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Short-term effects of ambient oxidation, and its interaction with fine particles on first-ever stroke: A national case-crossover study in China

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 907卷 168017页

作者： Jiang, Dongxia Wang, Luyang Han, Xueyan Pan, Zhaoyang Wang, Zhaokun Wang, Yaqi Li, Jing Guo, Jian Liu, Yuanli Huang, Shaodan Guan, Tianjia Peking Univ Sch Publ Hlth Dept Occupat & Environm Hlth Sci Beijing 100191 Peoples R China Beijing Key Lab Indoor Air Qual Evaluat & Control Beijing 100084 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Sch Hlth Policy & Management Beijing 100730 Peoples R China Peking Univ Inst Child & Adolescent Hlth Sch Publ Hlth Beijing 100191 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Peking Union Med Coll Hosp Med Res Ctr State Key Lab Complex Severe & Rare Dis Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Peking Union Med Coll Hosp Dept Cardiol State Key Lab Complex Severe & Rare Dis Beijing Peoples R China Peking Univ Key Lab Epidemiol Major Dis Minist Educ Beijing 100191 Peoples R China Peking Univ Sch Publ Hlth Dept Occupat & Environm Hlth Sci Beijing Peoples R China Peking Univ Key Lab Epidemiol Major Dis Minist Educ Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Sch Hlth Policy & Management Beijing Peoples R China

Stroke is a significant global cause of disability and death, and its burden has been on the rise, while ambient air pollution has been conclusively linked to stroke incidence. However, knowledge about effects of atmospheric oxidation on stroke and its interactions with fine particles (PM2.5) are still limited. In this study, we investigated the short-term effects of ambient NO2, O-3, and their combined oxidation (O-wt) on first-ever stroke, based on data from the China National Stroke Screening Survey (CNSSS) conducted from 2013 to 2015. We found significant association between ambient NO2 exposure at lag0 day with first-ever stroke, with a 13.1 % (95 % CI: 3.5 %, 23.6 %) increase in the first-ever stroke risk per 10 mu g/m(3 )exposure. We also found a significant interaction between NO2 and PM2.5 (p < 0.05): first-ever stroke risk increased 23.8 % (95 % CI: 9.6 %, 39.8 %) per 10 mu g/m(3) NO2 exposure in population exposed to higher PM(2.5 )concentrations, while no significant association was found in population exposed to lower PM(2.5 )concentrations. The results of stratified analyses indicated that physical inactivity enhanced the detrimental effects of O-3 and O-wt exposure, while smoking and transient ischemic attack (TIA) history enhanced the detrimental effects of NO2 exposure. However, TIA history appeared to mitigate the adverse effects of O-3 exposure. This study is helpful to better understand the impact of ambient oxidation on stroke, as well as its interaction with PM2.5, and has implications for policies and standards for atmospheric protection and governance.

关键词： Nitrogen dioxide Ozone Ambient oxidation Fine particle First -ever stroke

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Transfer Methods for Vehicle Carbon Emission Models Based on the Parallel Transportation System

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2024年第9期25卷 11664-11674页

作者： Sun, Yao Hu, Yunfeng Zhang, Hui Chen, Hong Wang, Fei-Yue Jilin Univ State Key Lab Automot Simulat & Control Changchun 130025 Peoples R China Jilin Univ Coll Commun Engn Changchun 130025 Peoples R China Beihang Univ Sch Transportat Sci & Engn Beijing 100091 Peoples R China Beihang Univ Ningbo Inst Technol NIT Ningbo 315323 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China Macau Univ Sci & Technol Macau 999078 Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100098 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Vehicle carbon emission models are essential for monitoring and managing transportation emissions. Considering the numerous vehicle types and diverse driving conditions, modeling each vehicle individually is not possible. A generalized carbon emission module (GCEM) is proposed to realize fast model transfer across both light-duty gasoline and heavy-duty diesel vehicles. It has five main components: normalized torque function, category-specified fuel consumption rate function, denormalized function, emission-standard correction, and fuel-specified conversion. GCEM estimates carbon emissions based on the engine bench results of a reference vehicle, avoiding time-consuming and costly experiments. By normalizing engine torque, the significant power disparities within the same vehicle category can be effectively addressed. Integrating GCEM within the parallel transportation system is a feasible solution for monitoring complex traffic emissions. Through comprehensive comparisons, GCEM demonstrates a superior generalized ability in this data-limited scenario than three baseline models, both for various test vehicles and diverse driving conditions. As real-world traffic is a typical data-limited scenario, GCEM is a promising and practical transfer method for estimating vehicle carbon emissions.

