• Korean Management Science Review
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• v.31 no.3
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• pp.95-111
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• 2014

It is essential to carry out an economic analysis on public water supply projects so that policy makers and water enterprises are aware of the actual value of the project. However, many popular approaches based on discounted cash flow analysis do not capture the uncertainties inherent in cash flow. In order to analyze the economic values of the water supply project of local governments, we utilize real option model, which considers uncertainty in future water price behavior and captures the value of real life flexibility. The real option model is designed to incorporate the option to expand and abandon, and it is applied to a local government case. Furthermore, we assess the project by exploring Luehrman's option space to accommodate the more efficient decision making. The results show that substantial amount of potential value is included in the public water supply service, and the overall value is greater than the value obtained from the discounted cash flow model.

• Journal of Korean Society for Quality Management
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• v.46 no.4
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• pp.821-842
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• 2018

Purpose: To derive key requirements and key technologies for weapon system acquisition business by using Qualify Function Deployment (QFD), and to reduce business cost by setting the target performance and key expense of weapon system. Methods: We propose a QFD methodology that can induce rational decision-making by translating analyst's subjective opinions into quantitative values when analyzing requirements at the initial stage of weapon system development project. Based on QFD methodology, QFD application model combining house of quality, value engineering, and analogy cost estimating technique is presented. Results: It was possible to analyze the specific requirements necessary for the development of the weapon system, to solve the communication problem of the participants, to set clear development direction and target. Conclusion: By applying the QFD application model at the early stage of the weapon system acquisition project, it is possible to reduce the business cost by establishing clear development direction and goal through the procedural analysis process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• The Journal of Korean Academic Society of Nursing Education
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• v.20 no.2
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• pp.212-222
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• 2014

Purpose: The purpose of this study was to evaluate the nursing students' clinical judgment skills in simulation using Tanner's Clinical Judgment Model. Method: Forty-five teams of a total 93 nursing students participated in a post-operative patient care scenario using human patient simulator. Data were collected from students' responses in scenario and guided reflective journaling according to the framework of Tanner's model which comprised noticing, interpreting, responding, and reflecting on response. Data were analyzed using descriptive statistics. Results: The students' responses of the situation were in accordance with the goals of scenario, i.e. relieving patient' pain and preventing pulmonary complications. However, most of students needed clinical cues and focused on a given clue to solve the issues. They were lack of ability to collect additional information as well as connect the relevant clues in simulated clinical situation. Conclusion: The nursing students have difficulty in what they notice, how they interpret finding, and respond appropriately to the situation. The simulation training using Tanner's model could provide faculty and nursing students with an effective teaching and learning strategy to develop the clinical judgment skills.

• Journal of the Korean Society of Safety
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• v.30 no.4
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• pp.151-158
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• 2015

Usually risk assessment is performed for the safety of diverse industries though, many kinds of risks cannot be analyzed effectively by using classical probability models due to lack of experience data and impreciseness of human decision making. For these reasons, fuzzy risk assessment utilizing subjective judgment and experience of skillful experts has been considered as a solution. In this study, to comprehend the relationship between conventional fuzzy theory and human conceptual images on risks, linguistic variables were reviewed with reference to fuzzy membership functions, especially in the Korean language. As interviewees, about a hundred people including students as well as safety engineers voluntarily participated. The research results showed that most people were in favor of adjective expressions decorated with adverbs rather than naive expressions such as "high" or "low", and that directly translated linguistic variables were not appropriate for the Korean people in risk assessment as far. Therefore, with consideration of the selection tendency by the Korean people in linguistic variables, it could be concluded that 5 level expressions would be most favorable for linguistic variables in risk assessments in Korea.

• Korean Journal of Adult Nursing
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• v.27 no.5
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• pp.604-611
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• 2015

Purpose: Knowledge, clinical reasoning, and self-confidence are the basis for undergraduate education, and determine students' level of competence. The purpose of this study was to assess the effects of the addition of a one-time simulation experience to the didactic curriculum on nursing students' knowledge acquisition, clinical reasoning skill, and self-confidence. Methods: Using a quasi-experimental crossover design consisted of intervention and wait-list control groups. Participants were non-randomly assigned to the first intervention group (Group A, n=48) or the wait-list control group (Group B, n=46). Knowledge level was assessed through a multiple choice written test, and clinical reasoning skill was measured using a nursing process model-based rubric. Self-confidence was measured using a self-reported questionnaire. Results: Results indicated that students in the simulation group scored significantly higher on clinical reasoning skill and related knowledge than those in the didactic lecture group; no difference was found for self-confidence. Conclusion: Findings suggest that undergraduate nursing education requires a simulation-based curriculum for clinical reasoning development and knowledge acquisition.

• ALGAE
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• v.24 no.2
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• pp.85-91
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• 2009

The filamentous cyanobacterium Anabaena sp. Strain PCC 7120 produces a developmental patten of single hete- rocysts separated by approximately 10 vegetative cells. Heterocysts differentiate from vegetative cells and are spe- cialized for nitrogen fixation. The patS gene, which encodes a small peptide that inhibits heterocyst differentiation, is expressed in proheterocysts and plays a critical role in establishing the heterocyst pattem. Another key regulator of heterocyst development is the hetR gene. hetR mutants fail to produce heterocysts and extra copies of hetR on a plas- mid cause a multiple contiguous heterocyst phenotype. To elucidate the relationship between these two counter act- ing factors in the genetic regulatory pathway during heterocyst differentiation, the expression patterns of a patS-gfp and a hetR-gfp fusion were examined in a patS deletion and a hetR deletion strain. The results, in combination with the result from a hetR and patS double deletion strain, suggest patS and hetR are mutually antagonistic and the bal- ance between these two factors in tow different cell types (heterocysts and vegetative cells) may be critical during the decision making process on their cell fates.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• Structural Monitoring and Maintenance
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• v.6 no.2
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• pp.125-145
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• 2019

Bridge scour is one of the predominant causes of bridge failure. Current climate deterioration leads to increase of flooding frequency and severity and thus poses a higher risk of bridge scour failure than before. Recent studies have explored extensively the vibration-based scour monitoring technique by analyzing the structural modal properties before and after damage. However, the state-of-art of this area lacks a systematic approach with sufficient robustness and credibility for practical decision making. This paper attempts to develop a data-driven methodology for bridge scour monitoring using support vector machines. This study extracts features from the bridge dynamic responses based on a generic sensitivity study on the bridge's modal properties and selects the features that are significantly contributive to bridge scour detection. Results indicate that the proposed data-driven method can quantify the bridge scour damage with satisfactory accuracy for most cases. This paper provides an alternative methodology for bridge scour evaluation using the machine learning method. It has the potential to be practically applied for bridge safety assessment in case that scour happens.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.256-264
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• 2024

In the last 10 years, AI has made rapid progress, and image classification, in particular, are showing excellent performance based on deep learning. Nevertheless, due to the nature of deep learning represented by a black box, it is difficult to actually use it in critical decision-making situations such as national defense, autonomous driving, medical care, and finance due to the lack of explainability of judgement results. In order to overcome these limitations, in this study, a model description algorithm capable of local interpretation was applied to the inception network-derived AI to analyze what grounds they made when classifying national defense data. Specifically, we conduct a comparative analysis of explainability based on confidence values by performing LIME analysis from the Inception v2_resnet model and verify the similarity between human interpretations and LIME explanations. Furthermore, by comparing the LIME explanation results through the Top1 output results for Inception v3, Inception v2_resnet, and Xception models, we confirm the feasibility of comparing the efficiency and availability of deep learning networks using XAI.