• 품질경영학회지
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• 제10권2호
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• pp.25-33
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• 1982

We discuss a model for acceptance/rejection decision regarding finite populations. The model is based on a beta-binomial prior distribution and additive costs -- relative sampling costs, relative sorting costs and costs of accepted defectives. A substantial part of the paper is devoted to constructing a Bayes sequential sampling acceptance plan (BSSAP) for attributes under the model. It is shown that the Bayes fixed size sampling acceptance plans (BFSAP) are better than the Hald's (1960) single sampling acceptance plans based on a uniform prior. Some tables and examples are provided for comprisons of the minimum Bayes risks of the BSSAP and those of the BFSAP based on a uniform prior and the model.

• 정보처리학회논문지D
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• 제15D권6호
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• pp.841-856
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• 2008

요구사항에 대한 우선순위는 릴리즈 플랜을 위한 핵심적 활동이기 때문에 요구공학에서 특히, 오픈 시장(Open Market)을 고객으로 하는 시장 주도형 제품개발에 있어서 중요하다. 또한, 요구사항 우선순위는 주어진 요구사항 간의 상호의존 관계를 바탕으로 프로세스 모델, 제품 종류 및 우선순위 프로세스에 대한 경험 등을 사전에 고려하여 우선순위화를 위한 방법과 관점 등을 선택하는 활동이 중요하다. 그러나, 기존 연구들은 요구사항간의 상호의존 관계를 정적 관계만 고려하였고, 고려된 관점들이 비용/가치 등으로 한정적이고 체계적인 우선순위 프로세스를 제공치 못하고 있다. 따라서 본 논문에서는 우선순위화를 위한 모델을 설계하고 개발 제품의 목표와 조직에 적합하도록 우선순위 방법과 관점 등을 선택할 수 있는 적응적 요구사항 우선순위 기법 및 프로세스를 제안한다. 특히, 요구사항간의 정적/동적 상호의존 관계 유형을 정의하고, 다양한 관점에 의한 우선순위화를 통해 릴리즈 플랜의 완성도를 높였다. 이로써 상호의존 관계 및 다양한 관점을 고려한 우선순위 모델기반의 체계적인 우선순위 프로세스를 정립하여 유연하고 충족스러운 우선순위화와 릴리즈 플랜을 통하여 합리적으로 의사결정을 도모할 수 있다.

• 대한산업공학회지
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• 제39권1호
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• pp.30-45
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• 2013

Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.

• 농촌계획
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• 제26권2호
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• pp.95-106
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• 2020

The Onishi model is an objective indicator which can be used to evaluate the relevance of city environmental management in regard to the capacities and processing status of existing urban infrastructure. This study is to analyze the facility carrying capacity and processing status of Jeju Island, a famous tourist site in South Korea. General variables covered by the Onishi model are considered, including water supply, wastewater treatment, waste disposal, and air pollution. Furthermore, the facility carrying capacities for transportation, such as airports and ports, as well as accommodations are assessed as variables pertinent to the characteristics of Jeju island. With the annual number of tourists exceeding that of residents on the island, more facilities for sewage treatment and waste disposal are required. Furthermore, transportation and accommodations used by tourists have already exceeded their capacity. For the future sustainability of Jeju Island, a plan will be needed for adjusting the volume of tourists based on the capacity of each relevant facility.

• 한국압력기기공학회 논문집
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• 제19권1호
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• pp.44-51
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• 2023

The RCP is one of the main components in nuclear power plants and plays an important role in circulating coolant to the RCS system. Currently, nuclear plants are monitored using various monitoring systems. However, since they operate independently according to their functional purpose, it is not able to analyze vibration and operation/performance information comprehensively, and thus failure diagnosis accuracy is limited. In addition, these systems do not provide some important information (such as fault type, parts and cause) necessary for emergency actions, but provide only alarm information. To improve these technical problems, this study proposes a diagnosis technique (M/L, Rule-based model, Data-driven model, Narrow band model) and methodology for comprehensive analysis.

