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Lei Jiang
University of Pittsburgh
  1.  1
    Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach.Shanglei Chai, Zhen Zhang, Mo Du & Lei Jiang - 2020 - Complexity 2020:1-18.
    Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time (...)
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    Applying Principal Component Analysis to Seismic Attributes for Interpretation of Evaporite Facies: Lower Triassic Jialingjiang Formation, Sichuan Basin, China.Suyun Hu, Wenzhi Zhao, Zhaohui Xu, Hongliu Zeng, Qilong Fu, Lei Jiang, Shuyuan Shi, Zecheng Wang & Wei Liu - 2017 - Interpretation: SEG 5 (4):T461-T475.
    In China and elsewhere, it is important to predict different lithologies and lithofacies for hydrocarbon exploration in a mixed evaporite-carbonate-siliciclastic system. The lower section of the second member of the Jialingjiang Formation is mainly composed of anhydrite, dolostone, limestone, and siliciclastic rocks, providing a rare opportunity to reconstruct detailed facies in a [Formula: see text] 3D seismic survey with 31 wells. Wireline logs calibrated by core analysis are essential in distinguishing anhydrite, siliciclastics, and carbonates. Although different lithologies are characterized by (...)
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    A Mode Selected Mixed Logic Dynamic Model and Model Predictive Control of Buck Converter.Lei Jiang, Enliang Liu & Ding Liu - 2020 - Complexity 2020:1-11.
    A Mixed Logic Dynamic model and control method based on mode selection are proposed for the Buck convertor. In establishing the hybrid system model, the factors such as the inductor current are neglected, and the Model Predictive Control is used to switch the most favorable working state of the control target. Since the modeling process ignores the inductance and current, it is necessary to convert the optimization prediction control resulting to avoid the problem that the model is inconsistent with the (...)
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    Characterization of Carbonate Microfacies and Reservoir Pore Types Based on Formation MicroImager Logging: A Case Study From the Ordovician in the Tahe Oilfield, Tarim Basin, China.Miaomiao Meng, Tailiang Fan, Ian J. Duncan, Shuai Yin, Zhiqian Gao, Lei Jiang, Chao Yu & Longyan Jiang - 2018 - Interpretation: SEG 6 (1):T71-T82.
    Improving the characterization of deep carbonate reservoirs requires developing a clear understanding of nature and distribution of their constituent microfacies and associated pore types. These aspects have been little studied in the middle-lower Ordovician of the Tahe Oilfield in the Tarim Basin in large part due to the limited available cores and relatively poor seismic data. Formation MicroImage logging provides a bridge to connect the core data and seismic data to facilitate study of the distribution of microfacies and pore types. (...)
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    Quantitative Identification of Coal Texture Using the Support Vector Machine with Geophysical Logging Data: A Case Study Using Medium-Rank Coal From the Panjiang, Guizhou, China.Zhenghui Xiao, Wei Jiang, Bin Sun, Yunjiang Cao, Lei Jiang, Taotao Cao, Qing Yang, Cailun Huang, Xiansheng Yang & Xiangkuan Huang - 2020 - Interpretation 8 (4):T753-T762.
    Coal texture is important for predicting coal seam permeability and selecting favorable blocks for coalbed methane exploration. Drilled cores and mining seam observations are the most direct and effective methods of identifying coal texture; however, they are expensive and cannot be used in unexplored coal seams. Geophysical logging has become a common method of coal texture identification, particularly during the CBM mining stage. However, quantitative methods for identifying coal texture based on geophysical logging data require further study. The support vector (...)
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