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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Li, Shijian Chugo, Daisuke Muramatsu, Satoshi Yokota, Sho She, Jin-Hua Hashimoto, Hiroshi Takashi, Uemura Kamishina, Hiroaki Hata, Yoshiharu Yamada, Takayuki Uchida, Takahiro Kwansei Gakuin University Graduate School of Science and Technology Hyogo Sanda6691330 Japan Tokai University School of Information Science and Technology Kanagawa Hiratsuka2591292 Japan Toyo University Faculty of Science and Engineering Saitama Kawagoe3508585 Japan Tokyo University of Technology School of Engineering Hachioji Tokyo1920982 Japan Advanced Institute of Industrial Technology Master Program of Innovation for Design and Engineering Shinagawa Tokyo1400011 Japan KyotoAR Animal Advanced Medical Center Kuse Kyoto6130034 Japan Rinrei Co. Ltd. Chuo Tokyo1040061 Japan

ISBN: (纸本)9798331531614

The objective of this paper is to identify the stresses placed on the musculoskeletal system when small dogs walk on floors with low friction coefficients. In many households, and small dogs in particular are often kept indoors. According to veterinary experience, it is known that small dogs kept indoors that walk with their lower limbs slipping cause a great deal of stress on the musculoskeletal system, and that the incidence of joint disease in the lower limbs of such small dogs is high. In this study, we measured the body movements of small dogs walking on slippery floors using a three-dimensional motion capture system and force plate system. Furthermore, by inputting these measurements into the musculoskeletal simulation model we have developed to simulate the musculoskeletal system of dogs, we derived the load on the musculoskeletal system of small dogs on slippery and non-slippery floors. As a result, it was identified that walking on a slippery floor causes a strong load on the lower limb muscles of small dogs. © 2025 IEEE.

关键词： Friction

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Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation

Medical Visual Prompting (MVP): A Unified Framework for Vers...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Yulin Huang, Guoheng Huang, Kai Lin, Zijin Zhong, Guo Luo, Shenghong Deng, Jie Zhou, Jian School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China University of Macau Faculty of Science and Technology China The First Affiliated Hospital of Sun Yat-Sen University Department of Otorhinolaryngology Guangzhou China Sun Yat-Sen University Cancer Center Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangzhou China South China Hospital Shenzhen University Shenzhen China

ISBN: (纸本)9781665410205

Accurate segmentation of lesion regions is crucial for clinical diagnosis and treatment across various diseases. While deep convolutional networks have achieved satisfactory results in medical image segmentation, they face challenges such as the loss of lesion shape information due to continuous convolution and downsampling, as well as the high cost of manually labeling lesions with varying shapes and sizes. To address these issues, we propose a novel Medical Visual Prompting (MVP) framework that leverages pre-training and prompting concepts from Natural Language Processing (NLP). The framework utilizes three key components: Super-Pixel Guided Prompting (SPGP) for superpixelating the input image, Image Embedding Guided Prompting (IEGP) for freezing patch embedding and merging with superpixels to provide visual prompts, and Adaptive Attention Mechanism Guided Prompting (AAGP) for pinpointing prompt content and efficiently adapting all layers. By integrating SPGP, IEGP, and AAGP, the MVP framework enables the segmentation network to better learn shape prompting information and facilitates mutual learning across different tasks. Extensive experiments conducted on five datasets demonstrate the superior performance of the proposed method in various challenging medical image tasks while simplifying single-task medical segmentation models. This novel framework offers improved performance with fewer parameters and holds significant potential for accurate segmentation of lesion regions in various medical tasks, making it clinically valuable. © 2024 IEEE.

关键词： Image segmentation

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Multi-agent Cellular Automaton Model for Traffic Flow Considering the Heterogeneity of Human Delay and Accelerations 23rd

Multi-agent Cellular Automaton Model for Traffic Flow Consi...

