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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Hsieh, Chi-Jung Wu, Kuang-Sung Wu, Tin-Yu Tey, Fu Jie Chen, Yi-Kai Ho, Jye Yi National Ilan University Department of Computer Science and Information Engineering Ilan County Taiwan National Ilan University Master Program of E-Learning for Multimedia and Network Communications Yilan Taiwan National Pingtung University of Science and Technology Department of Management Information Systems Pingtung County Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9781665492324

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method. © 2022 IEEE.

关键词： Genetic algorithms

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A Robust BMS Framework for DC Fast Charging in Smart Grid-Integrated EVs

A Robust BMS Framework for DC Fast Charging in Smart Grid-In...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： S.P. Sreeja D. Sreenivasulu Alok Jain Neeraj Chahuan Fatimah Dhari Jabbar Babu Naik Gugulothu Master of Computer Application New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering BMS Institute of Technology and Management Bengaluru Karnataka India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Smart grids are now connected to electric vehicles (EVs) so that micro-grids could radically in the future use EV batteries with the acronym V2G and G2V. It presents an electrical architecture of a smart Battery Management System (BMS) for DC fast charging as a Forward and Backward part which is designed to fit both V2G and G2V siding and the topological diagram. Such architecture uses a combined complex hardware associated to smart software algorithms to charge and discharge the battery using optimized all four operations of the energy transfer that are set up in order to achieve nominal state of charge and optimize the time charge for safety purpose. The real time operating software performs control over the system through the use of an on-board processor using dedicatedly designed algorithms, including novel predictive-based machine learning algorithms for smart energy performance optimization and preventive maintenance. Cyber-security protocols and renewable energy are also embedded in the design of the proposed BMS for a side of a renewable energy grid. Our simulation results prove that the proposed BMS improves vehicle range by storing more energy and increasing the battery life. A novel Battery Management System for DC fast charging EVs is presented in this paper.

关键词： Vehicle-to-grid Renewable energy sources Machine learning algorithms Software algorithms Battery management systems computer architecture Software Batteries Fast charging computer security

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Enhanced Particle Swarm Optimization based Node Localization Scheme in Wireless Sensor Networks

Enhanced Particle Swarm Optimization based Node Localization...

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Singh, Shashank Poonkuzhali, R. Nithya, G. Kumar, R. Anil Kartigeyan, J. Ramya, S. Department of Computer Science and Engineering Integral University Kurshi Road Uttar Pradesh Lucknow India Department of Information Technology K.S.Rangasamy College of Technology Tamilnadu Tiruchengode India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Maisammaguda Telangana Secunderabad India Electronics and Communication Engineering Aditya College of Engineering and Technology Andhra Pradesh Surampalem India Department of Electrical and Electronics Engineering J.B.Institute of Engineering and Technology Telangana Hyderabad India Master of Computer Applications M.Kumarasamy College of Engineering Karur Tamilnadu Thalavapalayam India

ISBN: (纸本)9781665489621

Wireless sensor networks (WSNs) are widely examined recently because of their various applications in processes which has to scatter over a vast region. sensor's locations in WSNs generally must be known. WSN comprises great quantity of sensors thereby installing global position system devices in all of them does not accept. Some of the anchor nodes with well-known locations are generally utilized. Forecasting sensor node locations from radio strength signal index are regarded as a hard optimization issue. This article introduces an Enhanced Particle Swarm Optimization based Node Localization Scheme (EPSONLS) in WSN. The presented EPSONLS technique mainly aims to determine the location of the unknown nodes in the network. To do so, the presented EPSONLS model applies the PSO algorithm to identify the node position. In addition, the EPSONLS model determines the node location with the goal of minimizing localization error and time. An extensive-ranging experimental examination is performed and the outcomes are investigated under distinct prospects. The simulation outcome demonstrated the betterment of the EPSONLS model over recent approaches. © 2022 IEEE.

