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Outlier-resistant distributed fusion filtering for nonlinear discrete-time singular systems under a dynamic event-triggered scheme

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Frontiers of Information Technology & Electronic Engineering 2024年第2期25卷 237-249页

作者： Zhibin HU Jun HU Cai CHEN Hongjian LIU Xiaojian YI Department of Applied Mathematics Harbin University of Science and TechnologyHarbin150080China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and TechnologyHarbin150080China School of Automation Harbin University of Science and TechnologyHarbin150080China School of Mathematics and Physics Anhui Polytechnic UniversityWuhu241000China School of Mechatronical Engineering Beijing Institute of TechnologyBeijing100081China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314003China Tangshan Research Institute Beijing Institute of TechnologyTangshan063099China

This paper investigates the problem of outlier-resistant distributed fusion filtering(DFF)for a class of multi-sensor nonlinear singular systems(MSNSSs)under a dynamic event-triggered scheme(DETS).To relieve the effect of measurement outliers in data transmission,a self-adaptive saturation function is ***,to further reduce the energy consumption of each sensor node and improve the efficiency of resource utilization,a DETS is adopted to regulate the frequency of data *** the addressed MSNSSs,our purpose is to construct the local outlier-resistant filter under the effects of the measurement outliers and the DETS;the local upper bound(UB)on the filtering error covariance(FEC)is derived by solving the difference equations and minimized by designing proper filter ***,according to the local filters and their UBs,a DFF algorithm is presented in terms of the inverse covariance intersection fusion *** such,the proposed DFF algorithm has the advantages of reducing the frequency of data transmission and the impact of measurement outliers,thereby improving the estimation ***,the uniform boundedness of the filtering error is discussed and a corresponding sufficient condition is ***,the validity of the developed algorithm is checked using a simulation example.

关键词： Distributed fusion filtering Multi-sensor nonlinear singular systems Dynamic event-triggered scheme Outlier-resistant filter Uniform boundedness

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Event-triggered Extended Kalman Filter for UAV Monitoring System

Event-triggered Extended Kalman Filter for UAV Monitoring Sy...

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Data Driven control and Learning systems (DDCLS)

作者： Yunge Zang Yan Li Yuting Duan Xiangyu Li Xi Chang Zhen Li Beijing Aerospace Automatic Control Institute Beijing P. R. China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing P. R. China Beijing Aerospace Institute for Metrology and Measurement Technology Beijing P. R. China Key Laboratory for Intelligent Control … Decision on Complex Systems School of Automation Beijing Institute of Technology Beijing China

To facilitate ground station monitoring and command uploading, unmanned aerial vehicles (UAVs) need to frequently exchange individual state data between units. However, this results in a significant usage of communication bandwidth. To address this issue, on the basis of an event-triggered strategy, this paper proposes an Extended Kalman Filter (EKF). aimed at reducing the communication burden of UAVs while maintaining high accuracy. Specifically, a state measurement triggered by an event is selected for filtering only if it contains innovation, thereby reducing the amount of data that needs to be communicated. Since UAV systems are nonlinear, EKF is adopted to fully utilize the information obtained from event-triggered strategies, thereby enhancing the estimation performance. In this paper, a physical UAV was used to verify the proposed algorithm, and it proved to have robust dynamic performance and to effectively reduce the communication rate.

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Isochronous Temporal Metric for Neighbourhood Analysis in Classification Tasks

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SN Computer Science 2023年第6期4卷 1-12页

作者： Kumar, Amit Krishan Mai, Nguyễn Ngọc Tian, Ke Xia, Yuanqing Institute of Research and Development Duy Tan University Da Nang Vietnam Faculty of Electrical-Electronic Engineering Duy Tan University Da Nang Vietnam Faculty of Chinese Language School of Foreign Language Duy Tan University Da Nang Vietnam State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Machine learning, classification, and clustering techniques use the distance functions to evaluate the proximity between data entries and deduce the best neighbouring element and the closest matching entry. The best neighbour is not only the closest neighbour but a neighbour that is quick to respond. In view of that, a time-based isochronous metric is introduced to evaluate the best neighbours and form linkages by grouping similar entities. The proposed method uses parametric equations of the fastest descent and solves the time variables for attributes localised in curved space–time. The time metric is compared with commonly used distance metrics for accuracy in classification and clustering using benchmark and commonly used datasets. The nearest-neighbour technique is used for evaluating classification accuracy, and an adjusted random index (ARI) is used to evaluate clustering. The proposed method shows better accuracy and ARI in comparison to distance functions. It also assigns better weights to attributes of the dataset and easily identifies repeated patterns in noisy time series data.

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Absolute Stability Analysis of Single-Phase Grid-Tied Inverters with Open-Loop Synchronization

Absolute Stability Analysis of Single-Phase Grid-Tied Invert...