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23rd International Conference on Computational science, ICCS 2023

作者： Malecki, Krzysztof Górka, Patryk Gokieli, Maria Faculty of Computer Science and Information Technology West Pomeranian University of Technology Żolnierska 52 Str. Szczecin71-210 Poland Faculty of Mathematics and Natural Sciences - School of Exact Sciences Cardinal Stefan Wyszyński University Wóycickiego 1/3 Warsaw01-938 Poland

ISBN: (纸本)9783031359941

We propose a multi-agent cellular automata model for analy-sing the traffic flow with various types of agents (drivers). Agents may differ by their vehicles’ acceleration/deceleration values and the delay value of their decision-making. We propose a model in which the main parameters are chosen to reflect different types of driving. Based on valuable previous works, accurate data for possible acceleration/deceleration are used. Additionally, to accurately reflect the cars’ dimensions and their limited movement in a traffic jam, a small-cell cellular automaton is used, where a set of cells represents one car. We present the results of a numerical simulation showing the influence of the main factors of the driving type on the traffic flow. Research shows that aggressive braking has a greater negative impact on traffic flow than aggressive acceleration. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cellular automata

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Localization of a Crack in Moving Cylindrical Ferromagnetic Rods by Measuring the Fourier Coefficients of the Leakage Magnetic Flux

Localization of a Crack in Moving Cylindrical Ferromagnetic ...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Kanta Shiku Masafumi Kuromizu Yuji Gotoh Takaaki Nara Graduate School of Information Science and Technology The University of Tokyo bunkyo-ku Japan Division of Materials Science and Production Engineering Graduate School of Engineering Oita University Oita Japan Intelligent Mechanical Systems Program Faculty of Science and Technology Oita University Oita Japan

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

Electromagnetic nondestructive inspection for high-speed moving ferromagnetic materials is necessary for manufacturing efficiency. In this paper, we present a novel leakage magnetic flux (LMF) method to estimate the crack position in a moving cylindrical ferromagnetic rod. The method directly measures the first-order Fourier coefficients of the LMF moving with the crack by using only two coils with DC excitation. The location of a crack is estimated by the proposed method, as demonstrated for an S45C cylindrical rod.

关键词： Location awareness Coils Magnetic flux Inspection Ferromagnetic materials Manufacturing Electromagnetics

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Bio-Signal Acquisition System for Psychological Stress Classification 15

Bio-Signal Acquisition System for Psychological Stress Class...

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15th International Conference on information and Communication technology Convergence, ICTC 2024

作者： Rahman, Daris Muhammad Permana, Arvanida Feizal Sakanti, Maria Mahardini Adristi, Cornelia Luo, Win-Jet Kuncoro, C. Bambang Dwi National Chin-Yi University of Technology Department of Refrigeration Air Conditioning and Energy Engineering Taichung41170 Taiwan Graduate Institute of Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung41170 Taiwan Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung41170 Taiwan Universitas Indonesia Faculty of Engineering Department of Electrical Engineering Depok16424 Indonesia

ISBN: (纸本)9798350364637

The increasing prevalence of psychological stress in modern society necessitates the development of effective monitoring and classification systems. This paper presents the design and implementation of the acquisition system for the bio-signal measurement. The proposed system utilizes a combination of sensors and a microcontroller to collect and analyze physiological data indicative of stress levels. The bio-signals data monitored include heart rate, pulse rate, galvanic skin response, body temperature, and acceleration. The data acquisition process is managed by a microcontroller, which processes the raw signals and transmits them to a central processing unit for further analysis. The built system captures the acquired data in real time, offering immediate feedback and long-term monitoring capabilities. The system operates on a small power supply of 5V and 250mA, featuring a modular and portable design for ease of use. This approach aims to offer a cost-effective, non-invasive solution for early detection and management of psychological stress, contributing to improved mental health outcomes. Experimental results demonstrate the system's accuracy and reliability in acquiring and processing bio-signal data. These results represent preliminary research, with future work focusing on integrating advanced data analysis techniques and machine learning algorithms to enhance stress classification and prediction, expanding the system's applicability in clinical and everyday settings. © 2024 IEEE.

