• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.4
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• pp.809-816
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• 2007

This paper presents an analysis of the SEMG signal quantitatively and automatically using spike variables : MSF, MSA, MSS, and MSD. The SEMG signals were recorded in three muscle parts, first dorsal interosseus, biceps brachii and abductor policis brevis, from 14 normal subjects. Emphasis was placed on the following 3 points in the experiments. 1) Suggest proper window length to estimate the spike variables 2) Investigate variation of the spike variables to varying %MVC. 3) Investigate variation of the spike variables to the sustained contraction for 30 minutes. Quantitative results were showed and examined in point of practical clinical application.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.285-298
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• 2022

Continuous shrinkage priors, as well as spike and slab priors, have been widely employed for Bayesian inference about sparse regression coefficient vectors or covariance matrices. Continuous shrinkage priors provide computational advantages over spike and slab priors since their model space is substantially smaller. This is especially true in high-dimensional settings. However, variable selection based on continuous shrinkage priors is not straightforward because they do not give exactly zero values. Although few variable selection approaches based on continuous shrinkage priors have been proposed, no substantial comparative investigations of their performance have been conducted. In this paper, We compare two variable selection methods: a credible interval method and the sequential 2-means algorithm (Li and Pati, 2017). Various simulation scenarios are used to demonstrate the practical performances of the methods. We conclude the paper by presenting some observations and conjectures based on the simulation findings.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1246-1254
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• 2009

The reproducibility of initial value and change over time of surface EMG spike variables(MSA, MSF, MSS, MSD) was investigated in the biceps brachii muscle of 11 healthy subjects. Surface EMG signals were recorded during sustained isometric voluntary contractions for 30 seconds at three contraction levels, 20%, 50%, and 80%MVC, respectively. Each contraction was repeated three times in each of three different days for a total of nine contractions and 99 contractions per %MVC level across the eleven subjects. A total of 297 EMG signals across the different trials, days, subjects, and %MVC levels was saved for the subsequent analysis. The degree of reproducibility was investigated using the intraclass correlation coefficient(ICC) and the standard error of the mean(SEM) based upon the analysis of variance(ANOVA), Results for intercept showed higher reproducibility of the spike variables with about 60%-98% ICC than the variable(ARV, MNF) which had been analyzed before in other researches. And results for slope showed poor reproducibility of the spike variables with about 30%-70% ICC and they were comparable with the variables of other researches.

• Journal of the Korean Society of Propulsion Engineers
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• v.23 no.1
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• pp.124-131
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• 2019

A design study of gas turbine engine simulation duct was conducted to investigate the operating characteristics and control gain tunning of the Altitude Engine Test Facility(AETF). The simulation duct design involved testing variable spike nozzle and ISO standard choking nozzle to verify the measurements such as mass flow rate and thrust. The simulation duct air flow area was designed to satisfy Ma 0.4 at the aerodynamic interface plane(AIP) at engine design condition. The test conditions for verifying the AETF controls and measurement devices were deduced from 1D analysis and CFD calculation results. The spike-cone driving part was designed to withstand the applied aero-load, and satisfy the axial traversing speed of 10 mm/s at whole operation envelops.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.491-505
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• 2021

We consider linear regression models in high-dimensional settings (p ≫ n) and compare various classes of priors. The spike and slab prior is one of the most widely used priors for Bayesian regression models, but its model space is vast, resulting in a bad performance in finite samples. As an alternative, various continuous shrinkage priors, including the horseshoe prior and its variants, have been proposed. Although each of the above priors has been investigated separately, exhaustive comparative studies of their performance have been conducted very rarely. In this study, we compare the spike and slab prior, the horseshoe prior and its variants in various simulation settings. The performance of each method is demonstrated in terms of the regression coefficient estimation and variable selection. Finally, some remarks and suggestions are given based on comprehensive simulation studies.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.258-258
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• 2017