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Sustainable Power and Energy Conference (iSPEC)

作者： Yu Qiao Jinming Xu Zhilei Chen Jiahua Kang Cheng Cheng College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems (China Electric Power Research Institute) Beijing China

For grid-tied inverters, rapid and accurate obtainment of real-time grid phase is the basic and important function for stable operation. Among existing grid synchronization schemes, open-loop synchronization (OLS) methods have attracted much concern in recent years due to fast response and the elimination of feedback loop. It is noted that near-term studies focus on the analysis and improvement of performance of OLS itself mainly, while the stability issue of OLS-based grid-tied inverter (OLS-GTI) under non-ideal grid conditions is not analyzed deeply. Because OLS owns a strongly nonlinear structure, it is hard to apply the linearized modelling and criterion to facilitate the investigation of OLS-GTI stability. Hence, to cope with this issue, based on Sector and Circle Criterion used for the stability analysis of nonlinear system, this study analyzes the OLS-GTI stability. Firstly, by setting the voltage and current amplitude benchmark, the bounded condition of nonlinear sector is acquired. Then, the absolute stability range of OLS-GTI is obtained by the Circle Criterion. Furthermore, the impact of non-ideal conditions and OLSGTI control parameters on the system stability are analyzed. Finally, the stability of OLS-GTI is tested. Using Sector and Circle Criterion can help judge the stability of OLS-GTI intuitively and facilitate the design of inverter under complex grid conditions.

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Research on Evolutionary Guiding Strategy for Self-organized Aggregates of Massive Flexible Resources

Research on Evolutionary Guiding Strategy for Self-organized...

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Advanced Electrical and Energy systems (AEES), International Conference on

作者： Tingfeng Mao Kaifeng Zhang Changwei Yin Xiaohui Pan Nan Ma Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China Nanjing Power Supply Company of State Grid Jiangsu Electric Power Co. Ltd. Nanjing China

With the improvement of communication technology and the management of demand-side, more and more researches focus on the aggregation technology of flexible resources on the demand side. This paper proposes to build a self-organizing response framework in the scenario of massive resources, and resources can independently choose to join or exit the aggregates to participate in the regulation of grid active power balance. However, self-organizing aggregates cannot generate aggregation scenarios that accurately match the dynamic needs of the power grid during autonomous operation, so the grid needs to further guide the evolution of the aggregates. Firstly, the process and principle of realizing self-organization and evolution of resources in cyberspace are outlined. Then, the evolutionary driving sources of the self-organizing aggregates are analyzed to construct the evolutionary dynamics model framework from both the steady and the dynamic dimensions, and the data-driven modeling is carried out through the back propagation neural network to obtain the evolution law of resource self-organization. Finally, an iterative evolutionary guiding strategy based on price feedback is designed. The optimal guiding value of the market price cap is obtained through iteration under the expected aggregation scale target of the power grid, and also the dynamic evolution time of the aggregates is predicted to determine the timing of the release of the guiding strategy. The proposed strategy is constructed and tested on the NETLOGO resource self-organization simulation platform. The results show that the self-organizing aggregates can evolve to the expected state of power grid demand with high accuracy.

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Perception for Coupler Rod of Freight Train Based on Image and Point Clouds

Perception for Coupler Rod of Freight Train Based on Image a...

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Chinese automation Congress (CAC)

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

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.

关键词： Point cloud compression Image segmentation Image recognition Image color analysis Filtering Neural networks Data integration

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Making a Higher Hit Ratio Cryptanalytic Time-Memory Trade-Off Attack on Passwords

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Chinese Journal of Electronics 2023年第4期22卷 671-676页

作者： ZOU Jing LIN Dongdai HAO Chunhui LI Zheuqi WANG Wenhao LU Yao State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Graduate University of Chinese Academy of Sciences Beijing China Huaiyin Normal University Huai'an China Laboratory of Complex Systems and Intelligence Science Institute Automation Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China

Most of implementations of the cryptanalytic time-memory trade-off attacks such as Hellman's original method, Rivest's distinguished points cracking and Oechslin's rainbow attack are also considered as an exhaustive attack to passwords in a limited length range on a certain charset. However, the distributions of structures and strings making up real human memorable passwords do not appear random. Based upon these, we propose a method to generate passwords in those cryptanalytic timememory trade-off methods. It achieves a higher hit ratio in attacking actual passwords and reduces search space drastically with requirement of only a little extra memory. It makes time-memory trade-off more practical. Even to attack long length passwords, the results of experiments show that our approach has a higher hit ratio compared with Oechslin's method. In addition, this method can be used in the distributed and parallel attack.

