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| WoS | SCOPUS | Document Type | Document Title | Abstract | Authors | Affiliation | ResearcherID (WoS) | AuthorsID (SCOPUS) | Author Email(s) | Journal Name | JCR Abbreviation | ISSN | eISSN | Volume | Issue | WoS Edition | WoS Category | JCR Year | IF | JCR (%) | FWCI | FWCI Update Date | WoS Citation | SCOPUS Citation | Keywords (WoS) | KeywordsPlus (WoS) | Keywords (SCOPUS) | KeywordsPlus (SCOPUS) | Language | Publication Stage | Publication Year | Publication Date | DOI | JCR Link | DOI Link | WOS Link | SCOPUS Link |
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| ○ | Article | Development of a High-Voltage Monitoring System for Experiments using Photomultiplier Tubes; [광전증폭관을 사용하는 실험에 응용 가능한 고전압 모니터링 프로그램 제작] | Photomultiplier tubes (PMTs) are widely used in particle physics experiments, such as neutrino precision measurement and dark matter search to observe events. Thus, the stable performance of PMTs is essential in such experiments. The stable operation and high-voltage monitoring are some of the most important factors. This paper reports a program that chronologically stores values provided by high-voltage suppliers in a database and visually monitors the currently given values simultaneously. © 2023 The Korean Physical Society. All rights reserved. | Ryu, Jiwon; Park, Jungsic | Department of Physics, Kyungpook National University, Daegu, 41566, South Korea; Department of Physics, Kyungpook National University, Daegu, 41566, South Korea | 58664936300; 57077274100 | New Physics: Sae Mulli | 0374-4914 | 73 | 9 | 0 | 2025-06-25 | 0 | Particle Physics | Korean | Final | 2023 | 10.3938/npsm.73.716 | 바로가기 | 바로가기 | |||||||||||||||||
| ○ | Article | Development of a Monitoring Tool to Manage Unregistered Mentally Ill Patients at Community Mental Health Centers: Using a Delphi Method | Purpose: This study was conducted to develop a monitoring tool to manage unregistered mentally ill patients at community mental health centers (M-MUMI). Methods: The Delphi method was used in this study. The participants of this study were 27 psychiatric experts. In the first Delphi round, content analysis was conducted. In the second Delphi round, Kendall’s W rank and weight rank were used. The third Delphi round was analyzed based on the content validity ratio (CVR). Results: The finally developed M-MUMI consisted of 2 domains, 7 categories, and 22 items. Assessment of the present status consisted of 4 basic elements affecting daily life, 5 psychological statuses, 2 elements related to the utilization of social service, and 3 causes for the refusal of registration. The registration strategy of the individual cases consisted of 2 human resources to help registration, 2 additional social services for registration, and 4 individual needs to induce registration. Conclusion: It is important to manage unregistered mentally ill patients at the community mental health centers. We hope that the developed M-MUMI will help to monitor unregistered mentally ill patients how to maintain mental health in the community. © 2023 The Korean Academy of Psychiatric and Mental Health Nursing. | Lee, Holyung; Park, Wanju | Mental Health Management Team, Yeongcheon Community Mental Health Center, Yeongcheon, South Korea; College of Nursing, Research Institute of Nursing Innovation, Kyungpook National University, Daegu, South Korea | 58634336600; 35788492900 | wanjupark@knu.ac.kr; | Journal of Korean Academy of Psychiatric and Mental Health Nursing | 1225-8482 | 32 | 3 | 0 | 2025-06-25 | 0 | Community mental health centers; Delphi technique; Mentally ill persons | Korean | Final | 2023 | 10.12934/jkpmhn.2023.32.3.291 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Article | Development of a pH/dissolved-oxygen Monitoring System Using HPTS and Rudpp | This study proposes a pH-dissolved-oxygen monitoring system using 8-HydroxyPyrene-1,3,6-trisulfonic acid Trisodium Salt (HPTS) and tris(4,7-diphenyl-1,10-phenanthroline)Ruthenium(II) chloride (Rudpp). Commercial water-quality sensors are electrochemical devices that require frequent calibration and cleaning, are subject to high maintenance costs, and have difficulties