Membrane bioreactor employs an efficient filtration technology for solid and liquid separation in wastewater treatment process. Development of membrane filtration model is significant as this model can be used to predict filtration dynamic which is later utilized in control development. Most of the available models only suitable for monitoring purpose, which are too complex, required many variables and not suitable for control system design. This work focusing on the simple time seris model for membrane filtration process using neural network technique. In this paper, submerged membrane filtration model developed using recurrent neural network (RNN) train using genetic algorithm (GA), inertia weight particle swarm optimization (IWPSO) and gravitational search algorithm (GSA). These optimization algorithms are compared in term of its accuracy and convergent speed in updating the weights and biases of the RNN for optimal filtration model. The evaluation of the models is measured using three performance evaluations, which are mean square error (MSE), mean absolute deviation (MAD) and coefficient of determination (R2). From the results obtained, all methods yield satisfactory result for the model, with the best results given by IW-PSO.
DCT AND DFT BASED BIOMETRIC RECOGNITION AND MULTIMODAL BIOMETRIC SECURITYIAEME Publication
This Research paper discusses the study and analysis conducted during this research on various techniques in biometric domain. A close glance on biometric enhancement techniques and their limitations are presented in this research paper. This process would enable researcher to understand the research contributions in the area of DCT and DFT based recognition and security, locate some crucial limitations of these notable research. This paper having summary about the different research papers that applicable to our topic of research which mentioned above. Biometric Recognition and security is a most important subject of research in this area of image processing.
Optimal Load Shedding Using an Ensemble of Artifcial Neural NetworksKashif Mehmood
This document describes using an ensemble of artificial neural networks for optimal load shedding in Pakistan's power system. It aims to maintain power system frequency stability by shedding an accurate amount of load. Real load and generation data from Pakistan's system is used to train individual neural networks. The outputs of the neural networks are then combined using a bagging algorithm with disjoint partitioning to form an ensemble. This ensemble approach helps reduce errors from the individual networks, improving load shedding accuracy and aiding power system stability.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
PERFORMANCE ASSESSMENT OF ANFIS APPLIED TO FAULT DIAGNOSIS OF POWER TRANSFORMER ecij
Continuous monitoring of Power transformer is very much essential during its operation. Incipient faults inside the tank and winding insulation needs careful attention. Traditional ratio methods and Duval triangle can be employed to diagnose the incipient faults. Many times correct diagnosis due to the
borderline problems and the existence of multiple faults may not be possible. Artificial intelligence (AI) techniques could be the best solution to handle the non linearity and complexity in the input data. In the proposed work, adaptive neuro fuzzy inference system (ANFIS), is utilized to deal with 9 incipient fault conditions including healthy condition of power transformer with sufficient DGA transformer oil samples. Comparison of the diagnosis performance of both the methods of ANFIS and the feasibility pertaining to the problem is presented. Diagnosis error in classifying the oil samples and the network structure are the main considerations of the present study.
IRJET- Overview of Artificial Neural Networks Applications in Groundwater...IRJET Journal
This document provides an overview of applications of artificial neural networks (ANNs) in groundwater studies. It discusses how ANNs mimic the human brain and can be used to model complex groundwater systems. It then summarizes several ways that ANNs have been successfully applied in groundwater hydrodynamics, water resources management, time series forecasting, and other areas. These include using ANNs to model coastal aquifers, predict groundwater levels, forecast water quality, and combine ANNs with other models for improved results. In summary, ANNs are a powerful tool for solving hydrogeological problems and have been widely used in groundwater research.
Applying Genetic Algorithms to Information Retrieval Using Vector Space ModelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
Applying genetic algorithms to information retrieval using vector space modelIJCSEA Journal
The document describes a study that applied genetic algorithms to information retrieval using the vector space model. The study used an adaptive genetic algorithm approach with two proposed fitness functions (cosine and Jaccard's), adaptive crossover and mutation probabilities. Experimental results on a test corpus showed improvements in precision and recall compared to traditional approaches, with the proposed cosine fitness function performing best. Precision generally decreased as recall increased. The modifications made to the genetic algorithm and fitness functions led to better weighting of query terms and improved results.
Applications of Artificial Neural Networks in Cancer PredictionIRJET Journal
This document discusses applications of artificial neural networks in cancer prediction and prognosis. It summarizes several studies that have used ANNs to predict breast cancer prognosis and recurrence, as well as classify types of lung cancer.
For breast cancer prognosis, a Maximum Entropy Estimation model was shown to outperform multi-layer perceptrons and probabilistic neural networks. For predicting breast cancer recurrence, an ANN achieved the best performance compared to other machine learning algorithms based on accuracy and AUC.
An ANN combined with a genetic algorithm was also able to successfully identify genes that classify lung cancer status. The ANN-GA model achieved over 97% accuracy in classifying different types of lung cancer based on gene expression data.
DCT AND DFT BASED BIOMETRIC RECOGNITION AND MULTIMODAL BIOMETRIC SECURITYIAEME Publication
This Research paper discusses the study and analysis conducted during this research on various techniques in biometric domain. A close glance on biometric enhancement techniques and their limitations are presented in this research paper. This process would enable researcher to understand the research contributions in the area of DCT and DFT based recognition and security, locate some crucial limitations of these notable research. This paper having summary about the different research papers that applicable to our topic of research which mentioned above. Biometric Recognition and security is a most important subject of research in this area of image processing.
Optimal Load Shedding Using an Ensemble of Artifcial Neural NetworksKashif Mehmood
This document describes using an ensemble of artificial neural networks for optimal load shedding in Pakistan's power system. It aims to maintain power system frequency stability by shedding an accurate amount of load. Real load and generation data from Pakistan's system is used to train individual neural networks. The outputs of the neural networks are then combined using a bagging algorithm with disjoint partitioning to form an ensemble. This ensemble approach helps reduce errors from the individual networks, improving load shedding accuracy and aiding power system stability.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
PERFORMANCE ASSESSMENT OF ANFIS APPLIED TO FAULT DIAGNOSIS OF POWER TRANSFORMER ecij
Continuous monitoring of Power transformer is very much essential during its operation. Incipient faults inside the tank and winding insulation needs careful attention. Traditional ratio methods and Duval triangle can be employed to diagnose the incipient faults. Many times correct diagnosis due to the
borderline problems and the existence of multiple faults may not be possible. Artificial intelligence (AI) techniques could be the best solution to handle the non linearity and complexity in the input data. In the proposed work, adaptive neuro fuzzy inference system (ANFIS), is utilized to deal with 9 incipient fault conditions including healthy condition of power transformer with sufficient DGA transformer oil samples. Comparison of the diagnosis performance of both the methods of ANFIS and the feasibility pertaining to the problem is presented. Diagnosis error in classifying the oil samples and the network structure are the main considerations of the present study.
