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Analysis of Power Consumption on Laser Solder Joints of Electric Connector

Chinese Journal of electronics 2025年第4期23卷 666-668页

作者： Zhi Zeng Xunbo Li Miao Li Bo Huang Rui Wang Chuanxi Xia School of Mechatronics Engineering University of Electronic Science and Technology of China Chengdu China School of Control Science and Engineering Hebei Institute of Technology Tianjin China Sichuan Aerospace Vocational and Technical College Chengdu China

Laser soldering process was introduced in Universal serial bus (USB) 2.0 electric connector to improve the mechanical and electrical bonding reliability. While, the effects of laser soldering technology on electric connector solder joints need to be estimated completely, especially on power consumption. The combined method based on numerical simulation and the Accelerated temperature experiment (ATE) was developed to analyze the power consumption of USB 2.0 electric connector in this paper. The ATE contains thermal cycle and thermal shock tests, and the four-electrode method is used to obtain the conductivity of solder joints. Numerical modeling and analysis was used to quantify the power consumption and optimize the geometry of solder joints, because the electric experimental measurements of power consumption during ATE are time-costing and often intractable. Accurate knowledge of the power consumption is a prerequisite for the reliability of the electric connector.

关键词： Connectors Laser theory Power demand Power lasers Conductivity Universal Serial Bus Laser modes Iron Reliability Soldering

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Analysis of the Operating Mode for the Photovoltaic Module by Experimental Methods

Analysis of the Operating Mode for the Photovoltaic Module b...

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2024 Conference of Young Researchers in Electrical and electronic engineering, ElCon 2024

作者： Mohammed, Hayder J. Ali, Qays A. Rasool, Bishro H. Al-Denenawe, Asra H. Peter the Great St. Petersburg Polytechnic University Institute of Energy Saint Petersburg Russia University of Misan College of Engineering Department of Mechanical Engineering Misan Iraq Northern Technical University College of Oil and Gas Techniques Engineering Department of Fuel and Energy Kirkuk Iraq Ministry of Electricity Kurdistan Region Government Erbil Iraq Peter the Great St. Petersburg Polytechnic University Institute of Electronic and Telecommunications Saint Petersburg Russia Ministry of Construction Housing Municipalities and Public Work Baghdad Iraq

ISBN: (纸本)9798350360646

conducting an experimental approach to improve the production of photovoltaic (PV) modules is important to raise the efficiency of the solar power system. Within the framework of this paper, the possibility of increasing the production of solar panels and optimizing their operation is considered by different conditions like irradiation, angle of incidence of light rays, temperature and load. In the Theoretical Electricity Laboratory at the Higher Voltage School, Institute of Energy, Peter the Great St. Petersburg Polytechnic University, several experiments were conducted on the laboratory solar station device (GalSen- NEE4-MFESE-N-R), which simulates the solar system as a model of a photovoltaic solar power plant. © 2024 IEEE.

关键词： Irradiation Power PV Renewable energy volt-Ampere

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An Enhanced Analysisof Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function

arXiv

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arXiv 2024年

作者： Ismaeel, Ayad Ghany Mary, S.J. Jereesha Anitha, C. Logeshwaran, Jaganathan Mahmood, Sarmad Nozad Alani, Sameer Shather, Akram H. Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk36001 Iraq Annai Velankanni College of Engineering Potalkulam Kanyakumari629401 India Saveetha School of Engineering Chennai600124 India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore641202 India Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk Iraq Computer Center University of Anbar Baghdad55431 Iraq Department of Computer Engineering Technology Al-Kitab University Altun Kopru Kirkuk Iraq

Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF-based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating theserich and diverse data. To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real-world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF-based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large-scale data sets. © 2024, CC BY-NC-ND.

关键词： Radial basis function networks

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Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

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Computer Systems Science & engineering 2023年第12期47卷 2791-2814页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering the Islamic UniversityNajafIraq

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and *** rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within *** the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information *** paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)***,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant *** by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for ***,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute *** propose a COA-based feature selection approach for medical data classification using machine learning *** approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature *** evaluate the performance of our approach on various medical datasets employing diverse machine learning *** results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification *** study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various *** provide further validation,we conduct detailed experiments on a benchmark

关键词： Association rule mining data classification healthcare data machine learning parameter tuning data mining feature selection MLARMC-HDMS COA stochastic gradient descent Apriori algorithm

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Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

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Computer Systems Science & engineering 2023年第12期47卷 3127-3144页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University forWomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering The Islamic UniversityNajafIraq

Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life *** CI is useful in the UAV domain as it produces efficient,precise,and rapid ***,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city *** the benefits of UAVs,security remains a major challenging *** addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,*** paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city *** purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class *** proposedMDLS-UAVIC model follows a two-stage process:encryption and image *** encryption technique for image encryption effectively encrypts the UAV ***,the image classification process involves anXception-based deep convolutional neural network for the feature extraction ***,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the *** experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various *** achieved a high accuracy of 98%.

