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International Conference on Big Data and information Analytics (BigDIA)

作者： Qishu Gong Jian Han Yan Liu Key Laboratory of Information System and Technology Beijing Institute of Control and Electronic Technology Beijing China

Constellation satellites are important space-based devices with wider irradiation range and less blind coverage. Passive target detection with the space-based constellation satellites as radiation source will play an increasingly important role for situational awareness. This paper analyzes signal model for the passive radar and the characteristics of the passive detection system with the satellite as radiation source. Then, to improve detection accuracy with weak signal and low signal-to-noise ratio, a few algorithms are presented from the perspective of increasing the available virtual sensors, i.e., degrees of freedom. Finally, theoretical analysis confirms the high detection accuracy and low hardware cost of the above algorithms compared to the traditional algorithms. The above performance superiority ensures accurate situational awareness, thus effectively supporting precise and efficient mission planning and command.

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Analysis of On-policy Policy Gradient Methods under the Distribution Mismatch

arXiv

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arXiv 2025年

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

Policy gradient methods are one of the most successful methods for solving challenging reinforcement learning problems. However, despite their empirical successes, many SOTA policy gradient algorithms for discounted problems deviate from the theoretical policy gradient theorem due to the existence of a distribution mismatch. In this work, we analyze the impact of this mismatch on the policy gradient methods. Specifically, we first show that in the case of tabular parameterizations, the methods under the mismatch remain globally optimal. Then, we extend this analysis to more general parameterizations by leveraging the theory of biased stochastic gradient descent. Our findings offer new insights into the robustness of policy gradient methods as well as the gap between theoretical foundations and practical implementations. © 2025, CC BY.

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Size effect on transverse free vibrations of ultrafine nanothreads

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Chinese Physics B 2023年第9期32卷 21-27页

作者：郑卓群李晗宿柱丁楠徐旭占海飞王立峰 State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and AstronauticsNanjing 210016China School of Management Science and Information Engineering Jilin University of Finance and EconomicsChangchun 130117China College of Mathematics Jilin UniversityChangchun 130012China College of Civil Engineering and Architecture Zhejiang UniversityHangzhou 310058China School of Mechanical Medical and Process EngineeringQueensland University of TechnologyBrisbane 4001Australia

Due to their unique properties and appealing applications,low dimensional sp^(3)carbon nanostructures have attracted increasing attention *** on the beam theory and atomistic studies,this work carries out a comprehensive investigation on the vibrational properties of the ultrathin carbon nanothreads(NTH).Size effect is observed in transverse free vibrations of *** quantify such effects,the modified couple stress theory(MCST)is utilized to modify the Timoshenko beam *** to the first four order frequencies of NTHs from atomistic simulations,the critical length scale parameter of MCST is calibrated as 0.1 *** is shown that MCST has minor effect on the first four order modal shapes,except for the clamped *** makes the modal shapes at the clamped boundary closer to those observed in atomistic *** study suggests that to some extent the MCST-based Timoshenko beam theory can well describe the transverse vibration characteristics of the ultrafine NTHs,which are helpful for designing and fabricating the NTH-based nanoscale mechanical resonators.

关键词： carbon nanothread size effect natural frequency modal shapes molecular dynamics simulation

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Framework for Designing Assessment Indicator system of Command and control systems Based on Task Element Flowchart

Framework for Designing Assessment Indicator System of Comma...

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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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Inspection Area Allocation for Forest Surveillance Based on Different Starting Points of Multi-UAVs 7th

Inspection Area Allocation for Forest Surveillance Based on ...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhou, Zhengjun Mu, Lingxia Ning, Kaikai Zhang, Youmin Xue, Xianghong Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China Department of Mechanical Industrial and Aerospace Engineering Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9789819733392

