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An improved branching algorithm for the proper interval edge deletion problem

Frontiers of Computer Science 2022年第2期16卷 178-180页

作者： Wenjun LI Xiaojing TANG Yongjie YANG School of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on TransportationChangsha University of Science and TechnologyChangsha 410015China School of Computer Science and Engineering Central South UniversityChangsha 410083China

1 Introduction For a graph class G,the G-EDGE DELETION problem is to determine whether a given graph can be transformed into a graph in G by deleting at most k *** G-EDGE DELETION problem for a large body of graph cla... 详细信息

关键词： branching problem transformed

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A Path Planning Method Based on Optimal Arm Angle of 7-Degrees-of-Freedom Manipulator

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JOURNAL OF intelligent & robotIC SYSTEMS 2025年第2期111卷 1-20页

作者： Zhang, Li Han, Bing Niu, Jianwei Tong, Kun Tian, Xinyang Yu, Xiaolong Hou, Renluan Guo, Yuliang Beihang Univ Hangzhou Innovat Inst Zhejiang Ind Big Data & Robot Intelligent Syst Key Hangzhou 310052 Peoples R China Zhejiang Univ Sch Mech Engn Hangzhou 310030 Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

This paper aims to address the issue of explicitly utilizing the joint limit avoidance, singularity avoidance, and self-motion obstacle avoidance properties of a 7-degrees-of-freedom manipulator during path planning, given a cartesian space task path. First, the redundant degree of freedom of the manipulator is represented using arm angle parameterization, and the mapping of joint limits and singular configurations to arm angle space is derived in detail, in conjunction with analytical inverse kinematics solution. Next, based on the cartesian space task path, the arm angle space is discretized to obtain the feasible arm angle range under the self-motion obstacle avoidance condition. The arm angle values determine the joint angles of the manipulator. To minimize task execution time and improve efficiency, the total joint angle displacement of the manipulator is set as the optimization objective, and an improved particle swarm optimization algorithm is employed to iteratively search for the optimal arm angles at each path point. Finally, the effectiveness of the proposed method is validated through both simulation and real machine experiments.

关键词： Redundant manipulator Inverse kinematics Path planning Joint limit avoidance Self-motion obstacle avoidance

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Digging out Discrimination Information from Generated Samples for Robust Visual Question Answering 61

Digging out Discrimination Information from Generated Sample...

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61st Annual Meeting of the the Association-for-Computational-Linguistics (ACL)

作者： Wen, Zhiquan Wang, Yaowei Tan, Mingkui Wu, Qingyao Wu, Qi South China Univ Technol Sch Software Engn Guangzhou Peoples R China PengCheng Lab Shenzhen Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China Univ Adelaide Sch Comp Sci Adelaide SA Australia

ISBN: (纸本)9781959429623

Visual Question Answering (VQA) aims to answer a textual question based on a given image. Nevertheless, recent studies have shown that VQA models tend to capture the biases to answer the question, instead of using the reasoning ability, resulting in poor generalisation ability. To alleviate the issue, some existing methods consider the natural distribution of the data, and construct samples to balance the dataset, achieving remarkable performance. However, these methods may encounter some limitations: 1) rely on additional annotations, 2) the generated samples may be inaccurate, e.g., assigned wrong answers, and 3) ignore the power of positive samples. In this paper, we propose a method to Dig out Discrimination information from Generated samples (DDG) to address the above limitations. Specifically, we first construct positive and negative samples in vision and language modalities, without using additional annotations. Then, we introduce a knowledge distillation mechanism to promote the learning of the original samples by the positive samples. Moreover, we impel the VQA models to focus on vision and language modalities using the negative samples. Experimental results on the VQA-CP v2 and VQA v2 datasets show the effectiveness of our DDG.

关键词： Distillation

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Research on Collaborative Storage of data Based on IPFS and Blockchain

Research on Collaborative Storage of Data Based on IPFS and ...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Boyuan Han Zhihong Liang Yuxiang Huang Mingming Qin Fei Xiong School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data Southwest Forestry University

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

The advent of the digital economy has necessitated secure and efficient storage solutions for large-scale data. Blockchain technology, characterized by its decentralization and immutability, offers unique advantages for protecting data privacy. However, as the scale of on-chain data processing expands, blockchain faces significant challenges in terms of storage costs and efficiency. This research introduces the IPFS-BC system, a novel collaborative storage solution that integrates the InterPlanetary File System (IPFS) with blockchain. This system harnesses IPFS’ s capability to manage large-scale data and employs blockchain smart contracts to facilitate automated interactions between the IPFS and blockchain systems, thereby ensuring data integrity and security. The IPFS-BC system combines the blockchain's transaction record layer with IPFS's storage solution to achieve decentralized data storage and enhanced efficiency. Experimental validation demonstrates that this system effectively reduces the storage load on the blockchain, improves storage performance, and maintains data security and integrity. This study not only provides a new solution for large-scale data storage but also lays a practical foundation for further integration and interaction between blockchain and IPFS.

