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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Kawai, Yasunori Miyazaki, Takato Miyoshi, Takanori National Institute of Technology Ishikawa College Department of Electrical Engineering Ishikawa Japan National Institute of Technology Ishikawa College Advanced Engineering Course Electronic and Mechanical Engineering Course Ishikawa Japan Nagaoka University of Technology Department of System Safety Niigata Japan

ISBN: (纸本)9798350324174

This paper considers a human assistive system of the rehabilitation for lower-limb using the exoskeleton. When the patient feels the fatigue in the rehabilitation, the power assist is needed to continue and to stop safety. First, the proposed power assist equipment using exoskeleton is indicated. The DC motor, the torsion spring, worm wheel, worm gear are used. Next, the control system using PI controller is proposed. The encoder attached to the knee joint is used as the reference value and the encoder attached to the motor is controlled by the motor. Finally, an experiment of repeated flexion and extension of the knee joint is conducted. It is verified that the motor can be controlled to be same angle of the knee angle. Therefore, the power assist by the exoskeleton is useful for the rehabilitation. © 2023 IEEE.

关键词： DC motors

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Stock Monitoring and Localization systems for FIFO Method Using RFID Based Pressure Sensing Tag and AI

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2024年第9期144卷 894-900页

作者： Hadi, Danang Kumara Song, Zequn Rahmadya, Budi Sun, Ran Takeda, Shigeki Graduate School of Science and Engineering Ibaraki University 4-12-1 Nakanarusawa Ibaraki Hitachi316-8511 Japan Department of Agroindustrial Technology Universitas Muhammadiyah Jember Jember68124 Indonesia Computer System Department Faculty of Information Technology Andalas University Padang25175 Indonesia

This research introduces real-time monitoring and localizing product stock using the First-In-First-Out (FIFO) method with radio frequency identification (RFID) pressure sensing tags. The proposed FIFO system has RFID pressure sensing tags that detect pressure changes, generate sensor codes, and estimate a stock location. Integrating artificial intelligence (AI) improves location estimation accuracy of up-to-date stock visibility. The ability of RFID pressure sensing tags and AI to detect stock from sensor code data enables more effective asset management, optimizes stock management, reduces product waste, and contributes to better efficiency in product inventory management. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Inventory control

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Design and performance investigation of a coherent optical system based multi-band OFDM with various indexes

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Journal of Optical Communications 2024年第s1期45卷 s2411-s2420页

作者： Dawood, Fatimah Imad Hadi, Wael Abdul Hassan Optical Communication System Engineering Communication Engineering Department University of Technology Baghdad Iraq

In this work, a comprehensive study of a multi-band optical orthogonal frequency division multiplexing (MB-OOFDM) system is investigated. Different OFDM indexes (128,256,512, and 1024) are utilized with the coherent detection CD technique. High-order modulation 16-quadrature amplitude modulation (16-QAM) is used to obtain high spectral efficiency. Transmission studies indicated that 128 subcarriers with 2-bands exhibit a higher optical signal-to-noise ratio (OSNR) of 23dB with lower bit error rate (BER) of 1 × 10-4, while 1024 subcarriers with 8-bands shows a lower OSNR of 22dB with a higher BER of 3 × 10-3. Even though the higher subcarrier type has the lowest BER, it provides a superior solution when combined with wavelength-division multiplexing (WDM) in the presence of forward error correction (FEC). The performance of the system with 40Gbps along 800km of uncompensated fiber has designed and simulated using Optisystem software. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Orthogonal frequency division multiplexing

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Innovative fusion of VGG16, MobileNet, EfficientNet, AlexNet, and ResNet50 for MRI-based brain tumor identification

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Iran Journal of Computer Science 2024年第1期8卷 185-215页

作者： Kia, Marjan Sadeghi, Soroush Safarpour, Homayoun Kamsari, Mohammadreza Jafarzadeh Ghoushchi, Saeid Ranjbarzadeh, Ramin Department of Business Information System Business Analysis (ISBA) Park University Parkville United States School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Software Engineering University of Szeged Szeged Hungary Faculty of Electrical Engineering Malek-Ashtar University of Technology(MUT) Esfahan Iran Faculty of Industrial Engineering Urmia University of Technology Urmia Iran School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland

