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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 74-87页

作者： Bogang Qu Zidong Wang Bo Shen Hongli Dong Hongjian Liu the College of Automation Engineering Shanghai University of Electric PowerShanghai 200090China IEEE the Department of Computer Science Brunel University LondonUxbridgeMiddiesex UB83PHUK the College of Information Science and Technology Donghua UniversityShanghai 200051 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast PetroleumDaqing 163318 the Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of EducationAnhui Polytechnic UniversityWuhu 2410000 the School of Mathematics and Physics Anhui Polytechnic UniversityWuhu 241000China

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

关键词： Decentralized state estimation(SE) measurements with anomalies minimum error entropy unscented Kalman filter wide-area power systems

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Research on coking diagnosis based on semi-supervised Bayesian T-distribution mixed model 4

Research on coking diagnosis based on semi-supervised Bayesi...

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4rth International Conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2024

作者： Yang, Hanmei Cui, Delong Li, Qirui Peng, Zhiping Zuo, Jinglong School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China School of Electronic and Information Engineering Guangdong University of Petrochemical Technology Maoming525000 China School of Computer Science Guangdong University of Petrochemical Technology Maoming525000 China Jiangmen Polytechnic College Jiangmen529090 China

ISBN: (纸本)9781510681637

In the process of ethylene production, the operating data of ethylene cracking furnace not only has the problems of time variability, non-gauss, strong noise, and outlier influence but also the collected data is incomplete, presenting the problem of scarce data with labels and abundant data without labels. To solve the above problems, this paper proposes a soft measurement model based on a semi-supervised Bayesian T-distribution mixture for the coking diagnosis of ethylene cracking furnaces. The model adopts a semi-supervised structure to solve the problem of the small number of data labels, adopts a T-distribution model to ensure the robustness of the model to strong noise and outliers under the Bayesian framework, and solves the solid variation of process data through the mixed model—complexity, non-Gaussian, and nonlinear problems. Finally, the experiment was carried out on the coking data of an ethylene cracking furnace in a petrochemical plant to verify the feasibility and prediction accuracy of the model under the condition of limited label data, and also to verify the robustness of the model to outliers. © 2024 SPIE.

关键词： Statistics

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Domain Adapted Object Detection with Intermediate Domain Generation

Domain Adapted Object Detection with Intermediate Domain Gen...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： He Yuhang Sun Yangyang Xue Zhijun Han Shijiao School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Autonomous driving detection technology in real-world road scenarios faces numerous challenges, including variable weather conditions and complex road environments. Therefore, developing an object detection model with robust domain adaptation is crucial. In this paper, we investigate the use of image style transfer techniques to leverage target domain images for enhancing model performance. Experimental results show that our proposed approach significantly improves detection efficacy. Notably, our method outperforms the Oracle results in tasks such as transitioning from Cityscapes to Foggy Cityscapes, highlighting its effectiveness in addressing domain adaptation challenges.

关键词： Training Adaptation models Roads Object detection Information processing Detectors Media Autonomous vehicles Faces Meteorology

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An Electromagnetic Covert Channel based on Neural Network Architecture 27

An Electromagnetic Covert Channel based on Neural Network Ar...

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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Gu, Chaojie Chen, Jiale Tan, Rui Jiang, Linshan College of Control Science and Engineering Zhejiang University China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781665408783

Outsourcing the design of deep neural networks may incur cybersecurity threats from the hostile designers. This paper studies a new covert channel attack that leaks the inference results over the air through a hostile design of the neural network architecture and the computing device's electromagnetic radiation when executing the neural network. Specifically, the hostile neural network consists of a series of binary models that correspond to all classes and are executed sequentially. The execution terminates once any binary model given the input is positive about its responsible class. We describe an approach to generate such binary models by pruning a benign neural network that is trained using the standard method to deal with all the classes. Compared with the benign neural network, the hostile one has similar memory usage and negligible classification accuracy drop, but distinct inference times for the samples of different classes. As a result, the hostile neural network's classification result can be eavesdropped by measuring the duration of the electromagnetic radiation emanated from the computing device. As neural networks are stored and transmitted as data files, this covert channel attack is more stealthy to the anti-malware than other code-based attacks. We implement the described attack on two edge computing devices that execute the hostile neural network on CPU or GPU. Evaluation shows 100% empirical accuracy in eavesdropping the inference results. © 2021 IEEE.

关键词： Electromagnetic wave emission

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Design and Simulation of Rogowski Coil with Pulse Compound Integrator

Design and Simulation of Rogowski Coil with Pulse Compound I...

