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Extension of Maxwell's equations for non-stationary magnetic fluids using gauss's divergence theorem

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Results in Engineering

Results in engineering 2025年 26卷

作者： Bouzidi, Mohammed NASRI, Abdelfatah Rahmoune, Mohamed Ben Hafsi, Oussama Aeggegn, Dessalegn Bitew Ghoneim, Sherif S. M. Ali, Enas Ghaly, Ramy N. R. Department of Sciences and Technology University of Tamanghasset Sersouf P.O. Box 10034 Tamanghasset11001 Algeria Energy and Materials Laboratory University of Tamanghasset Sersouf P.O. Box 10034 Tamanghasset11001 Algeria Laboratory Smart Grid and Renewable Energy SGRE University Tahri Mohamed Bechar Algeria Algeria Applied Automation and Industrial Diagnostics Laboratory Faculty of Science and Technology University of Djelfa DZ 17000 Algeria DDI Laboratory Ahmed Draia University Adrar01000 Algeria Department of Electrical and Computer Engineering Debre Markos University Debre Markos269 Ethiopia Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia University Centre for Research and Development Chandigarh University Punjab Mohali140413 India Faculty of Engineering and Technology Future University in Egypt New Cairo11835 Egypt Ministry of Higher Education & Scientific Research Industrial Technical Institute-Mataria Technical College Cairo11718 Egypt Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

The work presented in this paper focuses on formulating the development of time-dependent electromagnetic field laws through the application of Gauss's divergence theorem. The first part of the discussion looks at the basic ideas of electromagnetism. It focuses on how classical formulations of the laws of electromagnetism can be adapted to account for non-stationary conditions, especially regarding magnetic fluids that don't conduct electricity. It is suggested that employing Gauss's divergence theorem could help improve the computational analysis of these generalized equations, which would make them more useful in magnetic fluid dynamics. The paper examines the intricate interactions between non-conductive particles and conductive fluids under magnetic fields. By putting these interactions into a single theoretical framework, this work aims to help us understand non-stationary electromagnetic phenomena and how they affect many different scientific and engineering fields. The concluding section of the study examines the prospective practical applications of these extended equations. They could enable the development of more advanced electromagnetic devices and systems. Creating a strong set of analytical tools that can find new scientific paths and useful applications is the main goal of the study, particularly in the areas of electromagnetic induction and fluid dynamics. This research offers potential for substantial progress in both theoretical comprehension and technological advancement, The proposed method is applicable to real-world systems such as ferrofluid-based cooling, magnetic dampers, plasma generators, and smart electromagnetic devices. These applications demonstrate the practical benefits of coupling field behavior with boundary dynamics using Gauss's theorem. © 2025

关键词： Electromagnetic induction

A Retina-LGN-V1 Structure-like Spiking Neuron Network for Image Feature Extraction 21

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A Retina-LGN-V1 Structure-like Spiking Neuron Network for Im...

Proceedings of the 2021 5th International Conference on Video and Image Processing

作者： Sheng Ji Qijun Gu Ye Yuan Peng Zhao Tao Fang Hong Huo Xin Niu Department of Automation_School of Electronic Information and Electrical Engineering_Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China College of Computer Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China

ISBN: (纸本)9781450385893

SNNs have achieved great attention in recent years as they contain neurons more like those in the brain and use spikes to encode and transmit information efficiently among neurons with lower energy consumption. A Retina-LGN-V1 structure-like spiking neuron network (RLVSL-SNN) is proposed in this paper. It is inspired by the structure of mammalian primary visual pathway, and simulates different biological structures of Retina, LGN and V1. Noise reduction circuit and light adaptation circuit are also simulated for enhancing the robustness of its extracted features. RLVSL-SNN is a bio-plausible neuron network as it has firing rates of neurons in each layer that are similar to those of biological experiments. Besides, a full-connected SNN (FC SNN) is implemented following RLVSL-SNN for classification to evaluate the extracted features. The additive spiking timing dependent plasticity (STDP) learning rules and the ANN-to-SNN conversion method are utilized to train RLVSL-SNN and FC SNN, respectively. The experiments on MNIST dataset have verified that RLVSL-SNN is comparable to AlexNet for classification by features from spikes.

