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International Conference on Big Data and information Analytics (BigDIA)

作者： Yufan Zhao Chao Jiang Yuehan Wang Yuanyuan Wang Haifeng Liu Key Laboratory of Information System and Technology Beijing Institute of Control and Electronic Technology Beijing China

We propose a framework for designing an effectiveness assessment indicator system for command and control systems. This framework encompasses three key parts: the design of task element flowcharts, iterative optimization of indicators, and check of indicator completeness. Introducing the task element flowcharts and the physical mapping of indicators, a dual-layer iterative optimization guiding approach to indicator system design is provided. This method mitigates the complexity associated with indicator system design, enhancing design efficiency. The indicator system built using this approach shows advantages of non-redundancy, non-repetition, quantifiability, and high completeness. Concurrently, comparing supporting data leads to construct a high-trust assessment algorithm and generate the scientifically comprehensive data collection requirements.

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Edge-assisted Prediction and Predictive control for Flexible Platooning under Mixed Traffic Flow

Edge-assisted Prediction and Predictive Control for Flexible...

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IEEE Conference on Vehicular Technology (VTC)

作者： Fengkun Gao Bo Yang Jiahang Li Cailian Chen Xinping Guan Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China

The uncertainty of human-driven vehicles (HDVs) has a significant impact on the movement of connected and automated vehicles (CAVs), especially on the CAV platoon. In this regard, the future trajectory information of the preceding HDV is essential for CAV platoon to develop a safe and smooth speed control strategy. However, constrained by computation and energy resources, on-board unit can not provide accurate predictions, which results in inefficient control strategy and even safety issues. Hence, we propose an edge-assisted prediction and a flexible cooperative adaptive cruise control (CACC) strategy in this paper. The challenges mainly lie in the design of coordination mechanism and control strategy. Considering the inaccuracy of prediction, an error-aware communication trigger scheme is first designed to decide whether to request the roadside unit (RSU) for performing accurate prediction with the aid of mobile edge computing (MEC). Further, a novel flexible tracking policy is proposed to deal with prediction error, which is integrated with model predictive control method to determine control inputs. Simulations based on real data, collected in Shanghai, China, demonstrate that our proposed method can significantly improve driving safety and speed smoothness.

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Effect of Positive Temperature Coefficient Materials on the AC Breakdown Strength and Dielectric Properties of Epoxy Composites 4th

Effect of Positive Temperature Coefficient Materials on the ...

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4th International Symposium on Insulation and Discharge Computation for Power Equipment, IDCOMPU 2023

作者： Teng, Chenyuan Li, Shuo Zhou, Yuanxiang Zhang, Ling Zhang, Yunxiao Huang, Meng College of Information Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100084 China State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819974122

In operation, the negative temperature coefficient (NTC) electrical resistivity characteristics of polymeric insulating materials causes DC electric field distortion within power equipment insulation and lead to the failure. The doping of positive temperature coefficient (PTC) ceramic particles can suppress the NTC characteristics of the composites, but the electrical properties under AC voltages are unrevealed. In this paper, epoxy composites (0–20 wt%) doped with polydopamine-coated PTC particles were prepared, whose AC breakdown strength and dielectric properties were tested. The results shows that the decrease of AC dielectric strength is smaller than that under DC voltages after the doping PTC particles, and the coating of polydopamine can enhance the AC dielectric strength. As the doping content increases, the dielectric constant and tanδ of the epoxy composites increases, and the maximum increase of dielectric loss power is about 2% of the loss power caused by the current-carrying conductor. The research provides a theoretical basis for the optimization of the insulating performance of the equipment under the AC-DC composite voltages. © Beijing Paike Culture Commu. Co., Ltd 2024.

关键词： Epoxy resins

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A Classification and Recognition Model of Distributed Resources Based on Feature Extraction

A Classification and Recognition Model of Distributed Resour...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Zhu, Yu Yang, Le Yang, Xizai Lu, Jiaxin Wang, Yuqing Wang, Fei State Grid Shaanxi Electric Power Co. Ltd. Information and Communication Company Information and Communication Technology R & D Center Xi'an710048 China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China

ISBN: (纸本)9781665471640

The extensive integration of distributed resources such as distributed photovoltaic system and electric vehicles enhances the uncertainty of the load side greatly. Corresponding demand response strategies can be developed for different distributed resource types and characteristics of households' load, which is of great significance for new energy consumption and stable operation of the power system. However, only the net load power of households can be obtained through smart meters instead of the distributed resource types of households. Based on this, a distributed resource classification and identification model based on feature extraction is proposed in this paper, and effectively solves the problems of low accuracy and strong data dependence of existing recognition methods. Firstly, a generalized weather class generation algorithm based on clustering and voting is established, which is used to determine the weather class of each day to provide a basis for the following feature extraction. Then, based on the typical net load profiles under different generalized weather classes, a two-stage feature extraction and a classification identification model based on integrated learning algorithms are established to identify whether a user contains DPVS and EV, respectively. Finally, based on the category labels of each user for each stage, the final classification of the category to which the user belongs is carried out. Simulation experiments show that the recognition model built using the typical features extracted in the paper has good recognition accuracy. © 2023 IEEE.

