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Full-Text Articles in Engineering
Image Restoration Using Automatic Damaged Regions Detection And Machine Learning-Based Inpainting Technique, Chloe Martin-King
Image Restoration Using Automatic Damaged Regions Detection And Machine Learning-Based Inpainting Technique, Chloe Martin-King
Computational and Data Sciences (PhD) Dissertations
In this dissertation we propose two novel image restoration schemes. The first pertains to automatic detection of damaged regions in old photographs and digital images of cracked paintings. In cases when inpainting mask generation cannot be completely automatic, our detection algorithm facilitates precise mask creation, particularly useful for images containing damage that is tedious to annotate or difficult to geometrically define. The main contribution of this dissertation is the development and utilization of a new inpainting technique, region hiding, to repair a single image by training a convolutional neural network on various transformations of that image. Region hiding is also …
Fault Diagnosis Of High Speed Train Bogie Based On Multi-Domain Fusion Cnn, Yunpu Wu, Weidong Jin, Yingkun Huang
Fault Diagnosis Of High Speed Train Bogie Based On Multi-Domain Fusion Cnn, Yunpu Wu, Weidong Jin, Yingkun Huang
Journal of System Simulation
Abstract: The performance degradation and failures of high-speed train bogie components directly threaten the operation security of train. A fault detection method based on multi-domain fusion convolutional neural network is proposed to address the high complexity, high coupling and strong nonlinearity of vibration signals. Noise injection for time domain signal is used to enhance noise robustness and generalization of the model. Signal time-frequency representation information is obtained through embedded time-frequency transformation layer. Adaptive weight-based fusion is implemented through intrinsic characteristics of the convolutional neural network to handle the multi-domain multi-channel information. The experimental results show that the proposed method improves …
Cloud Fraction Of Satellite Imagery Based On Convolutional Neural Networks, Xia Min, Maoyang Shen, Jianfeng Wang, Yangguang Wang
Cloud Fraction Of Satellite Imagery Based On Convolutional Neural Networks, Xia Min, Maoyang Shen, Jianfeng Wang, Yangguang Wang
Journal of System Simulation
Abstract: Cloud fraction is the basis for the application of meteorological satellite. Existing methods cannot use all the characteristics and optical parameters of the satellite cloud, which results in the inaccuracy of cloud detection and cloud fraction. In order to solve this problem, convolutional neural network is used for cloud detection. Based on the improved convolutional neural network, the satellite cloud image is divided into thin cloud, thick cloud and clear sky. Based on the cloud detection, an improved spatial correlation method is used for cloud fraction. The results for Chinese HJ-1A/B satellite imagery show that convolutional neural network can …