Earticle

본 연구는 한우초음파생체단층촬영 자료를 이용하여 한우개량을 위한 기초자료로 활용코자 실시하였다. 당대검정우 1,125두에 대하여 초음파생체단층 촬영한 자료와 검정성적자료 및 12개월령에 거세한 후 30개월령까지 비육 후 출하하여 도체성적을 조사한 자료를 이용하였으며, 후대검정우는 921두에 대한 검정성적자료와 도체성적자료를 이용하였다. 12개월령에 측정한 초음파생체단층 촬영 배최장근단면적과 등지방두께의 유전력은 각각 0.57, 0.41로 추정되었으며, 24개월에서는 각각 0.57, 0.60으로 고도의 유전력을 보였다. 그러나, 초음파 측정 %지방함량 유전력 추정치에서는 12개월령과 24개월령에서 각각 0.14, 0.22로 저․중도의 유전력을 보였다. 12개월령에 초음파생체단층 촬영한 배최장근단면적, 등지방두께, %지방함량과 도체형질의 배최장근단면적, 등지방두께, 근내지방도의 유전상관은 각각 0.616, 0.544, 0.501로 추정되었고, 24개월령에서는 각각 0.894, 0.937, 0.263으로 높은 유전상관을 보였다. 12개월령에 측정한 초음파 측정 %지방함량과 근내지방도에서 높은 유전상관으로 추정되었는데, 이는 24개월령에서는 환경적요인(사양기술 등)에 의해 영향을 많이 받은 것으로 사료되며, 선발에 활용할 경우 24개월령 측정치 보다는 12개월령 측정치를 이용하는 것이 더 바람직할 것으로 사료된다. 12개월령 체중과 도체형질을 이용하여 씨수소를 선발하는 선발지수와 이와 유전상관이 높은 초음파생체단층촬영 자료를 바탕으로 선발하는 선발지수간의 순위상관을 분석한 결과 12개월령에 초음파생체단층 촬영한 자료에서 후보씨수소 선발지수는 0.140, 보증씨수소 선발지수는 0.843으로 높은 순위상관을 보였다. 본 연구결과 실시간으로 배최장근단면적, 등지방두께, 근내지방도와 같은 도축하지 않아도 도체성적을 파악할 수 있는 초음파생체단층 촬영 기술의 장점을 이용하여 선발에 활용한다면 근내지방도는 24개월령에 측정하는 것보다 12개월령에 측정하는 것이 더 효율적이라고 사료되어지며, 향후 초음파생체단층기술에 관한 더 많은 연구를 통하여 종축선발에 활용한다면 씨수소의 조기선발이 가능할 것으로 사료된다.

This study is conducted to use the real-time ultrasound measurement data of Hanwoo as basic data being available to improvement. We used the ultrasound measurement data of 1,125 heads of performance tested cattle and the carcass data after castrating at about 12 months of age, fattened to 30 months, and then sold. For 921 heads of progeny tested cattle, we used test data and slaughter data. Heritabilities of ultrasound data for longissimus muscle area and backfat thickness measured at 12 months of age were estimated as 0.57 and- 0.41, respectively, and at 24 months of age, it was 0.57 and- 0.60, respectively, with high heritability. However, in estimation value of heritability containing ultrasound measurement for percent intramuscular fat, it showed low and medium heritability as 0.14 at 12 months of age and 0.22 at 24 months of age for each. The longissimus muscle area, backfat thickness, and percent intramuscular fat of ultrasound measure traits and longissimus muscle area, backfat thickness, marbling score of carcass traits genetic correlation of at 12 months of age were estimated as 0.616, 0.544, 0.501, respectively and at 24 months of age, it showed high genetic correlation as 0.894, 0.937, 0.263, respectively. As a result of ranking correlation between selection index by using weight, carcass traits at 12 months of age and selection index based on ultrasound measurement data which has high genetic correlation, in data of ultrasound measurement at 12 months of age, it showed high ranking correlation as that selection index of young bull was 0.140 and that of proven bull was 0.843.

