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15th International Conference on Digital Image Processing, ICDIP 2023

作者： Zhang, Feng Liu, Guifu Yuan, Shuai Kong, Xiangjie Geng, Jinyu School of Electrical and Control Engineering Shenyang Jianzhu University China School of Computer Science and Engineering Shenyang Jianzhu University China

ISBN: (纸本)9798400708237

The tracker based on Siamese network usually adopts the cross-correlation of convolutional features between the object branch and the search branch to describe the visual object tracking task as a similarity matching. However, most trackers apply backbone networks to represent multi-scale features in a hierarchical manner, which has limitations in distinguishing similar object. In addition, the feature information on each channel and spatial region is treated equally in the calculation process. In fact, the feature importance of these locations is not the same, and the anchor box detection mechanism requires setting a large number of anchor boxes with redundant hyperparameters, which can lead to computational and memory overload. As for these problems, this paper proposes an object tracking algorithm using siamese attention mechanism based key point network, called SAMR (Siamese Attention Mechanism Reppoints). First of all, the backbone network res2net is used to represent fine-grained multi-scale features on a hierarchical basis for increasing the receptive field of each layer. Then the channel and spatial attention information of the attention mechanism is to enhance the discriminant ability and positioning ability of the feature map. Finally, a set of key points anchor-free frames is adopted to detect and track the object. Experiments on challenging datasets such as VOT2018, VOT2019, and OTB100 illustrate that the SAMR proposed in this paper is superior to other advanced trackers in tracking accuracy. © 2023 ACM.

关键词： Object detection

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Edge-Enabled Metaverse:The Convergence of Metaverseand Mobile Edge Computing

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Tsinghua science and Technology 2024年第3期29卷 795-805页

作者： Nyothiri Aung Sahraoui Dhelim Liming Chen Huansheng Ning Luigi Atzori Tahar Kechadi Information Engineering College Xinjiang Institute of EngineeringUrumqi 830023Chinaand also with School of Computer ScienceUniversity College DublinDublinD04 V1W8Ireland School of Computing Ulster UniversityBelfastBT151EDUK School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Electrical and Electronic Engineering University of CagliariSardinia 09124Italy School of Computer Science University College DublinDublinD04 V1W8Ireland

Metaverse is a virtual environment where users are represented by their avatars to navigate a virtual world having strong links with its physical *** state-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering *** current centralized architecture of such systems is unfavorable as it suffers from several drawbacks caused by the long latency of cloud access,such as low-quality *** this end,we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing *** applications leverage edge devices’computing power to perform the required computations for heavy tasks,such as collision detection in the virtual universe and high-computational 3D physics in virtual *** computational costs of a Metaverse entity,such as collision detection or physics emulation,are performed at the device of the associated physical *** validate the effectiveness of the proposed architecture,we simulate a distributed social Metaverse *** simulation results show that the proposed architecture can reduce the latency by 50%when compared with cloud-based Metaverse applications.

关键词： industrial Metaverse edge computing Blockchain virtual environment task offloading fog computing

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Performance-Guaranteed Finite-Time Tracking of Strict-Feedback Systems with Unknown control Directions: A Novel Switching Mechanism

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IEEE Transactions on Automatic control 2025年第6期70卷 4061-4068页

作者： Mao, Bing Wu, Xiaoqun Fan, Ziye Lu, Jinhu Chen, Guanrong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Wuhan University School of Mathematics and Statistics Hubei430072 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong

The problem of achieving performance-guaranteed finite-time exact tracking for uncertain strict-feedback nonlinear systems with unknown control directions is addressed. A novel logic switching mechanism with monitoring functions is designed to deal with unknown control directions. This approach is different from existing methods like the Nussbaum gain technique and smooth orientation functions, which are inapplicable to finite-time exact tracking. The main challenge lies in designing monitoring functions and determining switching rules, which is well resolved through three steps. Most significantly, in contrast to existing works that solely guarantee bounded tracking or asymptotic exact tracking, the new mechanism incorporates novel integral sliding mode techniques to achieve finite-time exact tracking with guaranteed performance. Finally, simulations are presented with comparison to demonstrate the effectiveness of the proposed methods. © 1963-2012 IEEE.

