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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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Quantification of Honey Adulteration Using a Planar Microstrip Metamaterial-Motivated Sensor and Software-Defined Radio

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IEEE Sensors Journal 2025年

作者： Hasar, Ugur C. Hasar, Hafize Kaya, Yunus Ozturk, Hamdullah Korkmaz, Huseyin Kosunalp, Selahattin Iliev, Teodor Islam, Mohammad Tariqul Nayyeri, Vahid Ramahi, Omar Mustafa Gaziantep University Department of Electrical and Electronics Engineering Gaziantep27310 Turkey Directorate of Provincial Agriculture and Forestry Ministry of Agriculture and Forestry of the Republic of Turkey Gaziantep Turkey Bayburt University Department of Electronics and Automation Bayburt69010 Turkey Gaziantep Islam Science and Technology University Department of Electrical and Electronics Engineering Gaziantep27010 Turkey Bandırma Onyedi Eylül University Department of Computer Technologies Bandırma10200 Turkey University of Ruse "Angel Kanchev" Department of Telecommunication Ruse7017 Bulgaria Faculty of Engineering and Built Environment Department of Electrical Electronic and Systems Engineering Bangi43600 Malaysia Iran University of Science and Technology School of Advanced Technologies Tehran*** Iran University of Waterloo Department of Electrical and Computer Engineering ON WaterlooN2L 3G1 Canada

This work presents the application of a planar microstrip metamaterial (MM)- motivated sensor in the form of a split-ring resonator (SRR) for the detection and quantification of water adulteration in honey samples. The proposed sensor utilizes inexpensive software-defined radio (SDR) measurements, offering a cost-effective solution. Unlike previous MM sensors employing SRR configuration, our proposed MM-motivated sensor is integrated with a microstrip feedline and an additional vertical bar, enhancing its sensing capability. To enable accurate measurements, a simple calibration procedure based on baseline normalization is implemented, allowing for amplitude-only transmission measurements (|S21|) using the SDR. A special sample holder was designed to increase repeatability of measurements. We separately employ two post-processing techniques, namely the rolling average (RA) approach and the Savitzky-Golay (SG) filter, to effectively eliminate ripples in the measured |S21| data obtained from the SDR. For validation and evaluation of the proposed sensor and post-processing techniques, measurements of flower honey samples with water adulteration levels (δ) up to 10% (mass-to-mass basis) were also performed using a vector network analyzer. The resonance frequency of |S21| is utilized as the basis for analysis. We establish a metric function (linear function) that correlates the shift in resonance frequency with the δ. It is observed that this function fits to measured resonance frequency values with an R2 value greater than 0.96 for both post-processing techniques with or without the sample holder. By inverting this function, we can predict δ with considerable accuracy for a given resonance frequency by using another linear function. It is observed that percentage variations between predicted and measured δ values are between 1.54% and 6.46% for testing samples with δ = 5% and δ = 7%. © 2001-2012 IEEE.

关键词： Adulteration honey metamaterials microwaves planar structures rolling average Savitzky-Golay filter sensors water

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Ensuring the quality of fuel equipment joints in series production conditions by graded kitting

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AIP Conference Proceedings 2023年第1期2889卷

作者： Oleksandr Kupriyanov Hanna Hrinchenko Roman Strelchuk Mykhailo Kupriianov Ukrainian Engineering Pedagogics Academy Innovative Technologies Faculty Department of Information Computer Technologies and Mathematics 16 Universitetska st. Kharkiv 61003 Ukraine Ukrainian Engineering Pedagogics Academy Power Engineering and Automation Faculty Department of Automatization Metrology and Energy-Saving Technologies 16 Universitetska st. Kharkiv 61003 Ukraine National Technical University «Kharkiv Polytechnic Institute» Department of Integrated Technologies of Mechanical Engineering named after M.F. Semko 2 Kirpicheva st. Kharkiv 61002 Ukraine Kharkiv National University of Radio Electronics Faculty of Computer Science 14 Nauky Ave. 61166 Kharkiv Ukraine

