• Archives of design research
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• v.17 no.3
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• pp.335-342
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• 2004

Recently there have been various research activities regarding Korean traditional aesthetics. However, those researches were mainly conducted individually, partially, and periodically, which resulted in unsystematic and incomprehensive works. Therefore, it is required to orginze all the precedent research works with more systematic and objective framework. Generally speaking, all the human activities including aesthetic activity have ends, procedure and means. In other words, human being needs three key elements for realizing any thought and those three elements include contents, formal, and practical element. Element of contents is ultimate goal to accomplish as value, concept, and meaning of thought with their aims. Formal element includes methods, principles, norms, procedure, formality and style comprising of thought in order to accomplish the goal. Finally, practical element refers to specific means, tool, media, material and techniques to concretize the contents through form. Almost all of thoughts and meaning which human being tries to express consist of language. Major elements in sentence include 'subject (omissible)' , 'objects (aim)', 'predicate (formality)', 'complement (means)' and they are composed systematically and hierarchically with rules in sentence. The study compared human activity model with language structure and analyzed their implication with design (aesthetics), which made it possible to propose analytic frameworks for traditional aesthetics. In addition, the study also systematically organized the way to realize traditional aesthetic value in the present context based on the methods developed in this study.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.271-284
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• 2008

Self-healing is an approach to evaluating constraints defined in target system and to applying an appropriate strategy when violating he constrains. Today, the computing environment is very complex, so researches that endow a system with the self-healing's ability that recognizes problem arising in a target system are being an important issues. However, most of the existing researches are that self-healing developers need much effort and time to analyze and model constraints. Thus, this paper proposes an automated approach to determine problem arising in external and internal system environment. The approach proposes: 1) Specifying the target system through the models created in design phase of target system. 2) Automatically creating constraints for external and internal system environment, by using the specified contents. 3) Deriving a dependency model of a component based on the created internal state rule. 4) Translating the constraints and dependency model into code evaluating behaviors of the target system, and determinating problem level. 5) Monitoring an internal and external status of system based on the level of problem determination, and applying self-healing strategy when detecting abnormal state caused in the target system. Through these, we can reduce the efforts of self-healing developers to analyze target system, and heal rapidly not only abnormal behavior of target system regarding external and internal problem, but also failure such as system break down into normal state. To evaluate the proposed approach, through video conference system, we verify an effectiveness of our approach by comparing proposed approach's self-healing activities with those of the existing approach.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.249-258
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• 2004

오늘날 활발하게 이루어지고 있는 유비쿼터스 컴퓨팅 관련 기술 연구는 사용자가 시간과 장소에 구애받지 않고 네트워크에 접근해 다양한 컴퓨터 관련 서비스를 제공 받을 수 있는 방법에 초점을 맞추고 있다. 이 처럼 시간과 공간의 한계를 뛰어 넘은 네트워크로의 자유로운 접근은 일상 생활의 패러다임을 바꾸어 놓게 될 것이다. 유비쿼터스 컴퓨팅 기술을 통해 가장 큰 변화가 일어나는 분야는 일반 가정환경에서 일어나는 인텔리전트 홈 네트워크 (Intelligent Home Network) 라고 할 수 있다. 집에 들어오면, 자동으로 문을 열어주고, 불을 켜주며, 놓쳤던 TV 프로그램을 자동으로 녹화해 놓았다가 원하는 시간에 보여주고, 적당한 시간에 목욕물을 미리 받아준다. 또한 집밖으로 나가기 전, 일기예보에 따라 우산을 챙겨주고, 일정을 확인시켜주며 입고 나갈 옷을 골라줄 수도 있다. 이 모든 일들이 유비쿼터스 컴퓨팅 기술이 가져올 인텔리전트 홈 네트워크의 모습이다. 그러나, 모든 사용자에게 효과적인 서비스를 제공하기 위해서는 홈 네트워크 상의 자원 관리에서 일어날 수 있는 에이전트들간의 자원 접근 권한 충돌을 효율적으로 방지할 수 있는 기술이 필요하다. 유비쿼터스 컴퓨팅 환경에서 자원관리 특성은 점유의 연속성, 자원 사이의 연관성, 그리고 자원과 사용자 사 사이의 연계성의 3 가지 특성을 지니고 있다. 본 논문에서는 유비쿼터스 컴퓨팅 환경에서 일어날 수 있는 자원 충돌 상황을 효율적으로 처리하기 위한 자원 협상 방법을 제안한다. 본 방법은 자원 관리 특성을 바탕으로 시간논리에 기반을 둔 자원 선점과 분배 규칙으로 구성된다.트 시스템은 b-Cart를 기반으로 할 것으로 예측할 수 있다.타났다. 또한, 스네이크의 초기 제어점을 얼굴은 44개, 눈은 16개, 입은 24개로 지정하여 MER추출에 성공한 영상에 대해 스네이크 알고리즘을 수행한 결과, 추출된 영역의 오차율은 각각 2.2%, 2.6%, 2.5%로 나타났다.해서 Template-based reasoning 예를 보인다 본 방법론은 검색노력을 줄이고, 검색에 있어 Feasibility와 Admissibility를 보장한다.매김할 수 있는 중요한 계기가 될 것이다.재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data b

