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Manipulating the Posterior Support of a Discrete Bayesian Estimator Under Full Sensor control

IFAC-PapersOnLine 2023年第2期56卷 252-257页

作者： Daniel Selvaratnam Farhad Farokhi Iman Shames Henrik Sandberg Decision and Control Systems School of Electrical Engineering & Computer Science KTH Royal Institute of Technology 114 28 Stockholm Sweden Department of Electrical and Electronic Engineering The University of Melbourne Parkville VIC 3010 Australia College of Engineering & Computer Science Australian National University Canberra ACT 2601 Australia

The asymptotic implausibility problem is introduced from the perspective of an adversary that seeks to drive the belief of a recursive Bayesian estimator away from a particular set of parameter values. It is assumed that the adversary controls all sensors informing the estimator, and can transmit false measurements stochastically according to a fixed distribution of its choice. First, we outline a method for verifying whether a given distribution solves the problem. We then consider the class of spoofing attacks, and show that the asymptotic implausibility problem has a solution if and only if it can be solved by a spoofing attack. Attention is restricted to finite parameter and observation spaces.

关键词： Network Security Bayesian Estimation Cyber-Physical Systems Network Systems Resilient control Systems Signal Processing

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A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection control Algorithms

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IEEE Transactions on Consumer Electronics 2025年

作者： Bilal, Hazrat Aslam, Muhammad Shamrooz Tian, Yibin Ullah, Inam Ayouni, Sarra Vasilakos, Athanasios V. College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou2211106 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences Saudi Arabia Grimstad Norway

The adoption of agricultural robots, or agrobots, has revolutionized modern farming operations, ranging from crop monitoring to automated harvesting, significantly boosting productivity. Motivated by the rapid advancements in agrobots and their integration into smart agricultural practices, this study proposes an autonomous trajectory tracking system for wheat farms using quadcopter UAVs. To address actuator fault detection, including stuck faults and partial loss of efficiency, a TSF-H∞-SMO (Takagi-Sugeno Fuzzy-based H∞ Sliding Mode Observer) fault detection framework is introduced. The approach initiates with the derivation of a TSF (Takagi-Sugeno Fuzzy) attitude control model that integrates an uncertainty term, constructed from the original nonlinear dynamics of the UAV and approximated through local linear models at four equilibrium positions. An actuator fault model is subsequently integrated to develop a comprehensive TSF-UAV model, accounting for actuator faults. The TSF-H∞-SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-H∞-SMO are evaluated through simulations on the TSF-UAV model under SISO (single-input single-output) actuator fault scenarios. The experimental results validate the proposed system, demonstrating its ability to detect a range of actuator faults accurately and promptly. The analysis reveals a proportional relationship between the amplitude of the state change and the severity of the fault, attributed to the interaction between system states and actuator flaps. This approach underscores the potential for deploying autonomous UAV-based fault detection and trajectory tracking systems in agricultural applications. Furthermore, integrating such advanced fault-tolerant control algorithms holds promise for consumer technology applications, where precision, reliability, and robustness are critical to enhancing system performance and operational efficiency. © 1

关键词： Agricultural robots

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Client Selection Based on Channel Capacity for Federated Learning Under Wireless Channels

Client Selection Based on Channel Capacity for Federated Lea...

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Asia-Pacific Conference on Communications

作者： Satoshi Yamazaki Takuma Furuki Dept. of Control and Computer Engineering National Institute of Technology Numazu College Numazu Japan Systems Innovation Faculty of Engineering Tokyo University Tokyo Japan

This paper proposes a user selection scheme for federated learning (FL) over wireless networks to reduce communication time based on channel capacity. In particular, the edge server calculates the Shannon channel capacity of each client for each round, and clients with a certain threshold capacity are randomly selected to participate in FL. We show that the convergence time of the proposed scheme outperformed that of the conventional scheme through computer simulation based on an image processing task under a wireless channel with pass-loss, shadowing, and Rician flat-fading. Moreover, the superiority of FL to centralized learning (CL) regarding total time is demonstrated theoretically and validated through computer simulation.

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Observer-Based control of Second-Order Multi-vehicle Systems in Bearing-Persistently Exciting Formations

arXiv

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arXiv 2024年

作者： Tang, Zhiqi Fidan, Baris Johansson, Karl H. Mårtensson, Jonas Hamel, Tarek Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-10044 Sweden Mechanical and Mechatronics Engineering Department University of Waterloo ON Canada France

This paper proposes an observer-based formation tracking control approach for multi-vehicle systems with second-order motion dynamics, assuming that vehicles' relative or global position and velocity measurements are unavailable. It is assumed that all vehicles are equipped with sensors capable of sensing the bearings relative to neighboring vehicles and only one leader vehicle has access to its global position. Each vehicle estimates its absolute position and velocity using relative bearing measurements and the estimates of neighboring vehicles received over a communication network. A distributed observer-based controller is designed, relying only on bearing and acceleration measurements. This work further explores the concept of the Bearing Persistently Exciting (BPE) formation by proposing new algorithms for bearing-based localization and state estimation of second-order systems in centralized and decentralized manners. It also examines conditions on the desired formation to guarantee the exponential stability of distributed observer-based formation tracking controllers. In support of our theoretical results, some simulation results are presented to illustrate the performance of the proposed observers as well as the observer-based tracking controllers. © 2024, CC BY-NC-ND.