A graded kitting method is proposed, which provides the required accuracy for a complete assemblage of a part batch. The new kitting method was tested on the example of fuel equipment produced by the Kharkiv Tractor Plant. The connection of the body and slide valve was studied under workshop conditions. Kitting accuracy and unfinished production were compared at full interchangeability, selective kitting, and graded kitting. The method proposes measuring the actual parameters of the parts and using them to improve and optimize the assembly process. The use of data on the part dimensions makes it possible to increase the efficiency of the assembly process at the expense of information support. These data must be linked to specific parts, which can be done by address buffer stations or special marking based on optical or magnetic tags. The graded kitting with full assemblage allows achieving the precision, which significantly exceeds the technological requirements. Therefore, by reducing the manufacturing requirements from the actual 5th-6th grade of accuracy for the body-slide valve connection to the 9th grade of accuracy, the total accuracy that is sufficient to ensure a structurally required fitting is achieved. The use of graded kitting makes it possible to achieve the required kitting accuracy for batches of 5 to 15 parts with easily achievable manufacturing precision requirements.

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Hyperchaos on the dynamics of memristive Tabu learning neuron model under influence of electromagnetic radiation: Application in biomedical data privacy

Franklin Open

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Franklin Open 2025年 10卷

作者： Boya, Bertrand Frederick Boui A Kengne, Jacques Nanfak, Arnaud Muni, Sishu Shankar Nkapkop, Jean de Dieu Kenmoe, Germaine Djuidje Babenko, Lyudmila Klimentyevna Unité de Recherche d'Automatique et d'Informatique Appliquée (UR-AIA) IUT-FV Bandjoun University of Dschang Bandjoun P.O. Box 134 Cameroon Laboratory of Energy-Electric and Electronic Systems Department of Physics Faculty of Science University of Yaoundé I Yaoundé P.O. Box 812 Cameroon Department of Physics Faculty of Science University of Yaoundé 1 Yaoundé Cameroon Institute of Computer Technology and Information Security Southern Federal University P.O. Box 347922 Taganrog Russian Federation Laboratory for electronics electrical engineering automation and telecommunications National Higher Polytechnic School of Douala University of Douala Douala PO Box 2701 Cameroon School of Digital Sciences Digital University Kerala Technocity campus Kerala Mangalapuram India Indian Institute of Information Technology and Management Kerala Technopark road Kerala India Department of Electrical Engineering and Industrial Computing University Institute of Technology Douala P.O. Box 8698 Cameroon

This paper presents a novel biological neural networks based on memristive Tabu learning neuron (MTLN) model influenced by electromagnetic radiation. Despite the model having an unstable equilibrium plane, numerical investigation without the influence of electromagnetic radiation reveal that when increasing the memristor strength induces complex phenomena, including the coexistence of infinite bubbles of bifurcations, offset plane coexistence of various attractors. Furthermore, electromagnetic radiation can significantly impact the system's behavior, giving rise to hyperchaotic dynamics. A decent understanding is seen between the mathematical outcomes, approved through trial execution on an Arduino microcontroller-based. The insights gained from neural dynamics could pave the way for applications in neuromorphic computing, engineering, and brain-machine interfaces. Based on the complex dynamics of the model, a data privacy of biomedical data has been build and reveals good results, and enable to resist to robust attacks. © 2024 The Author(s)

关键词： Data privacy Electromagnetic radiation Hyperchaos Microcontroller design MTLN model Offset plane coexisting behaviors

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Model-Free Optimization Scheme for Efficiency Improvement of Wind Farm Using Decentralized Reinforcement Learning ⁎

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IFAC-PapersOnLine 2020年第2期53卷 12103-12108页

作者： Zhiwei Xu Hua Geng Bing Chu Menghao Qian Ni Tan Department of Automation Tsinghua University and Beijing National Research Center for Information Science and Technology Beijing 100190 China School of Electronics and Computer Science Faculty of Physical Sciences and Engineering University of Southampton Southampton SO17 1BJ U.K. Department of Electrical Engineering Tsinghua University Beijing 100190 China

Wake interactions caused by the complex wakes between the turbines within a wind farm have significant adverse effect on the total power generation of the wind farm. To mitigate the effect of wake interactions and optimize the total power output of wind farm, this paper proposes a model-free control scheme using reinforcement learning by developing a decentralized Q learning method. The proposed approach guarantees that the output power of wind farm converges to the optimal total power under different wind conditions, and further ensures the gradual changes of control variables of wind turbines and thus avoids the unexpected sharp drop of the power generation performance of wind farm. Simulation results are provided to demonstrate the effectiveness of the proposed method.