关键词： classification and recognition Distributed photovoltaics system electric vehicles ensemble learning feature extraction

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Securing Mobile Robotic Networks Against Replacement Attack

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IEEE Transactions on Automatic control 2025年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping Cai, Lin Shanghai Jiao Tong University Dept. of Automation China Ministry of Education of China The Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China University of Victoria Dept. of Electrical and Computer Engineering BC Canada

The security of mobile robotic networks (MRNs) has been an active research topic in recent years. This paper aims to secure the ubiquitous formation control of MRNs against the replacement attack, where an external robot can replace a formation robot by compromising the communication and physically interfering with the victim simultaneously. To counter this advanced attack, the novel idea of this work is to leverage the physical proximity of the formation shape and the interaction topology among robots for defense design. First, from the physical proximity perspective, we propose the convex neighbor polygon (CNP) to capture the geometric characteristic of the formation robots, and design a CNP-based security mechanism for the robots during the replacement attack. Then, from the interaction perspective, we introduce the indirect controllability to characterize the possibility that the attacker leverages the interaction between robots to deactivate the CNP mechanism, and establish the conditions regarding the topology structure and the attack input to counter the replacement. Finally, based on the obtained conditions, we demonstrate how to design the initiatory topology among the formation robots to enhance the defense performance. Comparative simulations verify the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Topology

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Bev-Cfkt: A Lidar-Camera Cross-Modality-Interaction Fusion and Knowledge Transfer Framework with Transformer for Bev 3d Object Detection

SSRN

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SSRN 2023年

作者： Wei, Ming Lu, Jun-Guo Li, Jiachen Kang, Hongyi Huang, Yijie Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

The BEV-CFKT in this paper is proposed to LiDAR-Camera fusion using knowledge transfer with transformer in the Bird’s-Eye-View (BEV) space for accurate and robust 3D object detection. BEV-CFKT consists of three main parts, including generation of BEV features from images and point clouds, cross-modality interaction, and hybrid object queries based on a monocular detection head. By unifying the features from point clouds and images into the BEV space, we simplify the modal interaction and facilitate knowledge transfer, and obtain richer structural and semantic information from multimodal data, which effectively improves the network performance. BEVCFKT also adds a temporal fusion module to enhance the effect of detection. Additionally, a hybrid object queries module based on a monocular detection head is added to accelerate the convergence of our model. The effectiveness of our approach is demonstrated through a large number of experiments. © 2023, The Authors. All rights reserved.

关键词： Cameras

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V2X Based Cooperative Motion control and Energy Management for Electronic Vehicles

V2X Based Cooperative Motion Control and Energy Management f...

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IEEE Conference on Vehicular Technology (VTC)

作者： Jiahang Li Cailian Chen Fengkun Gao Bo Yang Xinping Guan Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China

V2X communication is a key technology for intelligent transportation system to improve traffic safety and efficiency. Electric vehicles can reduce greenhouse gas emissions and fossil fuel dependence, but they face challenges such as limited driving range, high battery cost, and long charging time. This paper proposes a V2X communication assisted hierarchical cooperative control strategy for EVs that aims at improving driving performance and energy efficiency. The upper layer uses improved model predictive control (MPC) method for cooperative motion control. A mechanism is designed for V2X communication loss in the algorithm. The lower layer employs a hybrid energy storage system for powertrain management. The algorithm in the lower layer uses the predictive information from the upper layer to enhance the powertrain efficiency and prolong the battery life. The proposed strategy is simulated using a Prescan-Carsim simulation platform. The results demonstrate that the proposed method can improve the battery lifetime over 13% compared with baseline method.

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Sensor Deception Attacks Against Initial-State Privacy in Supervisory control systems

Sensor Deception Attacks Against Initial-State Privacy in Su...

