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Retraction notice to “Automation industry in cyber physical systems based on 4G IoT networks for operation controlling and monitoring management” [Optik 272 (2023) 170308]

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Optik 2025年 333卷

作者： K.V. Mahesan Anupong Wongchai C. Tamizhselvan S. Manoj Kumar P. Vijayakumar Bharat Singh Dr. Ambedkar Institute of Technology Bangalore India Department of Agricultural Economy and Development Faculty of Agriculture Chiang Mai University Thailand Department of Data Science and Business System College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamilnadu India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore 641407 India School of Electronics Engineering Vellore Institute of Technology Chennai India Department of Mechanical Engineering GLA University Mathura UP India

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Generative method of self-organized swarm with designated global leader

Generative method of self-organized swarm with designated gl...

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2019 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2019

作者： Yu, Dengxiu Chen, C. L. Philip Lu, Gang Unmanned System Research Institute Northwestern Polytechnical University Xi'an China South China University of Technology School of Computer Science and Engineering Guangzhou China Northwestern Polytechnical University School of Mechanical Engineering Xi'an China

ISBN: (数字)9781728159287

ISBN: (纸本)9781728159287

The paper proposes the generative method of self-organized swarm with designated global leader. In previous work, the global leader of self-organized swarm is selected randomly. However, the global leader is designated in many situations and can not be replaced. To design the generative method of self-organized swarm with designated global leader, we propose the Molt Algorithm. Finally, the proposed method is verified by simulation. © 2019 IEEE.

关键词： Self-organized swarm designated global leader Molt Algorithm

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Surface EMG-Based Hand gesture recognition via hybrid and dilated deep neural network architectures for neurorobotic prostheses

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Journal of Medical Robotics Research 2020年第1-2期5卷

作者： Rahimian, Elahe Zabihi, Soheil Atashzar, Seyed Farokh Asif, Amir Mohammadi, Arash Concordia Institute for Information System Engineering Concordia University Montreal QC Canada Electrical and Computer Engineering Concordia University Montreal QC Canada Dept. of Electrical and Computer Engineering and Department of Mechanical and Aerospace Engineering New York University United States

Motivated by the potentials of deep learning models in significantly improving myoelectric control of neuroprosthetic robotic limbs, this paper proposes two novel deep learning architectures, namely the Hybrid Recognition Model (HRM) and the Temporal Convolutional Network Model (TCNM), for performing Hand Gesture Recognition (HGR) via multi-channel surface Electromyography (sEMG) signals. The work is aimed at enhancing the accuracy of myoelectric systems, which can be used for realizing an accurate and resilient man-machine interface for myocontrol of neurorobotic systems. The HRM is developed based on an innovative, unconventional, and particular hybridization of two parallel paths (one convolutional and one recurrent) coupled via a fully-connected multilayer network acting as the fusion center providing robustness across different scenarios. The hybrid design is specifically proposed to treat temporal and spatial features in two parallel processing pipelines and to augment the discriminative power of the model to reduce the required computational complexity and construct a compact HGR model. We designed a second architecture, the TCNM, as a compact architecture. It is worth mentioning that efficiency of a designed deep model, especially its memory usage and number of parameters, is as important as its achievable accuracy in practice. The TCNM has significantly less memory requirement in training when compared to the HRM due to implementation of novel dilated causal convolutions that gradually increase the receptive field of the network and utilize shared filter parameters. The NinaPro DB2 dataset is utilized for evaluation purposes. The proposed HRM significantly outperforms its counterparts achieving an exceptionally-high HGR performance of 98.01%. The TCNM with the accuracy of 92.5% also outperforms existing solutions while maintaining low computational requirements. © 2020 World Scientific Publishing Company.

关键词： convolutional neural network dilated causal convolution gesture recognition long-short term memory Surface Electromyography (sEMG)

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Electrical Grid-Independent Machine Learning-Assisted Wearable Gait Analysis Device with Triboelectric-Electromagnetic Hybrid Energy Harvester

SSRN

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SSRN 2025年

作者： Ra, Yoonsang Kim, Dongjun Nam, Mi Song Cho, Sumin Kim, Yu-Seop Cho, Seongwoo Um, Jumyung Heo, Yun Jung Choi, Dongwhi Department of Mechanical Engineering Integrated Engineering Program Kyung Hee University 1732 Deogyeong-daero Gyeonggi Yongin17104 Korea Republic of Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117576 Singapore National University of Singapore E6 L5 5 Engineering Drive 1 Singapore117608 Singapore Department of Artificial Intelligence Kyung Hee University Yongin17104 Korea Republic of Department of Mechanical Engineering Kyung Hee University Yongin17104 Korea Republic of Kyung Hee University Yongin17104 Korea Republic of Department of Industrial and Management System Engineering Kyung Hee University Yongin17104 Korea Republic of

