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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Imre Farkas Attila Kovari Mónika Rajcsanyi-Molnar Department of Computer Systems and Control Technology Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Institute of Electronics and Communication Systems Kandó Kalmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Economics and Management Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

In the last three years, a new partner has emerged for teachers and educators in the field of education. The mushrooming of applications based on large language models has greatly shaped the educational development fields of the present era. The use of various AI applications has become commonplace among students and teachers alike. Many questions are being raised by researchers in this field. What does the rapid development of AI applications bring to the field of pedagogy? Should the products of such applications be compared with human performance? The form presented in this thesis seeks to answer similar questions. What is the impact of frequent use of these applications on the literacy level of individuals? In which areas do students tend to use these applications? The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change. However, 88 % of students have already dealt with AI-based applications, with 64 % using them several times or more, suggesting a high level of integration into their educational processes.

关键词： Ethics Power engineering Large language models Education Collaboration Learning (artificial intelligence) Reliability Artificial intelligence Monitoring Guidelines

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

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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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GNNBoost: Accelerating sampling-based GNN training on large scale graph by optimizing data preparation

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Journal of Systems Architecture 2025年 167卷

作者： Tan, Yujuan Gan, Yan Zeng, Zhaoyang Bai, Zhuoxin Qiao, Lei Liu, Duo Zhong, Kan Ren, Ao Chongqing University College of Computer Science Chongqing China Beijing Institute of Control Engineering Beijing China Chongqing University School of Big Data & Software Engineering Chongqing China

Graph Neural Networks (GNNs) have successfully extended deep learning from traditional Euclidean spaces to complex graph structures. Sampling-based GNN training has been widely adopted for large-scale graphs without compromising accuracy. However, the graph irregularity results in imbalanced sampling workloads, making it challenging for existing GNN systems to effectively utilize GPU resources for graph sampling. Additionally, in GNN systems where both topology and feature caches are enabled, differences in characteristics and purposes of cache data complicate the allocation of GPU memory for these two caches with minimal overhead. To address these challenges, we propose GNNBoost, a framework designed to accelerate GNN training. GNNBoost consists of two key innovations. First, GNNBoost introduces a degree-oriented sampling schedule that groups training vertices based on their degrees and applies tailored sampling strategies to balance GPU workloads and improve sampling performance. Second, GNNBoost develops a low-overhead cache space allocation mechanism that accurately determines the optimal cache sizes for graph topology and features across different workloads, minimizing both space and time overheads. We conduct a comprehensive evaluation of GNNBoost through various GNN models and large graph datasets, demonstrating that it significantly outperforms existing GNN training systems. © 2025

关键词： Cache Graph neural networks Sample-based GNN training

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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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Design and testing of Hybrid Slime Mold Algorithm for Real-Time Adaptive Mobile Robot Path Planning in Dynamic Environments 3

Design and testing of Hybrid Slime Mold Algorithm for Real-T...

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3rd International Conference on Advancement in Computation and computer Technologies, InCACCT 2025

作者： Gupta, Sachin Gupta, Shivali Raith, Shaik Mohammed Kumar, Anand Bharany, Salil Kant, Vishnu Sandeep, Ch. Department of Robotics and Control Engineering School of Electronics and Control Engineering Lovely Professional University Phagwara India Department of Chemistry School of Chemical Engineering and Physical Sciences Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India

ISBN: (纸本)9798331543891

Dynamic path planning and collision prevention is one of the key tasks for autonomous vehicles in relation to dynamic movement of obstacles. In this paper, a novel hybrid approach based on integration of Slime Mold Algorithm (SMA), Triangle Inequality Principle (TIP), and Partition Method Strategy (PMS) for solution of this problem statement. The SMA algorithm is very flexible and adaptive by nature due to its biological working mechanism which is associated with TIP and PMS strategies in relation to their optimization and efficient path planning capabilities and robust solutions. This integrated module is tested in virtual environment and the results are compared with other updated algorithms such as A*, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for the same static and dynamic environment. It has been observed from the results that this hybrid strategy significantly improves the path planning task several times than other algorithms in terms of multiple aspects. Under testing conditions, the path length was reduced to 25% than other algorithms in a dynamic environment. During this calculation, the computational efficiency of the system has been improved to 60% than conventional methods. The chances of collision have been improved to 34.23% especially in environment where the obstacles are moving. The capability of SMA has been improved due to the synergy between TIP and PMS. The dynamic and adaptive nature of this algorithm is suitable for various applications such as search-and-rescue operations, manufacturing units’ automation and other hazardous environment exploration. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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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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