Dr. Fan Bai Profile

Dr. Fan Bai

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Proceedings Article | 25 May 2023 Paper

Engine fault detection algorithm EA-EFNC based on few-shot learning

Caiyun Wu, Fuqi Li, Fan Bai

Proceedings Volume 12636, 126364Z (2023) https://doi.org/10.1117/12.2675146

KEYWORDS: Education and training, Detection and tracking algorithms, Feature extraction

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In order to realize engine fault detection, a fault detection algorithm EA-ENC based on Few-shot learning is proposed in this paper. In recent years, more and more deep learning algorithms have been applied to the field of industrial detection, and certain achievements have been achieved. However, a common problem is that it is difficult to obtain available samples in the field of fault detection, and the labeled samples are too few. This problem of sample imbalance leads to the difficulty of model optimization, and the proposed method is difficult to be applied in engineering. This paper proposes a fault detection scheme based on Few-shot learning. The data involved in training is augmented by Few-shot learning, and the distribution reference line of the category is obtained by training with custom N_ways and N_shot. The classification reference line is constantly optimized during training, and the learning ability of the algorithm for features is strengthened by increasing the network depth of feature extractors. Through comparison and testing, the proposed algorithm can be applied in current industrial fault detection engineering.

Proceedings Article | 25 May 2023 Paper

research on UAV-assisted path planning system based on deep learning

Caiyun Wu, Yu Gong, Fan Bai

Proceedings Volume 12636, 126360N (2023) https://doi.org/10.1117/12.2675172

KEYWORDS: Unmanned aerial vehicles, Depth of field, Deep learning, Object detection, Depth maps

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With the widespread use of First Person View (FPV) Drone in film and television shooting, terrain reconnaissance, etc, the operator simultaneously handles the target task and obstacle avoidance trajectory planning flight task under greater pressure, which is very likely to lead to accidents, so combining monocular visual depth-of-field algorithm with Unmanned Aerial Vehicle(UAV) dynamic information to provide the controller with trajectory-assisted indication during flight becomes the focus of this research paper. In this paper, by combining the artificial potential field with deep learning, we use deep learning to regress the target motion trajectory under the action of depth-of-field potential, effectively predict multiple possible obstacle avoidance trajectories under the current position by introducing UAV positional information and UAV viewpoint images, improve the computational speed by streamlining the network, and deploy the algorithm model to Jetson TX2 for experimental validation. The experimental results show that the channel detection achieves the design requirements and realizes the effect of UAV aircraft to assist the controller in high dynamic scenes.

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