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Optimizing Artificial Neural Networks Trough Weight Adjustments

Procedia computer science 2024年 246卷 2158-2165页

作者： Syed Muhammad Abrar Akber Agnieszka Szczesna Sadia Nishat Kazmi Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice 44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice 44-100 Poland

Longer training times pose a significant challenge in Artificial neural networks (ANNs) as it may leads to increasing the computational costs and decreasing the effectiveness of the model. Therefore, it is imperative to reduce training times in ANNs to enhance the computational efficiency. The initialization of the weights between the layers in ANN plays a vital role in reducing training times. Appropriate weight initialization can help the network converge faster during the training by providing an optimum starting point for the network. Therefore, weight initialization techniques are essential for efficient training of ANNs. This paper revisits and implements different popular weight initialization techniques in ANNs and analyzes their impact on training time. Specifically, this paper implements Gaussian-based, Kaming-based, and Xavier-based weight initiation atop a popular DNN-based network. The experiments are conducted by employing a well-known dataset. The results show that the scenario when no weight initiation is applied consumed the highest training time, whereas different weight initiation techniques contribute in reducing the training times for the network.

关键词： Artificial neural network Deep neural network weight initiation

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Charging Load Prediction Method for Electric Vehicles using CNN-GRU-Attention model with SSA optimization strategy 7

Charging Load Prediction Method for Electric Vehicles using ...

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7th International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2025

作者： Fan, Xinyu Shvedov, Galaktion V. Cai, Chuqiao National Research University 'MPEI' Electrical Power Systems Department Moscow Russia Bauman Moscow State Technical University Faculty of Computer Science and Control Systems Information Processing and Management Systems Department Moscow Russia

ISBN: (纸本)9798331531836

This paper proposes a CNN-GRU-Attention model for short-term charging load prediction at electric vehicle (EV) charging stations. The model leverages the strengths of Convolutional Neural Networks (CNN) for feature extraction and dimensionality reduction, while Gated Recurrent Units (GRU) enhance temporal sequence learning. The attention mechanism assigns dynamic weights to the extracted features, improving the prediction accuracy. Hyperparameters are optimized using the Sparrow Search Algorithm (SSA), and an improved version, ISSA, further enhances model performance. Experimental results demonstrate that the CNN-GRU-Attention model outperforms baseline models, including CNN, GRU, LSTM (Long Short-Term Memory), and BiLSTM (Bidirectional Long Short-Term Memory), with higher prediction accuracy and fewer trainable parameters. The model's predictive capabilities enable optimization of power supply strategies in smart grids. However, it faces challenges in long-term load prediction and computational resource demands during hyperparameter optimization. © 2025 IEEE.

关键词： Convolutional neural networks

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Development of an "Artificial Lung" System for Use in Indoor Air Quality Testing 21st

Development of an "Artificial Lung" System for Use in Indoor...

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21st Polish control Conference, PCC 2023

作者： Kozyra, Andrzej Lipczynska, Aleksandra Koper, Piotr Babisz, Radoslaw Madej, Damian Nowakowski, Konrad Karwatka, Jakub Tomczok, Dominik Department of Measurements and Control Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Heating Ventilation and Dust Removal Technology Faculty of Energy and Environmental Engineering Silesian University of Technology Gliwice Poland

ISBN: (纸本)9783031351693

"Artificial lung" is a device that simulates breathing process of occupants in a room. This allows you to safely test, e.g., the impact of HVAC systems on the spread of pathogens. The paper describes the concept of the device, its construction and performed tests. The project was implemented by students of two faculties: Automatic control, Electronics and computer science, and Energy and Environmental Engineering. Project supervisors from both faculties provided substantive supervision and support for students. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Air quality

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Tire - Road Friction Characteristics Estimation Based on ESO Observer and LSTM Neural Networks

Tire - Road Friction Characteristics Estimation Based on ESO...

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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Tianyu, Hou Kulik, Alexey A. Faculty of Computer Science and Control Systems Bauman Moscow State Technical University Moscow Russia

ISBN: (纸本)9798331511241

To enhance the perception of road surface information during autonomous driving, this paper proposes a Tire-Road Friction Characteristics (TRFC) estimation algorithm that integrates a 7-DOF vehicle dynamics model, the Magic Formula, an improved adaptive Extended State Observer (ESO), and Long Short-Term Memory (LSTM) neural networks. The vehicle's key state parameters are obtained through the dynamics model, while the tire forces and the ESO observer provide accurate estimates of the utilized TRFC and slip ratio. Furthermore, an improved LSTM model, incorporating the Physics-Informed Neural Network (PINN) approach based on physical constraints, is employed to estimate the TRFC and critical vehicle state parameters. The algorithm's reliability and robustness are validated through MATLAB/Simulink and CarSim co-simulation experiments. The results demonstrate that the proposed method maintains high accuracy even under highly dynamic and uncertain conditions. This approach can serve as a complementary technique for fusion with vision and LiDAR-based TRFC identification methods, providing theoretical support for improving the safety and stability of autonomous vehicles. © 2025 IEEE.

