In this paper, with considerations of low efficiency of missile path planning (MPP) by traditional aggregation technology, it uses affinity propagation based multi-objective evolutionary algorithm with hypervolume env...
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In view of the disadvantages of Traditional tandem joint type thin-plate palletizing robots using push-down adsorption sorting, such as moving path complexity, large occupied space, joint error stack and low end contr...
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In this paper, a new optimal linear-quadratic guidance law with terminal acceleration constraints is proposed for intercepting maneuvering target. Because the angle of attack is approximately proportional to the norma...
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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 cont...
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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.
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 prov...
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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.
Recent object detection models have achieved satisfactory performance by deep learning with large-scale annotated datasets. However, these models often perform poorly when the training examples are not sufficient enou...
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In this paper, two types of practical angle of attack control schemes are proposed for hypersonic vehicle which is subject to large unmodeled, parametric uncertainties and external disturbances. In the first scheme, t...
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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 ...
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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.
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 prior...
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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.
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 solut...
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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.
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