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Boost 3-D Object Detection via Point Clouds Segmentation and Fused 3-D GIoU-L1 Loss

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2022年第2期33卷 762-773页

作者： Chen, Yaran Li, Haoran Gao, Ruiyuan Zhao, Dongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Coll Artificial Intelligence Beijing 100049 Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China

The 3-D object detection is crucial for many real-world applications, attracting many researchers' attention. Beyond 2-D object detection, 3-D object detection usually needs to extract appearance, depth, position, and orientation information from light detection and ranging (LiDAR) and camera sensors. However, due to more degrees of freedom and vertices, existing detection methods that directly transform from 2-D to 3-D still face several challenges, such as exploding increase of anchors' number and inefficient or hard-to-optimize objective. To this end, we present a fast segmentation method for 3-D point clouds to reduce anchors, which can largely decrease the computing cost. Moreover, taking advantage of 3-D generalized Intersection of Union (GIoU) and L-1 losses, we propose a fused loss to facilitate the optimization of 3-D object detection. A series of experiments show that the proposed method has alleviated the abovementioned issues effectively.

关键词： 3-D object detection generalized Intersection of Union (GIoU) loss segmentation

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Adaptive Critic Designs for Optimal Event-Driven control of a CSTR System

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第1期17卷 484-493页

作者： Yang, Xiong Wei, Qinglai Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This article presents an optimal event-driven control scheme for a continuous stirred tank reactor (CSTR) system. The CSTR system differs from most of studied plants in that its equilibrium point is nonzero. In order to obtain the optimal event-driven control without coordinate transformations, we first introduce a discounted cost for such a system. Then, under the framework of adaptive critic designs, we use a single critic network to solve the Hamilton Jacobi Bellman equation related to the discounted-cost optimal event-driven control problem. To update the critic network weights, we employ the gradient descent method together with the experience replay technique. An advantage of the experience replay technique is that it brings about an easy-checked persistency of excitation-like condition. The stability analysis of all the signals in the closed-loop system is conducted via the Lyapunov approach. Finally, we validate the present optimal event-driven control strategy through simulations of the CSTR system.

关键词： Chemical reactors Optimal control Nonlinear systems Adaptive systems Cost function Informatics Closed loop systems Adaptive critic designs (ACDs) continuous stirred tank reactor (CSTR) discounted cost event-driven control reinforcement learning (RL)

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Adaptive Critic Learning for Constrained Optimal Event-Triggered control With Discounted Cost

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2021年第1期32卷 91-104页

This article studies an optimal event-triggered control (ETC) problem of nonlinear continuous-time systems subject to asymmetric control constraints. The present nonlinear plant differs from many studied systems in that its equilibrium point is nonzero. First, we introduce a discounted cost for such a system in order to obtain the optimal ETC without making coordinate transformations. Then, we present an event-triggered Hamilton-Jacobi-Bellman equation (ET-HJBE) arising in the discounted-cost constrained optimal ETC problem. After that, we propose an event-triggering condition guaranteeing a positive lower bound for the minimal intersample time. To solve the ET-HJBE, we construct a critic network under the framework of adaptive critic learning. The critic network weight vector is tuned through a modified gradient descent method, which simultaneously uses historical and instantaneous state data. By employing the Lyapunov method, we prove that the uniform ultimate boundedness of all signals in the closed-loop system is guaranteed. Finally, we provide simulations of a pendulum system and an oscillator system to validate the obtained optimal ETC strategy.

关键词： Nonlinear systems Optimal control Robustness Cost function Adaptive systems Adaptive critic designs (ACDs) adaptive critic learning (ACL) adaptive dynamic programming (ADP) constrained optimal control event-triggered control (ETC) reinforcement learning (RL)

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A Pilot Study on Coupling Behaviors of Limb Movements and Brain Activation Based on EEG and fNIRS Synchronization Information

A Pilot Study on Coupling Behaviors of Limb Movements and Br...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Tang, Xi Li, Qingge Tang, Yao Tian, Lan Zheng, Yue Li, Xiangxin Jiang, Naifu Shang, Peng Li, Guanglin Peng, Liang Fang, Peng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems The Shenzhen Engineering Laboratory of Neural Rehabilitation Technology Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350327182

