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IEEE Journal on Flexible Electronics 2024年第7期3卷 292-299页

作者： Bhagavathi, K. Aditya Bonam, Satish Joseph, Jose Rao, Karri Trinadha Singh, Shiv Govind Vanjari, Siva Rama Krishna Indian Institute of Technology Hyderabad Department of Electrical Engineering Hyderabad502285 India Digital University Kerala School of Electronic Systems and Automation Trivandrum695317 India

Piezoelectric materials derived from biological sources are given greater consideration in the present era due to their advantages, such as biocompatibility, sustainability, and potential for tailoring properties through chemical modification and genetic engineering. Silk, a bio-piezopolymer, derived from the silk glands of various species of silkworms, is extensively studied for its piezoelectric properties and applications thereof. This article presents the development of a composite bio-piezopolymer, amalgamating the material properties of silk and the extracts taken from a tropical desert plant, aloe vera. A comprehensive material characterization protocol is embraced to assess the performance of the thin film formed from the silk-aloe vera (SAV) composite solution. In addition to an improvement in the piezoelectric characteristics, the composite film exhibited excellent surface uniformity and crystallinity, proving its potential for real-time applications. Pressure sensors were fabricated with SAV composite films and characterized using a custom-built test setup. The characterization results surpassed the performance achieved by pressure sensors fabricated using only silk thin film. © 2022 IEEE.

关键词： Pressure sensors

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Designated-time stabilizing strategy for a class of time-varying nonlinear systems and its application to pendulum system

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International Journal of Robust and Nonlinear control 2024年第12期34卷 7679-7697页

作者： Li, Jiao-Jiao Sun, Zong-Yao Wen, Changyun Chen, Chih-Chiang Institute of Automation Qufu Normal University Shandong China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Systems and Naval Mechatronic Engineering National Cheng Kung University Tainan Taiwan

In this paper, a new scheme is proposed to stabilize a class of nonlinear systems within a designated time, mainly motivated by two unsolved problems: the singularity caused by infinity control magnitudes at the prescribed time instant and the lack of formulating/manipulating state responses after the prescribed time. Under the proposed scheme, an exquisite controller is constructed by entailing both the state scaling approach and a finite-time stabilizing procedure. It is shown that the obtained control is well defined with bounded magnitude on the whole-time horizon and ensures the existence of the solutions of the closed-loop system. Superior to the existing prescribed time stabilization results, the proposed method is capable of ensuring that the states of the closed-loop system converge to a small compact set within the designated time, stay inside it thereafter and perform finite-time convergence eventually. Simulation examples are presented to illustrate the effectiveness of the proposed approach. © 2024 John Wiley & Sons Ltd.

关键词： Nonlinear systems

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Intelligent obstacle avoidance algorithm for safe urban monitoring with autonomous mobile drones

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Journal of Electronic Science and Technology 2024年第4期22卷 19-36页

作者： Didar Yedilkhan Abzal E.Kyzyrkanov Zarina A.Kutpanova Shadi Aljawarneh Sabyrzhan K.Atanov Department of Computer Engineering Astana IT UniversityAstana010000Kazakhstan Department of Computer and Software Engineering L.N.Gumilyov Eurasian National UniversityAstana010000Kazakhstan Department of Automation and Control Systems L.N.Gumilyov Eurasian National UniversityAstana010000Kazakhstan Department of Software Engineering Jordan University of Science and TechnologyIrbid22110Jordan

The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone *** deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition *** this context,this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring *** primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles,such as *** algorithm incorporates three key components to optimize the obstacle detection,navigation,and energy efficiency within a drone ***,the algorithm utilizes a method to calculate the position of a virtual leader,acting as a navigational beacon to influence the overall direction of the ***,the algorithm identifies observers within the swarm based on the current *** further refine obstacle avoidance,the third component involves the calculation of angular velocity using fuzzy *** approach considers the proximity of detected obstacles through operational rangefinders and the target’s location,allowing for a nuanced and adaptable computation of angular *** integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically,ensuring practical obstacle *** proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive *** results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications.

