• Journal of Korean Society for Quality Management
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• v.10 no.2
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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.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.841-856
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• 2008

The priority of requirement is important because the priority is a critical activity of release plan especially in software development which has an open market customer. Also, it is important for stakeholders to select a method and aspects to prioritize requirements. The selection is based on the organizational experience of a priority process, the process model of the product, goals and a type of products, and dependencies between requirements. But, the current researches considered only static dependency between requirements and did not suggest a systematic priority process. In addition, the current researches only suggest limited aspects to prioritize requirements, such as cost and value. Therefore, this paper proposes an adjustable priority process based on a priority model to select a method and aspects for the suitable priority for product and organization. Especially, this paper enhances the completeness of a release plan by a definition of static and dynamic dependency types between requirements. This paper suggests a priority model, which considers the dependencies between requirement and various viewpoint of software development. Based on the priority model, the paper suggests a systematic priority process to promote reasonable decisions to the priority and release plan of requirement.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.1
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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.

• Journal of Korean Society of Rural Planning
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• v.26 no.2
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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.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.19 no.1
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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.

• The Journal of Korea Robotics Society
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• v.19 no.2
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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.

• International conference on construction engineering and project management
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• 2009.05a
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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.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.57-66
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• 2016

In this paper, we describe capstone-related courses in the Computer information department of Dongyang-Mirae University and propose a capstone design model for computer engineering discipline based on operating experiences during the last year. Class schedules and contents, etc are presented from the case studies of two courses on system analysis and design and graduation work(capstone design) opened in 2015. Towards the successful operation of capstone design targeted to training practical and talented professionals with creative problem-solving skills, we propose six execution steps along with principal activities and outputs: Course plan, Project plan, Development, Reporting, Evaluation of students and coursework, and Course feedback. The capstone design model of this study is characterized by presenting the necessity and importance of pre and post steps such as 'Course Plan' and 'Course reflux' from a macroscopic view and by applying software engineering techniques such as prototyping, configuration managements, etc to it.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.4
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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.

• Proceedings of the KIEE Conference
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• 2006.11a
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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.