IRJET- Overview of Artificial Neural Networks Applications in Groundwater...IRJET Journal
This document provides an overview of applications of artificial neural networks (ANNs) in groundwater studies. It discusses how ANNs mimic the human brain and can be used to model complex groundwater systems. It then summarizes several ways that ANNs have been successfully applied in groundwater hydrodynamics, water resources management, time series forecasting, and other areas. These include using ANNs to model coastal aquifers, predict groundwater levels, forecast water quality, and combine ANNs with other models for improved results. In summary, ANNs are a powerful tool for solving hydrogeological problems and have been widely used in groundwater research.
Applying Genetic Algorithms to Information Retrieval Using Vector Space ModelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
Applying genetic algorithms to information retrieval using vector space modelIJCSEA Journal
The document describes a study that applied genetic algorithms to information retrieval using the vector space model. The study used an adaptive genetic algorithm approach with two proposed fitness functions (cosine and Jaccard's), adaptive crossover and mutation probabilities. Experimental results on a test corpus showed improvements in precision and recall compared to traditional approaches, with the proposed cosine fitness function performing best. Precision generally decreased as recall increased. The modifications made to the genetic algorithm and fitness functions led to better weighting of query terms and improved results.
Applications of Artificial Neural Networks in Cancer PredictionIRJET Journal
This document discusses applications of artificial neural networks in cancer prediction and prognosis. It summarizes several studies that have used ANNs to predict breast cancer prognosis and recurrence, as well as classify types of lung cancer.
For breast cancer prognosis, a Maximum Entropy Estimation model was shown to outperform multi-layer perceptrons and probabilistic neural networks. For predicting breast cancer recurrence, an ANN achieved the best performance compared to other machine learning algorithms based on accuracy and AUC.
An ANN combined with a genetic algorithm was also able to successfully identify genes that classify lung cancer status. The ANN-GA model achieved over 97% accuracy in classifying different types of lung cancer based on gene expression data.
Turbidity is a measure of water quality. Excessive turbidity poses a threat to health and causes pollution. Most of the available mathematical models of water treatment plants do not capture turbidity. A reliable model is essential for effective removal of turbidity in the water treatment plant. This paper presents a comparison of Hammerstein Wiener and neural network technique for estimating of turbidity in water treatment plant. The models were validated using an experimental data from Tamburawa water treatment plant in Kano, Nigeria. Simulation results demonstrated that the neural network model outperformed the Hammerstein-Wiener model in estimating the turbidity. The neural network model may serve as a valuable tool for predicting the turbidity in the plant.
The efficacy of neural network (NN) and partial least squares (PLS) methods is compared for the prediction of NMR chemical shifts for both 1H and 13C nuclei using very large databases containing millions of chemical shifts. The chemical structure description scheme used in this work is based on individual atoms rather than functional groups. The performances of each of the methods were optimized in a systematic manner described in this work. Both of the methods, least squares and neural network analysis, produce results of a very similar quality but the least squares algorithm is approximately 2-3 times faster.
Artificial intelligence based pattern recognition is
one of the most important tools in process control to identify
process problems. The objective of this study was to
evaluate the relative performance of a feature-based
Recognizer compared with the raw data-based recognizer.
The study focused on recognition of seven commonly
researched patterns plotted on the quality chart. The
artificial intelligence based pattern recognizer trained using
the three selected statistical features resulted in significantly
better performance compared with the raw data-based
recognizer.
Channel selection is an improvement technique to optimize EEG-based BCI performance.
In previous studies, many channel selection methods—mostly based on spatial information of signals—have
been introduced. One of these channel selection techniques is the energy calculation method. In this paper,
we introduce an energy optimization calculation method, called the energy extraction method. Energy
extraction is an extension of the energy calculation method, and is divided into two steps. The first step is
energy calculation and the second is energy selection. In the energy calculation step, l2-norm is used to
calculate channel energy, while in the energy selection method we propose three techniques: “high value”
(HV), “close to mean” (CM), and “automatic”. All proposed framework schemes for energy extraction are
applied in two types of datasets. Two classes of datasets i.e. motor movement (hand and foot movement)
and motor imagery (imagination of left- and right-hand movement) were used. The system used a Common
Spatial Pattern (CSP) method to extract EEG signal features and k-NN as a classification method to classify
the signal features with k=3. Based on the test results, all schemes for the proposed energy extraction
method yielded improved BCI performance of up to 58%. In summary, the energy extraction approach using
the CM energy selection method was found to be the best channel selection technique.
IRJET- Comparative Study of PCA, KPCA, KFA and LDA Algorithms for Face Re...IRJET Journal
This document compares the performance of four face recognition algorithms - PCA, KPCA, KFA, and LDA - on three standard datasets: AT&T, Yale, and UMIST. It finds that KFA generally achieves the highest recognition rates, particularly for the AT&T and Yale datasets which involve changes in facial expressions and lighting. The Yale dataset, with its variations, yields the best results overall for KFA and LDA. The UMIST dataset, with its profile images, produces lower recognition rates across algorithms due to less similarity between training and test images.
siRNA has become an indispensible tool for silencing gene expression. It can act as an antiviral agent in RNAi pathway against plant diseases caused by plant viruses. However, identification of appropriate features for effective siRNA design has become a pressing issue for researchers which need to be resolved. Feature selection is a vital pre-processing technique involved in bioinformatics data set to find the most discriminative information not only for dimensionality reduction and detection of relevance features but also for minimizing the cost associated with features to design an accurate learning system. In this paper, we propose an ANN based feature selection approach using hybrid GA-PSO for selecting feature subset by discarding the irrelevant features and evaluating the cost of the model training. The results showed that the performance of proposed hybrid GA-PSO model outperformed the results of general PSO.a
Mel Frequency Cepstral Coefficient is an efficient feature representation method for extracting human-audible audio signals. However, its representation of features is large and redundant. Therefore, feature selection is required to select the optimal subset of Mel Frequency Cepstral Coefficient features. The performance of two types of feature selection techniques; Orthogonal Least Squares and F-ratio for selecting Mel Frequency Cepstral Coefficient features of infant cry with asphyxia was examined. OLS selects the feature subset based on their contribution to the reduction of error, while F-Ratio selects them according to their discriminative abilities. The feature selection techniques were combined with Multilayer Perceptron to distinguish between asphyxiated infant cry and normal cry signals. The performance of the feature selection methods was examined by analysing the Multilayer Perceptron classification accuracy resulted from the combination of the feature selection techniques and Multilayer Perceptron. The results indicate that Orthogonal Least Squares is the most suitable feature selection method in classifying infant cry with asphyxia since it produces the highest classification accuracy.
Lung Cancer Detection using Decision Tree AlgorithmIRJET Journal
This document describes a study that uses a decision tree algorithm to detect lung cancer. The researchers used a dataset from the UCI Machine Learning Repository containing 32 instances with 57 attributes to train and test their model. They developed an artificial neural network to initially classify the data and select important input features. Then, a decision tree algorithm was applied to generate a tree structure based on the training data to classify test data as having lung cancer or not. The decision tree approach evaluates the information gain at each node to split the data into purer subgroups. The proposed system was found to effectively classify lung cancer status with high accuracy compared to other algorithms like support vector machines and naive Bayes.
