A5100375439- Seismic Imaging and Inversion Techniques
- Microstructure and Mechanical Properties of Steels
- Magnetic Properties and Applications
- Advancements in Battery Materials
- Advanced Battery Materials and Technologies
- Integrated Energy Systems Optimization
- Seismic Waves and Analysis
- Building Energy and Comfort Optimization
- Hydraulic Fracturing and Reservoir Analysis
- Smart Grid Energy Management
- Drilling and Well Engineering
- Image and Signal Denoising Methods
- Microstructure and mechanical properties
- Electromagnetic Effects on Materials
- Proteins in Food Systems
- Coal Properties and Utilization
- Microgrid Control and Optimization
- Energy Load and Power Forecasting
- Geothermal Energy Systems and Applications
- Optimal Power Flow Distribution
- Advanced machining processes and optimization
- Hydrocarbon exploration and reservoir analysis
- Extraction and Separation Processes
- Laser Material Processing Techniques
- Electric Power System Optimization
Zhengzhou University of Light Industry
2024-2025
Xi'an Jiaotong University
2022-2024
Southwest University
2024
Wuhan University of Technology
2024
Yangzhou University
2022-2024
China University of Geosciences
2024
Northeastern University
2022-2024
University of Nottingham Ningbo China
2024
Hohai University
2019-2023
Universidad del Noreste
2022
Seismic data reconstruction is one of the essential steps in seismic processing. Recently, deep learning (DL) models have attracted huge attention exploration, which has been applied to reconstruction, especially convolutional neural network (CNN)-based methods. However, general CNN-based only consider features time domain and do not take into account frequency features. Moreover, there are detailed lost due downsampling scheme. We propose a wavelet-based residual DL (WRDL) reconstruct...
Seismic image denoising is essential to enhance signal-to-noise ratio (SNR) of seismic images and facilitate processing geological structure interpretation. With the development deep learning (DL), several DL based models have been proposed for denoising. However, commonly used supervised require noise-free data as training labels, yet often difficult be obtained in field application scenarios. By considering similarity images, we propose a informed self-learning (SISL) address absence...
Attenuation of incoherent noise is an effective way to improve signal-to-noise ratio (SNR) seismic data. Recently, supervised deep learning based methods have been widely utilized for image denoising, which often need plenty noise-free data as training labels. However, are unavailable in field applications. We propose unsupervised method (NS2NS) train a denoising network by using single noisy The proposed model on two basic truths data: (1) High self-similarity data; (2) Spatially...
Peer-to-peer (P2P) energy trading is seen as a promising method for distributed transactive (TE) management. Since distribution network used electricity transfer, physical constraints should be imposed during the P2P trading. In this paper, we propose P2P-based TE management approach considering and product differentiation between different buses. A multi-period AC optimal power flow (OPF) model to monitor in system, exactness of second-order cone relaxation recovered by convex-concave...
Understanding the adsorption mechanism and precisely predicting thermodynamic properties of methane at high pressure are crucial while very challenging for shale gas development. In this study, we demonstrated that Langmuir model combining with different empirical methods to determine phase density makes calculated isothermal heat violate Henry's law low pressure. For instance, by Langmuir-Freundlich contradicts when absolute quantity is zero. Given current challenge in accurately...
Renewable energy producer is often exposed to huge financial losses in some imbalance hours (meaning that the contracted day-ahead market not equal actual output real time) caused by extremely large forecast error. To address this challenge, paper integrates end-user's risk profile into development of risk-averse combining approach for renewable trading. First, conditional value-at-risk (CVaR) applied evaluate extreme prediction error combined forecasts. Then, convex optimization models are...
A metal-free intramolecular alkoxylation-initiated cascade cyclization of allyl ether-tethered ynamides has been developed, leading to functionalized 3-isochromanones in high yields.
Suppressing random noise is an effective way to improve the signal-to-noise ratio (SNR) of seismic data. Supervised deep learning methods have recently been widely applied image denoising. However, these require a large amount noise-free data train network, which unavailable in practical applications. Moreover, most denoising focus on removing noise, assuming that zero-mean and independent signals. In field applications, real often exhibits band-limited spatial correlations. We propose...
The incorporation of nanoparticles hinders the spreading droplets after hitting wall.
The evolution of shear band (SB) and texture in strip-cast electrical steel was studied order to investigate the crystallographic character SB its influence on recrystallization texture. Shear deformation form played an effective role tailoring local lattice rotation accommodate heavy case inhomogeneous under conditions initial coarse grain steel. During rolling process, η-oriented SBs appeared at different stages as a result geometric softening. Cube-oriented mainly retained from exact Cube...
Hemiparesis is a common consequence of stroke that severely impacts the life quality patients. Active training key factor in achieving optimal neural recovery, but current systems for wrist rehabilitation present challenges terms portability, cost, and potential muscle fatigue during prolonged use. To address these challenges, this paper proposes low-cost, portable system with control strategy combines surface electromyogram (sEMG) electroencephalogram (EEG) signals to encourage patients...