检索结果-内蒙古大学图书馆

作者： Ruiyan Wang Yufeng Ke Shuang Liu Jiale Du Cheng Xu Bohao Xing Dong Ming Tianjin International Engineering Institute Tianjin University Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin University School of Precision Instruments & Opto-Electronics Engineering Tianjin University Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory

Brain-computer interface(BCI) provides a new way to express our minds without peripheral nerves and muscles. In this work, a process control recognition method based on continuous flickering is proposed to output continuous commands. Phase matching method was applied in this work, matching the test data with the template with the same phase to improve the accuracy. We also put forward a high tolerance criterion and give a new definition to the recognition result of attention shift period. We first conducted a screen-based continuous flickering feasibility verification experiment using correlation component analysis algorithm(TRCA) method, and the recognition accuracy reached 85% and 90% under high tolerance criterion. The average offline simulation information translates rate(ITR) was 455.8 bit/min, and the highest ITR reached 524.7 bit/min, which certificated the feasibility. Furthermore, we carried out a drone control experiment based on augmented reality(AR)-BCI using extended filter bank canonical correlation analysis(extended-FBCCA), and achieved an average accuracy of 90% and ITR of 49.1 bit/min, having better performance than repetitive visual stimulus(RVS) with intervals.

关键词： ITR red Design and Implement the Continuous Flickering SSVEP-BCI in Augmented Reality SSVEP

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A Fast Covariance Intersection Tracking Algorithm Based on CDKF

A Fast Covariance Intersection Tracking Algorithm Based on C...

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第三十八届中国控制会议

作者： Hang Zhang Chuang Song Mingrui Hao Linxiu Chen School of Software and Microelectronics Northwestern Polytechnical University Science and technology on complex system control and intelligent agent cooperation laboratory

With the enhancement of the anti-jamming ability of the target, it is difficult to track and strike the target stably and accurately by single guidance. It is necessary to use a variety of detectors as sensors to provide a variety of observation data to track the target stably and achieve accurate strike. In this paper, we mainly research the distributed multi-sensor estimation problem. First, the target motion model and observation model are established. Secondly, the central differential Kalman filter(CDKF) transformation is applied to address the non-linear filtering problem without the need for computation of Jacobian matrix. Then, a FCI fusion algorithm for the multi-sensor target tracking problem is proposed. Compared to the traditional covariance intersection fusion method, FCI is more efficient in computation without the need for complex optimization process. Finally, simulations are designed and implemented and the joint CDKF-FCI fusion estimation algorithm is validated.

关键词： central differential Kalman filter fast covariance intersection fusion information fusion target tracking

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Error Analysis of Object Depth Measurement Method Based on Feature Line Segments

Error Analysis of Object Depth Measurement Method Based on F...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Chuanqi Zhang Yunfeng Cao Meng Ding Donghui Wei College of Astronautic Nanjing University of Aeronautics and Astronautics Nanjing China College of Civil Aviation Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (数字)9781728180250

ISBN: (纸本)9781728180267

This paper focuses on the problem of object depth measurement in ultra-low altitude flight, which has always been a research hotspot in the field of machine vision. Compared with the previous object depth measurement method based on a single handcrafted feature point, the method based on feature line segments can effectively weaken the adverse effect caused by the feature matching error. However, since this method involves both image processing algorithms and the use of position data provided by navigation devices, various errors will be introduced. To this end, this paper points out the error sources of that may affect the accuracy of the object depth measurement results under this method. Then, through numerical simulation, the errors are simulated and their influence rules are analyzed, with the qualitative and quantitative results presented. Finally, we also give the allowable value range of each error under the minimum requirement that the object depth measurement error in ultralow altitude flight missions does not exceed 20%, which provides a reference and guidance for both algorithm selection and practical application.