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
This document presents a study on using an artificial neural network (ANN) and multi-sensor data to predict toxic gases. Researchers developed a multi-layer perceptron (MLP) neural network model using backpropagation to predict hydrogen sulfide, nitrogen dioxide, and their mixture using data from intelligent gas sensors. The network was trained on 160 samples and tested on the remaining 20 samples. Results showed the model with one hidden layer of 75 nodes achieved high prediction accuracy, approving the robustness of the developed ANN model for electronic nose prediction in challenging conditions like low gas concentrations and complex gas mixtures.
Evaluation of image segmentation and filtering with ann in the papaya leafijcsit
Precision agriculture is area with lack of cheap technology. The refinement of the production system brings
large advantages to the producer and the use of images makes the monitoring a more cheap methodology.
Macronutrients monitoring can to determine the health and vulnerability of the plant in specific stages. In
this paper is analyzed the method based on computational intelligence to work with image segmentation in
the identification of symptoms of plant nutrient deficiency. Artificial neural networks are evaluated for
image segmentation and filtering, several variations of parameters and insertion impulsive noise were
evaluated too. Satisfactory results are achieved with artificial neural for segmentation same with high
noise levels.
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
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1) The document discusses various medical image fusion techniques including pixel level, feature level, and decision level fusion.
2) It proposes a novel pixel level fusion method called Iterative Block Level Principal Component Averaging fusion that divides images into blocks and calculates principal components for each block.
3) Experimental results on fusing noise free and noise filtered MR images show that the proposed method performs well in terms of average mutual information and structural similarity compared to other algorithms.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
GPCODON ALIGNMENT: A GLOBAL PAIRWISE CODON BASED SEQUENCE ALIGNMENT APPROACHijdms
The alignment of two DNA sequences is a basic step in the analysis of biological data. Sequencing a long
DNA sequence is one of the most interesting problems in bioinformatics. Several techniques have been
developed to solve this sequence alignment problem like dynamic programming and heuristic algorithms.
In this paper, we introduce (GPCodon alignment) a pairwise DNA-DNA method for global sequence
alignment that improves the accuracy of pairwise sequence alignment. We use a new scoring matrix to
produce the final alignment called the empirical codon substitution matrix. Using this matrix in our
technique enabled the discovery of new relationships between sequences that could not be discovered using
traditional matrices. In addition, we present experimental results that show the performance of the
proposed technique over eleven datasets of average length of 2967 bps. We compared the efficiency and
accuracy of our techniques against a comparable tool called “Pairwise Align Codons” [1].
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
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AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...IAEME Publication
This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
The paper presents a technique called as Mobility-enabled Multi Level Optimization (MeMLO) that addressing the existing problem of clustering in wireless sensor net-work (WSN). The technique enables selection of aggregator node based on multiple optimi-zation attribute which gives better decision capability to the clustering mechanism by choosing the best aggregator node. The outcome of the study shows MeMLO is highly capable of minimizing the halt time of mobile node that significantly lowers the transmit power of aggregator node. The simulation outcome shows negligible computational com-plexity, faster response time, and highly energy efficient for large scale WSN for longer simulation rounds as compared to conventional LEACH algorithm.
This document discusses characterizing polymeric membranes under large deformations using an artificial neural network model. It presents an experimental study of blowing circular thermoplastic ABS membranes using free blowing technique. A multilayer neural network is used to model the non-linear behavior of the membrane under biaxial deformation. The neural network results are compared to experimental data and a finite difference model using a hyperelastic Mooney-Rivlin model. The neural network accurately reproduces the membrane behavior with minimal error margins compared to experimental measurements.
Artificial Neural Networks (ANNS) For Prediction of California Bearing Ratio ...IJMER
The behaviour of soil at the location of the project and interactions of the earth materials during and after construction has a major influence on the success, economy and safety of the work. Another complexity associated with some geotechnical engineering materials, such as sand and gravel, is the difficulty in obtaining undisturbed samples and time consuming involving skilled
technician. Knowledge of California Bearing Ratio (C.B.R) is essential in finding the road thickness. To cope up with the difficulties involved, an attempt has been made to model C.B.R in terms of Fine Fraction, Liquid Limit, Plasticity Index, Maximum Dry density, and Optimum Moisture content. A multi-layer perceptron network with feed forward back propagation is used to model varying the
number of hidden layers. For this purposes 50 soils test data was collected from the laboratory test
results. Among the test data 30 soils data is used for training and remaining 20 soils for testing using
60-40 distribution. The architectures developed are 5-4-1, 5-5-1, and 5-6-1. Model with 5-6-1 architecture is found to be quite satisfactory in predicting C.B.R of soils. A graph is plotted between
the predicted values and observed values of outputs for training and testing process, from the graph it
is found that all the points are close to equality line, indicating predicted values are close to observed
values
Application of support vector machines for prediction of anti hiv activity of...Alexander Decker
This document describes a study that used support vector machines (SVM) to develop a quantitative structure-activity relationship (QSAR) model to predict the anti-HIV activity of TIBO derivatives. The SVM model achieved high correlation (q2=0.96) and low error (RMSE=0.212), outperforming artificial neural networks and multiple linear regression models developed on the same data set. The results indicate that SVM is a valuable tool for QSAR modeling and predicting anti-HIV activity of chemical compounds.
Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifeti...IJECEIAES
This document presents a novel scheme for minimizing the number of iterative steps in the particle swarm optimization (PSO) algorithm to extend the lifetime of wireless sensor networks. It first discusses existing literature that uses PSO approaches to address issues like clustering, energy efficiency, and localization in wireless sensor networks. It then identifies problems with existing approaches, such as higher computational complexity due to many iterations of PSO. The proposed solution enhances the conventional PSO algorithm by introducing decision variables and optimizing parameters like inertia weight and learning coefficients to obtain the global best solution in fewer iterations. It aims to minimize the transmission energy of cluster heads using a radio energy model to improve network lifetime. The key contribution is a computationally efficient PSO algorithm that selects effective
Turbidity is a measure of water quality. Excessive turbidity poses a threat to health and causes pollution. Most of the available mathematical models of water treatment plants do not capture turbidity. A reliable model is essential for effective removal of turbidity in the water treatment plant. This paper presents a comparison of Hammerstein Wiener and neural network technique for estimating of turbidity in water treatment plant. The models were validated using an experimental data from Tamburawa water treatment plant in Kano, Nigeria. Simulation results demonstrated that the neural network model outperformed the Hammerstein-Wiener model in estimating the turbidity. The neural network model may serve as a valuable tool for predicting the turbidity in the plant.