关键词： Generalized carbon emission module modular integrated emission model parallel transportation system transfer method spatio-temporal characterization

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GAP: Goal-Aware Prediction with Hierarchical Interactive Representation for Vehicle Trajectory 7th

GAP: Goal-Aware Prediction with Hierarchical Interactive Re...

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7th International Conference on Data Mining and Big Data, DMBD 2022

作者： Li, Ding Zhang, Qichao Lu, Shuai Pan, Yifeng Zhao, Dongbin School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9789811992964

Predicting the future trajectories of surrounding vehicles plays a vital role in ensuring the safety of autonomous driving. It is extremely challenging for the pure imitation method due to the high degree of multimodality and uncertainty in the future. In fact, when driving in most traffic scenarios, vehicles should obey some traffic rules such as "vehicles follow the lane and do not collide with each other". Inspired by this, this paper proposes a goal-aware prediction (GAP) framework to predict the multimodal trajectories, where goals are chosen in the lanes with hierarchical interactive representation and a multi-task loss. Based on the graph-based vectorized input, a novel hierarchical interactive representation module is first designed to obtain the fine-grained goal features, which progressively models interactions between goal-to-goal, goal-to-lane, and lane-to-agent, corresponding to the individual, local and global levels, respectively. Then, an auxiliary collision loss is developed to take into account learning from demonstration and injecting common sense of collision avoidance, and is served as a part of the multi-task loss to guide the generation of multimodal plausible trajectories. In the end, the proposed method is verified on the Baidu In-house Cut-in dataset, which includes more than 370K interactive scenarios collected in the real road testing. The comparative results demonstrate the superior performance of our proposed GAP model than the mainstream prediction methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Trajectories

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A Vehicle-to-Vehicle Cooperative control Method for Intersections with Cav- Dedicated Lane

SSRN

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SSRN 2024年

作者： Zhang, Xiqiao Cui, Leqi Yang, Longhai Lv, Yisheng School of Transportation Science and Engineering Harbin Institute of Technology Harbin150090 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

With the development of intelligent connected technology and autonomous driving technology, connected autonomous vehicles (CAV) and connected human-driven vehicles (CHV) will coexist for a long time in the future. The popularization of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication enables the improvement of CAV platooning efficiency and the joint control of CAV trajectory and signal timing. Therefore, this paper proposes a CAV-dedicated lane-based vehicle-to-vehicle interactive intersection control method, which is suitable for managing mixed traffic flow of CHV and CAV at a single intersection. In the proposed method, the road capacity is enhanced by separating the mixed traffic flow with a CAV-dedicated lane. Through vehicle-to-vehicle interaction and CHV induction, the traffic flow spatiotemporal distribution is regulated by a limited number of controlled vehicles. Furthermore, by jointly controlling CAV trajectory and signal timing, vehicles can efficiently pass through the intersection in the CHV phase without the dedicated CAV phase. The optimization of signal timing and vehicle trajectory at the intersection aims to improve intersection capacity and smooth vehicle trajectories. In addition, the car-following and lane-changing models of CHV are revised based on the prospect theory to validate the performance of platooning strategies under cooperative induction information in practical traffic operations. Simulation results demonstrate that compared to control methods without considering vehicle-to-vehicle interaction, the proposed method in this paper has significant advantages in reducing average vehicle delay and improving intersection efficiency. © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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A control Framework for Adaptation of Training Task and Robotic Assistance for Promoting Motor Learning With an Upper Limb Rehabilitation Robot

