Since the cumbersome collection process and high cost, the collected degradation of the product is basically small samples, which will affect the accuracy of reliability evaluation. It is necessary to expand the degra...
Since the cumbersome collection process and high cost, the collected degradation of the product is basically small samples, which will affect the accuracy of reliability evaluation. It is necessary to expand the degradation to improve the accuracy of later reliability assessment. Therefore, a degradation generation and prediction method is proposed combining the time series generator adversarial network (TimeGAN) and stochastic process. Firstly, the input degradation is expanded by the sliding window to improve the later training accuracy; Then, the construction of the generator in TimeGAN is linked with the stochastic process to make the generation data more realistic. Finally, the results of degradation prediction by the Gated Recurrent Unit (GRU) can be obtained. Two datasets and different generation methods are adopted to evaluate the effectiveness of the proposed method. The results shows that the Kullback-Leibler(KL) divergence is the smallest, and the prediction error is the smallest compared with the other methods. So, the proposed method is proved that it is valid in the degradation generation and prediction, and can be used for the further reliability assessment of the product in the industrial system.
The synthetic aperture radar (SAR) is the recent all-weather technology used to monitor areas with low to moderate penetration. However, the scarcity of SAR imagery, and the high-level noise in the images makes it dif...
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For the regional search problem of multiple unmanned air vehicles (UAVs) ,a cooperative search strategy of multiple UAVs is proposed based on action matrix game (AMG) for intelligent moving targets on the ground in th...
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This paper studies the formation control and simultaneous fault detection of multi-UAVs. Firstly, a distributed observer is constructed for the second-order particle model of each UAV. Secondly, a novel formation cont...
This paper studies the formation control and simultaneous fault detection of multi-UAVs. Firstly, a distributed observer is constructed for the second-order particle model of each UAV. Secondly, a novel formation controller is designed based on the distributed observer. Then, the faulty UAV is detected by the residual signal. Finally, based on Lyapunov theory and Projection Lemma, sufficient conditions for UAVs formation are obtained. The effectiveness of the proposed method is verified by simulation.
With advancements in science and technology, photon-counting LiDAR applications are growing due to long-range measurement, high-definition imaging, and accuracy. Geiger-mode avalanche photodiodes (Gm-APD) provide sing...
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ISBN:
(数字)9798331542283
ISBN:
(纸本)9798331542290
With advancements in science and technology, photon-counting LiDAR applications are growing due to long-range measurement, high-definition imaging, and accuracy. Geiger-mode avalanche photodiodes (Gm-APD) provide single-photon sensitivity and picosecond resolution, but noise in depth estimation affects imaging accuracy. This study introduces a novel depth estimation method using time series correlation to mitigate these noise issues, improving accuracy in LiDAR imaging. Utilizing a photon-counting LiDAR system and time-correlated single photon counting (TCSPC), this study capture target echo photon flight times to maintain depth information. The proposed denoising approach incorporates multiple time series windows and Bayesian estimation to enhance depth accuracy, alongside a pruning algorithm to optimize computational efficiency. Evaluation metrics show significant improvements: RSNR improved by 4% and MAE decreased by 43 % compared to traditional methods. The findings demonstrate the robustness of this method across various scenarios, enhancing depth estimation and noise mitigation in photon-counting LiDAR technology, thereby providing practical improvements for a range of applications.
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...
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.
Modeling of scattering characteristics between ground/offing targets and environment is one of the necessary conditions to realize the internal field reconstruction of complex electromagnetic scenes. According to the ...
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In this paper, an autonomous positioning method based on coarse-to-fine multi-modal image matching is proposed for UAV navigation in GPS denied environment. Coarse image matching refers to roughly determining the appr...
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Low-light enhancement task is an essential component of computer low-level visual tasks, which involves processing images captured under dim lighting conditions to make them appear as if they were taken under normal i...
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ISBN:
(数字)9798350359145
ISBN:
(纸本)9798350359152
Low-light enhancement task is an essential component of computer low-level visual tasks, which involves processing images captured under dim lighting conditions to make them appear as if they were taken under normal illumination. Currently, deep neural networks have become the mainstream approach for image processing. However, recent works have devoted considerable efforts to designing high-performance models, which often come with high computational complexity and inference time, making real-time processing unfeasible. We observed that some convolutional methods are due to the need for deep layers which results in a large number of parameters. Moreover, enhancing details and removing noise in low-light images remains an open challenge. In order to solve the above problems, we propose a lightweight baseline that combines CNN and sparse grid attention transformer blocks to enable the model to capture a global receptive field at an early stage. Specifically, we propose a High-Frequency Wavelet-aware Block(HFWB) that focuses on processing high-frequency information in the wavelet domain to refine details and suppress noise. With a processing time of only 10.6ms, the performance of our model outperforms that of the current state-of-the-art lightweight models on benchmark low-light datasets. Compared to state-of-the-art models in the LOL dataset, our model achieves a reduction in inference time of over 90% and requires only about 1% of the FLOPS.
In order to solve the problem that the point feature tracking is not robust enough to reduce the accuracy of the system in a low-texture environment, this paper proposes a visual inertial odometry system based on poin...
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