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Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis

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npj Computational Materials 2021年第1期7卷 867-877页

作者： Jaimyun Jung Juwon Na Hyung Keun Park Jeong Min Park gyuwon kim Seungchul Lee Hyoung Seop kim Department of Materials AI&Big-Data Korea Institute of Materials Science(KIMS)Changwon-siRepublic of Korea Department of Mechanical Engineering Pohang University of Science and Technology(POSTECH)PohangRepublic of Korea Department of Materials Science and Engineering Pohrepresent the average grainang University of Science and Technology(POSTECH)PohangRepublic of Korea Graduate School of Artificial Intelligence Pohang University of Science and Technology(POSTECH)PohangRepublic of Korea Graduate Institute of Ferrous Technology Pohang University of Science and Technology(POSTECH)PohangRepublic of Korea

The digitized format of microstructures,or digital microstructures,plays a crucial role in modern-day materials ***,the acquisition of digital microstructures through experimental means can be unsuccessful in delivering sufficient resolution that is necessary to capture all relevant geometric features of the *** resolution-sensitive microstructural features overlooked due to insufficient resolution may limit one’s ability to conduct a thorough microstructure characterization and material behavior analysis such as mechanical analysis based on numerical ***,a highly efficient super-resolution imaging based on deep learning is developed using a deep super-resolution residual network to super-resolved low-resolution(LR)microstructure data for microstructure characterization and finite element(FE)mechanical *** characterization and FE model based mechanical analysis using the super-resolved microstructure data not only proved to be as accurate as those based on high-resolution(HR)data but also provided insights on local microstructural features such as grain boundary normal and local stress distribution,which can be only partially considered or entirely disregarded in LR data-based analysis.

关键词： microstructure Microstructure analysis

Deep learning acceleration of multiscale superresolution localization photoacoustic imaging

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Light(Science & Applications) 2022年第6期11卷 1166-1177页

作者： Jongbeom kim gyuwon kim Lei Li Pengfei Zhang Jin Young kim Yeonggeun kim Hyung Ham Kkim Lihong V.Wang Seungchul Lee Chulhong kim Departments of Electrical Engineering Mechanical EngineeringConvergence IT Engineeringand Interdisciplinary Bioscience and BioengineeringGraduate School of Artificial IntelligenceMedical Device Innovation CenterPohang University of Science and Technology(POSTECH)77 Cheongam-roNam-guPohangGyeongbuk 37673Republic of Korea Caltech Optical Imaging Laboratory Andrew and Peggy Cherng Department of Medical EngineeringDepartment of Electrical EngineeringCalifornia Institute of Technology1200 E.California Blvd.MC 138-78PasadenaCA 91125USA School of Precision Instruments and Optoelectronics Engineering Tianjin University92 Weijin RoadNankai DistrictTianjin 300072China Opticho 532CHANGeUP GROUND87 Cheongam-roNam-guPohangGyeongsangbuk 37673Republic of Korea

A superresolution imaging approach that localizes very small targets,such as red blood cells or droplets of injected photoacoustic dye,has significantly improved spatial resolution in various biological and medical imaging ***,this superior spatial resolution is achieved by sacrificing temporal resolution because many raw image frames,each containing the localization target,must be superimposed to form a sufficiently sampled high-density superresolution ***,we demonstrate a computational strategy based on deep neural networks(DNNs)to reconstruct high-density superresolution images from far fewer raw image *** localization strategy can be applied for both 3D label-free localization optical-resolution photoacoustic microscopy(OR-PAM)and 2D labeled localization photoacoustic computed tomography(PACT).For the former,the required number of raw volumetric frames is reduced from tens to fewer than *** the latter,the required number of raw 2D frames is reduced by 12 ***,our proposed method has simultaneously improved temporal(via the DNN)and spatial(via the localization method)resolutions in both label-free microscopy and labeled ***-learning powered localization PA imaging can potentially provide a practical tool in preclinical and clinical studies requiring fast temporal and fine spatial resolutions.

关键词： Deep frames networks

Temporal Statistical Downscaling of Precipitation and Temperature Forecasts Using a Stochastic Weather Generator

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Advances in Atmospheric Sciences 2016年第2期33卷 175-183页

作者： Yongku kim Balaji RAJAGOPALAN gyuwon LEE Department of Statistics Kyungpook National University Department of Civil Environmental and Architectural Engineering University of Colorado Department of Astronomy and Atmospheric Sciences Center for Atmospheric Remote SensingKyungpook National University

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society （IRI）. In conjunction with the GLM （generalized linear modeling） weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts （the IRI format only specifies probabilities for three categories： below normal, near normal, and above normal） into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.

