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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Li, Qiang Ju, Zhuoya Fan, Lili Liu, Xin Liu, Wenhua Wang, Feiyue Scientific and Technological Innovation Center Beijing China Beijing Institute of Technology The School of Automation Beijing China China University of Petroleum School of Big Data & Cyber Security Qingdao China Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9798350349252

As social production and living spaces gradually expand from the physical and information domains to the cognitive and social domains, this article addresses the new changes brought about by these expansions. Starting from the new impact of era of intelligent connectivity on social cognitive management, it analyzes the urgent need to enhance social cognitive management capabilities centered on cognitive decision-making, identifying the core key as Adversarial Generated Intelligence (AGI). It delves into the conceptual characteristics, operating mechanisms, and winning strategies of AGI, identifies the new challenges it faces, and proposes a Parallel Intelligence empowering AGI (PiAGI) system architecture through the empowerment of parallel intelligence. This provides theoretical support for improving social cognitive management capabilities centered on cognitive decision-making. © 2024 IEEE.

关键词： Adversarial machine learning

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A Positioning and Navigation System for Railway Technical Station Operators 4

A Positioning and Navigation System for Railway Technical St...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Yang, Fan Wang, Zhixue Xiong, Gang Hu, Xiangyu Xu, Kejie Chen, Shichao Shandong Jiaotong University State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Rail Transportation Jinan China Shandong Jiaotong University School of Rail Transportation Jinan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Beijing Technol. and Bus. Univ. State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Computer and Artificial Intelligence Beijing China China Mobile Communication Group Sichuan Co. Ltd Chengdu China Chinese Academy of Sciences Beijing Huairou Academy of Parallel Sensing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China

ISBN: (纸本)9798350349252

As an important node in the dismantling and grouping of freight trains, the operational tasks of the railway technical station are increasingly busy. Various business activities involve a large number of field personnel, and ensuring the personal safety and operational efficiency of these personnel is one of these issues that railway operations need to *** ensure the operational safety and standardization at the railway technical station, high-precision positioning system for the personnel is required. However, typical operational scenarios at railway technical stations often involve problems such as the carriage obstruction and the electromagnetic interference. Only rely on GNSS for positioning is often insufficient to meet practical requirements. Addressing the need of the high-precision positioning for the operational personnel at railway technical stations, this paper constructs a integrated navigation system based on RTK and IMU. It utilizes a Discrete Error State Kalman Filtering algorithm to fuse the solution data from RTK and IMU, thereby obtaining high-precision pedestrian positioning data. Compared to relying solely on GNSS positioning system, the integrated navigation system significantly improves its resistance to the interference and positioning accuracy. Experimental results demonstrate that this positioning system can achieve centimeter-level positioning in obstructed scenarios typical of railway technical stations, meeting the positioning requirements of the operational personnel at railway technical station work sites. © 2024 IEEE.

关键词： Kalman filters

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Toward parallel intelligence: An interdisciplinary solution for complex systems

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The Innovation 2023年第6期4卷 152-164页

作者： Yong Zhao Zhengqiu Zhu Bin Chen Sihang Qiu Jincai Huang Xin Lu Weiyi Yang Chuan Ai Kuihua Huang Cheng He Yucheng Jin Zhong Liu Fei-Yue Wang College of Systems Engineering National University of Defense TechnologyChangsha 410073China Hunan Institute of Advanced Technology Changsha 410073China Shanghai Institute of Infectious Disease and Biosecurity Shanghai 200032China Institute of Epidemiology Helmholtz Zentrum Minchen-German Research Center for Environmental Health(GmbH)NeuherbergGermany Department of Computer Science Hong Kong Baptist UniversityHong KongChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling,analysis,management,and *** meet these demands,the parallel systems method rooted in the artificial systems,computational experiments,and parallel execution(ACP)approach has been *** method cultivates a cycle termed parallel intelligence,which iteratively creates data,acquires knowledge,and refines the actual *** the past two decades,the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines,offering vers atile interdisciplinary solutions for complex systems across diverse *** review explores the origins and fundamental concepts of the parallel systems method,showcasing its accomplishments as a diverse array of parallel technologies and applica-tions while also prognosticating potential *** posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation.

