• Asian-Australasian Journal of Animal Sciences
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• v.15 no.12
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• pp.1690-1694
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• 2002

Genetic parameters were estimated for birth weight and weaning weight from three year (1991-1993) data totalling 1100 records of 25 rams to 205 ewes of Indigenous Sabi flock maintained at Grasslands Research Station in Zimbabwe. AIREML procedures were used fitting an Animal Model. The statistical model included the fixed effects of year of lambing, sex of lamb, birth type and the random effect of ewe. Weight of ewe when first joined with ram was included as a covariate. Direct heritability estimates of 0.27 and 0.38, and maternal heritability estimates of 0.24 and 0.09, were obtained for birth weight and weaning weight, respectively. The total heritability estimates were 0.69 and 0.77 for birth weight and weaning weight, respectively. Direct-aternal genetic correlations were high and positive. The corresponding genetic covariance estimates between direct and maternal effects were positive and low, 0.25 and 0.18 for birth weight and weaning weight, respectively. Responses to selection were 0.8 kg and 0.14 kg for birth weight and weaning weight, respectively. The estimated expected correlated response to selection for birth weight by directly selecting for weaning weight was 0.26. Direct heritabilities were moderate; as a result selection for any of these traits should be successful. Maternal heritabilities were low for weaning weight and should have less effect on selection response. Indirect selection can give lower response than direct selection.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.325-345
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• 2023

In the rapidly evolving domain of e-commerce, our study presents a cohesive approach to enhance customer satisfaction prediction from online reviews, aligning methodological innovation with practical insights. We integrate the RFE-SHAP feature selection with LDA topic modeling to streamline predictive analytics in e-commerce. This integration facilitates the identification of key features-specifically, narrowing down from an initial set of 28 to an optimal subset of 14 features for the Random Forest algorithm. Our approach strategically mitigates the common issue of overfitting in models with an excess of features, leading to an improved accuracy rate of 84% in our Random Forest model. Central to our analysis is the understanding that certain aspects in review content, such as quality, fit, and durability, play a pivotal role in influencing customer satisfaction, especially in the clothing sector. We delve into explaining how each of these selected features impacts customer satisfaction, providing a comprehensive view of the elements most appreciated by customers. Our research makes significant contributions in two key areas. First, it enhances predictive modeling within the realm of e-commerce analytics by introducing a streamlined, feature-centric approach. This refinement in methodology not only bolsters the accuracy of customer satisfaction predictions but also sets a new standard for handling feature selection in predictive models. Second, the study provides actionable insights for e-commerce platforms, especially those in the clothing sector. By highlighting which aspects of customer reviews-like quality, fit, and durability-most influence satisfaction, we offer a strategic direction for businesses to tailor their products and services.

• Fibers and Polymers
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• v.1 no.1
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• pp.55-58
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• 2000

We present a new approach to bale laydown grouping, which improves the laydown to laydown uniformities, compared to conventional approaches. In this approach, we use a frequency-relative picking method based on an HVI quality index for cotton bale selection and laydown formation. We demonstrate the effectiveness of this approach by computer simulation on real HVI data of 1500 cotton bales. Simulation results show that the proposed method significantly outperforms random picking.

• Journal of the Korean Statistical Society
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• v.12 no.2
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• pp.110-114
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• 1983

In this paper, a random subset selection procedure for the choice of the k best objects out of n primary measurements $Y_t$ is considered when only the associated measurements $X_t$ are available. In contrast to Yeo and David (1992), where only the ranks of the X's are needed, the present uses the observed X-values. The approach is illustrated numerically when X and Y are bivariate normal and the standard deviation of X is known.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.883-885
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• 1997

High impedance fault(HIF) is random in its behavior even in a similar environment. The detection of Ire HIF has focused on the development of algorithms based on harmonic, parameters of the arc currents. However, a fact that proper selection of the harmonic parameters, rather than algorithm selection, is more important is shown in this paper by applying three different performance evaluation methods on two HIF detection algorithms using eight harmonic parameters.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1327-1334
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• 2008

Mixed effect binomial regression models are widely used for analysis of correlated count data in which the response is the result of a series of one of two possible disjoint outcomes. In this paper, we consider kernel extensions with nonparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of hyperparameters, cross-validation techniques are employed. Examples illustrating usage and features of the proposed method are provided.

