• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1255-1258
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• 2012

클라우드 컴퓨팅에서 사용되는 웹 서비스들은 워크플로우에 따라 서비스가 설계되어 조합된다. 대표적인 웹서비스 명세언어인 BPEL의 검증방법에는 Petri nets, Abstract State Machine(ASM), BPE-Calculus 등이 존재한다. 하지만 기존의 방법은 설계와 검증이 분리되어 있어 일관성이 부족하고, 시각화 문제, 동일성, 시간에 대한 제약조건의 문제점이 존재한다. 이에 대한 해결방안으로 이동성, 재구성성, 동일성, 시간속성 등의 새로운 분석 방법을 제시하는 Onion 언어가 제안되었다. 본 논문은 BPEL로 명세된 서비스를 Onion 시스템에 적용시키기 위한, 변환 과정에 대해서 다룬다. 이에 대한 과정으로 BPEL의 액티비티를 Onion으로 변환하고, 워크플로우 패턴을 적용하여, 3 가지 패턴을 Onion OVL로 변환을 적용하였다. 이를 통하여 BPEL을 Onion OVL로 변환하는데 문제가 없음을 보였으며, 효율적인 표현이 가능함을 보였다. 추후 Onion 시스템의 컴포넌트로 적용하여, BPEL로 작성된 서비스를 Onion 시스템을 통해 분석/검증할 수 있다.

• The Transactions of the Korea Information Processing Society
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• v.5 no.7
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• pp.1908-1922
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• 1998

본 논문은 논리언어 프로그램의 효율적인 클로즈(Clause) 인덱싱을 위한 컴파일 기법에 대한 체계적인 접근방법을 제시한다. 본 접근방법의 핵심으로서 노드당 평균 병렬도와 클로즈 수행시도(clause trial) 횟수를 정확하게 나타낼 수 있는 기법으로서 인덱싱트리(Indexign Tree)를 제안한다. 인덱싱트리는 인덱싱 수행 시에 인덱싱을 위한 지시어(Instruction)의 수행 결과로 프로그램으 컨트롤이 실패처리코드로 이동하는 경우도 정량적으로 나타내 준다. 인덱싱트리를 사용하여 논리 프로그램을 위한 대표적인 가상머신인 WAM(Warren Abstract Machine)을 분석한 결과, WAM에서 사용하는 인덱싱 기법이 논리 프로그램의 병렬 처리에 있어 탐색트리의 병렬도를 감소시키며, 또한 스케쥴링의 효율성을 저하시키는 결점을 내포하고 있음을 발견할 수 있었다. 이러한 결점을 해결하기 위하여 본 논문은 플랫 인덱싱이라는 새로운 인덱싱 기법을 제안하고 이것을 실제 논리언어 컴파일러에 구현하여 측정한 향상 및 분석 결과를 보여준다.

• Korean Institute of Interior Design Journal
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• v.21 no.2
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• pp.74-83
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• 2012

This study is an historical consideration about the modern discourse of Japanese spacial tradition driven from cultural background. The purpose of this study is to establish a cartographic map of historical progress, and to shed light on the forming of identity in Japanese traditional space design on the schizo-analytical aspect. It adopts F. Guattari's psychoanalytic theory to the structural analysis of Japanese traditional space design. The process of this study is illustrated as follows: At first, it mentions Guattari's theory of Mechanical Unconscious, Schizo-analysis, Cartography, and Abstract machine as theoretical background. And, it considers the identity of Japanese traditional space constructed by various cultural sign over a long period of time as the statement of apriority. Secondly, it clarifies semiologic generation of Japanese traditional space design based on the analysis of spacial morphemes about each design stemmed from modernization process of Japan. Thirdly, it ascertains semiologic topography the representamens draw, i.e. schizo-analytic cartography from synchronic and diachronic point of view. Fourthly, it analyses traditional discourse structure in terms of generative schizo-analysis and transformational schizo-analysis with four categories- object, style, concept, strategy. Through this process, it studies the reproduction of Japanese tradition in terms of the 'social organization', and explores the way vitalized on the space-time coordinate system by the schizo-analysis of the mechanical unconscious. In conclusion, it clarifies Generative-schizo is accomplished in the level of formulating representamen, and Transformational-Schizo involves experimental mind that induce implantation of the heteromorphic elements and avant-garde experiments of abstract mechanical operation in the schizo-analysis of Japanese traditional space design. The significance of this study is to arrange an opportunity of introspection on Korean-ness seriously from inspecting logic of Japan-ness closely in traditional space design.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.867-871
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• 2022