• 로봇학회논문지
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• 제19권2호
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• pp.196-202
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• 2024

The ability to understand given environments and plan a sequence of actions leading to goal state is crucial for personal service robots. With recent advancements in deep learning, numerous studies have proposed methods for state representation in planning. However, previous works lack explicit information about relationships between objects when the state observation is converted to a single visual embedding containing all state information. In this paper, we introduce graph-based state representation that incorporates both object and relationship features. To leverage these advantages in addressing the task planning problem, we propose a Graph Neural Network (GNN)-based subgoal prediction model. This model can extract rich information about object and their interconnected relationships from given state graph. Moreover, a search-based algorithm is integrated with pre-trained subgoal prediction model and state transition module to explore diverse states and find proper sequence of subgoals. The proposed method is trained with synthetic task dataset collected in simulation environment, demonstrating a higher success rate with fewer additional searches compared to baseline methods.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.676-684
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• 2009

Decision making at the early stages of a construction project has a significant impact on the project, and various scenarios created based on the owner's requirements should be considered for the decision making. At the early stages of a construction project, the information regarding the project is usually limited and uncertain. As such, it is difficult to plan and manage the project (especially cost planning). Thus, in this study, a cost model that could be varied according to the owner's requirements was developed. The cost model that was developed in this study is based on the case-based reasoning (CBR) methodology. The model suggests cost estimation with the most similar historical case as a basis for the estimation. In this study, the optimization process was also conducted, using genetic algorithms that reflect the changes in the number of project characteristics and in the database in the model according to the owner's decision making. Two optimization parameters were established: (1) the minimum criteria for scoring attribute similarity (MCAS); and (2) the range of attribute weights (RAW). The cost model proposed in this study can help building owners and managers estimate the project budget at the business planning stage.

• 디지털융복합연구
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• 제14권5호
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• pp.57-66
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• 2016

본 연구에서는 동양미래대학 컴퓨터정보공학과의 캡스톤디자인 관련 교과목을 소개하고, 2015년 1년 동안의 운영 사례를 통하여 컴퓨터공학 분야의 캡스톤디자인 모델을 제안한다. 2015학년도 1학기와 2학기에 각각 운영된 '시스템분석설계'와 '졸업작품(종합설계)'에 대한 운영 사례에서 수업 일정과 내용 등을 제시하고, 컴퓨터공학 분야에서 창의적 문제해결 능력을 갖춘 현장실무형 인재양성을 위한 캡스톤디자인의 성공적인 운영을 위해 캡스톤디자인 모델의 수행 단계를 교과목계획, 프로젝트계획, 개발 구현, 결과보고, 학생 및 교과목평가, 교과목환류의 6단계로 제안하며, 각 단계에서의 주요 사항을 제시한다. 본 연구의 캡스톤디자인 모델의 특징은 거시적인 관점에서 '교과목계획'과 '교과목환류'와 같은 사전 사후 단계의 필요성과 중요성을 제시하며, 소프트웨어공학 요소인 프로토타입과 형상관리 등을 활용한다는 점이다.

• 대한건축학회논문집:계획계
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• 제36권4호
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• pp.41-49
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• 2020

The purpose of this study was to investigate the use of the Deep Neural Networks(DNN) model to classify user's emotions, in particular Electroencephalography(EEG) toward Virtual-Reality(VR) based 3D design alternatives. Four different types of VR Space were constructed to measure a user's emotion and EEG was measured for each stimulus. In addition to the quantitative evaluation based on EEG data, a questionnaire was conducted to qualitatively check whether there is a difference between VR stimuli. As a result, there is a significant difference between plan types according to the normalized ranking method. Therefore, the value of the subjective questionnaire was used as labeling data and collected EEG data was used for a feature value in the DNN model. Google TensorFlow was used to build and train the model. The accuracy of the developed model was 98.9%, which is higher than in previous studies. This indicates that there is a possibility of VR and Fast Fourier Transform(FFT) processing would affect the accuracy of the model, which means that it is possible to classify a user's emotions toward VR based 3D design alternatives by measuring the EEG with this model.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전력기술부문
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• pp.393-395
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• 2006

In competitive market, it is important to establish a plan of transmission expansion considering uncertainty of future generation and load behavior. For this reason, revised transmission expansion model is proposed in this paper. In the proposed model, information of predictable future condition are included in a cost function of transmission expansion investment. Also, to reduce risk of the investment, mean-variance Markowitz approach is added to the objective function of cost. By optimization programming, the most robust and the minimum cost plan can be obtained.