关键词： Sensor nodes

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Probabilistic Modeling and Uncertainty Quantification of Harmonic Distortions in Grid-Integrated Inverters Through Bayesian Neural Networks

Probabilistic Modeling and Uncertainty Quantification of Har...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Srinivasa Rao Neeta Awasthy V. Asha Ginni Nijhawan Gurmeet Singh Ali Albawi E. Annapoorna Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of Computer Science and Engineering GL Bajaj Group of Institutions Mathura U.P. India Master of Computer Application New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of AIMLE GRIET Hyderabad Telangana India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Harmonic distortion in grid connected inverters Thus, impacts on power quality and system stability are high, and more especially for systems that use renewable energy sources. This research presents a new stochastic predictive modelling approach based on Bayesian Neural Networks for forecasting of harmonic distortions with prediction uncertainty. The proposed methodology uses data from the National Renewable Energy Laboratory (NREL) database that consists of detailed grid voltage, current under various REMs. Harmonics components derived via sophisticated signal processing algorithms then yields the desired data base for training and testing. The uncertainty of grid conditions can be modelled by means of probabilities due to the utilization of BNNs for improving the reliability of predictions. The combination of stochastic modelling and harmonic analysis in this work creates a complex approach to enhancing grid stability and power quality. As applied to renewable energy challenges, the common method enhances the proposed approach as a scalable and adaptive solution for the mitigation of harmonic impacts for future power systems.

关键词： Renewable energy sources Adaptation models Uncertainty Stochastic processes Power system harmonics Predictive models Power system stability Harmonic analysis Inverters Harmonic distortion

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Effect of CLAHE-based Enhancement on Bean Leaf Disease Classification through Explainable AI

Effect of CLAHE-based Enhancement on Bean Leaf Disease Class...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Teuku Rizky Noviandy Aga Maulana Faris Khowarizmi Kahlil Muchtar Department of Informatics Master of Artificial Intelligence Universitas Syiah Kuala Aceh Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Telematics Research Center (TRC) Universitas Syiah Kuala Aceh Indonesia

Global agriculture faces a major threat from plant diseases, particularly those affecting bean leaves, resulting in significant crop yield losses and impacting farmers' livelihoods and food production. To overcome the limitations of manual visual inspection methods, researchers have adopted computer vision and deep learning techniques for automated disease detection. This study improves the accuracy of classifying bean leaf diseases using Contrast Limited Adaptive Histogram Equalization (CLAHE) and the EfficientNetB0 architecture. Explainable AI technique, specifically Grad-CAM, is employed to analyze predicted images for transparency and interpretability. The proposed approach outperforms models trained on original images, achieving higher accuracy, precision, recall, and F1 score. CLAHE proves to be effective in optimizing CNN-based models for plant disease detection. Grad-CAM analysis reveals the model's focus on disease-related features, enhancing understanding of its behavior. This research contributes to the development of robust automated systems for plant disease detection, benefiting farmers and agricultural practitioners in decision-making.

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Management of Covid-19 Detection Using Artificial Intelligence in 2020 Pandemic 21

Management of Covid-19 Detection Using Artificial Intelligen...

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5th International Conference on Medical and Health Informatics, ICMHI 2021

作者： Ghaderzadeh, Mustafa Aria, Mehrad Student Research Committee Department and Faculty of Health Information Technology and Management School of Allied Medical Science Shahid Beheshti University of Medical Sciences Tehran Iran Department of Computer Engineering Faculty of Electrical and Computer Engineering Shiraz University Shiraz Iran

ISBN: (纸本)9781450389846

Successful early detection of Covid-19 disease plays an important role in improving the effectiveness of treatment and managing the pandemic. Various diagnostic methods for the rapid detection of COVID-19 are presented. The first and most important test to detect Covid19 is the PCR test. Studies have shown that PCR testing is time-consuming, expensive, and has a large number of false negatives. As a trend in the scientific community, artificial intelligence has succeeded in Covid19 detection and diagnosis. This article identifies the key achievements reflected in the performance measurement indicators of the application of artificial intelligence algorithms in Covid-19 detection. Besides, this study discusses the finding and future lessons as a roadmap for Corona Pandemic Age. Mean diagnosis of all AI algorithms in the studies performed through Radiology modality had sensitivity with an average higher than 95% and a specificity of higher than 92%, which have a higher diagnostic rate than of traditional radiological methods. Based on relevant research in the field of diagnosis of Covid-19, the Health care managers and Specialists could be able to manage the Pandemic much better and scientifically. Equipping devices and Radiological Center by AI Algorithms and software can cause increases covid19 identification tests and hence can cover more people exposed to Diagnosing Test. This study presents a comprehensive review of Artificial Intelligence techniques and evolving deep learning techniques for Covid-19 Detection. © 2021 ACM.

