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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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Perceptive self-supervised learning network for noisy image watermark removal

arXiv

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arXiv 2024年

作者： Tian, Chunwei Zheng, Menghua Li, Bo Zhang, Yanning Zhang, Shichao Zhang, David School of Software Northwestern Polytechnical University Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China School of Electronics and Information Northwestern Polytechnical University Xi’an710129 China School of Computer Science Northwestern Polytechnical University National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology Xi’an710129 China Guangxi Key Lab of Multisource Information Mining & Security College of Computer Science & Engineering Guangxi Normal University Guilin541004 China Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China

Popular methods usually use a degradation model in a supervised way to learn a watermark removal model. However, it is true that reference images are difficult to obtain in the real world, as well as collected images by cameras suffer from noise. To overcome these drawbacks, we propose a perceptive self-supervised learning network for noisy image watermark removal (PSLNet) in this paper. PSLNet depends on a parallel network to remove noise and watermarks. The upper network uses task decomposition ideas to remove noise and watermarks in sequence. The lower network utilizes the degradation model idea to simultaneously remove noise and watermarks. Specifically, mentioned paired watermark images are obtained in a self-supervised way, and paired noisy images (i.e., noisy and reference images) are obtained in a supervised way. To enhance the clarity of obtained images, interacting two sub-networks and fusing obtained clean images are used to improve the effects of image watermark removal in terms of structural information and pixel enhancement. Taking into texture information account, a mixed loss uses obtained images and features to achieve a robust model of noisy image watermark removal. Comprehensive experiments show that our proposed method is very effective in comparison with popular convolutional neural networks (CNNs) for noisy image watermark removal. Codes can be obtained at https://***/hellloxiaotian/PSLNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Image denoising

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Centrifugal Microfluidic Based Painless Blood Sample Collection for Hematocrit Measurement

Centrifugal Microfluidic Based Painless Blood Sample Collect...

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Biomedical engineering International Conference (BMEiCON)

作者： Santi Rattanavarin Thanapat Sangkharat Ekachai Juntasaro Witsaroot Sripumkhai Pattaraluck Pattamang Norabadee Ranron Numfon Khemthongcharoen Chompunoot Sinthupibulyakit Chamras Promptmas Chayapitcha Saesue Jiraskul Udarwudhipong Wutthinan Jeamsaksiri Department of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok (KMUTNB) Bangkok Thailand Mechanical Engineering Simulation and Design Group (MESD) Sirindhorn International Thai-German Graduate School of Engineering (TGGS) King Mongkut’s University of Technology North Bangkok (KMUTNB) Bangkok Thailand Thai Microelectronics Center (TMEC) National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Chachoengsao Thailand Infrastructure Development Division (IDD) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand Faculty of Medical Technology Huachiew Chalermprakiet University Samut Prakan Thailand Department of Biomedical Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand

This work demonstrates a centrifugal microfluidic chip design for painless blood collection and hematocrit measurement. The fluid channel in the microfluidic chip was fabricated using a laser-cutting method. An optimal dimension of the negative pressure chamber was designed to control the maximum sample volume obtained for further hematocrit examination. The estimated blood sample of 20 µl could be obtained based on the negative pressure driven by finger press actuation on the negative pressure chamber of the microfluidic chip. The repeatability of blood volume collection evaluated by a laboratory technician was 21.126±0.001 µl with a coefficient of variation (CV) of 5.32%. The sample volume used in this centrifugal microfluidic chip was around three times lower than that used in the standard method. Pack red cell preparation for hematocrit examination in the microfluidic chip could be achieved using a centrifugation speed around 2.4 times lower than that used in the standard method. The hematocrit examination by our centrifugal microfluidic chip and the standard method was 41.75±0.77 (1.84 %CV), and 41.5±0.71 (1.7 %CV), respectively. The results showed that the hematocrit measurements in our centrifugal microfluidic chip were compatible with those measured by the standard method.

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Incentivizing mobile edge caching and sharing: An evolutionary game approach