Motion is one of the basic physiological functions of human beings. However, many brain diseases such as stroke may cause different degrees of motor dysfunctions for patients. As a commonly used rehabilitation method, active and passive exercise training may enhance patients' neuromuscular functions and recover their motor abilities. It is known that limb movements are strongly coupled with brain activation but there is currently insufficient exploration on the coupling behaviors from the perspective of informatics. In this study, the coupling relationship between limb movements and brain activation was preliminarily studied based on three healthy subjects. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals were synchronously collected during lower limb movements, and time-frequency analysis (TFA) and transfer entropy (TE) analysis were performed to quantitatively study the brain activation behaviors. In the experiments, a desynchronization phenomenon of μ rhythm in EEG was observed during exercise states, and the experimental results demonstrate the activation rule of motor and prefrontal cortexes upon limb movements. Calculations show that there exists a bidirectional flow of information between EEG and cerebral oxygen metabolism signals, but with a difference between different directions. This work may support the rehabilitation for patients with motor dysfunctions with a guidance of quantitative indicators and also benefit the exploration on neuroscience. © 2023 IEEE.

关键词： Electroencephalography

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Entity Matters in News: An Association Network-Enhanced Method for News Reprint Prediction

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IEEE INTELLIGENT systems 2022年第1期37卷 99-107页

作者： Li, Qiudan Liu, Hejing Yao, Riheng Xu, David Jingjun Zeng, Daniel D. 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 City Univ Hong Kong Coll Business Dept Informat Syst Hong Kong Peoples R China

Reprint is a fast and efficient way for news media to spread information and plays an increasingly important role in evaluating the influence of media and building a brand image. News reprint prediction is a novel research question in the news diffusion field, which predicts whether a news media will reprint a piece of news in the future. During reprinting, an association network among media and entities in the news is formed that reflects their multidimensional dynamic interaction. Existing research primarily focuses on integrating reprint historical records and news content while reprint prediction considering the associations among media and entities in the news remains under-researched. This work develops an entity association network-enhanced method for news reprint prediction, which adopts HIN2Vec to model the dynamic interaction and incorporates the learned embedding and news content through attention mechanism to generate entity-specific content representation. The efficacy of the proposed method is validated on real-world news reprint data. Experimental results show that the fusion of entity association network helps improve the performance of reprint prediction. This research contributes to media communication literature and has significant practical implications.

关键词： Media Semantics Publishing Predictive Models Urban Areas Mathematical Models Reprint Prediction Entity Association Network Attention Mechanism

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Parallel Intelligence for Smart Mobility in Cyberphysical Social System-Defined Metaverses: A Report on the International Parallel Driving Alliance

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IEEE INTELLIGENT TRANSPORTATION systems MAGAZINE 2022年第6期14卷 18-25页

作者： Liu, Kunhua Li, Leixin Lv, Yisheng Cao, Dongpu Liu, Zhongmin Chen, Long Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China North Automat Control Technol Inst Taiyuan 030000 Peoples R China

On 27 June 2018, the International Parallel Driving Alliance (iPDA) inaugural conference was held in Changshu, China. The iPDA consists of 24 well-known institutions, e.g., the University of Cambridge, Purdue University Indianapolis, and the Royal Institute of Technology of Sweden. The iPDA aims to co-establish a common shared research platform for parallel driving and a timely exchange of the latest research results and data related to parallel driving. During the conference, participants discussed the definition, applications, and future challenges of parallel driving and generally agreed that it is a solution to the current autonomous driving problem Five keynote speakers presented parallel driving with intelligent vehicle theme talks to share their perspectives, field applications, and outlooks on industry trends and future research.

关键词： Intelligent vehicles Metaverse Task analysis Predictive models Behavioral sciences Autonomous vehicles Ethics Cyber-physical systems

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A two-step method for 4-pin form-closure gripper with grasping force optimization

A two-step method for 4-pin form-closure gripper with graspi...

