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IEEE Conference on Cybernetics and Intelligent Systems

作者： Shaoru Zhang Muyan Jiang Yihan Liu Zhanping Huang Xiuju Du Jielu Zhang Fanglin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

This paper proposes a novel Halbach array structure based on the traditional radial and tangential magnetized Halbach array permanent magnet motor. This structure enables the magnetic field lines to be concentrated, thereby enhancing the motor torque. Concurrently, the magnetic axis offset is designed on this basis by combining ferrite with low magnetic conductivity and NdFeB with high magnetic conductivity. This design effectively reduces the gap between the electromagnetic torque and the peak current angle of the reluctance torque, and fully utilizes both the electromagnetic and reluctance torques to further improve the total output torque of the motor. The newly proposed motor, known as the Magnetic Axis Offset Halbach Motor (MAOHM), is a result of this design. Furthermore, this paper also employs a finite element method to study and compare the proposed motor with the traditional motor, serving to verify the rationality and effectiveness of the design.

关键词： Torque Shape Rotors Random access memory Conductivity Motors Permanent magnets

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Di-defects synergy boost electrocatalysis hydrogen evolution over two-dimensional heterojunctions

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Nano Research 2022年第1期15卷 677-684页

作者： Yan Cui Xiaoyu Guo Jian Zhang Xing’ao Li Xinbao Zhu Wei Huang Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China New Energy Technology Engineering Lab of Jiangsu Province School of ScienceNanjing University of Posts&Telecommunications(NUPT)Nanjing 210023China Key Laboratory for Organic Electronics and Information Displays&Institute of Advanced Materials(LAM) Jiangsu National Synergetic Innovation Center for Advanced Materials(SICAM)Nanjing University of Posts&Telecommunications(NUPT)Nanjing 210023China College of Chemical Engineering Nanjing Forestry UniversityNanjing 210037China Shaanxi Institute of Flexible Electronics(SIFE) Northwestern Polytechnical University(NPU)}127 West Youyi RoadXian 710072China

Electronic modulation on the inert basal plane of transition-metal dichalcogenides(TMDs)through vacancy defect excitation,although extremely challenging,is urgent for understanding the factors that impact the hydrogen evolution reaction(HER)catalytic ***,ultrathin WS2 nanosheets with precise quantitative single atomic S-vacancy on the inert basal plane were flexible prepared through hydrogen peroxide etching *** as-synthesized single atomic S-vacancy defect WS2(SVD-WS2)nanoflake with the activated basal plane exhibited an impressive overpotential of 137 mV at a current density of 10 mA·cm^(-2) and a Tafel slope of 53.9 mV dec^(-1).Furthermore,anchoring on the defect graphene matrix,the assembled two-dimensional(2D)stacking heterojunction exhibits further enhanced HER catalytic activity(an overpotential of 108 mV vs.10 mA·cm^(-2) and a Tafel slope of 48.3 mV·dec^(-1))and stability(〜10%decline after 9,000 cycles),which attributed to the electronic structure modulation from the synergetic interactions between SVD-WS_(2) and defect *** finding provides a smart defects introduce strategy to trigger high-efficiency hydrogen evolution over WS_(2) nanosheets and a general 2D heterojunctions fabricated inspiration based on strong interaction interface.

关键词： S-vacancies defect graphene hydrogen evolution reaction heterojunctions

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The short-term photovoltaic power generation forecasting based on Fruit Fly Optimization Algorithm optimized BP neural network

The short-term photovoltaic power generation forecasting bas...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Shaoru Zhang Haoxuan Zhu Likun Yang Zhanping Huang Xiuju Du Jielu Zhang Fang Lin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Accurately predicting photovoltaic power generation is crucial for ensuring the safe operation of power grids and advancing solar energy development and utilization. For the issue of large errors in current prediction methods, this paper introduces a forecasting method for photovoltaic power generation. The method utilizes Fruit Fly Optimization Algorithm (FOA) to optimize Back Propagation (BP) neural network. This involves analyzing the correlations among factors impacting photovoltaic power generation and selecting relevant meteorological data via the Pearson correlation coefficient method. Fruit Fly Algorithm demonstrates rapid convergence, a minimal parameter set and ease of adjustment, rendering it applicable across various domains. Employing Fruit Fly Algorithm to optimize weights and thresholds within BP neural network leads to the final prediction outcomes. Simulation results confirm the superior prediction accuracy of the FOA-BP model for photovoltaic power generation, particularly during spring, autumn and winter, showcasing its practical utility.

