• Bulletin of the Society of Naval Architects of Korea
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• v.3 no.1
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• pp.25-32
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• 1966

For preliminary estimation of ships' machinery weights, many papers giving well-judged data and discussions for rational method of estimation, such as [1], [2], [3], [4], [5], [6], are available, however, they are mostly concerned with large ships propelled by power more than about 2, 000 horsepower. Regarding the medium and small-sized ships, as far as the author is aware, fragmental data and vague discussions found in various technical literature are the all available. In this paper, available data concerned with machinery weights of commercial ships propelled by direct-drive diesel plants of power below 3, 000 horsepower with single screw propeller are collected and analysed to obtain systematic data Fig. 1 and Fig. 2 as weight to power ratio versus power per shaft diagrams together with suplementary data Fig. 1 and Fig. 3. Influences of various factor such as revolutions per minute, mean effective pressure, type and construction of the main units on machinery weights are also investigated in detail to give a better guidance for logical and rational utlization of the proposed diagrams in preliminary estimation of machinery weights.

• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.524-532
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• 2015

All data created in BigData times is included potentially meaning and correlation in data. A variety of data during a day in all society sectors has become created and stored. Research areas in analysis and grasp meaning between data is proceeding briskly. Especially, accuracy of meaning prediction and data imbalance problem between data for analysis is part in course of something important in data analysis field. In this paper, we proposed data prediction model based on data weights and neural network using R for meaning analysis between data. Proposed data prediction model is composed of classification model and analysis model. Classification model is working as weights application of normal distribution and optimum independent variable selection of multiple regression analysis. Analysis model role is increased prediction accuracy of output variable through neural network. Performance evaluation result, we were confirmed superiority of prediction model so that performance of result prediction through primitive data was measured 87.475% by proposed data prediction model.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1179-1189
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• 2007

In this paper, we estimate the proportion of individuals having health insurance in a given year for several small domains cross-classified by age, sex and other demographic characteristics using the data provided by the National Center for Health Statistics(NCHS). We employ Bayesian as well as frequentist methodology to obtain small domain estimates and the associated measures of precision. One of the new features of our study is that we utilize the survey weights along with the model to derive the small domain estimates.

• Korean Parent-Child Health Journal
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• v.5 no.1
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• pp.75-89
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• 2002

This study was conducted to compare the emotional state between the mothers with low-birth-weights and mothers with normal infants, and to analyze the effects of home visiting for the low-birth-weights in one city. Data were collected from 51 mothers with low-birth-weights and 90 mothers with normal infants to compare emotional state, and from 26 mothers with low-birth weights to evaluate the effect of home visiting care. Summaries of results were as follows; 1. In mothers with low-birth-weights, social support form others was significantly lower than those of mothers with normal infants. Although the differences were not significant, mothers with low-birth-weights have more stress and child rearing burden, and less maternal self-esteem than those of mothers with normal infants. 2. Mothers with low-birth-weights, the more burden, postpartum depression, and the less husbands' support they felt. When they had lower maternal self-esteem and lower husbands' support, child rearing burden was higher. Also there was significant negative correlation between maternal self-esteem and postpartum depression. 3. In mothers with low-birth-weights, the score of post-intervention stress, care-giving burden, and postpartum depression were somewhat decreased, and maternal self-esteem was increased than pre-intervention data, although they were not statistically significant. 4. Mothers' satisfaction on the home-visiting care was considered to be high. In summary, mothers with low-birth-weights had lower social support even though they experienced more stress than mothers with normal infants. Therefore, public health nurse in community should pay more attention to them.

• Journal of Preventive Medicine and Public Health
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• v.56 no.4
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• pp.291-302
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• 2023

Objectives: Disability weights require regular updates, as they are influenced by both diseases and societal perceptions. Consequently, it is necessary to develop an up-to-date list of the causes of diseases and establish a survey panel for estimating disability weights. Accordingly, this study was conducted to calculate, assess, modify, and validate disability weights suitable for Korea, accounting for its cultural and social characteristics. Methods: The 380 causes of disease used in the survey were derived from the 2019 Global Burden of Disease Collaborative Network and from 2019 and 2020 Korean studies on disability weights for causes of disease. Disability weights were reanalyzed by integrating the findings of an earlier survey on disability weights in Korea with those of the additional survey conducted in this study. The responses were transformed into paired comparisons and analyzed using probit regression analysis. Coefficients for the causes of disease were converted into predicted probabilities, and disability weights in 2 models (model 1 and 2) were rescaled using a normal distribution and the natural logarithm, respectively. Results: The mean values for the 380 causes of disease in models 1 and 2 were 0.488 and 0.369, respectively. Both models exhibited the same order of disability weights. The disability weights for the 300 causes of disease present in both the current and 2019 studies demonstrated a Pearson correlation coefficient of 0.994 (p=0.001 for both models). This study presents a detailed add-on approach for calculating disability weights. Conclusions: This method can be employed in other countries to obtain timely disability weight estimations.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.631-634
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• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.

• Journal of the Korean Society of Safety
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• v.31 no.5
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• pp.61-66
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• 2016

Many cut slopes are located along national roads, there were the collapse of cut slopes. In this study, the weights for condition evaluation of rock slopes was calculated using the entropy method and analytic hierachy process(AHP) method. The entropy analysis was performed using 95 cut slope data, and the AHP analysis was performed by a questionnaire to several expert. The weights based on analysis results were compared with evaluation weights of existing standard. As a result of this study, there was the difference of weights among the analytical methods. Later on, if this study's results is used to improvement current evaluation weights, it will be possible to perform the reliable condition evaluation.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.499-505
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• 2006

In this note, we give an elementary simple new proof of the main result of $Full{\acute{e}}r$ and Majlender [Fuzzy Sets and systems 136 (2003) 203-215] concerning obtaining minimal variability OWA operator weights.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.537-541
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• 2006

In this note, we give an elementary simple proof of the main result of $Full{\acute{e}}rand$ Majlender [Fuzzy Sets and systems 124(2001) 53-57] concerning obtaining maximal entropy OWA operator weights.

• Journal of Korean Academy of Nursing
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• v.40 no.1
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• pp.69-77
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• 2010

This methodological research was designed to develop performance evaluation key indicators (PEKIs) for management by objectives (MBO) and to estimate their weights for hospital nurses. Methods: The PEKIs were developed by selecting preliminary indicators from a literature review, examining content validity and identifying their level of importance. Data were collected from November 14, 2007 to February 18, 2008. Data set for importance of indicators was obtained from 464 nurses and weights of PEKIs domain was from 453 nurses, who worked for at least 2 yr in one of three hospitals. Data were analyzed using $X^2$-test, factor analysis, and the Analytical Hierarchy Process. Results: Based upon Content Validity Index of .8 or above, 61 indicators were selected from the 100 preliminary indicators. Finally, 40 PEKIs were developed from the 61 indicators, and categorized into 10 domains. The highest weight of the 10 domains was customer satisfaction, which was followed by patient education, direct nursing care, profit increase, safety management, improvement of nursing quality, completeness of nursing records, enhancing competence of nurses, indirect nursing care, and cost reduction, in that order. Conclusion: PEKIs and their weights can be utilized for impartial evaluation and MBO for hospital nurses. Further research to verify PEKIs would lead to successful implementation of MBO.