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소형 무인기 추진용 덕티드 팬의 공력특성에 관한 실험적 연구
김재경(Jaekyeong Kim),최현민(Hyunmin Choi),차봉준(Bongjun Cha),이상효(Sanghyo Lee),조진수(Jinsoo Cho) 한국추진공학회 2008 한국추진공학회 학술대회논문집 Vol.2008 No.5
본 연구에서는 소형 무인기의 추진체로 사용되는 덕티드 팬의 공력특성 연구를 위해 덕티드 팬 입/출구의 3차원 유동장 측정과 추력 특성 분석을 수행하였다. 3차원 유동장 측정은 정온형 열선유속계를 통하여 수행되었으며, 추력은 육분력 밸런스를 이용하여 측정하였다. 측풍의 영향을 고려하기 위해 덕티드 팬을 풍동시험기내 유동방향에 대하여 90° 회전시켜 설치하였다. 풍동시험을 통하여 4.5 m/s의 측풍으로 인한 덕티드 팬 유동장 및 추력의 변화를 분석하였다. The experimental analysis on a ducted fan for the propulsion system of a small UAV were performed. To investigate the aerodynamic characteristics of the ducted fan, flow fields at inlet and outlet were measured using a hot-wire anemometry. Thrusts were measured with the six-component balance with due regard to the cross wind. To reproduce the cross wind effect, the ducted fan was aligned to 90° rotated direction against flow direction in the wind tunnel. In this paper, the variation of the flow fields and thrust according to the cross wind were analyzed.
상용차용 Urea-SCR 시스템의 인젝터 냉각방식에 관한 연구
권봉수(Bongsu Kwon),김종훈(Jonghun Kim),김덕진(Deokjin Kim),이천환(Chunhwan Lee),오광철(Kwangchul Oh),이춘범(Chunbeom Lee),김재경(Jaekyeong Kim),김경남(Kyeongnam Kim) 한국자동차공학회 2010 한국자동차공학회 학술대회 및 전시회 Vol.2010 No.11
Urea-SCR system is a high-effective NOx reduction technology in diesel vehicles. Urea injectors typically are installed on the vehicle’s exhaust pipe. While the operating temperature of solenoid-type injector is 50℃, the exhaust gas temperature is higher than the temperature of the injector, and it will cause the overheating problem in the injector. Thus, in order to solve the problem, the cooling jacket (cooling system) is required to maintain the operating temperature of solenoid-type injector. In the paper, it was studied about the cooling performance of injector used in the heavy-duty commercial vehicles. First, the cooling performance by air was evaluated through the engine testing, and ESC and ETC mode are used for engine testing. Second, the cooling performance by water was evaluated through the CFD analysis. Therefore, through this study, we should select a more effective cooling system of the solenoid-type injector
온라인 리뷰의 제목과 내용의 일치성이 리뷰 유용성에 미치는 영향
이청용,김재경,Li, Qinglong,Kim, Jaekyeong 한국지식경영학회 2022 지식경영연구 Vol.23 No.3
Many studies have proposed several factors that affect review helpfulness. Previous studies have investigated the effect of quantitative factors (e.g., star ratings) and affective factors (e.g., sentiment scores) on review helpfulness. Online reviews contain titles and contents, but existing studies focus on the review content. However, there is a limitation to investigating the factors that affect review helpfulness based on the review content without considering the review title. However, previous studies independently investigated the effect of review content and title on review helpfulness. However, it may ignore the potential impact of similarity between review titles and content on review helpfulness. This study used text consistency between review titles and content affect review helpfulness based on the mere exposure effect theory. We also considered the role of information clearness, review length, and source reliability. The results show that text consistency between the review title and the content negatively affects the review helpfulness. Furthermore, we found that information clearness and source reliability weaken the negative effects of text consistency on review helpfulness.
설명 가능한 개인화 영화 추천 서비스를 위한 딥러닝 기반 텍스트 요약 모델
진요요,강경모,김재경,Chen, Biyao,Kang, KyungMo,Kim, JaeKyeong 한국IT서비스학회 2022 한국IT서비스학회지 Vol.21 No.2
The number and variety of products and services offered by companies have increased dramatically, providing customers with more choices to meet their needs. As a solution to this information overload problem, the provision of tailored services to individuals has become increasingly important, and the personalized recommender systems have been widely studied and used in both academia and industry. Existing recommender systems face important problems in practical applications. The most important problem is that it cannot clearly explain why it recommends these products. In recent years, some researchers have found that the explanation of recommender systems may be very useful. As a result, users are generally increasing conversion rates, satisfaction, and trust in the recommender system if it is explained why those particular items are recommended. Therefore, this study presents a methodology of providing an explanatory function of a recommender system using a review text left by a user. The basic idea is not to use all of the user's reviews, but to provide them in a summarized form using only reviews left by similar users or neighbors involved in recommending the item as an explanation when providing the recommended item to the user. To achieve this research goal, this study aims to provide a product recommendation list using user-based collaborative filtering techniques, combine reviews left by neighboring users with each product to build a model that combines text summary methods among deep learning-based natural language processing methods. Using the IMDb movie database, text reviews of all target user neighbors' movies are collected and summarized to present descriptions of recommended movies. There are several text summary methods, but this study aims to evaluate whether the review summary is well performed by training the Sequence-to-sequence+attention model, which is a representative generation summary method, and the BertSum model, which is an extraction summary model.
