• Journal of Animal Science and Technology
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• v.60 no.5
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• pp.13.1-13.7
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• 2018

Background: The objective of this study was to underline that litter size as a key trait of sows needs new parameters to be evaluated and to target an individual optimum. Large individual variation in litter size affects both production and piglet's survival and health negatively. Therefore, two new traits were suggested and analyzed. Two data sets on 5509 purebred German Landrace sows and 3926 Large White and crossing sows including at least two parental generations and at least five parities were subjected to variance components analysis. Results: The new traits for evaluating litter size were derived from the individual numbers of total born piglets (TBP) per parity: In most cases, sows reach their maximum litter size in their fourth parity. Therefore, data from at least five parities were included. The first observable maximum and minimum of TBP, and the individual variation expressed by the range were targeted. Maximum of TBP being an observable trait in pig breeding and management yielded clearly higher heritability estimates ($h^2{\sim}0.3$) than those estimates predominantly reported so far. Maximum TBP gets closer to the genetic capacity for litter size than other litter traits. Minimum of TBP is positively correlated with the range of TBP ($r_p=0.48$, $r_g$ > 0.6). The correlation between maximum of TBP and its individually reached frequency was negative in both data sets ($r_p=-0.28$ and - 0.22, respectively). Estimated heritability coefficients for the range of TBP comprised a span of $h^2=0.06$ to 0.10. Conclusion: An optimum both for maximum and range of total born piglets in selecting sows is a way contributing to homogenous litters in order to improving the animal-related conditions both for piglets' welfare and economic management in pig.

• Korean Institute of Interior Design Journal
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• no.38
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• pp.116-124
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• 2003

The purpose of this study was provide basic data for homemakers' housing notion according to unit size in apartment. This study was conducted by questionnaires that concerning about middle class homemakers whom they lived in Bundang. The results of this study were as follows; 1) In research on the homemakers' general quality, 20pyung unit size was alike 30pyung unit size and 40pyung unit size was similar to 50pyung unit size. 2) Homemakers' perception according to unit size in apartment was various; (a) 20pyungㆍ30pyung unit size-effective household space for husbands' participation, more sports facilities, parking lot space etc. (b) 40pyungㆍ50pyung unit size-homemakers' individual room, ordering and variable unit plan etc. 3) In research on the homemakers' needs, each of unit size was analogous to storage, common facility, new design and new housing form etc.

• Restorative Dentistry and Endodontics
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• v.49 no.2
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• pp.21.1-21.8
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• 2024

Objectives: This paper aims to serve as a useful guide for sample size determination for various correlation analyses that are based on effect sizes and confidence interval width. Materials and Methods: Sample size determinations are calculated for Pearson's correlation, Spearman's rank correlation, and Kendall's Tau-b correlation. Examples of sample size statements and their justification are also included. Results: Using the same effect sizes, there are differences between the sample size determination of the 3 statistical tests. Based on an empirical calculation, a minimum sample size of 149 is usually adequate for performing both parametric and non-parametric correlation analysis to determine at least a moderate to an excellent degree of correlation with acceptable confidence interval width. Conclusions: Determining data assumption(s) is one of the challenges to offering a valid technique to estimate the required sample size for correlation analyses. Sample size tables are provided and these will help researchers to estimate a minimum sample size requirement based on correlation analyses.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• Journal of Mechanical Science and Technology
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• v.17 no.11
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• pp.1784-1792
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• 2003

A hybrid method with supercompact multiwavelets is suggested as an efficient and practical method to compress CFD dataset. Supercompact multiwavelets provide various advantages such as compact support and orthogonality in CFD data compression. The compactness is a crucial condition for approximated representation of CFD data to avoid unnecessary interaction between remotely spaced data across various singularities such as shock and vortices. But the supercompact multiwavelet method has to fit the CFD grid size to a product of integer and power of two, m${\times}$2$^n$. To resolve this problem, the hybrid method with combination of 3, 2 and 1 dimensional version of wavelets is studied. With the hybrid method, any arbitrary size can be handled without any shrinkage or expansion of the original problem. The presented method allows high data compression ratio for fluid simulation data. Several numerical tests substantiate large data compression ratios for flow field simulation successfully.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.237-242
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• 2003

Various kinds of estimation methods have been developed for imputation of categorical missing data. They include modal category method, logistic regression, and association rule. In this study, we propose two imputation methods (neural network fusion and voting fusion) that combine the results of individual imputation methods. A Monte-Carlo simulation is used to compare the performance of these methods. Five factors used to simulate the missing data are (1) true model for the data, (2) data size, (3) noise size (4) percentage of missing data, and (5) missing pattern. Overall, neural network fusion performed the best while voting fusion is better than the individual imputation methods, although it was inferior to the neural network fusion. Result of an additional real data analysis confirms the simulation result.

• Journal of the Korean Society of Safety
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• v.32 no.6
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• pp.131-136
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• 2017

This study deals with the small-size roundabout accidents. The purpose of this study is to analyze the characteristics of small-size roundabout accidents from developing various types of accident models, and to discuss the improvement countermeasures by vehicle type. The geometric characteristics of 36 roundabouts are surveyed, and the traffic accident data from 2008 to 2014 are collected and classified as those of car, truck and motor cycle. In this study, dependent variable is the number of accident and independent variables are such 15 variables as geometry and traffic volume. The main results are as follows. First, the null hypotheses that the size of roundabout and type of vehicle are not related to traffic accident are rejected. Second, 8 count data models which are all statistically significant are developed. Third, the number of circulatory roadway lane and sidewalk are selected as common variables of roundabout size. Finally, the number of entry and circulatory roadway lane are selected as common variables of vehicle type.

• Journal of the Korean Society of Costume
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• v.57 no.10
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• pp.50-59
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• 2007

This study has been conducted to classify elderly men's feet with the three-dimensional body scanner data of Size Korea. It was intended to assist the manufacturing of the shoes that can appropriately perform the functions of feet, by providing the specific information about the shapes of elderly men's feet that are altered as a result of aging and shoe-wearing for a long time. The findings are as follows. 1. The investigations into the average and standard deviation of the measurements and index values for the elderly men's feet showed a large personal difference in the items of length and circumference. 2. The factors constituting the elderly men's feet were observed to be the size of foot width(Factor 1), the central angle of feet and the extension of toes(Factor 2), the size of ankle(Factor 3), the positions of lateral malleolus and pternion(Factor 4), and the position and size of medial malleolus(Factor 5). 3. The cluster analysis for the classification of elderly men's feet produced three types of them.

• Journal of Distribution Science
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• v.14 no.7
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• pp.53-61
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• 2016

Purpose - The production efficiency of agricultural crops has been the subject of numerous studies in the field of agricultural economics. This study examines the production efficiency of emerging vineyards in the 14 northern U.S. states and aims to understand raw input and managerial factors affecting the grape production with focusing on the effect of farm size. Research design, data, and methodology - Using a unique survey dataset that was collected from 176 vineyards in 2012, we employed data envelopment analysis (DEA) for estimation of production efficiency in individual vineyards. Production efficiency is regressed on various input and managerial covariates to understand factors influencing the productivity. Results - Although there exists positive correlation between the farm size and productivity of vineyards in Northern U.S. states, we find negative relationship when the farm size is instrumented by the additional farm size expansion indicator. The negative effect is more pronounced for the recently established vineyards. Conclusions - This study suggests that there needs to be adequate managerial improvements for emerging vineyards in northern states for the achievement of increased productivity.