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34th Chinese control and Decision Conference (CCDC)

作者： Tian, Lin Xue, Wenchao Cheng, Long Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Natl Ctr Math & Interdisciplinary Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781665478960

This paper proposes a hand position tracking algorithm based on optimized consistent extended Kalman filter (CEKF). By introducing the previous work of the authors and analyzing the parameter of the original CEKF algorithm, the key parameter that is negatively correlated with the degree of the estimation of uncertain dynamics is determined. Then, two metaheuristic methods, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) are used to optimize the original CEKF algorithm. To quantify the performance of the algorithms, the root-mean-square error (RMSE) is employed as the performance index. Finally, the numerical simulation and practical experiment of the hand position tracking are carried out, and the optimized algorithm achieves 9.52% and 10.94% improvements of the performance, respectively.

关键词： Consistency Extended Kalman Filter Genetic Algorithm Particle Swarm Optimization

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Model Predictive control-Based Depth control in Gliding Motion of a Gliding Robotic Dolphin

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2021年第9期51卷 5466-5477页

作者： Wang, Jian Wu, Zhengxing Tan, Min Yu, Junzhi Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ Coll Engn Beijing Innovat Ctr Engn Sci & Adv Technol Dept Mech & Engn SciState Key Lab Turbulence & C Beijing 100871 Peoples R China

This article proposes a model predictive control (MPC)-based depth control system for the gliding motion of a gliding robotic dolphin. An injector-based buoyancy-driven mechanism is employed to achieve more precise control of net buoyancy. In the system, a novel framework of depth control is proposed on the basis of a simplified model, including a depth controller with improved MPC, a heading controller with velocity-based proportional-integral-derivative, and a sliding mode observer. Extensive simulation and experimental results demonstrate the effectiveness of the proposed control methods. In particular, a variety of slider-based experiments are also conducted to explore the performance of a movable slider in the depth control so as to better govern the gliding angle. The results obtained reveal that it is feasible to realize regular gliding angles via regulating the slider, which offers promising prospects for bio-inspired gliding robots playing a key role in ocean exploration.

关键词： Dolphins Robot kinematics control systems Aquatic robots Predictive models complex systems Depth control gliding motion model predictive control (MPC) underwater robotics

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DSNRNet: A Network of LiDAR Data Denoising by Differential Stability in Underground Mine

DSNRNet: A Network of LiDAR Data Denoising by Differential S...

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International Conference on Intelligent Transportation

作者： Dejiang Meng Bin Tian Ziyu Pan Junhui Wang Long Chen The Waytous Inc. Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Data and Computer Science Sun Yat-sen University China

Some sprinklers in underground mines spray water mist to lessen the amount of dust in the air. But water mist causes LiDAR to provide inaccurate point measurements, which are referred to as water mist noise that undermines the effectiveness of LiDAR-based localization and object recognition. Therefore, in order to reduce water mist noise, we have developed a new noise segmentation network that can operate on a CPU in real time-differential stability noise removal network (DSNRNet). This network consists of two sub-networks. The first sub-network is aimed at extracting differential stability features, so we named it the differential stability feature extraction network (sub-network 1). The second sub-network: a fully connected neural network (sub-network 2), is used to segment noise. To evaluate the DSNRNet's performance, we built a LiDAR semantic segmentation dataset of underground mines and run the DSNRNet in an Intel i7-11800H CPU. The experimental results demonstrate that this method is able to strike a better balance between speed (26.3 milliseconds) and accuracy (97.8 % ) compared with the other two most possible methods-DSOR (24.2 milliseconds, 3.2 % ) and WeatherNet (436.8ms, 98.5 % ).

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The Shadow of Fraud: The Emerging Danger of AI-powered Social Engineering and its Possible Cure

arXiv

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arXiv 2024年

作者： Yu, Jingru Yu, Yi Wang, Xuhong Lin, Yilun Yang, Manzhi Qiao, Yu Wang, Fei-Yue Shanghai Artificial Intelligence Laboratory Shanghai200240 China The Macau Institute of Systems Engineering Macau University of Science and Technology China Eversec Technology Company Ltd. Beijing100080 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Social engineering (SE) attacks remain a significant threat to both individuals and organizations. The advancement of Artificial Intelligence (AI), including diffusion models and large language models (LLMs), has potentially intensified these threats by enabling more personalized and convincing attacks. This survey paper categorizes SE attack mechanisms, analyzes their evolution, and explores methods for measuring these threats. It highlights the challenges in raising awareness about the risks of AI-enhanced SE attacks and offers insights into developing proactive and adaptable defense strategies. Additionally, we introduce a categorization of the evolving nature of AI-powered social engineering attacks into"3E phases": Enlarging, wherein the magnitude of attacks expands through the leverage of digital media;Enriching, introducing novel attack vectors and techniques;and Emerging, signifying the advent of novel threats and methods. Moreover, we emphasize the necessity for a robust framework to assess the risk of AI-powered SE attacks. By identifying and addressing gaps in existing research, we aim to guide future studies and encourage the development of more effective defenses against the growing threat of AI-powered social engineering. Copyright © 2024, The Authors. All rights reserved.