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2022年第12期52卷 7737-7747页

作者： Wang, Chen Peng, Liang Hou, Zeng-Guang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Robot-assisted rehabilitation has been a promising solution to improve motor learning of neurologically impaired patients. state-of-the-art control strategies are typically limited to the ignorance of heterogeneous motor capabilities of poststroke patients and therefore intervene suboptimally. In this article, we propose a control framework for robot-assisted motor learning, emphasizing the detection of human intention, generation of reference trajectories, and modification of robotic assistance. A real-time trajectory generation algorithm is presented to extract the high-level features in active arm movements using an adaptive frequency oscillator (AFO) and then integrate the movement rhythm with the minimum-jerk principle to generate an optimal reference trajectory, which synchronizes with the motion intention in the patient as well as the motion pattern in healthy humans. In addition, a subject-adaptive assistance modification algorithm is presented to model the patient's residual motor capabilities employing spatially dependent radial basis function (RBF) networks and then combining the RBF-based feedforward controller with the impedance feedback controller to provide only necessary assistance while simultaneously regulating the maximum-tolerated error during trajectory tracking tasks. We conduct simulation and experimental studies based on an upper limb rehabilitation robot to evaluate the overall performance of the motor-learning framework. A series of results showed that the difficulty level of reference trajectories was modulated to meet the requirements of subjects' intended motion, furthermore, the robotic assistance was compliantly optimized in response to the changing performance of subjects' motor abilities, highlighting the potential of adopting our framework into clinical application to promote patient-led motor learning.

关键词： Robots Trajectory Task analysis Training Oscillators Rehabilitation robotics Real-time systems Assist as needed (AAN) motor learning nonlinear adaptive control rehabilitation robotics

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Development and Motion control of Biomimetic Underwater Robots: A Survey

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2022年第2期52卷 833-844页

作者： Wang, Rui Wang, Shuo Wang, Yu Cheng, Long Tan, Min 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 CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai 200031 Peoples R China

Biomimetic underwater robots have attracted considerable research attention globally, owing to their quieter actuations, higher propulsion efficiency, and stronger maneuverability when compared with conventional underwater vehicles equipped with axial propellers. This article provides a comprehensive survey of current research in this field. First, we review the development status of biomimetic underwater robots in both body/caudal fin (BCF), median/paired fin (MPF), and their hybrid propulsion modes. Then, we outline the motion control methods employed in biomimetic underwater robots, including open-loop swimming control and typical closed-loop control strategies. In particular, we detail our latest studies on the RobCutt series underwater robots. On this basis, some critical issues and future directions are summarized. We predict that biomimetic underwater robots will have excellent prospects in underwater environment exploration and resource utilization.

关键词： Unmanned underwater vehicles Robots Propulsion Motion control Prototypes Turning Biomimetic underwater robots motion control system development undulatory propulsion

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MLRNN: Taxi Demand Prediction Based on Multi-Level Deep Learning and Regional Heterogeneity Analysis

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第7期23卷 8412-8422页

作者： Zhang, Chizhan Zhu, Fenghua Lv, Yisheng Ye, Peijun Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Sch Future Technol Beijing 100049 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau 999078 Peoples R China

Taxi demand prediction is valuable for the decision-making of online taxi-hailing platforms. Data-driven deep learning approaches have been widely utilized in this area, and many complex spatiotemporal characteristics of taxi demand have been studied. However, the heterogeneity of demand patterns among different taxi zones has not been taken into account. To this end, this paper explores zone clustering and how to utilize the inter-zone heterogeneity to improve the prediction. First, based on the pairwise clustering theory, a taxi zone clustering algorithm is designed by considering the correlations among different taxi zones. Then, both the cluster-level and the global-level prediction modules are developed to extract intra-and inter-cluster characteristics, respectively. Finally, a Multi-Level Recurrent Neural Networks (MLRNN) model is proposed by combining the two modules. Experiments on two taxi trip records datasets from New York City demonstrate that our model improves the prediction accuracy compared with other state-of-the-art methods.

