Big data facial image is an important identity information for people. However, facial image inpainting using existing deep learning methods has some problems such as insufficient feature mining and incomplete semantic expression, leading to output…
Big data facial image is an important identity information for people. However, facial image inpainting using existing deep learning methods has some problems such as insufficient feature mining and incomplete semantic expression, leading to output…
Big data facial image is an important identity information for people. However, facial image inpainting using existing deep learning methods has some problems such as insufficient feature mining and incomplete semantic expression, leading to output…
Regular sampled seismic data is important for seismic data processing. However, seismic data is often missing due to natural or economic reasons. Especially, when encountering big obstacles, the seismic data will be missing in big gaps, which is more…
Regular sampled seismic data is important for seismic data processing. However, seismic data is often missing due to natural or economic reasons. Especially, when encountering big obstacles, the seismic data will be missing in big gaps, which is more…
Big data facial image is an important identity information for people. However, facial image inpainting using existing deep learning methods has some problems such as insufficient feature mining and incomplete semantic expression, leading to output…
Big data facial image is an important identity information for people. However, facial image inpainting using existing deep learning methods has some problems such as insufficient feature mining and incomplete semantic expression, leading to output…
Lithology recognition is an essential part of reservoir parameter prediction. Compared to conventional algorithms, deep learning that needs a large amount of training data as support can extract features automatically. In the process of real data…
Data-driven fault diagnosis for dynamic process faces three challenges. Firstly, models are hard to establish to describe the multivariate coupled correlations. Secondly, in an actual industrial process, the cost of fault data collection is huge, and…
The Internet of things (IoT) has certainly become one of the hottest technology frameworks of the year. It is deep in many industries, affecting people’s lives in all directions. The rapid development of the IoT technology accelerates the process of…
Most of the time the mechanical equipment is in normal operation state, which results in high imbalance between fault data and normal data. In addition, traditional signal processing methods rely heavily on expert experience, making it difficult for…