A5068844813- Mineral Processing and Grinding
- Hydrocarbon exploration and reservoir analysis
- Hydraulic Fracturing and Reservoir Analysis
- Geochemistry and Geologic Mapping
- Drilling and Well Engineering
- Domain Adaptation and Few-Shot Learning
- Image Processing and 3D Reconstruction
- Video Surveillance and Tracking Methods
- Reservoir Engineering and Simulation Methods
- Indoor and Outdoor Localization Technologies
- Green IT and Sustainability
- Multimodal Machine Learning Applications
- Robotics and Sensor-Based Localization
- Rock Mechanics and Modeling
- Advanced Battery Technologies Research
- Caching and Content Delivery
Changzhou University
2019-2024
Chengdu University of Technology
2017
State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation
2017
Abstract Lithology identification is vital for reservoir exploration and petroleum engineering. Recently, there has been growing interest in using an intelligent logging approach lithology classification. Machine learning emerged as a powerful tool inferring types with the curves. However, well logs are susceptible to parameter manual entry, borehole conditions calibrations. Most studies field of classification machine approaches have focused only on improving prediction accuracy...
Abstract Lithology identification is critical in the interpretation of well-logging data for petroleum exploration and development. However, limited availability labeled machine learning model training can lead to compromised accuracy lithology classification models. Here, we propose a semi-supervised overcome this challenge. Our framework consists Bayesian optimization tuning ensemble algorithms, including random forest, gradient boosting decision tree, extremely randomized trees, adaptive...
Lithology identification is an indispensable part in geological research and petroleum engineering study. In recent years, several mathematical approaches have been used to improve the accuracy of lithology classification. Based on our earlier work that assessed machine learning models formation classification, we optimize boosting classification ability with data collected from Daniudi gas field Hangjinqi field. Three models, namely, AdaBoost, Gradient Tree Boosting, eXtreme are evaluated...
Performances of smartphones are profoundly affected by battery life. Maximizing the amount usage energy is essential to extend However, developers might concentrate more on functionality applications while ignoring bugs that drain during development process. There no quantitative approaches detect these introduced in this fast-paced In paper, we employ a system-call-based approach develop power consumption model for Android devices. Data measure mobile devices under different testing...
Recently, there has been growing interest in improving the efficiency and accuracy of Indoor Positioning System (IPS). The Received Signal Strength- (RSS-) based fingerprinting technique is essential for indoor localization. However, it challenging to estimate position on RSS’s measurement under complex environment. This paper evaluates three machine learning approaches Gaussian Process (GP) regression with different kernels get best positioning model. hyperparameter tuning used select...