The efficacy of neural network (NN) and partial least squares (PLS) methods is compared for the prediction of NMR chemical shifts for both 1H and 13C nuclei using very large databases containing millions of chemical shifts. The chemical structure description scheme used in this work is based on individual atoms rather than functional groups. The performances of each of the methods were optimized in a systematic manner described in this work. Both of the methods, least squares and neural network analysis, produce results of a very similar quality but the least squares algorithm is approximately 2-3 times faster.
Artificial intelligence based pattern recognition is
one of the most important tools in process control to identify
process problems. The objective of this study was to
evaluate the relative performance of a feature-based
Recognizer compared with the raw data-based recognizer.
The study focused on recognition of seven commonly
researched patterns plotted on the quality chart. The
artificial intelligence based pattern recognizer trained using
the three selected statistical features resulted in significantly
better performance compared with the raw data-based
recognizer.
Channel selection is an improvement technique to optimize EEG-based BCI performance.
In previous studies, many channel selection methods—mostly based on spatial information of signals—have
been introduced. One of these channel selection techniques is the energy calculation method. In this paper,
we introduce an energy optimization calculation method, called the energy extraction method. Energy
extraction is an extension of the energy calculation method, and is divided into two steps. The first step is
energy calculation and the second is energy selection. In the energy calculation step, l2-norm is used to
calculate channel energy, while in the energy selection method we propose three techniques: “high value”
(HV), “close to mean” (CM), and “automatic”. All proposed framework schemes for energy extraction are
applied in two types of datasets. Two classes of datasets i.e. motor movement (hand and foot movement)
and motor imagery (imagination of left- and right-hand movement) were used. The system used a Common
Spatial Pattern (CSP) method to extract EEG signal features and k-NN as a classification method to classify
the signal features with k=3. Based on the test results, all schemes for the proposed energy extraction
method yielded improved BCI performance of up to 58%. In summary, the energy extraction approach using
the CM energy selection method was found to be the best channel selection technique.
IRJET- Comparative Study of PCA, KPCA, KFA and LDA Algorithms for Face Re...IRJET Journal
This document compares the performance of four face recognition algorithms - PCA, KPCA, KFA, and LDA - on three standard datasets: AT&T, Yale, and UMIST. It finds that KFA generally achieves the highest recognition rates, particularly for the AT&T and Yale datasets which involve changes in facial expressions and lighting. The Yale dataset, with its variations, yields the best results overall for KFA and LDA. The UMIST dataset, with its profile images, produces lower recognition rates across algorithms due to less similarity between training and test images.
siRNA has become an indispensible tool for silencing gene expression. It can act as an antiviral agent in RNAi pathway against plant diseases caused by plant viruses. However, identification of appropriate features for effective siRNA design has become a pressing issue for researchers which need to be resolved. Feature selection is a vital pre-processing technique involved in bioinformatics data set to find the most discriminative information not only for dimensionality reduction and detection of relevance features but also for minimizing the cost associated with features to design an accurate learning system. In this paper, we propose an ANN based feature selection approach using hybrid GA-PSO for selecting feature subset by discarding the irrelevant features and evaluating the cost of the model training. The results showed that the performance of proposed hybrid GA-PSO model outperformed the results of general PSO.a
Mel Frequency Cepstral Coefficient is an efficient feature representation method for extracting human-audible audio signals. However, its representation of features is large and redundant. Therefore, feature selection is required to select the optimal subset of Mel Frequency Cepstral Coefficient features. The performance of two types of feature selection techniques; Orthogonal Least Squares and F-ratio for selecting Mel Frequency Cepstral Coefficient features of infant cry with asphyxia was examined. OLS selects the feature subset based on their contribution to the reduction of error, while F-Ratio selects them according to their discriminative abilities. The feature selection techniques were combined with Multilayer Perceptron to distinguish between asphyxiated infant cry and normal cry signals. The performance of the feature selection methods was examined by analysing the Multilayer Perceptron classification accuracy resulted from the combination of the feature selection techniques and Multilayer Perceptron. The results indicate that Orthogonal Least Squares is the most suitable feature selection method in classifying infant cry with asphyxia since it produces the highest classification accuracy.
Lung Cancer Detection using Decision Tree AlgorithmIRJET Journal
This document describes a study that uses a decision tree algorithm to detect lung cancer. The researchers used a dataset from the UCI Machine Learning Repository containing 32 instances with 57 attributes to train and test their model. They developed an artificial neural network to initially classify the data and select important input features. Then, a decision tree algorithm was applied to generate a tree structure based on the training data to classify test data as having lung cancer or not. The decision tree approach evaluates the information gain at each node to split the data into purer subgroups. The proposed system was found to effectively classify lung cancer status with high accuracy compared to other algorithms like support vector machines and naive Bayes.
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
This document presents a study on using an artificial neural network (ANN) and multi-sensor data to predict toxic gases. Researchers developed a multi-layer perceptron (MLP) neural network model using backpropagation to predict hydrogen sulfide, nitrogen dioxide, and their mixture using data from intelligent gas sensors. The network was trained on 160 samples and tested on the remaining 20 samples. Results showed the model with one hidden layer of 75 nodes achieved high prediction accuracy, approving the robustness of the developed ANN model for electronic nose prediction in challenging conditions like low gas concentrations and complex gas mixtures.
Evaluation of image segmentation and filtering with ann in the papaya leafijcsit
Precision agriculture is area with lack of cheap technology. The refinement of the production system brings
large advantages to the producer and the use of images makes the monitoring a more cheap methodology.
Macronutrients monitoring can to determine the health and vulnerability of the plant in specific stages. In
this paper is analyzed the method based on computational intelligence to work with image segmentation in
the identification of symptoms of plant nutrient deficiency. Artificial neural networks are evaluated for
image segmentation and filtering, several variations of parameters and insertion impulsive noise were
evaluated too. Satisfactory results are achieved with artificial neural for segmentation same with high
noise levels.
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
1) The document discusses various medical image fusion techniques including pixel level, feature level, and decision level fusion.
2) It proposes a novel pixel level fusion method called Iterative Block Level Principal Component Averaging fusion that divides images into blocks and calculates principal components for each block.
3) Experimental results on fusing noise free and noise filtered MR images show that the proposed method performs well in terms of average mutual information and structural similarity compared to other algorithms.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
GPCODON ALIGNMENT: A GLOBAL PAIRWISE CODON BASED SEQUENCE ALIGNMENT APPROACHijdms
The alignment of two DNA sequences is a basic step in the analysis of biological data. Sequencing a long
DNA sequence is one of the most interesting problems in bioinformatics. Several techniques have been
developed to solve this sequence alignment problem like dynamic programming and heuristic algorithms.
In this paper, we introduce (GPCodon alignment) a pairwise DNA-DNA method for global sequence
alignment that improves the accuracy of pairwise sequence alignment. We use a new scoring matrix to
produce the final alignment called the empirical codon substitution matrix. Using this matrix in our
technique enabled the discovery of new relationships between sequences that could not be discovered using
traditional matrices. In addition, we present experimental results that show the performance of the
proposed technique over eleven datasets of average length of 2967 bps. We compared the efficiency and
accuracy of our techniques against a comparable tool called “Pairwise Align Codons” [1].
