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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Zhao, ErKai Hao, MingRui Yang, RuoHan Yue, KeYuan Yu, DeLin Harbin Normal University School of Computer Science and Information Engineering Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Northwestern Polytechnical University School of Electronics and Information Xi'an China Northwestern Polytechnical University School of Mathematics and Statistics Xi'an China

ISBN: (纸本)9798350312492

To reasonably improve the missing attribute data and effectively integrate sample data and uncertain expert knowledge, this paper proposes a new fault diagnosis method based on a belief rule base (BRB). In the case of missing attribute data, the maximum likelihood estimation (MLE) method is used to complete the missing attribute data, and the fault diagnosis method of the multi-UAVs is constructed by using other complete attribute data as the input information of BRB. Finally, the effectiveness of the proposed method is verified by experimental simulation. © 2023 IEEE.

关键词： Failure analysis

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Edge-guided image estimation for array Gm-APD Lidar in smoky environment using multi-scale depth images 6

Edge-guided image estimation for array Gm-APD Lidar in smoky...

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6th Optics Young Scientist Summit, OYSS 2023

作者： Zhang, Yinbo Li, Sining Sun, Jianfeng Jiang, Peng Zhang, Hailong Liu, Zhihui Zhu, Yuebing National Key Laboratory of Science and Technology on Tunable Laser Institute of Opto-Electronic Harbin Institute of Technology Heilongjiang Harbin150001 China Zhengzhou Research Institute of Harbin Institute of Technology Henan Zhengzhou450000 China Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing100074 China Sichuan Jiuzhou Electrical Group Company Limited Sichuan Mianyang621000 China

ISBN: (纸本)9781510672642

Limited statistical frame number and strong backscatter interference from smoke result in a photon-starved regime, severely limiting the depth imaging capability of array Gm-APD lidar in smoky environment. Here, we propose a depth image estimation algorithm that can significantly improve the integrity of targets in dense smoke environments when signal photons are starved. At the signal level, the algorithm improves the accuracy of extracting signal photons by constructing multi-scale superpixels. At the image level, using edge information of depth images at different scales to guide and fill the original scale depth image achieves efficient noise reduction and improves target integrity. It has been successfully demonstrated under different attenuation lengths and statistical frame numbers. Compared with other state-of-the-art methods, the proposed algorithm has maximum target recovery and structural similarity, especially for the attenuation length (AL) is 3.6 and statistical frame number is 1500 (imaging time is 75ms). This study is of great significance for the fast depth imaging of dynamic targets by array Gm-APD lidar in dense smoke environments. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Optical radar

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Image mosaicing algorithm for dynamic scenes using multi-scaled PHOG feature and optimal seam

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Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence 2012年第4期25卷 624-631页

作者： Zou, Li-Hui Chen, Jie Zhang, Juan Lu, Jing-Hua Key Laboratory of Complex System Intelligent Control and Decision of Ministry of Education School of Automation Beijing Institute of Technology Beijing 100081 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China

Aiming at the problems of registration error and synthetic movement ghost which are caused by moving objects in image mosaicing, a mosaicing algorithm for dynamic scene using multi-scale pyramid histogram of oriented gradients (PHOG) and optimal seam is proposed. Firstly, a new feature, multi-scaled PHOG, is generated by introducing PHOG to multi-scale space corner detections. The feature is used to align images for avoiding the local impact caused by moving objects in image registration. Then, an optimal seam, guaranteeing the minimum difference in geometry and gray value, is searched by graph cut algorithm through constructing an energy function to remove the movement ghost. The experimental results show that the proposed algorithm is efficient in dealing with the problems of image mosaicing with moving objects, and the mosaicing results are satisfactory with high precision.

关键词： Graphic methods

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Leader-follower Formation control for a Multi-missile system via Deep Reinforcement Learning

Leader-follower Formation Control for a Multi-missile System...