关键词： generalized linear model seasonal projection stochastic weather generator temporal statistical downscaling

A Case Study of Mesoscale Convective Band(MCB) Development and Evolution along a Quasi-stationary Front

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Advances in Atmospheric Sciences 2014年第4期31卷 901-915页

作者： Daeun JEONG Ki-Hong MIN gyuwon LEE Kyung-Eak kim Climate Research Department Asia-Pacific Economic Cooperation Climate Center Center for Atmospheric Remote Sensing Kyungpook National University Department of Astronomy and Atmospheric Sciences Kyungpook National University Department of Earth Atmospheric and Planetary Sciences Purdue University

This paper presents a case study of mesoscale convective band （MCB） development along a quasi-stationary front over the Seout metropolitan *** MCB,which initiated on 1500 UTC 20 September 2010 and ended on 1400 UTC 21 September 2010,produced a total precipitation amount of 259.5 *** MCB development occurred during a period of tropopause folding in the upper level and moisture advection with a low-level *** analyses show that the evolution of the MCB can be classified into five periods：（1） the cell-forming period,when convection initiated; （2） the frontogenetic period,when the stationary front formed over the Korean peninsula; （3） the quasi-stationary period,when the convective band remained over Seoul for 3 h; （4） the mature period,when the cloud cover was largest and the precipitation rate was greater than 90 mm h-1; and （5） the dissipating period,when the MCB diminished and *** synoptic,thermodynamic,and dynamic analyses show that the MCB maintained its longevity by a tilted updraft,which headed towards a positive PV *** was concentrated under this area,where a tilted ascending southwesterly converged with a tilted ascending northeasterly,at the axis of cyclonic *** formation of the convective cell was attributed in part by tropopause folding,which enhanced the cyclonic vorticity at the surface,and by the low-level convergence of warm moist air and upperlevel *** southwesterly flow ascended in a region with high moisture content and strong relative vorticity that maintained the development of an MCB along the quasi-stationary front.

关键词： convective band quasi-stationary front potential vorticity tropopause folding heavy precipitation

Toward Data-Driven Digital Therapeutics Analytics:Literature Review and Research Directions

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 42-66页

作者： Uichin Lee gyuwon Jung Eun-Yeol Ma Jin San kim Heepyung kim Jumabek Alikhanov Youngtae Noh Heeyoung kim Korea Advanced Institute of Science and Technology Daejeon 34141Republic of Korea Inha University Incheon 402-751Republic of Korea KENTECH Naju-siRepublic of Korea

With the advent of digital therapeutics(DTx),the development of software as a medical device(SaMD)for mobile and wearable devices has gained significant attention in recent *** DTx evaluations,such as randomized clinical trials,mostly focus on verifying the effectiveness of DTx *** acquire a deeper understanding of DTx engagement and behavioral adherence,beyond efficacy,a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for *** this work,the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets,to investigate contextual patterns associated with DTx usage,and to establish the(causal)relationship between DTx engagement and behavioral *** review of the key components of datadriven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets,which helps to iteratively improve the receptivity of existing DTx.

关键词： Causal inference data-driven analytics framework digital therapeutics(DTx) mobile and wearable data technical and behavioral engagement

Rapid determination of lithium-ion battery degradation: High C-rate LAM and calculated limiting LLI

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Journal of Energy Chemistry 2022年第4期31卷 663-671页

作者： gyuwon Seo Jaeyun Ha Moonsu kim Jihyeon Park Jaewon Lee Eunoak Park Sungyool Bong Kiyoung Lee Soon Jong Kwon Seung-pil Moon Jinsub Choi Jaeyoung Lee Electrochemical Reaction and Technology Laboratory School of Earth Sciences and Environmental EngineeringGwangju Institute of Science and Technology(GIST)Gwangju 61005South Korea Department of Chemistry and Chemical Engineering Inha UniversityIncheon 22212South Korea Ertl Center for Electrochemistry and Catalysis GISTGwangju 61005South Korea Korea Electric Power Corporation Research Institute Daejeon 34056South Korea International Future Research Center of Chemical Energy Storage and Conversion Processes(ifRC-CHESS) GISTGwangju 61005South Korea

Herein,incremental capacity-differential voltage (IC-DV) at a high C-rate (HC) is used as a non-invasive diagnostic tool in lithium-ion batteries,which inevitably exhibit capacity fading caused by multiple mechanisms during charge/discharge *** battery degradation modes are complex,the simple output of capacity fading does not yield any useful data in that *** IC and DV curves obtained under restricted conditions (<0.1C,25℃) were applied in non-invasive analysis for accurate observation of degradation symptoms,a facile,rapid diagnostic approach without intricate,complex calculations is critical in on-board ***,Li Ni_(0.5)Mn_(0.3)Co_(0.2)O_(2)(NMC532)/graphite pouch cells were cycled at 4 and 6C and the degradation characteristics,i.e.,loss of active materials (LAM) and loss of lithium inventory (LLI),were parameterized using the IC-DV *** the incremental current cycling,the initial steep LAM and LLI slopes underwent gradual transitions to gentle states and revealed the gap between low-and high-current measurements.A quantitative comparison of LAM at high and low C-rate showed that a IC;revealed the relative amount of available reaction region limited by cell ***,this did not provide a direct relationship for estimating the LAM at a low ***,the limiting LLI,which is calculated at a C-rate approaching 0,was obtained by extrapolating the LLI through more than two points measured at high C-rate,and therefore,the LLI at 0.1C was accurately determined using rapid cycling.

关键词： Battery degradation Rapid degradation diagnosis Loss of active materials(LAM) Loss of lithium inventory(LLI) Incremental capacity(IC) Differential voltage(DV)