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检索条件"机构=Intelligent Sensor Network Application Technology Research and Development Center"

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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...

引用

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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A WOA Based Switching PID Control for Overhead Crane

A WOA Based Switching PID Control for Overhead Crane

引用

Chinese Control Conference (CCC)

作者： Haoyong Yu Zhe Sun Yuhua Xu 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 Nanjing P.R. China Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing P.R. China Anhui Yougu Express Intelligent Technology Co. LTD Nanling Anhui P.R. China

Aiming at the problem that the amplitude of load oscillation is not easy to control in the movement process of overhead crane, this paper designs a switching PID control method based on whale optimization algorithm(WOA) to improve the performance of position control and anti-swing control for overhead crane. WOA is used to better adjust the parameters of PID controllers. The switching control is realized by a switching module, which defaults to the output of the Position PID Controller to provide control force for the trolley. When the absolute value of the load swing angle fed back by the crane system exceeds the warning value, the switching module immediately blocks the output of the Position PID Controller, and uses the Angle PID Controller to determine the control force. In addition, through theoretical analysis and simulation experiments under different conditions, the results verify the effectiveness of the WOA based switching control in position control and anti-swing control for overhead crane. Improve the efficiency and safety of overhead crane in practical industrial applications.

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LQR Control Method based on Improved Antlion Algorithm

LQR Control Method based on Improved Antlion Algorithm

引用

Chinese Control Conference (CCC)

作者： Zhe Sun Zhi Wen 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 Nanjing P.R. China Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing P.R. China Anhui Yougu Express Intelligent Technology Co. LTD Nanling Anhui P.R. China

In the face of the increasing demand of crane transportation speed and stability, an improved Ant-lion algorithm is proposed in this paper. On the original basis, the quasi-reverse learning method is used to improve the stability of the algorithm and make the initial data distributed evenly. Furthermore, an elite ant-lion growth mechanism is introduced to accelerate the convergence speed of the simulation and improve the simulation accuracy. Finally, the proposed algorithm is verified in the double pendulum system of crane based on LQR controller, and the feasibility and effectiveness of the proposed algorithm are proved by experiments.

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Design of Safety Monitoring System for Electrical Laboratory in Colleges and Universities under the Background of Informatization

Design of Safety Monitoring System for Electrical Laboratory...

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2021 International Symposium on Advances in Informatics, Electronics and Education, ISAIEE 2021

作者： Liu, Huahui Liu, Juan Hu, Xusheng Research Center for Wireless Sensor Network and Intellisense Ma'Anshan Engineering Technology Ma'anshan243031 China Key Laboratory of Unmanned Aerial Vehicle Development Data Application of Anhui Higher Education Institutes Wanjiang University of Technology Ma'anshan243031. China Ma'Anshan Technical College Ma'anshan243030 China

ISBN: (纸本)9781665478748

With the development of economic globalization, information technology has made great progress, and artificial intelligence technology has been widely used, such as car driving, robot automatic steering, rescue and disaster relief robot, underwater robot and so on. However, in the laboratory management of colleges and universities, the application of artificial intelligence is insufficient, and the traditional management mode is backward. Some universities still adopt manual management mode, which not only has high labor cost, but also has a large management gap, resulting in too many potential safety hazards, which is not conducive to the safety management of university laboratories. The market urgently needs to realize the combination of laboratory management and artificial intelligence. As a Key Laboratory of engineering, the electrical laboratory carries out and sets up multiple experimental courses according to the discipline training requirements. As an Engineering University, the course content spans multiple grades, faces more students and covers multiple majors in two colleges. It can be imagined that the experimental courses opened in the electrical laboratory basically take the power supply as the carrier, in this way, the requirements for students are very high. If the electrical experiment is misoperated or caused by emergencies, the risk is great and the accidents are very sudden. Based on the above situation, it fully shows that higher requirements are put forward for the safety management of the electrical laboratory and the management will be more difficult. In recent years, discipline construction has been paid more and more attention, but with the frequent occurrence of laboratory accidents, the fire safety related to the laboratory has attracted more and more attention. For the application-oriented laboratory in Colleges and universities, we must raise awareness, strengthen safety prevention, and strengthen the management of laboratory fire safet

关键词： Laboratories

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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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Chinese Control Conference (CCC)

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 Nanjing P.R. China Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing P. R. China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing P.R. China Anhui Yougu Express Intelligent Technology Co. LTD Nanling Anhui P.R. China

Logistics vehicle and cargo matching problem is a typical combination optimization problem, which has important theoretical and application value in the field of logistics distribution. The 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 speed. Based 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 experiment. The 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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A Review of the Evolutionary Algorithm Based VRP Problem

A Review of the Evolutionary Algorithm Based VRP Problem

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Chinese Control Conference (CCC)

作者： Lili Pan Bing Hu 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 Nanjing P. R. China Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing P. R. China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing P. R. China Anhui Yougu Express Intelligent Technology Co. LTD Nanling Anhui P. R. China

The problem of vehicle routing planning (VRP) has far-reaching influence, and is widely used in vehicle scheduling, industrial production, transportation, logistics and distribution. The study of VRP and its associated derivations has grown significantly in recent years. So, starting with the fundamental VRP, this paper categorizes the VRP based on its traits and real-world applications. It then in-depth analyses the capacity constrained VRP, time window VRP, batched delivery VRP, dynamic VRP, and stochastic demand VRP, and provides an update on each type of VRP's research. Additionally, a summary of the VRP problem-solving methods is provided. The study of evolutionary algorithms is the main topic of this paper, and the fundamental solution method is provided in accordance with the development of the algorithms.

