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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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Spin-dependent terahertz wavefront shaping based on hybrid phase in all-silicon chiral metasurfaces

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Photonics Research 2025年第05期 1271-1281页

作者： JIE LI LELING CHEN HANG XU TONG NAN XUEGUANG LU HUI LI WENHUI XU QI TAN JITAO LI YUANYUAN LV TINGTING LIU SHUYUAN XIAO TINGTING TANG LI LUO WANXIA HUANG JIN HE YAN ZHANG JIANQUAN YAO Sichuan Meteorological Optoelectronic Sensor Technology and Application Engineering Research Center College of Optoelectronic EngineeringChengdu University of Information Technology School of Precision Instruments and Opto-Electronics Engineering Tianjin University Key Laboratory of Terahertz Optoelectronics Ministry of EducationDepartment of PhysicsCapital Normal University College of Materials Science and Engineering Sichuan University School of Information Engineering Nanchang University

Independent manipulation of orthogonal circularly polarized wavefronts is one of the important goals for metaoptics,and chiral nanophotonics is one of the crucial *** this article,we propose a new scheme for spin-dependent terahertz wavefront shaping based on chiral metasurfaces,which involves a hybrid design of the propagation phase,chirality induced phase,and Pancharatnam-Berry phase in all-silicon meta-atoms.A controllable linear polarization conversion effect in the transmitted wave is obtained via breaking the mirror symmetry and preserving the C2symmetry of the resonator,which causes a different form of the Jones *** simultaneously adjusting the in-plane geometric dimensions and azimuth angle of chiral units,and utilizing the evolution characteristics of circularly polarized components in parameter space and polarization space,spin-dependent phase responses have been *** provide the main theoretical analysis and demonstrate the functional design of circularly polarized multiplexed beam deflection or vortex beam generation,through the expected simulation and experimental results near the operating frequency of 0.6 *** results may enrich the design of terahertz polarization-multiplexed devices.

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Incremental Learning Algorithm Based on Graph Regularized Non-negative Matrix Factorization with Sparseness Constraints 4

Incremental Learning Algorithm Based on Graph Regularized No...

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4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021

作者： Wang, Jintao Zhang, Meng Hu, Xusheng Ni, Tianwei Key Lab. of Unmanned Aer. Vehicle Devmt. and Data Application of Anhui Higher Education Institutes Maanshan Wireless Sensor Network and Intelligent Perception Engineering Technology Research Center Wanjiang University of Technology Maanshan China Wanjiang University of Technology Maanshan China

ISBN: (纸本)9780738131702

Focused on the issues that the sparseness of the data obtained after factorization is reduced and with the increasing of training samples, the computing scale increases rapidly, an incremental learning algorithm based on graph regularized non-negative matrix factorization with sparseness constraints was proposed. It not only considers the geometric structure in the data representation, but also introduces sparseness constraint to coefficient matrix and combines them with incremental learning. Using the results of previous factorization involved in iterative computation with sparseness constraints and graph regularized, the cost of the computation is reduced and the sparseness of data after factorization is highly improved. Experiments on both ORL and PIE face recognition databases demonstrate the effectiveness of the proposed method. © 2021 IEEE.

关键词： Non-negative matrix factorization

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Research on photovoltaic power generation based on multi-channel PID 2

Research on photovoltaic power generation based on multi-cha...

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2nd IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2021

作者： Liu, Juan Hu, Xusheng Zheng, Rui Ma'Anshan Technical College Department of Electrical Engineering Ma'anshan China Wanjiang University of Technology Ma'Anshan Engineering Technology Research Center for Wireless Sensor Network and IntelliSense Ma'anshan China Anhui Normal University Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot Wuhu China

ISBN: (纸本)9781728190181

In order to improve the utilization efficiency of solar energy, based on the in-depth study of the characteristics of solar energy, a control scheme based on daily motion trajectory was designed. In this scheme, single chip microcomputer is used as the controller to realize the output of SPWM waveform, and the compound PID with multiplexing selection is used to control the Angle of solar panel, so as to achieve the effect of fast and sensitive tracking of solar illumination. Through the angle sensor, on-line monitoring of the angle data of the solar panel, according to the intelligent control optimization algorithm, combined with single-chip microcomputer control technology, a more optimized control scheme is proposed. The experimental data show that the design effect is good and the expected control goal is achieved. © 2021 IEEE.