conducting measurements in real-time. The proposed pH-dissolved-oxygen monitoring system selects a thin-film sensing layer to measure the change in fluorescence intensity. This change in fluorescence intensity is based on reactions with hydrogen ions in an aqueous solution at a given pH and specific amount of dissolved oxygen. The change in fluorescence intensity is then measured using light-emitting diodes and photodiodes in response to HPTS and Rudpp. This method enables the development of a relatively small, inexpensive, and real-time measureable water-quality measurement system. © 2023, Korean Sensors Society. All rights reserved. | Jeong, Dong Hyuk; Jung, Daewoong | High-tech mechatronics R&D Group, Korea Institute of Industrial Technology (KITECH), Yeongcheon, 38822, South Korea, School of Electronic and Electrical Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu, South Korea; High-tech mechatronics R&D Group, Korea Institute of Industrial Technology (KITECH), Yeongcheon, 38822, South Korea | 58525972300; 36019307900 | dwjung@kitech.re.kr; | Journal of Sensor Science and Technology | 1225-5475 | 32 | 2 | 0 | 2025-06-25 | 0 | Dissolved O2 sensor; HPTS; pH sensor; Ru<sub>dpp</sub> ; Water pollution | Korean | Final | 2023 | 10.46670/jsst.2023.32.2.82 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Conference paper | Development of a Real-time BIM-VR Multi-Collaborative Design Environment | Construction 4.0 technologies are transforming AEC designs and processes. Metaverses are developing rapidly and are being adopted in various industries as the future of the internet. The metaverse is an augmented world that allows individuals to penetrate and engage. Nevertheless, many people and businesses are still unaware of the potential uses of this technology. Virtual Reality, which is part of the fundamentals of the metaverse convergence with BIM technology, has improved in research and application in the AEC industry. The AEC industry has recently adopted both Building Information Modeling (BIM) and Virtual Reality (VR) as combined tools aiming at increasing collaboration ability among project team members as well as detecting clashes and correcting flaws before construction begins. This presents a multi-collaborative design as a potential requirement for BIM processes in the metaverse. The authors presented a platform that connects multiple VR environments through an online network to create a real-time-shared VR space that supports BIM models in real-time for collaborative design. The BIM-VR environment uses a game engine to create a session where individuals can upload their 3D BIM models in real-time which can be viewed by all users. This study presents that a collaborative environment that supports users and BIM models is the initial step to a BIM-based metaverse in the AEC industry. © 2023, Education and research in Computer Aided Architectural Design in Europe. All rights reserved. | Panya, David Stephen; Kim, Taehoon; Gu, Hyeongmo; Choo, Seungyeon | School of Architecture, South Korea; School of Architecture, South Korea; Kyungpook National University, Daegu, South Korea; Kyungpook National University, Daegu, South Korea | 57210791927; 58260945000; 57209659182; 36835366900 | Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe | 2684-1843 | 2 | 1.44 | 2025-06-25 | 3 | BIM; Collaborative Design; Metaverse; Virtual Reality | English | Final | 2023 | 바로가기 | ||||||||||||||||||||
| ○ | Article | Development of an Predictive Model via Decision Tree-Based Algorithms for Forecasting Demolition Waste Generation | Management of demolition waste (DW), which accounts for a large portion of waste generation (WG), is a very important issue. Therefore, many researchers tried to apply various ML algorithms to predict WG, and tried to find the decisive factors affecting WG. This study conducted a study on the development of optimal ML model for predicting demolition waste generation (DWG). In this study, decision tree (DT), random forest (RF), and gradient boost machine (GBM) algorithms were applied to develop ML models to predictive DWG. For this, data preprocessing was performed and the optimal hyper parameter was searched for each