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Application of the Jiles–Atherton Model to Construct the Open-Circuit Voltage Hysteresis Model and Determine the Decommissioning of Lifepo4 Batteries

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SSRN 2024年

作者： Huang, Sy Ruen Chen, Sin Yan Lai, Siu Tong Energy Conversion Laboratory Master’s Program in Electrical Engineering Feng Chia University Taiwan

Here, the Jiles–Atherton (JA) hysteresis model is constructed based on the magnetic properties between the electrolyte and electrodes and is applied to the voltage hysteresis model of LiFePO4 batteries to evaluate the battery degradation degree. By constructing the voltage hysteresis model between the open-circuit voltage (OCV) and state of charge (SOC), the authors estimated the voltage hysteresis parameters of the JA model using differential algorithm and accurately described the OCV–SOC relationship of the battery. Finally, the battery degradation is evaluated according to changes in model parameters to determine whether the battery should be ***4 batteries of identical specifications (ANR26650M1-B, 3.3 Volt, 2.5Ah,) were tested, including 30 brand-new batteries and 13 batteries that had been used for more than two years. The SOC-OCV curve data were measured under the 1C charge-discharge mode with fixed current and voltage, and the changes in voltage hysteresis curve model parameters were estimated as the basis for theoretical validation. Furthermore, the battery degradation degree was determined using the area under the voltage hysteresis curve. Results showed that the constructed model can accurately collect the battery degradation information and determine the battery degradation degree. © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device 8

Objective Sleep Quality Measurement based on Fuzzy Logic and...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Wijaya, Jason A. Andersen, Pauw D. Surantha, Nico Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method. © 2023 The Authors. Published by Elsevier B.V.

关键词： actigraphy artificial intelligences fuzzy logic sleep sleep quality

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FA-FENet: A Feature Attention Front-End Network Based on a Lightweight CNN Architecture for Recognizing Abnormal Underwater Illegal Fishing Behavior

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IEEE Access 2025年 13卷 87753-87776页

作者： Huang, Xiang-Rui Chen, Liang-Bi National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu880011 Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan

In the past decade, studies on illegal fishing have neglected to consider illegal underwater fishing. Traditionally, supervisor-based methods have been used to manually interpret underwater behavior;however, existing artificial intelligence (AI) and Internet of Things (IoT) methods are not applicable to illegal underwater fishing. Therefore, in this study, we used a computer vision AI method to perform the challenging task of illegal underwater fishing image recognition. Illegal underwater fishing images have unclear features and high feature similarity. In this study, we define normal underwater travel as wearing diving clothing. Abnormal underwater behavior includes underwater personnel with illegal fishing equipment. Herein, we propose a feature attention front-end network (FA-FENet), a novel end-to-end convolutional neural network (CNN) architecture that differs from previous methods in that it allows flexible integration with various backbone networks. The front end of the backbone network can be directly replaced to improve the learning efficiency and performance of the proposed network. The convolutional layers in our proposed FA-FENet include dilated convolutions, group convolutions, padded convolutions, pooling layers, and activation functions. We also comprehensively review the performance of the proposed FA-FENet. A series of ablation studies demonstrate that the proposed FA-FENet can be applied in shallow backbone networks. In addition, FA-FENet has potential in emerging networks with good performance in illegal underwater fishing identification tasks. Finally, we conducted experiments with an underwater fishing behavior dataset. The experimental results demonstrate that our proposed method outperforms recently reported methods with attention mechanisms. Specifically, FA-FENet-RegNet achieves an accuracy of 88.81%, FA-FENet-ResNet18 reaches 78.32%, and FA-FENet-ShuffleNetV2 attains 80.41%. © 2013 IEEE.

关键词： Convolutional neural networks

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Verification of the Inactivation Effect of Near-UV LEDs using a Pulsed Lighting System on Staphylococcus aureus

Verification of the Inactivation Effect of Near-UV LEDs usin...

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2024 IEEE Sustainable Smart Lighting World Conference and Expo, LS24 2024

作者： Nomura, Hiroki Kihara, Seiji Zukeran, Akinori Sawai, Jun Misu, Takayuki Kanagawa Institute of Technology Graduate School of Engineering Master's Degree Program in Electrical and Electronic Engineering Atsugi Kanagawa243-0292 Japan Kanagawa Institute of Technology Faculty of Engineering Department of Electrical and Electronic Engineering Atsugi Kanagawa243-0292 Japan Kanagawa Institute of Technology Faculty of Health and Medical Sciences Department of Nutrition and Life Science Atsugi Kanagawa243-0292 Japan

ISBN: (纸本)9798331540197

The purpose of this study is to investigate the disinfection effect of Near-UV LED light, which is pulsed using PWM control, on Staphylococcus aureus. To evaluate the disinfection effect of Near-UV LED light, three types of Near-UV LEDs with wavelengths of 395 nm, 385 nm, and 365 nm were installed within a sealed space inside an incubator and energized with a forward current of 1000 mA and a forward voltage of 3.6 V, and the disinfection rate under each type of light was assessed. Additionally, the disinfection effect was evaluated when the same three types of Near-UV LEDs were pulsed with a 50% duty cycle. As a result, 100% disinfection was achieved with 365 nm Near-UV LED direct-current irradiation for 120 seconds, and a 93% disinfection rate was obtained with pulsed irradiation for 150 seconds. © 2024 IEEE.

关键词： Light emitting diodes

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Research on Track Fasteners and Rails Surface Detection using Image Recognition and Data Management System Technology

Research on Track Fasteners and Rails Surface Detection usin...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Ming-An Chung Chun-Chia Lin Department of Electronic Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan College of Mechanical and Electrical Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

This research uses image recognition to conduct track fastener and rail surface defect detection. To counteract the dataset imbalance, design an image manager system to change the image's size, brightness, flip, and rotation. Comparing original and enhanced datasets by the precision of three different image recognition, results indicate the improvement of YOLO V8 Nano and optimized YOLO V4. However, the YOLO V7 model has a lower precision rate but a more promising recall rate.

