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第42届中国控制会议

作者： Liyuan Zhang Wentao Wu Feng Dong Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University

As a kind of flow process with complex and variable flow structure,multiple process variables and nonlinear characteristics,the flow status of multiphase flow shows non-stationary characteristics,and it is difficult to achieve accurate online *** the nonlinear characteristics of the flow status of gas-liquid two-phase flow,a monitoring method based on principal polynomial analysis is proposed to extract the nonlinear characteristics of the flow status *** method performs dimensionality reduction in multiple steps and uses polynomials for feature fitting,which can fully extract flow status *** sensors are used to obtain data representing different fluctuation characteristics in the flow process,models of different flow statuses are established,and status monitoring is realized based on statistical *** the comparison with the principal component analysis method,it can be found that the principal polynomial analysis method can extract the nonlinear features and can describe the flow status more comprehensively,which can improve the accuracy of status monitoring.

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Learning-Based Motion Planning with Mixture Density Networks

Learning-Based Motion Planning with Mixture Density Networks

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yinghan Wang Xiaoming Duan Jianping He Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The trade-off between computation time and path optimality is a key consideration in motion planning algorithms. While classical sampling based algorithms fall short of computational efficiency in high dimensional planning, learning based methods have shown great potential in achieving time efficient and optimal motion planning. The SOTA learning based motion planning algorithms utilize paths generated by sampling based methods as expert supervision data and train networks via regression techniques. However, these methods often overlook the important multimodal property of the optimal paths in the training set, making them incapable of finding good paths in some scenarios. In this paper, we propose a Multimodal Neuron Planner (MNP) based on the mixture density networks that explicitly takes into account the multimodality of the training data and simultaneously achieves time efficiency and path optimality. For environments represented by point clouds, MNP first efficiently compresses point clouds into a latent vector by encoding networks that are suitable for processing point clouds. We then design multimodal planning networks which enables MNP to learn and predict multiple optimal solutions. Simulation results show that our method outperforms SOTA learning based method MPNet and advanced sampling based methods IRRT* and BIT*.

关键词： Point cloud compression Learning systems Training Simulation Neurons Training data Encoding

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Hemorrhage Detection by Magnetic Induction Tomography with Helmholtz Coil

Hemorrhage Detection by Magnetic Induction Tomography with H...

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第42届中国控制会议

作者： Zilong Zhu Chao Tan Yixuan Chen Feng Dong Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University

Due to the nonradioactive,noninvasive,low cost and portable features,magnetic induction tomography(MIT) is a promising technology in medical *** conventional MIT sensor array generates divergent excitation magnetic field,resulting in poor sensitivity intensity and *** improve the intensity and uniformity of the sensitivity in MIT for hemorrhage detection,a local MIT method with Helmholtz coil is *** Helmholtz coil provides a uniform background magnetic field in the measured *** obtain sufficient independent measurements for hemorrhage reconstruction,the open planar MIT sensor array with gradiometers is applied in combination with the Helmholtz *** structure and excitation-detection strategy of the MIT sensor array are *** distribution of the magnetic field,phase sensitivity and the phase response of hemorrhages under the designed MIT sensor array are *** results indicate that the open planar MIT sensor array with Helmholtz coil can effectively improve the intensity and uniformity of the phase *** addition,it can also enhance the intensity of the hemorrhage signals for different depths and volumes of the *** proposed method provides a solution to improve the detection accuracy and the detection depth of the hemorrhage by MIT.

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Distributed Task Allocation with Time Constraints Under Limited Communication Resources in Multi-Agent system

Distributed Task Allocation with Time Constraints Under Limi...

