• Quality Improvement in Health Care
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• v.25 no.2
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• pp.56-72
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• 2019

Purpose: The purpose of this study was to determine the factors that influence the performance and importance of clinical nutritionists. Methods: Ninety-nine nutritionists working in mental health institutions were assessed. Results: Factors affecting the performance of clinical nutritionists in certified hospitals were shown to have a significant positive effect on the description of treatment diet, status of clinical nutrition management, and presence of a nutrition counselor. The factors affecting importance were the number of permission beds and total work experience. Conclusions: In order to improve the accreditation system of mental hospitals, it is necessary to expand the scope of this study to include hospitals with fewer beds, and clinical nutrition management should be mandatory.

• 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$^{-8}$ .

• Korean journal of food and cookery science
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• v.19 no.3
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• pp.363-373
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• 2003

The purpose of this study was to identify university students' attitude to the foodservice attributes in their contribution to the selection or a fast food restaurant. Among 300 subjects, 214 were used in the analysis. The frequencies, Descriptives, ANOVA and t-test were used for the statistical analyses in this study. The findings from this study were as follows: firstly, there were no significant differences in the importance (or salience) of the foodservice attributes between the three different types of fast food restaurant. Secondly, there were no significant differences in the foodservice attributes between the means of the importance and salience for each of the selected fast food restaurants. Thirdly, there were no significant differences in the foodservice attributes in determining between the three types of fast food restaurant. Fourthly, there were no significant differences for the foodservice attributes between the mean importance (or salience) and universal mean for each of the selected fast food restaurants. Finally, the results of this study could provide some insight into the type of marketing strategies that may be successfully used by marketers who manage fast food restaurants.

• Nutrition Research and Practice
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• v.3 no.1
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• pp.49-55
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• 2009

This study investigated the management importance and performance level of foodservice managers at senior centers. Using the survey, perceived importance and performance levels of seven foodservice management areas were evaluated and analyzed. Data showed the foodservice facilities were being managed by dietitians (61.6%) or non-dietitians (38.9%). The result indicated that overall importance level (3.43) was higher than performance level (3.02) (p<.01). As of the IPA result, dietitians and non-dietitians had different perspectives in terms of managing the eight categories of foodservice areas. The differences in the IPA results between the two groups may reflect bias attributable to the respondents' degrees of knowledge and professional preparation. The research findings could enhance our understanding of importance of hiring professional dietitians to operate foodservice at senior centers and find out which management area should be concentrated for more effective foodservice management.

• Journal of the Korea Society for Simulation
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• v.8 no.3
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• pp.77-89
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• 1999

Simulating rare events, such as probability of cell loss in ATM networks, machine failure in highly reliable systems, requires huge simulation efforts due to the low chance of occurrence. Importance Sampling (IS) has been applied to accelerate the occurrence of rare events. However, it has a drawback of effective biasing scheme to make the estimator of IS unbiased. Adaptive Importance Sampling (AIS) employs an estimated sampling distribution of IS to the system of interest during the course of simulation. We propose Nonparametric Adaptive Importance Sampling (NAIS) technique which is nonparametrical version of AIS. We test NAIS to estimate a probability of rare event in M/M/1 queueing model. Comparing with classical Monte Carlo simulation, the computational efficiency and variance reductions gained via NAIS are substantial. A possible extension of NAIS regarding with random number generation is also discussed.

• The Journal of Natural Sciences
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• v.11 no.1
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• pp.165-171
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• 1999

The purpose of this study was to investigate the effects of body-cathexis and importance of meeting the ideal cody image on clothing style preferences. Body-cathexis and importance of meeting the ideal body image were measured for five areas of body parts: head/upper body, lower body, height, weight and torso. Clothing style preferences consisted of clothing preferences for sexy styles, waist emphasis, tight fit, and dark colors. The results showed that there were significant effects of body-cathexis and importance of meeting the ideal body image on clothing style preferences.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.6
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• pp.317-327
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• 2018

The perception of earthquake in Korea has changed due to the earthquake that occurred in Gyeongju and Pohang, and the earthquake has become an important factor in infrastructure management. Damage to infrastructures in the event of an earthquake is extensive. In particular, damage to infrastructures that perform public function used to spread to the whole area. From the point of view of earthquake disaster prevention, it is very important for the public to define what infrastructures are important management targets and to prepare a countermeasure. In this study, we propose a method to evaluate the importance of infrastructure to effectively manage infrastructures for earthquakes. For this purpose, important factors for the railway bridges, power generation and electric power facilities, and apartment complexes are suggested. AHP analysis is conducted to suggest priorities. In addition, the evaluation criteria for infrastructure importance are presented.

• Korean Journal of Organic Agriculture
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• v.26 no.4
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• pp.585-597
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• 2018

The purpose of this study is to analyze the cases of Asan-si and Hongseong-gun in Chungcheongnam-do to derive the direction of development of the school meal support center and improvement of the school meal policy in connection with local food. We conducted a survey on the importance and performance of school meal support policy and local food supply policy for nutritionists in two areas. In addition, we identified the issues to be improved intensively and the issues to be continuously managed over the medium to long term, and suggested directions for improvement using IPA. As a result of the importance-performance analysis of school meal support policy and local food supply policy, both Asan-si and Hongseong-gun. focused on improving the price and quality of local food.

• 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.