关键词： File systems data integrity Smart contracts Memory Collaboration data processing Blockchains InterPlanetary File System Information technology Secure storage

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A Belief Propagation Cooperative Localization Algorithm in GNSS-Loss Environments 3

A Belief Propagation Cooperative Localization Algorithm in G...

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3rd International Conference on Digital Signal and Computer Communications, DSCC 2023

作者： Ma, Fei Tan, Guoping Zhou, Siyuan Hu, Hexuan College of Computer and Information Hohai University Jiangsu Intelligent Transportation and Intelligent Driving Research Institute Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University College of Computer and Information Hohai University Nanjing China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

When vehicles are in driving environments such as urban roads and tunnels, the global navigation satellite system (GNSS) signal can fail to achieve positioning due to occlusion. Cooperative positioning can effectively improve the accuracy and coverage of vehicle positioning. In this paper, we address the vehicle localization issue in GNSS-Loss driving environments and with random packet loss due to wireless communications. We propose an improved cooperative localization algorithm based on belief propagation, which first linearizes the measurement model of distance by statistical linear regression method, then performs belief propagation based on the linearized model, and solves the packet loss problem by expanding the dimension of the vehicle state. From the simulation results, the algorithm can effectively reduce the impact of packet loss and improve the vehicle localization accuracy. © 2023 SPIE.

关键词： Belief propagation

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GBFRS: Robust Fuzzy Rough Sets via Granular-ball Computing

arXiv

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arXiv 2025年

作者： Xia, Shuyin Lian, Xiaoyu Sang, Binbin Wang, Guoyin Gao, Xinbo Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China College of Computer and Information Science the National Center for Applied Mathematics in Chongqing Chongqing Normal University Chongqing401331 China

Fuzzy rough set theory is effective for processing datasets with complex attributes, supported by a solid mathematical foundation and closely linked to kernel methods in machine learning. Attribute reduction algorithms and classifiers based on fuzzy rough set theory exhibit promising performance in the analysis of high-dimensional multivariate complex data. However, most existing models operate at the finest granularity, rendering them inefficient and sensitive to noise, especially for high-dimensional big data. Thus, enhancing the robustness of fuzzy rough set models is crucial for effective feature selection. Muiti-garanularty granular-ball computing, a recent development, uses granular-balls of different sizes to adaptively represent and cover the sample space, performing learning based on these granular-balls. This paper proposes integrating multi-granularity granular-ball computing into fuzzy rough set theory, using granular-balls to replace sample points. The coarse-grained characteristics of granular-balls make the model more robust. Additionally, we propose a new method for generating granular-balls, scalable to the entire supervised method based on granular-ball computing. A forward search algorithm is used to select feature sequences by defining the correlation between features and categories through dependence functions. Experiments demonstrate the proposed model’s effectiveness and superiority over baseline methods. The source codes and datasets are both available on the public link: https://***/lianxiaoyu724/GBFRS Copyright © 2025, The Authors. All rights reserved.

关键词： Granular computing

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On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving

On the Road to Portability: Compressing End-to-End Motion Pl...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Kaituo Feng Changsheng Li Dongchun Ren Ye Yuan Guoren Wang Beijing Institute of Technology ALLRIDE.AI Hebei Province Key Laboratory of Big Data Science and Intelligent Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

End-to-end motion planning models equipped with deep neural networks have shown great potential for enabling full autonomous driving. However, the oversized neu-ral networks render them impractical for deployment on resource-constrained systems, which unavoidably requires more computational time and resources during reference. To handle this, knowledge distillation offers a promising approach that compresses models by enabling a smaller stu-dent model to learn from a larger teacher model. Neverthe-less, how to apply knowledge distillation to compress motion planners has not been explored so far. In this paper, we propose PlanKD, the first knowledge distillation frame-work tailored for compressing end-to-end motion planners. First, considering that driving scenes are inherently complex, often containing planning-irrelevant or even noisy in-formation, transferring such information is not beneficial for the student planner. Thus, we design an information bottleneck based strategy to only distill planning-relevant information, rather than transfer all information indiscrim-inately. Second, different waypoints in an output planned trajectory may hold varying degrees of importance for motion planning, where a slight deviation in certain crucial waypoints might lead to a collision. Therefore, we devise a safety-aware waypoint-attentive distillation module that as-signs adaptive weights to different waypoints based on the importance, to encourage the student to accurately mimic more crucial waypoints, thereby improving overall safety. Experiments demonstrate that our PlanKD can boost the performance of smaller planners by a large margin, and significantly reduce their reference time.

