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Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA), International Conference on

作者： Yufei Wang Baihua Xiao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences & University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Deep convection can cause a variety of severe weather conditions such as thunderstorms, strong winds, and heavy rainfall. Satellite observations provide all-weather and multi-directional observations, facilitating the timely detection of such weather systems, which is crucial to saving lives and property. However, previous methods based on channel feature extraction and threshold filtering did not make full use of information in satellite images, which led to limitations on such complex problems as strong convection detection. In this study, we propose a novel framework of a deep learning-based model Convection-UNet to detect convection. We use channel 4 to 7 of FY-4B GHI that we select according to the microphysical properties of convection as input and radar reflectivity as label. We combine the detailed training time and test time data augmentation strategies and build a deep neural network to automatically extract spatial context features and achieve end-to-end learning. Results show that the performance of our method far exceeds the previous channel extraction combined with threshold filtering methods such as BT and BTD at least 0.24 on Fi-measure. We also show that our channel selection and data augmentation strategies are of great significance to detect convection.

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A Comparative Study of Machine Learning Models for Parkinson's Disease Detection

A Comparative Study of Machine Learning Models for Parkinson...

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International Conference on Decision Aid Sciences and Applications (DASA)

作者： Bunterngchit, Chayut Bunterngchit, Yuthachai Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Siam Univ Grad Sch Engn Bangkok Thailand

ISBN: (纸本)9781665495011

Parkinson's disease is a major public health concern, affecting over 6 million people worldwide. The objective of this paper is to assist doctors and clinicians in accurately detecting the disease at an early stage. Previous research proposed various models that gave very high accuracy. However, very few of them examined the processing time of each model, which is an important consideration in decision making. The most common method for diagnosing this disease is through voice signal recordings. This paper formulates 10 machine learning-based predictive models on a biomedical voice measurement dataset. A genetic algorithm is applied as a feature selection algorithm. The highest prediction accuracy after running 10 generations is 97.96%. The features of the most accurate model are reduced from 22 to 9 features. The processing time of the most accurate model is 1.83 seconds. The best improvement in accuracy after applying this feature selection algorithm is 16.33%.

关键词： Parkinson's disease genetic algorithm machine learning feature selection

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Containment control with input and velocity constraints

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AUTOMATICA 2022年 142卷

作者： Xu, Jiahao Lin, Peng Cheng, Long Dong, Hairong Cent South Univ Sch Automat Changsha 410083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

In this paper, containment control for double-integrator discrete-time multi-agent systems is investigated with switching graphs, nonconvex input constraints and nonconvex velocity constraints. In order to eliminate the nonlinearities caused by the nonconvex input and velocity constraints, we first introduce two time-varying scaling factors to transform the original constrained system into an unconstrained time-varying linear one. By proposing an inductive method based on the lower bounds of the scaling factors, we decouple the relevance of the two kinds of constraints and transform the obtained time-varying system into a new equivalent system. It is shown that all agents converge into the convex hull of the leaders even when switching graphs, nonconvex control input constraints and nonconvex velocity constraints coexist, as long as there exists at least one directed path from one of the leaders to each agent in the union of the communication graphs. Finally, a simulation example is given to illustrate the obtained theoretical results. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Multi-agent systems Containment control Nonconvex constraints Switching graphs

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4D mmWave Radar for Autonomous Driving Perception: A Comprehensive Survey

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第4期9卷 4606-4620页

作者： Fan, Lili Wang, Junhao Chang, Yuanmeng Li, Yuke Wang, Yutong Cao, Dongpu Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Jiangsu Ind Innovat Ctr Intelligent Equipment Co L Beijing 100081 Peoples R China Dalian Minzu Univ Sch Sci Dalian 116600 Peoples R China Waytous Co Ltd Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Tsinghua Univ State Key Lab Intelligent Green Vehicle & Mobil Beijing 100084 Peoples R China

The rapid development of autonomous driving technology has driven continuous innovation in perception systems, with 4D millimeter-wave (mmWave) radar being one of the key sensing devices. Leveraging its all-weather operational characteristics and robust perception capabilities in challenging environments, 4D mmWave radar plays a crucial role in achieving highly automated driving. This review systematically summarizes the latest advancements and key applications of 4D mmWave radar in the field of autonomous driving. To begin with, we introduce the fundamental principles and technical features of 4D mmWave radar, delving into its comprehensive perception capabilities across distance, speed, angle, and time dimensions. Subsequently, we provide a detailed analysis of the performance advantages of 4D mmWave radar compared to other sensors in complex environments. We then discuss the latest developments in target detection and tracking using 4D mmWave radar, along with existing datasets in this domain. Finally, we explore the current technological challenges and future directions. This review offers researchers and engineers a comprehensive understanding of the cutting-edge technology and future development directions of 4D mmWave radar in the context of autonomous driving perception.