The application of Artificial Intelligence (AI) to body motion analysis has garnered increasing interest for advancing the related technologies including individually optimized healthcare. Accordingly, an energy-efficient AI capable of onsite operation is required to advance the related fields to the next-generation such as in-sensor computing, near-sensor computing, on-device AI, and embedded AI. In this study, an Electrical grid-independent Machine learning-assisted Wearable device for Gait analysis (EMWG) with a ground reaction force (GRF) sensor was developed. Various machine learning architectures, including multi-layer perceptron (MLP), long short-term memory units, and gated recurrent units, were evaluated. MLP was found to be the optimal model in terms of analysis performance (accuracy of 88% and model conversion rate of 84.22%) and power consumption (discharging time of 1 h 47 min when using a 10 min-charged 110 mAh battery). The machine learning model was fine-tuned and deployed on a microcontroller unit. A triboelectric-electromagnetic hybrid energy harvesting module was developed to supply power to the AI-embedded microcontroller by harvesting walking energy of users. The hybrid energy harvester generates up to 184 V and 4.72 μA of voltage and current from the triboelectric nanogenerator and 32 V and 49 mA from the electromagnetic generator when the user moves. As a proof-of-concept demonstration, drunken person determination was performed using EMWG-mounted smart shoes with an accuracy of 88%. The concept of wearable gait analysis device with electrical grid-independent machine learning proposed in this work is expected to provide a technical guideline for developing next-generation motion data analysis systems. © 2025, The Authors. All rights reserved.

关键词： Microcontrollers

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Adaptive Switching Control of Wind Turbine Generators for Necessary Frequency Response

arXiv

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arXiv 2020年

作者： Liu, Yang Zhang, Yichen Sun, Kai Zhao, Xiaopeng Department of Electrical Engineering and Computer Science Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee KnoxvilleTN37996 United States Energy System Division Argonne National Laboratory LemontIL60439 United States

This letter proposes a new control strategy for wind turbine generators to decide the necessity of switches between the normal operation and frequency support modes. The idea is to accurately predict an unsafe frequency response using a differential transformation method right after power imbalance is detected so as to adaptively activate a frequency support mode only when necessary. This control strategy can effectively avoid unnecessary switches with a conventional use of deadband but still ensure adequate frequency response. Copyright © 2020, The Authors. All rights reserved.

关键词： Frequency response

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Comprehensive insights into electrical properties enhancement of boron‑sulfur co-doped diamond via ion implantation: First principles calculation and experimental validation

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Diamond and Related Materials 2025年 157卷

作者： Wang Zhao Bing Wang Zhanqiang Liu Honglu Li Zhaoliang Yu Pengyang Wang School of Mechanical Engineering Shandong University Jinan 250061 PR China Shandong Key Laboratory of High Performance Tools and System Jinan 250061 PR China Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of Education Jinan 250061 PR China State Key Laboratory of Advanced Equipment and Technology for Metal Forming Shandong University Jinan 250061 PR China Department of Computer and Information Science University of Macau PR China

Diamond presents great potential for applications as high‑temperature and high-power electronic devices as a new generation of semiconductor material. However, the inherent low electrical conductivity limits diamond applications. This study aims to reveal the conductive mechanism of boron (B)‑sulfur (S) co-doped diamond for conductivity enhancement with density-functional theory (DFT) calculations and ion implantation experiments. Firstly, the effects of B-S co-doping on diamond's electrical properties are investigated by analyzing the band structure and electron transport at different doping ratios using DFT. Secondly, Precision doping into single-crystal diamond is achieved via ion implantation, followed by annealing to remove lattice defects. Thirdly, the lattice integrity after doping is assessed by Raman spectroscopy, the chemical compositions and bonding states are analyzed by X-ray photoelectron spectroscopy, and the electrical properties of the co-doped diamonds are evaluated by Hall effect measurements. First-principles calculations show that B-S co-doping also generates additional acceptor and donor energy levels that couple, leading to more uniformly distributed impurity levels, a narrower band gap, altered electron transitions, and coupling in specific orbitals. These modifications imply that a B:S co-doping ratio of 1:2 may be favorable for enhanced carrier activation and improved electrical transport behavior. Based on first-principles calculations and Hall effect experiments the optimal conductivity occurs at a B:S ratio of 1:2, which gives better conductivity compared to single doping or other ratios. These findings Preliminary provide complementary theoretical and experimental evidence supporting the modulation of electrical conductivity through B-S co-doping.