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...IAEME Publication
This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
The paper presents a technique called as Mobility-enabled Multi Level Optimization (MeMLO) that addressing the existing problem of clustering in wireless sensor net-work (WSN). The technique enables selection of aggregator node based on multiple optimi-zation attribute which gives better decision capability to the clustering mechanism by choosing the best aggregator node. The outcome of the study shows MeMLO is highly capable of minimizing the halt time of mobile node that significantly lowers the transmit power of aggregator node. The simulation outcome shows negligible computational com-plexity, faster response time, and highly energy efficient for large scale WSN for longer simulation rounds as compared to conventional LEACH algorithm.
This document discusses characterizing polymeric membranes under large deformations using an artificial neural network model. It presents an experimental study of blowing circular thermoplastic ABS membranes using free blowing technique. A multilayer neural network is used to model the non-linear behavior of the membrane under biaxial deformation. The neural network results are compared to experimental data and a finite difference model using a hyperelastic Mooney-Rivlin model. The neural network accurately reproduces the membrane behavior with minimal error margins compared to experimental measurements.
Artificial Neural Networks (ANNS) For Prediction of California Bearing Ratio ...IJMER
The behaviour of soil at the location of the project and interactions of the earth materials during and after construction has a major influence on the success, economy and safety of the work. Another complexity associated with some geotechnical engineering materials, such as sand and gravel, is the difficulty in obtaining undisturbed samples and time consuming involving skilled
technician. Knowledge of California Bearing Ratio (C.B.R) is essential in finding the road thickness. To cope up with the difficulties involved, an attempt has been made to model C.B.R in terms of Fine Fraction, Liquid Limit, Plasticity Index, Maximum Dry density, and Optimum Moisture content. A multi-layer perceptron network with feed forward back propagation is used to model varying the
number of hidden layers. For this purposes 50 soils test data was collected from the laboratory test
results. Among the test data 30 soils data is used for training and remaining 20 soils for testing using
60-40 distribution. The architectures developed are 5-4-1, 5-5-1, and 5-6-1. Model with 5-6-1 architecture is found to be quite satisfactory in predicting C.B.R of soils. A graph is plotted between
the predicted values and observed values of outputs for training and testing process, from the graph it
is found that all the points are close to equality line, indicating predicted values are close to observed
values
Application of support vector machines for prediction of anti hiv activity of...Alexander Decker
This document describes a study that used support vector machines (SVM) to develop a quantitative structure-activity relationship (QSAR) model to predict the anti-HIV activity of TIBO derivatives. The SVM model achieved high correlation (q2=0.96) and low error (RMSE=0.212), outperforming artificial neural networks and multiple linear regression models developed on the same data set. The results indicate that SVM is a valuable tool for QSAR modeling and predicting anti-HIV activity of chemical compounds.
Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifeti...IJECEIAES
This document presents a novel scheme for minimizing the number of iterative steps in the particle swarm optimization (PSO) algorithm to extend the lifetime of wireless sensor networks. It first discusses existing literature that uses PSO approaches to address issues like clustering, energy efficiency, and localization in wireless sensor networks. It then identifies problems with existing approaches, such as higher computational complexity due to many iterations of PSO. The proposed solution enhances the conventional PSO algorithm by introducing decision variables and optimizing parameters like inertia weight and learning coefficients to obtain the global best solution in fewer iterations. It aims to minimize the transmission energy of cluster heads using a radio energy model to improve network lifetime. The key contribution is a computationally efficient PSO algorithm that selects effective
This summarizes a document presenting a case study on using a self-evolving neural network algorithm for fault prognosis in wind turbines. The study uses SCADA data from a real 2 MW wind turbine located in Sweden. An artificial neural network model is developed to estimate gearbox bearing temperatures based on inputs like oil temperature, nacelle temperature, turbine power, and RPM. The model is used to detect anomalies by comparing estimated vs. measured temperatures. When a major maintenance activity like gearbox replacement occurs, the model must be retrained due to changes in operating conditions. The case study demonstrates selecting an initial training dataset and later updating the model after a gearbox replacement using the proposed approach. Results show the approach allows the neural
Prediction of Extreme Wind Speed Using Artificial Neural Network ApproachScientific Review SR
Prediction of an accurate wind speed of wind farms is necessary because of the intermittent nature
of wind for any region. Number of methods such as persistence, physical, statistical, spatial correlation, artificial
intelligence network and hybrid are generally available for prediction of wind speed. In this paper, ANN based
methods viz., Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are used. The
performance of the networks applied for prediction of wind speed is evaluated by model performance indicators
viz., Correlation Coefficient (CC), Model Efficiency (MEF) and Mean Absolute Percentage Error (MAPE).
Meteorological parameters such as maximum and minimum temperature, air pressure, solar radiation and
altitude are considered as input units for MLP and RBF networks to predict the extreme wind speed at Delhi.
The study shows the values of CC, MEF and MAPE between the observed and predicted wind speed (using
MLP) are computed as 0.992, 95.4% and 4.3% respectively while training the network data. For RBF network,
the values of CC, MEF and MAPE are computed as 0.992, 95.9% and 3.0% respectively. The model
performance analysis indicates the RBF is better suited network among two different networks studied for
prediction of extreme wind speed at Delhi.
Virtual private networks (VPN) provide remotely secure connection for clients to exchange information with company networks. This paper deals with Site-to-site IPsec-VPN that connects the company intranets. IPsec-VPN network is implemented with security protocols for key management and exchange, authentication and integrity using GNS3 Network simulator. The testing and verification analyzing of data packets is done using both PING tool and Wireshark to ensure the encryption of data packets during data exchange between different sites belong to the same company.
The performance of an algorithm can be improved using a parallel computing programming approach. In this study, the performance of bubble sort algorithm on various computer specifications has been applied. Experimental results have shown that parallel computing programming can save significant time performance by 61%-65% compared to serial computing programming.
Classification of Churn and non-Churn Customers in Telecommunication CompaniesCSCJournals
Telecommunication is very important as it serves various activities, services of electronic systems to transmit messages via physical cables, telephones, or cell phones. The two main factors that affect the growth of telecommunications are the rapid growth of modern technology and the market demand and its competition. These two factors in return, create new technologies and products, which open a series of options and offers to customers, in order to satisfy their needs and requirements. However, one crucial problem that commercial companies in general and telecommunication in particular, suffer from is a loss of valuable customers to competitors; this is called customer churn prediction. In this paper, the dynamic training technique is introduced. The dynamic training is used to improve the prediction of performance. This technique is based on two ANN network configurations to minimise the total error of the network to predict two different classes; names churn and non-customers.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
The document proposes using a particle swarm optimization (PSO) algorithm to optimize the hyperparameters of a convolutional neural network (CNN) for image classification. The PSO algorithm is used to find optimal values for CNN hyperparameters like the number and size of convolutional filters. In experiments on the MNIST handwritten digit dataset, the optimized CNN achieved a testing error rate of 0.87%, which is competitive with state-of-the-art models. The proposed approach finds optimized CNN architectures automatically without requiring manual design or encoding strategies during training.