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Asian control Conference

作者： Weilin Li Jing Wu Chengnian Long Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, a new distributed allocation algorithm based on performance impact with collective influence (PI-CI) is proposed. Considering inefficiency of creating time slots in PI-MaxAss, two completely independent phases with different communication frequencies are designed to ensure allocation both real-time and conflict-free under limited communication. To solve the misconvergence of the baseline algorithm PI-MaxAss in allocation results, an evaluation function is put forward in consensus phase to directly determines the final attribution of repeatedly assigned tasks, guaranteeing convergence of allocation. Comparison experiments validate the performance improvement of the PI-CI algorithm over the baseline algorithm.

关键词： Time-frequency analysis Real-time systems Resource management Time factors Multi-agent systems Radio spectrum management Convergence

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Multi-block Integrated Monitoring of Gas-liquid Two-Phase Flow Based on Conductance Signal Analysis

Multi-block Integrated Monitoring of Gas-liquid Two-Phase Fl...

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第42届中国控制会议

作者： Zhao Li Shumei Zhang Chao Tan Feng Dong Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University

The state monitoring of gas-liquid two-phase flow with complex and diverse structures is the key to ensure the safe and stable operation of related industrial *** order to achieve effective monitoring with limited sensing equipment,a multi-block integrated monitoring strategy for gas-liquid two-phase flow based on conductance signal analysis in time,frequency and multiscale domain is ***-block information including water holdup in time-domain,continuous wavelet transforms(CWT) based coarse scale approximation(CSA) coefficients in frequency-domain and CWT based local wavelet energy(LWE) coefficient maps in multiscale-domain are used for block-based modeling and *** the original structure of each block,the traditional principal component analysis(PCA) is adopted to model and monitor 1-D information including water holdup and CSA coefficients,while the tensor PCA is adopted for the 2-D LWE coefficient ***-block monitoring results are integrated through Bayesian inference to realize final *** dynamic experiment results demonstrate that the proposed multi-block integrated monitoring outperforms single-block information monitoring.

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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Spatial Dynamics of Chickenpox Outbreaks in Rapidly Developing Regions:Implications for Global Public Health

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Biomedical and Environmental Sciences 2024年第7期37卷 687-697页

作者： Li Wang Miaomiao Wang Chengdong Xu Peihan Wang Meiying You Zihan Li Xinmei Chen Xinyu Liu Xudong Li Yuanyuan Wang Yuehua Hu Dapeng Yin College of Geography and Environmental Science Henan UniversityKaifeng 475001HenanChina State Key Laboratory of Resources and Environmental Information System Institute of Geographic Science and Natural Resource ResearchChinese Academy of SciencesBeijing 100101BeijingChina Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions(Henan University) Ministry of EducationKaifeng 475001HenanChina Office of Epidemiology Chinese Center for Disease Control and PreventionBeijing 102206BeijingChina University of Chinese Academy of Sciences(UCAS) Beijing 100101BeijingChina Weifang Municipal Center for Disease Control and Prevention Weifang 261061ShandongChina Hainan Center for Disease Control and Prevention Haikou 570203HainanChina

Objective The occurrence of chickenpox in rapidly developing areas poses substantial seasonal risk to ***,certain factors influencing local chickenpox outbreaks have not been ***,we examined the relationship between spatial clustering,heterogeneity of chickenpox outbreaks,and socioeconomic factors in Southern *** We assessed chickenpox outbreak data from Southern China between 2006 and 2021,comprising both relatively fast-growing parts and slower sub-regions,and provides a representative sample of many developing *** analyzed the spatial clustering attributes associated with chickenpox outbreaks using Moran’s I and local indicators of spatial association and quantified their socioeconomic determinants using Geodetector q *** There were significant spatial heterogeneity in the risk of chickenpox outbreaks,with strong correlations between chickenpox risk and various factors,particularly demographics and living ***,interactive effects among specific are factors,such as population density and per capita residential building area,percentage of households with toilets,percentage of rental housing,exhibited q statistics of 0.28,0.25,and 0.24,*** This study provides valuable insights into the spatial dynamics of chickenpox outbreaks in rapidly developing regions,revealing the socioeconomic factors affecting disease *** implications extend the formulation of effective public health strategies and interventions to prevent and control chickenpox outbreaks in similar global contexts.