• Journal of the Chungcheong Mathematical Society
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• v.34 no.2
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• pp.169-180
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• 2021

This paper investigates the time-inconsistent agent's optimal consumption and investment problem under inflation risk. The agents' discount factor is governed by hyperbolic discounting, which has a random time to change. We impose the inflation risk which plays a crucial role in long-term financial planning. We derive the semi-analytic solution to the problem of sophisticated agents when the time horizon is finite.

• Korean journal of food and cookery science
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• v.29 no.6
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• pp.637-645
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• 2013

The purpose of this study was to investigate the consumers' selection attributes of social enterprises type cafe by IPA(Importance-Performance Analysis). Data were collected through a self-administered questionnaire by 309 random consumers in several social enterprises type cafes in Seoul and Gyonggi area. The data was analyzed using SPSS windows(ver. 17.0) for descriptive analysis, t-test, one-way ANOVA and factor analysis. The importance of cafe's selection attributes were divided into five factors including 'promotion and menu', 'service quality and atmosphere', 'location', 'taste', and 'brand name'. The mean scores of importance and performance for cafe selection attributes were 4.01 and 3.68 out of 5, showing a significant difference between importance and performance. According to the IPA results of 17 selection attributes for social enterprises type cafe, the selection attributes with relatively low performance but high importance(II quadrant) was 'easy accessibility'. The factor to be improved through the IPA was accessibility factor, showing that the consumers had a low satisfaction compared to the significance of cafe's location.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.381-394
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• 2016

Graphical model represents conditional dependencies between variables as a graph with nodes and edges. It is widely used in various fields including physics, economics, and biology to describe complex association. Conditional dependencies can be estimated from a inverse covariance matrix, where zero off-diagonal elements denote conditional independence of corresponding variables. This paper proposes a efficient BCDR (block coordinate descent with random permutation) algorithm using graphics processing units and random permutation for the CONCORD (convex correlation selection method) based on the BCD (block coordinate descent) algorithm, which estimates a inverse covariance matrix based on pseudo-likelihood. We conduct numerical studies for two network structures to demonstrate the efficiency of the proposed algorithm for the CONCORD in terms of computation times.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.3
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• pp.120-125
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• 2002

The switching-mode power converter has been widely used because of its features of high efficiency and small weight and size. These features are brought by the ON-OFF operation of semiconductor switching devices. However, this switching operation causes the surge and EMI(Electromagnetic Interference) which deteriorate the reliability of the converter themselves and entire electronic systems. This problem on the surge and noise is one of the most serious difficulties in AC-to-DC converter. In the switching-mode power converter, the output voltage is generally controlled by varying the duty ratio of main switch. When a converter operates in steady state, duty ratio of the converter is kept constant. So the power of switching noise is concentrated in specific frequencies. Generally, to reduce the EMI and improve the immunity of converter system, the switching frequency of converter needs to be properly modulated during a rectified line period instead of being kept constant. Random Pulse Width Modulation (RPWM) is performed by adding a random perturbation to switching instant while output-voltage regulation of converter is performed. RPWM method for reducing conducted EMI in single switch three phase discontinuous conduction mode boost converter is presented. The more white noise is injected, the more conducted EMI is reduced. But output-voltage is not sufficiently regulated. This is the reason why carrier frequency selection topology is proposed. In the case of carrier frequency selection, output-voltage of steady state and transient state is fully regulated. A RPWM control method was proposed in order to smooth the switching noise spectrum and reduce it's level. Experimental results are verified by converter operating at 300V/1kW with 5%~30% white noise input. Spectrum analysis is performed on the Phase current and the CM noise voltage. The former is measured with Current Probe and the latter is achieved with LISN, which are connected to the spectrum analyzer respectively.