关键词： Dictionaries Memory management Passwords

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Cooperative Resource Allocation for NOMA-MEC Multi-Cell Network

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IEEE Transactions on Vehicular Technology 2025年

作者： Cai, Yujin Zhang, Zaichen Huang, Yongming Yu, Wenwu Nie, Xiaokai Liu, Hongzhe Southeast University School of Mathematics Jiangsu Nanjing211189 China Southeast University National Mobile Communications Research Laboratory Nanjing211189 China Purple Mountain Laboratories Nanjing211111 China Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518057 China

Integrating non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) enables IoT nodes to offload their complicated tasks to MEC servers. Most of the existing resource allocation schemes in NOMA-MEC networks are based on the centralized optimization framework, which may increase the communicational and computational burden on the central unit. In this paper, the NOMA-MEC multi-cell network without the central unit is considered. To minimize the task completion time and offloading cost, a joint optimization problem including sub-channel allocation, transmit power, offloaded proportions, computing frequencies of IoT nodes and MEC servers, and price of MEC resources is investigated. In order to tackle the formulated mixed integer nonlinear programming problem, a two-stage low-complexity distributed algorithm based on the cooperation of all base stations (BSs) is proposed. Specifically, in Stage 1, a distributed heuristic sub-channel allocation algorithm is designed based on the sub-channel gains. In Stage 2, with the obtained sub-channel allocation result, a cooperative distributed continuous variable optimization algorithm is devised based on the distributed optimization and alternating optimization methods. Numerical results demonstrate the convergence of the proposed two-stage algorithm for three interference cases. Besides, the impacts of system parameters and schemes on the network performance are analyzed. More importantly, the resource allocation time can be significantly shortened by employing the cooperation of BSs compared with the centralized algorithm © 1967-2012 IEEE.

关键词： Nonlinear programming

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Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation

arXiv

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arXiv 2022年

作者： Li, Ding Zhang, Qichao Lu, Shuai Pan, Yifeng Zhao, Dongbin School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Baidu Inc. Beijing100085 China

Predicting joint future trajectories of two interacting vehicles faces great challenges, due to the high degree of multimodality and uncertainty in the future interaction process. For the traditional joint prediction models, each vehicle is treated as an individual agent, and its multimodal future modes can be learned from an efficient Goal-oriented Trajectory Prediction (GTP) method. However, such marginal methods produce multimodal self-consistent trajectories over individual agents without considering their future interactions. Motivated by this, this paper aims to tackle the interactive behavior prediction task, and proposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP) framework to jointly generate scene-compliant trajectories of two interacting agents. Our CGTP framework is an end-to-end and interpretable model, including three main stages: context encoding, goal interactive prediction and trajectory interactive prediction. First, a Goals-of-Interest Network (GoINet) is designed to extract the interactive features between agent-to-agent and agent-to-goals using a graph-based vectorized representation. Further, the Conditional Goal Prediction Network (CGPNet) focuses on goal interactive prediction via a combined form of marginal and conditional goal predictors. Finally, the Goal-oriented Trajectory Forecasting Network (GTFNet) is proposed to implement trajectory interactive prediction via the conditional goal-oriented predictors, with the predicted future states of the other interacting agent taken as inputs. In addition, a new goal interactive loss is developed to better learn the joint probability distribution over goal candidates between two interacting agents. In the end, the proposed method is conducted on Argoverse motion forecasting dataset, In-house cut-in dataset, and Waymo open motion dataset. The comparative results demonstrate the superior performance of our proposed CGTP model than the mainstream prediction methods. Copyright © 202

关键词： Forecasting

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AI Graphical Programming Learning Platform for Children

AI Graphical Programming Learning Platform for Children

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2021 China automation Congress, CAC 2021

作者： Zhao, Hongxia Liu, Xiwei Gong, Xiaoyan Li, Qiang Jing, Sifeng Xue, Yaofeng The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China University of Chinese Academy of Sciences Beijing China Shanghai Engineering Research Center of Digital Education Equipment Department of Education Information Technology East China Normal University Shanghai China

ISBN: (纸本)9781665426473

In this era of rapid development of artificial intelligence, the truth of competition in the future is the talent. Countries all over the world attach great importance to the programming and artificial intelligence education of children. In the future, the theoretical development and practical application of this emerging field will usher in a period of rapid development. This paper presents an artificial intelligence graphical programming learning platform based on Jetson Nano, which combines artificial intelligence education with modular software programming, provides students with a more open, intelligent, and interesting programming environment through the cross-platform development mode of "cloud service + intelligent hardware", and adopts visual programming to reduce the difficulty of artificial intelligence programming. The platform can fully tap learning resources, reduce education costs, and make artificial intelligence education more intuitive and humanized. © 2021 IEEE

关键词： Computer graphics

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