Based on a set of reaction-diffusion equations of oxygen, glucose and growth inhibitory factor which were built to describe the tumor growth environment, a cell cycle control factor was proposed for the simulation of the avascular tumor growth. In order to fix the model parameters, a two-level heuristic searching was given for the parameter selection. Finally, a total finite element energy equation was present to combine the chemical simulation and the physical simulation for the avascular tumor growth. Experimental results showed that the presented approach outperformed the baseline approach in the simulation of the mouse mammary tumor cells EMT6/Ro.

Electrical discharge machining (EDM) is one of the most extensively used nonconventional material removal process. The Taguchi method has been utilized to determine the optimal EDM conditions in several industrial fields. The method was design to optimize only a single performance characteristic. To remove this limitation, the Grey relational theory has been used to resolve the complicated interrelationship among the multiple performance characteristics. In the present study, we attempt to find the optimal machining conditions under which the Material removal rate(MRR) to be maximize and Tool wear rate(TWR) to be minimize. This paper summarizes the Grey relation theory and Taguchi optimization technique in order to optimize the cutting parameters in EDM for SS304. The Taguchi method was used to determine the relations between the machining and Response parameters. GRA was used to investigate the optimal machining parameters, among which the pulse on-time, pulse off-time are found to be the most desirable. Finally optimal machining conditions are pulse on time (50 μs), pulse off time (35 μs), discharge current (12A), and voltage (50V). Experimentation was planned as per Taguchi’s L9 (33) orthogonal array. Analysis of variance (ANOVA) is applied to identify the level of importance of the machining parameters on the multiple performance characteristics considered. Finally confirmation result was carried out to identify the effectiveness of this proposed method.

Recently, the importance of big data continues to be emphasized, and it is applied in various fields based on data mining techniques, which has a great influence on the health care industry. There are many healthcare industries, but only home health care is considered here. However, applying this to real problems does not always give perfect results, which is a problem. Therefore, data mining techniques are used to solve these problems, and the algorithms that affect performance are evaluated. This paper focuses on the effects of healthcare services on patient satisfaction and satisfaction. In order to use the CVParameterSelectin algorithm and the SMOreg algorithm of the classify method of data mining, it was evaluated based on the experiment and the verification of the results. In this paper, we analyzed the services of home health care institutions and the patient satisfaction analysis based on the name, address, service provided by the institution, mood of the patients, etc. In particular, we evaluated the results based on the results of cross validation using these two algorithms. However, the existence of variables that affect the outcome does not give a perfect result. We used the cluster analysis method of weka system to conduct the research of this paper.

A procedure for antenna parameter selections is proposed that considers the relationships between synthetic aperture radar performance and the antenna parameters of a parabola-type reflector antenna with a central flat dish. The effects of a central dish designed for weight reduction on the antenna beam pattern are also quantitatively analyzed using commercially available software based on the physical optics algorithm. The results of the theoretical analysis and simulation predict that a larger size of the central dish results in an increase in the sidelobe level, which is the reason for the increase in two important ambiguities, such as range ambiguity ratio (RAR) and azimuth ambiguity ratio (AAR). The dependence of RAR and AAR on Pulse repetition frequency is also analyzed and discussed.

재료의 중량과 강도는 기계부품 특히 항공기의 부품에 중요한 요소가 되므로 가볍고 강인한 열처리 강화 알루미늄이나 티타늄 등이 많이 사용된다. 그러나 알루미늄은 용융점이 낮기 때문에 기계 가공 시 발생되는 열에 의해 부품이 얇고 길수록 쉽게 변형된다. 본 연구는 end milling 가공에서 최적의 절삭 parameter를 선정하여 열 변형을 최소화한다. 밀링 가공의 절삭속도, 이송속도, 절삭 깊이를 실험 인자로 정하여 다구찌 방법으로 실험을 계획하고 얇은 시편을 절삭하여 특성을 측정한다. 결과를 분산분석 (ANOVA) 과 signal to noise 비를 (SNR) 분석하여 최소 열 변형의 절삭 parameter를 찾는다. 실험의 data를 SQL database 프로그램화하여 다양한 절삭 환경에서 최소 열 변형과 최소 표면거칠기의 parameter를 찾을 수 있도록 하였다.