Amid the recent rapid trend change, the change in design has a great impact on the sales of fashion companies, so it is inevitable to be careful in choosing new designs. With the recent development of the artificial intelligence field, various machine learning is being used a lot in the fashion market to increase consumers' preferences. To contribute to increasing reliability in the development of new products by quantifying abstract concepts such as preferences, we generate new images that do not exist through three adversarial generative neural networks (GANs) and numerically compare abstract concepts of preferences using pre-trained convolution neural networks (CNNs). Deep convolutional generative adversarial networks (DCGAN), Progressive growing adversarial networks (PGGAN), and Dual Discriminator generative adversarial networks (DANs), which were trained to produce comparative, high-level, and high-level images. The degree of similarity measured was considered as a preference, and the experimental results showed that D2GAN showed a relatively high similarity compared to DCGAN and PGGAN.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.775-782
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• 2002

As electronic commerce systems have been widely used, the necessity of adaptive e-commerce agent systems has been increased. These kinds of agents can monitor customer's purchasing behaviors, clutter them in similar categories, and induce customer's preference from each category. In order to implement our adaptive e-commerce agent system, we focus on following 3 components-the monitor agent which can monitor customer's browsing/purchasing data and abstract them, the conceptual cluster agent which cluster customer's abstract data, and the customer profile agent which generate profile from cluster, In order to infer more accurate customer's preference, we propose a 2 layered structure consisting of conceptual cluster and inductive profile generator. Many systems have been suffered from errors in deriving user profiles by using a single structure. However, our proposed 2 layered structure enables us to improve the qualify of user profile by clustering user purchasing behavior in advance. This approach enables us to build more user adaptive e-commerce system according to user purchasing behavior.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.1-11
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• 2021

The purpose of this study was to analyze the trends and characteristics of 'stress urinary incontinence' research through word frequency analysis, and their relationships were modeled using word embedding. Abstract data of 9,868 papers containing abstracts in PubMed's MEDLINE were extracted using a Python program. Then, through frequency analysis, 10 keywords were selected according to the high frequency. The similarity of words related to keywords was analyzed by Word2Vec machine learning algorithm. The locations and distances of words were visualized using the t-SNE technique, and the groups were classified and analyzed. The number of studies related to stress urinary incontinence has increased rapidly since the 1980s. The keywords used most frequently in the abstract of the paper were 'woman', 'urethra', and 'surgery'. Through Word2Vec modeling, words such as 'female', 'urge', and 'symptom' were among the words that showed the highest relevance to the keywords in the study on stress urinary incontinence. In addition, through the t-SNE technique, keywords and related words could be classified into three groups focusing on symptoms, anatomical characteristics, and surgical interventions of stress urinary incontinence. This study is the first to examine trends in stress urinary incontinence-related studies using the keyword frequency analysis and word embedding of the abstract. The results of this study can be used as a basis for future researchers to select the subject and direction of the research field related to stress urinary incontinence.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.83-90
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• 2004

Strategic games are missing special qualities of genre these days. Game engines neither reason about behaviors of computer objects nor have learning ability that can prepare countermeasure in variously command user's strategy. This paper suggests a strategic game engine that applies non-monotonic reasoning and inductive machine learning. The engine emphasizes three components -“user behavior monitor”to abstract user's objects behavior,“learning engine”to learn user's strategy,“behavior display handler”to reflect abstracted behavior of computer objects on game. Especially, this paper proposes two layered-structure to apply non-monotonic reasoning and inductive learning to make behaviors of computer objects that learns strategy behaviors of user objects exactly, and corresponds in user's objects. The engine decides actions and strategies of computer objects with created information through inductive learning. Main contribution of this paper is that computer objects command excellent strategies and reveal differentiation with behavior of existing computer objects to apply non-monotonic reasoning and inductive machine learning.

