• Convergence Security Journal
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• v.8 no.4
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• pp.167-179
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• 2008

As ubiquitous technology develops, there are many studies to provide various contents proper to users through a mobile device. However, there is a limit of information provision due to a small user interface of a mobile device. This study proposes a system that can solve a problem and provide an intelligent agent model appropriate to a mobile environment and job information positively that an individual user is interested. It is composed of a personalization engine to monitor users' behavior patterns and a learning algorithm to provide information to a mobile device. Analysis shows that preferred job items are different by sex, age and education, while a region affects job searching significantly.

• 전자공학회논문지 IE
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• v.47 no.1
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• pp.12-21
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• 2010

In this paper, we suggest Location Recognition System using Sensor Network; it distinguishes locations. Furthermore, this paper proposes Intelligent Navigation System which presents the proper route for the user. INS evaluates the user's preference, tendency and environmental state using Sensor Network Module and current driving information. This system also uses Soft-computing method to learn and infer the person’s preference and tendency. This paper defines Intelligent Assistance Module (IAM) which is a connector in between a user and a robot; it is portable. All in all, we created a basic intelligent robot, Location Recognition System, and Environment Sensor Modules; we verified the proposed algorithm through computer simulations.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.767-769
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• 2022

본 논문에서는 현대인들의 일상생활 속 누적된 스트레스를 완화하고 사용자의 편의를 고려한 "아로마테라피를 지원하는 지능형 샤워부스" 시스템을 제안한다. 제안하는 시스템의 주요 기능은 다음과 같다. 첫째, 적외선 온도 센서와 초음파 센서, 카메라를 통해 사용자의 신체 정보와 기분을 측정한다. 둘째, 측정된 사용자의 신체 정보를 반영하여 Linear actuator를 이용해 샤워기의 높낮이 및 수온을 자동으로 조절한다. 셋째, OpenCV와 앱 내에 만족도 평가를 통해 사용자의 기분에 따라 알맞은 아로마오일을 추천하고 이를 샤워기 필터에 주입한다. IoT기술과 연동된 샤워부스 시스템을 통해 사용자 컨디션에 맞춘 아로마테라피를 지원하여 현대인의 지친 심신 회복과 사용자 편의성이 증대될 것으로 기대된다.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.509-517
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• 2023

The reactive web service provides a related word recommendation system using the TextRank algorithm and a word-based idea generation service selected by the user. In the related word recommendation system, the method of weighting each word using the TextRank algorithm and the probability output method using SoftMax are discussed. The idea generation service discusses the idea generation method and the artificial intelligence reinforce-learning method using mini-GPT. The reactive web discusses the linkage process between React, Spring Boot, and Flask, and describes the overall operation method. When the user enters the desired topic, it provides the associated word. The user constructs a mind map by selecting a related word or adding a desired word. When a user selects a word to combine from a constructed mind-map, it provides newly generated ideas and related patents. This web service can share generated ideas with other users, and improves artificial intelligence by receiving user feedback as a horoscope.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.407-413
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• 2022

Existing clothing recommendation systems remain at the level of showing appropriate photos when a user selects a type of clothing he or she likes after entering his or her own body size or body size. When a user purchases clothing using such recommendation systems, there are many cases in which it does not fit or does not fit the user's body size. In this study, to solve these problems of existing clothing recommendation systems, a system was implemented in which the user receives not only size but also skin tone and recommends clothing suitable for the user's body size as well as skin tone. In this system, clothing size information obtained through web crawling was periodically stored in a database for eight male tops to recommend clothing, and the entire pixel of the clothing image was analyzed to extract color text values. In order to confirm the performance of this system, a survey was conducted on 100 male college students, and the satisfaction level was 70%. Most of the reasons for not being satisfied are that the recommended clothing is limited, so it is judged that it is necessary to expand the target clothing in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.583-585
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• 2022

기존 군사 분야 영상 판독 시스템은 영상 판독관들의 작업 부담이 크고, 판독관들의 경험과 숙련도에 의존적이다. 이전 연구에서 판독관들의 부담을 줄이고 경험 및 숙련 의존도를 낮추기 위해 문장 추천 시스템을 제안하였다. 하지만 학습에 사용된 데이터의 양이 적고, 학습에 사용되지 않은 장비 혹은 지역 등의 단어가 등장 시 제대로 동작하지 않는 한계점이 있었다. 이를 해결하기 위해 학습 데이터 단계와 디코딩 단계에 지식그래프를 적용하여 문장의 다양성과 확장성을 확보하고, 데이터 부족 문제를 완화하였다. 이 연구는 추후 판독관들의 업무 과부화를 완화하고 업무 효율을 높일 수 있을 것이다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1290-1298
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• 2006