关键词： Chickenpox Spatial dynamics Socioeconomic factors

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Multi-Agent Path Planning for Finite Horizon Tasks with Counting Time Temporal Logics

Multi-Agent Path Planning for Finite Horizon Tasks with Coun...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Peng Lv Shaoyuan Li Xiang Yin Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we consider the multi-agent path planning problem for high-level tasks with finite horizons. In many situations, there is the need to count how many times a sub-task is satisfied in order to achieve the overall task. However, existing temporal logic languages, such as linear temporal logic, may not be efficient in describing such requirements. To address this issue, we propose a new temporal logic language called Counting Time Temporal Logic (CTTL) that extends linear temporal logic by explicitly counting the number of times that some tasks are satisfied. To solve the CTTL path planning problem, we use integer linear programming to encode the satisfaction of the task. We demonstrate that our approach is both sound and complete. To validate our results, we present numerical experiments to show the scalability of the proposed approach. Furthermore, we provide a simulation case study of a team of autonomous robots to illustrate the synthesis procedure.

关键词： Computer aided software engineering Automation Scalability Integer linear programming Path planning Planning Delays Logic Standards Autonomous robots

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Jointly Learning Time-Structure-History Graph Embedding for Temporal Knowledge Graph Reasoning 19

Jointly Learning Time-Structure-History Graph Embedding for ...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Duan, Hao Jin, Haoyu Chen, Kang Du, Shaochong Fang, Tao Huo, Hong Shanghai Jiao Tong University Department of Automation Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9798350346558

The Temporal Knowledge Graph(TKG) reasoning is an imperative task for various applications of TKG. However, reasoning over TKGs which aims to predict future facts has been less explored. Intuitively, entities and relations in TKG change differently over time, and the variations are different. It also can be observed that what happens in the past may affect the future. Apart from these, structure information that exists in the neighborhood of a given entity in a KG has been proven to be important. Inspired by these, a novel graph embedding method for TKG reasoning, called time-structure-history graph embedding (TSHGE), is proposed in this paper. TSHGE consists of three progressive learning units. The first time information learning unit employs a novel time-aware mechanism to encode the time influence on entities and relations. Then a relation-aware GAT (Graph Attention Network) is used in the structure information learning unit to model the 1-hop neighborhoods of all entities in a KG at each timestamp in TKG. Finally, the history information learning unit utilizes GRU (Gated Recurrent Unit) to learn history information from various facts. Then the ultimate embedding representations of entities and relations can be obtained for the prediction of future facts. The evaluations on three public TKG datasets have demonstrated the effectiveness and superiority of TSHGE for predicting future facts. © 2022 IEEE.

关键词： Forecasting

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Distributed Fault-Tolerant Containment control for Nonlinear Multi-Agent systems Under Directed Network Topology via Hierarchical Approach

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IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 806-816页

作者： Shuyi Xiao Jiuxiang Dong the College of Information Science and Engineering Northeastern UniversityShenyang 110819 the State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819 the Key Laboratory of Vibration and Control of AeroPropulsion Systems Ministry of Education Northeastern UniversityShenyang 110819China

This paper investigates the distributed fault-tolerant containment control(FTCC)problem of nonlinear multi-agent systems(MASs)under a directed network *** proposed control framework which is independent on the global information about the communication topology consists of two *** from most existing distributed fault-tolerant control(FTC)protocols where the fault in one agent may propagate over network,the developed control method can eliminate the phenomenon of fault *** on the hierarchical control strategy,the FTCC problem with a directed graph can be simplified to the distributed containment control of the upper layer and the fault-tolerant tracking control of the lower ***,simulation results are given to demonstrate the effectiveness of the proposed control protocol.

关键词： Adaptive fault-tolerant control directed network topology distributed control hierarchical control multi-agent systems(MASs)

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