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.299-306
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• 2022

In recent, sensors embedded in robots, equipment, and circuits have become common, and research for diagnosing device failures by learning measured sensor data is being actively conducted. This failure diagnosis study is divided into a classification model for predicting failure situations or types and a regression model for numerically predicting failure conditions. In the case of a classification model, it simply checks the presence or absence of a failure or defect (Class), whereas a regression model has a higher learning difficulty because it has to predict one value among countless numbers. So, the reason that regression modeling is more difficult is that there are many irregular situations in which it is difficult to determine one output from a similar input when predicting by matching input and output. Therefore, in this paper, we focus on input and output data with periodicity, analyze the input/output relationship, and secure regularity between input and output data by performing sliding window-based input data patterning. In order to apply the proposed method, in this study, current and temperature data with periodicity were collected from MMC(Modular Multilevel Converter) circuit system and learning was carried out using ANN. As a result of the experiment, it was confirmed that when a window of 2% or more of one cycle was applied, performance of 97% or more of fit could be secured.

• Journal of The Korean Association For Science Education
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• v.38 no.6
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• pp.885-899
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• 2018

As 'Science Inquiry Experiment' is newly introduced in the high school curriculum, where inquiry and experiment oriented education is insufficient, this study aims to analyze teacher's practice of 'Science Inquiry Experiment' in depth and identify contradictions during its process in the perspective of Cultural Historical Activity Theory. The research participant is teacher SHIN who is exclusively responsible for Science Inquiry Experiment. Starting with reflection on the practice of Science Inquiry Experiment class conducted in the first semester, interviews with participants, participatory observation and local materials were used during the 2nd semester's Science Inquiry Experiment class. A descriptive analysis of the teacher SHIN's practice of Science Inquiry Experiment was carried out and the contradictions in the activity system of the teacher SHIN were identified. The result reveals that in the overall practice of teaching Integrated Science and Science Inquiry Experiment, there were contradictions between teacher SHIN's recognition about cooperation(subject) and shared responsibility with other teachers(division of labor), and between teacher SHIN's recognition about the subjects(subject) and contrasting contents in teacher training courses(community). In the practice of teaching Science Inquiry Experiment, there were specific contradictions between teacher SHIN's recognition about the subject(subject) and time of job assignment(rule), between experimental activities(object) and experimental tools(tool), and between purpose of the subject(object) and directions about assessment(rule). These contradictions directly or indirectly influence the practice of teaching Science Inquiry Experiment. There needs to be support for constructing an activity system capable of supporting and promoting teachers' practice of Science Inquiry Experiment, and we made several suggestions to resolve the problems.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.6
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• pp.286-304
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• 2017