This study presents a novel approach for brain MRI classification by integrating multiple state-of-the-art deep learning (DL) architectures, including VGG16, EfficientNet, MobileNet, AlexNet, and ResNet50, with an attention mechanism. The primary objective was to accurately detect the presence or absence of brain tumors—a critical challenge in medical diagnosis. The methodology was validated on a comprehensive dataset comprising 3264 brain MRI scans, systematically divided into training, validation, and testing subsets. Preprocessing techniques tailored for MRI data were employed to enhance input quality. Each model was initially developed and evaluated individually to establish a performance baseline before their integration. The proposed ensemble technique capitalized on the complementary strengths of these models to enhance classification accuracy and robustness. The inclusion of an attention mechanism further refined the models by emphasizing critical regions in the MRI scans, thereby boosting predictive performance. In addition, data augmentation techniques were applied to mitigate overfitting and increase dataset resilience. Performance metrics, including precision, accuracy, and recall, demonstrated significant improvements across all evaluations, particularly for the models incorporating the attention mechanism. These findings highlight the potential of combining advanced DL architectures with attention mechanisms as a promising strategy for precise and reliable brain tumor classification in MRI scans. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： AlexNet Attention mechanism Brain tumor classification MobileNet ResNet50

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Enhanced Video Streaming Based Computing Encoder for Digital Visual Processing 2

Enhanced Video Streaming Based Computing Encoder for Digital...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Kumar, Tarun Shukla, Amita Galgotias University Department of Computer Science and Engineering Greater Noida India Noida Institute of Engineering and Technology Department of Computer Science and Business System Greater Noida India

ISBN: (纸本)9798350347456

The rise of video streaming for digital visual processing has been a boon for the industry of visual processing. Video streaming technology has made it easier for companies to capture, analyze, and interpret visual data faster than ever before. It has allowed for the storage and transmission of large amounts of visual data at high speeds, providing businesses with the ability to process and interpret this data in real-time. Video streaming technology can be used in a wide variety of applications, including facial recognition, 3D mapping, and object recognition. By streaming video data, companies can quickly and accurately identify individuals, recognize objects, and track movement. This technology can also be used in security applications, such as surveillance and monitoring, as well as in medical imaging, such as MRI and CT scans. Video streaming technology has also allowed companies to create more efficient visual processing systems. The streaming video data can be used to automate the process of image recognition and object classification. This has allowed companies to reduce the amount of time and effort needed to interpret visual data. Additionally, streaming video data can be used to create virtual reality experiences, providing users with an immersive experience when viewing digital images. © 2023 IEEE.

关键词： Video streaming

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Time-Varying Formation Tracking Control of Heterogeneous Multi-Agent systems With Intermittent Communications and Directed Switching Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems Control theory Autonomous systems Stability

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The Innovation Development of Data Prediction and Clustered Compressive Sensing (CCS) in Environmental Application 2

The Innovation Development of Data Prediction and Clustered ...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Kukreja, Sonia Jain, Garima Galgotias University Department of Computer Science and Engineering Greater Noida India Noida Institute of Engineering and Technology Department of Computer Science and Business System Greater Noida India

ISBN: (纸本)9798350347456

Data prediction in environmental applications is a quickly evolving field that provides tremendous potential for improving environmental management and decision-making. By leveraging the power of data, predictive models can be developed to identify potential environmental risks, mitigate the impacts of climate change, and improve the efficiency of environmental management. Data prediction in environmental applications can be used to better understand the relationships between environmental phenomena and the environment. For instance, predictive models can be used to identify areas that are more likely to experience extreme weather events and inform the development of strategies for responding to such events. Predictive models can also be used to analyze the effects of changing climate on ecosystems and help inform decisions regarding the management of natural resources. Data prediction in environmental applications can also be used to develop more effective management strategies. Predictive models can be used to determine which areas are more likely to experience water scarcity or air pollution, and which areas are more likely to benefit from certain conservation practices. By leveraging data, predictive models can also help predict how certain management practices will affect the environment and inform decision-makers on how to best allocate resources. © 2023 IEEE.

关键词： Decision making

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Developing a Person-Organization Fit Method for Candidate or Employees Placement 5

Developing a Person-Organization Fit Method for Candidate or...