关键词： Feedback

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A strategy for accurate prediction of dielectric permittivity and dielectric strength in polyimide

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High Voltage 2024年第6期9卷 1393-1401页

作者： Yu Wang Changhai Zhang Jiaqi Lin Hongguo Sun Ying Yang Wenlong Yang School of Electrical and Electronic Engineering Harbin University of Science and TechnologyHarbinChina School of Science Harbin University of Science and TechnologyHarbinChina Polymer Composites Engineering Laboratory Changchun Institute of Applied ChemistryChinese Academy of SciencesChangchunChina State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and AstronauticsNanjingChina School of Measurement and Communication Engineering Harbin University of Science and TechnologyHarbinChina

A strategy was proposed to predict accurately the dielectric permittivity and dielectric strength based on the Onsager local field *** molecular dynamic simulation was utilised to analyse the dipole moment fluctuation in polyimide(PI)to reflect the polar-isation response of applied electric *** simulation results revealed that the optical dielectric permittivity and static dielectric permittivity were 2.62 and 3.34,showing that the electronic displacement polarisation of the PI acted as a major *** deviation between simulated and measured values of the frequency-dependent relative dielectric permittivity was no more than 5%,which exhibited high *** the polarisation response of the polar groups in the PI was at infrared frequencies,the conductive loss may be the dominant role in 102–106 Hz at room ***,the effect of Joule heat on the structure,dielectric permittivity and Young modulus was considered to accurately predict the dielectric strength of the PI(359 kV/mm),which was in agreement with experimental values in the *** results establish a clear correlation between structural characteristics and dielectric properties of the PI,which would be the theoretical insights into the design and synthesis of the PI with tailored dielectric properties.

关键词： dielectric strength permittivity

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Dynamic Artificial Neural Network-Assisted GPS-Less Navigation for IoT-Enabled Drones

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IEEE Internet of Things Magazine 2024年第3期7卷 92-99页

作者： Simsek, Murat Boukerche, Azzedine Kantarci, Burak Bitirgen, Rahman Hancer, Muhsin Bayezit, Ismail School of Electrical Engineering and Computer Science The University of Ottawa Ottawa Canada Istanbul Technical University Faculty of Aeronautics and Astronautics Turkey

Uncrewed Aerial Vehicles (UAVs) have enabled key duties in emergency preparedness, traffic monitoring, environmental monitoring, and public safety. Since the presence of GPS-enabled contexts is not always guaranteed, a grand challenge with the UAVs is the lack of accomplishing their tasks without the presence of GPS coordinates (latitude, longitude, and altitude). Hence, the performance of UAVs in GPS-denied environments is expected to degrade dramatically when compared to the UAVs employed in GPS-enabled environments. In this article, an alternative approach to the state-of-the-art, Dynamic Artificial Neural Network (D-ANN)-based solution is proposed to assist UAV navigation without GPS positions during a mission. Besides accelerometer and gyroscope data, Pulse Width Modulation (PWM) signals, which have been traditionally used in the design of UAV flight controllers, are proposed to be a part of the input for D-ANN-assisted UAV navigation without GPS data. Since the latitude, longitude, and altitude values of the UAV are not correlated, each position is obtained through a separate D-ANN system. The proposed D-ANN location of a quadrotor UAV assisted by D-ANN has less than 3m average destination error at the end of the testing trajectory and also less than 0.12 average normalized mean square error during the testing trajectory in terms of the 3D GPS coordinates. © 2018 IEEE.