algorithm to derive an optimal ML model. In consideration of dataset size, leave one out cross validation (LOOCV) was applied to the model validation and mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R squared), and mean square error (MSE) were used as the performance evaluation index of the models. As a result of this study, it was found that the predictive performance of the RF model (MAE 72.837, MSE 12198.236, RMSE 110.446, R2 0.880) was better than one of DT (MAE 87.081, MSE 17348.052, RMSE 131.712, R2 0.829) and GBM (MAE 87.883, MSE 18175.125, RMSE 134.815, R2 0.821) models. The error from the observed mean (987.1806 kg m-2) was 8.82%, 7.38%, and 8.90% for the DT, RF, and GBM models, respectively. Therefore, it can be seen that the ML model using the DT-based algorithms is very good at predicting DWG. Finally, this study presented a reliable and optimal ML model for predicting DWG for a domestic waste management strategy. © 2023 Architectural Institute of Korea. | Cha, Gi-Wook; Hong, Won-Hwa | School of Science and Technology Acceleration Engineering, Kyungpook National University, South Korea; School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, South Korea | 55754413300; 7401527968 | cgwgnr@gmail.com; | Journal of the Architectural Institute of Korea | 2733-6239 | 39 | 3 | 0 | 2025-06-25 | 0 | Decision Tree; Demolition Waste; Gradient Booting Machine; Machine Learning; Random Forest; Waste Management | Korean | Final | 2023 | 10.5659/jaik.2023.39.3.179 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Article | Development of Autonomous Mobile Robot Control System for Changeable Target-of-interest Object Tracking using Specific Vest | To support delivery tasks using autonomous mobile robots (AMRs), an AMR tracks target-of-interest (ToI) objects, such as workers. ToI objects that are tracked using AMRs are specified in advance or through the robot's settings. It is difficult for an object to be directly set as a ToI object. By making an AMR follow a ToI object wearing a specific vest, the object can be set easily. As such, it is essential to develop a ToI object tracking system that can follow a ToI object wearing a specific vest with AMR. This paper proposes a ToI object tracking system using a specific vest to track changeable ToI objects. The proposed system comprises a recognition unit to recognize a ToI object wearing a specific vest and a control unit to control the AMR. The recognition unit tracks the ToI object and estimates its position while the control unit controls the AMR based on the ToI object’s position. The contributions of this paper are as follows. 1) A system that can follow a changeable ToI object is proposed. 2) This tracking system can easily designate and change ToI objects. In the experiment, the proposed system enabled the AMR to track a changeable ToI object. The recognition unit then calculated the position of the ToI object wearing a specific vest, and the control unit calculated a control signal for the AMR to track the ToI object based on its position. © ICROS 2023. | Kwak, Jeonghoon; Yang, Kyon-Mo; Lee, Ji-Won; Koo, Jaewan; Seo, Kap-Ho | Innovation Lab-Seoul, Korea Institute of Robotics and Technology Convergence (KIRO), South Korea; Innovation Lab-Seoul, Korea Institute of Robotics and Technology Convergence (KIRO), South Korea; Innovation Lab-Seoul, Korea Institute of Robotics and Technology Convergence (KIRO), South Korea; Innovation Lab-Seoul, Korea Institute of Robotics and Technology Convergence (KIRO), South Korea; Innovation Lab-Seoul, Korea Institute of Robotics and Technology Convergence (KIRO), South Korea, Department of Robot and Smart System Engineering, Kyungpook National University (KNU), South Korea | 56963328600; 55698480000; 57214780494; 57217782518; 7201838999 | neoworld@kiro.re.kr; | Journal of Institute of Control, Robotics and Systems | 1976-5622 | 29 | 5 | 0 | 2025-06-25 | 0 | Autonomous Mobile Robot; Deep Learning; Human Following; Object Tracking | Deep learning; Mobile robots; Target tracking; Wear of materials; Autonomous Mobile Robot; Control unit; Deep learning; Delivery task; Human following; Mobile robot control systems; Object Tracking; Recognition units; Targets of interest; Tracking system; Navigation | Korean | Final | 2023 | 10.5302/j.icros.2023.23.0006 | 바로가기 | 바로가기 | |||||||||||||||
