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2022 Australian and New Zealand control Conference

作者： Chen, Yuanyuan Lv, Yisheng Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China

ISBN: (数字)9781665498876

ISBN: (纸本)9781665498876

With the fast development of new technologies, such as Internet of Things, big data and Internet plus, Intelligent Transportation systems (ITS) have made remarkable achievements and the intelligence in ITS has also been continuously increased, which a new field, i.e., "Social Transportation", is emerging. In social transportation systems, physical and cyber elements are tightly conjoined, coordinated, and integrated with human and social characteristics. In this paper, we collect and analyze traffic data from physical world and social media data from cyberspace to sense the human mobility patterns during holidays under the COVID-19 pandemic.

关键词： COVID-19 Social networking (online) Pandemics Transportation Cyberspace Big Data Internet of Things

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Efficient Calibration of Agent-Based Traffic Simulation Using Variational Auto-Encoder 25

Efficient Calibration of Agent-Based Traffic Simulation Usin...

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IEEE 25th International Conference on Intelligent Transportation systems (ITSC)

作者： Ye, Peijun Zhu, Fenghua Lv, Yisheng Wang, Xiao Chen, Yuanyuan Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

In agent-based traffic simulation, calibration is an essential stage before the models applied to reproduce the individual/group travel behaviors. While traditional methods suffer from a high computational complexity, this paper proposes an improved method to alleviate the computational burden for large-scaled simulations. Specifically, we introduce variational auto-encoder to compress the original agent state vector into a lower dimensional hidden space, where the state transfer probability is calculated fast. Then the probability is mapped into the original space through a decoder, to achieve the agent travel parameters. The dynamic calibration method is tested with other baselines in urban travel demand analysis. Experiment results demonstrate that our method brings about 19% elevation of efficiency with the same accuracy of calibration.

关键词： Technological innovation Computational modeling Traffic control Probability Calibration Space exploration Decoding

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Tumor lesion detection in patients with cervical cancer by indocyanine green near-infrared imaging

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EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 2023年第4期50卷 1252-1261页

作者： Li, Pengfei Liu, Jiaqi He, Kunshan Gong, Shipeng Chi, Chongwei Liu, Pan Su, Guidong Li, Weili Duan, Hui Liu, Ping Tian, Jie Chen, Chunlin Southern Med Univ Nanfang Hosp Dept Obstet & Gynecol 1838 Guangzhou Ave Guangzhou 510515 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Med & Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Big Data Based Precis Med Minist Ind & Informat Technol Beijing 100191 Peoples R China Chinese Acad Sci Inst Automation CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Purpose To investigate the feasibility and accuracy of near-infrared fluorescence (NIRF) imaging for detecting the extent of tumor invasion in cervical cancer using indocyanine green (ICG). Methods We enrolled 51 patients who were diagnosed with cervical cancer with FIGO stage IB1-IIA2 disease. Patients were administered indocyanine green (ICG) at a dose of 5 mg/kg 24 h prior to surgery. A customized near-infrared fluorescence (NIRF) imaging system was used to identify the extent of tumor invasion when radical hysterectomy specimens were harvested. The relationship between tumor fluorescence intensity and clinicopathological characteristics was analyzed. Results Of the 51 enrolled patients, 3 patients did not have residual tumors after cervical conization, and tumor lesions were identified by NIRF imaging in all the remaining 48 patients. The results of NIRF imaging were in agreement with the postoperative pathological findings in 95.8% of the patients with stromal invasion, 100% of those with surgical margin invasion, 100% of those with parametrial tumor involvement, and 100% of patients with uterine corpus invasion. The mean signal-to-background ratio (SBR) of the cervical tumors was 2.91 +/- 1.64, and the SBR was independent of clinicopathological characteristics. Fluorescence microscopy confirmed that ICG fluorescence was present in the tumor nests. Conclusions NIRF imaging enables objective, accurate, and safe identification of tumor invasion during cervical cancer surgery.

