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SSRN 2023年

作者： Wang, Quan Wang, Fei Li, Zhongheng Wang, Zheng Nie, Feiping Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing100094 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Northwestern Polytechnical University Shaanxi Xi’an710072 China

Joint clustering and dimensionality reduction methods are a promising solution to clustering due to its scalability to high-dimensional data. Some methods leverage trace ratio criterion and attain clusters by borrowing the K-means algorithm. However, trace ratio criterion has no close-formed solution for the discriminative projection matrix and the K-means algorithm has a limited capacity to handle the many-cluster problem. In this paper, Coordinate Descent Clustering with Trace Difference criterion (CDCTD) is proposed for joint clustering and feature extraction. Formulating the objective function as a direct trace difference criterion containing a balance parameter, CDCTD harmonizes between-cluster scatter maximization and within-cluster scatter minimization by the balance parameter. Using the direct trace difference criterion, CDCTD can straightforward solve for the discriminative projection matrix and avoid obtaining a poor discriminative projection matrix in the iterative manner when a bad cluster start is given. CDCTD uses the coordinate descent method for clustering, improving the ability to address the many-cluster problem. Extensive experiments show that CDCTD has achieved significant performance improvements compared to previous trace ratio criterion related joint clustering and feature extraction methods, and also outperformed other clustering methods in most cases. © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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Efficient Random Subspace Decision Forests with A Simple Probability Dimensionality Setting Scheme

SSRN

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SSRN 2022年

作者： Wang, Quan Wang, Fei Li, Zhongheng Jiang, Peilin Ren, Fuji Nie, Feiping Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing100094 China School of Software Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Northwestern Polytechnical University Shaanxi Xi’an710072 China

The random subspace concept is widely used in decision forests. However, there is not a reasonable approach to specify the appropriate number of randomly selected features. Previous random subspace decision forests simply specify the random subspace dimensionality d s to a preset function value such as [log 2 d ] or [√ d ] where d is the number of the total feature dimensions. Actually, this preset value may be a relatively poor choice for high dimensional data sets. In this paper, a novel framework named Efficient Random Subspace decision forest (ERS) is proposed which pays attention to how to set the random subspace dimensionality for each decision tree of decision forests. Specifically, a simple discrete uniform distribution is employed to set with probability the number of randomly selected features for each tree in random subspace decision forests. The proposed Half Range Discrete Uniform distribution-based Varied Dimensionality setting (HRDUVD) method removes the hesitation of how to preset an appropriate d s for different data sets but also results in good enough classification performance along with relatively short running time. Plenty of experiments on public benchmark data sets have demonstrated the effectiveness and the efficiency of the proposed ERS. © 2022, The Authors. All rights reserved.

关键词： Decision trees

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Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning

arXiv

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arXiv 2024年

作者： Wang, Jingyao Mou, Luntian Zheng, Changwen Gao, Wen The University of Chinese Academy of Sciences Beijing100049 China The National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing100086 China Beijing Institute of Artiicial Intelligence Faculty of Information Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing100124 China The School of Electronics Engineering and Computer Science Peking University Beijing100871 China

Freeform handwriting authentication verifies a person’s identity from their writing style and habits in messy handwriting data. This technique has gained widespread attention in recent years as a valuable tool for various fields, e.g., fraud prevention and cultural heritage protection. However, it still remains a challenging task in reality due to three reasons: (i) severe damage, (ii) complex high-dimensional features, and (iii) lack of supervision. To address these issues, we propose SherlockNet, an energy-oriented two-branch contrastive self-supervised learning framework for robust and fast freeform handwriting authentication. It consists of four stages: (i) pre-processing: converting manuscripts into energy distributions using a novel plug-and-play energy-oriented operator to eliminate the influence of noise;(ii) generalized pre-training: learning general representation through two-branch momentum-based adaptive contrastive learning with the energy distributions, which handles the high-dimensional features and spatial dependencies of handwriting;(iii) personalized fine-tuning: calibrating the learned knowledge using a small amount of labeled data from downstream tasks;and (iv) practical application: identifying individual handwriting from scrambled, missing, or forged data efficiently and conveniently. Considering the practicality, we construct EN-HA, a novel dataset that simulates data forgery and severe damage in real applications. Finally, we conduct extensive experiments on six benchmark datasets including our EN-HA, and the results prove the robustness and efficiency of SherlockNet. We will release our code and dataset at https://***/WangJingyao07/SherlockNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Research on obstacle detection and path planning based on visual navigation for mobile robot

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Journal of Physics: Conference Series 2020年第6期1601卷

作者： Yixiao Zheng Bixi Yan Chao Ma Xinyu Wang Haoyang Xue School of Instrument Science and Optoelectronic Engineering Beijing Information Science and Technology University Beijing China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing China