| ○ | Conference paper | Development of Building Component Combination Algorithms for Generative Design-based DfMA Applications | The AEC industry faces challenges such as low productivity, high carbon emissions, labor shortages, and construction site accidents. To address these issues, the industry focuses on MMC and DfMA based on BIM. This research paper develops building component combination algorithms for generative design-based applications. Using GD, the proposed method optimises the layout and selection of building components while considering construction costs and a specified budget range. A case study of a five-component building system with four types of components demonstrates the method's ability to generate diverse design alternatives. Designers can efficiently explore and evaluate these alternatives based on economic and design criteria. However, the method has limitations, such as the exclusion of MEP facilities as GD parameters and the focus on optimising the budget as a single goal. Nevertheless, this study lays the foundation for applying DfMA in the early design stage and utilizing GD technology in construction projects. © 2023, Education and research in Computer Aided Architectural Design in Europe. All rights reserved. | Hong, Soon Min; Kim, Geunjae; Gu, Hyeongmo; Kim, Taehoon; Choo, Seungyeon | School of Architecture, Kyungpook National University, South Korea; School of Architecture, Kyungpook National University, South Korea; School of Architecture, Kyungpook National University, South Korea; School of Architecture, Kyungpook National University, South Korea; School of Architecture, Kyungpook National University, South Korea | 57734398300; 57543331500; 57209659182; 58260945000; 36835366900 | Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe | 2684-1843 | 2 | 0.72 | 2025-06-25 | 1 | DfMA; Generative Design; Optimisation; OSC | English | Final | 2023 | 바로가기 | ||||||||||||||||||||
| ○ | Conference paper | Development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments | Early detection of skin conditions is crucial, and some skin conditions can become more difficult to treat if left untreated. The gold standard Dermatoscope is a non-invasive technique used for the examination and evaluation of skin lesions, which is equipped with a magnifying lens and a light source. However, precise inspection of existing dermatoscopes has become a limitation due to the unavailability of image-analyzing methods. Herein, this study reports the successful development of a Convolutional Neural Networks (CNN) based, Artificial intelligence (AI)-Dermatoscope integrating optics and a smart illumination system to enhance the accurate examination of acne conditions of the skin. The system was trained on a large dataset of acne to accurately identify and classify skin conditions. Finally, the system utilizes CNN knowledge to predict new images of skin and provide diagnostic information to doctors and other healthcare professionals. Thus, this system will improve the accuracy and speed of skin diagnosis, and consequently, improve the health-related quality of life of patients. © 2023 IEEE. | Kahatapitiya, Nipun Shantha; Wijethunge, Akila; Edirisinghe, Sajith; Silva, Bhagya Nathali; Jeon, Mansik; Kim, Jeehyun; Wijenayake, Udaya; Wijesinghe, Ruchire Eranga | University of Sri Jayewardenepura, Faculty of Technology, Dept. of Materials and Mechanical Technology, Pitipana, Sri Lanka; University of Sri Jayewardenepura, Faculty of Technology, Dept. of Materials and Mechanical Technology, Pitipana, Sri Lanka; University of Sri Jayewardenepura, Faculty of Medical Sciences, Dept. of Anatomy, Gangodawila, Sri Lanka; University of Sri Jayewardenepura, Faculty of Engineering, Dept. of Computer Engineering, Nugegoda, Sri Lanka; School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, Buk-gu, Daegu, South Korea; School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, Buk-gu, Daegu, South Korea; University of Sri Jayewardenepura, Faculty of Engineering, Dept. of Computer Engineering, Nugegoda, Sri Lanka; Sri