• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.277-280
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• 2006

Most real world design evaluation and risk-based decision support combine quantitative and qualitative (linguistic) variables. Decision-making based on conventional mathematics that combines qualitative and quantitative concepts always exhibit difficulty in modelling actual problems. The successful selection process for choosing a design/procurement proposal is based on a high degree of technical integrity, safety levels and low costs in construction, corrective measures, maintenance, operation, inspection and preventive measures. However, the objectives of maximising the degree of technical performance, maximising the safety levels and minimising the costs incurred are usually in conflict, and the evaluation of the technical performance, safety and costs is always associated with uncertainties, especially for a novel system at the initial concept design stage. In this paper, a design-decision support framework using a composite structure methodology grounded in approximate reasoning approach and evidential reasoning method is suggested for design evaluation of machinery space of a ship engine room at the initial stages. It is a Multiple Attribute Decision-Making (MADM) or Multiple Criteria Decision Making (MCDM) framework, which provides a juxtaposition of cost, safety and technical performance of a system during evaluation to assist decision makers in selecting the winning design/procurement proposal that best satisfies the requirement in hand. An illustrative example is used to demonstrate the application of the proposed framework.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.203-211
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• 2009

Korean public owners who order public multi-family housing construction projects have yet to gain access to a model for predicting construction cost. For this reason, their construction cost prediction is mainly dependent upon historic data and experience. In this paper, a cost-prediction model based on Case-Based Reasoning (CBR) in the design phase of public multi-family housing construction projects was developed. The developed model can determine the total construction cost by estimating the different Building, Civil, Mechanical, Electronic and Telecommunication, and Landscaping work costs. Model validation showed an accuracy of 97.56%, confirming the model's excellent viability. The developed model can thus be used to predict the construction cost to be shouldered by public owners before the design is completed. Moreover, any change orders during the design phase can be immediately applied to the model, and various construction costs by design alternative can be verified using this model. Therefore, it is expected that public owners can exercise effective design management by using the developed cost prediction model. The use of such an effective cost prediction model can enable the owners to accurately determine in advance the construction cost and prevent increase or decrease in cost arising from the design changes in the design phase, such as change order. The model can also prevent the untoward increase in the duration of the design phase as it can effectively control unnecessary change orders.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.103-116
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• 2017

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner, which can derive new qualitative spatial knowledge representing both topological and directional relationships between two arbitrary spatial objects in efficient way using Aparch Spark SQL. Apache Spark SQL is well known as a distributed parallel programming environment which provides both efficient join operations and query processing functions over a variety of data in Hadoop cluster computer systems. In our spatial reasoner, the overall reasoning process is divided into 6 jobs such as knowledge encoding, inverse reasoning, equal reasoning, transitive reasoning, relation refining, knowledge decoding, and then the execution order over the reasoning jobs is determined in consideration of both logical causal relationships and computational efficiency. The knowledge encoding job reduces the size of knowledge base to reason over by transforming the input knowledge of XML/RDF form into one of more precise form. Repeat of the transitive reasoning job and the relation refining job usually consumes most of computational time and storage for the overall reasoning process. In order to improve the jobs, our reasoner finds out the minimal disjunctive relations for qualitative spatial reasoning, and then, based upon them, it not only reduces the composition table to be used for the transitive reasoning job, but also optimizes the relation refining job. Through experiments using a large-scale benchmarking spatial knowledge base, the proposed reasoner showed high performance and scalability.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.373-378
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• 2005

The diagrammatic language for pathways is widely used for representing systems knowledge as a network of causal relations. Biologists infer and hypothesize with pathways to design experiments and verify models, and to identify potential drug targets. Although there have been many approaches to formalize pathways to simulate a system, reasoning with incomplete and high level knowledge has not been possible. We present a qualitative formalization of a pathway language with incomplete causal descriptions and its translation into propositional temporal logic to automate the reasoning process. Such automation accelerates the identification of drug targets in pathways.

• Journal of Intelligence and Information Systems
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• v.3 no.1
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• pp.101-109
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• 1997

철강제품의 품질설계란 제품의 주문요구조건을 만족시키기 위해 제품의 성분 및 생산공정을 결정하는 과정을 의미한다. 본 연구에서는 품질설계업무를 지원하기 위한 시스템을 개발하였다. 설계업무의 특성을 고려하여 과거사례를 설계에 이용하기 위해, 사례기반추론(Case-based Reasoning)접근방법을 이용하였다. 본 연구에서는 또한 유사사례의 효율적 검색을 위해 품질설계 문제에 적합한 유사성척도를 제안하고 있으며, 문제에 적합한 유사성척도를 제안하고 있으며, 문제에 적합한 지식관리 방법 및 설계조정 방법을 개발하였다.