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5th International Conference on Advancement in Data Science, E-Learning and Information systems, ICADEIS 2023

作者： Arifin, Farah Fitriavida Suakanto, Sinung Siswanto, Joko Putri, Alifiah Darmawan, Irfan Telkom University Faculty of Industrial Engineering Department of Information System Bandung Indonesia Faculty of Industrial Technology Industrial Engineering Research Group Bandung of Institute Technology Bandung Indonesia

ISBN: (纸本)9798350303414

Building a high-quality company necessitates optimizing human resources aligned with organizational goals. However, this study face challenges related to human resource management due to a lack of standardized evaluation procedures. To address this issue, a method was devised to assist the Human Resource department in effectively placing prospective employees or current staff. This research analyzes and designs a method that quantitatively measures employee competencies in relation to job requirements, employing the person-organization fit approach. The research findings demonstrate that the method's accuracy is deemed appropriate and reasonable. Consequently, it can be concluded that this method effectively facilitates the placement of prospective employees or current staff. Future research endeavors can expand upon this method by exploring its applicability in other organizations or companies utilizing the person-organization fit approach. Furthermore, the method can be further enhanced by incorporating more accurate and advanced concepts, such as descriptive statistics or machine learning algorithms. Accuracy results show that 86% of employees acknowledge a fit between themselves and the organization, while 14% of employees acknowledge a mismatch between themselves and the organization. The results and recommendations presented can guide organizations in optimizing their human resources, enhancing employee performance, and achieving organizational success. © 2023 IEEE.

关键词： Human resource management

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Development of a Simple Dielectric Equivalent Circuit Model from Polarization and Depolarization Current Measurements

Development of a Simple Dielectric Equivalent Circuit Model ...

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2024 IEEE IAS Industrial and Commercial Power system Asia, I and CPS Asia 2024

作者： Yutthagowith, Peerawut Nimsanong, Phethai Baba, Yoshihiro King Mongkut's Institute of Technology Ladkrabang School of Engineering Bangkok Thailand Power System Analysis Section Metropolitan Electricity Authority Department of Power System Control Bangkok Thailand Doshisha University Graduate School of Science and Engineering Kyoto Japan

ISBN: (纸本)9798350352290

Assessing insulation performance is vital for sustainable energy systems, which aim to minimize visual impact while ensuring reliability and resilience against weather-related disruptions. With increasing reliance on renewable energy sources, the importance of insulation performance becomes even more pronounced. Regular evaluations of insulation performance are critical for maintaining the safety, reliability, and resilience of electricity networks. By promptly identifying risks such as insulation degradation, utilities can improve maintenance practices, prioritize repairs, and enhance the integration of renewables, thereby boosting overall infrastructure performance. This paper introduces a straightforward and precise technique for determining the equivalent circuit parameters of insulation systems in high-voltage equipment. The proposed method facilitates the accurate determination of the dielectric loss factor from the equivalent circuit. To validate this approach, test cases were utilized to demonstrate its effectiveness. The results obtained through this method were then compared with those obtained using commercial software. The comparison reveals that the proposed approach results in a lower number of depolarization current branches compared to the commercial software. Also, it provides almost the same relative root mean square error and the lower root mean square relative error of the depolarization current. These findings underscore the appeal and effectiveness of the proposed method for determining the equivalent circuit of the dielectric model. © 2024 IEEE.

关键词： Depolarization

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Performance Improvement of Poem Genre Classification using a Combination of SMOTE and Support Vector Machine

Performance Improvement of Poem Genre Classification using a...

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2023 International Seminar on Application for technology of Information and Communication, iSemantic 2023

作者： Aini, Quratu Muljono Yakub, Fitri Universitas Dian Nuswantoro Informatics Engineering Department Semarang Indonesia Malaysia-Japan International Institute of Technology Utm Electronic System Engineering Department Kuala Lumpur Malaysia

ISBN: (纸本)9798350339215

Text classification aims to be able to classify documents into the correct class. In this study, the use of SVM to perform text classification. The existence of this research is to find out how good the performance produced by SVM is in classifying text. A total of 841 poetry data were categorized into 4 genres namely affection, death, environment, and music. The data is cleaned at the text preprocessing stage with the stages of case folding (lowercase, remove punctuation and whitespace), tokenization, stopword removal, and lemmatizing. Feature extraction using Bag of Word (BoW) produces 6860 features. Features resulting from BoW will be weighted using TF - IDF. Data separation is carried out with a separation ratio of 80:20, 70:30, and 60:40. There is data imbalance, so it needs to be balanced. In this research, data balancing is done using SMOTE. Data separation is done for original data, balancing result data, and balancing result data with PCA. The highest accuracy result of train data is obtained by balancing data with a 60:40 separation of 97%. While the highest test data accuracy result is obtained by balancing data with an 80:20 separation of 87%. Thus the highest accuracy of each train data and test data is obtained by balancing data. © 2023 IEEE.

关键词： Support vector machines

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