关键词： Radar Radar antennas Millimeter wave communication Microstrip antennas Antenna arrays Radar detection Autonomous vehicles 4D mmWave radar autonomous driving perception multi-modal fusion 3D object detection

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Toward Generalizable Robotic Dual-Arm Flipping Manipulation

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第5期71卷 4954-4962页

作者： Huang, Haifeng Zeng, Chao Cheng, Long Yang, Chenguang South China Univ Technol Coll Automat Sci & Engn Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510640 Peoples R China South China Univ Technol Coll Automat Sci & Engn GuangDong Engn Technol Res Ctr Control Intelligen Guangzhou 510640 Peoples R China Univ Hamburg Dept Informat Tech Aspects Multimodal Syst TAMS Grp D-22527 Hamburg Germany 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

Robotic dual-arm manipulation often requires close cooperation between the arms. Dual-arm manipulation tasks are always difficult to program in advance, and then, executed autonomously by the robots. Learning from demonstration is an efficient programming method for robots that can transfer human skills to robots. However, conventional skill learning methods, e.g., dynamic movement primitives (DMPs) can only characterize the motion information of each dimension independently, and cannot take into account the relationship between multidimensional information. Participially, flipping manipulation is quite common in industry production lines, but it has not been well addressed yet to provide a robotics solution to this task. In this article, we propose an improved DMP model, called the object-level constrained DMP, which effectively preserves the association between multidimensional information. Similarly, we also propose an orientation generalization method for the flipping task. In addition, we show how to demonstrate the flipping task via a teleoperation system. Finally, experiments are performed on a Baxter robot to verify the effectiveness of the methods.

关键词： Dynamic movement primitive (DMP) flipping task learning from demonstration (LfD) skill generalization

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Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2023年第7期37卷 1555-1581页

作者： Zhao, Tianhong Huang, Zhengdong Tu, Wei Biljecki, Filip Chen, Long Shenzhen Univ Sch Architecture & Urban Planning Shenzhen Peoples R China Natl Univ Singapore Dept Architecture Singapore Singapore Natl Univ Singapore Dept Real Estate Singapore Singapore Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

The accurate prediction of travel demand by bus is crucial for effective urban mobility demand management. However, most models of travel demand prediction by bus tend to focus on the bus's spatiotemporal dependencies, while ignoring the interactions between buses and other transportation modes, such as metros and taxis. We propose a Multiview Spatiotemporal Graph Neural Network (MSTGNN) model to predict short-term travel demand by bus. It emphasizes the ability to capture the interaction dependencies among the travel demand of buses, metros, and taxis. Firstly, a multiview graph consisting of bus, metro, and taxi views is constructed, with each view containing both a local and global graph. Secondly, a multiview attention-based temporal graph convolution module is developed to capture spatiotemporal and cross-view interaction dependencies among different transport modes. Especially, to address the uneven spatial distributions of features in multiview learning, the cross-view spatial feature consistency loss is introduced as an auxiliary loss. Finally, we conduct intensive experiments using a real-world dataset from Shenzhen, China. The results demonstrate that our proposed MSTGNN model performs better than the existing models. Ablation experiments validate the contributions of various modes of transportation to the improvement of the model's performance.

关键词： Graph deep learning multiview learning travel demand prediction multimodal transportation smart card data

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Modeling Digital Personality: A Fuzzy-Logic-Based Myers-Briggs Type Indicator for Fine-Grained Analytics of Digital Human

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第1期11卷 1096-1107页

作者： Wang, Tan Ye, Peijun Lv, Hongqiang Gong, Weichao Lu, Hao Wang, Fei-Yue Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China