关键词： execution artificial offering

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Parallel Learning:Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 603-631页

作者： Qinghai Miao Yisheng Lv Min Huang Xiao Wang Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Artificial Intelligence Anhui UniversityHefei 266114China Qingdao Academy of Intelligent Industries Qingdao 230031China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE

The virtual-to-real paradigm,i.e.,training models on virtual data and then applying them to solve real-world problems,has attracted more and more attention from various domains by successfully alleviating the data shortage problem in machine *** summarize the advances in recent years,this survey comprehensively reviews the literature,from the viewport of parallel ***,an extended parallel learning framework is proposed to cover main domains including computer vision,natural language processing,robotics,and autonomous ***,a multi-dimensional taxonomy is designed to organize the literature in a hierarchical ***,the related virtual-toreal works are analyzed and compared according to the three principles of parallel learning known as description,prediction,and prescription,which cover the methods for constructing virtual worlds,generating labeled data,domain transferring,model training and testing,as well as optimizing the strategies to guide the task-oriented data generator for better learning *** issues remained in virtual-to-real are ***,the future research directions from the viewpoint of parallel learning are suggested.

关键词： Machine learning parallel learning parallel systems sim-to-real syn-to-real virtual-to-real

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Energy-aware Integrated Scheduling for Container Terminals with Conflict-free AGVs

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Journal of systems Science and systems Engineering 2023年第4期32卷 413-443页

作者： Zhaolin Zhong Yiyun Guo Jihui Zhang Shengxiang Yang Institute of Complexity Science School of AutomationQingdao UniversityQingdao266071China Shandong Key Laboratory of Industrial Control Technology Qingdao266071China Qingdao Port International Company Ltd.Qingdao266011China Institute of Artificial Intelligence School of Computer Science and InformaticsDe Montfort UniversityLeicesterLE19BHUK

For automated container terminals,the effective integrated scheduling of different kinds of equipment such as quay cranes(QCs),automated guided vehicles(AGVs),and yard cranes(YCs)is of great significance in reducing energy consumption and achieving sustainable *** at the joint scheduling of AGVs and YCs with consideration of conflict-free path planning for AGVs as well as capacity constraints on AGV-mate which is also called buffer bracket in blocks,a mixed integer programming model is established to minimize the energy consumption of AGVs and YCs for the given loading/unloading task.A solution method based on a novel bi-level genetic algorithm(BGA),in which the outer and the inner layer search the optimal dispatching strategy for QCs and YCs,respectively,is *** validity of the model and the algorithm is verified by simulation experiments,which take the Port of Qingdao as an example and the performance under different conflicting resolution strategies is *** results show that,for the given task,the proposed solution to conflict-free path and the schedule provided by the algorithm can complete the task with minimum energy consumption without loss of AGVs utilization,and the number of AGV-mates should be adjusted according to the task rather than keeping *** results indicate that our proposed approach could efficiently find solutions within 6%optimality *** consumption is dropped by an average of 15%.

关键词： Automated container terminal conflict-free routing energy saving improved genetic algorithm

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On the reduction of Linear Parameter-Varying State-Space models

arXiv

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arXiv 2024年

作者： Olucha, E. Javier Terzin, Bogoljub Das, Amritam Tóth, Roland Control Systems Group Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

This paper presents an overview and comparative study of the state of the art in state-order reduction (SOR) and scheduling dimension reduction (SDR) for linear parameter-varying (LPV) state-space (SS) models, comparing and benchmarking their capabilities, limitations and performance. The use case chosen for these studies is an interconnected network of nonlinear coupled mass spring damper systems with three different configurations, where some spring coefficients are described by arbitrary user-defined static nonlinear functions. For SOR, the following methods are compared: linear time-invariant (LTI), LPV and LFR-based balanced reductions, moment matching (MM) and parameter-varying oblique projection. For SDR, the following methods are compared: principal component analysis (PCA), trajectory PCA, Kernel PCA and LTI balanced truncation, autoencoders and deep neural network. The comparison reveals the most suitable reduction methods for the different benchmark configurations, from which we provide use case SOR and SDR guidelines that can be used to choose the best reduction method for a given LPV-SS model. © 2024, CC BY.