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Chinese control and Decision Conference, CCDC

作者： Jianfeng Li Keyuan Yu Shenmin Song Center for Control Theory and Guidance Technology School of Astronautics Harbin Institute of Technology Harbin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The methods for the formation tracking control problem are mainly based on nonlinear control theories. Usually, the control effect of such methods heavily depends on an accurate control model and a lot of controller parameters need to be selected properly. To cope with these shortcomings, a novel reinforcement learning-based formation controller is proposed for a leader-follower multi-missile system. The deep deterministic policy gradient (DDPG) algorithm is implemented to generate the formation control signals. An interaction environment containing a leader and a follower is formulated, in which a model-free optimal control policy is obtained by a training process. The convergent actor-network is tested for a multi-missile system. Simulation results show that the formation control objective can be rapidly achieved in spite of the change of leader maneuvers. There is no need to change the hyperparameters of the policy for different kinds of formation scenarios, which shows great advantages over the conventional nonlinear control theory-based methods in terms of parameter selection.

关键词： Training Three-dimensional displays Target tracking Simulation Heuristic algorithms Process control Optimal control

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Modeling Method of Aircraft Simulation Based on Formal Description of Petri Net 18th

Modeling Method of Aircraft Simulation Based on Formal Des...

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18th Chinese Intelligent systems Conference, CISC 2022

作者： Zhan, Tianming Huang, Yu Geng, Huapin Tong, Jiahui TAIJI Computer Co. Ltd Beijing100102 China Peking University Beijing100871 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100190 China Beijing Electro-Mechanical Engineering Institute Beijing102299 China

ISBN: (纸本)9789811962028

The simulation of aircraft guidance and control system has characteristics such as multiple model classification, large parameter influence,complex information interaction and so on. The traditional code-level model development method has many limitations, such as time consuming, low reusability, low modeling efficiency, and model quality can not be guaranteed. Based on the concurrency, graphic representation, mathematics and simulation characteristics of Petri net, this paper studies the aircraft simulation modeling method of formal description based on Petri net, constructs a general modeling development and integration software tool that can carry out component, parametric and graphical modeling, solves the problems of multi granularity model modeling and automatic generation of real-time simulation model code, and realizes the visual graphic modeling and comprehensive integration of different granularity models such as components, modules and meta models. The research method has been successfully applied in the simulation modeling of an aircraft. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Petri nets

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Estimate depth information from monocular infrared images based on deep learning 7

Estimate depth information from monocular infrared images ba...

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7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020

作者： Li, Xu Ding, Meng Wei, Donghui Wu, Xiaozhou Cao, Yunfeng Nanjing University of Aeronautics and Astronautics College of Civil Aviation Nanjing China Beijing Electro-.Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing China

ISBN: (纸本)9781728170862

In the field of automatic driving, vision-based driving assistance system(V-DAS) is a research direction. Aiming at the demand of V-DAS for low visibility environment perception, we propose a depth estimation method based on monocular infrared images. Our method uses an end-to-end self-supervised deep learning framework, and uses the reprojection relationship between monocular video frames to construct the loss function, which makes it easy to collect dataset. In order to mitigate the influence of infrared noise, we adopt the minimum reprojection loss method. To ensure the neural network can learn infrared features better, we use transfer learning from RGB image dataset first. Qualitative experiments on FLIR datasets show that the proposed method can obtain pixel-lev el dense depth from monocular infrared images. Experiments on real roads show that the proposed method can effectively perceive the depth information of the target in the night, and the absolute error is 13.2% within 15m. This can meet the requirements of collision avoidance in most emergency situations during automatic driving. © 2020 IEEE.

关键词： Deep learning

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Entity Recognition for Military Situation Awareness Knowledge Graph with Wikipedia Data

Entity Recognition for Military Situation Awareness Knowledg...