关键词： Global positioning system

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Cloud-edge-based We-Market:Autonomous Bidding and Peer-to-peer Energy Sharing Among Prosumers

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1282-1293页

作者： Fangyuan Si Yinghua Han Qinqin Xu Jinkuan Wang Qiang Zhao College of Information Science and Engineering Northeastern UniversityShenyangChinaand F.Si is also with the State Key Laboratory of Power System and Generation EquipmentDepartment of Electrical EngineeringTsinghua UniversityBeijingChina School of Computer and Communication Engineering Northeastern University at QinhuangdaoQinhuangdaoChina School of Control Engineering Northeastern University at QinhuangdaoQinhuangdaoChina

With the extensive penetration of distributed renewable energy and self-interested prosumers,the emerging power market tends to enable user autonomy by bottom-up control and distributed *** paper is devoted to solving the specific problems of distributed energy management and autonomous bidding and peer-to-peer(P2P)energy sharing among prosumers.A novel cloud-edge-based We-Market is presented,where the prosumers,as edge nodes with independent control,balance the electricity cost and thermal comfort by formulating a dynamic household energy management system(HEMS).Meanwhile,the autonomous bidding is initiated by prosumers via the modified Stone-Geary utility *** the cloud center,a distributed convergence bidding(CB)algorithm based on consistency criterion is developed,which promotes faster and fairer bidding through the interactive iteration with the edge ***,the proposed scheme is built on top of the commercial cloud platform with sufficiently secure and scalable computing *** results show the effectiveness and practicability of the proposed We-Market,which achieves 15%cost reduction with shorter running *** analysis indicates better scalability,which is more suitable for largerscale We-Market implementation.

关键词： We-Market bidding energy sharing prosumer peer-to-peer cloud-edge

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An efficient cloud based face recognition system for e-health secured login using steerable pyramid transform and local directional pattern

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1017-1027页

作者： Dosaj, Ayush Satapathy, Suresh Chandra Soundrapandiyan, Rajkumar Kaur, Mannat Hannoon, Naeem School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Kalinga Institute of Industrial Technology Deemed to be University Odisha Bhubaneswar India Faculty of Electrical Engineering Universiti Teknologi MARA Shah Alam Malaysia

This paper proposes a face recognition system based on steerable pyramid transform (SPT) and local directional pattern (LDP) for e-health secured login in cloud domain. In an e-health login, patients periodically forget their self-made login username and password. This face recognition system can replace the traditional login system. In the proposed system, SPT can decompose a face image into several sub-bands of different scales and orientations, and LDP can encode the sub-bands in binary texture pattern. The LDP binary pattern was obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Therefore, SPT–LDP design represents a face image in a robust way which includes multiple information sources from different scales and orientations. The proposed system is tested on standard benchmark facial recognition technology (FERET) and extended Yale-B databases. Further, the efficiency of the proposed system is proved by comparing recognition rates with other existing descriptor based methods. From the experimental results it is observed that the proposed system achieves 99.79% recognition in fb set, 90.72% in fc set, 84.97% in dup I set, and 87.69% in dup II set for FERET database and 100% in sub2 set, 100% in sub3 set, 96% in sub4 set and 95% in sub5 set for extended Yale-B database which is better than the existing methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Face recognition

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Specificity-preserving RGB-D saliency detection

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Computational Visual Media 2023年第2期9卷 297-317页

作者： Tao Zhou Deng-Ping Fan Geng Chen Yi Zhou Huazhu Fu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of System Control and Information Processing Ministry of EducationShanghaiChina Computer Vision Lab ETH Z¨urichZ¨urichSwitzerland School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’anChina School of Computer Science and Engineering Southeast UniversityNanjingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Salient object detection(SOD)in RGB and depth images has attracted increasing research *** RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities,while few methods explicitly consider how to preserve modality-specific *** this study,we propose a novel framework,the specificity-preserving network(SPNet),which improves SOD performance by exploring both the shared information and modality-specific ***,we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction *** effectively fuse cross-modal features in the shared learning network,we propose a cross-enhanced integration module(CIM)and propagate the fused feature to the next layer to integrate cross-level ***,to capture rich complementary multi-modal information to boost SOD performance,we use a multi-modal feature aggregation(MFA)module to integrate the modalityspecific features from each individual decoder into the shared *** using skip connections between encoder and decoder layers,hierarchical features can be fully *** experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection *** project is publicly available at https://***/taozh2017/SPNet.