关键词： Drone swarms Fuzzy logic Intelligent solution Smart city Urban monitoring

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Hierarchically Distributed Optimization with a Flexible and Complexity-Reducing Algorithm

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Journal of systems Science & Complexity 2024年第6期37卷 2530-2555页

作者： LIANG Shu ZHANG Lei WEI Yiheng LIU Yemo Department of Control Science and Engineering Tongji UniversityShanghai 200092China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai 200092China School of Mathematics Southeast UniversityNanjing 211189China Beijing Aerospace Automatic Control Institute Beijing 100854China

In this paper,the authors consider distributed convex optimization over hierarchical *** authors exploit the hierarchical architecture to design specialized distributed algorithms so that the complexity can be reduced compared with that of non-hierarchically distributed *** this end,the authors use local agents to process local functions in the same manner as other distributed algorithms that take advantage of multiple agents'computing ***,the authors use pseudocenters to directly integrate lower-level agents'computation results in each iteration step and then share the outcomes through the higher-level network formed by *** authors prove that the complexity of the proposed algorithm exponentially decreases with respect to the total number of *** support the proposed decomposition-composition method for agents and pseudocenters,the authors develop a class of *** operators are generalizations of the widely-used subgradient based operator and the proximal operator and can be used in distributed convex ***,these operators are closed with respect to the addition and composition operations;thus,they are suitable to guide hierarchically distributed design and ***,these operators make the algorithm flexible since agents with different local functions can adopt suitable operators to simplify their ***,numerical examples also illustrate the effectiveness of the method.

关键词： Complexity convergence convex optimization distributed algorithm hierarchical network

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of Computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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HKB-YOLO: transmission line fire detection method based on hierarchical feature fusion

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International Journal of Security and Networks 2024年第4期19卷 188-198页

作者： Ying, Zhilei Meng, Yanan Chen, Ruoxi Lou, Jianlou School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin 132022 China Department of Automation Jilin Institute of Chemical Technology Jilin 132022 China School of Computer Engineering Northeast Electric Power University Jilin 132012 China

Timely transmission line fire inspections are vital for power system safety. Although deep learning models are widely used for flame detection, struggle with small target recognition due to background interference and are vulnerable to input data perturbations, posing security risks. This study introduces a hierarchical feature fusion method based on the you only look once Version 8 (YOLOV8) framework. It employs high-performance GPU network version 2 (HGNetV2) to enhance small-target feature extraction while reducing computational complexity. A spatial pyramid pooling-fast module with large separable kernel attention is designed to highlight key features and suppress background noise. The proposed bidirectional slim neck structure (BiSlimneck) reduces feature loss. Adversarial training enhances the model’s robustness. Experimental results show a 1.4% accuracy improvement, with a reduction in parameters and complexity by 18.3% and 21.6%, respectively. After adversarial training, the accuracy of the network in the face of attacks increased by 10% compared to the pre-training. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Premixed flames

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Data-Driven control of Distributed Event-Triggered Network systems

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 351-364页

作者： Xin Wang Jian Sun Gang Wang Frank Allgower Jie Chen the National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China IEEE the Institute for Systems Theory and Automatic Control University of StuttgartStuttgart 70550Germany the Department of Control Science and Engineering Tongji UniversityShanghai 201804 the National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(*** systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional *** the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is ***,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input ***,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.

关键词： Data-driven control distributed event-triggered network system(ETS) linear matrix inequalitie(LMI) looped-functional stability

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Feedback-Based Quantum Algorithm for Constrained Optimization Problems 15th

Feedback-Based Quantum Algorithm for Constrained Optimizati...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Abdul Rahman, Salahuddin Karabacak, Özkan Wisniewski, Rafal Automation and Control Section Department of Electronic Systems Aalborg University Aalborg Denmark Department of Mechatronics Engineering Kadir Has University Istanbul Turkey

ISBN: (纸本)9783031856990

The feedback-based algorithm for quantum optimization (FALQON) has recently been proposed to find ground states of Hamiltonians and solve quadratic unconstrained binary optimization problems. This paper efficiently generalizes FALQON to tackle quadratic constrained binary optimization (QCBO) problems. For this purpose, we introduce a new operator that encodes the problem’s solution as its ground state. Using control theory, we design a quantum control system such that the state converges to the ground state of this operator. When applied to the QCBO problem, we show that our proposed algorithm saves computational resources by reducing the depth of the quantum circuit and can perform better than FALQON. The effectiveness of our proposed algorithm is further illustrated through numerical simulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Constrained optimization

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High-Capacity DWDM Transmission System for Free-Space Optical Network 6

High-Capacity DWDM Transmission System for Free-Space Optica...