Lanka Institute of Information Technology, Faculty of Engineering, Dept. of Electrical and Electronic Engineering, Malabe, Sri Lanka | 58781581100; 56826149000; 57643298300; 57192304387; 24171094000; 7601373350; 55547801900; 56018152300 | akilawijethunge@sjp.ac.lk; | ICAC 2023 - 5th International Conference on Advancements in Computing: Technological Innovation for a Sustainable Economy, Proceedings | 1 | 2025-06-25 | 2 | acne; computer vision; convolutional neural network (CNN); dermatoscope; image processing; optics; skin conditions | Classification (of information); Convolution; Convolutional neural networks; Diagnosis; Large datasets; Light sources; Optical data processing; Acne; Convolutional neural network; Dermatoscope; Gold standards; Images processing; Network-based; Noninvasive technique; Skin conditions; Skin lesion; Computer vision | English | Final | 2023 | 10.1109/icac60630.2023.10417424 | 바로가기 | 바로가기 | ||||||||||||||||||
| ○ | Article | Development of Drone-attached Spraying Device for Active Maintenance of Structures | Exteriors of structures (apartments, buildings, bridges, dams, power plants, etc.) are subject to deterioration and damage (cracks, rust, etc.), mainly due to thermal expansion/contraction and environmental humidity. The damages shorten the lifespan of structures and cause unnecessary reconstruction, increasing social costs. The existing damage maintenance methods, which are directly constructed by the workers, have problems such as reduced work efficiency, increased work cost, lack of timely maintenance, and high work risks. In this paper, a spraying device attached to a drone for active and flexible maintenance of structures is developed. To simplify maintenance, the device consists of a solenoid motor, detachable parts for maintenance agent, and a lightweight-designed frame, manufactured with a 3D printer. In particular, the lever mechanism that amplifies the pushing force of the solenoid motor is designed to spray the maintenance agent when a switch comes into contact with the exterior of the structure. The prototype of a spraying device is attached to a commercial drone (Mavic3, DJI) and tested for effectiveness in structure maintenance. It demonstrates successful, cost-effective maintenance of structural damages in less than 10 minutes. © The Korean Society for Precision Engineering. | Yang, Seung-Han; Lee, Kwang-Il | School of Mechanical Engineering, Kyungpook National University, South Korea; School of Mechanical and Automotive Engineering, Kyungil University, South Korea | 8407949900; 57196250383 | kilee@kiu.kr; | Journal of the Korean Society for Precision Engineering | 1225-9071 | 40 | 12 | 0 | 2025-06-25 | 0 | Active maintenance; Damage; Maintenance agent; Spraying device; Structure | Korean | Final | 2023 | 10.7736/jkspe.023.086 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | ○ | Article | Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas | Owing to a rapid increase in waste, waste management has become essential, for which waste generation (WG) information has been effectively utilized. Various studies have recently focused on the development of reliable predictive models by applying artificial intelligence to the construction and prediction of WG information. In this study, research was conducted on the development of machine learning (ML) models for predicting the demolition waste generation rate (DWGR) of buildings in redevelopment areas in South Korea. Various ML algorithms (i.e., artificial neural network (ANN), K-nearest neighbors (KNN), linear regression (LR), random forest (RF), and support vector machine (SVM)) were applied to the development of an optimal predictive model, and the main hyper parameters (HPs) for each algorithm were optimized. The results suggest that ANN-ReLu (coefficient of determination (R-2) 0.900, the ratio of percent deviation (RPD) 3.16), SVM-polynomial (R-2 0.889, RPD 3.00), and ANN-logistic (R-2 0.883, RPD 2.92) are the best ML models for predicting the DWGR. They showed average errors of 7.3%, 7.4%, and 7.5%, respectively, compared to the average observed values, confirming the accurate predictive performance, and in the uncertainty analysis, the