关键词： Feature extraction Atmospheric modeling Pipelines Object segmentation Measurement uncertainty Measurement errors intelligent agents

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Motion Parallax Estimation for Ultra Low Altitude Obstacle Avoidance

Motion Parallax Estimation for Ultra Low Altitude Obstacle A...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Changqing Wang Xinkai Liang Shan Zhang Chao Shen Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9781728137933

By making full use of the curvature of the earth and ground clutter, the ultra-low altitude flight technique can significantly increase the difficulty of detecting and intercepting unmanned aerial vehicles (UAVs), and improve their penetration probabilities. However, unpredicted static/dynamic obstacles seriously threaten the flight safety of the ultra-low altitude UAVs. Therefore, fine autonomous spatial perception capability is essential for ultra-low altitude UAVs with limited loads and computing resources to avoid obstacles. To solve the difficult problem of spatial perception for ultra-low altitude obstacle avoidance, a motion parallax estimation algorithm is proposed by combing the deep learning and the epipolar geometry in this paper. By utilizing the deep neural network, the end-to-end mapping relationship from sequence images to depth values of pixels is constructed for spatial perception. At the same time, to overcome the interference caused by the image consistency hypothesis, the loss function is constructed based on the 3D geometric information of the scene rather than the re-projection error between images. Finally, the proposed algorithm is evaluated on the KITTI dataset and infrared image dataset to verify its effectiveness and generalization.

关键词： Motion parallax Deep learning Epipolar geometry component Motion Parallax Space Perception flight safety Unmanned Aerial Vehicle Obstacle avoidance Collision avoidance

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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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Observability Enhancement of the Target Tracking Model Based on Infrared Semi-strapdown Seeker

Observability Enhancement of the Target Tracking Model Based...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Peng Siting Zhao Qian Ma Yichao Xu Cheng Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory The Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9781728137933

As an important device for target interception, The seeker is used to search, recognize and track the target autonomously. Infrared semi-strapdown seekers are widely used in applications due to their low cost and small size. However, one non-negligible disadvantage of semi-strapdown infrared seekers is that the observability of target tracking model is poor, especially for the interception of high-speed maneuvering weapons. In this paper, the target tracking model ( i.e., the filtering estimation model) based on the infrared simi-strapdown seeker is established. The observability of the target tracking model is analyzed and the evaluation method of the observability is studied. Simulation results show that the observability of the filtering estimation model can be improved by increasing the relative motion between missile and target which results in faster changes of the line-of-sight (LOS) angle. According to the results on the observability analysis, the desired trajectory is obtained through the design of proper guidance law. Nonlinear filtering algorithm is also introduced for the trajectory design. Simulation experiments are conducted for the integrated filtering and guidance, and simulation results illustrate the effectiveness of the proposed observability enhancement method.

关键词： Observability Missiles Filtering Estimation Mathematical model Target tracking Navigation

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A Multi-branch Parallel Simulation Method based on Symbiotic Potential

A Multi-branch Parallel Simulation Method based on Symbiotic...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Lu Zhao Tu Zhen-biao Wei Jia-ning Zhu Yu-tong Liu Tong-lin Liu Qing-hua Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Insititute Beijing China

A multi-branch parallel simulation method and an architecture of application system are proposed in this paper based on symbiotic potential, and method of construction and calibration of parallel battlefield based on real data, as well as generation of simulation branch based on situation interpretation etc. are proposed. Fast building and adjustment of situation of real time simulation are realized based on simulation to meet the requirements of consistency and parallel operation between virtual simulation and real systems. Multibranch operation management, multi-branch highperformance operation support and other methods are proposed to support the dynamic generation of massive parallel simulation copies based on real-time intelligence and ultra-realtime and large-sample operation to meet the requirements of advanced evaluation of real-time situation. At the same time, a set of simulation application verification frameworks for typical task situation is presented to support the construction of multibranch simulation operation environment, and provide technical verification means for real-time situation prediction and evaluation.

