Article
Engineering, Environmental
Vinky Chow, Raphael C-W Phan, Anh Cat Le Ngo, Ganesh Krishnasamy, Siang-Piao Chai
Summary: Photocatalysis is a powerful technology with beneficial impacts on science and engineering. However, experimental-based research in this field has been disrupted due to the COVID-19 pandemic. Machine learning is playing a vital role in ensuring the continuity of photocatalysis research and solving relevant problems.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2022)
Article
Computer Science, Information Systems
Kai Yang, Jie Lu, Wanggen Wan, Guangquan Zhang, Li Hou
Summary: This paper proposes a transfer learning method based on sparse Gaussian process to tackle regression problems, and maintains transfer performance through adaptive neural kernel network and transfer inducing point algorithm.
INFORMATION SCIENCES
(2022)
Article
Engineering, Multidisciplinary
Jan N. Fuhg, Michele Marino, Nikolaos Bouklas
Summary: This paper introduces a method that combines data-driven constitutive prediction and macroscopic calculations, ensuring prediction accuracy and reliability by using local approximate Gaussian process regression (laGPR). A modified Newton-Raphson approach specific to laGPR is proposed to solve the global structural problem.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Mohammad Nabati, Seyed Ali Ghorashi, Reza Shahbazian
Summary: This paper introduces a low-cost multi-target Gaussian process regression algorithm, called joint GPR, which employs a shared covariance matrix and solves a sub-optimal cost function for hyperparameter optimization during the training phase. Experimental results show that the proposed method outperforms existing approaches on multiple benchmark datasets.
Article
Energy & Fuels
Claudia Buerhop Lutz, Larry Lueer, Oleksandr Stroyuk, Jens Hauch, Ian Marius Peters
Summary: To fully benefit from high-efficiency solar cells and modules, the quality of polymer encapsulants and backsheet (BS) materials is crucial. By studying inverter data, we found that different types of BS are associated with varying rates of ground impedance (GI) loss and degradation onset.
SOLAR ENERGY MATERIALS AND SOLAR CELLS
(2023)
Article
Engineering, Multidisciplinary
Porya Ghasemi, Masoud Karbasi, Alireza Zamani Nouri, Mahdi Sarai Tabrizi, Hazi Mohammad Azamathulla
Summary: Forecasting of drought using three machine learning models showed that the accuracy declined as the predictive period increased. The Gaussian process regression model performed the best across all intervals and clusters, while the MLP model had the worst results.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Environmental Sciences
Dapeng Feng, Jiangtao Liu, Kathryn Lawson, Chaopeng Shen
Summary: This paper introduces a differentiable and learnable process-based model (delta model) that approaches the performance level of purely data-driven deep learning models (such as LSTM) in predicting hydrologic variables. Experimental results show that the delta model performs similarly to LSTM in simulating variables like streamflow and can also output other untrained variables, such as soil and groundwater storage.
WATER RESOURCES RESEARCH
(2022)
Article
Energy & Fuels
Kwang-Jae Lee, Won-Hyung Lee, Kwang-Ki K. Kim
Summary: This paper presents methods of battery state-of-charge (SoC) estimation using Gaussian processes (GPs): GP-Unscented Kalman filter (GP-UKF) and GP-Particle filter (GP-PF). Both methods utilize data-driven battery GP prediction and observation models trained via machine learning. The proposed GP-UKF incorporates a straightforward technique for calculating predicted voltage sigma points, while the proposed GP-PF involves calculating initial samples and weights of the previous SoC and adjusting the likelihood function. Evaluation of the proposed methods shows significant improvements in SoC estimation accuracy and uncertainty compared to classical methods.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Environmental Sciences
Tianfang Xu, Qianqiu Longyang, Conor Tyson, Ruijie Zeng, Bethany T. Neilson
Summary: This study presents a hybrid modeling approach that combines a physically based snow model with a deep learning karst model to predict streamflow in mountainous karst watersheds. The approach is tested on a watershed in northern Utah and shows high accuracy in simulating streamflow. The deep learning model captures the spatiotemporal recharge and discharge patterns and provides valuable insights into hydrologic responses influenced by complex surface and subsurface properties.