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6th International Conference on Electrical, control and Instrumentation engineering, ICECIE 2024

作者： Rahman, M.T. Jahid, M.A. Anha, S.Y. Rahman, Md. Shohanur Azmi Orpy, F. Bakibillah, A.S.M. Department of Computer Science and Engineering Bangladesh Tokyo Institute of Technology Department of Systems and Control Engineering Tokyo152-8552 Japan

ISBN: (纸本)9798350380040

This study presents the architecture and performance evaluation of a high-capacity free-space optical (FSO) communication system that makes use of dense wavelength division multiplexing (DWDM) and a 1.28 Tb/s link. The system under consideration comprises 64 individual channels, each of which is capable of transferring data streams at a speed of 20 Gbps. A return-to-zero (RZ) line coding algorithm is incorporated to maximize performance across 2.0 km of FSO link. Important parameters that affect the system's performance, like beam divergence and atmosphere attenuation, are taken into account in the analysis. The signals' bit-error-rate (BER), quality factor (Q), and eye diagrams are calculated and examined to assess the system's performance. For future optical-wireless networks that require significant bandwidth capabilities, cost-effective implementation, and increased flexibility, this novel high-capacity design offers a promising solution. The results aid in the development of dependable and useful high-speed FSO communication systems. © 2024 IEEE.

关键词： Cost effectiveness

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Working Condition Identification Method for the Scarifying Tooth Harrow Monitoring System of a Roller-type Residual Film Collector

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Journal of engineering Science and Technology Review 2024年第2期17卷 58-65页

作者： Xue, Dangqin Zhang, Xiaodong Dong, ZhaoSen Zhang, Jiaxi Shi, Huojie Nanyang Institute of Technology Nanyang473000 China College of Automation Engineering Henan Polytechnic Institute Nanyang473000 China Mechanical and Electrical Engineering Institute Xinjiang Agricultural University Urumqi830052 China Department of Biological Systems Engineering Washington State University PullmanMI98101 United States

The roller-type residual film collector is the main machine type for collecting residual films. The scarifying tooth harrow bears the largest resistance among all the components of the entire machine, and it directly affects the quality and efficiency of the residual film collector. In this study, the arithmetic optimization algorithm- variational mode decomposition - bidirectional long short-term memory (AOA-VMD-BiLSTM) model was proposed to improve the working condition identification accuracy of the scarifying tooth harrow. First, the locations of three scarifying tooth harrow monitoring points were analyzed, the arithmetic optimization algorithm was introduced to optimize the variational mode decomposition (VMD) parameters, and the optimal parameter combination of the three monitoring points was acquired. Second, the reconstructed signal for the three monitoring points on the scarifying tooth harrow after VMD decomposition was subjected to two-way updating and recursion with bidirectional long short-term memory (BiLSTM) as the core of the model, and iterative training of the scarifying tooth harrow under four working conditions was performed. Last, the new sample set and the original dataset were imported into two deep learning models, namely, BiLSTM and long short-term memory (LSTM), followed by a comparative validation of the training results of VMD-BiLSTM, VMD-LSTM, BiLSTM, and LSTM models. Results demonstrate that among the models, VMD-BiLSTM exerts the best processing effect on complex signals and has a higher convergence rate, better convergence stability, and higher accuracy. In the VMD-BiLSTM diagnostic model, each performance index can exceed 97.3%. This model performs better than VMD-LSTM, BiLSTM, and LSTM models and realizes intelligent classification under four signal conditions of the scarifying tooth harrow: no load, normal operation, mild overload, and serious overload. The proposed method provides great significances for improving the intelligenc

关键词： Long short-term memory

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