关键词： Cervical cancer Near-infrared fluorescence Indocyanine green Tumor invasion

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Robot Precision Assembly Combining With Passive and Active Compliant Motions

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第8期69卷 8157-8167页

作者： Su, Jianhua Liu, Chuankai Li, Rui 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 Beijing Aerosp Control Ctr Beijing 100094 Peoples R China Chongqing Univ Chongqing 400044 Peoples R China

Precision assembly is one of the primary goals of robots in manufacturing. Assembly strategies combining active and passive compliant control are presented herein. In this article, we construct a high-dimensional configuration space of robot assembly and then divide the configuration space to its subspaces. We further map the active compliant motion and the passive compliant motion of the manipulator into different subspaces. In one subspace, we construct the constraint function and design the passive compliant motion of the manipulator in the constraint region, where the uncertainties of the system should be eliminated by the environment constraints. In another subspace, we design a force controller based on the low-resolution force sensory information to control the position of the robot. The proposed method avoids the design of precision mechanism systems and the usage of high-quality sensors. Several experiments pertaining to peg-in-hole insertions are conducted to demonstrate the efficiency of the proposed method.

关键词： Robots Robot sensing systems Force Robot kinematics Force feedback Uncertainty Trajectory Assembly compliant motion constraint region force controller

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An Online Traffic Simulation Modeling Framework for Real-Time Road Traffic control and management 25

An Online Traffic Simulation Modeling Framework for Real-Tim...

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IEEE 25th International Conference on Intelligent Transportation systems (ITSC)

作者： Qin, Yuanqi Jin, Junchen Hua, Wen Dai, Xingyuan Wang, Xiao Zhejiang Lab Hangzhou 310030 Peoples R China Zhejiang Supcon Informat Technol Co Ltd Hangzhou 310030 Peoples R China ECARX Technol Co Ltd Shanghai Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Online traffic simulation feeds from online information to simulate vehicle movement in real-time, which has recently seen substantial advancement in road traffic control and management. It has been a challenging problem due to three aspects: 1) the diversity of traffic patterns caused by heterogeneous layouts of urban intersections;2) the complexity of spatiotemporal correlations;3) the requirement of adjusting traffic model parameters in a real-time system. To cater to these challenges, this paper proposes an online traffic simulation modeling framework via a meta-learner. In particular, simulation models with various intersection layouts are automatically generated using an open-source simulation tool, SUMO, according to static traffic geometry attributes. Through a meta-learning technique, the proposed modeling framework enables an automated learning process for estimating model settings capable of adapting traffic model parameters according to dynamic traffic information in real-time. Such a process is featured with various traffic scenarios and different spatiotemporal correlations. Through computational experiments, we demonstrate that the meta-learning-based framework is able to self-adapt its effectiveness according to real-time traffic data.

关键词： Adaptation models Correlation Computational modeling Layout Real-time systems Data models Spatiotemporal phenomena

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Probabilistic Data-Driven Modeling of a Melt Pool in Laser Powder Bed Fusion Additive Manufacturing