• Journal of Korean Academy of Nursing
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• v.53 no.1
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• pp.87-100
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• 2023

Purpose: This study aimed to develop a nursing simulation learning module for coronavirus disease 2019 (COVID-19) patient-care and examine its effects on clinical reasoning competence, clinical competence, performance confidence, and anxiety in COVID-19 patient care for nursing students. Methods: A non-equivalent control group pre- and post-test design was employed. The study participants included 47 nursing students (23 in the experimental group and 24 in the control group) from G City. A simulation learning module for COVID-19 patient-care was developed based on the Jeffries simulation model. The module consisted of a briefing, simulation practice, and debriefing. The effects of the simulation module were measured using clinical reasoning competence, clinical competence, performance confidence, and anxiety in COVID-19 patient-care. Data were analyzed using χ²-test, Fisher's exact test, t-test, Wilcoxon signed-rank test, and Mann-Whitney U test. Results: The levels of clinical reasoning competence, clinical competence, and performance confidence of the experimental group were significantly higher than that of the control group, and the level of anxiety was significantly low after simulation learning. Conclusion: The nursing simulation learning module for COVID-19 patient-care is more effective than the traditional method in terms of improving students' clinical reasoning competence, clinical competence, and performance confidence, and reducing their anxiety. The module is expected to be useful for educational and clinical environments as an effective teaching and learning strategy to empower nursing competency and contribute to nursing education and clinical changes.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.349-359
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• 2010

The purpose of this study is to design the On-line Insurance Sales Support System using Case-Based Reasoning(CBR). In on-line insurance subscription process, this system provides the personalized insurance payment cases and insurance statistics for customers to entice an insurance subscription. By measuring, specifically, similarities between the user profile and insurance payment cases, it suggests the best insurance payment case which has the highest similarity and reflects the latest in the insurance payment cases. In addition, it serves the insurance statistical information that matches with the attributes of the finally-selected case. These functions can be useful in on-line insurance sales.

• Journal of Korean Elementary Science Education
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• v.37 no.1
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• pp.12-26
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• 2018

The purpose of this study was to investigate the characteristics of reasoning in which $3^{rd}$ grade elementary school students form ideas, design experiments, and interpret the results to solve problems in small group argumentation. For this purpose, 12 3rd-grade students' small group argumentations including experiments were observed. The researchers analyzed students' pre- and post-open questionnaires, field notes, and video recordings of small group argumentation. The results of the research are as follows. First, in the initial opinion formation process, a hasty unification of opinions and a transformation of inquiry problem occurred. In the design and execution of experiments, verification experiments and unplanned and arbitrary experiments were performed. They also selectively noticed or accepted claims, evidence, interpretation, and criticism. They could distinguish between the condition and the cause, but they were confused by using inaccurate terms and tended to keep the initial opinions when interpreting the results and drawing conclusions.

• Management & Information Systems Review
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• v.2
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• pp.169-193
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• 1998

Case- based reasoning(CBR) offers a new approach for developing knowledge based systems. Unlike the rule-based paradigm, in which domain knowledge is encoded in the form of production rules, in the case-based approach the problem solving experience of the domain expert is encoded in the form of cases stored in a casebase(CB). CBR allows a reasoner (1) to propose solutions in domains that are not completely understood by the reasoner, (2) to evaluate solutions when no algorithmic method is available for evaluation, and (3) to interprete open-ended and ill-defined concepts. CBR also helps reasoner (4) take actions to avoid repeating past mistakes, and (5) focus its reasoning on important parts of a problem. Owing to the above advantages, CBR has successfully been applied to many kinds of problems such as design, planning, diagnosis and instruction. In this paper, I propose case-based DSS(CBDSS). CBDSS is an intelligent DSS using CBR technique. CBDSS consists of interface, case-based reasoner, maintainer, casebase management system, domain dependent CB, domain independent CB, and so on.

• Korean Journal of Child Studies
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• v.20 no.3
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• pp.275-291
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• 1999

The effect of prosocial story telling and discussion on the development of children's helping and sharing behaviors and prosocial reasoning was studied. Subjects were 36 five-year-old kindergarten children assigned to control or experimental groups. The research design consisted of a pre-test and pre-observation, 6 week intervention, and post-test and post-observation. Data were collected by means of a video camera and analyzed by adjusted means and ANCOVA, using the SPSS/PC+. Both children's helping behavior and prosocial reasoning was higher in the group exposed to prosocial story telling with discussion as compared with the group with story telling only and the control group.