WATER RESOURCES RESEARCH
(2022)
Article
Mechanics
Xu Yan, Dehua Liu, Wenhua Xu, Denghui He, Haiyang Hao
Summary: This paper applied Gaussian process regression (GPR) model to predict shale gas production rate based on geological and field data from 175 wells in the Fuling gas field, China. The results showed that geological factors dominated the gas production rate, fluid loss had a negative impact on production rates, and increasing the consumption of 40-70 mesh proppant could increase gas production in Fuling.
ENGINEERING FRACTURE MECHANICS
(2023)
Article
Computer Science, Artificial Intelligence
Myong Chol Jung, Rifai Chai, Jinchuan Zheng, Hung Nguyen
Summary: Biomechatronic systems have made significant advancements through integrating pattern recognition and regression algorithms, but are often affected by disturbances in real-world environments. The proposed WRGP algorithm in this study shows improved performance by adapting weights based on covariate shift, outperforming traditional LR and MLP models in robustness.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Adhishree Srivastava, S. K. Parida
Summary: In this paper, a model for detection, location and isolation of faults in AC microgrid is presented. Real-time data is processed to provide reliable protection and predict fault location using a fault locator module. The superiority of GPR method over others is established through comparison of various machine learning techniques.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Environmental Sciences
Muhammad Tariq Khan, Muhammad Shoaib, Raffaele Albano, Muhammad Azhar Inam, Hamza Salahudin, Muhammad Hammad, Shakil Ahmad, Muhammad Usman Ali, Sarfraz Hashim, Muhammad Kaleem Ullah
Summary: The science of hydrological modeling has evolved with advancements in software and hardware technologies. Researchers focus on accurately converting rainfall into runoff and assessing uncertainty. Alternative data-driven methods, such as coupling machine learning models with wavelet transformation, have gained attention in hydrology.
Article
Environmental Sciences
Elahe Akbari, Ali Darvishi Boloorani, Jochem Verrelst, Stefano Pignatti, Najmeh Neysani Samany, Saeid Soufizadeh, Saeid Hamzeh
Summary: This study proposes a kernel-based machine learning algorithm for spatio-temporal estimation of vegetation biophysical variables using Sentinel-2 images. The developed GPR-PSO algorithm outperformed other algorithms in terms of robustness and accuracy, and it is capable of generating pixel-based uncertainty maps for prediction purposes.
Article
Chemistry, Analytical
Esme Isik
Summary: Thermoluminescence (TL) is a method of monitoring absorbed dose using luminescence properties of crystals. This study investigated the TL dosimetric properties of calcite and used Gaussian process regression (GPR) to examine stimulated TL characteristics.
Article
Meteorology & Atmospheric Sciences
Won Chang, Jiali Wang, Julian Marohnic, V. Rao Kotamarthi, Elisabeth J. Moyer
Article
Computer Science, Interdisciplinary Applications
Youngdeok Hwang, Hang J. Kim, Won Chang, Kyongmin Yeo, Yongku Kim
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2019)
Article
Geosciences, Multidisciplinary
Won Chang, Sunghoon Kim, Heewon Chae
SPATIAL STATISTICS
(2020)
Article
Statistics & Probability
Matthew Plumlee, Taylor G. Asher, Won Chang, Matthew Bilskie
Summary: This article demonstrates the good forecasting performance of probabilistic hurricane storm surge forecasting using a small number of carefully chosen simulations by utilizing modern statistical tools and an optimal design criterion. The study also addresses the missing data and output handling issues in surge modeling, showing evidence of efficacy in comparison to existing methods through a case study on Hurricane Michael (2018).
ANNALS OF APPLIED STATISTICS
(2021)
Article
Business
Sunghoon Kim, Wayne S. DeSarbo, Won Chang
Summary: The study introduces a new spatial modeling approach to calibrate the potential impact of spatial dependency and heterogeneity on customer service and satisfaction measurements. By utilizing a hierarchical Bayes framework with geographical boundary effects, the proposed method shows improved performance compared to existing procedures.
INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING
(2021)
Article
Computer Science, Interdisciplinary Applications
Jacob Tracy, Won Chang, Sarah St George Freeman, Casey Brown, Adriana Palma Nava, Patrick Ray
Summary: This paper introduces a data-driven dynamic emulation method that successfully simulates high-dimensional model outputs, overcoming challenges from traditional multivariate statistics. The method is demonstrated on a regional groundwater model in metropolitan Mexico City and successfully emulates the dynamic of land subsidence and aquifer level fluctuation, providing new methodological advances for computationally expensive planning and optimization applications.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Mathematics, Interdisciplinary Applications
Saumya Bhatnagar, Won Chang, Seonjin Kim, Jiali Wang
Summary: Computer model calibration plays a key role in scientific and engineering problems. However, the existing standard calibration framework faces inferential issues when dealing with high-dimensional dependent data. To overcome this problem, we propose a new calibration framework based on deep neural networks, which can accurately estimate input parameters and quantify the uncertainty of the estimates.
SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION
(2022)
Article
Geosciences, Multidisciplinary
Jaewoo Park, Won Chang, Boseung Choi
Summary: This article analyzes COVID-19 contact tracing data from Seoul to understand the spatial patterns of patient visits and detect local cluster centers. The study develops a novel interaction Neyman-Scott process to account for the complex behavior of cluster centers. The results show that the method can effectively describe the spatial patterns of patient visits and provide visualizations for public health interventions.
SPATIAL STATISTICS
(2022)
Article
Biology
Carter Allen, Yuzhou Chang, Brian Neelon, Won Chang, Hang J. Kim, Zihai Li, Qin Ma, Dongjun Chung
Summary: High throughput spatial transcriptomics (HST) is a rapidly emerging experimental technology that allows for analyzing gene expression in tissue samples at or near single-cell resolution. We developed SPRUCE, a Bayesian spatial multivariate finite mixture model, to identify distinct cellular sub-populations in HST data.
Article
Statistics & Probability
Won Chang, Bledar A. Konomi, Georgios Karagiannis, Yawen Guan, Murali Haran
Summary: Calibrating ice sheet models is challenging due to the nature of the data and the uncertainties in model parameters. However, a hierarchical latent variable model can overcome these challenges and provide improved projections for future ice-volume change.
ANNALS OF APPLIED STATISTICS
(2022)
Article
Environmental Sciences
Christopher K. Wikle, Abhirup Datta, Bhava Vyasa Hari, Edward L. Boone, Indranil Sahoo, Indulekha Kavila, Stefano Castruccio, Susan J. Simmons, Wesley S. Burr, Won Chang
Summary: Explainable AI is a sub-discipline of computer science and machine learning that aims to address the lack of uncertainty quantification and inability to do inference in machine learning and deep neural models. This article focuses on explaining which inputs are important in models for predicting environmental data, and describes three general methods for explainability.
Review
Biochemistry & Molecular Biology
Hyeongseon Jeon, Juan Xie, Yeseul Jeon, Kyeong Joo Jung, Arkobrato Gupta, Won Chang, Dongjun Chung
Summary: In this paper, power analysis for three types of gene expression profiling technologies, bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics, is reviewed and discussed from a practical standpoint. Existing power analysis tools and recommendations are described for bulk RNA-seq and scRNA-seq experiments. Factors influencing power analysis are investigated for high-throughput spatial transcriptomics, as there are currently no power analysis tools available.
Article
Environmental Sciences
Jaehong Jeong, Won Chang
Summary: As the risk of climate change becomes more apparent, countries worldwide are actively searching for alternative energy sources. Wind energy presents great potential for future energy portfolios without negative environmental impacts. Understanding the spatio-temporal patterns of wind is crucial in developing energy plans at national and global levels.
Article
Clinical Neurology
Minki P. Lee, Kien Hoang, Sungkyu Park, Yun Min Song, Eun Yeon Joo, Won Chang, Jee Hyun Kim, Jae Kyoung Kim
Summary: Sleep is crucial for health and well-being, but collecting and analyzing accurate longitudinal sleep data can be challenging. Researchers propose a neural network model called SOMNI to impute missing sleep-activity data, allowing clinicians to monitor sleep-wake cycles of individuals with irregular sleep patterns.
Article
Geosciences, Multidisciplinary
Jiali Wang, Zhengchun Liu, Ian Foster, Won Chang, Rajkumar Kettimuthu, V. Rao Kotamarthi
Summary: This study develops a neural-network-based approach for emulating high-resolution precipitation data with greatly reduced computational cost, which trains on a combination of low- and high-resolution simulations to produce realistic results. Results show that CNNs trained by CGAN generated more physically reasonable results, better capturing data variability and extremes than conventional methods.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2021)
