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2022 Chinese Automation Congress, CAC 2022

作者： Guo, Renjie Feng, Jun Yang, Huachang Luan, Dejie Yang, Guodong Li, En Institute of Automation CAS State Key Laboratory of Management and Control for Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China China Academy of Railway Sciences Corporation Limited Signal & Communication Research Institute Beijing100081 China

ISBN: (纸本)9781665465335

In the construction of automated marshaling yards, the automatic uncoupling operation of freight trains has always been the research and development direction. The coupler rod is the operation object of the uncoupling operation. The identification of coupler rod type and estimating position and posture are the prerequisites to realizing automatic uncoupling operation. In this paper, we propose a perception method of coupler rod by RGB image combined with the point cloud. We use an RGBD camera to synchronously collect frame-aligned RGB and depth images and generate point cloud data with colors according to the mapping relationship. Then, we use the YOLOv5 neural network to detect the collected RGB images and recognize the type of coupler rod in the image. Clear clutter in the point cloud and the bounding box is mapped to a spatial coordinate system for straight-through filtering of the point cloud. We use RANSAC for coarse registration and ICP for fine registration. The result of registration is carried out with the lower sampling point cloud model to achieve the registration of the coupler rod. Experiments show that this method can effectively segment the point cloud of the region of interest with high robustness. © 2022 IEEE.

关键词： Data fusion

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A New Perspective for Computational Social systems: Fuzzy Modeling and Reasoning for Social Computing in CPSS

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第1期11卷 101-116页

作者： Wang, Tan Zhu, Yifan Ye, Peijun Gong, Weichao Lu, Hao Mo, Hong Wang, Fei-Yue Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau Peoples R China Chinese Acad Sci Inst Automat Chinese Assoc Automat Beijing 100190 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 10086 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China Changsha Univ Sci & Technol Coll Elect & Informat Engn Changsha 410114 Peoples R China

The evolution of modern mobile terminals, social networks, and other intelligent services makes everyone become a ubiquitous information perceiver, producer, and propagator. Also known as "social sensor" and "social IoT", these individuals and communities generate a huge volume of social signals, which has shown prominent value for mining. These unstructured social signals provide a new perspective in the research of complex systems, which makes the traditional cyber-physical system (CPS)-oriented information computing sublimate to the cyber-physical-social system (CPSS)-oriented knowledge computing. However, there still exist great uncertainties, ambiguities, and complexities in modeling behaviors of social individuals or groups. Especially when we apply big-data-driven learning-based models in specific fields and scenarios, the lack of domain expert knowledge and characteristics of system uncertainty severely limits the performance and accuracy of these models. The introduction of fuzzy system modeling integrates data and knowledge in the social computing area, which has shown its unique advantages in solving the above issues and has drawn more attention to this topic. In this article, we conduct a review of recent advances in social computing with fuzzy technologies in CPSS. First, we briefly review the development of social computing, and analyze the characteristics and advantages of social computing through fuzzy methods. Second, we refine core fuzzy system methods for social computing and elaborate on existing fuzzy-technology-empowered social computing methodologies. As in a range of social spaces, we also review and analyze related advances in human-in-the-loop systems. We also reveal the trend of decentralized, autonomous, and organized computing in cyber-physical-social space with fuzzy-based methods and proposed a framework to categorize related studies in CPSS. Finally, we conclude the research trends and hotspots based on current studies, and discuss th

关键词： Social computing Computational modeling Fuzzy systems Uncertainty complex systems Analytical models Human in the loop Computational systems cyber-physical-social systems (CPSSs) fuzzy paradigm parallel society social computing

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A hidden demethylation pathway removes mercury from rice plants and mitigates mercury flux to food chains

NATURE FOOD

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NATURE FOOD 2024年第1期5卷 72-82页