In this study, we proposed a new-type of circular perforated caisson breakwater consisting of a bundle of latticed blocks that can be applied to a small port such as a fishing port, and numerically investigated the hydraulic characteristics of the breakwater. The numerical method used in this study is OLAFOAM which newly added wave generation module, porous media analysis module and reflected wave control module based on OpenFOAM that is open source CFD software published under the GPL license. To investigate the applicability of OLAFOAM, the variations of wave pressure acting on the three-dimensional slit caisson were compared to the previous experimental results under the regular wave conditions, and then the performance for irregular waves was examined from the reproducibility of the target irregular waves and frequency spectrum analysis. As a result, a series of numerical simulations for the new-type of circular perforated caisson breakwaters, which is similar to slit caisson breakwater, was carried out under the irregular wave actions. The hydraulic characteristics of the breakwater such as wave overtopping, reflection, and wave pressure distribution were carefully investigated respect to the significant wave height and period, the wave chamber width, and the interconnectivity between them. The numerical results revealed that the wave pressure acting on the new-type of circular perforated caisson breakwaters was considerably smaller than the result of the impermeable vertical wall computed by the Goda equation. Also, the reflection of the new-type caisson breakwater was similar to the variation range of the reflection coefficient of the existing slit caisson breakwater.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.250-250
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• 2013

단일벽 탄소나노튜브(Single-walled nanotubes, SWNTs)는 나노스케일의 크기와 우수한 물성으로 인하여, 전자, 에너지, 바이오 분야로의 응용이 기대되고 있다. 특히 SWNTs의 직경을 제어하게 되면 튜브의 전도성 제어가 훨씬 수월하게 되어, 차세대 나노전자소자의 실현을 앞당길 수 있으며 이러한 이유로 많은 연구들이 현재 행해지고 있다. SWNTs의 직경제어 합성을 위해서는 현재 열화학기상증착법(Thermal chemical vapor deposition; TCVD)이 가장 일반적으로 이용되고 있으며, 합성 촉매와 합성되는 튜브의 직경과의 크기 연관성이 알려진 후로는, 촉매의 크기를 제어하여 SWNTs의 직경을 제어하고자 하는 연구들이 활발하게 보고되고 있다. 특히, 촉매 나노입자의 직경이 1~2 nm 이하로 감소될 경우, SWNTs의 직경 분포가 어떻게 변화할 것인지가 최근 가장 중요한 관심사로 남아 있으나, 이러한 크기의 금속입자는 나노입자의 융점저하 현상이 발현되는 영역이므로, SWNTs의 합성온도 영역에서 촉매 금속입자는 반액체(Semi-liquid) 상태로 존재할 것으로 추측하고 있다. 본 연구에서는 고온의 SWNTs 합성환경에서 금속나노촉매의 유동성을 제한하기 위하여 나노사이즈의 기공이 규칙적으로 정렬된 다공성 물질인 제올라이트를 촉매담지체로 이용하였고, 이 때 다양한 합성변수가 SWNTs의 직경에 미치는 영향을 살펴보고자 하였다. SWNTs의 합성을 위해 실리콘 산화막 기판 위에 제올라이트를 도포한 후, 합성 촉매로서 전자빔증발법을 통하여 수 ${\AA}$에서 수 nm 두께의 철 박막을 증착하였다. 합성은 메탄을 원료가스로 하여 TCVD법으로 실시하였다. 주요변수로는 제올라이트 종류, 증착하는 철 박막의 두께, 합성온도를 설정하였으며, 이에 따라 합성된 SWNTs의 합성수율 및 직경분포의 변화를 체계적으로 살펴보았다. SWNTs의 전체적인 합성수율의 변화는 SEM 관찰결과를 이용하였으며, SWNTs의 직경은 AFM 관찰 및 Raman 스펙트럼의 분석에서 도출하였다. 실험결과, 제올라이트 종류에 따라서는 명확한 튜브직경 분포의 변화 없이 비교적 좁은 직경분포를 갖는 SWNTs가 합성되었으며, 합성온도가 $850^{\circ}C$ 이하로 감소되면 합성수율이 현저히 감소되는 것을 알 수 있었다. 촉매박막의 두께가 1 nm 이상인 경우에서는 직경 5 nm 전후의 나노입자가 형성되었으며, 이때 SWNTs의 합성수율은 높았으나 다양한 직경의 튜브가 합성이 된 것을 확인할 수 있었다. 반면, 촉매입자의 크기가 2 nm 이하에서는 합성수율은 다소 저하되었으나, SWNTs의 직경분포의 폭이 상대적으로 훨씬 좁아지는 것을 알 수 있었다. 추후, 극미세 촉매와 저온합성 환경에서의 합성수율 향상을 위한 합성공정의 개량이 지속적으로 요구된다.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.6
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• pp.727-737
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• 1999