关键词： Acceleration measurement

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Convolution without multiplication:A general speed up strategy for CNNs

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Science China(Technological Sciences) 2021年第12期64卷 2627-2639页

作者： CAI GuoRong YANG ShengMing DU Jing WANG ZongYue HUANG Bin GUAN Yin SU SongJian SU JinHe SU SongZhi College of Computer Engineering Jimei UniversityXiamen 361021China Department of Artificial Intelligence Xiamen UniversityXiamen 361005China Laboratory of Big Data and Artificial Intelligence NetDragon Websoft Inc.Fuzhou 350001China College of Computer and Control Engineering Minjiang UniversityFuzhou 350108China The Third Research Institute RopeokInc.Xiamen 361008China

Convolutional Neural Networks(CNN)have achieved great success in many computer vision ***,it is difficult to deploy CNN models on low-cost devices with limited power budgets,because most existing CNN models are computationally ***,CNN model compression and acceleration have become a hot research topic in the deep learning *** schemes for speeding up the feed-forward process with a slight accuracy loss include parameter pruning and sharing,low-rank factorization,compact convolutional filters and knowledge *** this study,we propose a general acceleration scheme that replaces the floating-point multiplication with integer *** this end,we propose a general accelerate scheme,where the floating point multiplication is replaced by integer *** motivation is based on the fact that every floating point can be replaced by the summation of an exponential ***,the multiplication between two floating points can be converted to the addition among *** the experiment section,we directly apply the proposed scheme to AlexNet,VGG,ResNet for image classification,and Faster-RCNN for object *** results acquired from ImageNet and PASCAL VOC show that the proposed quantized scheme has a promising performance,even with only one item of ***,we analyzed the eciency of our method on mainstream *** experimental results show that the proposed quantized scheme can achieve acceleration on FPGA with a slight accuracy loss.

关键词： deep learning convolutional neural network network quantization network speed up

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Experimental and numerical study on the shrinkage-deformation of carrot slices during hot air drying

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International Journal of Agricultural and Biological engineering 2023年第1期16卷 260-272页

作者： Dalong Jiang Congcong Li Zifan Lin Yuntian Wu Hongjuan Pei Magdalena Zielinska Hongwei Xiao School of Computer and Control Engineering Yantai UniversityYantai 264005ShandongChina Hebei Agricultural University Baoding 071001HebeiChina Hebei Key Laboratory of Agricultural Big Data Baoding 071001HebeiChina Department of Electrical and Electronic Engineering University of Western AustraliaPerth 6000Australia BeiGene Guangzhou Biologics Manufacturing Co. LtdGuangzhou 510555China Neuroscience and Intelligent Media Institute Communication University of ChinaBeijing 100024China Department of Systems Engineering University of Warmia and MazuryOlsztyn 004889Poland College of Engineering China Agricultural UniversityBeijing 100083China

In order to further understand the mechanism of material volume change in the drying process,numerical simulations(considering or neglecting shrinkage)of heat and mass transfer during convective drying of carrot slices under constant and controlled temperature and relative humidity were carried *** results were validated with experimental *** results of the simulation show that the Quadratic model fitted well to the moisture ratio and the material temperature data trend with average relative errors of 5.9%and 8.1%,***,the results of the simulation considering shrinkage show that the moisture and temperature distributions during drying are closer to the experimental data than the results of the simulation disregarding *** material moisture content was significantly related to the shrinkage of dried *** and relative humidity significantly affected the volume shrinkage of carrot *** volume shrinkage increased with the rising of the constant temperature and the decline of relative *** model can be used to provide more information on the dynamics of heat and mass transfer during drying and can also be adapted to other products and dryers devices.