• Korean Institute of Interior Design Journal
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• v.18 no.6
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• pp.150-157
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• 2009

In the 1970's the Interior Design Associations was established and the activity of youth designers who werecalled the "first generation of Korean interior designers" were created. This study is aimed to analyze characteristics and trends of commercial interior design in the 1970's. The design methods include the documentary research and the actual proof research conducted. The frames of analysis were made by the background theories about Korean interior design, and the annual case studies were analyzed and estimated according to the design types. The design types were analyzed by the geometrical simplicity research, the romantic emotional expression, the Korean identity expression, the machine technical asthetic expression and the eclectic style with western classics. In the early 1970's, the abstract, brief, and simple expression were presented most frequently by the geometrical form and the repetition of the pattern. From the mid-1970's the romantic and emotional atmosphere of the youth culture that was popular at that time were expressed as vernacular design by the rough finishing of the natural materials such as plaster, brick, and wood floorings etc. The space such as a Korean food restaurant relates to the Korean traditional culture aims to be different through the expression by the Korean traditional patterns, furniture, and materials. In the late 1970's the metals and glass were used for the expression of the machine aesthetic form but was not popular because of the rare application. The type that revived the past western traditional form was presented by using the arch, dome, and the curved and luxurious moldings.

• Journal of KIISE:Software and Applications
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• v.26 no.8
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• pp.1010-1017
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• 1999

불완전한 계획 영역 이론은 오류 영역(noisy domain)에서 하나의 상태에 상반된 연산자들이 적용되는 불일치성 문제를 야기할 수 있다. 이 문제를 해결하기 위해서 본 논문은 상태를 기술하기 위해 다치 논리를 도입하여 제어지식으로서의 부정적 선행조건을 학습하는 새로운 방법을 제안한다. 기계에는 알려지지 않은 이러한 제어지식이 인간에게는 반대개념으로 잠재적으로 사용되고 있다. 이러한 잠재된 개념을 학습하기 위해 본 논문은 반대 연산자들로 구성된 사이클을 영역이론으로부터 기계적으로 생성하고, 이 연산자들에 대한 실험을 통해 반대 리터럴(literal)들을 추출한다. 학습된 규칙은 불일치성을 방지하면서 동시에 중복된 선행조건을 제거하여 연산자를 단순화시킬 수 있다.Abstract An incomplete planning domain theory can cause an inconsistency problem in a noisy domain, allowing two opposite operators to be applied to a state. To solve the problem, we present a novel method to learn a negative precondition as control knowledge by introducing a three-valued logic for state description. However, even though the control knowledge is unknown to a machine, it is implicitly known as opposite concept to a human. To learn the implicit concept, we mechanically generate a cycle composed of opposite operators from a domain theory and extract opposite literals through experimenting the operators. A learned rule can simplify the operator by removing a redundant precondition while preventing inconsistency.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1282-1290
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• 2017

Since the mitigation of fear of crime significantly enhances the consumptions in a city, studies focusing on urban safety analysis have received much attention as means of revitalizing the local economy. In addition, with the development of computer vision and machine learning technologies, efficient and automated analysis methods have been developed. Previous studies have used global features to predict the safety of cities, yet this method has limited ability in accurately predicting abstract information such as safety assessments. Therefore we used a Convolutional Context Neural Network (CCNN) that considered "context" as a decision criterion to accurately predict safety of cities. CCNN model is constructed by combining a stacked auto encoder with a fully connected network to find the context and use it in the CNN model to predict the score. We analyzed the RMSE and correlation of SVR, Alexnet, and Sharing models to compare with the performance of CCNN model. Our results indicate that our model has much better RMSE and Pearson/Spearman correlation coefficient.