d-factor of the models appeared less than 1, showing that the presented models are reliable. Through a comparison with ML algorithms and HPs applied in previous related studies, the results herein also showed that the selection of various ML algorithms and HPs is important in developing optimal ML models for WG management. | Cha, Gi-Wook; Choi, Se-Hyu; Hong, Won-Hwa; Park, Choon-Wook | Kyungpook Natl Univ, Sch Sci & Technol Accelerat Engn, Daegu 41566, South Korea; Kyungpook Natl Univ, Sch Architectural Civil Environm & Energy Engn, Daegu 41566, South Korea; Kyungpook Natl Univ, Ind Acad Cooperat Fdn, Daegu 41566, South Korea | ; Choi, Se-Hyu/R-9264-2019 | 55754413300; 7408119153; 7401527968; 56181530400 | shchoi@knu.ac.kr; | INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH | 1660-4601 | 20 | 1 | 2.53 | 2025-06-25 | 11 | 17 | waste management; demolition waste; machine learning; optimal predictive model; waste generation rate; redevelopment area | SUPPORT VECTOR MACHINE; MULTIPLE LINEAR-REGRESSION; CONSTRUCTION; HYBRID; CHINA; PERFORMANCE; ALGORITHMS | demolition waste; machine learning; optimal predictive model; redevelopment area; waste generation rate; waste management | Algorithms; Artificial Intelligence; Machine Learning; Neural Networks, Computer; Support Vector Machine; Waste Management; South Korea; artificial intelligence; artificial neural network; demolition; machine learning; redevelopment; solid waste; waste management; article; artificial neural network; construction and demolition waste; k nearest neighbor; linear regression analysis; machine learning; prediction; predictive model; random forest; sensitivity analysis; South Korea; support vector machine; uncertainty; waste management; algorithm; artificial intelligence; machine learning; procedures | English | 2023 | 2023-01 | 10.3390/ijerph20010107 | 바로가기 | 바로가기 | 바로가기 | |||||||||
| ○ | Article | Development of Machine Learning Predictive Model for Forecasting Demolition Waste Generation | Due to the rapid increase in Construction & Demolition (C&D) waste, C&D waste management (WM) management is an important challenge, and Artificial Intelligence (AI) technology is being actively used as a smart WM strategy. Demolition waste (DW) predictive models were developed and tested by applying artificial neural network (ANN) and support vector machine (SVM) based on a dataset consisting of categorical input variables in this study. For this, DW predictive models with optimal performance were derived through hyper-parameter tuning of ANN and SVM algorithms. As a result of this study, the predictive performance of the ANN and SVM models showed mean absolute error (MAE) 71.730 and 79.437, root mean square error (RMSE) 119.414 and 104.979, coefficient of determination (R squared) 0.891 and 0.859 mean square error (MSE) 11020.556 and 14259.820 respectively. Therefore, the ANN model was confirmed to be a better model for predicting the DW than the SVM model in this study. At this time, the mean of the observed values is 987.181 kg·m-2, and the mean of the predictive values of the ANN and SVM models are 986.180 kg·m-2 and 991.050 kg·m-2, respectively. © 2023 Architectural Institute of Korea. | Cha, Gi-Wook; Hong, Won-Hwa | School of Science and Technology Acceleration Engineering, Kyungpook National University, South Korea; School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, South Korea | 55754413300; 7401527968 | cgwgnr@gmail.com; | Journal of the Architectural Institute of Korea | 2733-6239 | 39 | 2 | 0.15 | 2025-06-25 | 1 | Artificial Neural Network; Construction & Demolition Waste; Demolition Waste; Machine Learning; Predictive Model; Support Vector Machine; Waste Management | Korean | Final | 2023 | 10.5659/jaik.2023.39.2.229 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Article | Development of Mean Radiant Temperature Virtual Sensor for Core and Perimeter Zones during the Summer using Random Forest | Mean radiant temperature (MRT) is one of many significant factors that influence an occupant’s thermal comfort. There is a deviation in the MRT between the indoor core and perimeter zones depending on a building’s thermal properties; this deviation must be