关键词： Solar energy

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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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Generalized Metasurface Design for Polarization Reconstruction in a Single Snapshot

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Journal of Lightwave technology 2025年

作者： Xu, Wenhui Li, Hui Liu, Yufei Li, Jie Tan, Qi Xu, Hang Zhang, Yan Yao, Jianquan Tianjin University Key Laboratory of Opto-Electronics Information Technology Ministry of Education School of Precision Instruments and Opto-Electronics Engineering Tianjin300072 China Capital Normal University Beijing Key Laboratory for Metamaterials and Devices Key Laboratory of Terahertz Optoelectronics Ministry of Education Beijing Advanced Innovation Center for Imaging Technology Department of Physics Beijing100048 China Chengdu University of Information Technology Sichuan Meteorological Optoelectronic Sensor Technology and Application Engineering Research Center Chengdu610225 China

Metasurfaces enable the design of compact devices that can detect polarization with high precision. This capability is highly significant in applications where space and weight are constraints, such as in portable sensors, optical systems, or communications. Nevertheless, traditional metasurface-based polarization measurement techniques may not work efficiently at terahertz (THz) frequencies due to the different behavior of THz waves compared to visible light. In this work, we propose and experimentally demonstrate a design strategy for THz polarization recognition that utilizes all-dielectric metasurface. This approach necessitated only the extraction of the electric field distribution at the focal plane in the transmission mode. The focal spot located at the center of the focal plane carries orbital angular momentum (OAM), which assisted in determining the azimuthal angle of the incident polarization state. Furthermore, off-axis focal spot positioned in four quadrants provided additional information regarding ellipticity and enable characterization of both axis ratio and the handedness of the polarization ellipse. The pivotal information delineated by the Stokes parameter S3, the axial ratio (AR), and ellipticity (χ) can effectively identify arbitrary polarization states that encompass the entire surface of Poincaré sphere. The consistency between the experimental and simulated results confirms the effectiveness of the designed metasurface in accurately reconstructing THz polarization states. The ability of the designed metasurface to manipulate and detect polarization states with high precision can improve performance in imaging, sensing, and communication systems. © 1983-2012 IEEE.

关键词： Spin polarization

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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 Smart Home System Based on Internet of Things 1

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3rd International Conference on Artificial Intelligence, 2022

作者： Wang, Hai Sun, Peng Sun, Guiling Wang, Zhihong Jiang, Xiaomei Bi, Chaoran Zhang, Ying Teaching Center for Experimental Electronic Information College of Electronic Information and Optical Engineering Nankai University Tianjin300350 China Tianjin Key Laboratory of Optical Thin Film Tianjin Jinhang Technical Physics Institute Tianjin300308 China College of Media Design Tianjin Modern Vocational Technology College Tianjin300350 China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Tianjin300071 China

ISBN: (数字)9789811694233

ISBN: (纸本)9789811694226

In the era of Internet of Things (IOT), a more convenient and efficient life concept has created by the smart home system. In this integrated home solution, in order to realize the intelligent control, information technique is frequently utilized to interconnect ordinary household appliances to form an internal family network. With the help of sand table, the real environment is simulated in this design. Different types of sensors are integrated on ZigBee module which controller is CC2531. The intelligent gateway is responsible for analyzing and processing the environmental information obtained by the sensors. The proprietor can control one or more household electrical appliance and set personalized and intelligent home control scenarios according to the actual needs, such as home mode, out mode, sleep mode, etc. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Zigbee

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