Overview of soft intelligent computing technique for supercritical fluid extr...IJAAS Team
Optimization of Supercritical Fluid Extraction process with mathematical modeling is essential for industrial applications. The response surface methodology (RSM) has been proven to be a useful and effective statistical method for studying the relationships between measured responses and independent factors. Recently there are growing interest in applying smart system or artificial technique to model and simulate a chemical process and also to predict, compute, classify and optimize as well as for process control. This system works by generalizing the experimental result and the process behavior and finally predict and estimate the problem. This smart system is a major assistance in the development of process from laboratory to pilot or industrial. The main advantage of intelligent systems is that the predictions can be performed easily, fast, and accurate way, which physical models unable to do. This paper shares several works that have been utilizing intelligent systems for modeling and simulating the supercritical fluid extraction process.
The document summarizes a study that used artificial neural networks (ANN) to predict chemical oxygen demand (COD) levels in an anaerobic wastewater treatment system. Four ANN backpropagation training algorithms - Levenberg-Marquardt, gradient descent with adaptive learning, gradient descent with momentum, and resilient backpropagation - were tested on a model using COD input data. The Levenberg-Marquardt algorithm produced the best results with the lowest mean squared error of 0.533 and highest regression value of 0.991, accurately predicting COD levels. The study demonstrates ANNs can effectively model and predict values in nonlinear wastewater treatment processes.
Artificial Neural Network and Multi-Response Optimization in Reliability Meas...inventionjournals
Neural network is an important tool for reliability analysis, including estimation of reliability or utility function which are too complicated to be analytical expressed for large or complex system. It has been demonstrated the neural network has significant improvement in the parameter estimation accuracy over the traditional chi-square test. There are many parameters of a neural network that should be determined while training the dataset, since different setups of algorithm parameters affect the estimation performance in either accuracy or computation efficiency. In this paper, neural network training is used to estimate the utility function for the parallel-series redundancy allocation problem, and weighted principal component based multi-response optimization method is applied to find the optimal setting of neural network parameters so that the simultaneous minimizations of training error and computing time are achieved.
Intelligent Controller Design for a Chemical ProcessCSCJournals
Abstract - Chemical process control is a challenging problem due to the strong on-line non-linearity and extreme sensitivity to disturbances of the process. Ziegler – Nichols tuned PI and PID controllers are found to provide poor performances for higher-order and non–linear systems. This paper presents an application of one-step-ahead fuzzy as well as ANFIS (adaptive-network-based fuzzy inference system) tuning scheme for an Continuous Stirred Tank Reactor CSTR process. The controller is designed based on a Mamdani type and Sugeno type fuzzy system constructed to model the dynamics of the process. The fuzzy system model can take advantage of both a priori linguistic human knowledge through parameter initialization, and process measurements through on- line parameter adjustment. The ANFIS, which is a fuzzy inference system, is implemented in the framework of adaptive networks. The proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In this method, a novel approach based on tuning of fuzzy logic control as well as ANFIS for a CSTR process, capable of providing an optimal performance over the entire operating range of process are given. Here Fuzzy logic control as well as ANFIS for obtaining the optimal design of the CSTR process is explained. In this approach, the development of rule based and the formation of the membership function are evolved simultaneously. The performance of the algorithm in obtaining the optimal tuning values has been analyzed in CSTR process through computer simulation.
Performance comparison of SVM and ANN for aerobic granular sludgejournalBEEI
To comply with growing demand for high effluent quality of Domestic Wastewater Treatment Plant (WWTP), a simple and reliable prediction model is thus needed. The wastewater treatment technology considered in this paper is an Aerobic Granular Sludge (AGS). The AGS systems are fundamentally complex due to uncertainty and non-linearity of the system makes it hard to predict. This paper presents model predictions and optimization as a tool in predicting the performance of the AGS. The input-output data used in model prediction are (COD, TN, TP, AN, and MLSS). After feature analysis, the prediction of the models using Support Vector Machine (SVM) and Feed-Forward Neural Network (FFNN) are developed and compared. The simulation of the model uses the experimental data obtained from Sequencing Batch Reactor under hot temperature of 50˚C. The simulation results indicated that the SVM is preferable to FFNN and it can provide a useful tool in predicting the effluent quality of WWTP.
IRJET- Reliable Transmission of Packets using Multiple ChannelsIRJET Journal
This document summarizes research on reliable transmission of packets using multiple channels. It discusses how the presence of time-varying channels and reconfiguration delays reduces network stability and optimal policies. Frame-based dynamic control and myopic policies are proposed to provide throughput-optimal network control despite delays. Simulation results show memory in channels can be exploited to improve stability region and throughput compared to memoryless channels. The work aims to stabilize networks using scheduling policies and Markov decision processes to achieve higher throughput with reduced delays.
Multimode system condition monitoring using sparsity reconstruction for quali...IJECEIAES
In this paper, we introduce an improved multivariate statistical monitoring method based on the stacked sparse autoencoder (SSAE). Our contribution focuses on the choice of the SSAE model based on neural networks to solve diagnostic problems of complex systems. In order to monitor the process performance, the squared prediction error (SPE) chart is linked with nonparametric adaptive confidence bounds which arise from the kernel density estimation to minimize erroneous alerts. Then, faults are localized using two methods: contribution plots and sensor validity index (SVI). The results are obtained from experiments and real data from a drinkable water processing plant, demonstrating how the applied technique is performed. The simulation results of the SSAE model show a better ability to detect and identify sensor failures.
Semelhante a Neural Network Model Development with Soft Computing Techniques for Membrane Filtration Process (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
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represent the fouling layer in the membrane filtration process to develop the filtration model. Similar to other
mechanistic or first principle model, tedious calibration process involving many parameters is required.
An alternative technique to represent a dynamic behavior of the filtration process is using artificial
neural network (ANN), which has high accuracy of prediction capability. The ANN technique is employed in
numerous areas including chemical process, mechanical applications, financial forecasting and trending,
environmental prediction and others. On the area of membrane filtration modeling, Geissler et al [2] has
developed models which are semi empirical model and ANN based model for permeate flux modeling in
submerged capillary MBR. The ANN model is based on Elman neural network structure to predict the
permeate flux. The techniques yield very good results, where the semi empirical model required small input
variables compared to ANN. However, the ANN model gave high accuracy with the average error of 2.7%.
In [3], a flat sheet submerged membrane bioreactor (SMBR) filtration for wastewater filtration is
modeled using ANN model to represent the backwash effect to the permeate flux. Multilayer feed forward
neural network is used to model the system. Various backwashed and filtration interval are used as the input
to the model, meanwhile flux is used as the output. With the given backwashed to permeate ratio, the model
is able to predict the permeate flux performance. Nevertheless, the application of ANN using conventional
back propagation (BP) algorithm for training has faced several problems such as slow convergent and the
algorithm has tendency to tarp in the local minima. Thus, the application of heuristic search optimization
technique is one of the solutions.