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IEEE Conference on Decision and control

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

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper investigates the problem of synthesizing sensor deception attackers against privacy in the context of supervisory control of discrete-event systems (DES). We consider a plant controlled by a supervisor, which is subject to sensor deception attacks. Specifically, we consider an active attacker that can tamper with the observations received by the supervisor. The privacy requirement of the supervisory control system is to maintain initial-state opacity, i.e., it does not want to reveal the fact that it was initiated from a secret state during its operation. On the other hand, the attacker aims to deceive the supervisor, by tampering with its observations, such that initial-state opacity is violated due to incorrect control actions. We investigate from the attacker’s point of view by presenting an effective approach for synthesizing sensor attack strategies threatening the privacy of the system. To this end, we propose the All Attack Structure (AAS) that records state estimates for both the supervisor and the attacker. This structure serves as a basis for synthesizing a sensor attack strategy. We also discuss how to simplify the synthesis complexity by leveraging the structural properties. A running academic example is provided to illustrate the synthesis procedure.

关键词： Privacy Estimation Supervisory control Discrete-event systems Complexity theory

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The Regional Distributed PV Ultra-short-term Power Forecasting Based on Static-dynamic Spatiotemporal Correlation Modeling

The Regional Distributed PV Ultra-short-term Power Forecasti...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Zhu, Yu Yang, Le Yang, Xizai Xu, Fei Su, Zinuo Zhen, Zhao Information and Communication Technology R & D Center State Grid Shaanxi Electric Power Co. Ltd. Information and Communication Company Xi'an710048 China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China

ISBN: (纸本)9781665471640

Accurate prediction of distributed photovoltaic power can help the arrange the scheduling plan reasonably to Maintain the safe and stable operation of the distribution network. However, distributed photovoltaic power generation exists lack of meteorological data and real-time monitoring data, the distribution range is large and dispersed, the installed capacity is relatively small and so on, the centralized photovoltaic power prediction method is not applicable to distributed photovoltaic power prediction, so it is necessary to carry out a regional power prediction study for distributed photovoltaic. Distributed PV is located in close proximity, in a similar external environment, and the power output between stations has strong spatial and temporal correlation, which can be captured to improve the accuracy of power forecasting. To this end, a regional distributed PV power prediction model based on static-dynamic spatiotemporal correlation modeling is proposed. The k-means is firstly used to cluster the regional distributed photovoltaic sites into several sub-regions, according to the geographical location and output characteristics that reflect the long-term static correlation between power stations. Then, graph convolutional neural network are applied to capture the dynamic correlation between subregions according to the power. In this way, the static and dynamic correlation are effectively considered in power prediction modeling, and the prediction accuracy has been improved. The superiority of the proposed method is verified by a real world dataset in China. © 2023 IEEE.

关键词： distributed photovoltaic graph convolution Regional power prediction

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LBM modeling of three-dimensional mixed convection in CZ crystal growth on curvilinear coordinates system

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Results in Physics 2024年 61卷

作者： Zhang, Ni Liu, Ding The Department of Automation Xi'an Key Laboratory of Advanced Control and Intelligent Process Xi'an University of Posts and Telecommunications Xi'an 710121 China The Shaanxi Key Laboratory of Complex System Control and Intelligent of Information Processing Xi'an University of Technology Xi'an 710048 China

A three-dimensional (3D) thermal lattice Boltzmann model of crystal growth is presented to study the magneto-hydrodynamics (MHD) mixed convection in the melt of Czochralski silicon single crystal growth in this paper. In order to solve the problem of lattice Boltzmann method's difficulty in solving the flow of 3D cylindrical cavity, the 3D physical model of crystal growth is transformed from the Cartesian coordinate system to the Curvilinear coordinate system. And the irregular grids in the Cartesian coordinate system are transformed into regular grids in the Curvilinear coordinate system to facilitate the establishment of the Lattice Boltzmann model. Under the above operations, two coupled distribution functions are established to solve the governing equations. Two D3Q19 models are adopted to solve the melt velocity and temperature. And the 3D lattice Boltzmann modeling of crystal growth with cylinder boundary is realized by interpolation in Curvilinear coordinate system. The comparison with the experiment and other references proves that the proposed model and boundary treatment scheme is correct and it proposes a new way for studying the 3D crystal growth or other similar 3D cylindrical cavity simulations. Furthermore, the unstable melt flow for two cases are also simulated in this paper and the critical Reynolds number are also obtained. Results show that the introduction of the transverse magnetic field enhances the stability of the crystal growth system and increases the adjustable range of crystal rotation parameter. The obtained conclusion has an important reference for adjusting the process parameters during the CZ crystal growth. © 2024 The Author(s)

关键词： 3D mixed convection Computer simulation Heat transfer Lattice Boltzmann Method Silicon single crystal growth

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