关键词： Microcontrollers

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Deep Liver Lesion AI System: A Liver Lesion Diagnostic System Using Deep Learning in Multiphase CT 10th

Deep Liver Lesion AI System: A Liver Lesion Diagnostic Syste...

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10th KES International Conference on Innovation in Medicine and Healthcare, KES-InMed 2022

作者： Kitrungrotsakul, Titinunt Xu, Yingying Hu, Jihong Liu, Jing Li, Yinghao Lin, Lanfen Tong, Ruofeng Li, Jingsong Chen, Yen-Wei Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Graduate School of Information Science and Engineering Ritsumeikan University Kyoto Japan College of Computer Science and Technology Zhejiang University Hangzhou China College of Biomedical Engineering & Instrument Science Zhejiang University Hangzhou China

ISBN: (纸本)9789811934391

In the medical field, computer-aided diagnosis (CAD) systems are an important tool that can assist doctors in the diagnosis process. However, the powerful higher performance and accuracy models in CAD systems require high computation costs. Furthermore, collaboration between doctors is problematic when the CAD system is run on a single machine. To solve these problems, we developed a deep liver lesion AI system for a liver lesion diagnostic system based on a web foundation, in which core processing and data storage are implemented on the backend system. The core processing functions of the system consist of 5 modules: liver segmentation, liver lesion detection, liver lesion segmentation, liver lesion classification, and hepatocellular carcinoma early recurrent prediction. The visualization and interaction functions between the system and doctors are implemented on the web interface. With all processing functions implemented on the backend and the interface shown with a web interface, data sharing between doctors becomes easier than the standalone version. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Medical imaging

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High-Quality Font Generation Based on StyleGAN2 and FSFont Font Generation Model

High-Quality Font Generation Based on StyleGAN2 and FSFont F...

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Asia-Pacific Signal and information Processing Association Annual Summit and Conference (APSIPA)

作者： Yuki Shimamura Michiharu Niimi Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka-shi Fukuoka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka-shi Fukuoka Japan

This paper proposes a high-quality font generation method based on StyleGAN2 and FSFont font generation model. In our previous work, we proposed a system that expresses emotions by using font changes and call it an EmoEditor (Emotion-Expressive Editor). This system uses a deep neural network based on zi2zi that is able to generate a desired rendered character by giving a fixed rendered character and a font label which distinguishes between fonts. By changing the font label, the system generates the different font characters which corresponds to several emotions. However, the system was not able to reconstruct the expected ones for our collected fonts that is needed to express emotions. Considering the rapid development of DNN technology, in this paper, we propose an extension of the StyleGAN2 model for font generation. Specifically, we enhance StyleGAN2 by incorporating an Encoder which is come from FSFont generation model and a class label input structure. By adopting a class label input, the proposed model is able to generate indicated font by users. Experimental results show that the proposed method can generate better fonts qualitatively and quantitatively than previous studies on font generation.

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Web Scraping Methods on Odoo Framework to Collect Rupiah Exchange Rate From Bank Indonesia Website 10

Web Scraping Methods on Odoo Framework to Collect Rupiah Exc...

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10th International Congress on Advanced Applied Informatics, IIAI-AAI 2021

作者： Tarigan, Masmur Gaol, Ford Lumban Matsuo, Tukoro Universitas Bina Nusantara Doctor of Computer Science Graduate Program Jakarta Indonesia Doctor of Computer Science Universitas Bina Nusantara Jakarta Indonesia Japan Department of M-Commerce and Multimedia Applications Graduate School of Industrial Technology Asia University Japan Faculty of Computer Science Universitas Esa Unggul Jakarta Indonesia

