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Computation and field experiment validation of greenhouse energy load using building energy simulation model

International Journal of Agricultural and Biological Engineering 2015年第6期8卷 116-127页

作者： Taehwan Ha In-bok Lee Kyeong-seok Kwon se-woon hong College of Agriculture and Life Sciences Seoul National University1 Gwanak-roGwanak-guSeoul 151-921Korea Research Institute of Agriculture and Life Sciences 1 Gwanak-roGwanak-guSeoul 151-921Korea Institute for Agricultural and Fisheries Research(ILVO) Burg.Van Gansberghelaan 1159820 MerelbekeBelgium

Greenhouse Building Energy Simulation(BES)models were developed to estimate the energy load using TRNSYS(ver.16,University of Wisconsin,USA),a commercial BES *** was conducted based on data recorded during field *** BES greenhouse modeling is reliable,as validation showed 5.2%and 5.5%compared with two field experiments,*** the next step,the heating characteristics of the greenhouses were analyzed to predict the maximum and annual total heating loads based on the greenhouse types and target locations in the Republic of Korea using the validated greenhouse *** BES-computed results indicated that the annual heating load was greatly affected by the local climate conditions of the target *** annual heating load of greenhouses located in Chuncheon,the northernmost region,was 44.6%higher than greenhouses in Jeju,the southernmost area among the studied *** regression models for prediction of maximum heating load of Venlo type greenhouse and widespan type greenhouse were developed based on the BES computed results to easily predict maximum heating load at field and they explained nearly 95%and 80%of the variance in the data set used,respectively,with the predictor *** a BES model of geothermal energy system was additionally designed and incorporated into the BES greenhouse *** feasibility of the geothermal energy system for greenhouse was estimated through economic analysis.

关键词： Greenhouse building energy simulation(BES) energy load dynamic analysis geothermal energy heating load

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Optimal seismic retrofit model for steel moment resisting frames with brittle connections

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Earthquake Engineering and Engineering Vibration 2018年第4期17卷 835-847页

作者： Hyo seon Park se woon Choi Byung Kwan Oh Department of Architectural Engineering Yonsei University Center for Structural Health Care Technology in Building Yonsei University Department of Architecture Catholic University of Daegu Department of Civil and Environmental Engineering Princeton University

Based on performance-based seismic engineering, this paper proposes an optimal seismic retrofit model for steel moment resisting frames（SMRFs） to generate a retrofit scheme at minimal cost. To satisfy the acceptance criteria for the Basic Safety Objective（BSO） specified in FEMA 356, the minimum number of upgraded connections and their locations in an SMRF with brittle connections are determined by evolutionary computation. The performance of the proposed optimal retrofitting model is evaluated on the basis of the energy dissipation capacities, peak roof drift ratios, and maximum interstory drift ratios of structures before and after retrofitting. In addition, a retrofit efficiency index, which is defined as the ratio of the increment in seismic performance to the required retrofitting cost, is proposed to examine the efficiencies of the retrofit schemes derived from the model. The optimal seismic retrofit model is applied to the SAC benchmark examples for threestory and nine-story SMRFs with brittle connections. Using the retrofit efficiency index proposed in this study, the optimal retrofit schemes obtained from the model are found to be efficient for both examples in terms of energy dissipation capacity, roof drift ratio, and maximum inter-story drift ratio.

关键词： steel moment resisting frame performance-based seismic engineering optimal seismic retrofit evolutionarycomputation

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Simply Fine-Tuned Deep Learning-Based Classification for Breast Cancer with Mammograms

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Computers, Materials & Continua 2022年第12期73卷 4677-4693页

作者： Vicky Mudeng Jin-woo Jeong se-woon Choe Department of Medical IT Convergence Engineering Kumoh National Institute of TechnologyGumi39253Korea Department of Electrical Engineering Institut Teknologi KalimantanBalikpapan76127Indonesia Department of Data Science Seoul National University of Science and TechnologySeoul01811Korea Department of IT Convergence Engineering Kumoh National Institute of TechnologyGumi39253Korea