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作者： Li, Rui Li, Yinlin Su, Xiaojie School of Automation Chongqing University Chongqing400040 China State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Grasp planning and the grasping force optimization are challenging issues associated with the dexterous robotic hand grasping problem. To ensure a safe, fast, and stable grasp for robots, careful determination of the grasping points and the grasping forces is required. The former creates an open question because of the availability of multiple solutions. The latter is formulated as a problem for minimizing an objective function subject to form-closure constraints and balance constraints of external forces. In this work, we propose a novel approach for the grasping force optimization of the 4-pin environmental constraint-based form-closure grasp using dynamic neural networks. To find a form-closure grasp, we propose an environmental constraint-based algorithm and convert it to a problem of finding the local minima of a constraint region, resulting in an easy determination of the grasping points with the guarantee of grasping safety. To satisfy the grasping force limit of the contact points, we solve the grasping force optimization problem with friction constraint, which maximize the efficiency of the applied force and power of the dexterous hand. The proposed approach is evaluated with 4-pin gripper, and the result demonstrates that we can plan a safe, fast, and stable grasp from any initial state to a form-closure grasping state. © 2021 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Grippers

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On the Approximation of the Quotient of Two Gaussian Densities for Multiple-Model Smoothing 26

On the Approximation of the Quotient of Two Gaussian Densiti...

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26th International Conference on Information Fusion, FUSION 2023

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Xue, Yanbo University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Career Science Lab Beijing China

ISBN: (纸本)9798890344854

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target. © 2023 International Society of Information Fusion.

关键词： Cost functions

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Physical-Informed Neural Network for MPC-Based Trajectory Tracking of Vehicles With Noise Considered

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4493-4503页

作者： Jin, Long Liu, Longqi Wang, Xingxia Shang, Mingsheng Wang, Fei-Yue Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Big Data & Intelligent Comp Chongqing 400714 Peoples R China 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

The trajectory tracking plays a vital role in unmanned driving technology. Although traditional control schemes may yield satisfactory outcomes in dealing with simple linear tasks, they may fall short when handling dynamic systems with time-varying characteristics or lack of ability to complete a given task with the disturbance of noise. Therefore, a predictive control scheme under the framework of artificial systems, computational experiments, and parallel execution (ACP) is proposed. Within the ACP framework, the scheme integrates a model predictive control (MPC) controller and a physical-informed neural network (PINN) model to tackle intricate trajectory tracking tasks effectively with noise considered. Moreover, soft constraints that can enhance model robustness and improve solution efficiency are considered in the scheme. Then, theoretical analyses on the PINN model are provided with rigorous mathematical proofs. Finally, experiments and comparisons with existing works are conducted to illustrate the effectiveness and superiority of the constructed PINN model for MPC-based trajectory tracking of vehicles.

关键词： Mathematical models Trajectory tracking Task analysis Predictive models Intelligent vehicles Computational modeling Trajectory Artificial systems computational experiments model predictive control (MPC) controller parallel execution (ACP) physical-informed neural network (PINN) trajectory tracking tasks

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Neural Dynamics for Computing Perturbed Nonlinear Equations Applied to ACP-Based Lower Limb Motion Intention Recognition

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2022年第8期52卷 5105-5113页

作者： Jin, Long Li, Jiachang Sun, Zhongbo Lu, Jingwei Wang, Fei-Yue Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Changchun Univ Technol Dept Control Engn Changchun 130012 Peoples R China 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

Many complex nonlinear optimization or control issues can be transformed into the solving of time-varying nonlinear equations (TVNEs), playing a fundamental role in the control and management of complex systems. As a result, a robust and high-precision online solution method is significant for TVNE. However, there are three main challenges for handling TVNE via the existing methods: First, short-time invariance assumption frequently leveraged in the existing methods leads to lagging errors that are difficult to eliminate. Second, it is difficult in dealing with unknown noise disturbance during the solution process, which causes low solution accuracy or solution failure. Third, existing continuous-time methods are hard to be implemented on digital equipments. In this article, an anti-noise discrete-time neural dynamics (DTND) is designed and studied to overcome the above issues systematically. The theoretical analysis and numerical simulations demonstrate that the proposed model effectively eliminates the lagging errors and achieves the accurate solution of the TVNE in a noisy environment. Moreover, to verify the superior numerical computational property of the DTND model, the intention recognition of lower limbs is explored from the artificial systems, computational experiments, and parallel execution (ACP) framework. Specifically, a nonlinear artificial dynamic system (NADS) concerning the human surface electromyogram (sEMG) signals and joint information is established, which performs in parallel with the actual human lower limb physical experiments. Simulation results illustrate that, within the acceptable range of the digital computer, the controller designed by the DTND model can well guide the NADS to accurately recognize the motion intention of the human lower limb.

关键词： Mathematical models Computational modeling Numerical models Nonlinear equations Convergence Analytical models Time-varying systems Artificial systems computational experiments and parallel execution (ACP) discrete-time neural dynamics (DTND) model human lower limb physical experiment nonlinear equations

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