关键词： Stiction

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Biological Influence of Low-Frequency Electromagnetic Field Exposition Based on Selected Cell Death Markers 15

Biological Influence of Low-Frequency Electromagnetic Field ...

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15th International Conference Elektro, ELEKTRO 2024

作者： Cichorowski, Robert Kocikowska, Olga Bajtos, Marek Radil, Roman Janousek, Ladislav De Carrillo, Daria Gendosz Student, Sebastian Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Faculty of Automatic Control Electronics and Computer Science Department of Engineering and Systems Biology Gliwice Poland Silesian University of Medicine Faculty of Medical Sciences Department of Physiology Katowice Poland Faculty of Electrical Engineering and Informational Technology Department of Electromagnetic and Biomedical Engineering Žilina Slovakia Silesian University of Medicine Faculty of Medical Sciences in Zabrze Department of Histology Zabrze Poland Silesian University of Technology Biotechnology Center Gliwice Poland

ISBN: (纸本)9798350372359

This work presents the results of the examination of the HeLa cell line exposure on the ELF-EMF (extremely low-frequency electromagnetic field). In particular, the relationship between ELF-EMF exposition time and cell death. The examination of cell death hallmarks were examined by estimation levels of selected proteins - FACL4 (a protein that is a part of the ferroptosis pathway) and CK18 (Cytokeratin related to necrosis and apoptosis pathways) in the proposed model of workers exposed to ELF-EMF week. © 2024 IEEE.

关键词： Electromagnetic fields

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Tire—Road Friction Characteristics Estimation Based on ESO Observer and LSTM Neural Networks

Tire—Road Friction Characteristics Estimation Based on ESO ...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Hou Tianyu Alexey A. Kulik Faculty of Computer Science and Control Systems Bauman Moscow State Technical University Moscow Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

关键词： Adaptation models Accuracy Friction Heuristic algorithms Neural networks Mathematical models Vehicle dynamics Long short term memory Autonomous vehicles Thermal stability

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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

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computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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Reference Tracking control of a Nonlinear Epidemiological Model with State Estimation 9

Reference Tracking Control of a Nonlinear Epidemiological Mo...

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9th International Conference on control, Decision and Information Technologies, CoDIT 2023

作者： Csutak, Balázs Szederkényi, Gábor Pazmany Peter Catholic University Faculty of Information Technology and Bionics Budapest Hungary Institute for Computer Science and Control Systems and Control Laboratory Kende u. 13-17 BudapestH-1111 Hungary

ISBN: (纸本)9798350311402

In this paper, a reference tracking controller for an 8-compartment epidemic model is proposed. The dynamical model describing the disease spread and progression is given in nonlinear input-affine form. The manipulable input is the transmission rate, while the output to be tracked is the number of infected people. The model parameters correspond to the COVID-19 pandemic. The control design uses a simple SEIR model and it is based on feedback linearization combined with extended Kalman filter for state estimation. Simulation results show good tracking performance even with model mismatch and significant parameter uncertainty. © 2023 IEEE.

关键词： COVID-19

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Analog, Programmable Switched Capacitor FIR Filter Based on Rotator Architecture Implemented in CMOS Technology 26

Analog, Programmable Switched Capacitor FIR Filter Based on ...

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26th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Pawlowski, Pawel Dlugosz, Rafal Radkowski, Marek Watly, Mateusz Dabrowski, Adam Poznań University of Technology Division of Signal Processing and Electronic Systems Faculty of Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Poznań60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350304985

The paper presents project and its verification of a prototype integrated circuit containing an analog, programmable finite impulse response (FIR) filter, implemented in CMOS 350 nm technology. The structure of the filter is based on the switched capacitor technique. In circuits of this type, one of main challenges is an efficient implementation of filter coefficients, which result from several factors described in this work. When implementing such filters as programmable circuits, the values of their coefficients have to be limited to a selected range, i.e. a given resolution in bits. In the implemented prototype filter, the filter coefficients are represented by 6 bits in sign-magnitude notation, so they can take 63 different values only. In such filters, it is not possible to directly implement any frequency response of the filter. Each time, it is necessary to properly round the theoretical values of the coefficients so that they fit into the available range of discrete values resulting from the implementation. The authors of the work designed an algorithm that allows such matching. The paper also presents results of measurements of the prototype chip. © 2023 Division of Signal Processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： FIR filters

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