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5th International Conference on Automation, Control and Robotics engineering, CACRE 2020

作者： Zhao, Liang Wang, Guangwen School of Control Science and Engineering Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian China Dalian University of Technology School of Control Science and Engineering Dalian China

ISBN: (纸本)9781728198880

The increasing number of wireless sensor nodes brings more application markets and challenges to the Internet of things (IoT). The traditional IPv4 address resources cannot meet the needs of the development of the IoT, so we need a communication mechanism with better compatibility and scalab.lity. With the proposal of the 6LoWPAN protocol, the application of 6LoWPAN to the communication network of the IoT has become the focus of research. This paper studies the research status of 6LoWPAN in the field of Internet of things in the past four years, and analyzes the system architecture, system composition and monitoring parameters of 6LoWPAN through different application scenarios. Experiments in related literatures show that 6LoWPAN has stable network transmission performance and can be used for precise monitoring and management in combination with web applications. In addition, RPL routing protocol security algorithm can be one of the future research directions. © 2020 IEEE.

关键词： Internet of things

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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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Limit-Cycle-Based Design of Formation Control for Mobile Agents

Limit-Cycle-Based Design of Formation Control for Mobile Age...

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作者： Wang, Chen Xia, Weiguo Xie, Guangming National Engineering Research Center for Software Engineering Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian116024 China State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this article, we study the formation problem for a group of mobile agents in a plane, in which the agents are required to maintain a distribution pattern, as well as to rotate around or remain static relative to a static/moving target. The prescribed distribution pattern is a class of general formations where the distances between neighboring agents or the distances between each agent and the target do not need to be equal. Each agent is modeled as a double integrator and can merely perceive the relative information of the target and its neighbors, and the acceleration of the target. In order to solve the formation problem, a limit-cycle-based controller design is delivered. We divide the overall control objective into two subobjectives, where the first is target circling that each agent keeps its own desired distance to the static/moving target as well as rotating around or remaining static relative to the target as expected, and the second is distribution adjustment that each agent maintains the desired distance to its neighbors. Then, we propose a controller comprised of two parts, where a limit cycle oscillator named a converging part is designed to deal with the first subobjective, while a layout part is introduced to address the second subobjective. One key merit of the controller is that it can be implemented by each agent in its local frame so that only local information is utilized without knowing global information. Theoretical analysis of the convergence to the desired formation, of which the agents are required to be evenly distributed on a circle around the target, is provided for the multiagent system under the proposed controller. Numerical simulations are given to validate the effectiveness of the proposed controller for the cases of general formations, and to show that no collision between agents ever takes place throughout the system's evolution. © 1963-2012 IEEE.

关键词： Multi agent systems

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Size-fixed group discovery via multi-constrained graph pattern matching

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Information Sciences 2025年 690卷

作者： Guliu Liu Lei Li Guanfeng Liu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education 230601 Hefei China School of Computer Science and Information Engineering Hefei University of Technology 230601 Hefei China Department of Computing Macquarie University Sydney 2122 Australia

Multi-Constrained Graph Pattern Matching (MC-GPM) aims to match a pattern graph with multiple attribute constraints on its nodes and edges, and has garnered significant interest in various fields, including social-based e-commerce and trust-based group discovery. However, the existing MC-GPM methods do not consider situations where the number of each node in the pattern graph needs to be fixed, such as finding experts group with expert quantities and relations specified. In this paper, a Multi-Constrained Strong Simulation with the Fixed Number of Nodes (MCSS-FNN) matching model is proposed, and then a Trust-oriented Optimal Multi-constrained Path (TOMP) matching algorithm is designed for solving it. Additionally, two heuristic optimization strategies are designed, one for combinatorial testing and the other for edge matching, to enhance the efficiency of the TOMP algorithm. Empirical experiments are conducted on four real social network datasets, and the results demonstrate the effectiveness and efficiency of the proposed algorithm and optimization strategies.

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Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification

arXiv

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arXiv 2022年

作者： Wang, Jiabao Li, Yang Wei, Xiu-Shen Li, Hang Miao, Zhuang Zhang, Rui Army Engineering University of PLA Nanjing210007 China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Lab. for Novel Software Technology Nanjing University Nanjing210023 China

Unsupervised learning technology has caught up with or even surpassed supervised learning technology in general object classification (GOC) and person re-identification (re-ID). However, it is found that the unsupervised learning of fine-grained visual classification (FGVC) is more challenging than GOC and person re-ID. In order to bridge the gap between unsupervised and supervised learning for FGVC, we investigate the essential factors (including feature extraction, clustering, and contrastive learning) for the performance gap between supervised and unsupervised FGVC. Furthermore, we propose a simple, effective, and practical method, termed as UFCL, to alleviate the gap. Three key issues are concerned and improved: First, we introduce a robust and powerful backbone, ResNet50-IBN, which has an ability of domain adaptation when we transfer ImageNet pre-trained models to FGVC tasks. Next, we propose to introduce HDBSCAN instead of DBSCAN to do clustering, which can generate better clusters for adjacent categories with fewer hyper-parameters. Finally, we propose a weighted feature agent and its updating mechanism to do contrastive learning by using the pseudo lab.ls with inevitable noise, which can improve the optimization process of learning the parameters of the network. The effectiveness of our UFCL is verified on CUB-200-2011, Oxford-Flowers, Oxford-Pets, Stanford-Dogs, Stanford-Cars and FGVC-Aircraft datasets. Under the unsupervised FGVC setting, we achieve state-of-the-art results, and analyze the key factors and the important parameters to provide a practical guidance. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification

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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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Towards Feature Distribution Alignment and Diversity Enhancement for data-Free Quantization

Towards Feature Distribution Alignment and Diversity Enhance...