关键词： Computational intelligence unmanned aerial vehicles deep learning metaheuristics smart city image encryption image classification

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Traditional Chinese Medicine Health Status Identification with Graph Attention Network 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Fu, Amin Ma, Jishun Wang, Chuansheng Zhou, Changen Li, Zuoyong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona08034 Spain Research Base of Traditional Chinese Medicine Syndrome Fujian University of Traditional Chinese Medicine Fuzhou350122 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The Traditional Chinese Medicine Health Status Identification plays an important role in TCM diagnosis and prescription recommendation. In this paper, we propose a method of Status Identification via Graph Attention Network, named SIGAT, which captures the complex medical correlation in the symptom-syndrome graph. More specifically, we construct a symptom-syndrome graph in that symptoms are taken as nodes and the edges are connected by syndromes. And we realize automatic induction of symptom to state element classification by using the attention mechanism and perceptron classifier. Finally, we conduct experiments by using hamming loss, coverage, 0/1 error, ranking loss, average precision, macro-F1 score, and micro-F1 score as evaluation metrics. The results demonstrate that the SIGAT model outperforms comparison algorithms on Traditional Chinese Medicine Prescription Dictionary dataset. The case study results suggest that the proposed method is a valuable way to identify the state element. The application of the graph attention network classification algorithm in TCM health status identification is of high precision and methodological feasibility. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Motor-Temperature-Aware Optimal Energy Management for Dual-Motor Electric Buses

Motor-Temperature-Aware Optimal Energy Management for Dual-M...

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IEEE Conference on Vehicle Power and Propulsion (VPPC)

作者： Sheng Yu Cuneyt Haspolat Yaprak Yalcin Saleh Msaddi Boli Chen Simos A. Evangelou Imad M. Jaimoukha Dept. of Electrical and Electronic Engineering Imperial College London UK AVL Türkiye Research & Engineering Istanbul Türkiye Dept. of Control and Automation Engineering Istanbul Technical University Türkiye Dept. of Electronic and Electrical Engineering University College London UK

ISBN: (数字)9798331541606

ISBN: (纸本)9798331541613

This work studies the energy management performance of heavy-duty electric buses (EBs) equipped with two electric motors onboard their powertrains. Instead of merely considering the effects of load torque and angular speed on motor efficiency, the proposed optimal scheme further includes the effect of different temperatures on motor performance. After identifying a novel motor temperature model and generating temperature-dependent three-dimensional motor efficiency maps based on experimentally collected data, the energy management task is formulated as an optimal control problem and solved by dynamic programming that guarantees the global optimality of the generated solution. Compared with an existing single-motor powertrain and dual-motor powertrain controlled by benchmark strategies, the heavy-duty EB can benefit from a maximum of 11.96% battery electric energy consumption saving after utilizing the proposed temperature-aware optimal dual-motor energy management scheme.

关键词： Temperature distribution Solid modeling Energy consumption Torque Optimal control Mechanical power transmission Propulsion Temperature control Energy management Load modeling

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A Simulation of a Design and Implementation of a New Low- Cost Prototype Portable Smart Ventilator

A Simulation of a Design and Implementation of a New Low- Co...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Shereen Qisam Bless Jameel Kadhim Abed Mohannad Jabbar Mnati Medical Instrumentation Techniques Engineering Electrical Engineering Technical College Middle Technical University Baghdad Iraq Department of Electronic Technology Institute of Technology Baghdad Middle Technical University Al-Za ‘franiya Baghdad Iraq

The outbreak of the Covid-19 virus prompted many engineers and researchers around the world to seek to develop mechanical ventilation devices and make them easy to use and affordable. This paper presents a simulation model for a group of medical sensors and gives very accurate results. This model contributes to the development and improvement of the artificial breathing system by comparing the results between the simulation model and the realistic response of the human lung.

关键词： Mechanical sensors Ventilators Costs Computer viruses Pulmonary diseases Computational modeling Lung

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Improved Malaria Cells Detection Using Deep Convolutional Neural Network

Improved Malaria Cells Detection Using Deep Convolutional Ne...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Sarmad Nozad Mahmood Swash Sami Mohammed Ayad Ghany Ismaeel Hülya Gükalp Clarke Iman Nozad Mahmood Dlnya Abdulahad Aziz Sameer Alani Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk Iraq Electrical and Electronics Engineering Ondokuz Mayıs University Samsun Turkey Kirkuk General Education Directorate Kirkuk Iraq Computer Center University of Anbar Anbar Iraq

This research presents a deep convolutional neural network (CNN) as a solution for identifying malarial cells that are infected. The AI model suggested in this work comprises a three-layered CNN and a two-layered dense neural network. The model can capture both minor and significant features by utilizing CNN, thereby extracting a maximum amount of information from the input data. The model is trained over 20 epochs and evaluated using the binary cross entropy loss function and accuracy metric to assess its performance. Remarkably, the proposed model achieved an impressive accuracy of 96% and maintained a loss value below 0.2 for both the training and validation datasets. Ultimately, this research demonstrates promising potential for automating the detection of malaria through parasite cell counting.

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Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

arXiv

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arXiv 2024年

作者： Ismaeel, Ayad Ghany Krishnadas, J. Manishankar, S. Natarajan, Yuvaraj Mahmood, Sarmad Nozad Alani, Sameer Shather, Akram H. Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk36001 Iraq Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur Kodakara680684 India Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore641062 India Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk36001 Iraq Computer Center University of Anbar Ramadi55431 Iraq Department of Computer Engineering Technology Al-Kitab University Altun Kopru Kirkuk36001 Iraq

This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns' dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods. © 2024, CC BY-NC-ND.

关键词： Classification (of information)

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