Both obstacle and path planning are of significance for autonomous visual navigation of a mobile robot which is able to realize free movement and obstacle avoidance. In this paper, a scheme of obstacle detection which is based on binocular vision to identify obstacles is constructed and it is combined with improved A* algorithm for obstacle avoidance motion for mobile robot platform. First, a binocular vision system to detect the robot's perception of the environment is used and the visual information is obtained. Then a Gaussian filter is used to reduce the noise in the image, and the grayscale image is obtained through the binocular vision system. The gray scale map is matched with SAD (Sum of Absolute Differences) in order to obtain the disparity image with comparison. Second, the location information of the obstacle is extracted through the environmental depth information contained in the disparity image, and the improved A* algorithm is used as the path planning algorithm in this paper, which can modify the path of the planning error. Furthermore, the detection experiment of the robot visual obstacle avoidance system was established and the optimal path was obtained. The examples show that, the obstacle avoidance system can complete the task of obstacle detection and give a better path for the mobile robot.

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Discrete Element Simulation of Durability for Bionic Wheels of Mars Rover

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Journal of Physics: Conference Series 2020年第5期1507卷

作者： Y C Lin Y P Yan B F Yuan Y F Liu M Zou Beijing Key Laboratory of Intelligent Space Robotic System Technology and application Institute of Spacecraft System Engineering CAST Beijing 100094 China Key Laboratory for Bionics Engineering of Education Ministry Jilin University Changchun 130022 China

Four kinds of bionic surfaces (convex, scale-like, ribbed and concave shape) for Mars rover wheels were designed to improve wheel durability. A discrete element wheel-soil simulation system was developed, and three-dimensional discrete element analysis and soil bin tests were conducted. The results show that the masses of the convex and scale-like forms increased by 5.4% and 17%, respectively, and that the tangential cumulative forces decreased by about 40% and 70%, respectively. The wear mass decreased by 53% and 47%, respectively, compared with a prototypical wheel. The simulation shows that the durability increased significantly. The average tangential cumulative forces of the concave and ribbed shapes increased by more than 50%, and the wear mass decreased by 35% and 32%, respectively, compared with a prototypical wheel. The simulation shows that the results were not definitive. The analysis shows that the durability of the wheels can be improved via bionic optimization method. These results therefore provide a basis and reference for the design and optimization of the Mars rover's wheel surface.

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A Survey of space robotic Technologies for On-Orbit Assembly

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空间科学与技术（英文） 2023年第3期3卷 291-303页

作者： Delun Li Lou Zhong Wei Zhu Zhipeng Xu Qirong Tang Wenhao Zhan Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System EngineeringCASTBeijingChina College of Mechanical Engineering Tongji UniversityShanghaiChina National Key Laboratory of Human Factors Engineering China Astronaut Research and Training CenterBeijingChina

The construction of large structures is one of the main development trends of the space exploration in the future,such as large space stations,large space solar power stations,and large space *** is one of important development tendency,which aims to make full use of space robots to assemble space structures autonomously in the aerospace *** that on-orbit assembly is an effective method to solve the problem of construction of large-scale spatial structures,it is necessary to motivate and facilitate the research of space robotics technologies for on-orbit ***,in this paper,the development status of space robot technology and the relevant space robot on-orbit assembly technology in recent decades are ***,based on the space robot motion planning and assembly sequence planning,the development of space robot planning algorithms is *** space robot assembly task,the space robot assembly method is *** the control point of view,how to solve the vibration suppression and compliant assembly of on-orbit assembly is reviewed,which provides a reference for the autonomous intelligent assembly of space robots for large-scale structures in *** order to simulate the space assembly scene on the ground,this paper introduces the development of ground verification experiments and provides ideas for the effective verification of space on-orbit assembly *** summary,though some of these problems have been satisfactorily solved in the past research,further research is still necessary in the ***,it looks forward to the future research direction of space machine on-orbit assembly.

关键词： technologies space assembly on-orbit robotic survey

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A Soft robotic system Imitating the Multimodal Sensory Mechanism of Human Fingers for intelligent Grasping and Recognition

SSRN

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SSRN 2024年

作者： Xu, Jinsui Xu, Boyi Zhan, Hao Xie, Zhijie Tian, Zhen Lu, Yifan Wang, Zheping Yue, Honghao Yang, Fei South China University of Technology China State Key Laboratory of Robotics and System School of Mechatronics Engineering Harbin Institute of Technology Harbin150001 China Wu Xianming School of Intelligent Engineering South China University of Technology Guangzhou510641 China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin150042 China Science and Technology on Space Physics Laboratory China Academy of Launch Vehicle Technology Beijing100000 China

