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IEEE Sensors Letters 2023年 1-4页

作者： Bhagavathi, Aditya Bonam, Satish Joseph, Jose Singh, Shiv Govind Vanjari, Siva Rama Krishna Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana India School of Electronic Systems Automation Digital University Kerala Trivandrum Kerala India

Triboelectric nanogenerators (TENGs) are efficient, sustainable, low-maintenance, and environment-friendly power sources that are suitable for wireless, portable, and wearable devices. This study, for the first time, presents a vertical contact mode TENG utilizing a thin film of silk and copper film as the triboelectric pair. This contact separation mode TENG is highly flexible, cost-effective, and biocompatible. The silk triboelectric layer is spin-coated on a PET sheet which serves as the flexible substrate of the TENG. The response was maximized by enhancing the surface roughness of the copper film, which in turn increases the contact area during triboelectrification, leading to an increase in charge generation. The optimized TENG yielded a maximum output voltage of 350 $V_{pp}$ and a power density of 4 $W/m^{2}$ and was able to power up commercially available LEDs. Repeatability analysis and TENG performance at different forces yielded satisfactory results. IEEE

关键词： Electrodes

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High-Accuracy and Low-Multiplication Recursive Discrete Cosine Transform Algorithm Design and Its Realization in Mel-Scale Frequency Cepstral Coefficients

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IEEE Transactions on Very Large Scale Integration (VLSI) systems 2024年第11期32卷 2139-2143页

作者： Lai, Shin-Chi Wang, Szu-Ting Zhu, Yi-Chang Hung, Ying-Hsiu Lee, Jeng-Dao Chen, Wei-Da National Formosa University Smart Machinery and Intelligent Manufacturing Research Center Department of Automation Engineering Yunlin 63208 Taiwan National Formosa University Yunlin 63208 Taiwan National Yunlin University of Science and Technology Department of Electronic Engineering Douliu64002 Taiwan

This brief introduces an innovative recursive discrete cosine transform (DCT) algorithm characterized by its exceptional precision and minimal multiplication requirements. Through the strategic implementation of data reordering and 'q' value adjustment schemes, the proposed algorithm entails only a single constant-multiplication operation featuring a fixed cosine coefficient within the iterative phase. By judiciously selecting an appropriate 'q' value (q =41), it achieves outstanding results, reaching peak signal-to-noise ratios (PSNRs) of 94.9 and 100.9 dB under 18-bit and 20-bit word length (WL) conditions, respectively, in terms of decimal places. Notably, the proposed algorithm substantially diminishes the number of multiplications by 86.08%, offset by an increase of 2688 additions. The proposed design has a simpler structure and utilizes fewer hardware resources. In field programmable gate array (FPGA) implementation, the device is composed of 43 combinational adaptive look-up tables (ALUTs) specifically allocated for constant multiplication (CM). Overall, the proposed accelerator totally takes 158 combinational ALUTs, 65 registers, a 960-bit read-only memory (ROM), and a 1024-bit random access memory (RAM) in hardware realization and can be operated at a maximum frequency of 156.62 MHz. Therefore, it is particularly well-suited for VLSI implementation in a parallel calculation of Mel-scale frequency cepstral coefficients (MFCCs). © 1993-2012 IEEE.

关键词： Table lookup

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Development of a Bio-inspired Tailless FWMAV with High-Frequency Flapping Wings Trajectory Tracking Control

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Journal of Bionic engineering 2024年第5期21卷 2145-2166页

作者： Qingcheng Guo Chaofeng Wu Yichen Zhang Feng Cui Wu Liu Xiaosheng Wu Junguo Lu National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong UniversityShanghai200240China Department of Micro/Nano Electronics School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai200240China Department of Automation Shanghai Jiao Tong UniversityShanghai200240China The Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China

The development of a tailless Flapping Wing Micro Aerial Vehicle(FWMAV)inspired by the hummingbird is presented in this *** implementing mechanical simplifications,it is possible to use planar machining technology for manufacturing of the FWMAV’s body,greatly reducing assembly ***,studies on flapping wing aircraft are limited to open-loop wing kinematics *** this work,an instantaneous closed-loop wing trajectory tracking control system is introduced to minimize wings’trajectory tracking *** control system is based on Field-Oriented Control(FOC)with a loop shaping compensation technique near the flapping *** frequency analysis,the loop shaping compensator ensures the satisfactory bandwidth and performance for the closed-loop flapping *** implement the proposed controller,a compact autopilot board integrated with FOC hardware is designed,weighing only 2.5 *** utilizing precise wing trajectory tracking control,the hummingbird-inspired FWMAV demonstrates superior ability to resist external disturbances and exhibits reduced attitude tracking errors during hovering flight compared to the open-loop wing motion.

关键词： Flapping Wing Micro Aerial Vehicle(FWMAV) Bio-inspired robotics Trajectory control Loop shaping compensator Field-Oriented Control(FOC)

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Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy 63

Approximate state estimation with large-scale sensor network...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Liu, Xinhui Cheng, Meiqi Shi, Dawei Shi, Ling Beijing Institute of Technology Miit Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong

ISBN: (纸本)9798350316339

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of E(varepsilon)-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement. © 2024 IEEE.