关键词： Principal component analysis

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Learning-based model augmentation with LFRs

arXiv

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arXiv 2024年

作者： Hoekstra, Jan H. Verhoek, Chris Tóth, Roland Schoukens, Maarten Control Systems Group Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Nonlinear system identification (NL-SI) has proven to be effective in obtaining accurate models for highly complex systems. Especially, recent encoder-based methods for artificial neural networks state-space (ANN-SS) models have achieved state-of-the-art performance on various benchmarks, while offering consistency and computational efficiency. The inclusion of prior knowledge of the system can be exploited to increase (i) estimation speed, (ii) accuracy, and (iii) interpretability of the resulting models. This paper proposes an encoder based model augmentation method incorporating prior knowledge from first-principles (FP) models. We introduce a novel linear-fractional-representation (LFR) model structure that allows for the unified representation of various augmentation structures including the ones that are commonly used in the literature, and an identification algorithm for estimating the proposed structure together with appropriate initialization methods. The performance and generalization capabilities of the proposed method are demonstrated on a hardening mass-spring-damper simulation. © 2024, CC BY-NC-SA.

关键词： Nonlinear systems

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IMTRAC: Image-Based Modeling for Transformative Traffic control via Foundation Models

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IFAC-PapersOnLine 2024年第10期58卷 225-230页

作者： Xingyuan Dai Yiqing Tang Chao Guo Yuanyuan Chen Yisheng Lv Fei-Yue Wang The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 Shanghai Artificial Intelligence Laboratory Shanghai 200232

This paper introduces the Image-based Modeling for Transformative Traffic control (IMTRAC) framework, which revolutionizes urban traffic management by integrating image-based representations, foundation models, and dynamic signaling. It transitions from textual to visual data analysis, transforming complex traffic scenes into quantifiable insights. The image-based representation is then analyzed by foundation models to infer refined and effective traffic control policies. Our experimental results show marked improvements in traffic flow and reductions in waiting times, highlighting IMTRAC’s ability to outperform traditional control methods. The proposed IMTRAC promises a significant leap towards smarter, efficient urban traffic systems, leveraging the synergy of advanced imaging techniques and multi-modal foundation models to enhance control decisions.

关键词： Foundation models Intelligent transportation systems Traffic control

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Lane-Change Intention Prediction of Surrounding Vehicles Using BiLSTM-CRF Models with Rule Embedding

Lane-Change Intention Prediction of Surrounding Vehicles Usi...

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2022 Chinese automation Congress, CAC 2022

作者： Wang, Kai Hou, Jie Zeng, Xianlin School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

ISBN: (纸本)9781665465335

Predicting lane-change intentions of surrounding vehicles can effectively help autonomous vehicles reduce collisions caused by lane changes and ensure driving safety. Because prediction methods based on black-box models will lead to passengers' distrust of machine prediction, the intention prediction methods used to autonomous driving need to be interpretable and trustworthy. This paper presents a method for intention prediction of surrounding vehicles by using a bidirectional long short term memory network (BiLSTM) with a conditional random field (CRF) layer above it. Compared with intention prediction methods using deep network, the proposed method can find the features that contribute most to the prediction, thereby improving the interpretability and ensuring the prediction performance. In addition, by employing the transfer characteristic of the CRF layer, traffic rules and the experience of skilled drivers can be embedded to the prediction in the form of rules. Use rules to constrain the intention prediction, thereby improving the trustworthiness of prediction results. Test results on naturalistic driving dataset show that the proposed method can predict the lane-change intention with an accuracy of 97.22%, which is higher than that of Bi-LSTM. © 2022 IEEE.

关键词： Embeddings

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A lightweight insulator string defect detection method based on improved YOLOv5 4

A lightweight insulator string defect detection method based...

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4th International Conference on Electronic Communication, computer Science and Technology, ECCST 2024

作者： Gao, Pengpei Song, Chunhe Zhang, Yue State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

In recent years, the use of drones to assess the safety status of insulators in power transmission systems has become a trend. To facilitate the application of intelligent algorithms on drone platforms, this paper proposes a lightweight insulator string defect detection method based on an improved YOLOv5. Firstly, to decrease the parameter count in the YOLOv5 backbone module, we opted for GhostNet. Considering the limitations of GhostNet in feature extraction, a lightweight attention mechanism is designed and integrated with GhostNet to enhance the accuracy of capturing defect features in insulator strings. Additionally, to further reduce parameters in the neck part of the YOLOv5 model, Ghost Convolution is introduced. Experimental results demonstrate that the proposed model performs excellently on the insulator string dataset and meets the requirements for real-time defect detection of insulators using drones. © Published under licence by IOP Publishing Ltd.

关键词： Insulating materials

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