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

作者： Wang, Xin Sun, Jian Wang, Gang State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

The present paper considers the data-driven control of unknown linear time-invariant discrete-time systems under an event-triggering transmission scheme. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional (DLF) approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which offers a way of co-designing the ETS matrix and the controller using pre-collected noisy input-state data. Finally, numerical simulations showcase the efficacy of ETS in reducing data transmissions as well as of the proposed co-design methods. © 2022 IEEE.

关键词： Stability criteria

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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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EGCN: Ensemble Graph Convolutional Network for Neural Architecture Performance Prediction

EGCN: Ensemble Graph Convolutional Network for Neural Archit...

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2021 International Conference on Information, Cybernetics, and Computational Social systems, ICCSS 2021

作者： Liu, Xin DIng, Zixiang Li, Nannan Chen, Yaran Zhao, Dongbin Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China

ISBN: (纸本)9781665402453

Neural Architecture Search (NAS) is proposed to automatically search novel neural networks. Currently, one typical problem of NAS is that its computation requirements are too high to stand for most researchers. In fact, it consumes a lot of resources to train subnetworks for architecture search. If the performance of each subnetwork can be predicted accurately without training, the computational burden will be alleviated. Graph Convolutional Network (GCN) is proven to have powerful capabilities for topological information perception and extraction. It is suitable to use GCN for predicting neural architecture performance which is related to its *** this paper, we treat GCN as the performance predictor with two improvements. First, a novel neural architecture data processing method named DATAPRO2 is designed to improve GCN's performance. Then, we propose EGCN, a model-based performance predictor which employs ensemble technique on GCN with DATAPRO2 to alleviate the overfitting issue caused by the imbalanced dataset for neural architecture performance prediction. Experimental results on CVPR-2021-NAS-TRACK2 dataset show that EGCN contributes to obtaining better predictive performance than vanilla GCN and other popular predictors. © 2021 IEEE.

关键词： Topology

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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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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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DeCAST in TransVerse for Parallel Intelligent Transportation systems and Smart Cities: Three Decades and Beyonds

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IEEE INTELLIGENT TRANSPORTATION systems MAGAZINE 2022年第6期14卷 6-17页

作者： Zhao, Chen Lv, Yisheng Jin, Junchen Tian, Yonglin Wang, Jiangong Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Zhejiang Supcon Informat Co Ltd Intelligent Transportat Business Grp Hangzhou 310052 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China

Parallel transportation management and control was proposed three decades ago as a new paradigm for conducting complex transportation operations and has led to today's DeCAST in TransVerse platform designed and constructed according to the principle of decentralized/distributed autonomous operations and organizations. This article presents an overview of its architectures, processes, operating procedures, and major applications. The developments and applications have demonstrated clearly that parallel transportation systems are effective for networked traffic control and distributed logistical operations. The existing challenges and emerging opportunities are also addressed. A transportation foundation model based on parallel learning and federated intelligence is proposed as a potential path to the next-generation parallel intelligent transportation systems.

关键词： Transportation Data models Traffic control Roads Computational modeling complex systems Adaptation models

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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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Neural event-triggered optimal filtering co-design of Markovian jump systems with hidden mode detections

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2023年

作者： Ma, Chao Lu, Yanfeng Wu, Wei Univ Sci & Technol Beijing Sch Mech Engn Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst 95 Zhongguancun East Rd Beijing 100190 Peoples R China

The modified H-infinity filtering problem for a class of Markovian jump systems with unknown nonlinear dynamics is investigated in the work by developing the neural event-triggered filter co-design method. Moreover, the true system modes are assumed to be inaccessible such that the estimated jumping modes are utilized for the mode-dependent filters. In particular, a novel event-triggered mechanism is introduced to improve filtering communication efficiency, where the unknown nonlinearity approximation is conducted by a neural network. By virtue of employing Lyapunov-Krasovskii method, sufficient filtering conditions are constructed to ensure the optimal H-infinity performance under the mean-square framework, based on which desired mode-dependent filter gains, event-triggering, and neural network parameters are co-designed with an aid of matrix techniques. Illustrative simulations with two practical examples are finally carried out to validate the usefulness and advantages of our developed approach.

关键词： Markovian jump system neural event-triggered scheme optimal filtering unknown nonlinearity hidden mode detections

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From cadCAD to casCAD2: A Mechanism Validation and Verification System for Decentralized Autonomous Organizations Based on Parallel Intelligence

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第2期11卷 2853-2862页

作者： Liang, Xiaolong Ding, Wenwen Qin, Rui Hou, Jiachen Yuan, Yong Wang, Xiao Wang, Fei-Yue Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Renmin Univ China Sch Math Beijing 100872 Peoples R China Renmin Univ China Engn Res Ctr Finance Computat & Digital Engn Minist Educ Beijing 100872 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

The governance technology of decentralized autonomous organizations (DAOs) provides an effective solution for solving existing organizational management issues. Governance mechanisms of DAOs are usually encoded in smart contracts in the form of rule sets and executed automatically. However, the mechanism's logical flaws and code errors expose DAOs to unpredictable risks. complex adaptive dynamics computer-aided design (CadCAD) can test the effectiveness of the mechanisms through simulation. Nonetheless, as DAOs are typical complex systems with social and engineering complexity, managing, controlling, and supervising their operation through traditional methods are difficult. The parallel intelligence theory based on artificial societies, computational experiments, and parallel execution (ACP) method provides an effective research framework and practical method for solving DAOs' governance issues. Therefore, in this article, we propose a parallel mechanism verification method and execution system, namely, complex adaptive systems for computer-aided dynamic design (casCAD2) as an extension of cadCAD. Leveraging parallel intelligence and cyber-physical-social systems (CPSS), casCAD2 is capable of probing into the laws that govern system evolution within a simulated environment. It serves as a robust tool for verifying the efficacy of DAOs' mechanisms and predicting their potential risks. We also build a parallel market-based anchoring mechanism (MAM) system to demonstrate how it can be used for DAOs' mechanism verification. This study can provide a new research method and application system for DAOs' effective governance.

关键词： Blockchain complex adaptive systems for computer-aided dynamic design (casCAD2) decentralized autonomous organizations (DAOs) parallel governance parallel intelligence smart contracts

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