作者： Tang, Wenli Bai, Xu Zhou, Yang Sonne, Christian Wu, Mengjie Lam, Su Shiung Hintelmann, Holger Mitchell, Carl P. J. Johs, Alexander Gu, Baohua Nunes, Luis Liu, Cun Feng, Naixian Yang, Sihai Rinklebe, Joerg Lin, Yan Chen, Long Zhang, Yanxu Yang, Yanan Wang, Jiaqi Li, Shouying Wu, Qingru Ok, Yong Sik Xu, Diandou Li, Hong Zhang, Xu-Xiang Ren, Hongqiang Jiang, Guibin Chai, Zhifang Gao, Yuxi Zhao, Jiating Zhong, Huan Nanjing Univ Sch Environm State Key Lab Pollut Control & Resource Reuse Nanjing Peoples R China Chinese Acad Sci IHEP Key Lab Biomed Effects Nanomat & Nanosafety Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Aarhus Univ Arctic Res Ctr Dept Ecosci Roskilde Denmark Univ Petr & Energy Studies Sch Engn Sustainabil Cluster Dehra Dun Uttar Pradesh India Univ Malaysia Terengganu Higher Inst Ctr Excellence HICoE Inst Trop Aquaculture & Fisheries AKUATROP Kuala Nerus Malaysia Saveetha Univ Saveetha Inst Med & Tech Sci SIMATS CGHR Chennai Tamil Nadu India Trent Univ Dept Chem Peterborough ON Canada Trent Univ Sch Environm Peterborough ON Canada Univ Toronto Scarborough Dept Phys & Environm Sci Scarborough ON Canada Oak Ridge Natl Lab Environm Sci Div Oak Ridge TN USA Univ Algarve Fac Sci & Technol Civil Engn Res & Innovat Sustainabil Ctr Faro Portugal Chinese Acad Sci Inst Soil Sci Key Lab Soil Environm & Pollut Remediat Nanjing Peoples R China Jinan Univ Coll Life Sci & Technol Guangzhou Peoples R China Nanjing Univ Sch Life Sci State Key Lab Pharmaceut Biotechnol Nanjing Peoples R China Univ Wuppertal Inst Fdn Engn Water & Waste Management Sch Architecture & Civil Engn Lab Soil & Groundwater Management Wuppertal Germany Nankai Univ Coll Environm Sci & Engn Tianjin Key Lab Environm Technol Complex Trans Me Tianjin Peoples R China East China Normal Univ Sch Geog Sci Key Lab Geog Informat Sci Minist Educ Shanghai Peoples R China Nanjing Univ Sch Atmospher Sci Nanjing Peoples R China Tsinghua Univ Sch Environm State Key Joint Lab Environm Simulat & Pollut Con Beijing Peoples R China State Environm Protect Key Lab Sources & Control Beijing Peoples R China Korea Univ Korea Biochar Res Ctr APRU Sustainable Waste Management Program Seoul South Korea Korea Univ Div Environm Sci & Ecol Engn Seoul South Korea Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Environm Chem & Ecotoxicol Beijing Peoples R China Sooc

Dietary exposure to methylmercury (MeHg) causes irreversible damage to human cognition and is mitigated by photolysis and microbial demethylation of MeHg. Rice (Oryza sativa L.) has been identified as a major dietary source of MeHg. However, it remains unknown what drives the process within plants for MeHg to make its way from soils to rice and the subsequent human dietary exposure to Hg. Here we report a hidden pathway of MeHg demethylation independent of light and microorganisms in rice plants. This natural pathway is driven by reactive oxygen species generated in vivo, rapidly transforming MeHg to inorganic Hg and then eliminating Hg from plants as gaseous Hg degrees. MeHg concentrations in rice grains would increase by 2.4- to 4.7-fold without this pathway, which equates to intelligence quotient losses of 0.01-0.51 points per newborn in major rice-consuming countries, corresponding to annual economic losses of US$30.7-84.2 billion globally. This discovered pathway effectively removes Hg from human food webs, playing an important role in exposure mitigation and global Hg cycling. A natural pathway in rice and in seven other major crop species degrades 57-89% of accumulated methylmercury and releases Hg into the air. The MeHg demethylation within rice mitigates Hg flux into human food webs, diminishing human cognitive damage and generating global economic benefits of US$30.7-84.2 billion.

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S⁴TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 3220-3231页

作者： Wang, Xiao Tang, Ke Dai, Xingyuan Xu, Jintao Du, Quancheng Ai, Rui Wang, Yuxiao Gu, Weihao Anhui Univ Engn Res Ctr Autonomous Unmanned Syst Technol Minist Educ Hefei 230031 Peoples R China Haomo Technol Co Ltd Beijing 100192 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266114 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning ((STP)-T-4) framework. Specifically, (STP)-T-4 integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV's planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. (STP)-T-4 employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, (STP)-T-4 executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed (STP)-T-4 in terms of safety and rationality. (STP)-T-4 achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%.