关键词： Artificial intelligence

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3-D Electromagnetic Position Estimation System Using High-Magnetic-Permeability Metal for Continuum Medical Robots

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 2581-2588页

作者： Wang, Heng Wang, Shuangyi Liu, Hongbin Rhode, Kawal Hou, Zeng-Guang Rajamani, Rajesh South China Univ Technol Sch Intelligent Engn Guangzhou 511442 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Kings Coll London St Thomas Hosp Sch Biomed Engn & Imaging Sci London SE1 7EH England Univ Minnesota Dept Mech Engn Minneapolis MN 55455 USA

In this letter, a new 3-D electromagnetic position sensing method is proposed for localization of continuum medical robots. An electromagnet and magnetic sensors are placed outside the human body while only a piece of passive mu-metal with high magnetic permeability is attached to the robot moving inside the body, resulting in a wireless non-contacting position estimation system. The mu-metal gets easily magnetized by the electromagnet and thus exerts position-dependent influence on the external magnetic field, which is measured for position estimation using a particle filter. An alternating magnetic field from the electromagnet is used and hence disturbances from nearby ferromagnetic objects can be rejected. The 3-D position estimation system is evaluated on a flexible trans-esophageal robot for ultrasound imaging with motions of insertion and maneuver. Experiments show that the mean position estimation error is about 5 mm and the system is robust in the presence of magnetic disturbances from a ferromagnetic object. This new wireless and robust 3-D position estimation system is demonstrated to have the potential to localize a continuum medical robot, which can enable autonomous navigation of the robot.

关键词： Electromagnetic tracking 3D position estimation high-magnetic-permeability metal continuum medical robot

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Trip Purposes Mining From Mobile Signaling Data

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第8期23卷 13190-13202页

作者： Li, Zhishuai Xiong, Gang Wei, Zebing Zhang, Yu Zheng, Meng Liu, Xiaoli Tarkoma, Sasu Huang, Min Lv, Yisheng Wu, Chuheng Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan 523808 Peoples R China Beijing Municipal Inst City Planning & Design Beijing 100045 Peoples R China Univ Helsinki Dept Comp Sci Helsinki 00014 Finland

With the widespread application of mobile phones, it has become possible to study human mobility and travel behaviors based on cellular network data. Contrary to call detail records, the data is triggered by mobile cellular signaling and can provide fine-grained information about users' daily routines. However, it does not explicitly provide semantic details about traveling traces, e.g., trip purposes. In this paper, we propose a methodological framework to handle large-scale cellular network data and discover the underlying trip purposes in an unsupervised way. We first devise heuristic rules to identify home/work purposes. Then, a flexible latent Dirichlet allocation (LDA) model is presented to discover the activities for remaining trips, in which each trip is depicted by four attributes, i.e. arrival time, age group, stay duration, and the point of interest tag for the destination. Experimental results show that the proposed method can identify diverse trip purposes by explaining their structures over trip attributes and outperform baselines in terms of log-likelihood and perplexity. We also analyze the difference between the automatically discovered trip purposes and those estimated from household census, and the analyzed results demonstrate the feasibility of our proposed method.

关键词： Cellular networks Trajectory Semantics Unsupervised learning Supervised learning Resource management Public transportation Trip purpose inference cellular network data latent Dirichlet allocation travel behavior big data

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Investigating the dynamic memory effect of human drivers via ON-LSTM

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Science China(Information Sciences) 2020年第9期63卷 83-93页

作者： Shengzhe DAI Zhiheng LI Li LI Dongpu CAO Xingyuan DAI Yilun LIN Department of Automation Tsinghua University Shenzhen International Graduate School Tsinghua University Department of Mechanical and Mechatronics Engineering University of Waterloo State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

It is a widely accepted view that considering the memory effects of historical information(driving operations) is beneficial for vehicle trajectory prediction models to improve prediction accuracy. However,many commonly used models(e.g., long short-term memory, LSTM) can only implicitly simulate memory effects, but lack effective mechanisms to capture memory effects from sequence data and estimate their effective time range(ETR). This shortage makes it hard to dynamically configure the most suitable length of used historical information according to the current driving behavior, which harms the good understanding of vehicle motion. To address this problem, we propose a modified trajectory prediction model based on ordered neuron LSTM(ON-LSTM). We demonstrate the feasibility of ETR estimation based on ON-LSTM and propose an ETR estimation method. We estimate the ETR of driving fluctuations and lane change operations on the NGSIM I-80 dataset. The experiment results prove that the proposed method can well capture the memory effects during trajectory prediction. Moreover, the estimated ETR values are in agreement with our intuitions.