关键词： Additive Spiking Timing Dependent Plasticity Learning Primary Visual Pathway Image Classification Spiking Neural Network

Submodularity-based False Data Injection Attack Scheme in Multi-agent Dynamical Systems

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arXiv 2022年

作者： Luo, Xiaoyu Zhao, Chengcheng Fang, Chongrong He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China The Department of Electrical and Computer Engineering University of Victoria BC Canada

Consensus in multi-agent dynamical systems is prone to be sabotaged by the adversary, which has attracted much attention due to its key role in broad applications. In this paper, we study a new false data injection (FDI) attack design problem, where the adversary with limited capability aims to select a subset of agents and manipulate their local multi-dimensional states to maximize the consensus convergence error. We first formulate the FDI attack design problem as a combinatorial optimization problem and prove it is NP-hard. Then, based on the submodularity optimization theory, we show the convergence error is a submodular function of the set of the compromised agents, which satisfies the property of diminishing marginal returns. In other words, the benefit of adding an extra agent to the compromised set decreases as that set becomes larger. With this property, we exploit the greedy scheme to find the optimal compromised agent set that can produce the maximum convergence error when adding one extra agent to that set each time. Thus, the FDI attack set selection algorithms are developed to obtain the near-optimal subset of the compromised agents. Furthermore, we derive the analytical suboptimality bounds and the worst-case running time under the proposed algorithms. Extensive simulation results are conducted to show the effectiveness of the proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

Insights from an Industrial Collaborative Assembly Project: Lessons in Research and Collaboration

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arXiv 2022年

作者： Chen, Tan Huang, Zhe Motes, James Geng, Junyi Ta, Quang Minh Dinkel, Holly Abdul-Rashid, Hameed Myers, Jessica Mun, Ye-Ji Lin, Wei-Che Huang, Yuan-Yung Liu, Sizhe Morales, Marco Amato, Nancy M. Driggs-Campbell, Katherine Bretl, Timothy The Coordinated Science Laboratory The University of Illinois Urbana-Champaign United States The Department of Electrical and Computer Engineering The University of Illinois Urbana-Champaign United States The Department of Computer Science The University of Illinois Urbana-Champaign United States The Robotics Institute Carnegie Mellon University United States The Department of Aerospace Engineering The University of Illinois Urbana-Champaign United States Foxconn Interconnect Technology Taiwan

Significant progress in robotics reveals new opportunities to advance manufacturing. Next-generation industrial automation will require both integration of distinct robotic technologies and their application to challenging industrial environments. This paper presents lessons from a collaborative assembly project between three academic research groups and an industry partner. The goal of the project is to develop a flexible, safe, and productive manufacturing cell for sub-centimeter precision assembly. Solving this problem in a high-mix, low-volume production line motivates multiple research thrusts in robotics. This work identifies new directions in collaborative robotics for industrial applications and offers insight toward strengthening collaborations between institutions in academia and industry on the development of new technologies. © 2022, CC BY.

关键词： Collaborative robots

Highly Differentiated Target Detection Method Based on Yolov3 Model Under Extremely Low Light Conditions

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SSRN

SSRN 2022年

作者： Yan, Chenxu Shao, Haijian Zhai, Patrick Jiang, Yingtao Deng, Xing School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China Department of Electrical and Computer Engineering University of Nevada Las VegasNV89154 United States School of Automation Key Laboratory of Measurement and Control for CSE Ministry of Education Southeast University Jiangsu Nanjing210096 China

