• Korean Institute of Interior Design Journal
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• v.23 no.6
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• pp.42-50
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• 2014

The purpose of this study is to analyze the change of perception and to measure the degree of interaction between the lighting and the finishing materials according to preference types and the impacted factors to the spatial sensitivity of users. To do that, the this change was grasped through the experiment with Likert Scale and ANOVA of SPSS program to the simulated images. The result of this study is as following. Firstly, the personal difference in the perception of space comes from 'attention' in information processing process. The degree of the perception of users change strongly by lighting in the space expressed the 'modern natural'. Secondly, the atmosphere and the degree of perception are different in the preference of users to the spatial sensitivity and in impacted factors which are thought by users in the space sensitives. The users who prefer the 'decoration' feel the mood more strongly in the space. But the users who prefer the 'active' and 'intimacy' are opposite to that. The difference to the degree of perception is greater in the 'classic natural' than the others under the lighting turns on. Thirdly, an in-depth research which is considered of the interaction among the various factors is needed for finding the design methods for inducing the sensitivity of users in the space. And the ranking of the impacted factors should be understanded and applied to the research regarding the changes of perception in the space. Finally, this study has the limitation to be adapted to all of the situation for the space design methods. But this will be a basic data to study the design methods for users's sensitivity in the space.

• Journal of the Korean Society of Groundwater Environment
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• v.4 no.3
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• pp.122-129
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• 1997

Spills or leaks of hazardous organic. compounds from underground storage tanks (USTs) are common contaminant sources of soil and groundwater. It would aid in managing USTs to assess and rank the potential environmental hazard posed by the USTs. Therefore, a preliminary hazard ranking system of USTs is developed in this study. The system is combined with GIS and consists of five steps: 1) selection of significant factors, 2) determination of the hierarchy of the factors. 3) determination of the weights, 4) calculation of the potential hazard, and 5) hazard assessment. The system is applied to the gas stations in Kwanak-gu, Seoul. The results indicate that the gas stations can be categorized in three groups as highly hazardous, less highly hazardous, and weakly hazardous. Seven gas stations belong to the highly hazardous group. Through the sensitivity analysis, four stations appear to possess high hazard potentials regardless of weights assigned to the factors. It appears that a user can make flexible application of the hazard ranking system with the user's experience and particular purposes. However, the system still needs validations against field survey data.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2266-2273
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• 2024

A machine learning platform is proposed for the diagnosis of a severe accident progression in a nuclear power plant. To predict the key parameters for accident management including lost signals, a long short term memory (LSTM) network is proposed, where multiple accident scenarios are used for training. Training and test data were produced by MELCOR simulation of the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident at unit 3. Feature variables were selected among plant parameters, where the importance ranking was determined by a recursive feature elimination technique using RandomForestRegressor. To answer the question of whether a reduced order ML model could predict the complex transient response, we performed a systematic sensitivity study for the choices of target variables, the combination of training and test data, the number of feature variables, and the number of neurons to evaluate the performance of the proposed ML platform. The number of sensitivity cases was chosen to guarantee a 95 % tolerance limit with a 95 % confidence level based on Wilks' formula to quantify the uncertainty of predictions. The results of investigations indicate that the proposed ML platform consistently predicts the target variable. The median and mean predictions were close to the true value.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.542-549
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• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.594-609
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• 1998

In this dissertation, a new process capability index $C_{psk}$ is introduced for non-normal process. The Pearson curve and the Johnson curve are selected for capability index calculation and data modeling the normal-based index $C_{psk}$ is used as the model for non-normal process. A significant result of this research find that the ranking of the seven indices, $C_p,\;C_{pk},\;C_{pm},\;C^{\ast}_{pm},\;C_{pmk},\;C_s,\;C_{psk}$ in terms of sensitivity to departure of the process median from the target value T=M from the most sensitive one up to the least sensitive are $C_{psk},\;C_{s},\;C_{pmk},\;C^{\ast}_{pm},\;C_{pm},\;C_{pk},\;C_p$. i.e, By the criteria adopted for evaluation of PCI's $C_{psk}$ is the most sensitive to the departure of the process median from target and $C_p$ is least

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.4
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• pp.39-60
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• 2003

In this study, we evaluate financial performance of 21 domestic life insurers using SAW (simple additive weighting), ELECTREII, cluster analysis respectively, and suggest a hybrid approach of combining cluster analysis and ELECTREII to reclassify the life insurers into more meaningful groups according to their respective financial features. We also perform the sensitivity analysis employing ANOVA and Tukey's test to examine the robustness of ELECTREII, which would be influenced by decision maker's subjective preference parameters. Consequently, it is shown that ELECTREII turns out to be a flexible method providing decision makers with useful ranking Information especially under fuzzy decision making situation with incomparable alternatives, and hence it can serve as a complementary method to overcome the weakness of classical cluster analysis.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.133-136
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• 1987

This paper presents a new algorithm of optimal measurement system by using band matrix characteristic respectively for state estimation. A performance index of measurement system is established to reflect relation among measurement sets, probability of measurement failure and cost of individual meter installation. Selection ranking in the candidates of measurement sets is composed to guarantee the observability for any any single meter outage. Performance index sensitivity is introduced and recursive formula which based on the matrix inversion lemma used for selection. The proposed algorithm is composed of successive addition algorithm, successive elimination algorithm and combinatorial algorithm. The band matrix characteristic could save in memory requirements and calculate the performance index faster than earlier.

• Ocean Systems Engineering
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• v.5 no.1
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• pp.41-54
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• 2015

This study presents the development of predictive models for uplift capacity of suction caisson in clay using an artificial intelligence technique, extreme learning machine (ELM). Other artificial intelligence models like artificial neural network (ANN), support vector machine (SVM), relevance vector machine (RVM) models are also developed to compare the ELM model with above models and available numerical models in terms of different statistical criteria. A ranking system is presented to evaluate present models in identifying the 'best' model. Sensitivity analyses are made to identify important inputs contributing to the developed models.

• Structural Engineering and Mechanics
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• v.41 no.5
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• pp.575-589
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• 2012

This paper presents a reliability analysis of steel I-beams with rectangular web openings, based on a combination of the common probabilistic reliability methods, such as RSM, FORM and SORM and using data obtained from experimental tests performed at the Istanbul Technical University. A procedure is proposed to obtain the optimum design load that can be applied to this type of structural members, by taking into account specified target values of reliability indices for ultimate and serviceability limit states. The goal of the paper is to present an algorithm to obtain more realistic and economical design of beams and to demonstrate that it can be applied efficiently to steel I-beams with web openings. Finally, a sensitivity analysis is performed allowing to ranking the random variables according to their importance in the reliability analysis.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.115-118
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• 2008

In this study, PROMETHEE(Preference Ranking Organization METHod for Enrichment Evaluations) which is one of the multi criteria decision making methods is applied to estimate the relative inundation risk of the urban subcatchment. For this purpose, five factors which have an effect on the inundation risk are selected and used to perform PROMETHEE. Those are elevation average, slope average, density of conduit, population and sediment yields per unit area of each subcatchment. Based on them, PROMETHEE is performed and the relative inundation risk for each subcatchment is estimated. Sensitivity anlysis is conducted to evaluate each factor's effect on subcatchment and it is found that suggested method can be used to establish a practical guide to mitigate the inundation.