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The Influence of Overcharging and Over-Discharging on the Capacity Degradation of Lithium-Ion Batteries

The Influence of Overcharging and Over-Discharging on the Ca...

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IEEE Conference on Vehicle Power and Propulsion (VPPC)

作者： Joelton Deonei Gotz Marco Antonio Simões Teixeira Fernanda Cristina Corrêa Emilson Ribeiro Viana Alceu André Badin Milton Borsato Graduate Program in Electrical and Computer Engineering Federal Technological University of Paraná (UTFPR-CT) Curitiba Brazil Graduate Program in Informatics (PPGIa) Pontifical Catholic University of Paraná (PUC-PR) Curitiba Brazil Graduate Program in Electrical Engineering Federal Technological University of Paraná (UTFPR-PG) Ponta Grossa Brazil Physics Department Graduate Program in Physics and Astronomy Federal Technological University of Paraná (UTFPR-CT) Curitiba Brazil Graduate Program in Mechanical Engineering Federal Technological University of Paraná (UTFPR-CT) Curitiba Brazil

ISBN: (数字)9798331541606

ISBN: (纸本)9798331541613

Lithium-ion batteries (LIB) are the best technology for supplying and storing energy for electric mobility systems. Despite that, this technology is sensitive to abuses that can compromise its lifetime and cause fire and explosion risks. In this context, if LIB is not correctly controlled, some abuses, such as overcharging (OC) and over-discharging (OD), can be observed and provoke a loss of capacity. Therefore, this work presented the application of several experiments in cells, of the NMC type, under OC and the combination of OC and OD. The results indicated that cells charged until 4.4 and 4.5 V and discharged until 3.0 V do not lose capacity for twenty-five cycles, but more cycles should be done to understand the impact over time. On the other side, the cell charged until 4.6 and 4.8 V had its security protection romped in the fourth charging cycle, and the cell charged until 5.0 V had its safety compromised in the first charging cycle. Conversely, cells with a combination of OC and OD were more compromised. In this context, while a cell is submitted only with OC and does not lose capacity, a cell under OC and then under OD had its protection romped in the third charging cycle. Cells under 4.6 V and 4.8 V lost capacity during the second charging cycle. In conclusion, OC provokes a high level of degradation in the cell, but the combination of OC and OD causes even more degradation.

关键词： Degradation Lithium-ion batteries Battery management systems Propulsion Security Reliability Protection Stress Monitoring Predictive maintenance

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Efficient Utilization of Metaheuristic Techniques for Profit Maximization in Combinatorial Production Planning: A Comparative Study

Efficient Utilization of Metaheuristic Techniques for Profit...

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International Conference on Control, Instrumentation, Energy and Communication (CIEC)

作者： Arijit Mukherjee Surojit Mukherjee Deptartment of Electrical Engineering Future Institute of Technology Kolkata India Post-Graduate Program in Management Great Lakes Institute of Management Chennai India

Efficient production planning requires well-structured models compatible with metaheuristic and other optimization techniques. This study compares three established metaheuristic methods - sanitized-teaching-learning-based optimization (s-TLBO), Particle Swarm Optimization (PSO), and Differential Evolution (DE) with a termination criterion of equal number of functional evaluations - for profit-maximizing production planning. The focus is on a single-level problem involving complex constraints, non-linear costs, budget and raw material limitations. The paper presents two solution strategies: a penalty-based and a correction-based approach. Analyzing 300 unique instances of the problem reveals that s-TLBO implemented with correction-based approach consistently outperforms the other methods in devising optimal production plans for two Saudi Arabian petrochemical industries with 24 products, 54 processes, and three raw materials for plant 1 and two raw materials for plant 2.

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Optimal Integration of Metaheuristic Techniques with Correction-Based Approaches for Profit Maximization in Combinatorial Production Planning: A Comparative Analysis

Optimal Integration of Metaheuristic Techniques with Correct...