关键词： Diagnosis

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Innovations in Teaching Vibrations and Control - Reinforcing Learning by Combining Portable Lab Equipment and Virtual Labs

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IFAC-PapersOnLine 2024年第28期58卷 66-71页

作者： Britt Walker Fahim Dorsey Vanessa Young Connor Talley Coskun Tekes Tris Utschig Ayse Tekes Undergraduate students (Walker Dorsey and Young) and Ph.D. student (Talley) Department of Mechanical Engineering Kennesaw State University Marietta GA USA Assistant Professor of Electrical and Computer Engineering (C. Tekes) Professor of Mechanical Engineering (Utschig) Kennesaw State University Marietta GA USA Associate Professor of Mechanical Engineering Kennesaw State University Marietta GA USA

Most educational laboratory equipment utilized in vibrations and control labs is heavy and bulky, limiting its use within lab spaces. Further, mechanical engineering students typically gain hands-on experience only in labs that are offered separately from fundamental courses, unlike other engineering programs. Thus, the extent of student learning from labs, given their limited time, remains a concern considering students’ learning opportunities. This study presents the design and development of two portable and low-cost lab equipment devices: a compliant mechanism with unbalanced rotating masses and a DC motor with beam control under disturbances. While the former is utilized to demonstrate fundamentals of vibrations, derivation of the mathematical model, simulation, and comparison of theory and experimental data, the latter can be used for demonstration of disturbance control. Virtual labs are developed for both mechanisms in MATLAB Simscape.

关键词： Lab equipment design virtual lab simulations fundamentals of vibrations

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning 7

Impact of CLAHE-based image enhancement for diabetic retinop...

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7th International Conference on computer Science and Computational Intelligence, CSCI 2022

作者： Hayati, Mira Muchtar, Kahlil Roslidar Maulina, Novi Syamsuddin, Irfan Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-Aided diagnosis of diabetic retinopathy. © 2022 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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A Mixed-Integer Linear Programming Approach to Maintenance Budgeting in electrical Distribution Networks Considering Repair Time Uncertainty

A Mixed-Integer Linear Programming Approach to Maintenance B...

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Future Energy Solutions (FES), International Conference on

作者： Hamed Mirsaeedi Alireza Hassankashi Mahsa Sajjadi Pierluigi Siano Graduated Student from University of Tehran Tehran Iran Graduated Student from Iran University of Science and Technology Tehran Iran Department of Electrical and Computer Engineering University of Tennessee Knoxville TN USA Department of Management & Innovation Systems University of Salerno Salerno Italy

As a component of asset management strategies, maintenance management is essential for improving the reliability of the electrical Distribution Networks (EDNs). To increase system efficiency, electricity companies invest a significant effort to assign their financial sources to important system components. In this paper, a mixed-integer linear programming approach to maintenance budgeting considering restoration time uncertainty is suggested. The suggested technique finds the optimal budget for doing maintenance tasks in distribution network feeders. The objective is to reduce overall cost, which includes total interruption cost of customer $\left(\boldsymbol{T} \boldsymbol{C}_{\boldsymbol{C I C}}\right)$, total energy not supplied ${cost}\left(T C_{E N S}\right)$, total cost of materials and labor for repair $\left(T C_{R E P}\right)$, and total cost of maintenance $\left(T C_{P M}\right)$. Three cases are also taken into account to observe the effectiveness of the suggested maintenance method: Case I assumes that no maintenance actions are performed in the system, in Case II, maintenance budget for each distribution feeder is taken into consideration equally, in Case III, according to constraints, the suggested formulation allocates the optimal maintenance budget. The Roy Billinton test system’s bus number two (RBTS2) is subjected to the proposed reliability improvement approaches, and the results are discussed.

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Lightweight Pedestrian Detection through Guided Filtering and Deep Learning 10

Lightweight Pedestrian Detection through Guided Filtering an...

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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Muchtar, Kahlil Saddami, Khairun Bintang, Akhyar Cenggoro, Tjeng Wawan Pardamean, Bens Lin, Chih-Yang Ernita, Tia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Bina Nusantara University School of Computer Science Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Yuan-Ze University Department of Electrical And Engineering Taoyuan Taiwan

ISBN: (纸本)9781665436762

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. © 2021 IEEE.

关键词： Convolutional neural networks

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