mitigated to ensure thermal comfort. However, there are various practical limitations involved in directly measuring the MRT of these zones. Therefore, this study developed a model that virtually sensed the MRT of the core and perimeter zones using the random forest. To verify the model’s performance, the experiment was conducted during the summer season when the MRT deviation between these zones are often the largest. As a result, the proposed model showed an MRT inference performance of 0.0568°C in the core zone and 0.123°C in the perimeter zone, based on the mean absolute error. This study demonstrated the potential of the MRT virtual sensor for evaluating the inference performance of the core and perimeter zones. The virtual sensor can be used in HVAC control systems to improve an occupant’s thermal comfort. © 2023 Architectural Institute of Korea. | Sung, Seung-Ho; Yun, Woo-Seung; Ryu, Wontaek; Seo, Hyuncheol; Hong, Won-Hwa | School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, South Korea; School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, South Korea; School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, South Korea; School of Architecture, Kyungpook National University, South Korea; School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, South Korea | 58203498300; 57221104908; 58203608600; 56083741500; 7401527968 | hongwh@knu.ac.kr; | Journal of the Architectural Institute of Korea | 2733-6239 | 39 | 3 | 0.15 | 2025-06-25 | 1 | Machine learning; Mean radiant temperature; Thermal comfort; Virtual sensor | Korean | Final | 2023 | 10.5659/jaik.2023.39.3.171 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Article | Development of rice-based gluten-free muffins enriched with tigernut dietary fiber | The effects of tigernut dietary fiber (TDF: 5, 10, and 20% w/w) inclusion in rice muffin formulations on the functional and pasting properties of composite powders, as well as the nutritional and sensory properties of muffins were investigated. The results showed a significant (p<0.05) proportional increase in the water and oil holding capacity as TDF increased in the powder blends. Moreover, pasting viscosity was found to decrease with the inclusion of TDF. TDF muffins showed improved nutritional quality, with increased protein (~14%), insoluble fiber (~128%) and total fiber (~34%) contents compared to 100% rice muffins. Also, TDF-muffins had lower baking losses (~22%) and better texture, including firmness and chewiness. Sensory scores of TDF-muffins (up to 10% w/w) showed similar consumer acceptability for all parameters considered. Overall, this study suggests tigernut fiber as a functional additive that balances the growing consumers’ demands for healthy and quality gluten-free rice muffins. © 2023 Korean Society of Food Preservation. All rights reserved. | Na, Yoo-Jin; Olawuyi, Ibukunoluwa Fola; Cho, Ha-Seong; Said, Nurul Saadah Binti; Lee, Wonyoung | School of Food Science and Technology, Kyungpook National University, Daegu, 41566, South Korea; School of Food Science and Technology, Kyungpook National University, Daegu, 41566, South Korea, Research Institute of Tailored Food Technology, Kyungpook National University, Daegu, 41566, South Korea; School of Food Science and Technology, Kyungpook National University, Daegu, 41566, South Korea; School of Food Science and Technology, Kyungpook National University, Daegu, 41566, South Korea; School of Food Science and Technology, Kyungpook National University, Daegu, 41566, South Korea, Research Institute of Tailored Food Technology, Kyungpook National University, Daegu, 41566, South Korea | 58749049300; 57204471854; 58188941600; 57211398275; 57195940408 | wonyoung@knu.ac.kr; | Korean Journal of Food Preservation | 1738-7248 | 30 | 6 | 0.14 | 2025-06-25 | 1 | dietary fiber; gluten-free; Keywords tigernut; muffin; rice flour | English | Final | 2023 | 10.11002/kjfp.2023.30.6.918 | 바로가기 | 바로가기 | ||||||||||||||||