A lump growing in the breast may be referred to as a breast mass related to the ***,not all tumors are cancerous or *** masses can cause discomfort and pain,depending on the size and texture of the *** an appropriate diagnosis,non-cancerous breast masses can be diagnosed earlier to prevent their cultivation from being *** the development of the artificial neural network,the deep discriminative model,such as a convolutional neural network,may evaluate the breast lesion to distinguish benign and malignant cancers frommammogram breast masses *** work accomplished breastmasses classification relative to benign and malignant cancers using a digital database for screening mammography image datasets.A residual neural network 50(ResNet50)model along with an adaptive gradient algorithm,adaptive moment estimation,and stochastic gradient descent optimizers,as well as data augmentations and fine-tuning methods,were *** addition,a learning rate scheduler and 5-fold cross-validation were applied with 60 training procedures to determine the best *** results of training accuracy,p-value,test accuracy,area under the curve,sensitivity,precision,F1-score,specificity,and kappa for adaptive gradient algorithm 25%,75%,100%,and stochastic gradient descent 100%fine-tunings indicate that the classifier is feasible for categorizing breast cancer into benign and malignant from the mammographic breast masses images.

关键词： Medical image analysis convolutional neural network mammogram breast masses breast cancer

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Fatigue strength evaluation of self-piercing riveted joints of AZ31 Mg alloy and cold-rolled steel sheets

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Journal of Magnesium and Alloys 2020年第1期8卷 241-251页

作者： se-Hyung Kang Dong-woon Han Ho-Kyung Kim Graduate School Seoul National University of Science and TechnologySeoul01811South Korea Department of Mechanical and Automotive Engineering Seoul National University of Science and TechnologySeoul01811 South Korea

The application of magnesium alloys to automobiles is increasing due to their superior specific strength and specific *** this study,an upper sheet of AZ31 magnesium alloy and a lower sheet of cold-rolled steel were joined by self-piercing riveting(SPR),a method commonly used to join automotive panels.A cross-shaped specimen was fabricated with a punching force of 35 kN,which exhibited the best joint strength for the SPR joint specimen *** and fatigue strengths were evaluated using cross-shaped specimens at loading angles of 0°,45°,and 90°.The load amplitude corresponding to the fatigue endurance limit was assumed to be at 106 cycles,and the fatigue ratios(=fatigue endurance limit/static strength)at the loading angles of 0°,45°,and 90°are 22%,13%,and 9%,*** all three loading angle specimens,fatigue cracks initiated at the triple point where the rivet shank,the upper sheet and the lower sheet are in contact with each other,with the cracks propagating through the thickness of the upper sheet and ultimately leading to *** fatigue lifetimes were evaluated through the von-Mises stress,maximum principal stress,and equivalent stress intensity *** was found that the fatigue lifetimes could be evaluated most appropriately through the maximum principal stress.

关键词： self-piercing riveting Magnesium alloy Fatigue strength Maximum principal stress Fatigue lifetime

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Monitoring of non-point source pollutants load from a mixed forest land use

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Journal of Environmental Sciences 2010年第6期22卷 801-805页

作者： Sung Wan Yoon se woong Chung Dong Geun Oh Jae woon Lee Department of Environmental Engineering Chungbuk National University Geum River Environmental Research Center National Institute of Environmental Research