关键词： salient object detection(SOD) RGB-D cross-enhanced integration module(CIM) multi-modal feature aggregation(MFA)

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Multi-Period Resilient Model for VSC-Based AC-DC HDS Considering Public-Safety Power Shutoff to Mitigate Wildfires

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 821-833页

作者： Zekai Wang Tao Ding Xiaosheng Zhang Chenggang Mu Pengwei Du Fangxing Li School of Electrical Engineering Xi'an Jiaotong UniversityXi'an 710049China Electric Reliability Council of Texas AustinTX76574 USA Department of Electrical Engineering and Computer Science The University of TennesseeKnoxville(UTK)TN37996USA

This paper proposes a voltage source converter (VSC) -based AC-DC hybrid distribution system (HDS) resilient model to mitigate power outages caused by wildfires. Before a wildfire happens, the public-safety power shutoff (PSPS) strategy is applied to actively cut some vulnerable lines which may easily cause wildfires, and reinforce some lines that are connected to critical loads. To mitigate load shedding caused by active line disconnection in the PSPS strategy, network reconfiguration is applied before the wildfire occurrence. During the restoration period, repair crews (RCs) repair faulted lines, and network reconfiguration is also taken into consideration in the recovery strategy to pick up critical loads. Since there exists possible errors in the wildfire prediction, several different scenarios of wildfire occurrence have been taken into consideration, leading to the proposition of a stochastic multi-period resilient model for the VSC-based AC-DC HDS. To accelerate the computational performance, a progressive hedging algorithm has been applied to solve the stochastic model which can be written as a mixed-integer linear program. The proposed model is verified on a 106-bus AC-DC HDS under wildfire conditions, and the result shows the proposed model not only can improve the system resilience but also accelerate computational speed.

关键词： AC-DC hybrid distribution system restoration network reconfiguration progressive hedging public safety power shutoffs strategy resilience

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Research of STF-CKF-SLAM algorithm using Variational Bayes 23

Research of STF-CKF-SLAM algorithm using Variational Bayes

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6th International Conference on Electronics, Communications and control engineering, ICECC 2023

作者： Yuan, Shuai Bai, Yueyan Wu, Jian Li, Aichun Guo, Pengcheng School of Computer Science and Engineering Shenyang Jianzhu University China School of Electrical and Control Engineering Shenyang Jianzhu University China

ISBN: (纸本)9798400700002

Aiming at the problem of poor robustness of the driving model and divergence of the filtering process due to time-varying or abrupt-varying noise of the driving model during simultaneous localization and mapping of mobile robots, this paper proposes a strong tracking cubature kalman based SLAM algorithm using Variational Bayes, which uses the variational bayes algorithm to iteratively estimate the prediction covariance of the cubature kalman filter. Then the strong tracking cubature kalman algorithm is used to filter and fuse the observation data and the driving model, and the optimized pose data is obtained to further construct a more accurate feature map. Finally, the effectiveness of the algorithm is illustrated by simulation and practical experiments using a mobile robot equipped with lidar sensors. The simulation results represent that the simultaneous localization and mapping errors of this algorithm are reduced by 58.43% compared with the traditional CKF algorithm. The experimental results in real environment show that the average error of acquiring environmental features is reduced by 44.46%, which further verifies the feasibility and effectiveness of the proposed algorithm. This algorithm provides a new idea for simultaneous localization and mapping of mobile robots. © 2023 ACM.

关键词： Mobile robots

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