We received 73 wheat resources from CIMMYT for analysis of agricultural characteristics and qualities of them. We sowed 73 lines and 4 Korean varieties in fall and spring to compare differences of wheat traits between fall and spring seeding. The heading and maturity date of fall seeding lines has coming faster average 18 days than spring seeding lines. Fall seeding lines have more number of spike per $m^2$ and grain number of spike. Spike and awn length of spring seeding lines were shorter than fall seeding lines and stem length was variable to varieties in both condition. Protein contents of five of 73 lines, Jokyoung and Jopoom varieties decreased but the others increased. The sedimentation values of 34 lines include Jokyoung and falling number (F/N) of 44 lines include two varieties, Keumkang and Jokyoung, decreased but the others increased, respectively. Particle size was presented that most spring seeding lines were bigger. However, when it sowed in fall, the lightness (L) of wheat flour was lighter. Consequently, we confirmed when it was sowed in spring, agricultural traits and qualities of wheat got weak.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.103-118
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• 2024

The horseshoe prior is notably one of the most popular priors in sparse regression models, where only a small fraction of coefficients are nonzero. The parameter space of the horseshoe prior is much smaller than that of the spike and slab prior, so it enables us to efficiently explore the parameter space even in high-dimensions. However, on the other hand, the horseshoe prior has a high computational cost for each iteration in the Gibbs sampler. To overcome this issue, various MCMC algorithms for the horseshoe prior have been proposed to reduce the computational burden. Especially, Johndrow et al. (2020) recently proposes an approximate algorithm that can significantly improve the mixing and speed of the MCMC algorithm. In this paper, we compare (1) the traditional MCMC algorithm, (2) the approximate MCMC algorithm proposed by Johndrow et al. (2020) and (3) its variant in terms of computing times, estimation and variable selection performance. For the variable selection, we adopt the sequential clustering-based method suggested by Li and Pati (2017). Practical performances of the MCMC methods are demonstrated via numerical studies.

• Korean Journal of Applied Biomechanics
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• v.16 no.4
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• pp.125-134
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• 2006

The purpose of this study was to analyze the Biomechanical elements by looking at the differences on the motions of the right and left spikes of right-handed offense volleyball players, using 3D image analysis and force platform. For that purpose, spike motions of six male university volleyball players were recorded three times each using two 16mm high speed cameras and the speed of recording was set at 60 frames/sec. The coordinated raw data was leveled as 6Hz using low pass filtering method and the calculation of 3D coordinates was done by using a DLT (Direct Linear Transformation) method. Also KWON 3D program was used to analyze the variables. Through the experiments and research, the following results were found: That is, in case of the right spike, the required time from the toss to the impact, which affected the success rate of offense showed as longer and on the take-off, the exact timing to touch the ball was longer because the pace between right and left feet was wider, and also after the jump, the distance between the feet indicated shorter, than the left. In addition, the degree of somersault and horizontal adduction of shoulder joint was smaller and the degree of medial rotation of shoulder joint showed bigger than the left, so it indicated that it was not centered on the body, but by the arm with an axis of shoulder using a swing motion. After the impact, the speed of the ball indicated slower compared to the left spike.

• Environmental and Resource Economics Review
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• v.13 no.3
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• pp.417-440
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• 2004

For the behavior of the wholesale spot price, a regime switching model with time-varying transition probabilities was estimated using the data from the PJM (Pennsylvania-New Jersey-Maryland) market. By including the temperature as an explanatory variable in the transition probability equations, the threshold effect of changing regime is clearly enhanced. And hence the predictability of the price spikes was improved. This means that the model showed a very clear threshold effect, with a low probability of switching for low loads and low temperatures and a high probability for high loads and high temperatures. And temperature showed a clearer threshold effect than load does. This implies that weather-related contracts may help to hedge against the risk in the cost of buying electricity during a summer.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.445-455
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• 2022

In this paper, we introduce existing Bayesian methods for high-dimensional sparse regression models and compare their performance in various simulation scenarios. Especially, we focus on the variational Bayes approach proposed by Ray and Szabó (2021), which enables scalable and accurate Bayesian inference. Based on simulated data sets from sparse high-dimensional linear regression models, we compare the variational Bayes approach with other Bayesian and frequentist methods. To check the practical performance of the variational Bayes in logistic regression models, a real data analysis is conducted using leukemia data set.