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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...

引用

Chinese Control and Decision Conference, CCDC

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 Nanjing P.R. China Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology) Nanjing University of Posts and Telecommunications Nanjing P.R. China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing P.R. China School of Information Technology and Electrical Engineering The University of Queensland Brisbane Brisbane Australia

Traffic prediction is the basis for dynamic network services and resource optimization. Software Defined networking (SDN) provides a global view for network measurement and management, and a reliable way for network information collection and network traffic prediction. Recently, 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 explored. From the perspective of the problem, a novel Spatial-Temporal Residual Graph Convolutional network (STRGCN) model for SDN Traffic Matrix (TM) prediction is proposed. Combined with SDN network topology, TM prediction is defined as time series data prediction in non-Euclidean space. Temporal Gated-Conv Blocks are based on Gated Convolutional Layer, and Spatial Graph-Conv Blocks are based on Graph Convolutional network (GCN). Through identity mapping between Spatial-Temporal Conv Blocks, the deep spatial and temporal features extraction is implemented. Tested by TM data from CERNET and GÉANT datasets, the accuracy and the timely performance of our model are evaluated. Experimental results suggest that the general performance of STRGCN for TM prediction in SDN is superior to the current deep learning models.

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An obstacle avoidance design of UAV based on genetic algorithm 3

An obstacle avoidance design of UAV based on genetic algorit...

引用

3rd International Conference on Mechanical, Electric and Industrial Engineering, MEIE 2020

作者： Hu, Xusheng Liu, Juan Key Laboratory of Unmanned Aerial Vehicle Development Data Application of Anhui Higher Education Institutes Wanjiang University of Technology Ma'anshan243031 China Ma'anshan Engineering Technology Research Center for Wireless Sensor Network and IntelliSense Ma'anshan243031 China Ma'anshan Technical College Ma'anshan243030 China

In order to improve the effect of obstacle avoidance, a PID control algorithm combined with genetic algorithm is proposed. By establishing the mathematical model of UAV, the auxiliary control system of UAV is analyzed, and then the control system is optimized by genetic algorithm. Taking STM32 as the control platform, the obstacle avoidance experiment was carried out by building the experimental platform. The experimental results show that the algorithm can avoid obstacles effectively. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Genetic algorithms

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Design of Safety Monitoring System for Electrical Laboratory in Colleges and Universities under the Background of Informatization

Design of Safety Monitoring System for Electrical Laboratory...

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Advances in Informatics, Electronics and Education (ISAIEE), International Symposium on

作者： Huahui Liu Juan Liu Xusheng Hu Ma'anshan Engineering Technology Research Center for Wireless Sensor Network and Intellisense Ma'anshan China Key Laboratory of Unmanned Aerial Vehicle Development Data Application of Anhui Higher Education Institutes Wanjiang University of Technology Ma'anshan China Ma'anshan Technical College Ma'anshan China

ISBN: (纸本)9781665478755

关键词： Buildings Safety management Process control Control systems Software Safety Artificial intelligence

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Efficient Object Detection Method Based on Improved YOLOv3 network for Remote Sensing Images

Efficient Object Detection Method Based on Improved YOLOv3 N...

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International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Jintao Wang Wen Xiao Tianwei Ni Key Laboratory of Unmanned Aerial Vehicle Development & Data Application of Anhui Higher Education Institutes Maanshan China Wanjiang University of Technology Maanshan Wireless Sensor Network and Intelligent Perception Engineering Technology Research Center Maanshan China Wanjiang University of Technology Maanshan China

ISBN: (数字)9781728197418

ISBN: (纸本)9781728197425

In the future, Drones or general Unmanned Aerial Vehicles(UAVs) need to implement online real-time object detection, but their memory and computing power are limited. An improved YOLOv3 is adopted in this paper, making full use of the lightweight network instead of the feature extraction network of YOLOv3 to achieve high-efficiency object detection for remote sensing images. In the case of similar detection accuracy, parameters and FLOPs (floating point operations) of the improved model are 2.5 times and 3.3 times smaller than YOLOv3, respectively. In addition, an IoU K-medians algorithm is proposed, which improves mAP by 7.0% on YOLOv3 and by 2.3% on the improved *** show that the detection speed of the improved YOLOv3 is 101 frame/s at the fastest, and it is still 1.6 times faster than that of YOLOv3 when its mAP is 6% higher than that of YOLOv3. The efficient remote sensing object detection method proposed in this paper lays the foundation for future UAVs.

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