The aim of this study was to determine the unit load of NPS （non-point source） pollutants including organic variables such as BOD （biochemical oxygen demand）, COD （chemical oxygen demand） and DOC （dissolved organic carbon）, nitrogen and phosphorus constituents, and suspended solids （SS） and their event mean concentration （EMC） of runoff flows from a water-shed of mixed forest land use by intensive field experiments. Field monitoring for continuous measurements of rainfall, flow, and water quality was conducted over 12 storm events during 2008-2009 using automated and manual sampling methods. The EMCs of individual runoff event were estimated for each water quality constituent based on the flow rate and concentration data of runoff discharge. The average EMCs of BOD, COD, DOC, SS, TN （total nitrogen）, NH4^＋-N, NO3^--N, TP （total phosphorus）, PO4^3-P from the mixed forest land were 1.794, 3.498, 1.462, 10.269, 0.862, 0.044, 0.634, 0.034, and 0.005 mg/L, respectively. The annual unit loads of BOD, COD, DOC, SS, TN, NH4^＋-N, NO3^--N, TP and PO4^3--P were estimated as 66.9, 133.2, 55.5, 429.8, 36.5, 1.6, 26.9, 1.3 and 0.1 kg/（ha·yr）, respectively. In addition, affecting parameters on the EMCs were investigated by statistic analysis of the field data. As a result, significant correlations with precipitation, rainfall intensity, and total runoff flows were found in most constituents.

关键词： event mean concentration mixed forest land use non-point source pollutant load unit load

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Advances in Wireless,Batteryless,Implantable Electronics for Real‑Time,Continuous Physiological Monitoring

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Nano-Micro Letters 2024年第3期16卷 254-302页

作者： Hyeonseok Kim Bruno Rigo Gabriella Wong Yoon Jae Lee woon‑hong Yeo IEN Center for Wearable Intelligent Systems and Healthcare Georgia Institute of TechnologyAtlantaGA 30332USA George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Tech and Emory University School of MedicineAtlantaGA 30332USA Parker H.Petit Institute for Bioengineering and Biosciences Institute for MaterialsInstitute for Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaGA 30332USA

This review summarizes recent progress in developing wireless,batteryless,fully implantable biomedical devices for real-time continuous physiological signal monitoring,focusing on advancing human health *** considerations,such as biological constraints,energy sourcing,and wireless communication,are discussed in achieving the desired performance of the devices and enhanced interface with human *** addition,we review the recent achievements in materials used for developing implantable systems,emphasizing their importance in achieving multi-functionalities,biocompatibility,and *** wireless,batteryless devices offer minimally invasive device insertion to the body,enabling portable health monitoring and advanced disease ***,we summarize the most recent practical applications of advanced implantable devices for human health care,highlighting their potential for immediate commercialization and clinical uses.

关键词： Implantable electronics Biomedical systems Batteryless devices Wireless electronics Physiological signal monitoring

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An AIoT Monitoring System for Multi-Object Tracking and Alerting

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Computers, Materials & Continua 2021年第4期67卷 337-348页

作者： Wonseok Jung se-Han Kim seng-Phil hong Jeongwook seo Korea Electronics Technology Institute Seongnam13509Korea Electronics and Telecommunications Research Institute Daejeon34129Korea Hancom With Inc. Seongnam13493Korea Hanshin University Osan-si18101Korea

Pig farmers want to have an effective solution for automatically detecting and tracking multiple pigs and alerting their conditions in order to recognize disease risk factors *** this paper,therefore,we propose a novel monitoring system using an Artificial Intelligence of Things(AIoT)technique combining artificial intelligence and Internet of Things(IoT).The proposed system consists of AIoT edge devices and a central monitoring ***,an AIoT edge device extracts video frame images from a CCTV camera installed in a pig pen by a frame extraction method,detects multiple pigs in the images by a faster region-based convolutional neural network(RCNN)model,and tracks them by an object center-point tracking algorithm(OCTA)based on bounding box regression outputs of the faster ***,it sends multi-pig tracking images to the central monitoring server,which alerts them to pig farmers through a social networking service(SNS)agent in cooperation with an oneM2M-compliant IoT alerting *** results showed that the multi-pig tracking method achieved the multi-object tracking accuracy performance of about 77%.In addition,we verified alerting operation by confirming the images received in the SNS smartphone application.