关键词： Data models Analytical models Predictive models Real-time systems Computational modeling Symbiosis Integrated circuit modeling

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Evaluation method of UAV cluster simulation models with different granularity based on sensitivity analysis

Evaluation method of UAV cluster simulation models with diff...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Qing Zhang Jing Li Chao Shen Science and Technology on omplex-System Control and Intelligent-Agent Cooperation Laboratory Beijing China

Simulation test evaluation of the unmanned aerial vehicle (UAV) cluster operational effectiveness requires the support of simulation models with different granularity levels such as precise, medium, and coarse levels. In order to ensure the confidence of the evaluation, difficulties in evaluating the effectiveness of different granularity simulation models of the UAV cluster need to be solved. Aiming at the problem that the existing evaluation methods are not fully applicable due to the constraints of a large number of actual flight data, this paper proposes the evaluation method of different granularity simulation models, completes the rationality analysis of the granularity partitioning of UAV cluster at three levels: precise, medium and coarse, and the rationality analysis of the support relationship between different granularity models. Based on the special input method, capability meta-models of the UAV cluster with different granularity are evaluated. The evaluation method of different granularity simulation models of the UAV cluster based on sensitivity analysis is proposed. The sensitivity factor extraction and simulation analysis of precise granularity in the motion layer, medium granularity in the perception layer, and coarse granularity in the decision layer are completed. The validity evaluation of the different granularity simulation models has laid a solid foundation for the simulation test evaluation of the UAV cluster operational effectiveness.

关键词： Analytical models Sensitivity analysis

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A survey of few shot learning based on intelligent perception

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Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica 2020年 41卷

作者： Song, Chuang Zhao, Jiajia Wang, Kang Liang, Xinkai Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China School of Computer and Technique Fudan University Shanghai200433 China

Few-shot learning refers to using only a small amount of supervision information of the target class to train the machine learning model. Due to its practical values, recent advances in few-shot learning by academia and industry have made significant contributions. However, there were few reviews on this issue in China. This paper systematically summarizes and explores the few-shot learning algorithms and the object detection algorithms based on few-shot learning. Firstly, the problem definition of few-shot learning is given, and its connections with other classic machine learning problems are also enumerated. Meanwhile, the theoretical challenges of the problem of few-shot learning are explained. Then, we summarize the image classification based on few-shot learning, and analyze its representative works. Based on this, we focus on the problem of few-shot object detection, especially the problem of zero-shot object detection, and analyze the existing research works in detail. Finally, we look forward to the future development of few-shot learning in terms of problem setting, theoretical research, implementation technology, and application scenarios based on the advantages and disadvantages of the existing methods. It is expected to provide inspirations for the subsequent research works in this field. © 2020, Beihang University Aerospace Knowledge Press. All right reserved.

关键词： Image classification

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Cross-cycle iterative unmanned aerial vehicle reentry guidance based on reinforcement learning

Cross-cycle iterative unmanned aerial vehicle reentry guidan...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Yang Cheng Zunshi Shui Cheng Xu Tianyu Feng Yiyang Jiang Beijing Aerospace Technology Research Institute Beijing China Science and technology on complex system control and intelligent agent cooperation laboratory Beijing China

The traditional predictive correction algorithm requires a large number of iterative calculations for the predicted trajectory, which greatly occupies a large amount of computing resources, so that the real-time solution of the guidance command can not be guaranteed, and the guidance accuracy will have a large impact. And the prediction correction guidance requires the algorithm to have the ability of selfadaptation and intelligent learning. Therefore, this paper proposes a cross-cycle iterative hypersonic UAV predictive correction guidance method based on reinforcement learning. The parametric control variable (CVP) method is used to construct the parametric model of the guidance command. The actor-critic-based reinforcement learning method is used to solve the guidance command in real time, and the guidance information is effectively transmitted in the adjacent guidance solution cycle. The guidance error converges to within the allowable accuracy range during the cross-cycle iteration. Monte Carlo simulation shows that the proposed method has good adaptability to initial conditions and flight parameter uncertainty, and can guarantee the real-time performance of the guidance command while achieving high-precision guidance.

关键词： Trajectory Aircraft Atmospheric modeling Earth Mathematical model Iterative algorithms Prediction algorithms

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