• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.914-919
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• 2017

While industry understands the importance of keeping equipment operational and well maintained, the importance of tracking maintenance information in reliability models is often overlooked. Prognostic models can be used to predict the failure times of critical equipment, but more often than not, these models assume that all maintenance actions are the same or do not consider maintenance at all. This study investigates the influence of integrating maintenance information on prognostic model prediction accuracy. By incorporating maintenance information to develop maintenance-dependent prognostic models, prediction accuracy was improved by more than 40% compared with traditional maintenance-independent models. This study acts as a proof of concept, showing the importance of utilizing maintenance information in modern prognostics for industrial equipment.

• Journal of the Korean Society of Food Culture
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• v.16 no.5
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• pp.423-430
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• 2001

The facts of job satisfaction and their perceived importance among elementary school foodservice dietitians were assessed. The survey instrument consisted of three parts: the job satisfaction survey was used to measure facets of job satisfaction and the level of total satisfaction; perceived importance questions for corresponding job facets; and demographic items. A survey of school food service operations was undertaken and detailed information was collected from 170 dietitians. The collected data were processed using the SPSS package program for descriptive analysis and analysis of valiance. School foodservice dietitians' importance and satisfactions scores on their job and working environment were 4.25 and 2.83, respectively. The respondents rated the subscales of 'communication' and 'nature of work' the highest and the subscales of 'pay' and 'working environment' the lowest The results of facet satisfaction scores and corresponding perceived importance scores were paired to be plotted on the Importance-Performance Analysis Grid. IPA grid was used to provide a strategy for food service managers to counteract dietitian dissatisfaction.

• Journal of the Korean Society for information Management
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• v.39 no.2
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• pp.325-346
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• 2022

Social changes characterized by new normal also demand changes in libraries and libraries are making preemptive efforts. This study compared and analyzed the difference in librarian perceptions between the start of COVID-19 in 2020 and after experiencing the pandemic in 2022 in terms of the relative importance of library policies considered by pairwise comparative analysis. In the 1st survey, the importance of factors related to 'Library Management' and 'Library Services' was high and in the 2nd survey, the importance of factors related to 'Human Resources' and 'Information Resources' increased. This study insists that the public libraries invest in human resources and information resources and establish a user-customized policy in order to quickly adapt to the new paradigm and to provide sustainable library services.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.283-285
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• 2005

To account for the importance of variable in multi-sensor data fusion, random forests are applied to supervised land-cover classification. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. Its distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Supervised classification with a multi-sensor remote sensing data set including optical and polarimetric SAR data was carried out to illustrate the applicability of random forests. From the experimental result, the random forests approach could extract important variables or bands for land-cover discrimination and showed good performance, as compared with other non-parametric data fusion algorithms.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.443-446
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• 2007

A new association rule mining algorithm, which reflects the strategic importance of associative relationships between items, was developed and presented in this paper. This algorithm exploits the basic framework of Apriori procedures and TSAA(transitive support association Apriori) procedure developed by Hyun and Choi in evaluating non-frequent itemsets. The algorithm considers the strategic importance(weight) of feature variables in the association rule mining process. Sample feature variables of strategic importance include: profitability, marketing value, customer satisfaction, and frequency. A database with 730 transaction data set of a large scale discount store was used to compare and verify the performance of the presented algorithm against the existing Apriori and TSAA algorithms. The result clearly indicated that the new algorithm produced substantially different association itemsets according to the weights assigned to the strategic feature variables.

• Korean journal of food and cookery science
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• v.16 no.2
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• pp.112-120
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• 2000

The purpose of this study was to examine the needs of high school students on foodservice. A survey from 8 high school foodservice operations in Seoul was undertaken and detailed information was collected from 538 high school students. Completed questionnaires were received from 3 self-operated and 5 contracted school foodservice operations. Statistical analyses were performed by SPSS including descriptive analysis and t-test. The 49.8% of the respondent students were male and 50.2% female. The whole students assessed the importance and performance of school foodservice as 4.18 and 2.83 out of 5 respectively, which suggests that the school foodservice needs to be improved. The Importance-Performance Analysis(IPA) used for obtaining information on high school foodservice management suggested that foodservice attributes with fair to poor performance but with high importance were sanitation of food, service of foodservice personnel, dealing with complaints and the reflection of students'opinion in menu.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1165-1172
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• 2008

A considerable amount of research has focused on the dimensionality of service quality construct. To achieve and maintain their comprehensiveness and profitability, restaurant managers should manage and aim to continuously improve the level of service quality offered to their customers. This paper is focused on service quality in the Korean native cattle and imported beef restaurant industry in the Korea. So, this paper has adapted DINESERV scale so that restaurant managers can use it to determine how customers perceive the service quality in Korean native cattle beef restaurant and imported beef restaurant. The purpose of this research is to test the difference in importance and actual performance of 5 dimensions between the restaurants that sell the beef of Korean native cattle and imported cattle. The t-value is used to test difference of the importance and actual performance for DINESERV's 5 dimensions of the 2 restaurant types. But, there is no difference between Korean native cattle and imported beef restaurant.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.1097-1106
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• 1997

Most works on importance sampling (IS) as an efficient evaluation technique havd been done in an additibe white gaussian noise channel (Awgn). In this paper we propose a CQ(conventional importance sampling and quasi-translantion) IS technique for the mobile radio channel modeled as Rician fading, and analyze the IS estimator's variance to determine optimum IS parameters and the minimum number of run times. Reference showed that CIS technique has a poor performance for systems with meories, but it is shown that the CIS technique can be improved by combining with quasi-translation technique even for systems with memories. Here the CQ IS technique modifies the variance of additive noise and also performs quasi-translation for the fading distribution. We determine the optimum IS parameters of the proposed CQ IS estimator and whow that the simulation gains are about 10$^{3}$~10$^{6}$ for the mobile communication systems with memories in case of the expected BERs 10$^{-5}$ ~10$^{-8}$ .

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1990.04a
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• pp.272-288
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• 1990

The uncertainty quantification process in probabilistic Risk Assessment usually involves a specification of the uncertainty in the input data and the propagation of this uncertainty to the final risk results. The distributional sensitivity analysis is to study the impact of the various assumptions made during the quantification of input parameter uncertainties on the final output uncertainty. The uncertainty importance of input parameters, in this case, should reflect the degree of changes in the whole output distribution and not just in a point estimate value. A measure of the uncertainty importance is proposed in the present paper. The measure is called the distributional sensitivity measure(DSM) and explicitly derived from the definition of the Kullback's discrimination information. The DSM is applied to three typical discrimination information. The DSM is applied to three typical cases of input distributional changes: 1) Uncertainty is completely eliminated, 2) Uncertainty range is increased by a factor of 10, and 3) Type of distribution is changed. For all three cases of application, the DSM-based importance ranking agrees very well with the observed changes of output distribution while other statistical parameters are shown to be insensitive.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.82-89
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• 2023

Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.