关键词： Photovoltaic systems Correlation coefficient Atmospheric modeling Electricity Neural networks Predictive models Prediction algorithms

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Deep Learning-based Visual Recognition System for Fruit-Picking Robots

Deep Learning-based Visual Recognition System for Fruit-Pick...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Rui Ma Yihan Liu Qike Wu Shaoru Zhang Nan Wang Jielu Zhang Fang Lin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Fruit recognition plays an important role in automated picking, in order to improve the accuracy and real-time picking, this study uses deep learning methods to design a fruit-picking robot visual recognition system. First, multiple preprocessing methods are used to expand the sample data, and the images are proportionally cropped and scaled to make the image dataset more complete; subsequently, to improve the accuracy of image recognition, a multilayer Convolutional Neural networks (CNN) is established; and the Adam algorithm is utilized to train the model parameters several times to determine the optimal hyperparameters, thus overcoming the defects of the multilayer neural network that has a local optimal search. The experimental results show that compared with the random forest method, this system has the characteristics of high-speed recognition and high accuracy in fruit picking, and can quickly and accurately recognize fruit images, with the recognition speed of a single image taking only 0.081 seconds, and the recognition accuracy reaching more than 97.35%. The method has important theoretical and application value and provides an effective means for automatic fruit recognition.

关键词： Visualization Image recognition Accuracy Random access memory Nonhomogeneous media Real-time systems Character recognition

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Crop-picking Robot Grasping System Based on Deep Learning

Crop-picking Robot Grasping System Based on Deep Learning

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Qike Wu Yihan Liu Rui Ma Shaoru Zhang Sai Gao Jielu Zhang Fang Lin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

The crop-picking system plays a key role in automatic picking. In this study, a set of crop-picking robot grasping system based on convolutional neural network (CNN) was designed by using deep learning technology. Firstly, a variety of preprocessing methods are used to expand and process the image data to ensure the diversity and integrity of the sample data. Then, CNN and gradient descent (GD) algorithm were combined to optimize the training model to identify the types of crops accurately. Then, the lidar used ToF (Time of Flight) technology and AGglomerative NESting(AGNES) algorithm to obtain the key positioning points. After excluding invalid cluster centers, the identification results were accurately located. The experimental results show that compared with the 3D pose estimation method of the manipulator based on multi-view, the calculation error of the 3D coordinates of crops is smaller, and the reasoning speed for a single image is only 0.000316 seconds. Finally, the comprehensive accuracy of the system reaches 97.58%, which provides practical technical support for automatic crop picking.

关键词： Training Three-dimensional displays Accuracy Laser radar Robot kinematics Pose estimation Crops

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Research on the Loading Method of Logistics Vehicle and Cargo Based on IPSO Algorithm

Research on the Loading Method of Logistics Vehicle and Carg...

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第42届中国控制会议

作者： Zewei Zhao Huqing Wang Zhe Sun Lei Xu Guangfu Gong Xiangpeng Xie Zhixin Sun Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology) Nanjing University of Posts and Telecommunications Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and Telecommunications

Logistics vehicle and cargo matching problem is a typical combination optimization problem,which has important theoretical and application value in the field of logistics *** proved particle swarm optimization(PSO) algorithm is one of the most extensive optimization methods studied in recent years,with excellent global search ability and convergence *** on the IPSO algorithm,this paper proposes an effective IPSO algorithm model for the matching problem of logistics vehicles and goods,and conducts the corresponding comparative *** final experimental results show that the algorithm has good results in practical application and can provide a feasible optimization method for solving the problem of logistics vehicles and goods.

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Traffic Matrix Prediction in SDN based on Spatial-Temporal Residual Graph Convolutional network

Traffic Matrix Prediction in SDN based on Spatial-Temporal R...

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第35届中国控制与决策会议

作者： Xintong Wang Yibo Sun Xuan Wang Enliang Wang Zhixin Sun Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology) Nanjing Universiry of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and Telecommunications School of Information Technology and Electrical Engineering The University of Queensland Brisbane

Traffic prediction is the basis for dynamic network services and resource *** Defined networking(SDN) provides a global view for network measurement and management,and a reliable way for network information collection and network traffic ***,SDN traffic prediction model enabled by deep learning mainly focuses on mining the temporal correlation of traffic data,while the modeling of its spatial correlation remains to be *** the perspective of the problem,a novel Spatial-Temporal Residual Graph Convolutional network(STRGCN) model for SDN Traffic Matrix(TM) prediction is *** with SDN network topology,TM prediction is defined as time series data prediction in non-Euclidean *** GatedConv Blocks are based on Gated Convolutional Layer,and Spatial Graph-Conv Blocks are based on Graph Convolutional network(GCN).Through identity mapping between SpatialTemporal Conv Blocks,the deep spatial and temporal features extraction is *** by TM data from CERNET and GEANT datasets,the accuracy and the timely performance of our model are *** results suggest that the general performance of STRGCN for TM prediction in SDN is superior to the current deep learning models.