호텔 방문객들의 문화적 특성이 호텔 선택속성에 끼치는 영향: Hofstede 문화차원을 중심으로
장재원 ( Jaewon Jang ),이병현 ( Byunghyun Lee ),김재경 ( Jaekyeong Kim ) 한국지식경영학회 2023 지식경영연구 Vol.24 No.1
문화적 배경은 사회 구성원이 특정한 방향으로 인지하고 행동하도록 기여하는 역할을 하므로, 서로 다른 문화적 배경을 가진 고객들은 같은 서비스를 제공받아도 각자 다른 반응을 보인다. 호텔 방문객들은 서로 다른 문화적 배경을 가지고 있으므로, 호텔에서 제공되는 서비스나 시설에 대한 인식과 만족도 또한 다르다. 이에 따라 기존 연구에서는 Hofstede 문화차원을 활용하여, 호텔 방문객들의 문화적 배경에 따라 제공되는 서비스에 대한 만족도가 어떻게 달라지는지 파악하였다. 그러나 기존 연구에서는 호텔 방문객들의 문화적 배경만 고려하였으며, 여행 유형까지 고려한 연구는 많지 않은 실정이다. 그러나 많은 선행 연구에서 여행 유형에 따라 중요하게 고려하는 호텔 서비스 속성 요인들은 서로 상이한 것으로 나타났다. 따라서 본 연구에서는 호텔 방문객의 여행 유형을 비즈니스 방문객과 여가 관광여행 방문객으로 분류하고, Hofstede의 문화차원이 호텔 선택속성에 끼치는 영향이 여행 유형에 따른 차이를 분석하였다. 이를 위해, Hofstede의 6가지 문화차원에 대한 정보는 Hofstede insights에서 제공하는 오픈 데이터를 사용하였고, 호텔 선택속성에 대한 만족도는 대표적인 관광 플랫폼인 TripAdvisor에서 뉴욕 호텔에 대한 선택속성 평점 204,261개를 수집하였다. 따라서 본 연구는 향후 호텔에 방문하는 다양한 문화권 고객들이 어떠한 서비스 속성에 더 중점을 두는지를 파악할 수 있고, 그에 적합한 서비스를 제공할 수 있을 것으로 기대한다. As cultural background contributes members of society to recognize and behave in a specific direction, customers with different cultural backgrounds show various reactions even when they are provided with the same service. Previous studies have used the Hofstede cultural dimension to understand how hotel visitors’ satisfaction varies with the provided service as per their cultural background. However existing research only considered the cultural background of the guests, and there are not many studies focused on the types of travel. Therefore, in this study, the travel types of hotel visitors are classified into business travel visitors and leisure tourism visitors, and analyzed the effect of Hofstede's cultural dimension on hotel selection attributes according to the styles of travel. In this study, we collected information on six cultural dimensions of Hofstede, and from TripAdvisor, a representative tourism platform, 204,261 optional attribute ratings for hotels in New York to investigate the satisfaction of hotel selection attributes. In conclusion, it is expected that this study will be able to identify which service attributes the customers of various cultures who visit hotels put emphasis in advance, and therefore provide suitable service accordingly.
명시적 및 암시적 피드백을 활용한 그래프 컨볼루션 네트워크 기반 추천 시스템 개발
이흠철(Xinzhe Li),김동언(Dongeon Kim),이청용(Qinglong Li),김재경(JaeKyeong Kim) 한국IT서비스학회 2023 한국IT서비스학회지 Vol.22 No.1
With the development of the e-commerce market, various types of products continue to be released. However, customers face an information overload problem in purchasing decision-making. Therefore, personalized recommendations have become an essential service in providing personalized products to customers. Recently, many studies on GCN-based recommender systems have been actively conducted. Such a methodology can address the limitation in disabling to effectively reflect the interaction between customer and product in the embedding process. However, previous studies mainly use implicit feedback data to conduct experiments. Although implicit feedback data improves the data scarcity problem, it cannot represent customers' preferences for specific products. Therefore, this study proposed a novel model combining explicit and implicit feedback to address such a limitation. This study treats the average ratings of customers and products as the features of customers and products and converts them into a high-dimensional feature vector. Then, this study combines ID embedding vectors and feature vectors in the embedding layer to learn the customer-product interaction effectively. To evaluate recommendation performance, this study used the MovieLens dataset to conduct various experiments. Experimental results showed the proposed model outperforms the state-of-the-art. Therefore, the proposed model in this study can provide an enhanced recommendation service for customers to address the information overload problem.