关键词： YOLO Rails Training Image recognition Accuracy Force Fasteners

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Beyond the Scroll: Eye-Tracking Evaluations on the National Gallery of Indonesia’s Instagram 26th

Beyond the Scroll: Eye-Tracking Evaluations on the National ...

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26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024

作者： Sari, Desy Novita Prameswari, Clarissa Rahmi Anshori, Isa Master’s Program in Library Science Universitas Indonesia Kota Depok Indonesia Department of Library and Information Sciences Universitas Indonesia Kota Depok Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia National Gallery of Indonesia Central Jakarta Indonesia

ISBN: (纸本)9789819608676

Eye-tracking studies focused on art galleries and museums within the realm of Instagram, especially in the Indonesian context, are notably scant. This investigation elucidates the user interaction and experience of the art gallery’s Instagram account, @galerinasional. Employing a quantitative approach, we implemented eye-tracking experiments using the well-known online eye-tracking software RealEye. The study participants were followers of the National Gallery of Indonesia’s (Galnas) Instagram profile, seven women and three men, in two age brackets: 18–24 years (n = 5) and 25–34 years (n = 5). The experimental protocol required participants to execute five tasks centered on locating exhibition details and artwork on the Galnas Instagram platform, with a duration of 100 s allocated to each task. The Participant Quality Grade (PQG), examination duration, optimal browser dimensionality, K-factor mean, gaze and fixation frequencies, and heat maps were determined for the participants. The key findings are as follows. (1) The participants’ PQG scores were predominantly between 4 and 6. (2) Investigative browsing typically exceeded the 100-s limit. (3) The consensus on the ideal browser dimensions hovered around 1,368.6 × 814.8 px. (4) The K-factor mean across participants tended toward K > 0. (5) The average gaze occurrence was 27 items per second for most participants. (6) A noticeable disparity in fixation counts was observed between the two age groups, with individuals aged 25–34 demonstrating prolonged engagement with Galnas’ content. (7) Heat maps accentuated a pronounced concentration on visual content, encompassing photographs, videos, and their associated captions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Economic and social effects

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Modification of Deeplab V3+ for Detecting Cracks on Steel Rail Surfaces

Modification of Deeplab V3+ for Detecting Cracks on Steel Ra...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Ming-An Chung Yi-Xuan Ma Department of Electronic Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan College of Mechanical and Electrical Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

This study proposes an improved deep learning model for detecting surface cracks on railway steel tracks, addressing the urgent safety needs in the growth of railway construction. The improved Deeplab V3+ model architecture can improve feature extraction efficiency and reduce computational costs. Based on experimental results, the model outperforms others in pixel accuracy and processing speed. This study provides an effective approach for improving railway crack detection, and future work will focus on further optimizing the model for practical applications in railway inspections.

关键词： Accuracy Computational modeling Semantic segmentation Refining Computer architecture Rail transportation Safety

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Data Enhancement Based on Adding Noise to Improve The Detection of Rail Surface Defects and Sleeper Fractures

Data Enhancement Based on Adding Noise to Improve The Detect...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Ming-An Chung Jia-Wei Lin Department of Electronic Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan College of Mechanical & Electrical Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Railway track inspection and maintenance is a significant topic. This research introduces a track inspection method based on image recognition to attain a rapid and effective track inspection methodology. The approach employs image enhancement technology to augment the dataset’s content, thereby improving the detection accuracy of the track detection model. The experiment indicates an improvement of substantial accuracy when employing image enhancement technology to augment the dataset, irrespective of the YOLOv5n or YOLOv5s model. The model’s average accuracy (mAP50) in detecting sleeper breaks reached the highest score of 99.5%. The datasets are publicly available at https://***/softteacherrail-xxepi/l10_ul_50_2.

关键词： Rails YOLO Training Accuracy Noise Inspection Predictive models

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Improving Track Fastener And Rail Crack Detection Based On Data Enhancement Of Defect Images

Improving Track Fastener And Rail Crack Detection Based On D...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Ming-An Chung Pu-Chun Chen Department of Electronic Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan College of Mechanical & Electrical Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

In order to solve the defect images of fastener damage and rail fracture are seriously less than the normal images, this study proposes to use the lightweight and fast-identifying YoLov5 to train the model for fastener and rail defects based on the data enhancement of the track defect *** show that the detection data of the model after data enhancement is better than that without data enhancement.

关键词： Rails YOLO Accuracy Fasteners Data models Consumer electronics

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Analysis stage in the development of a virtual laboratory electric motor installation for vocational high schools 6

Analysis stage in the development of a virtual laboratory el...

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6th International Conference on Technology and Vocational Teachers, ICTVT 2020

作者： Wati, M.S. Djatmiko, I.W. Master Education Program of Electrical Engineering Faculty of Engineering Universitas Negeri Yogyakarta Indonesia

The study aimed to find out the design of instructional media which is suitable for practical learning of electric motor installation. It used needs analysis and front-end analysis which was carried out at Vocational High Schools in Yogyakarta. The result showed that the developing two-or three-dimensional media in the form of virtual laboratories was strongly recommended. Virtual laboratories was able to visualize the components according to real conditions. The concepts and work principles of electric motor installations were showed in the form of a two-or three-dimensional simulation. Virtual laboratories was expected to help virtual learning and train students' independence and competence in practicing electric motor installation. © Published under licence by IOP Publishing Ltd.

关键词： Laboratories

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