arXiv

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arXiv 2021年

作者： Li, Mingyu Jiang, Changkun Gao, Lin Wang, Tong Jiang, Yufei The School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China The Department of Computer Engineering Shenzhen University Shenzhen China The School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Mobile Edge Caching is a promising technique to enhance the content delivery quality and reduce the backhaul link congestion, by storing popular contents at the network edge or mobile devices (e.g. base stations and smartphones) that are proximate to content requesters. In this work, we study a novel mobile edge caching framework, which enables mobile devices to cache and share popular contents with each other via device-to-device(D2D) links. We are interested in the following incentive problem of mobile device users: whether and which users are willing to cache and share what contents, taking the user mobility and cost/reward into consideration. The problem is challenging in a large-scale network with a large number of users. We introduce the evolutionary game theory, an effective tool for analyzing large-scale dynamic systems, to analyze the mobile users' content caching and sharing strategies. Specifically, we first derive the users' best caching and sharing strategies, and then analyze how these best strategies change dynamically over time, based on which we further characterize the system equilibrium systematically. Simulation results show that the proposed caching scheme outperforms the existing schemes in terms of the total transmission cost and the cellular load. In particular, in our simulation, the total transmission cost can be reduced by 42.5%∼55.2% and the cellular load can be reduced by 21.5%∼56.4%. © 2021, CC BY-SA.

关键词： Game theory

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Value Stream Mapping for Smart Pharmaceutical Management in a Thai Hospital

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Procedia computer Science 2025年 253卷 495-504页

作者： Ganda Boonsothonsatit Rardchawadee Silapunt Supachai Vongbunyong Kamol Kaemarungsi Wirachchaya Chanpuypetch Nilubon Chonsawat Graduate School of Management and Innovation King Mongkut’s University of technology Thonburi Thailand Electronic and Telecommunication Engineering King Mongkut’s University of Technology Thonburi Thailand Hospital Automation Research Center Institute of Field Robotics King Mongkut’s University of Technology Thonburi Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Faculty of Agro-Industry Chiang Mai University Thailand Department of Agricultural Economy and Developement Faculty of Agriculture Chiang Mai University Thailand

The evolution of healthcare systems worldwide necessitates continual improvement in hospital management practices, particularly pharmaceutical management. This paper explores transforming traditional pharmaceutical management systems in large and medium-sized hospitals in Thailand into smart systems using Value Stream Mapping (VSM) as a guiding tool. Traditional systems often face challenges such as inefficiencies and medication errors. VSM, rooted in lean management principles, offers a structured approach to visualize and optimize processes. Additionally, smart technologies like automated inventory systems and electronic health records enhance pharmaceutical management effectiveness. Through a comprehensive research methodology encompassing case studies, data collection, and VSM analysis, this study identifies inefficiencies and proposes evidence-based strategies for improvement. Results from a case study highlight the benefits of VSM implementation in enhancing pharmacy operations, emphasizing improved patient care and operational efficiency. The implementation of a smart medication dispensing system shows promising results in reducing workload and accommodating increased patient arrivals. Continued evaluation and refinement are crucial for maximizing benefits and addressing emerging challenges effectively.

关键词： lean management smart medication dispensing smart pharmaceutical smart technologies Value Stream Mapping

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IoT based stir casting system of aluminium MMC

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Materials Today: Proceedings 2021年 45卷 2818-2820页

作者： P. Anantha Christu Raj P. Kavitha S. Sophia K. Thilagam V. Devi J. Mohammed Feros Khan Department of Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore 641114 Tamilnadu India Department of Electrical and Electronics Engineering R.M.K. Engineering College Kavaraipettai 60120 Tamilnadu India Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore 641008 Tamilnadu India Department of Electronics and Communication Engineering Velammal Engineering College Chennai 600066 Tamilnadu India Department of Computer Applications Gurunanak College Chennai 600042 Tamilnadu India Department of Electrical and Electronics Engineering Vickram College of Engineering Enathi 630561 Tamilnadu India

In present days, an Internet of Things (IoT) based smart industry was established for casting sections that produces the different components in automobile, construction, railways and shipping. The effect of casting parameters and their performances on quality were analyzed through collected data from the system. A monitoring system has been introduced in every sector but the collected data are not being utilized properly. Hence, it is essential to set up a system suitable for the casting through IoT. In this paper, stir casting process was processed under IoT. Stir casting parameter and data were analyzed through IoT. It is expected that a process of efficient completion to be offered to the casting industries that desire to construct smart casting divisions in the future. The microcontroller, network and sensors have been used to improve the stir casting process. IoT based stir casting experimental investigation has been conducted on Aluminium Metal Matrix Composite (Al-MMC).

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Design and Implementation of Neuro-Fuzzy Control Approach for Robot's Trajectory Tracking

Design and Implementation of Neuro-Fuzzy Control Approach fo...