Several works has found in literature to find an optimal weight and bias value in the training of the
neural network. Among the widely used heuristic search algorithm for ANN model is genetic algorithm
(GA).this algorithm is among the pioneer in heuristic optimization which is inspired from the population
evolutionary theory. GA was successful used in [4], [5] and [6] to train the neural network model in various
applications. This algorithm is able to produce a global solution for a given optimization problems. Particle
swarms optimizations (PSO) in particular, have become a very effective optimization technique in various
fields. The algorithm is fast and very reliable in searching for minimization. Several well known
advancement of PSO algorithm such as random PSO (RPSO), constriction factor PSO (CPSO) and inertia
weight PSO (IW PSO) have been developed throughout the years. These algorithms have been tested in
previous neural networks application as reported in [7], [8] and [9]. Gravitational search algorithm (GSA)
[10] is another type of optimization technique. This method is inspired by the law of gravity and the mass
interaction in universe. The GSA was reported to yield faster convergent rate and is reliable to meet the
objective function in many applications as reported in [10]-[12].
This work focuses on model development of membrane filtration process using recurrent neural
network (RNN) model train by three type’s of soft computing optimization techniques which are GA, PSO
and GSA. These three algorithms will be used to search for the best weights and biases of the recurrent neural
network model. The trained mode is then compared in term of its accuracy on the training and testing of
membrane filtration data set.
2. EXPERIMENT SETUP
The data set is collected from the developed membrane bioreactor pilot plant located in Process
Control Lab, Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM). Random magnitude
steps input were given to the suction pump to stimulate the dynamic behavior of the process. Figure 1 shows
the plant schematic diagram while Figure 2 shows the data collected from the pilot plant. The experiments
were carried out in single tank submerged membrane bioreactors, with working volume of 20 L Palm Oil
Mill Effluent (POME) taken from Sedenak Palm Oil Mill Sdn. Bhd. in Johor, Malaysia. The aeration during
filtration is set around 6 to 8 litter per minute (LPM) to maintain the dissolved oxygen level to be more than
2 ppm during the experiment. In this work, voltage input of the permeate pump is used for the input, mean
while the flux and TMP are the measurement outputs.
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Figure 1. Plant schamatic Figure 2. Experimental data
In this work, Polyethersulfone (PES) material with approximately 80-100kda pore size and a surface
area of approximately 0.35 m2
is used in the filtration system
3. MODEL DEVELOPMENT
Recurrent neural network (RNN) model is a mathematical model developed based on the past input
and past output of the system. The RNN is developed based on the basic feed forward neural network
(FFNN). The training algorithm is employed to obtain suitable weights and biases of the network in order to
minimize the error in the training procedure. Figure 3 shows the basic structure of the neural network model.
Figure 3. Neural network structure Figure 4. Recurrent neural network structure
The neural network equation can be represented as:
̂ ( ) [∑ (∑ ) ] (1)
where ̂ ( )is the prediction output. Fi is the function of the network, φ is the input vector, Wij and wij
represent the network connection layer weights and biases respectively. In this work three and five hidden
neurons are tested with basic three layers neural networks which are the input layer, single hidden layer and
output layer. This work employed nonlinear autoregressive with exogenous input (NARX) ANN structure
which shows in Figure 4. The optimization techniques applied in the ANN model training is optimize the
weights and biases of the ANN structure by minimizing the mean square error (MSE) of the model
3.1. Genetic algorithm (GA)
Optimization algorithm is a developing area in the soft computing study. GA algorithm is one of the
pioneers in evolution based optimization techniques and is a reliable technique in optimization. This
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ANN
𝑢(𝑡)
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optimization technique is widely applied in many industrial applications such as in [13] and [14]. The flow
chart of the algorithm can be represented in Figure 5.
Figure 5. GA Optimization flowchart
3.2. Particle swarm optimization
Particle swarm optimization (PSO) is inspired by behavior of a group of animals hunting. This
heuristic search optimization is very effective in finding optimal solution for many problems. In PSO
algorithm, number of swarm must be selected to search for the solution. Each swarm contains an individual
calls a particle. The PSO main algorithm is to update the position of each particle with an estimated velocity.
Each of the components of the velocity equation represents the exploration ability and capability of
individual learning as well as social learning. The PSO algorithm execution flow chart is shown in Figure 6.
Figure 6. PSO flowchart
3.2. Gravitational search algorithm (GSA)
The GSA algorithm is an effective optimization algorithm developed in [10]. It is inspired from
gravity law and mass interaction in the universe. Figure 7 presents the flow chart of the GSA optimization
algorithm. The details development of the algorithm is presented in [10].
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Figure 7. GSA flowchart
4. RESULTS AND ANALYSIS
Generally, all the training methods using soft computing techniques are able to produce acceptable
result for the model obtained. From the training result it shows that PSO algorithm performs much better
when compared with GA and GSA. From Figure 8(a) it can be observed that GA has a much faster searching
for the right weights and biases of the neural network compared with the other. However, when approaching
the first 50 iterations, PSO shows superiority in finding global minimum from the given cost function.
Meanwhile, Figure 8(b) shows the zooming convergent area of the training algorithms. In term of modeling
performance criteria, the PSO algorithm performs better compared with the other methods. Table 1(a) present
the permeate flux training result. Table 1(b) shows the result for the TMP modeling.
(a) (b)
Figure 8. (a) The performance of the training algorithm (b) Zoom at the convergent area of the training
algorithm
Table 1. (a) Permeate Flux Modeling Training Result
Method R2
MSE MAD
GA-RNN 93.48 0.0066 0.0473
PSO-RNN 94.38 0.0057 0.0418
GSA-RNN 93.12 0.0070 0.0493
Table 1. (b) TMP Modeling Training Result
Method R2
MSE MAD
GA-RNN 97.90 0.0014 0.0287
PSO-RNN 97.82 0.0015 0.0296
GSA-RNN 83.59 0.0113 0.0848
Figure 9 (a) and Figure 9(b) shows the comparison between actual and predicted training model using PSO-
RNN for the permeate flux and TMP.
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(a) (b)
Figure 9. (a) Permeate Flux modeling training result using RNN-PSO three hidden neuron and the residuals
between actual and model (b) TMP modeling training result using RNN-PSO three hidden neuron and the
residuals between actual and model
The validation of the model is done by using data not utilized for the model development. From the
result given in the Table 2(a) and (b), it is proven that the training performances of all the algorithms are able
to model the system. Generally, the testing result is less accurate compared with the training. However, from
the testing performance it showed that the models are able to replicate the dynamic of the membrane
filtration system as shown in Figure 10.