This paper proposes a parameter design method for multi-element resonant converters (MERCs) with a unique resonant zero point (RZP). This method is mainly composed of four steps. These steps include program filtration, loss comparison, 3D figure fine-tuning and priority compromise. It features easy implementation, effectiveness and universal applicability for almost all of the existing RZP-MERCs. Meanwhile, other design methods are always exclusive for a specific topology. In addition, a novel dual-CTL converter is also proposed here. It belongs to the RZP-MERC family and is designed in detail to explain the process of parameter selection. The performance of the proposed method is verified experimentally on a 500W prototype. The obtained results indicate that with the selected parameters, an extensive dc voltage gain is obtained. It also possesses over-current protection and minimal switching loss. The designed converter achieves high efficiencies among wide load ranges, and the peak efficiency reaches 96.9%.

In the past decades, various image regularization methods have been introduced. Among them, total variation model has drawn much attention for the reason of its low computational complexity and well-understood mathematical behavior. However, regularization parameter estimation of total variation model is still an open problem. To deal with this problem, a novel adaptive regularization parameter selection scheme is proposed in this paper, by means of using the local spectral response, which has the capability of locally selecting the regularization parameters in a content-aware way and therefore adaptively adjusting the weights between the two terms of the total variation model. Experiment results on simulated and real noisy image show the good performance of our proposed method, in visual improvement and peak signal to noise ratio value.

Q-algorithm is proposed at EPC global Class-1 Generation-2 RFID systems to determine the frame size of next query round. In Q-algorithm, the reader calculates the frame size without estimating the number of tags. But, it uses only the slot conditions: empty, success, or collision. Therefore, it wastes less computational cost and is simpler than other algorithms. However, the constant parameter C value, which is used for calculating the next frame size, is not optimized. In this paper, we propose the optimized C values of Q-algorithm according to the number of tags within the identification range of reader through a lot of computer simulations.

The smoothing parameter <TEx>$\lambda$</TEx> in smoothing spline regression is usually selected by minimizing cross-validation (CV) or generalized cross-validation (GCV). But, simple CV or GCV is poor candidate for estimating prediction error. We defined MGCV (Multifold Generalized Cross-validation) as a criterion for selecting smoothing parameter in smoothing spline regression. This is a version of cross-validation using $leave-\kappa-out$ method. Some numerical results comparing MGCV and GCV are done.

In this paper we consider kernel type estimator of the spectral density at a point in the analysis of stationary time series data. The kernel entails choice of smoothing parameter called bandwidth. A data-based bandwidth choice is proposed, and it is obtained by solving an equation similar to Sheather(1986) which relates to the probability density estimation. A Monte Carlo study is done. It reveals that the spectral density estimates using the data-based bandwidths show comparatively good performance.

An automated signal-acquisition method for the NASA's space geodesy satellite laser ranging (SGSLR) system is described as a selection of two system parameters with specified probabilities. These parameters are the correlation parameter: the minimum received pulse number for a signal-acquisition and the frame time: the minimum time for the correlation parameter. The probabilities specified are the signal-detection and false-acquisition probabilities to distinguish signals from background noise. The steps of parameter selection are finding the minimum set of values by fitting a curve and performing a graph-domain approximation. However, this selection method is inefficient, not only because of repetition of the entire process if any performance values change, such as the signal and noise count rate, but also because this method is dependent upon system specifications and environmental conditions. Moreover, computation is complicated and graph-domain approximation can introduce inaccuracy. In this study, a new method is proposed to select the parameters via a conditional equation derived from characteristics of the signal-detection and false-acquisition probabilities. The results show that this method yields better efficiency and robustness against changing performance values with simplicity and accuracy and can be easily applied to other satellite laser ranging (SLR) systems.

This paper proposes an improved particle swarm optimization named PSO with Controllable Random Exploration Velocity (PSO-CREV) behaving an additional exploration behavior. Different from other improvements on PSO, the updating principle of PSO-CREV is constructed in terms of stochastic approximation diagram. Hence a stochastic velocity independent on cognitive and social components of PSO can be added to the updating principle, so that particles have strong exploration ability than those of conventional PSO. The conditions and main behaviors of PSO-CREV are described. Two properties in terms of "divergence before convergence" and "controllable exploration behavior" are presented, which promote the performance of PSO-CREV. An experimental method based on a complex test function is proposed by which the proper parameters of PSO-CREV used in practice are figured out, which guarantees the high exploration ability, as well as the convergence rate is concerned. The benchmarks and applications on FCRNN training verify the improvements brought by PSO-CREV.