The sensory function is crucial for achieving precise feedback control and environmental interaction in soft robots. Therefore, the multimodal sensory capabilities that mimic human fingers have always been a research hotspot. This study proposes a design method for a soft robotic gripper that can emulate the multimodal perception mechanism of human fingers and achieve high-precision recognition of grasped objects using machine learning algorithms. In addition, a finger texture structure inspired by human fingerprints is designed using a negative pressure-induced buckling method to enhance the soft gripper's ability to perceive minute surface textures. Inspired by slow adapting (SA) receptors and fast adapting (FA) receptors of human fingers, this study designs a capacitive curvature sensor (CS) and a triboelectric texture sensor (TS) to detect the size, material, and texture of objects. Furthermore, a non-contact medium identification sensor (MIS), having a sensitivity of more than 1300 times higher compared to the traditional MISs, is fabricated using the principle of electromagnetic resonance. It is shown that by employing a deep learning-based multi-channel feature fusion network, the soft grasping system with multimodal sensing can achieve an object recognition accuracy of 98.43%. This work provides an innovative approach for the application of soft robots in various fields, such as logistics sorting and food safety. © 2024, The Authors. All rights reserved.

关键词： Textures

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Characterizing the temporally stable structure of community evolution in intra-urban origin-destination networks

arXiv

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arXiv 2023年

作者： Chen, Xiao-Jian Zhao, Yuhui Kang, Chaogui Xing, Xiaoyue Dong, Quanhua Liu, Yu Institute of Remote Sensing and Geographical Information Systems School of Earth and Space Sciences Peking University Beijing China Beijing Key Lab of Spatial Information Integration & Its Applications Peking University Beijing China Research Centerfor Intelligent Society and Governance Zhejiang Lab Zhejiang China Zhejiang Pilot Laboratory of Philosophy and Social Science Zhejiang China National Engineering Research Center of Geographic Information System China University of Geosciences Wuhan China Peking University Beijing100871 China

Intra-urban origin-destination (OD) network communities evolve throughout the day, indicating changing groups of closely connected regions. Under this variation, groups of regions with high consistency of community affiliation characterize the temporally stable structure of the evolution process, aiding in comprehending urban dynamics. However, how to quantify this consistency and identify these groups are open questions. In this study, we introduce the consensus OD network to quantify the consistency of community affiliation among regions. Furthermore, the temporally stable community decomposition method is proposed to identify groups of regions with high internal and low external consistency (named "stable groups"), where each group consists of temporally stable cores and attaching peripheries. Wuhan taxi data is used to verify our methods. On the hourly time scale, eleven stable groups containing 82.9% of regions are identified. This high percentage suggests that dynamic communities can be well organized via cores. Moreover, stable groups are spatially closed and more likely to distribute within a single district and separated by water bodies. Cores exhibit higher POI entropy and more healthcare and shopping services than peripheries. Our methods and empirical findings contribute to some practical issues, such as urban area division, polycentric evaluation and construction, and infectious disease control. Copyright © 2023, The Authors. All rights reserved.

关键词： Disease control

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DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach

arXiv

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arXiv 2024年

作者： Tang, Xin Chen, Qian Weng, Wenjie Liao, Binhan Wang, Jiacheng Cao, Xianbin Li, Xiaohuan Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Guangxi Nanning530001 China College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Electronic and Information Engineering The Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beihang University Beijing100191 China

Unmanned Aerial Vehicles (UAVs) possess high mobility and flexible deployment capabilities, prompting the development of UAVs for various application scenarios within the Internet of Things (IoT). The unique capabilities of UAVs give rise to increasingly critical and complex tasks in uncertain and potentially harsh environments. The substantial amount of data generated from these applications necessitates processing and analysis through deep neural networks (DNNs). However, UAVs encounter challenges due to their limited computing resources when managing DNN models. This paper presents a joint approach that combines multiple-agent reinforcement learning (MARL) and generative diffusion models (GDM) for assigning DNN tasks to a UAV swarm, aimed at reducing latency from task capture to result output. To address these challenges, we first consider the task size of the target area to be inspected and the shortest flying path as optimization constraints, employing a greedy algorithm to resolve the subproblem with a focus on minimizing the UAV’s flying path and the overall system cost. In the second stage, we introduce a novel DNN task assignment algorithm, termed GDM-MADDPG, which utilizes the reverse denoising process of GDM to replace the actor network in multi-agent deep deterministic policy gradient (MADDPG). This approach generates specific DNN task assignment actions based on agents’ observations in a dynamic environment. Simulation results indicate that our algorithm performs favorably compared to benchmarks in terms of path planning, Age of Information (AoI), energy consumption, and task load balancing. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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