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Experimental Study and State of Charge Estimation of A123 Li-Ion Cylindrical Cell Using Passivity Observer in Different Temperatures 11

Experimental Study and State of Charge Estimation of A123 Li...

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11th IEEE International Conference on Power Electronics, Drives and Energy systems, PEDES 2024

作者： Aakash, P.A. Manivannan, R. School of Electrical and Electronics Engineering Sastra Deemed To Be University Tamil Nadu Thanjavur 613401 India Sashe Sastra Deemed To Be University Artificial Intelligence and Control Systems (AI & CS) Lab Dept. of Mathematics Tamil Nadu Thanjavur 613401 India

Accurate estimation of State of Charge (SoC) in lithium-ion batteries (LIBs) is vital for effective energy management, particularly under varying temperatures. This study develops a temperature-dependent passivity observer for SoC estimation using the Lyapunov and linear matrix inequality (LMI) approaches, applying passivity theory for the first time. Using a thermal chamber, SoC measurements were taken at 0°C, 25°C, and 50°C. By integrating temperature effects into the observer framework, we significantly improved SoC estimation accuracy. Extensive simulations and experimental validation demonstrated superior performance over traditional methods. This advancement promotes more efficient and reliable battery management systems (BMS), optimizing LIB usage in various electric vehicle (EV) applications. © 2024 IEEE.

关键词： A123 Cyclindrical Cell Electric Vehicle Hybrid Pulse Power Characterization Linear Matrix Inequality Passivity State of Charge

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Methods for Developing Effective Models of Biomedical Processes in MATLAB

Methods for Developing Effective Models of Biomedical Proces...

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International Conference on Modern Problems of Radio engineering, Telecommunications and computer Science

作者： Kostyantyn Kolisnyk Viktoriia Kolisnyk Torsten Wik Dept. Industrial and Biomedical Electronics National Technical University “Kharkiv Polytechnic Institute” Kharkov Ukraine Dept. Media Engineering and Information Radioelectronic Systems Kharkiv National University of Radio Electronics Kharkiv Ukraine Dept. Systems and Control Technology Electrical Technology Institutionen for elektroteknik Chalmers tekniska hogskola Gothenburg Sweden

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

Mathematical modeling in the study of dynamic processes in biomedicine is currently widely used. The use of mathematical statistics and formal logic methods in the analysis of complex biomedical processes is especially important for increasing the efficiency of biomedical research. Taking into account the established growth of the technical base of research, the development of new technologies and technical means for obtaining and processing statistical biomedical information, the use of mathematical modeling methods is a fairly promising direction in scientific and practical work. In this paper the authors studies the possibility of improving the quality of biomedical process modeling using the MATLAB software environment. The research was carried out on real statistical material obtained by the authors during a full-scale experiment to study the impact of a complex of external factors on patients. The results obtained in this work can be used in the implementation of scientific and applied research of biomedical processes.

关键词： Process modeling Biological system modeling Market research Mathematical models Data models Software Telecommunications Logic MATLAB Medical diagnostic imaging

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Multi-core parallel processing technique to prepare the time series data for the early detection of DDoS Flooding Attacks 8

Multi-core parallel processing technique to prepare the time...

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8th International Conference on Computing for Sustainable Global Development, INDIACom 2021

作者： Kumar, S. Ratan Kumari, V. Valli Raju, K.V.S.V.N. Anil Neerukonda Institute of Technology and Sciences Dept. of Computer Sc. and Engineering Visakhapatnam India Andhra University Dept. of Computer Sc. and Systems Engineering Visakhapatnam India Gayatri Vidya Parishad College for Degree and PG Courses Dept. of Computer Sc. and Engineering Visakhapatnam India

ISBN: (纸本)9789380544434

Distributed Denial of Service (DDoS) attacks pose a considerable threat to Cloud Computing, Internet of Things (IoT) and other services offered on the Internet. The victim server receives terabytes of data per second during the DDoS attack. It may take hours to examine them to detect a potential threat, leading to denial of service to legitimate users. Processing vast volumes of traffic to mitigate the attack is a challenging task for network administrators. High-performance techniques are more suited for processing DDoS attack traffic compared to Sequential Processing Techniques. This paper proposes a Multi-Core Parallel Processing Technique to prepare the time series data for the early detection of DDoS flooding attacks. Different time series analysis methods are suggested to detect the attack early on. Producing time series data using parallel processing saves time and further speeds up the detection of the attack. The proposed method is applied to the benchmark data set CICDDoS2019 for generating four different time series to detect TCP-based flooding attacks, namely TCP-SYN, TCP-SYN-ACK, TCP-ACK, and TCP-RST. The implementation results show that the proposed method can give a speedup of 2.3 times for processing attack traffic compared to sequential processing © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Denial-of-service attack

