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Spiking Adaptive Dynamic Programming Based on Poisson Process for Discrete-Time Nonlinear systems

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2022年第5期33卷 1846-1856页

作者： Wei, Qinglai Han, Liyuan Zhang, Tielin 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 Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China Chinese Acad Sci Inst Automat Res Ctr Brain Inspired Intelligence Beijing 100190 Peoples R China

In this article, a new iterative spiking adaptive dynamic programming (SADP) method based on the Poisson process is developed to solve optimal impulsive control problems. For a fixed time interval, combining the Poisson process and the maximum likelihood estimation (MLE), the three-tuple of state, spiking interval, and probability of Poisson distribution can be computed, and then, the iterative value functions and iterative control laws can be obtained. A property analysis method is developed to show that the value functions converge to optimal performance index function as the iterative index increases from zero to infinity. Finally, two simulation examples are given to verify the effectiveness of the developed algorithm.

关键词： Process control control systems Optimal control Maximum likelihood estimation Performance analysis Dynamic programming Nonlinear systems Maximum likelihood estimation (MLE) nonlinear systems optimal control Poisson process spike train spiking adaptive dynamic programming (SADP)

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Hand Position Tracking based on Optimized Consistent Extended Kalman Filter 34

Hand Position Tracking based on Optimized Consistent Extende...

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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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Making Standards for Smart Mining Operations: Intelligent Vehicles for Autonomous Mining Transportation

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2022年第3期7卷 413-416页

作者： Ge, Shirong Wang, Fei-Yue Yang, Jianjian Ding, Zhen Wang, Xiao Li, Yuchen Teng, Siyu Liu, Zhongmin Ai, Yunfeng Chen, Long China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China Minist Emergency Management Key Lab Smart Min & Robot Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China CHN Energy Investment Grp Co Ltd Dept Coal Transportat Beijing 100011 Peoples R China Hong Kong Baptist Univ Fac Sci Dept Comp Sci Hong Kong 999077 Peoples R China North Automat Control Technol Inst Taiyuan Taiyuan 030006 Shanxi Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

In face of the safety risk and ageing workforce in traditional mining industry, smart mining is becoming the new trend, especially the autonomous mining, which could completely address the safety issue while boosting efficiency. However, the lack of specific standards for the smart mining has led to the various types of equipment and high development costs. Therefore, we present the effort by Chinese mining research organizations and industrial enterprises in creating standards for automation and autonomy. Besides, we introduce a few key intelligent technologies for the autonomous mining transportation, and several current applications of autonomous trucks in mines around the world. These key intelligent technologies are likely to be further improved with following standards and become more affordable for mines.

关键词： Autonomous vehicles Standards Production Safety Data mining Transportation Roads Intelligent mining parallel intelligence parallel mining operation systems autonomous transportation smart mining standards

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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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Detecting Product Adoption Intentions via Multiview Deep Learning

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INFORMS JOURNAL ON COMPUTING 2022年第1期34卷 541-556页

作者： Zhang, Zhu Wei, Xuan Zheng, Xiaolong Li, Qiudan Zeng, Daniel Dajun Chinese Acad Sci Inst Automat State Key Lab ofManagement & Control Complex Syst Beijing 100190 Peoples R China Shenzhen Artificial Intelligence & Data Sci Inst Shenzhen 518129 Peoples R China Shanghai Jiao Tong Univ Antai Coll Econ & Management Dept Informat Technol & Innovat Shanghai 200030 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China

Detecting product adoption intentions on social media could yield significant value in a wide range of applications, such as personalized recommendations and targeted marketing. In the literature, no study has explored the detection of product adoption intentions on social media, and only a few relevant studies have focused on purchase intention detection for products in one or several categories. Focusing on a product category rather than a specific product is too coarse-grained for precise advertising. Additionally, existing studies primarily focus on using one type of text representation in target social media posts, ignoring the major yet unexplored potential of fusing different text representations. In this paper, we first formulate the problem of product adoption intention mining and demonstrate the necessity of studying this problem and its practical value. To detect a product adoption intention for an individual product, we propose a novel and general multiview deep learning model that simultaneously taps into the capability of multiview learning in leveraging different representations and deep learning in learning latent data representations using a flexible nonlinear transformation. Specifically, the proposed model leverages three different text representations from a multiview perspective and takes advantage of local and long-term word relations by integrating convolutional neural network (CNN) and long short-term memory (LSTM) modules. Extensive experiments on three Twitter datasets demonstrate the effectiveness of the proposed multiview deep learning model compared with the existing benchmark methods. This study also significantly contributes research insights to the literature about intention mining and provides business value to relevant stakeholders such as product providers.

关键词： web mining business intelligence intention detection deep learning social media analytics

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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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LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGVs and USVs Based on Foundation Models and Scenarios Engineering

LogisticsVISTA: 3D Terminal Delivery Services With UAVs, UGV...

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2024 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2024

作者： Tian, Yonglin Lin, Fei Zhang, Xinyuan Ge, Jingwei Wang, Yutong Dai, Xingyuan Lv, Yisheng Wang, Fei-Yue University of Chinese Academy of Sciences Beijing100049 China Tsinghua University Department of Automation Beijing100084 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Macau University of Science and Technology Faculty of Innovation Engineering Department of Engineering Science 999078 China

Intelligent logistics with autonomous vehicles is promising to improve the efficiency of rapidly expanding delivery systems. However, complex environments, diverse packages, and irregular human interaction have imposed challenges to the application of unmanned delivery vehicles. Combining various forms of vehicles and developing knowledge-driven delivery services have been the key to further facilitating the upgrading of logistics systems. In this paper, we propose an integrated framework named LogisticsVISTA (Logistics on Vehicles with Intelligent systems for Transport Automation) comprised of UAVs (Unmanned Aerial Vehicles), UGVs (Unmanned Ground Vehicles), and USVs (Unmanned Surface Vehicles) to provide timely and flexible 3D delivery services. In LogisticsVISTA, we design Large language models (LLMs) and large multimodal models (LMMs) for task planning, active perception, autonomous decision, and human-machine interaction. Besides, virtual-real integrated parallel scenarios are constructed to provide the playing ground for embodied agents efficiently and flexibly. © 2024 IEEE.

关键词： Drones

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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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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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