• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.277-287
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• 2021

The purpose of this study is to establish a model that can quantitatively diagnose personal color. Representative color systems for personal colors have limitations in that it oversimplify personal color diagnosis types or it is difficult to distinguish objective differences between diagnosis types. To develop a brand new color system that enhances this, a PCCS color system capable of logical color was introduced and reclassified based on the four main properties of color. Twenty diagnostic types, which are more diverse than the existing color system were proposed and a quantitative method was used to evaluate the degree of harmony with a subject to find an optimized type of subject. The experimenter's individual competency and subjective intervention were minimized by devising a matrix in which a type suitable for the subject is derived when the coded evaluation result is substituted. Finally a quantitative diagnosis model of personal color consisting of three stages: property diagnosis, coding, and seasonal diagnosis was constructed. It can be seen that this will give diversity, reliability, and accuracy to the existing diagnostic methods.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.4
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• pp.685-702
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• 2021

This study aims to discover how the perceived attributes of luxury fashion platforms affect consumer trust and satisfaction as well as online word-of-mouth intention. Based on a literature review, this study derived four dimensions of perceived attributes: brand assortment size, exclusivity, convenience, and personalization. The paper presents findings from an online survey targeting 359 consumers in their 20s to 30s who had recent experience with luxury fashion platforms. Based on the collected data, a structural model equation analysis was performed using AMOS 22.0 and SPSS 26.0. The findings illustrated that brand assortment size, exclusivity, and personalization had positive effects on consumers' platform trust. In addition, brand assortment size and convenience had a positive impact on satisfaction. Overall, the findings of the study illustrate that perceived attributes of luxury fashion platforms have a significant impact on consumers' platform trust and satisfaction and online word-of-mouth intentions. This study reveals that consumers' trend orientation moderates the effects of consumer attitude and behavioral intention. The academic practice of this study has laid the foundation for understanding mechanisms of marketing strategies by providing the characteristics of platforms in the luxury fashion industry.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.537-542
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• 2021

Smart TVs provide a variety of services and information compared to existing TVs based on the Internet. In order to provide more personalized services or information, it is necessary to analyze users' viewing patterns and provide customized services or information based on them. The proposed system receives the user's TV viewing pattern, analyzes it, and recommends a TV program or movie as customized information to the user. For this, the system was constructed with a preprocessor and a deep learning model. The preprocessor refines the name of the TV program watched by the user, the date the TV program was watched, and the watched time. Then, the multi-attribute LSTM model trains the refined data and performs prediction.The proposed system is a system that provides customized information to users, and is believed to be a leading technology in digital convergence that combines existing IoT technology and deep learning technology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.411-416
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• 2020

Field investigations of forests are carried out by writing measured data by hand, and it is a hassle to reorganize the results after a field survey. In this study, a method using object modeling and augmented reality (AR) was applied in a test forest to increase the efficiency of a field investigations. Using a 3D laser scanner, data on were acquired 387 trees within an area of 1 ha at the study site. The coordinates, height, and diameter were calculated through object extraction and modeling of a tree. The proposed can reduce the time required to acquire data in the field and can be used as basic data for building related systems. In addition, the modeling results of trees and a survey using GNSS and AR techniques can be used check coordinates, labor, and attribute information, such as the chest height diameter of the trees being surveyed in the field. The shortcomings of the survey method could be improved. In the future, the method could greatly improve the efficiency of tree surveys and monitoring by reducing the manpower and time required for field surveys.

• Korean Journal of Culture and Social Issue
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• v.26 no.4
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• pp.525-549
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• 2020

As the attitudes toward women is ambivalent (both hostile and ambivalent), people have a tendency to have ambivalent attitudes toward men. Despite conflicts between men and women caused by misogyny and misogyny have recently worsened in a Korean society, most of previous Korean studies have focused on the attitudes toward women. In addition, there has been no scale to measure such ambivalent attitudes toward men in Korea. Therefore, this study was designed to translate and validate the Ambivalence toward Men Inventory, a scale developed and currently utilized in the United State. Sample 1 (183 college students), sample 2 (300 college students), and sample 3 (317 adults) were used. Exploratory and confirmatory factor analyses resulted in 16 items and 2 factors. The tests of convergent and concurrent validity revealed strong evidence for the validity of the Korean version of the Ambivalence toward Men Inventory and the reliabilities of the two factors were .830~.917.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.5-10
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• 2022

Ground Subsidence has been continuously occurring in densely populated downtown. The main cause of ground subsidence is the damaged underground facility like sewer. Currently, ground subsidence is being dealt with by discovering cavities in ground using GPR. However, this consumes large amount of manpower and cost, so it is necessary to predict hazardous area for efficient operation of GPR. In this study, ◯◯city is divided into 500 m×500 m grids. Then, data set was constructed using the characteristics of the underground facility and ground subsidence in grids. Data set used to machine learning model for ground subsidence risk grade prediction. The purposed model would be used to present a ground subsidence risk map of target area.