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 4908-4925页

作者： Fang, Qihang Xiong, Gang Zhao, Meihua Tamir, Tariku Sinshaw Shen, Zhen Yan, Chao-Bo Wang, Fei-Yue 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 Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr Printing & Intelligent Mfg Cloud Comp Ctr Dongguan 523808 Peoples R China Guangdong Univ Technol State Key Lab Precis Elect Mfg Technol & Equipment Guangzhou Peoples R China Debre Markos Univ Inst Technol Debre Markos 269 Ethiopia Xi An Jiao Tong Univ Fac Elect & Informat Engn State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Automat Sci & Engn Xian 710049 Shaanxi Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The widespread adoption of laser powder bed fusion (LPBF) additive manufacturing is hampered by process unreliability problems. Modeling the melt pool behavior in LPBF is crucial to develop process control methods. While data-driven models linking melt pool dynamics to specific process parameters have shown appreciable advancements, existing models often oversimplify these relationships as deterministic, failing to account for the inherent instability of LPBF processes. Such simplifications can lead to overconfident and unreliable predictions, potentially resulting in erroneous process decisions. To address this critical issue, we propose a probabilistic data-driven approach to melt pool modeling that incorporates process noise and uncertainty. Our framework formulates a problem that includes distribution approximation and uncertainty quantification. Specifically, the Gaussian distribution with higher order priors, aided with variational inference and importance sampling, is used to approximate the probability distribution of melt pool characteristics. The uncertainty inherent in both LPBF process data and the modeling approach itself are then decomposed and approximated by using Monte Carlo sampling. The melt pool model is improved further by using a novel grid-based representation for the neighborhood of a fusion point, and a neural network architecture designed for effective feature fusion. This approach not only refines the accuracy of the model but also quantifies the uncertainty of the predictions, thereby enabling more informed decision-making with reduced risk. Two potential applications, including LPBF process planning and anomaly detection, are discussed. The implementation of our model is available at https://***/qihangGH/probabilistic_melt_pool_model. Note to Practitioners-Modeling the melt pool behavior in laser powder bed fusion (LPBF) processes is pivotal for enhancing its quality control. However, a problem is that most existing data-driven me

关键词： Laser powder bed fusion (LPBF) additive manufacturing melt pool uncertainty quantification quality control process planning anomaly detection Laser powder bed fusion (LPBF) additive manufacturing melt pool uncertainty quantification quality control process planning anomaly detection

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TrajSGAN: A Semantic-Guiding Adversarial Network for Urban Trajectory Generation

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2024年第2期11卷 1733-1743页

作者： Xiong, Gang Li, Zhishuai Zhao, Meihua Zhang, Yu Miao, Qinghai Lv, Yisheng Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent M Cloud Comp Ctr Dongguan 523808 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Beijing Municipal Inst City Planning & Design Beijing 100045 Peoples R China Shandong Jiaotong Univ Shandong Key Lab Smart Transportat Preparat Jinan 250000 Peoples R China

Simulating human mobility contributes to city behavior discovery and decision-making. Although the sequence-based and image-based approaches have made impressive achievements, they still suffer from respective deficiencies such as omitting the depiction of spatial properties or ordinal dependency in trajectory. In this article, we take advantage of the above two paradigms and propose a semantic-guiding adversarial network (TrajSGAN) for generating human trajectories. Specifically, we first devise an attention-based generator to yield trajectory locations in a sequence-to-sequence manner. The encoded historical visits are queried with semantic knowledge (e.g., travel modes and trip purposes) and their important features are enhanced by the multihead attention mechanism. Then, we designate a rollout module to complete the unfinished trajectory sequence and transform it into an image that can depict its spatial structure. Finally, a convolutional neural network (CNN)-based discriminator signifies how "real" the trajectory image looks, and its output is regarded as a reward signal to update the generator by the policy gradient. Experimental results show that the proposed TrajSGAN model significantly outperforms the benchmarks under the MTL-Trajet mobility dataset, with the divergence of spatial-related metrics such as radius of gyration and travel distance reduced by 10%-27%. Furthermore, we apply the real and synthetic trajectories, respectively, to simulate the COVID-19 epidemic spreading under three preventive actions. The coefficient of determination metric between real and synthetic results achieves 91%-98%, indicating that the synthesized data from TrajSGAN can be leveraged to study the epidemic diffusion with an acceptable difference. All of these results verify the superiority and utility of our proposed method.

关键词： Trajectory Semantics Hidden Markov models Generators Measurement Computational modeling Task analysis Generative adversarial networks (GANs) human mobility simulation urban trajectory

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Unsupervised Domain Adaptation Based Automatic COVID-19 CT Segmentation 7

Unsupervised Domain Adaptation Based Automatic COVID-19 CT S...