프로세스 중심 소프트웨어 개발 환경(PSEE : Process-centered Software Engineering Environment)은 소프트웨어 개발자를 위한 여러가지 정보의 제공과 타스크의 수행, 소프트웨어 개발 도구의 수행 및 제어, 필수적인 규칙이나 업무의 수행등과 같은 다양한 행위를 제공하는 프로세스 모형의 수행을 통하여 소프트웨어 개발 행위를 지원한다. SEED(Software Engineering Environment for Development)는 효율적인 소프트웨어 개발과 프로세스 모형의 수행을 제어하기 위해 ETRI에서 개발된 PSEE이다.본 논문에서는 SEED에서 프로세스 모형을 설계하기 위해 사용되는 SimFlex 프로세스 프로그래밍 언어와, 수행지원시스템인 SEED Engine의 구현에 대하여 기술한다. SimFlex는 간단한 언어 구조를 가진 프로세스 프로그래밍 언어이며, 적절한 적합화를 통하여 다른 PSEE에서 사용될 수 있다. SimFlex 컴파일러는 SimFlex에 의해 기술된 프로세스 모형을 분석하고, 모형의 오류를 검사하며, SEED Engine에 의해 참조되는 중간 프로세스 모형을 생성한다. 중간 프로세스 모형을 사용하여 SEED Engine은 외부 모니터링 도구와 연관하여 사용자를 위한 유용한 정보뿐만 아니라 SimFlex에 의해 기술된 프로세스 모형의 자동적인 수행을 제공한다. SimFlex 언어와 수행지원 시스템의 지원을 통하여 소프트웨어 프로세스를 모형화하는데 드는 비용과 시간을 줄일 수 있으며, 편리하게 프로젝트를 관리하여 양질의 소프트웨어 생산물을 도출할 수 있다. Abstract Process-centered Software Engineering Environments(PSEEs) support software development activities through the enaction of process models, providing a variety of activities such as supply of various information for software developers, automation of routine tasks, invocation and control of software development tools, and enforcement of mandatory rules and practices. The SEED(Software Engineering Environment for Development) system is a PSEE which was developed for effective software process development and controlling the enactment of process models by ETRI.In this paper, we describe the implementation of the SimFlex process programming language used to design process models in SEED, and its runtime support system called by SEED Engine. SimFlex is a software process programming language to describe process models with simple language constructs, and it could be embedded into other PSEEs through appropriate customization. The SimFlex compiler analyzes process models described by SimFlex, check errors in the models, and produce intermediate process models referenced by the SEED Engine. Using the intermediate process models, the SEED Engine provides automatic enactment of the process models described by SimFlex as well as useful information for agents linked to the external monitoring tool. With the help of the SimFlex language and its runtime support system, we can reduce cost and time in modeling software processes and perform convenient project management, producing well-qualified software products.