关键词： Internet of Things multi-object tracking pig pen social network

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Effects of Cr interlayer on mechanical and tribological properties of Cr-Al-Si-N nanocomposite coating

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Transactions of Nonferrous Metals Society of China 2011年第A01期21卷 62-67页

作者： Young Su hong se Hun KWON Tiegang WANG Doo-In KIM Jihwan CHOI Kwang Ho KIM School of Materials Science and Engineering Pusan National University National Core Research Center for Hybrid Materials Solution Pusan National University

Cr-A1-Si-N coatings were deposited on SUS 304 substrate by a hybrid coating system. A Cr interlayer was introduced between Cr-A1-Si-N coating and SUS 304 substrate to improve the coating adherence. The effects of Cr interlayer on the microhardness, adhesion, and tribological behavior of Cr-A1-Si-N coatings were systematically investigated. The results indicate that the microhardness of the Cr-A1-Si-N coatings gradually deceases with increasing thickness of Cr interlayers. The adhesion between Cr-A1-Si-N and SUS 304 substrate is improved by addition of the Cr interlayers. A peak critical load of-50 N is observed for the coating containing Cr interlayer of 60 nm as compared - 20 N for the coating without Cr interlayer. The thicker Cr interlayers result in reduced critical load values. Moreover, the wear resistance of the Cr-AI-Si-N coatings is greatly enhanced by introducing the Cr interlayer with thickness of 60 nm in spite of the decreased microhardness. The friction coefficient of the coating system is also moderately reduced.

关键词： Cr-A1-Si-N coating Cr interlayer hardness adhesion wear

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Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

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National Science Review 2024年第2期11卷 231-263页

作者： Kyung Rok Pyun Kangkyu Kwon Myung Jin Yoo Kyun Kyu Kim Dohyeon Gong woon-hong Yeo seungyong Han seung Hwan Ko Department of Mechanical Engineering Seoul National University IEN Center for Human-Centric Interfaces and Engineering Institute for Electronics and NanotechnologyGeorgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology Department of Chemical Engineering Stanford University Department of Mechanical Engineering Ajou University George W.Woodruff School of Mechanical Engineering Georgia Institute of Technology Institute of Advanced Machinery and Design(SNU-IAMD) Seoul National University

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human–machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

关键词： wearable sensor soft electronics artificial intelligence machine learning human–machine interfaces gesture recognition

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Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

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Computers, Materials & Continua 2021年第3期66卷 2493-2507页

作者： Hyun Kyu Shin Si woon Lee Goo Pyo hong Lee Sael Sang Hyo Lee Ha Young Kim Architectural Engineering Hanyang UniversityERICAAnsan15588Korea Department of Artificial Intelligence Ajou UniversitySuwon16499Korea Division of Architecture and Civil Engineering Kangwon National UniversitySamcheok25913Korea Division of Smart Convergence Engineering Hanyang UniversityERICAAnsan15588Korea Graduate School of Information Yonsei UniversitySeoul03722Korea

The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of ***,conventional manpower-based inspection methods not only incur considerable cost and time,but also cause frequent disputes regarding defects owing to poor ***,the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent,as the reduction in maintenance costs is significant from a long-term ***,this paper proposes a deep learning-based image objectidentification method to detect the defects of paint peeling,leakage peeling,and leakage traces that mostly occur in underground parking lots made of concrete *** deep learning-based object-detection method can replace conventional visual inspection methods.A faster region-based convolutional neural network(R-CNN)model was used with a training dataset of 6,281 images that utilized a region proposal network to objectively localize the regions of interest and detect the surface *** defects were classified according to their type,and the learning of each exclusive model was ensured through test sets obtained from real underground parking *** a result,average precision scores of 37.76%,36.42%,and 61.29%were obtained for paint peeling,leakage peeling,and leakage trace defects,***,this study verified the performance of the faster RCNN-based defect-detection algorithm along with its applicability to underground parking lots.

关键词： Faster R-CNN deep learning defect detection concrete structures

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