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International conference of electronics, Communication and Aerospace Technology (ICECA)

作者： Kiruba Sandou D Sunad kumara A N Binod Kumar Choudhary Shashikala Gurpur Sarishma Natrayan L M. SivaramKumar Department of Robotics and Automation Manakula Vinayagar Institute of Technology Pondicherry India Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University Mandya Karnataka India Department of Electrical and Electronics Engineering ARKA Jain University Jamshedpur Jharkhand India Faculty of Law Symbiosis Law School (SLS) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India Department of Management Studies Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamilnadu India Department of EEE Karpagam Academy of higher education Coimbatore Tamiinadu

Achieving precise trajectory tracking while minimizing errors, reducing settling time, and curbing overshoot presents a significant challenge in the domain of robot-manipulator control. In response, this paper proposes a novel controller design for robot manipulators, incorporating two distinct approaches: a conventional fuzzy controller and an advanced Neuro-Fuzzy controller. These controllers are tailored to elevate trajectory tracking performance by simultaneously optimizing time characteristics in robot manipulator responses. To assess their efficacy, a range of test scenarios is devised, encompassing different trajectory types, including linear and nonlinear paths. Upon comparative analysis, the results highlight an intriguing contrast: while the fuzzy controller exhibits higher maximum overshoot and longer settling times in the robot's response, the Neuro-Fuzzy controller consistently demonstrates superior performance. The comprehensive error analysis such as Integral Square Error (ISE) and Integral Absolute Error (IAE) further underscores the capability of the Neuro-Fuzzy-based robot manipulator in delivering trajectory tracking precision. This study contributes to the advancement of trajectory control techniques for robot manipulators, paving the way for enhanced automation and performance across various applications.

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IoT enabled temperature sensing system of molten metal in welding

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Materials Today: Proceedings 2021年 45卷 2514-2517页

作者： S. Sanjith N. Balaji L.D. Vijay Anand W. Regis Anne P. Shanmugapriya S. Sheeba Rani Department of Computer Science St. Alphonsa College of Arts and Science Soosaipuram 629157 Tamilnadu India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai 600044 Tamilnadu India Department of Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore 641114 Tamilnadu India Department of Computer Applications Sri Krishna College of Engineering and Technology Coimbatore 641008 Tamilnadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai 600044 Tamilnadu India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore 641008 Tamilnadu India

The control of temperature in welding is essential to maintain the quality of metal joints. Due to current fluctuations and instrument errors, the quality of joint may be affected. The excess of temperature or molten stage may leads to change the effect of micro structure. An automatic temperature sensing system is needed to eliminate this problem. It is fulfilled through Internet of Things (IoT) based temperature sensing on molten metal in welding. The temperature sensors, micro controller, LAN/Wireless communications and server are used to enhance the automatic temperature sensing on molten metal in welding process. The experimentation is conducted on arc welding of mild steel. The quality characteristics and optimization studied through Taguchi method.

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Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

arXiv

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arXiv 2024年

作者： Handa, Palak Mahbod, Amirreza Schwarzhans, Florian Woitek, Ramona Goel, Nidhi Dhir, Manas Chhabra, Deepti Jha, Shreshtha Sharma, Pallavi Thakur, Vijay Chawla, Simarpreet Singh Gunjan, Deepak Kakarla, Jagadeesh Raman, Balasubramanian Research Center for Medical Image Analysis and Artificial Intelligence Department of Medicine Danube Private University Krems Austria Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Machine Learning University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India Department of Electronics and Communication Engineering Delhi Technological University Delhi India Columbia University New YorkNY United States Department of Gastroenterology and HNU All India Institute of Medical Sciences Delhi India Chennai Kancheepuram India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria in collaboration with the 9th International Conference on computer Vision & Image Processing (CVIP 2024) being organized by the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document provides an overview of the challenge, including the registration process, rules, submission format, description of the datasets used, qualified team rankings, all team descriptions, and the benchmarking results reported by the organizers. © 2024, CC BY.

关键词： Endoscopy

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computer Vision and Image Understanding

SSRN

引用

SSRN 2022年

作者： Tang, Wenming Gong, Yuanhao Qiu, Guoping College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science The University of Nottingham United Kingdom

Graph neural networks (GNNs) are ideally suited for mesh denoising. However, existing solutions such as those based on graph convolutional networks (GCNs) are built for a fixed graph thus making them not naturally generalisable to unseen meshes. Furthermore, their graph Laplacian based global node embedding algorithms can cause excessive smoothing while achieving feature preserving mesh denoising requires a GNN to posses local processing capability. This paper presents a mesh denoising method via a new Dense lOcal Graph neural NETwork (DOGNET). DOGNET implements a local node embedding algorithm that generates node embeddings through aggregating information from a node’s connected local neighbours which automatically make DOGNET inductive as well as effective for feature preserving mesh denoising. We present extensive experimental results to demonstrate quantitatively and qualitatively that DOGNET is superior to SOTA meshing denoising techniques. © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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