A5003786701- X-ray Diffraction in Crystallography
- Crystallization and Solubility Studies
- Machine Fault Diagnosis Techniques
- Fault Detection and Control Systems
- Anomaly Detection Techniques and Applications
- Electrochemical Analysis and Applications
- Electrocatalysts for Energy Conversion
- Advanced Image Processing Techniques
- Hydraulic Fracturing and Reservoir Analysis
- Oil and Gas Production Techniques
- Water Quality Monitoring and Analysis
- Advanced Measurement and Detection Methods
- Rock Mechanics and Modeling
- Water Quality Monitoring Technologies
- Advanced Image Fusion Techniques
- Drilling and Well Engineering
- Engineering Diagnostics and Reliability
- Fuel Cells and Related Materials
- Advanced battery technologies research
- Mineral Processing and Grinding
- Image and Signal Denoising Methods
- Hydrological Forecasting Using AI
Shandong University of Science and Technology
2023-2024
Xidian University
2024
China University of Petroleum, East China
2023
China University of Petroleum, Beijing
2019
In the era of industrial big data, traditional shallow machine learning-based data analytical technologies can not handle complex system fault detection issue effectively. regard this problem, paper presents a deep method, called one-dimensional residual GANomaly (1DRGANomaly). This method builds semi-supervised feature extraction mechanism where unsupervised generator network is utilized to capture latent representation and discriminator adopted improve reconstruction quality. Furthermore,...
Nitrogen-carbon (N-C) materials have been recently explored to activate Fenton-reaction-inactive Zn single atoms as active sites for electrocatalytic oxygen reduction reaction (ORR). However, achieving Zn-N-C electrocatalysts with high activity and...
Deep learning is an important research topic in the field of image super-resolution. Problematically, performance existing hyperspectral super-resolution networks limited by feature for images. Nevertheless, current algorithms exhibit some limitations extracting diverse features. In this paper, we address to networks, focusing on challenges. We introduce Channel-Attention-Based Spatial–Spectral Feature Extraction network (CSSFENet) enhance diversity and optimize loss functions. Our...
Risk assessment of deep shale reservoirs is very important for subsurface energy development. However, due to complex geological environments and physicochemical interactions, reservoir fabric parameters exhibit variability. Moreover, the actual investigation testing information limited, which a typical small-sample problem. In this paper, heterogeneity statistical characteristics are clarified by measured mechanical parameters. A learning method with limited survey data proposed. The...
Abstract Crankshaft is a pivotal mechanical unit in the power-end system of fracturing pump and its fault inference could facilitate optimal condition-based maintenance. Fracturing pumps are equipped with advanced instrumentation systems able to acquire vibration information for crankshaft analysis, but there exist complex uncertain dependences between faults symptoms as well incomplete symptom information, further increasing difficulty by operators. To achieve effective case or diagnosis...