This paper consider penalized likelihood regression with data from exponential family. The fast computation method applied to Gaussian data(Kim and Gu, 2004) is extended to non Gaussian data through asymptotically efficient low dimensional approximations and corresponding algorithm is proposed. Also smoothing parameter selection is explored for various exponential families, which extends the existing cross validation method of Xiang and Wahba evaluated only with Bernoulli data.

We propose an alternative procedure to select penalty parameter in $L_1$ penalized robust regression. This procedure is based on marginalization of prior distribution over the penalty parameter. Thus, resulting objective function does not include the penalty parameter due to marginalizing it out. In addition, its estimating algorithm automatically chooses a penalty parameter using the previous estimate of regression coefficients. The proposed approach bypasses cross validation as well as saves computing time. Variable-wise penalization also performs best in prediction and variable selection perspectives. Numerical studies using simulation data demonstrate the performance of our proposals. The proposed methods are applied to Boston housing data. Through simulation study and real data application we demonstrate that our proposals are competitive to or much better than cross-validation in prediction, variable selection, and computing time perspectives.

Objective It takes significant time and energy to collect data on explicit networks. This study used graph machine learning to identify hidden networks and predict mental health conditions in the middle-aged and old.Methods Data came from the Korean Longitudinal Study of Ageing (2016?2018), with 2,000 participants aged 56 or more. The dependent variable was mental disease (no vs. yes) in 2018. Twenty-eight predictors in 2016 were included. Graph machine learning with systematic hyper-parameter selection was conducted.Results The area under the curve was similar across different models in different scenarios. However, sensitivity (93%) was highest for the graph random forest in the scenario of 2,000 participants and the centrality requirement of life satisfaction 90. Based on the graph random forest, top-10 determinants of mental disease were mental disease in previous period (2016), age, income, life satisfaction?health, life satisfaction?overall, subjective health, body mass index, life satisfaction?economic, children alive and health insurance. Especially, life satisfaction?overall was a top-5 determinant in the graph random forest, which considers life satisfaction as an emotional connection and a group interaction.Conclusion Improving an individual’s life satisfaction as a personal condition is expected to strengthen the individual’s emotional connection as a group interaction, which would reduce the risk of the individual’s mental disease in the end. This would bring an important clinical implication for highlighting the importance of a patient’s life satisfaction and emotional connection regarding the diagnosis and management of the patient’s mental disease.

The objective of this research is to investigate the problem of goodness-of-fit testing based on nonparametric density estimation with a data-driven smoothing parameter. The small and large sample properties of a new test statistic $\hat{\lambda_a}$ is investigated. The test statistic $\hat{\lambda_a}$ is itself a smoothing parameter which is selected to minimize an estimated MISE for a truncated series estimator of the comparison density function. Therefore, this test statistic leads immediately to a point estimate of the density function th the event that $H_0$ is rejected. The limiting distribution of $\hat{\lambda_a}$ is obtained under the null hypothesis. It is also shown that this test is consistent against fixed alternatives.

The Proposed goodness-of-fit test Statistic $\hat{\lambda}_{\alpha}$ derived from the test Statistc in Kim (1992) is itself a smoothing parameter which is selected to minimize an estimated MISE for a truncated series estimator, $d_{\hat{\lambda}{n}}$, of the comparison density function. Therefore, this test statistic leads immediately to a point estimate of the density function in the event that $H_{0}$ is ejected. The limiting distribution of $\hat{\lambda}_{\alpha}$ was obtained under the null hypothesis. It is also shown that this test is consistent against fixed alternatives.

The Gompertz law is a widely-used human mortality model, especially for old ages. In this paper, we propose a new parameter estimation method for the Gompertz mortality law based on the weighted least squares regression framework. Unlike other existing weight schemes, we derive a statistically appropriate weight structure using the delta method in the spirit of the Gauss-Markov theorem. In addition, we propose a new criterion to select the Gompertz threshold age, from which the Gompertz law starts, another important aspect in practice. The new criterion is designed with actuarial applications in mind. In particular, it identifies the threshold by minimizing the difference in the actuarial present value between the observed mortality and the estimated one. We illustrate our findings using the Korean assured mortality dataset and show that the proposed methods to find the Gompertz parameters and the threshold work well.