关键词： Boron and sulfur co-doping Diamond semiconductor Electrical property First principles calculation Ion implantation

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Key Ingredients for Improving Process Quality at High-Level Cyber-Physical Robot Grinding systems

Key Ingredients for Improving Process Quality at High-Level ...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Chih-Hsuan Shih Yuan-Chieh Lo Hsuan-Yu Yang Feng-Li Lian Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Mechanical and Mechatronics System Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

Industrial robots have been applied in labor-intensive and high-risk jobs such as grinding, polishing, deburring, etc. However, traditional robotic manufacturing systems require skillful experts to advise the robot programming for controlling manufacturing efficiency and quality. Based on the level of automation, an advanced cyber-physical robot grinding system has been developed to increase efficiency, improve quality, and save human effort. The proposed system consists of robot trajectory generation and optimization, force-based trajectory modification, vision-based grinding machine localization, and acoustic emission based abrasive belt wear-life analysis. Compared with traditional robot tuning processes, the process quality of the proposed system is better than that, and the tuning time is shortened from 14 days to 1-2 days.

关键词： Service robots Robot kinematics Robot sensing systems Trajectory Collision avoidance

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Student-Centered engineering Education: The Renewable Energy Case

Student-Centered Engineering Education: The Renewable Energy...

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IEEE Education engineering (EDUCON)

作者： Bashar Hammad Abdallah Al-Zoubi Haneen Hijazi Ahmad Al-Khasawneh Dept.of Mechanical and Maintenance Engineering German Jordanian University Amman Jordan Dept. of Communication Engineering Princess Sumaya University for Technology Amman Jordan Dept. of Computer Information System Hashemite University Zarqa Jordan

ISBN: (数字)9781728109305

ISBN: (纸本)9781728109312

The emergence of information and communication technologies has paved the way for new online teaching methodologies to spread widely in recent years. Students have consequently moved into the heart of the learning process while instructors have acquired the role of facilitators in a new paradigm centered on students. Student-centered learning has therefore manifested itself as an opportunity for higher education institutions; particularly in developing countries, to meet many of the challenges posed by an increasingly changing landscape.A new renewable energy online course is described as a pilot to support the concept of student-centered learning at Hashemite University. Students in this course utilize study guides as the backbone of course delivery and implementation, interact with and navigate content, read and/or listen to explanation, watch videos, answer questions, uncover answers interactively. Several types of assessment tools are arranged weekly to ensure continuous monitoring and follow up. Three tools are utilized in order to evaluate the effectiveness and the quality of the course, including pre-survey, satisfaction survey and grades analysis. The correlation between students' grades and grade point average is then revealed to demonstrate the validity and effectiveness of course delivery and comprehension as well as the concept of student-centered learning. The results support the idea that online student-centered learning is promising in higher education institutions in Jordan, although there is still a great debate in the literature on its effectiveness.

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Infrared Vision systems for Emergency Vehicle Driver Assistance in Low-Visibility Conditions

arXiv

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arXiv 2025年

作者： Naddaf-Sh, M-Mahdi Lee, Andrew Yen, Kin Amini, Eemon Soltani, Iman Department of Mechanical and Aerospace Engineering University of California - Davis One Shields Ave DavisCA95616 United States Department of Computer Science University of California - Davis 2063 Kemper Hall DavisCA95616 United States The Division of Research Innovation and System Information California Department of Transportation 1120 N Street SacramentoCA95814 United States

This study investigates the potential of infrared (IR) camera technology to enhance driver safety for emergency vehicles operating in low-visibility conditions, particularly at night and in dense fog. Such environments significantly increase the risk of collisions, especially for tow trucks and snowplows that must remain operational in challenging conditions. Conventional driver assistance systems often struggle under these conditions due to limited visibility. In contrast, IR cameras, which detect the thermal signatures of obstacles, offer a promising alternative. The evaluation combines controlled laboratory experiments, real-world field tests, and surveys of emergency vehicle operators. In addition to assessing detection performance, the study examines the feasibility of retrofitting existing Department of Transportation (DoT) fleets with cost-effective IR-based driver assistance systems. Results underscore the utility of IR technology in enhancing driver awareness and provide data-driven recommendations for scalable deployment across legacy emergency vehicle fleets. © 2025, CC BY-NC-ND.

关键词： Ambulances

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Generating infrared image from visible image using Generative Adversarial Networks

Generating infrared image from visible image using Generativ...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： An, Lei Zhao, Jiajia Di, Huijun Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Coorperation Laboratory Beijing China

ISBN: (纸本)9781728137926

Infrared images have properties that are unaffected by illumination compared to visible images, object can be clearly recognized at day or night. Therefore, it is a better choice to use infrared images when training deep learning algorithms in unmanned systems. However, how to obtain a large number of infrared images makes it possible to train deep learning algorithm, which is the focus of our attention. In contrast to difficulties in direct shooting, computer simulations can generate large infrared images from visible images. But models that are trained on simulated data usually do not translate well to real scenarios. To bridge the domain gap between simulated and real infrared images, we exploit recent advances in paired image-to-image translation. We extend Pixel2Pixel to generate infrared image from visible image and propose a novel way of multilayer semantic information fusion, which significantly improves the quality of generated images. Finally, the image generated by our proposed PixelPro network is almost consistent with the real image distribution, achieved the purpose of expanding the dataset. © 2019 IEEE.

关键词： Generative adversarial networks

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