关键词： Taxi demand prediction taxi zone clustering heterogeneity analysis deep learning

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A Novel 3D LiDAR SLAM Based on Directed Geometry Point and Sparse Frame

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第2期6卷 374-381页

作者： Liang, Shuang Cao, Zhiqiang Wang, Chengpeng 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 BIC ESATDept Mech & Engn Sci State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

Simultaneous localization and mapping is an indispensable yet challenging direction for mobile robots. Attracted by 3D LiDAR with accurate depth information and robustness to illumination variations, many 3D LiDAR SLAM methods based on scan-to-map matching have been developed. However, there is a critical issue of existing approaches, where a large and dense map is generally required to achieve satisfactory localization accuracy, leading to low efficiency of scan-to-map matching. To solve this problem, in this letter, we propose a novel 3D LiDAR SLAM based on directed geometry point (DGP) and sparse frame. The former is used to provide a sparse distribution of points in the spatial dimension and the latter gives rise to a sparse distribution of frames in the temporal sequence. The sparsity of points and frames impove the efficiency of 3D LiDAR SLAM, and the strict data association based on directed geometric points also brings in good accuracy of pose estimation. To compensate the accuracy loss of the localization and mapping caused by frame sparsity, point propagation is proposed to improve the quality of directed geometric points in the map and the accuracy of scan-to-map matching. Also, loop detection and pose graph optimization are conducted for global consistency. The experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and efficiency.

关键词： 3D LiDAR SLAM directed geometric point sparse frame point propagation

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Data-Driven Optimal control for Half-Vehicle Suspension System via Adaptive Dynamic Programming 11

Data-Driven Optimal Control for Half-Vehicle Suspension Syst...

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11th IEEE Data Driven control and Learning systems Conference, DDCLS 2022

作者： Li, Hongyang Wei, Qinglai School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, a data-driven optimal control method is provided for the half-vehicle suspension system via adaptive dynamic programming. The main contribution of this paper is that the data-driven adaptive dynamic programming method is applied to the optimal control problem of half-vehicle suspension system, which only requires the input-state data of the system. First, the structure of the half-vehicle suspension system is analyzed, and the optimal control problem is introduced. Next, the model-based adaptive dynamic programming method is provided. Based on the model-based method, a data-driven adaptive dynamic programming method is given. The properties of the provided methods are analyzed. Finally, simulation example is given to show the effectiveness of the data-driven adaptive dynamic programming method. © 2022 IEEE.

关键词： Dynamic programming

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Underwater Target Tracking control of an Untethered Robotic Fish With a Camera Stabilizer

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2021年第10期51卷 6523-6534页

作者： Yu, Junzhi Wu, Zhengxing Yang, Xiang Yang, Yueqi Zhang, Pengfei Peking Univ State Key Lab Turbulence & Complex Syst Dept Mech & Engn Sci BIC ESATColl Engn Beijing 100871 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

Implementing underwater target tracking remains difficult for a free-swimming robotic fish owing to the intrinsically reciprocating motion in fishlike propulsion. In this article, we present a novel robotic fish platform with a camera stabilizing system and achieve real-time two-dimensional target tracking assisted by reinforcement learning (RL) in continuous environments. More specifically, we first develop an active visual tracking system based on cascade control structure to obtain the relative orientation between the robotic fish and the underwater target. Then, we propose a target tracking controller dealing with continuous state and action spaces based on deep RL (DRL). The controller takes the position of the target object as input and yields the motion parameters of the bioinspired central pattern generator governed robotic fish. The robustness and adaptability of the proposed controller as well as the influence of time-delays on the control system are explored via simulated experiments under different scenarios. Finally, both static and dynamic tracking experiments on the actual robotic fish demonstrate the effectiveness of the proposed mechatronic design and control methods, providing insights to executing aquatic vision-based tracking tasks.

关键词： Cameras Robot vision systems Target tracking Oscillators Sports Active visual tracking camera stabilizer reinforcement learning (RL) robotic fish underwater target tracking

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