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International Conference on Control, Instrumentation, Energy and Communication (CIEC)

To enhance production planning efficiency, it’s crucial to have well-structured models that align with metaheuristic and other optimization techniques. This research conducts a comparative analysis of three unique metaheuristic methods: sanitized-teaching-learning-based optimization (sTLBO), Particle Swarm Optimization (PSO), and Differential Evolution (DE) with a termination criterion of equal number of functional evaluations. The primary focus lies on profit-maximizing production planning within a single-level framework, encompassing intricate domain constraints, nonlinear costs, budget limitations and raw material constraints. The paper introduces three correction-based solution approaches. Upon evaluating 300 distinct instances of the problem, the study reveals that s-TLBO integrated with correction-based approach 1 and 3 consistently outperforms the other methods when it comes to devising optimal production plans for a Saudi Arabian petrochemical industry. This industry scenario involves 24 products, 54 processes, and three raw materials.

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3D Stackable Vertical Ferroelectric Tunneling Junction (V-FTJ) with on/off Ratio 1500x, Applicable Cell Current, Self-Rectifying Ratio 1000x, Robust Endurance of 109Cycles, Multilevel and Demonstrated Macro Operation Toward High-Density BEOL NVMs

3D Stackable Vertical Ferroelectric Tunneling Junction (V-FT...

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2023 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2023

作者： Lee, J.-Y. Chang, F.-S. Hsiang, K.-Y. Chen, P.-H. Luo, Z.-F. Li, Z.-X. Tsai, J.-H. Liu, C.W. Lee, M.H. National Taiwan Normal University Institute and Undergraduate Program of Electro-Optical Engineering Taiwan National Taiwan University Graduate School of Advance Technology Taiwan National Yang Ming Chiao Tung University Institute of Electronics Department of Electronics Engineering Taiwan National Taiwan Normal University Department of Electrical Engineering Taiwan

ISBN: (纸本)9784863488069

A 1500x ILRS/IHRS with a high cell current of 100 nA/ cell (J = 83 A/cm2) is achieved by antiferroelectric (AFE) vertical ferroelectric tunnel junctions (V-FTJs) that demonstrates multilevel, a self-rectifying rate > 1000x, and macro operation. The stackable 3 D architecture integrating multiple layers of AFE V-FTJs with contact optimization further increases memory density. Robust endurance > 109 cycles at |4V| and stable nonvolatile data retention > 104 sec with extrapolation to 10 years are achieved. The proposed cell contact V-FTJ by AFE is a promising pathway toward BEOL NVMs by a NAND/NOR-based framework. © 2023 JSAP.

关键词： Ferroelectricity

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ADHD Subtype Diagnosis Through Convolutional Neural Networks Evaluation of the Connectivity Networks in Brain fMRI 9th

ADHD Subtype Diagnosis Through Convolutional Neural Networks...

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9th Latin American Congress on Biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical engineering, CBEB 2022

作者： Pedrollo, Guilherme Rodrigues Franco, Alexandre Rosa Balbinot, Alexandre Post Graduate Program in Electrical Engineering Universidade Federal do Rio Grande do Sul RS Porto Alegre90020-000 Brazil Child Mind Institute New YorkNY10022 United States Nathan Kline Institute for Psychiatric Research New YorkNY10962 United States

ISBN: (纸本)9783031494031

Attention-deficit hyperactivity disorder (ADHD) affects at least 5% of the world population. ADHD typically starts in childhood and can affect normal development that can cause serious consequences during adulthood. Moreover, there are three subtypes of ADHD, each with its symptomatic differences that require particular treatment. The development of tools capable of detecting the brain differences between each subtype and a control group can help in directing future neuropsychological studies and guide treatments. Here we investigate the hypothesis of using Convolutional Neural Networks (CNN) as a tool to detect ADHD in connectivity networks of the brain. This research used the Yeo brain parcellation maps from 28 healthy volunteers, 36 combined subtype ADHD, and 26 inattentive type ADHD. The CNN achieved a 64.71% accuracy at detecting the multi-class diagnosis (Control, ADHD combined, or ADHD inattentive) when using the complete connectivity network matrix, surpassing the individual accuracies achieved when using only the within network connectivity measures. However, the separate analysis of each network showed that the Dorsal, Default and Frontoparietal networks have better accuracy of performing the classification compared to using other within brain connectivity measures. The Default Network has shown greater accuracy at identifying ADHD-I than the other within networks or whole brain connectivity measures. These results suggest that the neurological differences of the presence of ADHD and its subtypes may be present and, therefore, affect more than one connectivity network functionality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolution

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A DEA-CCR Model Application in Clustered Stocks Portfolio with Technical Investment Strategies and Mean-Variance Model

A DEA-CCR Model Application in Clustered Stocks Portfolio wi...