An inspection allocation algorithm based on different starting points and endurance capabilities of unmanned aerial vehicles (UAVs) is proposed for the multi-UAVs forest inspection process. Firstly, the area map to be inspected is rasterized, and the distances between the starting points of each UAV and individual grid cells are computed, which will be used for area decomposition. Subsequently, an evaluation function is employed to allocate individual grid cells reasonably to each UAV. Then, the boundary lines of the allocated sub-regions are generated, by which the raster map is converted into an inspection map with continuous boundaries. This ultimately yields the sub-regions for inspection by each UAV. Simulation results demonstrate that this method effectively allocates the forest inspection area to the various UAVs in a rational manner. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Inspection

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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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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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Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method

arXiv

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arXiv 2025年

作者： Zhang, Zhong-Yi Xu, Chen-Yang Zhao, Li-Xuan Hou, Hui-Rang Meng, Qing-Hao Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China

Electroencephalogram (EEG) is a non-invasive tool for real-time neural monitoring, widely used in depression detection via deep learning. However, existing models primarily focus on binary classification (depression/normal), lacking granularity for severity assessment. To address this, we proposed the DepL-GCN, i.e., Depression Level classification based on GCN model. This model tackles two key challenges: (1) subjectivity in depression-level labeling due to patient self-report biases, and (2) class imbalance across severity categories. Inspired by the model learning patterns, we introduced two novel modules: the sample confidence module and the minority sample penalty module. The former leverages the L2-norm of prediction errors to progressively filter EEG samples with weak label alignment during training, thereby reducing the impact of subjectivity;the latter automatically upweights misclassified minority-class samples to address imbalance issues. After testing on two public EEG datasets, DepL-GCN achieved accuracies of 81.13% and 81.36% for multi-class severity recognition, outperforming baseline models. Ablation studies confirmed both modules’ contributions. We further discussed the strengths and limitations of regression-based models for depression-level recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

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A novel feature enhancement module for small object detection

A novel feature enhancement module for small object detectio...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Ruogu Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (纸本)9781728119489

Generic object detection has made great progress in recent years, yet small object detection is still facing a serious problem of missed detection. The problem is caused by two reasons: low resolution in small targets and feature loss during deep convolution downsampling. Some previous studies used feature pyramid structures and multi-scale training predictions to alleviate this problem, but still cannot efficiently utilise the feature information of small targets. In this paper, firstly we propose a novel and effective feature enhancement module to extract more features;secondly, a filter size design method is presented to extract features of matching objects and incorporate context information;finally, an adaptive fusion method is used to process outputs of our module and improve model robustness. We select YOLOv3 as base model and carry out several experiments on a special Small Object Dataset and the results show that our work can achieve an increase of about seven percentage points (35.73%) compared to 28.78% on baseline. © 2022 IEEE.

关键词： Object detection

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Online Learning Enabled Hierarchical Distributionally Robust Model Predictive control of Green-Hydrogen Microgrids under Uncertainties 5

Online Learning Enabled Hierarchical Distributionally Robust...

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Ning, Chao Li, Longyan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

This paper proposes a novel online-learning enabled hierarchical operation framework for a green-hydrogen microgrid (MG) with a high penetration of renewable energy (RE) and hybrid energy storage. The operation framework features a bi-level multi-timescale structure which integrates a day-ahead optimal scheduling with online learning distributionally robust model predictive control (OL-DRMPC). Day-ahead scheduling optimization is employed at the high level to provide MG energy dispatch references, while the OL-DRMPC is carried out in the low level for reference-tracking under uncertainties incurred by RE and load forecast errors. To confront the imperfect knowledge of forecast-error distributions, distributionally robust conditional value-at-risk constraints are imposed on MG system states, namely the storage of battery and hydrogen, for a family of distributions called ambiguity set. By exploiting a Dirichlet process mixture model, a streaming-data-driven ambiguity set for uncertain parameters is devised over the runtime of controller and flexibly captures multimodal structure and fine-grained moment information of each mixture component. Enabled by online learning, the proposed strategy enjoys the merit of offering a reliable and intelligent MG operation. Case studies are used to demonstrate its effectiveness and superiority. © 2022 IEEE.

关键词： Energy storage

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