오늘날 B2B 전자상거래의 폭발적인 성장과 더불어 SCM상에서 e-Procurement의 중요성이 부각되고 있다. 그 중에서 다수의 구매자와 공급자가 참여하여 다양한 형태의 상거래를 수행하는 3자 관리 모델은 점차 시장이 확대되어 e-Procurement의 핵심요소로 여겨지고 있다. 하지만 현재의 e-Procurement의 3자 관리 모델은 방대한 제품 정보 중에서 구매자가 원하는 제품을 정확히 검색할 수 있는 기능이 미비하고 구매 물량에 따른 할인 가격과 공급자의 배송 정책을 고려한 납기일을 실시간으로 구매자에게 제공해 줄 수 있는 기능이 미흡한 실정이다. 또한 구매 프로세스의 주요 기능인 공급자의 선택에 있어 공급자에 대한 신뢰성이 결여되어 구매자의 비즈니스 요구사항을 채워주지 못하고 있다. 본 연구에서는 이러한 e-Procurement의 3자 관리 모델의 문제점을 해결하고 발전시키기 위해 (1)구매자의 제품 검색 시 시맨틱 웹을 이용한 카테고리 기반 검색 기법과 (2)비즈니스 룰과 웹서비스 기반으로 공급자의 가격 정책, 배송 정책에 따른 납기일을 고려한 검색, (3)구매자와 공급자의 단일 거래에 대한 서비스 품질을 측정하여 구매자에게 공급자의 신뢰성을 보장하는 제품 추천 e-Procurement 시스템을 제안한다.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.3
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• pp.349-360
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• 2019

This study analyzes the trends of recommendation services for customized fashion styles in relation to artificial intelligence. To achieve this goal, the study examined filtering technologies of collaborative, content based, and deep-learning as well as analyzed the characteristics of recommendation services in the users' purchasing process. The results of this study showed that the most universal recommendation technology is collaborative filtering. Collaborative filtering was shown to allow intuitive searching of similar fashion styles in the cognition of need stage, and appeared to be useful in comparing prices but not suitable for innovative customers who pursue early trends. Second, content based filtering was shown to utilize body shape as a key personal profile item in order to reduce the possibility of failure when selecting sizes online, which has limits to being able to wear the product beforehand. Third, fashion style recommendations applied with deep-learning intervene with all user processes of buying products online that was also confirmed to penetrate into the creative area of image tag services, virtual reality services, clothes wearing fit evaluation services, and individually customized design services.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.210-211
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• 2023

To reduce maritime accidents and improve safety for domestic ships, the government has developed and is operating the Korean e-Navigation Service. The ship collision and grounding monitoring service and the route planning service were developed based on the experience of captains and navigators who navigated large ships. However, in Korea, small ships account for a large portion of the users of these services, so there are various difficulties and additional requirements for applying services based on large ships to small ships. To improve this, a data science-based algorithm model using maritime digital traffic information is being developed for small ships, and it is important to evaluate the performance of the developed model and compare its accuracy with existing services. In this study, we propose a performance evaluation method for the developed algorithm model and study how to compare and evaluate its accuracy with existing e-navigation services.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.526-529
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• 2003

Recommender systems or collaborative filtering are methods to identify potentially interesting or valuable items to a particular user Under the assumption that people with similar interest tend to like the similar types of items, these methods use a database on the preference of a set of users and predict the rating on the items that the user has not rated. Usually the preference of a particular user is liable to vary with time and this temporal variation may cause an inaccurate identification and prediction. In this paper we propose a method to adapt the temporal variation of the user preference in order to improve the predictive performance of a collaborative filtering algorithm. To be more specific, the correlation weight of the GroupLens system which is a general formulation of statistical collaborative filtering algorithm is modified to reflect only recent similarity between two user. The proposed method is evaluated for EachMovie dataset and shows much better prediction results compared with GrouPLens system.