关键词： Scheduling algorithms

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Enhanced UAV Pursuit-Evasion Using Boids Modelling:A Synergistic Integration of Bird Swarm Intelligence and DRL

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Computers, Materials & Continua 2024年第9期80卷 3523-3553页

作者： Weiqiang Jin Xingwu Tian Bohang Shi Biao Zhao Haibin Duan Hao Wu School of Information and Communications Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China China Academy of Electronics and Information Technology China Electronics Technology Group Corporation(CETC)Beijing100041China Department ofAutomatic Control School of Automation Science and Electrical EngineeringBeihangUniversityBeijing100191China

TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and *** address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our *** integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion *** further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic *** also leverage self-play training to continuously optimize the performance of pursuit *** experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each *** simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed *** overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.

关键词： UAV pursuit-evasion swarm intelligence algorithm Boids model deep reinforcement learning self-play training

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Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui Wang Zhiming Zhou Kaiwen Li Tao Zhang Ling Wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

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Light-Weight Low-Cost Drive Control Modules Design and Mass Production Research in SAR Satellites 4

Light-Weight Low-Cost Drive Control Modules Design and Mass ...

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4th China International SAR Symposium, CISS 2023

作者： Liu, Pengfei Ma, Xiaoyan Wang, Jun Qian, Weixin Feng, Zhengkang Aerospace System Engineering Shanghai Department of Electrical Control Shanghai China Shanghai Aerospace Electronic Technology Institute Department of Radar System Shanghai China

ISBN: (纸本)9798350307955

In order to adapt to the development trend of light-weight and low-cost SAR (Synthetic Aperture Radar) satellites, products of drive control modules, especially drive control modules of SAR antenna deployment mechanism and solar array, are required to be light-weight and low-cost. At the level of product design, the mechanical structure of light-weight drive control module and low-cost hardware circuit design are given. The design scheme mainly involves the electromechanical integration interface of the drive control module and the drive mechanism, and the protection and reinforcement of the industrial components of the hardware circuit. At the level of product production, firstly, the product is designed according to modularization. Secondly, a series of standard systems from production to test are established. Finally, automatic production lines and automatic testing equipment are used for mass production and testing of products. The above design and production optimization measures reduce the weight and cost of drive control module products. The light-weight and low-cost drive control modules is conducive to carrying more payloads on SAR satellites and networking between satellites. © 2023 IEEE.

关键词： Synthetic aperture radar

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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of systems Science & Complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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A Multi-Bit ECRAM-Based Analog Neuromorphic system With High-Precision Current Readout Achieving 97.3% Inference Accuracy

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IEEE Transactions on Biomedical Circuits and systems 2025年第3期19卷 590-604页

作者： Um, Minseong Kang, Minil Eom, Kyeongho Kwak, Hyunjeong Noh, Kyungmi Lee, Jimin Son, Jeonghoon Kwon, Jiseok Kim, Seyoung Lee, Hyung-Min Korea University School of Electrical Engineering Seoul02841 Korea Republic of Korea University Department of Semiconductor System Engineering Seoul02841 Korea Republic of Pohang University of Science and Technology Department of Materials Science and Engineering Pohang37673 Korea Republic of The Catholic University of Korea School of Information Communications and Electronic Engineering Bucheon14662 Korea Republic of

This article proposes an analog neuromorphic system that enhances symmetry, linearity, and endurance by using a high-precision current readout circuit for multi-bit nonvolatile electro-chemical random-access memory (ECRAM). For on-chip training and inference, the system uses activation modules and matrix processing units to manage analog update/read paths and perform precise output sensing with feedback-based current scaling on the ECRAM array. The 250nm CMOS neuromorphic chip was tested with a 32 × 32 ECRAM synaptic array, achieving linear and symmetric updates and accurate read operations. The proposed circuit system updates the 32 × 32 ECRAM across 100 levels, maintaining consistent synaptic weights, and operates with an output error rate of up to 2.59% per column. It consumes 5.9 mW of power excluding the ECRAM array and achieves 97.3% inference accuracy on the MNIST dataset, close to the software-confirmed 97.78%, with only the final layer (64 × 10) mapped to the ECRAM. © 2007-2012 IEEE.

关键词： CMOS integrated circuits

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Improved Artificial Neural Networks Based Whale Optimization Algorithm

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for Computer Science and Mathematics 2023年第3期4卷 195-202页

作者： Ibrahem Alhayali, Royida A. Aladamey, Mostafa Khaled Abd Alrahman Subhi, Mohammed Rashid Mohammed, Mostafa Abdulghafoor Amir, Amiza Abdalkareem, Zahraa A. Department of Architecture Engineering College of Engineering University of Diyala Diyala Iraq Alimam Aladham University College Baghdad Iraq Department of Petroleum System Control Engineering College of Petroleum Processes Engineering Tikrit University Iraq Faculty of Engineering & Electronic Engineering Technology University Malaysia Malaysia

Owing to the increasing interest in artificial neural networks (ANNs) across various fields of study, many studies have focused on enhancing their performance through the utilisation of different learning algorithms. This study examines the use of the Whale Optimization Algorithm (WOA) as a training algorithm to improve the classification accuracy of ANNs. To achieve a high level of classification accuracy with ANN models, it is imperative to ensure that the model is appropriately designed in terms of the employed structure, training algorithm and activation function. In this work, WOA was adopted to train ANN models using 10 well-known datasets sourced from the UCI machine learning repository. The classification accuracy of a WOA-trained ANN was compared with that of a backpropagation-trained ANN, and the results showed that the WOA-trained ANN exhibited superior performance. © 2023 College of Education, Al-Iraqia University. All rights reserved

关键词： Neural networks

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