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IEEE International Conference on data Mining (ICDM)

作者： Yangcheng Gao Zhao Zhang Richang Hong Haijun Zhang Jicong Fan Shuicheng Yan School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Department of Computer Science Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China National University of Singapore Singapore

To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require original data for the fine-tuning or calibration of quantized model, which makes them inapplicable to the cases that original data are not accessed due to privacy or security. This gives birth to the data-free quantization method with synthetic data generation. While current data-free quantization methods still suffer from severe performance degradation when quantizing a model into lower bit, caused by the low inter-class separability of semantic features. To this end, we propose a new and effective data-free quantization method termed ClusterQ, which utilizes the feature distribution alignment for synthetic data generation. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated. Moreover, we incorporate the diversity enhancement to solve class-wise mode collapse. We also employ the exponential moving average to update the centroid of each cluster for further feature distribution improvement. Extensive experiments based on different deep models (e.g., ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate that our proposed ClusterQ model obtains state-of-the-art performance.

关键词： Degradation Training Quantization (signal) Semantics Neural networks Memory management data models

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Unsupervised vehicle re-identification with progressive adaptation 29

Unsupervised vehicle re-identification with progressive adap...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Peng, Jinjia Wang, Yang Wang, Huibing Zhang, Zhao Fu, Xianping Wang, Meng College of Information and Science Technology Dalian Maritime University Liaoning Dalian China Pengcheng Laboratory Shenzhen Guangdong China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology China

ISBN: (纸本)9780999241165

Vehicle re-identification (reID) aims at identifying vehicles across different non-overlapping cameras views. The existing methods heavily relied on well-lab.led datasets for ideal performance, which inevitably causes fateful drop due to the severe domain bias between the training domain and the real-world scenes;worse still, these approaches required full annotations, which is lab.r-consuming. To tackle these challenges, we propose a novel progressive adaptation learning method for vehicle reID, named PAL, which infers from the abundant data without annotations. For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlab.led target domain as "pseudo target samples". These pseudo samples are combined with the unlab.led samples that are selected by a dynamic sampling strategy to make training faster. We further proposed a weighted lab.l smoothing (WLS) loss, which considers the similarity between samples with different clusters to balance the confidence of pseudo lab.ls. Comprehensive experimental results validate the advantages of PAL on both VehicleID and VeRi-776 dataset. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Vehicles

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Social Group Query Based on Multi-fuzzy-constrained Strong Simulation

arXiv

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arXiv 2021年

作者： Liu, Guliu Li, Lei Liu, Guanfeng Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology China Department of Computing Macquarie University Australia Mininglamp Academy of Sciences Mininglamp Technology China

Traditional social group analysis mostly uses interaction models, event models, or other methods to identify and distinguish groups. This type of method can divide social participants into different groups based on their geographic location, social relationships, and/or related events. However, in some applications, it is necessary to make more specific restrictions on the members and the interaction between members of the group. Generally, graph pattern matching (GPM) is used to solve this problem. However, the existing GPM methods rarely consider the rich contextual information of nodes and edges to measure the credibility between members. In this paper, a social group query problem that needs to consider the trust between members of the group is proposed. To solve this problem, we propose a Strong Simulation GPM algorithm (NTSS) based on the exploration of pattern Node Topological ordered sequence. Aiming at the inefficiency of the NTSS algorithm when matching pattern graph with multiple nodes with zero in-degree and the problem of repeated calculation of matched edges shared by multiple matching subgraphs, two optimization strategies are proposed. Finally, we conduct verification experiments on the effectiveness and efficiency of the NTSS algorithm and the algorithms with the optimization strategies on four social network datasets in real applications. Experimental results show that the NTSS algorithm is significantly better than the existing multi-constrained GPM algorithm, and the NTSS_Inv_EdgC algorithm, which combines two optimization strategies, greatly improves the efficiency of the NTSS algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Constrained optimization

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Multi-scale Graph Convolutional Neural Network for Object Recognition from Point Cloud data 2nd

Multi-scale Graph Convolutional Neural Network for Object Re...

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2nd International Conference on Urban Intelligence and Applications, ICUIA 2020

作者： Lu, Qiang Chen, Chao Teng, Jinfeng Zhang, Chunyuan Huang, Yi Xuan, Shanli Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China School of Computer and Information Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230009 China

ISBN: (纸本)9789813346000

How to make robots understand the point cloud data which is collected from the 3D sensor and complete the recognition has become a hot research direction in recent years. In this paper, we propose a new approach to improve the critical robotic capability, semantic understanding of the environment (i.e., 3D object recognition). The convolutional neural network (CNN) method has a very good recognition result in the 2D image domain, but it has certain difficulty in applying irregular and unordered 3D point clouds data. The network for point cloud data generally uses the convolution to realize the extraction of point cloud features by finding the neighborhood features on the point set. Due to the different neighborhood scales caused by the irregularity of 3D point cloud data, we propose a CNN structure that combines multi-scale features. By finding multiple neighborhoods of the point set and establishing local graph extraction features, the stable expression of the local neighborhood is obtained. At the same time, the key point calibration method is added, so that the network can dynamically focus on key point features to improve the recognition result. In a series of analytical experiments, we demonstrate competing results that demonstrate the effectiveness of the network structure. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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