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Chinese control and Decision Conference, CCDC

作者： Linxiu Chen Weili Guan Xudong Guo Yuan Li Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China School of Automation Beijing Institute of Technology Beijing China

Entity recognition is an essential component of knowledge representation and knowledge extraction research. To enhance military situation awareness through the construction of a knowledge graph, this paper presents a novel method, BERTATT_POSBiLSTMLSTMCRF, which is based on the traditional entity recognition model BERT_BiLSTM_CRF. The local location information and the impact of the entity's position in the sentence on the entity recognition task are both fully considered by introducing the attention mechanism. Additionally, an LSTM layer is added after the BiLSTM layer to deal with long-distance label dependencies while improving the model's ability to recognize long entities. Comparative experiments demonstrate that the improved model proposed in this paper is effective in entity recognition with Wikipedia data.

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RGB-D Co-attention Network for Semantic Segmentation 15th

RGB-D Co-attention Network for Semantic Segmentation

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Zhou, Hao Qi, Lu Wan, Zhaoliang Huang, Hai Yang, Xu National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China The Chinese University of Hong Kong Hong Kong China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030695248

Incorporating the depth (D) information for RGB images has proven the effectiveness and robustness in semantic segmentation. However, the fusion between them is still a challenge due to their meaning discrepancy, in which RGB represents the color but D depth information. In this paper, we propose a co-attention Network (CANet) to capture the fine-grained interplay between RGB’ and D’ features. The key part in our CANet is co-attention fusion part. It includes three modules. At first, the position and channel co-attention fusion modules adaptively fuse color and depth features in spatial and channel dimension. Finally, a final fusion module integrates the outputs of the two co-attention fusion modules for forming a more representative feature. Our extensive experiments validate the effectiveness of CANet in fusing RGB and D features, achieving the state-of-the-art performance on two challenging RGB-D semantic segmentation datasets, i.e., NYUDv2, SUN-RGBD. © 2021, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Correlation between time and frequency domain polarisation measurements for transformer moisture assessment

Correlation between time and frequency domain polarisation m...

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作者： Saha, T.K. Middleton, R. Thomas, A. School of Information Technology and Electrical Engineering University of Queensland School of Electrical Engineering and Computer Science University of Newcastle Department of Electrical Engineering School of Information Technology and Electrical Engineering University of Queensland Australia Engineers Australia University of Newcastle Centre for Complex Dynamic Systems and Control CRC Board of Governors IEEE Control System Society University of Queensland's School of Information Technology and Electrical Engineering

Preventative diagnosis and maintenance of transformers has become more popular in recent times in order to improve the reliability of electric power systems. A number of transformers have recently been tested using Return Voltage (RV) and Dielectric Dissipation Factor (tanδ) measurements in the time and frequency domain, respectively. This paper outlines a circuit model which describes the dielectric behaviour of the transformer insulation system and has been parameterised from the frequency domain data. This model was then used to simulate RV and Polarisation-Depolarisation current (PDC) results. Some recommendations about the diagnosis of the transformers have been proposed. © Institution of Engineers.

关键词： Electric transformers

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Failure mode and effects analysis based on intuitionistic fuzzy sets and evidential correlation coefficient

Failure mode and effects analysis based on intuitionistic fu...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Hang Zhang Chan Huang Mingsheng Lu Xiaofei Dong Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory School of Software Northwestern Polytechnical University Xi’an Beijing China School of Electronics and Information Northwestern Polytechnical University Xi’an China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781728137933

As an effective implement, failure mode and effects analysis (FMEA) is widely applied in the security of system for practical application. Nowadays, many methods determine the order of fault mode by a crisp risk priority number (RPN). However, these methods exist several shortcomings, for instance, the correlation of the assessments given by team members are not fully considered. In this article, a new method for risk assessment and sequence for failure modes in FMEA is proposed on account of the D-S evidence theory and the evidential correlation coefficient. By using the proposed approach, the weights of team members for each failure mode and risk factor is obtained. Then the weighted assessments are used to perform the aggregation process by Intuitionistic fuzzy weighted averaging (IFWA) operator. A classic application regarding risk assessment is used to verify the effectiveness of the proposed method.

关键词： Correlation Fuzzy sets Risk management Uncertainty Aggregates Linguistics control systems

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