关键词： carrot drying numerical simulation heat and mass transfer shrinkage

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Influence of agglomerations on magnetic properties of polymer matrices filled with magnetic nanoparticles

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Materials Today: Proceedings 2022年 67卷 792-796页

作者： Blachowicz, Tomasz Grzybowski, Jacek Ehrmann, Andrea Institute of Physics – Centre for Science and Education Silesian University of Technology Gliwice 44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice 44-100 Poland Faculty of Engineering and Mathematics Bielefeld University of Applied Sciences Bielefeld 33619 Germany

Magnetic nanoparticles can be embedded in electrospun nanofibers and other polymeric matrices to prepare magnetic composites with defined magnetic and mechanical properties. Metal-oxide nanoparticles, such as magnetite or nickel-ferrite, are of special interest since they do not need a coating to avoid oxidation. Like other nanoparticles, these metal-oxide nanoparticles tend to form agglomerations, in this way modifying the magnetic properties of the composites. After studying this effect for the magnetic elements Co, Fe, Ni as well as permalloy (Py) in a previous study, defining a new method to quantify the nanoparticle distribution in a polymer, here we concentrate on the influence of agglomerations on the magnetic properties of metal-oxide nanoparticles with different diameters in non-magnetic matrices. © 2022

关键词： Magnetite

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High-Performance Image Compression and Reconstruction Using Lightweight Deep Neural Networks

High-Performance Image Compression and Reconstruction Using ...

引用

China Instrument and control Society (ACCIS), Academic Conference of

作者： Shuhua Gao Zekun Qin Xingyu Jiang Zhengfang Wang Wenjun Xu Jing Wang School of Control Science and Engineering Shandong University Jinan China Electrical and Computer Engineering National University of Singapore Singapore Research Institute of MA&EI Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350350326

ISBN: (纸本)9798350350333

In many remote areas, tunnels, and underground spaces, network bandwidth is extremely limited. However, with the development of high-resolution cameras, the size of image data has significantly increased, making it challenging to frequently transmit large-scale images for detection and monitoring purposes. To address this issue, this paper proposes an image compression and reconstruction method based on lightweight neural networks, utilizing a dual-module deep learning architecture comprising a Compressed Convolutional Neural Network (CCNN) and a Reconstructed Convolutional Neural Network (RCNN). The lightweight design facilitates easy deployment on edge devices. The CCNN module compresses the original image for fast network transmission, and upon reception, the RCNN module reconstructs the image. This approach significantly reduces the transmission payload and enhances speed under limited bandwidth conditions. Furthermore, the developed method outperforms the classic JPEG algorithm across four representative metrics as evaluated on a widely-used public dataset. To further optimize the CCNN for edge deployment, a model distillation technique is employed, reducing the model size by approximately 70% with negligible performance impact. In summary, the proposed deep neural network model offers high performance and a low memory footprint for image compression and reconstruction, making it particularly suitable for resourcelimited edge devices and bandwidth-constrained applications.

关键词： Performance evaluation Deep learning Image coding Image edge detection Computational modeling Transform coding Bandwidth Cameras Convolutional neural networks Image reconstruction

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Analysis of the Characteristics of a Six-phase Induction Motor

Analysis of the Characteristics of a Six-phase Induction Mot...

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International Conference on Electrical Drives and Power Electronics (EDPE)

作者： Mariusz Korkosz Andriy Kutsyk Krystyna Krzywdzińska-Kornak Marek Nowak Mykola Semeniuk Faculty of Electrical and Computer Engineering Rzeszow University of Technology Rzeszow Poland Institute of Power Engineering and Control System Lviv Polytechnic National University Lviv Ukraine

The paper includes a report on the preliminary results of six-phase induction motor tests. Using a power supply with more than three phases requires a slightly different approach in the motor design process. In theory, a six-phase induction motor design should be more resistant to the loss of power to one, two, or three phases. A numerical analyzis of selected operating states of an asymmetrical power supply to an induction motor was carried out. Verification of the numerical results was carried out under laboratory conditions.

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MPC-based Optimization Design for 3D Collision Avoidance of a Mobile Manipulator Based-on Obstacle Velocity Estimation

MPC-based Optimization Design for 3D Collision Avoidance of ...

引用

International Automatic control Conference

作者： Kai-Tai Song Chih-Hsuan Lin Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University 1001 Ta Hsueh Road Hsinchu Taiwan Taiwan R.O.C

ISBN: (数字)9798350354904

ISBN: (纸本)9798350354911

This paper presents an optimization design of 3D dynamic obstacle avoidance for a mobile manipulator based on model predictive control (MPC). The design enables a mobile manipulator to achieve optimized 3D collision avoidance motion with shorter avoidance path and faster avoidance time. A 3D LiDAR is installed onboard the robot to acquire environmental point cloud and estimate obstacle velocity. The MPC is designed to track an initial 3D path of the mobile manipulator and avoid any static and dynamic obstacles in real time. Experimental results show that the proposed method can simultaneously avoid static and dynamic obstacles in 3D space. Compared with baseline algorithms without velocity estimation, the proposed method reduces the avoidance path length by 8.27% and path execution time by 13.79%.

关键词： Solid modeling Three-dimensional displays Tracking Dynamics Estimation Real-time systems Collision avoidance Manipulator dynamics Optimization Predictive control

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