关键词： Autonomous driving social interactive traffic scenarios trajectory planning intention prediction Social-Aware Driving Risk Field

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Dynamic Testing for Autonomous Vehicles Using Random Quasi Monte Carlo

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第3期9卷 4480-4492页

作者： Ge, Jingwei Zhang, Jiawei Chang, Cheng Zhang, Yi Yao, Danya Tian, Yonglin Li, Li Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRist Dept Automat Beijing 100084 Peoples R China Tsinghua Berkeley Shenzhen Inst TBSI Shenzhen 518055 Peoples R China Jiangsu Prov Collaborat Innovat Ctr Modern Urban T Nanjing 210096 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Tsinghua Univ Dept Automat BNRIST Beijing 100084 Peoples R China

The substantial resource usage required to create ample scenarios for testing Autonomous Vehicles (AV) presents a bottleneck in their implementation. At present, research relies on sampling the driving behaviour of Surrounding Vehicles (SV) based on naturalistic datasets in simulation. However, these methods still generate huge amounts of scenarios, making it impossible to synthetically evaluate AV intelligence in a very small number of tests (especially in real-world situations). Simultaneously, the unknown distribution of critical scenarios leads to the problem that more critical scenarios cannot be accurately sampled. In this article, a novel optimization problem is described and a dynamic scenario sampling method is proposed to cover more critical scenarios with finite samples. First, the sampling space is constructed by extracting the behavioural model parameters of the SVs. Second, multiple rounds of sampling are carried out successively to learn the distribution of critical scenarios, which in turn gradually improves the coverage of the critical scenarios. To do this, in each round, we divide the sampling space into several subspaces using two-step sampling, sample the scenarios using Random Quasi Monte Carlo (RQMC), evaluate the criticality of the subspace, and then use the evaluation results to guide the selection of the sampling space for the next round. The purpose of RQMC is to uniformly sample in the critical subspace rather than Standard Monte Carlo (SMC). Experimental results show that our method can better narrow the gap with the distribution of critical scenarios and discover more critical scenarios when compared to the baseline method.

关键词： Autonomous vehicles Automation intelligence testing multi-rounds testing random quasi Monte Carlo

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RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts

arXiv

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arXiv 2024年

作者： Yang, Jing Wang, Xiao Zhao, Yu Liu, Yuhang Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Artificial Intelligence Anhui University Hefei266114 China Qingdao Academy of Intelligent Industries Qingdao230031 China National Key Laboratory of Information Systems Engineering National University of Defense Technology China Macau University of Science and Technology 999078 China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing100098 China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China

Crowdsourcing is a critical technology in social manufacturing, which leverages an extensive and boundless reservoir of human resources to handle a wide array of complex tasks. The successful execution of these complex tasks relies on task decomposition (TD) and allocation, with the former being a prerequisite for the latter. Recently, pre-trained language models (PLMs)based methods have garnered significant attention. However, they are constrained to handling straightforward common-sense tasks due to their inherent restrictions involving limited and difficult-to-update knowledge as well as the presence of "hallucinations". To address these issues, we propose a retrieval-augmented generation-based crowdsourcing framework that reimagines TD as event detection from the perspective of natural language understanding. However, the existing detection methods fail to distinguish differences between event types and always depend on heuristic rules and external semantic analyzing tools. Therefore, we present a Prompt-Based Contrastive learning framework for TD (PBCT), which incorporates a prompt-based trigger detector to overcome dependence. Additionally, trigger-attentive sentinel and masked contrastive learning are introduced to provide varying attention to trigger and contextual features according to different event types. Experiment results demonstrate the competitiveness of our method in both supervised and zero-shot detection. A case study on printed circuit board manufacturing is showcased to validate its adaptability to unknown professional domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Correlation analysis and text classification of chemical accident cases based on word embedding

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2022年 158卷 698-710页

作者： Jing, Sifeng Liu, Xiwei Gong, Xiaoyan Tang, Ying Xiong, Gang Liu, Sheng Xiang, Shuguang Bi, Rongshan Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Univ Sci & Technol Coll Chem Engn Qingdao 266042 Peoples R China Qingdao Acad Intelligent Ind Inst Smart Educ Syst Qingdao 266044 Peoples R China Rowan Univ Dept Elect & Comp Engn Glassboro NJ 08028 USA