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7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Zhang, Zhen Guo, Dalei University of Chinese Academy of Sciences School of Artificial Intelligence China Chinese Academy of Sciences State Key Lab. of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

The outbreak of COVID-19 has brought huge challenges to health systems around the world. Computed Tomography (CT), as an effective method to screen COVID-19, can provide explainable visual information which can be used for the diagnosis and quantification of COVID-19. It will make diagnosis easier and more effective by providing segmentation results of infections from given CT scans. Deep learning based methods can perform well in tasks that have abundant annotated data, while they may suffer from great performance drops when there is little annotated data available. To collect and annotate CT scans of COVID-19 cases is time-consuming and labor-intensive, which will demand highly-proficient radiology expertise. To tackle this bottleneck, this paper provides a novel unsupervised domain adaptation method that bridges the gap of distributions between the available annotated CT scans and private CT scans, thus reducing dependence on private annotated CT scans. Quantitative analysis demonstrates the effectiveness of our proposed method, which achieves 20 increments in Dice score compared to models without adaptation. © 2022 IEEE.

关键词： COVID-19

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Foundation Intelligence for Smart Infrastructure Services in Transportation 5.0

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 39-47页

作者： Han, Xu Meng, Zonglin Xia, Xin Liao, Xishun He, Brian Yueshuai Zheng, Zhaoliang Wang, Yutong Xiang, Hao Zhou, Zewei Gao, Letian Fan, Lili Li, Yuke Ma, Jiaqi Univ Calif Los Angeles UCLA Mobil Lab Los Angeles CA 90095 USA Univ Calif Los Angeles FHWA Ctr Excellence New Mobil & Automated Vehicle Los Angeles CA 90095 USA Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100045 Peoples R China Waytous Co Ltd Beijing 100083 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

This perspective paper delves into the concept of foundation intelligence that shapes the future of smart infrastructure services as the transportation sector transitions into the era of Transportation 5.0. First, the discussion focuses on a suite of emerging technologies essential for foundation intelligence. These technologies encompass digital twinning, parallel intelligence, large vision-language models, traffic simulation and transportation systems modeling, vehicle-to-everything (V2X) connectivity, and decentralized/distributed systems. Next, the paper introduces the present landscape of Transportation 5.0 applications as illuminated by the foundational intelligence, and casts a vision towards the future including cooperative driving automation, smart intersection/infrastructure, parallel traffic management, virtual drivers, and mobility systems planning and operations, laying out prospects that are poised to redefine the mobility ecosystem. Last, through a comprehensive outlook, this paper aspires to offer a guiding framework for the intelligent evolution in data generation and model calibration, digital twinning and simulation, scenario development and experimentation, feedback loop for management and control, and continuous learning and adaptation, fostering safety, efficiency, reliability, and sustainability in the future smart transportation infrastructure.

关键词： Foundation intelligence foundation models smart infrastructure transportation 5.0

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Robust Optimal Parallel Tracking control Based on Adaptive Dynamic Programming

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第7期54卷 4308-4321页

作者： Wei, Qinglai Jiao, Shanshan Wang, Fei-Yue Dong, Qi Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 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 Qingdao Acad Intelligent Ind Parallel Intelligence Innovat Ctr Qingdao 266109 Peoples R China China Acad Elect & Informat Technol Grp Elect & Informat Beijing 100049 Peoples R China

This article focuses on a novel robust optimal parallel tracking control method for continuous-time (CT) nonlinear systems subject to uncertainties. First, the designed virtual controller facilitates the transformation of the original nonlinear system into an affine system with an augmented state vector, which promotes the introduction of the optimal parallel tracking control problem. Then, this article generates fresh insight into counteracting the effects of uncertainty by developing a novel parallel control system that invokes the formulated virtual control law and an auxiliary variable obtained from the relationship between the solutions of the optimal control problems for the uncertain system and the nominal one. Next, critic neural networks (NNs) approximate the Hamilton-Jacobi-Bellman (HJB) equations' solution to implement the proposed robust optimal control method via adaptive dynamic programming (ADP). Finally, simulation experiments demonstrate the proposed method's remarkable effectiveness.

关键词： Optimal control control systems Uncertainty Robust control Performance analysis Dynamic programming Uncertain systems ADP NN nonlinear robust optimal control parallel control tracking control

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