Digital human in cyberspace can help provide humanized services in specific applications, such as question & answer systems, recommender systems, chatter robots, and intelligent assistants. While most researches focus on behavior analytics, few of them integrate the personality that is also a closely related factor. As a classic indicator for personality representation, Myers-Briggs type indicator (MBTI) categorizes an individual into mutually exclusive types from four dichotomous axes (extraversion versus introversion, sensing versus intuition, thinking versus feeling, judging versus perceiving). Traditional recognition method using MBTI simply measures the user's preference frequency in each axis through questionnaires, treating the dominant value as the identified result. Such a paradigm, however, represents all the people with only 16 types and cannot distinguish heterogeneous users clearly. This article proposes a novel personality recognition method using fuzzy logic. Different from previous classifications, our new method categorizes the individual in a continuous space and represents one's personality in a more fine-grained level. We have designed comparative psychological tests for 77 people. The validation experiments on such tests indicate that the fuzzy-logic-based method is not only consistent with the classic MBTI tests (in the sense of defuzzification) but also provides the uncertainty for each personality type. Therefore, it can be viewed as a generalization of the classic MBTI tests and promotes the representation of individual's heterogeneity for fine-grained analytics of digital human.

关键词： Psychology Fuzzy logic Automation Technological innovation Uncertainty Soft sensors Social networking (online) Digital human digital personality fuzzy logic reasoning fuzzy personality recognition Myers-Briggs type indicator (MBTI)

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D-PBS: Dueling Priority-Based Search for Multiple Nonholonomic Robots Motion Planning in Congested Environments

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第7期9卷 6288-6295页

作者： Zhang, Xiaotong Xiong, Gang Wang, Yuanjing Teng, Siyu Chen, Long Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Univ Durham Dept Nat Sci Durham DH1 3LE England Hong Kong Baptist Univ Hong Kong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Waytous Inc Qingdao 266109 Peoples R China

This letter focuses on the multiple nonholonomic robots motion planning (MRMP) problem in congested and complex environments, where the complexity escalates dramatically with the increase in the number of robots, frequently leading to deadlocks. We present the Dueling Priority-Based Search (D-PBS), an efficient and scalable priority-based motion planner for multiple nonholonomic car-like robots, capable of enabling robots to move safely to destinations in spatially-constrained settings. We achieve this by adopting the alternate dueling collision resolution approach, coupled with the exploration of comprehensive priority relationships, effectively addressing the deadlock situations. We also introduce a novel priority-binding algorithm to enhance the scalability of our planner in restricted spaces densely populated with robots. Experimental evaluations in various scenarios demonstrate that D-PBS outperforms standard approaches to MRMP, offering superior path quality and scalability for larger robot swarms.

关键词： Multi-robot system motion planning nonholonomic robot

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Online Adaptive Dynamic Programming for Optimal Self-Learning control of VTOL Aircraft systems With Disturbances

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第1期21卷 343-352页

作者： Wei, Qinglai Yang, Zesheng Su, Huaizhong Wang, Lijian 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 Macau 999078 Peoples R China COMAC Beijing Aeronaut Technol Res Inst Beijing 102211 Peoples R China

In this paper, a novel online adaptive dynamic programming control algorithm is developed to solve optimal control problems for Vertical Take Off and Landing (VTOL) aircraft systems. Considering the input disturbances of the VTOL systems and adding perturbation interference term to the value function, a robust policy iteration-based ADP method will be introduced to obtain the optimal controller with an approximate optimal control approach. The convergence property is developed to ensure the value function converges to a finite neighborhood of the optimal one. The uniform ultimate boundedness (UUB) of the iterative errors will be strictly proved by Lyapunov stability theory. Finally, we will give mathematical simulation results with the developed method. Compared with sliding mode control (SMC) and linear quadratic regulator (LQR) methods, the simulation results illustrate better overall performance of the novel method.

关键词： Adaptive dynamic programming (ADP) VTOL aircraft system policy iteration neural network (NN) optimal control iterative errors

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Completed attention convolutional neural network for MRI image segmentation

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High Technology Letters 2022年第3期28卷 247-251页

作者： ZHANG Zhong LV Shijie LIU Shuang XIAO Baihua Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin Normal UniversityTianjin 300387P.R.China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190P.R.China

Attention mechanism combined with convolutional neural network(CNN) achieves promising performance for magnetic resonance imaging(MRI) image segmentation,however these methods only learn attention weights from single scale,resulting in incomplete attention learning.A novel method named completed attention convolutional neural network(CACNN) is proposed for MRI image ***,the channel-wise attention block(CWAB) and the pixel-wise attention block(PWAB) are designed to learn attention weights from the aspects of channel and pixel *** a result,completed attention weights are obtained,which is beneficial to discriminative feature *** method is verified on two widely used datasets(HVSMR and MRBrainS),and the experimental results demonstrate that the proposed method achieves better results than the state-of-theart methods.

关键词： magnetic resonance imaging(MRI)image segmentation completed attention convolutional neural network(CACNN)

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