| ○ | Article | Development of the Iconography of Maitreya Preaching in Tusita Heaven and Pensive Image as Heavenly Multitude through the Northern Route; [북방루트를 통한 兜率天 說法圖像의 전개와 聖衆으로서의 思惟像] | The iconography of Maitreya preaching in Tusita Heaven illustrates the contents of Foshuoguan Milepusa Shangsheng Doushuaitian jing(hereafter, Shangsheng jing) in which Maitreya, who was promised that he will attain Buddhahood after Sakyamuni’s nirvana, preaches in Tusita Heaven. It originated from Gandhara region that includes Kapisa and was introduced to Central Asia and China by the Northern Route. In the Kizil Caves, there are Maitreya images sitting cross-legged and preaching to the heavenly multitude. Compared to the examples found in the Gandhara region, the heavenly multitudes here are depicted with increased importance. Moreover, the multitudes seating cross-legged or with two feet drawn together in the art of Gandhara region became pensive bodhisattvas in the Kizil Caves, and those in the lower rows in the cave also seem to represent the pensive multitude mentioned in Shangsheng jing. As the iconography was introduced to China, the heavenly multitude who stood with attending bodhisattvas were reduced in size whereas images of Maitreya became more dominant. The fact that cross-legged Maitreya is usually attended by pensive bodhisattvas to form a triad shows that the Chinese perception of Shangsheng jing differed from that of Gandhara and Central Asia. The palaces of Tusita Heaven that resemble the wooden architectures of China show that the iconography has been Sinicized. There are many inscriptions on the pensive bodhisattvas of late Northern Wei that include the word ‘pensive image,’ suggesting that they were made to represent pensive heavenly multitude listening to the preaching of Maitreya in Tusita Heaven. Accordingly, it can be deduced that the images of pensive bodhisattva reflect not only Sakyamuni meditating when still Prince Siddhartha, but also pensive heavenly multitude in accordance with the iconography of Maitreya preaching in Tusita Heaven. © 2023 Korean Journal of Art History. All rights reserved. | Seo, Nam-Young | Kyungpook National University, South Korea | 58393673500 | Korean Journal of Art History | 1225-2565 | 317 | 0 | 2025-06-25 | 0 | Foshuo guan mile pusa shangsheng doushuaitian jing; Heavenly Multitude; Maitreya Bodhisattva; Northern Route; Pensive Image; Preaching Iconography; rebirth in Maitreya’s paradise; Tusita Heaven | Korean | Final | 2023 | 10.31065/kjah.317.202303.002 | 바로가기 | 바로가기 | ||||||||||||||||||
| ○ | Conference paper | Development of V2G Simulator Considering System Uncertainty for Designing MILS | As Vehicle-to-grid(V2G) services are being actively researched, the importance of uncertainty parameters in V2G optimization is increasing. By considering the uncertainty, the aggregator can generate a more efficient and robust charging/discharging schedule for each purpose. The currently researched V2G methodologies are focused on the EV's own, and not considered sudden events such as plug errors and communication errors. This model proposes an improved simulator by assigning EV user behavior and transient events of EV and EVCS. Furthermore, this model analyzes cost, power, and battery status by comparing general V2G schedules with uncertainty-applied schedules through loop simulation. © 2023 IEEE. | Gong, Deokho; Seo, Mingyu; Son, Hyeongyu; Han, Sekyung | Kyungpook National University, Department of Electronic and Electrical Engineering, Daegu, South Korea; Kyungpook National University, Department of Electronic and Electrical Engineering, Daegu, South Korea; Kyungpook National University, Department of Electronic and Electrical Engineering, Daegu, South Korea; Kyungpook National University, Department of Electronic and Electrical Engineering, Daegu, South Korea | 58575902000; 57215358477; 58165869800; 36023785800 | 2023 13th International Conference on Power, Energy and Electrical Engineering, CPEEE 2023 | 0 | 2025-06-25 | 0 | binomial distribution; EV; EVCS; MILS; uncertainty | Behavioral research; Charging (batteries); Electric power distribution; Binomial distribution; Charging/discharging; EV; EVCS; MILS; Optimisations; System uncertainties; Uncertainty; Uncertainty parameters; Vehicle to Grid (V2G); Vehicle-to-grid | English | Final | 2023 | 10.1109/cpeee56777.2023.10217566 | 바로가기 | 바로가기 |
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