关键词： Wind farm power optimization model-free approach decentralized control Q learning method

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Gesture Interpretation Control System Using Convolutional Neural Networks

Gesture Interpretation Control System Using Convolutional Ne...

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International Conference on Biomedical Innovations and Applications (BIA)

作者： Benedikt Baldursson Behnood Rasti Karl Gudmundsson Dorian Cojocaru Kristinn Andersen Saemundur E. Thorsteinsson Faculty of Electrical and Computer Engineering University of Iceland Hafnarfjordur Iceland Faculty of Automation Computers and Electronics University of Craiova Craiova Romania

ISBN: (纸本)9781728147550

This paper proposes a non-invasive control system for electrical wheelchairs utilizing facial gestures of individuals captured by real-time monocular camera. The images are interpreted with a convolutional neural network that achieves up to ~99.5% overall accuracy. The control system uses an embedded system with a graphics processing unit for predicting real-time throughput with fast inference time. This solution offers great versatility, where the user can make a gesture to depict a command of his choice.

关键词： Non-invasive Gestures Real-time Convolutional neural network Machine learning Gestures control systems Artificial Intelligence Machine Learning Real time Embedded systems Neural network commands

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Real-time deep learning for multi-label retinal disease diagnosis with embedded system

Computational and Structural Biotechnology Reports

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Computational and Structural Biotechnology Reports 2025年 2卷

作者： Feudjio Ghislain Saha Tchinda Beaudelaire Romain Atangana Tchiotsop Daniel Research Unit of Automation and Applied Computer (UR-AIA) Electrical Engineering Department of IUT-FV University of Dschang P.O. Box: 134 Bandjoun Cameroon Research Unit of Condensed Matter of Electronics and Signal Processing (UR‑MACETS) Department of Physics Faculty of Sciences University of Dschang P.O. Box 67 Dschang Cameroon Department of Computer Science Higher Teacher Training College University of Bertoua-Cameroon P.O.Box 652 Bertoua Cameroon

Background Ophthalmological consultations are essential for the early detection of retinal pathologies. The development of advanced diagnostic devices that combine portability, ease of access, and energy efficiency offers significant benefits, especially in rural or peri-urban areas, which often struggle with limited medical resources and time constraints. Method In this study, we develop a classification platform for various retinal diseases using a Raspberry Pi 4 board. The system relies on a lightweight deep learning model based on convolutional neural networks (CNNs). To enhance the model's efficiency, we integrate biorthogonal wavelet transforms, which facilitate the effective extraction of relevant features from the input images. This approach reduces computational complexity while maintaining the quality of the extracted information. The model, optimized for real-time deployment, was trained on a dataset containing 4303 retinal images, representing four classes of pathologies. Results The proposed embedded model achieves an accuracy of 0.9806 across the four classes of pathologies collected from the two public databases ODIR (Ocular Disease Intelligence Recognition) and RFMiD (Retinal Fundus Multi-Disease Image Dataset). Conclusions Compared to several state-of-the-art methods published to date, the outstanding performance of the proposed embedded system demonstrates its potential as a valuable tool for clinicians to diagnose various ocular diseases in underprivileged healthcare settings.