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2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Navarro, M.M. Young, M.N. Prasetyo, Y.T. Camino, J.L. Navarro, B.B. Ramos, V.T. School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines School of Graduate Studies Mapúa University Manila Philippines Technological Institute of the Philippines Department of Industrial Engineering Quezon City Philippines Yuan Ze University International Bachelor Program in Engineering Taiwan Technological Institute of the Philippines Department of Civil Engineering Quezon City Philippines Technological Institute of the Philippines Department of Electrical Engineering Quezon City Philippines

ISBN: (纸本)9798350323153

The stock market is one of the investment options that offer the best potential returns. However, investors are aware of the risk associated with their investments, which is why stock portfolios are established to diversify investments or distribute investments. Managing a portfolio may be challenging because there are so many different stocks. One of the challenges is deciding how much money to invest and how to allocate it for the best results. This paper used the K-means Algorithm for cluster analysis in identifying Decision Making Unit (DMU), the stocks will be grouped according to their characteristics considering their risk and return. Asset allocation per clustered portfolio was performed and the Mean-Variance Model was used to optimize clustered portfolios. This paper aims to use Data Envelopment Analysis (DEA) to evaluate the efficiency of these clustered portfolios/DMUs. In addition, this paper introduces subjective choice in decision-making using the criterion's level of importance to help investors decide which among these efficient DMUs is a better option. The results show 4 Efficient DMUs for 2019 Data, and 3 Efficient DMUs in 2020 Data. All simulations were carried out using the MATLAB environment platform. © 2023 IEEE.

关键词： Data Envelopment Analysis K-means Mean-Variance Model Stock Market Technical Investment Strategy

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Robustness challenges in Reinforcement Learning based time-critical cloud resource scheduling: A Meta-Learning based solution

Robustness challenges in Reinforcement Learning based time-c...

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作者： Liu, Hongyun Chen, Peng Ouyang, Xue Gao, Hui Yan, Bing Grosso, Paola Zhao, Zhiming Informatics Institute University of Amsterdam Amsterdam1098 XH Netherlands Graduate School of Informatics University of Amsterdam Amsterdam1098 XH Netherlands School of Computer and Software Engineering Xihua University Chengdu610039 China School of Computer Science National University of Defense Technology Changsha410073 China College of Electrical and Control Engineering Shaanxi University of Science and Technology Xi'an710021 China School of Electrical and Electronic Engineering University of Adelaide Adelaide South Australia5005 Australia

Cloud computing attracts increasing attention in processing dynamic computing tasks and automating the software development and operation pipeline. In many cases, the computing tasks have strict deadlines. The cloud resource manager (e.g., orchestrator) effectively manages the resources and provides tasks Quality of Service (QoS). Cloud task scheduling is tricky due to the dynamic nature of task workload and resource availability. Reinforcement Learning (RL) has attracted lots of research attention in scheduling. However, those RL-based approaches suffer from low scheduling performance robustness when the task workload and resource availability change, particularly when handling time-critical tasks. This paper focuses on both challenges of robustness and deadline guarantee among such RL, specifically Deep RL (DRL)-based scheduling approaches. We quantify the robustness measurements as the retraining time and investigate how to improve both robustness and deadline guarantee of DRL-based scheduling. We propose MLR-TC-DRLS, a practical, robust Meta Deep Reinforcement Learning-based scheduling solution to provide time-critical tasks deadline guarantee and fast adaptation under highly dynamic situations. We comprehensively evaluate MLR-TC-DRLS performance against RL-based and RL advanced variants-based scheduling approaches using real-world and synthetic data. The evaluations validate that our proposed approach improves the scheduling performance robustness of typical DRL variants scheduling approaches with 97%–98.5% deadline guarantees and 200%–500% faster adaptation. © 2023 The Author(s)

关键词： Reinforcement learning

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A Method for Automatic Generation of 3D Background Models Using Fractal Models and Genetic Algorithms 11

A Method for Automatic Generation of 3D Background Models Us...