ISBN: (纸本)9781665424202

The foreign currency exchange rate against the rupiah in the Odoo ERP framework system accommodates multi-currencies. However, Odoo has not updated the rupiah exchange rate based on the value from Bank Indonesia regularly. The exchange rate is the price at which one country's currency can be exchanged for another country's currency. The rupiah exchange rate in business has an influence on the cost revenue as well as the profit of the Indonesian company. The problem is updating the rupiah exchange rate must be done manually by looking at the exchange rate on the Bank Indonesia website and entering it into the ERP system according to the current date. The Rupiah exchange rate in foreign currencies needs to be updated regularly by Bank Indonesia exchange rates. To collect rupiah exchange rate uses the Odoo Framework ERP system using the web scraping methods to extract data from Bank Indonesia exchange rates, the library used in scraping is Beautiful Soup. Beautiful Soup is a Python package for parsing HTML and XML. Is used to extract data from HTML, which is useful for web scraping. Rupiah exchange rate updates will be scheduled by the system every day at 12.30 p.m. GMT+07. © 2021 IEEE.

关键词： Websites

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Fuzzy Knowledge Distillation 25

Fuzzy Knowledge Distillation

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Proceedings of the 2025 3rd International Conference on Communication Networks and Machine Learning

作者： Chuanbin Zhang Kai Li Jianbin Wang Qiqiao He Hongbo Li Zhenghao Liang School of Computer Science and Software Zhaoqing University Zhaoqing Guangdong China Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Selangor Malaysia School of Computer Science and Artificial Intelligence Foshan University Foshan Guangdong China College of Coastal Agricultural Sciences Guangdong Ocean University Zhanjiang Guangdong China

ISBN: (纸本)9798400713231

Knowledge Distillation (KD) is a technique that transfers knowledge from a teacher model to a student model. KD is designed to reduce the number of model parameters while achieving better performance than training student model alone. This paper introduces a novel fuzzy knowledge distillation method that employs fuzzy clustering algorithms to cluster intermediate layer features of both teacher and student models. The differences between features are evaluated using fuzzy membership vectors. Specifically, the Kullback-Leibler (KL) divergence is used to calculate the distance between the fuzzy membership vectors corresponding to the intermediate layer features of the teacher and student models. This KL term is then integrated into the knowledge distillation framework. Additionally, the updating process of the cluster centers for intermediate layer features is implemented using mini-batch stochastic gradient descent. Experimental results demonstrate that the proposed fuzzy knowledge distillation method efficiently facilitates knowledge transfer and achieves higher accuracy than existing methods.

关键词： Deep learning

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Deep reinforcement learning for near-fieldwideband beamforming in STAR-RIS networks

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Frontiers of information technology & Electronic Engineering 2024年第12期25卷 1651-1663页

作者： Ji WANG Jiayi SUN Wei FANG Zhao CHEN Yue LIU Yuanwei LIU Department of Electronics and Information Engineering College of Physical Science and TechnologyCentral China Normal UniversityWuhan 430079China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Faculty of Applied Sciences Macao Polytechnic UniversityMacao SARChina School of Electronic Engineering and Computer Science Queen Mary University of LondonLondon E14NSUK Department of Electrical and Electronic Engineering The University of Hong KongHong Kong 999077China

A simultaneously transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)assisted multiuser near-field wideband communication system is investigated,in which a robust deep reinforcement learning(DRL)based algorithm is proposed to enhance the users’achievable rate by jointly optimizing the active beamforming at the base station(BS)and passive beamforming at the *** mitigate the beam split issue,the delay-phase hybrid precoding structure is introduced to facilitate wideband *** the coupled nature of the STARRIS phase-shift model,the passive beamforming design is formulated as a problem of hybrid continuous and discrete phase-shift control,and the proposed algorithm controls the high-dimensional continuous action through hybrid action ***,to address the issue of biased estimation encountered by existing DRL algorithms,a softmax operator is introduced into the algorithm to mitigate this *** results illustrate that the proposed algorithm outperforms existing algorithms and overcomes the issues of overestimation and underestimation.

关键词： Deep reinforcement learning Near-field beamforming Simultaneously transmitting and reflecting reconfigurable intelligent surface(STAR-RIS) Wideband beam split

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