Because of the dark atmosphere created by YOLO v3's poor detection accuracy, the photographs obtained in reality usually considerably improve the difficulty of target detection tasks in extremely low light settings. Under extremely low light conditions, the YOLOv3 model is used to suggest a high-discrimination target detection approach. Target detection is an essential task in the field of computer vision, in which the image is the source of input, but the image obtained in reality may make the target detection task in extremely low light conditions because of the dark environment and poor-quality imaging equipment, which increases the difficulty of target detection and affects the accuracy of detection. Therefore, based on the above reasons, this paper proposes a high-discrimination target detection method based on the YOLOv3 model under extremely low light conditions. The low light enhancement algorithm based on dehazing algorithm is improved for restraining the part of the transmission rate less than 0.5 in the pixel, to enhance the contrast of the processed images. The improved low light enhancement algorithm is 5.32% higher based on the evaluation index of target detection than the original images. Coiflet wavelet transform is applied to achieve high-discrimination feature extraction of images and decompose the low-frequency features of image information at different resolutions to obtain high-frequency features in the horizontal, vertical, and diagonal directions. The eventually constructed YOLOv3 network has reduced the negative impact of the detected target on the detection accuracy under the illumination change. The results reveal that the target detection method established in this paper has been enhanced by 40.6% on low light photos when compared to the traditional YOLOv3 model, and it still has considerable advantages in comparison to other benchmark approaches in target detection models. © 2022, The Authors. All rights reserved.

关键词： Wavelet transforms

Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

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IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge Computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

Machine Learning for Smart and Energy-Efficient Buildings

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arXiv 2022年

作者： Das, Hari Prasanna Lin, Yu-Wen Agwan, Utkarsha Spangher, Lucas Devonport, Alex Yang, Yu Drgoňa, Ján Chong, Adrian Schiavon, Stefano Spanos, Costas J. Department of Electrical Engineering and Computer Sciences University of California Berkeley United States School of Automation Science and Engineering Xi’an Jiaotong University China Pacific Northwest National Laboratory United States Department of Building National University of Singapore Singapore Center for Built Environment University of California Berkeley United States

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning. © 2022, CC BY.

关键词： Machine learning

Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction

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arXiv 2022年

作者： Chen, Kenny Nemiroff, Ryan Lopez, Brett T. The Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States The Department of Mechanical and Aerospace Engineering University of California Los Angeles Los AngelesCA United States The Verifiable and Control-Theoretic Robotics Laboratory University of California Los Angeles Los AngelesCA United States

Aggressive motions from agile flights or traversing irregular terrain induce motion distortion in LiDAR scans that can degrade state estimation and mapping. Some methods exist to mitigate this effect, but they are still too simplistic or computationally costly for resource-constrained mobile robots. To this end, this paper presents Direct LiDAR-Inertial Odometry (DLIO), a lightweight LiDAR-inertial odometry algorithm with a new coarse-to-fine approach in constructing continuous-time trajectories for precise motion correction. The key to our method lies in the construction of a set of analytical equations which are parameterized solely by time, enabling fast and parallelizable point-wise deskewing. This method is feasible only because of the strong convergence properties in our nonlinear geometric observer, which provides provably correct state estimates for initializing the sensitive IMU integration step. Moreover, by simultaneously performing motion correction and prior generation, and by directly registering each scan to the map and bypassing scan-to-scan, DLIO's condensed architecture is nearly 20% more computationally efficient than the current state-of-the-art with a 12% increase in accuracy. We demonstrate DLIO's superior localization accuracy, map quality, and lower computational overhead as compared to four state-of-the-art algorithms through extensive tests using multiple public benchmark and self-collected datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： State estimation

Quantized Distributed Nonconvex Optimization with Linear Convergence

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Quantized Distributed Nonconvex Optimization with Linear Con...

IEEE Conference on Decision and Control

作者： Lei Xu Xinlei Yi Jiayue Sun Yang Shi Karl Henrik Johansson Tao Yang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper considers distributed nonconvex op-timization for minimizing the average of local cost functions, by using local information exchange over undirected communication networks. Since the communication channels often have limited bandwidth or capacity, we first introduce a quantization rule and an encoder/decoder scheme to reduce the transmission bits. By integrating them with a distributed algorithm, we then propose a distributed quantized nonconvex optimization algorithm. Assuming the global cost function satisfies the Polyak– Łojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that the proposed algorithm linearly converges to a global optimal point. Moreover, a low data rate is shown to be sufficient to ensure linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical simulation examples.

关键词： Quantization (signal) Communication channels Bandwidth Cost function Numerical simulation Communication networks Distributed algorithms