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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Watanabe, Kaito Nakayama, Hirotaka Shiraki, Atsushi Ito, Tomoyoshi Hirata, Katsumi Hoshikawa, Naoto National Institute of Technology Oyama College Advanced Course General Engineering Program Oyama Japan National Astronomical Observatory of Japan Center for Computational Astrophysics Mitaka Japan Chiba University Graduate School of Engineering Chiba Japan National Institute of Technology Oyama College Department of Innovative Electrical and Electronic Engineering Oyama Japan University of Tsukuba Graduate School of Science and Tecnology Tsukuba Japan

ISBN: (纸本)9798350306941

In recent years, 3DCG-related industries have grown rapidly and the demand for 3DCG content has increased. However, 3DCG production requires special skills and a lot of time. In particular, the creation of background data requires a lot of human resources at the production site because the data is huge and complex. Therefore, we propose an automatic creation system that generates 3D background data for 3DCG production. The proposed system consists of three functions: 3D model generation, generation parameter optimization, and fitness evaluation. The user inputs a landscape photograph as idea information into the system, and the system optimizes the generation parameters so that the 3D object shape approximates the input image. To evaluate the accuracy of the system's performance, we observe the optimization process and the 3D object output when a previously generated fractal image or landscape photos are input. Experimental results show that the system accurately optimizes the generation parameters and generates 3D objects that are almost identical to the shape of the fractal image. In addition, the parameters were also optimized with a certain degree of accuracy for landscape photographs for which no specific solution had been determined. © 2023 IEEE.

关键词： Genetic algorithms

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Evaluation of Various Transformer Field Conditions Using Cross-Combined Frequency Response Analysis to Identify Specific Deformity 4

Evaluation of Various Transformer Field Conditions Using Cro...

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4th International Conference on High Voltage engineering and Power Systems, ICHVEPS 2023

作者： Sutardi, Bagas Maulana Wulung, Ario Prakoso, Muhammad Helmi Pudiatmoko, Ary Abd Azis Reskiawan, M. Khayam, Umar Suwarno Pln Corporate University Graduate Program PLN-ITB Jakarta Indonesia Transformation and Ceo Office Pln Head Office Jakarta Indonesia UP3B Kalbar Pln UIP3BK Pontianak Indonesia School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350318678

In HV equipment, the most critical and valuable components used is transformers. Power transformers should go through standard diagnostic tests to identify deterioration or to avoid the emergence of defects that might possibly cause severe failure. Frequency Response Analysis already proven to be a promising tool in diagnostic techniques that make use of this understanding to find such changes. FRA has so far been used to detect deformation in transformers by observing changes in the frequency response curve, despite a limited understanding of what caused the changes in the initial case. Several FRA methods have been implemented recently all around the world, primarily for transformer deformation detection and diagnosis. Nonetheless, there is now an increasing amount of research focused on determining specifically the type and degree of deformation in a transformer by looking at its FRA response curve. It is very important to refer to the guidance given in the recently published IEC, IEEE, SPLN, and CIGRE standards to ensure the accuracy of the analysis of the frequency response results. An approach for transformer diagnostics is discussed in this research by doing a comparative analysis with four study cases related to various physical phenomena and examining the cross-combined between standards and field FRA results. Four FRA measurement cases are taken from the experience in PLN, namely: Tayan (water contamination), Sanggau (transformers relocation), Sambas (rebuilding transformer foundation), and Cianjur (earthquake). The evaluation results indicate that the IEEE and IEC diagnostic standards have an assessment that is closest to the actual condition that is influenced by variations in wide band frequency measurement. © 2023 IEEE.

关键词： Power transformers

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