关键词： software defined networking traffic prediction traffic matrix graph convolutional neural network spatialtemporal residual graph network

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An Incentive Mechanism Considering Privacy Metric for Personalized Application

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IEEE Transactions on Consumer Electronics 2025年

作者： Xie, Yun Li, Peng Zhang, Jindan Attar, Razaz Waheeb Arya, Varsha Gupta, Brij B. Nanjing Vocational College of Information Technology Jiangsu Province Service Customization Network Application Engineering Research Center Jiangsu Province Service Customization Network Application Engineering Research Center Nanjing210023 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210023 China Xianyang Polytechnic Institute China Princess Nourah bint Abdulrahman University College of Business Administration Management Department Riyadh11671 Saudi Arabia Hong Kong Metropolitan University Hong Kong Hong Kong Center for Interdisciplinary Research Dehradun India Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan China Medical University Hospital China Medical University Department of Medical Research Taichung Taiwan Pune India Korea University School of Cybersecurity Seoul Korea Republic of Kyung Hee University Kyungheedae-ro Seoul26 Korea Republic of

Edge-based federated learning (EFL) is a solution for personalized applications. It addresses the limitations of cloud-based consumer service systems, such as data privacy and network latency. To further enhance the EFL system’s ability to protect data privacy, differential privacy (DP) is introduced. It is crucial to motivate high-quality collaboration among participants for optimal performance of the DP-EFL system. Researchers primarily focus on efficient learning algorithms, optimized resource scheduling, and user-oriented incentive mechanisms. Existing research exhibits notable limitations in modeling and analyzing the heterogeneity of resource costs (e.g., communication costs), particularly failing to comprehensively account for the additional costs imposed by participants’ privacy protection mechanisms‌. To address this gap, we propose a novel market transaction model based on Stackelberg game theory. This framework integrates incentive-driven reward allocation mechanisms to rigorously investigate the collaborative behaviors and strategic interactions between edge nodes (ENs) and the cloud server (CS)‌. We incorporate privacy costs into the multidimensional costs of participants with privacy metric, and based on this, derived collaboration strategies between ENs and CS, thus solving the Stackelberg game problem by theoretical analysis. Furthermore, numerical experiments validated the rationality of our proposed model, the impact of strategies on the performance of collaborators, and the advantages over other incentive mechanisms. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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Joint DBF and SAO parallel filtering based on multithread load balancing

Joint DBF and SAO parallel filtering based on multithread lo...

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2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Ma, Hao Hu, Dong Li, Yi Jiangsu Province’s Key Lab of Image Procession and Image Communications Nanjing210003 China Education Ministry’s Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China Education Ministry’s Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing210003 China

This paper presents a joint parallel loop filtering algorithm based on multi-thread load balancing in HEVC decoding, which implements the parallel processing of deblocking filtering (DBF) and sample adaptive compensation (SAO). Because of the diversity of video, the texture of different regions in an image is also different, which leads to various CTU partition methods. Therefore, the number of the boundary to be filtered is greatly different, resulting the computation load among multiple threads unbalanced in parallel processing. To solve this problem, an area division scheme is proposed, which divides the image into multiple areas, and the number of boundaries to be filtered in each area is similar. Then, the mapping relationship table is used to allocate these areas to multiple threads for parallel processing, so as to achieve the load balancing among the filtering threads. Finally, the cache technology is used to combine DBF and SAO to reduce the delay between them and improve the overall parallelism of the loop filter. Experimental results show that the performance of the proposed load balancing joint filtering algorithm is 8.15% higher than the previous scheme. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Textures

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Scale adaptive block target tracking based on multi-layer convolution features and Kernel correlation filter

Scale adaptive block target tracking based on multi-layer co...

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2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Zhang, Ting Hu, Dong Zhang, Jing Jiangsu Province’s Key Lab of Image Procession and Image Communications Nanjing210003 China Education Ministry’s Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China Education Ministry’s Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing210003 China

Target tracking is currently a hot research topic in Computer Vision and has a wide range of use in many research fields. However, due to factors such as occlusion, fast motion, blur and scale variation, tracking method still needs to be deeply studied. In this paper, we propose a block target tracking method based on multi-convolutional layer features and Kernel correlation filter. Our method divides the tracking process into two parts: target position estimation and target scale estimation. First, we block the target frame based on the condition number. Second, we extract the features by the convolutional layer and apply it to the kernel correlation filter to get the center position of different block targets. With the reliability of different blocks measured by the Barker coefficient, the overall target position center is obtained. Then, the affine transformation is adopted to achieve the scale adaptation. The algorithm in this paper is evaluated by the public video sequences in OTB-2013. Numerous experimental results demonstrate that the proposed tracking method can achieve target scale adaptation and effectively improve the tracking accuracy. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Target tracking

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