Accident precursors can provide valuable clues for risk assessment and risk warning. Trends such as the main characteristics, common causes, and high-frequency types of chemical accidents can provide references for formulating safety-management strategies. However, such information is usually documented in unstructured or semistructured free text related to chemical accident cases, and it can be costly to manually extract the information. Recently, text-mining methods based on deep learning have been shown to be very effective. This study, therefore, developed a text-mining method for chemical accident cases based on word embedding and deep learning. First, the word2vec model was used to obtain word vectors from a text corpus of chemical accident cases. Then, a bidirectional long short-term memory (LSTM) model with an attention mechanism was constructed to classify the types and causes of Chinese chemical accident cases. The case studies revealed the following results: 1) Common trends in chemical accidents (e.g., characteristics, causes, high-frequency types) could be obtained through correlation analysis based on word embedding;2) The developed text-classification model could classify different types of accidents as fires, explosions, poisoning, and others, and the average p (73.1%) and r (72.5%) of the model achieved ideal performance for Chinese text classification;3) The developed text-classification model could classify the causes of accidents as personal unsafe act, personal habitual behavior, unsafe conditions of equipment or materials and vulnerabilities management strategy;p and r were 63.6% for the causes of vulnerabilities management strategy, and the average p and r are both 60.7%;4) the accident precursors of explosion, fire, and poisoning were obtained through correlation analyses of each high-frequency type of chemical accident case based on text classification;5) the text-mining method can provide site managers with an efficient tool for extracting

关键词： Text mining Correlation analysis Text classification Word embedding Deep learning Chemical accident cases

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

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

ISBN: (数字)9798350379167

ISBN: (纸本)9798350379174

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.

关键词： Human computer interaction Training Three-dimensional displays Foundation models Transportation Transforms Real-time systems Resource management Logistics Testing

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Singular perturbation-based saturated adaptive control for underactuated Euler-Lagrange systems

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ISA TRANSACTIONS 2022年 119卷 74-80页

作者： Sun, Tairen Zhang, Xuexin Yang, Hongjun Pan, Yongping Jiangsu Univ Sch Elect Informat & Engn Zhenjiang 212013 Jiangsu Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

This paper proposes a saturated smooth adaptive controller for regulating a certain type of underactuated Euler-Lagrange systems (UELSs) with modeling uncertainties and control saturations based on a singular perturbation approach. Compared with relevent literature, the advantages of the proposed controller include: (1) it renders the UELS semiglobally asymptotically track the desired position without the violation of control input constraints;(2) high-order derivatives of positions are not required in its implementation. The Hoppensteadt's Theorem is employed to show that the proposed saturated controller renders the UELS semiglobally asymptotically stable about the desired set point with the satisfaction of control input constraints. The control effectiveness is validated by simulations on a two-link compliant robot arm. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Underactuated system Euler-Lagrange system Compliant robot Nonlinear control Adaptive control control saturation Output regulation

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Evaluation and Spatial-Temporal Difference Analysis of Urban Water Resource Utilization Efficiency Based on Two-Stage DEA Model

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2022年第5期9卷 1282-1296页

作者： Xie, Qiwei Ma, Hewen Zheng, Xiaolong Wang, Xiao Wang, Fei-Yue Beijing Univ Technol Res Base Beijing Modern Mfg Dev Beijing 100124 Peoples R China Hubei Univ Fac Math & Stat Wuhan 430062 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 100190 Peoples R China

In the present study, a two-stage data envelopment analysis (DEA) model and spatial econometric method were employed to evaluate and analyze the utilization efficiency of urban water resources and spatial-temporal differences in cities of China. The traditional DEA model was enhanced by adopting the Shannon entropy in the first stage. After selecting variables based on the previous step and the Bayes information criterion (BIC), redundant variables were removed. In the meanwhile, a comprehensive efficiency score (CES) was generated to rank the efficiency. Finally, spatial econometric analysis was applied to explore the spatial-temporal differences of urban water resource utilization efficiency. Results demonstrate that: 1) according to the calculations and analysis, communities should concentrate on increasing investment in equipment and technology that can help enhance water consumption efficiency, while overlooking some minor aspects, such as per capita gross domestic product (PCGDP);2) most cities have poor water resource utilization efficiency (low CES). However, both input and output have much room for the improvement;3) Lhasa, Beijing, Haikou, and Shanghai have high CES, indicating that the utilization efficiency of water resources is not entirely dependent on economic development;and 4) through performing the Lagrange multiplier (LM) test, the spatial error model (SEM) test is passed at the significant level of 5%. Moreover, the water resource utilization efficiency of a city may be enhanced with the economic development in neighboring cities.

关键词： Water resources Biological system modeling Urban areas Economics Econometrics Analytical models Water pollution Bayes information criterion (BIC) cities data envelopment analysis (DEA) Shannon entropy spatial econometric utilization efficiency of urban water resources

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