• Journal of Korean Library and Information Science Society
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• v.53 no.2
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• pp.95-115
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• 2022

The purpose of this study is to transform the sub-categorization terms of the National Science and Technology Standards Classification System into technical keywords by applying a machine learning algorithm. For this purpose, AttentionMeSH was used as a learning algorithm suitable for topic word recommendation. For source data, four-year research status files from 2017 to 2020, refined by the Korea Institute of Science and Technology Planning and Evaluation, were used. For learning, four attributes that well express the research content were used: task name, research goal, research abstract, and expected effect. As a result, it was confirmed that the result of MiF 0.6377 was derived when the threshold was 0.5. In order to utilize machine learning in actual work in the future and to secure technical keywords, it is expected that it will be necessary to establish a term management system and secure data of various attributes.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.383-383
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• 2023

오늘날 수문학 분야에서는 유역에 대한 강우-유출 시뮬레이션을 머신 러닝(ML: Machine Learning)을 활용하여 다양한 연구를 실행하고 있다. 본 연구에서는 시간별 강우-유출 예측 모델인 GR4H(Génie Rural à 4 paramètres Horaires)를 사용하여 충주댐 유역을 대상으로 연구를 수행하였다. 유역의 속성에 따라서 모델의 성능이 어떻게 달라지는지 비교하여 특성에 맞는 모델을 알아내고. 또한 이 과정에서 기상 및 유출 데이터의 보정 길이를 가지고 어느 정도의 데이터 기간이 모델에서 좋은 성능을 보이는지 파악하였다. 뿐만 아니라 모델에 필요한 선행기간의 데이터가 있는 경우와 없는 경우를 비교하여 어떠한 차이를 보이는지, 그리고 선행기간은 얼마나 필요한지 연구를 통하여 알아냈다. 본 연구를 통하여 충주댐 유역에 대한 모델의 적용성 및 성능을 파악하고 수문 모형 구축에 제한이 있는 유역에 대해서도 사용이 가능한지 판단한다. 실험 유역의 관측 값을 모델에 입력한 후 각 모델에 해당하는 매개변수의 최적값을 찾아내는 과정을 거쳐 시뮬레이션을실 행했다. 본 연구에서 사용한 강우-유출 모델인 GR4H는 프랑스의 INRAE-Antony(Institut National de la recherche agronomique-Antony)에서 만들어진 airGR의 일종으로, 시간별 강우-유출 예측을 위해 개발된 공정 기반(process-based)의 집중적, 개념적 수문학 모델이다. 4개의 매개변수(parameter)가 있으며 이는 유역의 특정 속성을 나타낸다. GR4H를 시뮬레이션 하는 과정에서 매개변수의 최적화를 위해 적절한 보정 길이를 파악하여야 한다. 이러한 과정은 4년, 5년, 6년 등 1년씩 데이터의 양을 늘려가며 매개변수를 최적화한다. 이 과정에서 기상 및 유출 데이터의 적절한 보정 길이를 찾아낸다. 시뮬레이션을 통해 얻은 데이터를 관측 값과 비교하여 모델의 성능을 평가하고 다른 관측 값을 통해 시뮬레이션을 실행하여 검증을 거친다.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.107-116
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• 2009

Overcrowding in an Emergency Department (ED) of hospital is a common phenomenon. To improve the service quality and system performance of the ED, a task assignment rule for the Registered Nurses (RNs) is proposed in this paper. At each task assignment point, the rule prioritizes all treatment requests based on the urgency which is determined by the multiple attributes such as accomplishment time of treatment task, elapsed time of treatment request, total remain time to patient discharge, and number of remain treatments. The values of partial urgency with a single criterion are determined and then overall urgency is computed to find the most urgent one among current requests with the importance weights assigned to the criteria. Through computer simulation, the performance of the proposed rule is compared with current rule in terms of the length of stay and system throughput in a simplified ED system of the hospital M.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.2-5
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• 2022

With the advent of deep learning and the rapid development of ICT (Information and Communication Technology), research using artificial intelligence is being actively conducted in various fields of society such as politics, economy, and culture and so on. Deep learning-based artificial intelligence technology is subdivided into various domains such as natural language processing, image processing, speech processing, and recommendation system. In particular, as the industry is advanced, the need for a recommendation system that analyzes market trends and individual characteristics and recommends them to consumers is increasingly required. In line with these technological developments, this paper extracts and classifies attribute information from structured or unstructured text and image big data through deep learning-based technology development of 'language processing intelligence' and 'image processing intelligence', and We propose an artificial intelligence-based 'customized fashion advisor' service integration system that analyzes trends and new materials, discovers 'market-consumer' insights through consumer taste analysis, and can recommend style, virtual fitting, and design support.