DATA DRIVEN ANALYSIS, MODELING, AND SEMI-SUPERVISED MACHINE LEARNING FOR QUALITATIVE AND QUANTITATIVE DETERMINATIONS

• 1. 使用数据驱动不确定性范围的储层属性趋势建模指导 [P] . 中国专利： CN106663234A . 2017-05-10
• 2. 用于对尿样进行化学、定性、定量和半定量分析的方法和装置 [P] . 中国专利： CN1181343C . 2004.12.22
• 3. DATA DRIVEN ANALYSIS, MODELING, AND SEMI-SUPERVISED MACHINE LEARNING FOR QUALITATIVE AND QUANTITATIVE DETERMINATIONS [P] . 美国专利： US2018374575A1 . 2018-12-27

  机译： 数据驱动的分析，建模和半监督机器学习，可进行定性和定量确定

• 4. Data driven analysis, modeling, and semi-supervised machine learning for qualitative and quantitative determinations [P] . 美国专利： US10068666B2 . 2018-09-04

  机译： 数据驱动的分析，建模和半监督机器学习，以进行定性和定量确定

• 5. PAIN DETERMINATION USING TREND ANALYSIS, MEDICAL DEVICE INCORPORATING MACHINE LEARNING, ECONOMIC DISCRIMINANT MODEL, AND IOT, TAILORMADE MACHINE LEARNING, AND NOVEL BRAINWAVE FEATURE QUANTITY FOR PAIN DETERMINATION [P] . US2022004913A1 . 2022-01-06

  机译： 使用趋势分析，医疗器械融合机器学习，经济判别模型和物联网，定制机器学习和新型脑波特征数量的疼痛确定

• 1. A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidence [J] . Christopher M. Yeomans, Robin K. Shail, Stephen Grebby, 地学前缘(英文版) . 2020,第006期
• 2. Semi-supervised LIBS quantitative analysis method based on co-training regression model with selection of effective unlabeled samples [J] . Xiaomeng LI, Huili LU, Jianhong YANG, 等离子体科学和技术（英文版） . 2019,第003期
• 3. Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models [J] . Vahid Najafi Moghaddam Gilani, Seyed Mohsen Hosseinian, Meisam Ghasedi, Mathematical Problems in Engineering: Theory, Methods and Applications . 2021,第a期
• 4. Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data [J] . Pan Leyun, Cheng Caixia, Haberkorn Uwe, Physics in medicine and biology. . 2017,第9期
• 5. Combining data assimilation and machine learning to build data-driven models for unknown long time dynamics-Applications in cardiovascular modeling [J] . Regazzoni Francesco, Chapelle Dominique, Moireau Philippe International journal for numerical methods in biomedical engineering . 2021,第7期
• 6. A NEW PORTABLE SPECTROMETRIC SURFACE DATA ANALYSIS SYSTEM FOR QUALITATIVE AND QUANTITATIVE DETERMINATION OF PETROLEUM HYDROCARBONS IN CONVENTIONAL AND UNCONVENTIONAL RESERVOIRS [C] . John D. Hanby, Charles D. Fator, E. Ray Oswalt SPWLA Annual Logging Symposium;Society of Petrophysicists and Well Log Analysts, inc . 2015
• 7. Statistical Approaches for Big Data Analytics and Machine Learning: Data-Driven Network Reconstruction and Predictive Modeling of Time Series Biological Systems. [D] . Farhangmehr, Farzaneh. 2014
• 8. Identification of the high-risk area for schistosomiasis transmission in China based on information value and machine learning: a newly data-driven modeling attempt [O] . Yan-Feng Gong, Ling-Qian Zhu, Yin-Long Li, 2021
• 9. Data Driven Determination of Reaction Conditions in Oxidative Coupling of Methane via Machine Learning [O] . Junya Ohyama, Shun Nishimura, Keisuke Takahashi 2019

• 1. Machine Learning for 5G and Beyond:From ModelBased to Data-Driven Mobile Wireless Networks [J] . Tianyu Wang ,Shaowei Wang ,Zhi-Hua Zhou . 中国通信：英文版 . 2019,第1期
• 2. Semi-Supervised Learning Based Big Data-Driven Anomaly Detection in Mobile Wireless Networks [J] . Bilal Hussain ,Qinghe Du ,Pinyi Ren . 中国通信 . 2018,第004期
• 3. Quantitative versus Qualitative Geospatial Data in Spatial Modelling and Decision Making [J] . Ko Ko Lwin ,Yuji Murayama ,Chiaki Mizutani . 地理信息系统（英文） . 2012,第3期
• 4. 不确定性知识的定性定量表达方法分析与展望 [C] . Shuiie LI ,李书杰 ,Zonghai CHEN . Thirteenth Chinese Conference,SSTA 2011(第十三届中国系统仿真技术及其应用学术会议） . 2011
• 5. 数据驱动的高比例可再生能源电力系统不确定性分析研究 [A] . 王冠中 . 2019