• Proceedings of the Korean Information Science Society Conference
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• 1998.10a
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• pp.697-699
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• 1998

본 논문은 프로세서 알제브라의 하나인 ACSR을 이용하여 파이프라인 비순차 슈퍼스칼라 프로세서의 타이밍 특성과 자원 제한을 묘사하기 위한 정형기법을 제시한다. ACSR의 두드러진 특징은 시간, 자원, 우선 순위의 개념이 알제브라에서 직접적으로 제공되어 진다는 것이다. 여기서의 접근 방식은 슈퍼스칼라 프로세서의 레지스터를 ACSR 자원으로, 명령어를 ACSR 프로세서로의 모델링하는 것이다. 결과적으로 얻어지는 ACSR식에서 각각의 클럭 주기에서 어떻게 명령어가 실행되고 레지스트들이 이용되는지 확인할 수 있으며 이 모델링을 이용해서 비순차 슈퍼스칼라 프로세서 구조를 검증하거나 분석하는 것이 가능하다.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.827-832
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• 2001

In this paper, we propose a sequentially optimization method for fuzzy inference system using fuzzy equalization and linguistic hedge. The fuzzy equalization does not require the usual learning step for generating fuzy rules. However, it is too sensitive for the given input-output data set. So, we adopt a sequential scheme which sequentially optimizes the fuzzy inference system. Here, the parameters of fuzzy membership function obtained from the fuzzy equalization are optimized by the genetic algorithm, and then they are also modified to increase the performance index using the linguistic hedge. Finally, we applied it to rice taste data and got better results than previous ones.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1567-1570
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• 2012

기존 연구에서는 순차적, 상태, 엑티브 다이어그램 기반의 테스트케이스 추출을 초점을 두고 있다. 하지만 현재 최신의 모델링 언어인 UML2.4.1(Unified Modeling Language) 기반으로 한 테스트케이스 추출 메커니즘은 없다. 그래서 본 논문은 UML2.4.1 기반에 기존의 원인-결과 다이어그램의 접목을 통해 테스트케이스 추출 메커니즘을 제안 한다. 이를 위해 UML2.4.1 의 메시지-순차적 다이어그램에 ECA Rule(Event Condition Action)기법을 적용하고, 제안한 접목 알고리즘을 통해 확장된 메시지-순차적 다이어그램을 원인-결과 다이어그램과 접목한 후, 결정 테이블화로 테스트케이스를 발생한다. 이러한 절차를 통해 모델링 기반에서 테스트케이스 추출 가이드가 제공된다. 본 논문에서는 복잡한 메시지-순차적 다이어그램을 통해 테스트케이스 발생 사례연구로서 자동차 와이퍼 시스템을 적용한다.

• Smart Media Journal
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• v.11 no.9
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• pp.9-20
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• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.330-332
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• 2012

시계열 데이터 모델링은 시간 간격의 길이에 따라 단기적인 패턴이 주로 반영된다. 특히, 모델에 마코프 가정을 적용하였을 경우 이전 시간의 값에 따라 현재값이 결정된다. 시계열 데이터의 장기적인 변화를 다루기 위해, 특정 길이의 순차적 패턴을 부호화 하고, 이를 상위 모델의 입력으로 사용하는 과정을 통해 추상화를 시도하고자 한다. 실제로 사람의 감각기억은 200~500 밀리초 가량의 짧은 기억 유지기간을 갖는데, 이 기간의 정보를 상위 처리기의 입력 단위로 보고자 하는 것이다. 이에 본 고에서는 에너지기반 모델링 기법을 이용하여 반복적으로 나타나는 순차적 패턴을 부호화 하는 방법을 제안한다. 이 부호화 방법은 시간 순서에 따른 패턴의 유사도를 이용하여 확률적으로 다음 패턴과의 관계를 표현할 수 있으며, 이는 향후 시계열 데이터를 간략하게 표현하여 분석 및 시각화에 도움을 줄 수 있다.

• KIISE Transactions on Computing Practices
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• v.23 no.12
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• pp.698-702
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• 2017

Since the introduction of Deep Neural Networks to the Natural Language Processing field, two major approaches have been considered for modeling text. One method involved learning embeddings, i.e. the distributed representations containing abstract semantics of words or sentences, with the textual context. The other strategy consisted of composing the embeddings trained by the above to get embeddings of longer texts. However, most studies of the composition methods just adopt word embeddings without consideration of the optimal embedding unit and the optimal method of composition. In this paper, we conducted experiments to analyze the optimal embedding unit and the optimal composition method for modeling longer texts, such as documents. In addition, we suggest a new discourse-based composition to overcome the limitation of the sequential composition method on composing sentence embeddings.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.1
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• pp.87-94
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• 2017

Reliability analysis(RA) and Reliability-based design optimization(RBDO) require statistical modeling of input random variables, which is parametrically or nonparametrically determined based on experimental data. For the parametric method, goodness-of-fit (GOF) test and model selection method are widely used, and a sequential statistical modeling method combining the merits of the two methods has been recently proposed. Kernel density estimation(KDE) is often used as a nonparametric method, and it well describes a distribution function when the number of data is small or a density function has multimodal distribution. Although accurate statistical models are needed to obtain accurate RA and RBDO results, accurate statistical modeling is difficult when the number of data is small. In this study, the accuracy of two statistical modeling methods, SSM and KDE, were compared according to the number of data. Through numerical examples, the RA results using the input models modeled by two methods were compared, and appropriate modeling method was proposed according to the number of data.

• Journal of the Korea Society for Simulation
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• v.25 no.4
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• pp.21-30
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• 2016

Recently manufacturing companies have used PLC control programs popularly for their automated production systems. Since the life cycle of production process is not so long, the change of the production lines occur frequently. Most of changes happen with modification of the position information and control process of the equipment. PLC control program is also modified based on the fundamental process. Therefore, to verify new PLC program by configuring virtual space according to real environment is needed. In this paper we show a logical modeling method, based on Timed-FSA useful for sequence control and dead-lock prevention. There is a problem wasting user's labor and time when defining a variety of states in a device. To overcome this problem, we propose an extended I-O model based on existing methods by adding a token concept of Petri Nets. Also we will show the usability of the extended I-O modeling through user study.

• IE interfaces
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• v.8 no.3
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• pp.47-60
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• 1995

물류시스템간의 상호작용이 복잡한 자동화제조시스템을 시뮬레이션하기 위해서는 전통적인 사건중심의 모델링이나 고객중심의 프로세스묘사방식 모델링보다 서버중심의 프로세스상호작용방식 모델링이 유리하다. 이러한 모델링 방식은 기존의 동적시스템 모델링 이론과도 일치하며 최근의 소프트웨어엔지니어링기법인 객체지향적 모델링기법과도 상통한다. 본 연구에서는 객체지향적 모델링 기법을 바탕으로 서버중심의 프로세스상호작용방식에 기초한 시뮬레이션 모델링 방법을 제안한다. 이러한 방법을 자동화제조시스템의 시뮬레이션모델링에 작용한 예를 소개하고, 순차적 처리언어인 C＋＋ 사용하여 프로세스상호작용방식의 시뮬레이션 실행기를 구현하는 방법을 설명한다.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.