关键词： driving behavior memory effect trajectory prediction historical information ON-LSTM

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Fuzzy Approximation-Based Task-Space control of Robot Manipulators With Remote Center of Motion Constraint

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IEEE TRANSACTIONS ON FUZZY systems 2022年第6期30卷 1564-1573页

作者： Su, Hang Qi, Wen Chen, Jiahao Zhang, Dandan Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2BX England Univ Bristol Dept Engn Math Bristol BS8 1TH Avon England

The presence of unknown physical interaction between the patients' body and surgical tool in laparoscopic surgery requires a secure end-effector positioning while assuring a reliable constraint motion. In this work, a task-space control approach based on fuzzy approximation is proposed for a teleoperated surgery scenario utilizing a serial redundant robot manipulator (7 degrees of freedom), the motions of which are constrained with respect to a point known as remote center of motion (RCM). The dynamical uncertainties due to the physical interaction are considered and estimated to maintain operational accuracy by introducing the decoupled adaptive fuzzy approximation technique. Experiments verify the presented approach's effectiveness. Results indicate that with the proposed control method, the accuracy of the surgical operation is improved while the safety can be ensured for teleoperated surgery with RCM constraint.

关键词： Robots Task analysis Minimally invasive surgery Kinematics Force Jacobian matrices End effectors Fuzzy approximation null space physical interaction remote center of motion (RCM) teleoperation

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A Real 3D Embodied Dataset for Robotic Active Visual Learning

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第3期7卷 6646-6652页

作者： Zhao, Qianfan Zhang, Lu Wu, Lingxi Qiao, Hong Liu, Zhiyong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China Univ Calif Santa Barbara Dept Math Coll Letters & Sci Santa Barbara CA 93106 USA

Active interaction with environments is one of the striking characteristics of robotic active vision, which allows robots to move to facilitate visual tasks. Recently, several embodied AI platforms have been proposed as the synthetic environments to study robotic active vision, without needing to interact in real world. However, by using synthetic data, model trained on these platforms will suffer performance degradation when applied in reality. In this letter, a real 3D embodied dataset is proposed for robotic active visual learning. The proposed dataset consists of real point cloud data densely collected in 7 real-world indoor scenes. In our embodied dataset, researchers are able to simulate the movements and interactions of robots in indoor environments and obtain real visual data, which will not lead to performance degradation in reality. Furthermore, we proposed a 3D divergency policy that can guide robots to move and collect data to improve visual performance in novel environments. The proposed policy is designed following a simple fact: a good 3D detector should produce consistent 3D detection results for the same object from different viewpoints. Therefore, our policy encourages the robot to explore the area where the detector generates different 3D bounding boxes for the same object and helps the robot improve its visual performance in novel scenes.

关键词： Data sets for robotic vision deep learning for visual perception reinforcement learning

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SRGCN: Graph-based multi-hop reasoning on knowledge graphs

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NEUROCOMPUTING 2021年 454卷 280-290页

作者： Wang, Zikang Li, Linjing Zeng, Daniel Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China Shenzhen Artificial Intelligence & Data Sci Inst Shenzhen Peoples R China

Learning to infer missing links is one of the fundamental tasks in the knowledge graph. Instead of reasoning based on separate paths in the existing methods, in this paper, we propose a new model, Sequential Relational Graph Convolutional Network (SRGCN), which treats the multiple paths between an entity pair as a sequence of subgraphs. Specifically, to reason the relationship between two entities, we first construct a graph for the entities based on the knowledge graph and serialize the graph to a sequence. For each hop in the sequence, Relational Graph Convolutional Network (R-GCN) is then applied to update the embeddings of the entities. The updated embedding of the tail entity contains information of the entire graph, hence the relationship between two entities can be inferred from it. Compared to the existing approaches that deal with paths separately, SRGCN treats the graph as a whole, which can encode structural information and interactions between paths better. Experiments show that SRGCN outperforms path-based baselines on both link and fact prediction tasks. We also show that SRGCN is highly efficient in the sense that only one epoch of training is enough to achieve high accuracy, and even partial datasets can lead to competitive performance. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Knowledge graph Multi-hop reasoning Graph convolutional network

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