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Improving E-commerce Fraud Detection via Machine Learning: Comparative Evaluation of Model Effectiveness

Improving E-commerce Fraud Detection via Machine Learning: C...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Gangalakshmi, T. Sathya, V. Vinodhini, S. Chandralekha, E. Usha, V. Ravikumar, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept.of CSE Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Dept.of Computer Science and Business Systems Chennai India Velammal Engineering College Dept. of Information Technology Chennai India

ISBN: (纸本)9798350319200

In the fast-growing E-commerce world, fraud has become a major concern, causing economic losses and eroding consumer trust. This work aimed to detect E-commerce fraud using machine learning on a customer dataset. The dataset contained transactional and customer data, requiring preprocessing to handle missing values and categorical features. Four models - Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Classifier - were trained and evaluated for fraud detection accuracy using metrics like Accuracy, Precision, Recall, and F1-Score. Results revealed distinct model strengths and weaknesses. Logistic Regression achieved balanced performance, while Random Forest and Gradient Boosting excelled in certain aspects. Support Vector Classifier also demonstrated competitive results. This work highlights the potential of machine learning in E-commerce fraud prevention, enhancing transaction security and trust. Further research could refine and advance fraud detection techniques for online platforms. © 2023 IEEE.

关键词： E-commerce fraud Fraud detection Hybrid approach Machine learning

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POCS-based Image Compression: An Empirical Examination

POCS-based Image Compression: An Empirical Examination

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International Conference on Green Technology and Sustainable Development (GTSD)

作者： Truong-Dong Do Le-Anh Tran Thanh-Dat Nguyen Nghe-Nhan Truong Dong-Chul Park My-Ha Le Dept. of Aerospace Systems Engineering Sejong University Seoul South Korea Dept. of Electronics Engineering Myongji University Yongin South Korea Dept. of Research and Development OCST Co. Ltd. Seoul South Korea Dept. of Computer Science & Engineering Sejong University Seoul South Korea Dept. of Electrical & Electronics Engineering HCMC University of Technology & Education Ho Chi Minh City Vietnam

ISBN: (数字)9798350386073

ISBN: (纸本)9798350386080

This paper investigates the applicability of the Pro-jection onto Convex Set (POCS)-based clustering algorithm to image compression tasks. The POCS-based clustering approach treats all data points in a given dataset as non-intersecting convex sets and performs POCS-based parallel projections from each cluster prototype onto corresponding member data points to minimize an objective function and update cluster prototypes. The POCS-based clustering algorithm has been proven to be able to yield promising results against other prevailing clustering approaches in terms of convergence time and clustering error on general clustering tasks. In this study, a comparison of various clustering schemes for image compression applications has been conducted. The evaluations and analyses on various standard test images verify that the POCS-based clustering algorithm can perform competitively against other conventional clustering methods in image compression problems.

关键词： Image coding Clustering methods Clustering algorithms Prototypes Linear programming Sustainable development Standards

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Online Trained Model of Tweets Expertise

Online Trained Model of Tweets Expertise

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Information Technology and Nanotechnology (ITNT), International Conference on

作者： Elena Mozaidze Sergei Zuev Petr Kabalyants Dept. of computer software and automated systems Belgorod State Technological University named after V.G. Shukhov Belgorod Russia Dept. of computer engineering and modelling V.I. Vernadsky Crimean Federal University Simferopol Russia

ISBN: (数字)9798350372939

ISBN: (纸本)9798350372946

The paper proposes a model for the examination of tweets (brief texts and messages) in Russian. The model is based on the authors' explorations on the identification of tweets meanings. The main idea is to apply the latest developed quantum machine learning method to the hierarchical classification models of texts. This approach avoids the text corpus necessity and supports the online learning due to the very design. The model proposed is tested in the experiment. The target content was given in the experiment and the model is used to measure the correspondence between the target topics and the test messages. The result of the experiment shows that the proposed method allows to distinguish the target content in the test text.

关键词： Natural languages Machine learning Information technology Nanotechnology

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Multi-agent and Reinforcement Learning Schemes for Demand Response Estimation in Distributed Microgrids

Multi-agent and Reinforcement Learning Schemes for Demand Re...