关键词： Computer vision Computational modeling Roads Artificial neural networks Planning Trajectory Safety

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GNEA: A Novel GCN-based Network Embedding Algorithm for Semantic Social Network 10

GNEA: A Novel GCN-based Network Embedding Algorithm for Sema...

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10th International Symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Zhao, Jinbo He, Jinling Du, Yuanhan Tang, Ming Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China

ISBN: (纸本)9798350362930

With the development of semantic social networks, social networks become more complex and their size expands rapidly, which brings significant challenges to social network analysis. Network Embedding can transform the network from a high-dimensional adjacency matrix to a low-dimensional one, which has excellent foreground in link prediction, vertex classification, and graph visualization. Nowadays, most algorithms are proposed for static networks with little consideration of the dynamic changes in the network. However, most real-world networks change over time. Directly applying static network embedding algorithms to dynamic networks will result in poor stability, flexibility, and efficiency. Because of these shortcomings, we propose a graph convolution-based dynamic network representation learning algorithm (GNEA), which combines Graph Convolutional Network (GCN) with Long Short-Term Memory (LSTM) to train the time series networks. GCN is applied to extract features in the network, and LSTM is utilized to capture temporal information. GNEA can not only perform characterization learning on the network but also obtain more abundant information during the dynamic evolution of the network. GNEA is compared with several typical algorithms on the datasets Stochastic Block Model (SBM), Autonomous Systems (AS), and Digital Bibliography and Library Project (DBLP). The results prove that GNEA has good performance in MAP and precision@k. © 2024 IEEE.

关键词： Complex networks

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Micromotion Gesture Recognition Technology Based on Millimeter-wave Radar 7

Micromotion Gesture Recognition Technology Based on Millimet...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Li, Zhaoqi Gao, Fei Li, Yingchun Tang, Panpan Cai, Dayu Li, Zheyi Wang, Cong Nan, Jingchang Liaoning Technical University Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao China Harbin Institute of Technology School of Information Science and Engineering Weihai China Harbin Institute of Technology School of Electronics and Information Engineering Harbin China

ISBN: (纸本)9798350384437

Different from the traditional FMCW radar signal processing method, this paper mainly explores the application of the time-frequency analysis method of the reflected intermediate frequency of millimeter wave radar in fretting detection. The radar echo data are analyzed by FFT, wavelet transform and Hilbert yellow transform. This topic mainly uses human gesture targets as the micromotion feature to detect. Under the three designed gestures, the time-frequency analysis method proposed in this topic combined with a convolutional neural network achieves a high recognition success rate. The motion detection technology proposed in this paper can be applied to millimeter wave radar under THE FMCW system. It can detect and classify tiny motions under complex backgrounds and show good recognition ability under strong interference. It can be used in medical treatment, automobile and other fields to play the role of accurate identification. © 2024 IEEE.

关键词： Gesture recognition

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A Broadband SIW-Feed Microstrip Array Antenna for Millimeter-Wave Applications

A Broadband SIW-Feed Microstrip Array Antenna for Millimeter...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Chang Ge Mingming Gao Hongliang Niu Hang Yuan Congying Wang Xinyue Zhang Liaoning Technical University Huludao Liaoning China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

A broadband array antenna with high gain is proposed in this paper. The proposed antenna consists of microstrip series-fed line arrays, power divider based on Substrate Integrated Waveguide (SIW). Series-fed line arrays achieve broadband characteristics by periodically loading inverted trapezoidal branches on each side of the microstrip line. In contrast to the traditional microstrip feed network, this antenna array adopts the SIW technique for its feed network design, known for its lower loss. Finally, the relative bandwidth of this broadband millimeter-wave array antenna is 12.7% (41.85-47.54 GHz), and the peak gain is 17.97 dB. The antenna is characterized by broadband capability, high gain, low profile, simple structure, and ease of manufacture, making it one of the most prospective candidates for millimeter-wave applications.

关键词： Power dividers Microstrip antenna arrays Millimeter wave technology Microstrip antennas Broadband communication Broadband antennas Microstrip

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