Table 2. (a) Permeate Flux Modeling Testing Result
Method %R2
MSE MAD
GA-RNN 93.62 0.0062 0.0452
PSO-RNN 94.19 0.0056 0.0418
GSA-RNN 92.76 0.0070 0.0448
Table 2. (b) TMP Modeling Testing Result
Method %R2
MSE MAD
GA-RNN 97.85 0.0013 0.0275
PSO-RNN 97.87 0.0012 0.0283
GSA-RNN 81.33 0.0115 0.0839
(a) (b)
Figure 10. (a) Permeate Flux modeling testing result using RNN-PSO three hidden neuron and the residuals
between actual and model (b) TMP modeling testing result using RNN-PSO three hidden neuron and the
residuals between actual and model
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The result of the increment of the hidden neurons of the neural network has shown significant
difference from the initial (three hidden neurons) training. It is an interesting finding where some of the
training algorithm shows improvement of the model while the other is not able to find suitable weights and
biases of the neural network model until the end of the iterations. Figure 11(a) shows the performance for
each of the training methods in term of it fitness and iterations. Figure 11(b) shows the zoomed image at the
convergent area.
(a) (b)
Figure 11. (a) The performance of the training algorithm for five hidden neuron (b) Zoom at the convergent
area of the training algorithm for five hidden neuron
The overall of the modeling performance for the training result is shows in Table 3(a) and
Table 3(b). Permeate flux modeling had shown the PSO and GA can be used for the RNN training.
Figure 12(a) and Figure 12(b) represents the training result for the permeate flux and TMP using the PSO-
RNN modeling technique.
Generally, the testing results have shown that the result is much improved compared with the three
hidden neuron. The PSO and GA training algorithm are able to maintain the accuracy of the model with
minimum error increment compared with the training dataset. The testing result reveal similar trend of
accuracy with the training result where the PSO algorithm obtains higher accuracy of the model followed
with GA and GSA. Table 4(a) and Table 4(b) present the testing performance of the models with the %R2
,
MSE and MAD performance. The testing result for the permeate flux and TMP were shown in Figure 13(a)
and Figure 13(b).
Table 3. (a) Permeate Flux Modeling five hidden Neuron Training Result
Method %R2
MSE MAD
GA-RNN 93.00 0.0071 0.0489
PSO-RNN 95.26 0.0048 0.0321
GSA-RNN 76.10 0.0242 0.0974
Table 3. (b) TMP Modeling Training five hidden Neuron Result
Method %R2
MSE MAD
GA-RNN 96.10 0.0027 0.0349
PSO-RNN 98.47 0.0011 0.0259
GSA-RNN 65.12 0.0586 0.0837
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(a) (b)
Figure 12. (a) Permeate Flux modeling training result using RNN-PSO five hidden neuron and the residuals
between actual and model (b) TMP modeling training result using RNN-PSO five hidden neuron and the
residuals between actual and model
Table 4. (a) Permeate Flux Modeling Testing Result
Method %R2
MSE MAD
GA-RNN 93.22 0.0066 0.0468
PSO-RNN 94.91 0.0049 0.0341
GSA-RNN 74.47 0.0248 0.0995
Table 4. (b) TMP Modeling Testing Result
Method %R2
MSE MAD
GA-RNN 96.68 0.0020 0.0322
PSO-RNN 98.46 9.4640e-004 0.0243
GSA-RNN 60.43 0.0620 0.0818
(a) (b)
Figure 13. (a) Permeate Flux modeling testing result using RNN-PSO three hidden neuron and the residuals
between actual and model (b): TMP modeling testing result using RNN-PSO three hidden neuron and the
residuals between actual and model
5. CONCLUSION
This paper presents the dynamic modeling technique for submerged membrane filtration process
using RNN with various soft computing techniques. From the result it showed that the training algorithm
using soft computing technique is able to produce high accuracy of RNN model. This work compares
optimization algorithms of GA, PSO and GSA method to train the RNN model. It is observed that the PSO
algorithm is able to gives higher accuracy compared with the others followed with GA and GSA. The
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Time(s)
TMP(mbar)
Actual
PSO-RNN
0 500 1000 1500 2000
-1
-0.5
0
0.5
1
Time(s)
Error
0 500 1000 1500 2000
0
0.2
0.4
0.6
0.8
1
Time(s)
Flux(L/m2h)
Actual
Predicted
0 500 1000 1500 2000
-1
-0.5
0
0.5
1
Time(s)
Error
0 500 1000 1500 2000
0
0.2
0.4
0.6
0.8
1
Time(s)
TMP(mbar)
Actual
PSO-RNN
0 500 1000 1500 2000
-1
-0.5
0
0.5
1
Time(s)
Error
9. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 8, No. 4, August 2018 : 2614 – 2623
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performance of the PSO and GA algorithm are almost similar especially for the TMP model. Increment from
three to five hidden neuron can increase the accuracy of the model, however, it is can also be troublesome for
GSA algorithm to find an optimum weights and biases value for the model. Finally this model will be useful
in order to do prediction performance and controller design strategy in the future.
ACKNOWLEDGEMENTS
The authors would like to thank the Research University Grant (GUP) vote 13H70, Universiti
Teknologi Malaysia for the financial support. The first author wants to thank the Universiti Teknologi
MARA (UiTM) and the MOHE for the TPM-SLAI scholarship
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BIOGRAPHIES OF AUTHORS
Zakariah Yusuf received his B. Eng. Hons (Electrical) and MSc. Electrical Engineering from Universiti
Teknologi MARA, Malaysia in 2007 and 2012 respectively. He is currently working toward his PhD in
process control at the Faculty of Electrical Engineering, Universiti Teknologi Malaysia. He has a few
years’ experiences in control and instrumentation from various industries. His current research interests
include membrane filtration and application of soft computing in the area of process modeling and
control
Ir. Dr. Norhaliza Abdul Wahab is currently an Associate Professor at Universiti Teknologi Malaysia
(UTM). She is currently the Head Department of Control and Mechatronics Engineering Department at
the Faculty of Electrical Engineering, UTM. She completed her PhD in Electrical Engineering majoring
in Control in July 2009. She is actively involved in researching and teaching in the field of industrial
process control. Her expertise is in modelling and control of industrial process plant. Recently she has
10. Int J Elec & Comp Eng ISSN: 2088-8708
Neural Network Model Development with Soft Computing Techniques for … (Norhaliza Abdul)
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worked primarily on different types of domestic and industrial wastewater treatment technology towards
optimization and energy saving system.
Shafishuhaza Sahlan graduated from University of Sheffield, UK in 2002 with an MEng in Control
Systems Engineering. In 2010, she graduated from University of Western Australia upon completion of
her studies majoring in Control System Algorithm. Currently, she is working in the Control &
Mechatronics Engineering Department in Faculty of Electrical Engineering in Universiti Teknologi
Malaysia, Skudai, Johor as a senior lecturer. Her research interest includes Control Algorithm and its
advancement, as well as animal biotechnology.