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International Conference on Energy Conservation and Efficiency (ICECE)

作者： Muhammad Ikram Salman Ahmed Safdar Nawaz Khan Marwat Muhammad Nasir Dept. of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan Department of Physics Allama Iqbal Open University Islamabad Pakistan

Demand response (DR) is an approach that encourages consumers to shape consumption patterns in peak demands for the reliability of the power system and cost minimization. The optimal DR scheme has not only leverages the distribution system operators (DSOs) but also the consumers in the energy network. This paper introduced a multi-agent coordination control and reinforcement learning approach for optimal DR management. Each microgrid is considered an agent for the state and action estimation in the smart grid and programmed rewards and incentive plans. In this regard, the Multi-agent Markov game (MAMG) is utilized for the state and action. At the same time, the reward is articulated through reinforcement learning deep Q-network (DQN) and deep deterministic policy gradient (DDPG) schemes. The proposed DR model also encourages consumer participation for long-term incentivized benefits through integrating battery energy storage systems (BESS) in the SG network. The reliability of DQN and DDPG schemes is demonstrated and observed that the dynamically changing electricity cost is reduced by 19.86%. Moreover, the controllability of complex microgrids is achieved with limited control information to ensure the integrity and reliability of the network. The proposed schemes were simulated and evaluated in MATLAB and Python (PyCharm IDE) environments.

关键词： Costs Estimation Microgrids Reinforcement learning Mathematical models Power system reliability Smart grids

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jiahui Zhai Jing Bi Haitao Yuan Jinhong Yang Jia Zhang MengChu Zhou College of Computer Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions.

关键词： Energy consumption Quantum computing Multitasking Energy efficiency Internet Manufacturing Optimization Sorting Industrial Internet of Things Genetic algorithms

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Machine Learning Models for Cardiovascular Disease Prediction: A Comparative Study 7

Machine Learning Models for Cardiovascular Disease Predictio...

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Yan, Chao Xing, Yiluan Liu, Sensen Gao, Erdi Wang, Jinyin Northeastern University Department of Electrical and Computer Engineering Sunnyvale United States Indiana University Bloomington Economics Department Bloomington United States Washington Univ. in St. Louis Dept. Electr. and Systems Eng St. LouisMO United States New York University Tandon School of Engineering New York United States Stony Brook University Department of Computer Science New York United States

ISBN: (纸本)9798350352009

Cardiovascular diseases (CVDs) pose a significant threat to global public health, affecting individuals across various age groups. Factors such as cholesterol levels, smoking, alcohol consumption, and physical inactivity contribute to their onset and progression. Enhancing our understanding of CVD etiology and informing targeted interventions for disease prevention and management remains a critical challenge. In this study, we address the task of predicting the likelihood of individuals developing CVDs using machine learning techniques. Specifically, we explore three approaches: the k-nearest neighbors (KNN) algorithm, logistic regression, and the random forest algorithm. Leveraging a comprehensive dataset sourced from Kaggle, encompassing 11 relevant factors, we conduct a series of experiments to identify the most influential predictors of CVDs. Our analysis aims not only to forecast disease occurrence but also to elucidate the primary determinants contributing to its manifestation. Through comparative analysis of the three methodologies, we demonstrate that the random forest algorithm exhibits superior performance in terms of predictive accuracy. This research represents a significant step towards leveraging machine learning techniques to enhance our understanding of CVD dynamics and inform targeted interventions for disease prevention and management. © 2024 IEEE.

关键词： Contrastive Learning

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Strategies for Optimizing Time Series Visual Data Exploration

Strategies for Optimizing Time Series Visual Data Exploratio...

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ACS/IEEE International Conference on computer systems and Applications

作者： Heba Helal Mohamed A. Sharaf College of Information Technology United Arab Emirates University UAE Dept. of Computer Engineering and Systems College of Engineering Mansoura University Egypt

Dealing with high-dimensional time series data makes the process of recommending visualizations with "interesting" insights difficult. The challenge originates from finding a way to obtain the recommended visualizations efficiently without compromising their quality. Identifying such visualizations manually is considered a labor-intensive and time-consuming process. In response, this paper introduces different techniques designed to optimize the automated recommendation process. These techniques are entirely based on the concept of computation sharing and pruning. Furthermore, we provide a glimpse into our future research works in PhD thesis. The objective is to broaden the scope of our current work and enhance the generality of our problem statement.

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