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Mechatronics Technology and Intelligent Manufacturing (ICMTIM), International Conference on

作者： Haowen Yang Mingcheng Wang Ran Wei School of Control Science and Engineering Dalian University of Technology Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9798350363265

ISBN: (纸本)9798350363272

This paper describes a Rogowski coil integrator for pulsed high current measurements. Based on the principle and equivalent model of Rogowski coil, this paper designs an impulse composite integrator that is compatible with the coil sensitivity, bandwidth and phase by comparing the shortcomings of the first-order RC integrator and the classical active integrator at the design stage. The integrator consists of a passive part and an active part. The passive part is used for mid-frequency integration, which reduces the gain of the coil. The active part is used for low-frequency integration, which has a lifting effect on the system gain. In addition to this, the overall sensitivity can be changed by adjusting the gain coefficient between the frequency band of the passive integrator and the active integrator. Finally, the circuit simulation was carried out using MULTISIM to verify the effectiveness of the integrator design.

关键词： Coils Sensitivity Mechatronics Pulse measurements Current measurement Oscilloscopes Bandwidth

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Detection of Self-explosive Insulators in Aerial Images Based on Improved YOLO v4 15

Detection of Self-explosive Insulators in Aerial Images Base...

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2022 15th International Conference on computer and Electrical engineering, ICCEE 2022

作者： Yi, Li Jinguo, Li Yongjie, Zhai Ping, Meng College of Computer Science and Technology Shanghai University of Electric Power Shanghai200000 China School of Control and Computer Engineering North China Electric Power University Baoding Hebei071000 China

In view of the low detection efficiency of traditional aerial image-based self-explosive insulators and the need to manually extract the features of self-explosive insulators, a self-explosive insulator detection algorithm based on the improved YOLO v4 is proposed: We use the hybrid data augmentation method to increase the number of defective samples of self-explosive insulators and increase the diversity of self-explosive insulators to improve the detection accuracy of the algorithm. In addition, the channel attention mechanism is combined with the YOLO v4 algorithm to improve the feature extraction capability of algorithms to self-explosion insulators. The proposed algorithm is verified using the expanded self-explosive insulator dataset and the traffic signs dataset TT100K. The experimental results show that the proposed algorithm has a mAP of 92.0 % on the self-explosive insulator dataset and has an F1-score of 78.70% on TT100K dataset, which has positive significance for the research on self-explosive insulator detection. © 2022 Published under licence by IOP Publishing Ltd.

关键词： Explosives

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Perimeter Defense Using a Turret with Finite Range and Startup Time

Perimeter Defense Using a Turret with Finite Range and Start...

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American control Conference (ACC)

作者： Shivam Bajaj Shaunak D. Bopardikar Alexander Von Moll Eric Torng David W. Casbeer Department of Electrical and Computer Engineering Michigan State University Control Science Center Air Force Research Laboratory Department of Computer Science and Engineering Michigan State University

We consider a perimeter defense problem in a planar conical environment comprising a turret that has a finite range and non-zero startup time. The turret seeks to defend a concentric perimeter against N ≥ 2 intruders. Upon release, each intruder moves radially towards the perimeter with a fixed speed. To capture an intruder, the turret's angle must be aligned with that of the intruder's angle and must spend a specified startup time at that orientation. We address offline and online versions of this optimization problem. Specifically, in the offline version, we establish that in general parameter regimes, this problem is equivalent to solving a Travelling Repairperson Problem with Time Windows (TRP-TW). We then identify specific parameter regimes in which there is a polynomial time algorithm that maximizes the number of intruders captured. In the online version, we present a competitive analysis technique in which we establish a fundamental guarantee on the existence of at best (N – 1)-competitive algorithms. We also design two online algorithms that are provably 1 and 2-competitive in specific parameter regimes.

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Res2net based Siamese Network for Object Tracking via Efficient Multi-scale Attention and Border Region Reppoints*

Res2net based Siamese Network for Object Tracking via Effici...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Shuai Yuan Jinyu Geng Huize Dou School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

Object tracking is an important and challenging task. The changes of the object itself and the complex background can affect tracking performance. Siamese networks based object tracking are widely adopted due to their advantages in efficient similarity measurement, end-to-end learning, and modular design. However, existing object tracking algorithms based on Siamese networks do not extract object features sufficiently, resulting in lower tracking accuracy. And excessive parameter settings adversely affect real-time performance. Therefore, we propose a new algorithm, SiamBRR, which can more accurately and quickly track objects. We first introduce the Res2Net residual network into the Siamese network framework as the backbone feature extraction network to fully extract features. Then, we use the EMA to enhance feature representation. Furthermore, our proposed border region reppoints accurately locate the object border while avoiding the need for extensive parameter settings. Finally, we conducted experiments on three challenging public datasets: VOT2018, VOT2019, and OTB100. The experimental results illustrate that the proposed SiamBRR outperforms other advanced trackers in tracking accuracy.

关键词： Location awareness Accuracy Attention mechanisms Feature extraction Real-time systems Computational efficiency Object tracking

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology 33

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Bogacki, P. Dlugosz, M. Talaska, T. Dlugosz, R. Aptiv Services Poland Kraków30-399 Poland Agh University of Science and Technology Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350347760

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock. © 2023 IEEE.

关键词： Pixels

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