关键词： Embedded board Real-time classification Lightweight CNN model Wavelet transforms

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MACHINE LEARNING APPROACHES FOR COVID-19 DETECTION FROM CHEST X-RAY IMAGING: A SYSTEMATIC REVIEW

arXiv

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arXiv 2022年

作者： Arteaga-Arteaga, Harold Brayan delaPava, Melissa Mora-Rubio, Alejandro Bravo-Ortíz, Mario Alejandro Alzate-Grisales, Jesus Alejandro Arias-Garzón, Daniel López-Murillo, Luis Humberto Buitrago-Carmona, Felipe Villa-Pulgarín, Juan Pablo Mercado-Ruiz, Esteban Orozco-Arias, Simon Hassaballah, M. de la Iglesia-Vaya, Maria Cardona-Morales, Oscar Tabares-Soto, Reinel Department of Electronics and Automation Universidad Autónoma de Manizales Manizales Colombia Department of Chemical Engineering Universidad Nacional de Colombia Manizales Colombia Department of Computer Science Universidad Autónoma de Manizales Manizales Colombia Department of Systems and Informatics Universidad de Caldas Manizales Colombia Faculty of Computers and Information South Valley University Qena Egypt Unidad Mixta de Imagen Biomédica FISABIO-CIPF Fundación para el Fomento de la Investigación Sanitario y Biomédica de la Comunidad Valenciana Valencia Spain

There is a necessity to develop affordable, and reliable diagnostic tools, which allow containing the COVID-19 spreading. Machine Learning (ML) algorithms have been proposed to design support decision-making systems to assess chest X-ray images, which have proven to be useful to detect and evaluate disease progression. Many research articles are published around this subject, which makes it difficult to identify the best approaches for future work. This paper presents a systematic review of ML applied to COVID-19 detection using chest X-ray images, aiming to offer a baseline for researchers in terms of methods, architectures, databases, and current limitations. © 2022, CC BY.

关键词： COVID-19

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The Relational Parts of Speech in Text Analysis for Definition Detection, for Romanian Language

The Relational Parts of Speech in Text Analysis for Definiti...

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Roedunet International Conference (RoEduNet)

作者： Cristian Niculiţă Luminiţa Dumitriu Faculty of Automation Computer Sciences Electronics and Electrical Engineering “Dunărea de Jos” University Galaţi Romania

This article presents a method for detecting definitions in text, as well as identifying their formal parts, using the predictive capacity of certain relational parts of speech, mainly prepositions but also including words from other lexical categories such as adverbs, pronouns and conjunctions. The method is specifically tailored for Romanian language. Based on their predicting capacity, the relational words can be distributed into four categories that influence the way the definitions are detected. Using an annotated corpus of definitions, a series of statistics is extracted and used to formulate measures describing the impact and usage patterns of these relational words.

关键词： Semantics Syntactics Machine learning Natural language processing Statistical analysis Text analysis

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New simplified energy flow management algorithms for a DC bus-coupled photovoltaic-grid hybrid system

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Energy Systems 2025年

作者： Mbende, Ernest Titi Pesdjock, Mathieu Jean Pierre Muluh, Fombu Andrew Kenne, Godpromesse Department of Electrical and Power engineering Research Unit of Automation and Applied Computer Science IUT-FV Bandjoun University of Dschang P.O. Box 134 west Bandjoun Cameroon Department of Physics Research Unit of Condensed matter electronics and signal processing Faculty of Science University of Dschang P.O. Box 67 west Dschang Cameroon Department of Electrical and Electronic Engineering National Higher Polytechnic Institute University of Bamenda P.O. Box 39 North West Bambili Bamenda Cameroon

This paper presents a new simplified linear self-adjusting algorithm and an improved artificial ant colony self adjusting algorithm, for optimal energy flow management of a photovoltaic-grid hybrid energy system coupled through a DC link. The two proposed algorithms rely on the voltage error in the stabilization of the AC link to self determine a compensation current and hence power deficit, which is automatically extracted from the back-up storage to maintain an uninterrupted service and ensure the balance between power production and consumption. Numerical simulation results in Matlab/simulink under different scenarios show the effectiveness of the algorithms in mitigating the challenges posed by the intermittent nature of the photovoltaic source and also in facilitating the rapid and flexible integration of the renewable energy source. The algorithms also contribute effectively in regulating the AC bus voltage and in maintaining the frequency and total harmonic distortion within acceptable standards. The specificity of the two proposed algorithms is the fast response speed of the system in balancing power output and consumption. A comparative study of the proposed algorithms with a proportional integral (PI) controller under different operating modes clearly show the superiority of the new linear self-adjusting algorithm in terms of system stability, response time, error reduction and total harmonic distortion mitigation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Power management (telecommunication)

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