• 한국지형학회지
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• 제26권2호
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• pp.1-14
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• 2019

This study analyzed the relativity between block stream and talus distributions by employing a likelihood ratio approach. Possible distribution sites for each debris slope landform were extracted by applying a spatial integration model, in which we combined fuzzy set model, Bayesian predictive model, and logistic regression model. Moreover, to verify model performance, a success rate curve was prepared by cross-validation. The results showed that elevation, slope, curvature, topographic wetness index, geology, soil drainage, and soil depth were closely related to the debris slope landform sites. In addition, all spatial integration models displayed an accuracy of over 90%. The accuracy of the distribution potential area map of the block stream was highest in the logistic regression model (93.79%). Eventually, the accuracy of the distribution potential area map of the talus was also highest in the logistic regression model (97.02%). We expect that the present results will provide essential data and propose methodologies to improve the performance of efficient and systematic micro-landform studies. Moreover, our research will potentially help to enhance field research and topographic resource management.

• Structural Engineering and Mechanics
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• 제48권5호
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• pp.661-679
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• 2013

In this work, the safety performance of a commercial motorcycle helmet already placed on the market is assessed. The assessed motorcycle helmet is currently homologated by several relevant motorcycle standards. Impacts including translational and rotational motions are accurately simulated through a finite element numerical framework. The developed model was validated against experimental results: firstly, a validation concerning the constitutive model for the expanded polystyrene, the material responsible for energy absorption during impact; secondly, a validation regarding the acceleration measured at the headform's centre of gravity during the linear impacts defined in the ECE R22.05 standard. Both were successfully validated. After model validation, an oblique impact was simulated and the results were compared against head injury thresholds in order to predict the resultant head injuries. From this comparison, it was concluded that brain injuries such as concussion and diffuse axonal injury may occur even with a helmet certified by the majority of the motorcycle helmet standards. Unfortunately, these standards currently do not contemplate rotational components of acceleration. Conclusion points out to a strong recommendation on the necessity of including rotational motion in forthcoming motorcycle helmet standards and improving the current test procedures and head injury criteria used by the standards, to improve the safety between the motorcyclists.

• Journal of the Korean Data and Information Science Society
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• 제24권1호
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• pp.125-133
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• 2013

이 논문에서는 호흡곤란을 주호소로 내원한 668명의 환자를 대상으로 11개 혈액검사 결과를 이용하여 퇴원여부를 결정하는 벌점 이항 로지스틱 회귀 기반 통계모형을 유도하였다. 구체적으로 $L^2$ 벌점에 근거한 능형 모형과 $L^1$ 벌점에 근거한 라소 모형을 고려하였다. 이 모형의 예측력 비교 대상으로는 일반 로지스틱 회귀의 11개 전체 변수를 사용한 모형과 변수선택된 모형이 사용되었다. 10-묶음 교차타당성 (10-fold cross-validation) 비교 결과 능형 모형의 예측력이 우수한 것으로 나타났다.

• Journal of the Korean Data and Information Science Society
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• 제23권4호
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• pp.643-648
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• 2012

여러 임상자료를 이용하여 반응변수와 설명변수간의 관계를 규명하는 분석이 많이 이루어지고 있다. 이를 위해서 회귀분석이 흔히 사용되고 있으며, 이를 통해 설명변수가 반응변수를 얼마나 설명하는지 또한 모형이 얼마나 자료에 적합한지에 대해 분석하고 있다. 그러나 임상자료로 분석된 회귀모형에 대한 타당성 확인은 대부분 분석된 회귀모형이 얼마나 자료를 설명하는가를 나타내는 결정계수만을 살펴보는 것에 그치고 있다. 결정계수 이외의 다른 방법으로도 분석된 회귀모형의 회귀계수에 대한 타당성을 확인할 필요가 있다. 따라서 본 논문에서는 잭나이프 회귀분석과 붓스트랩 회귀분석을 이용하여 임상자료로 분석한 회귀모형의 회귀계수에 대한 타당성을 확인하는 방법을 소개하고자 한다.

• Healthcare Informatics Research
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• 제24권4호
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• pp.276-282
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• 2018

Objectives: Due to the uncontrolled increase of the mobile health applications and their scarce use by elderly for reason of absence credibility of measurements by lack scientific support, the aim of this study was to evaluate the differences between the biophysical measurements based on standard instrument against a mobile application using controlled experiments with elderly to propose an effective validation model of the developed apps. Methods: The subjects of the study (50 people) were elderly people who wanted to check their weight and cardiac status. For this purpose, two mobile applications were used to measure energy expenditure based on physical activity (Activ) and heart rate (SMCa) during controlled walking at specific speeds. Minute-by-minute measurements were recorded to evaluate the average error and the accuracy of the data acquired through confidence intervals by means of statistical analysis of the data. Results: The experimental results obtained by the Activ/SMCa apps showed a consistent statistical similarity with those obtained by specialized equipment with confidence intervals of 95%. All the subjects were advised and trained on the use of the applications, and the initial registration of data to characterize them served to significantly affect the perceived ease of use. Conclusions: This is the first model to validate a health-app with elderly people allowed to demonstrate the anthropometric and body movement differences of subjects with equal body mass index (BMI) but younger. Future studies should consider not only BMI data but also other variables, such as age and usability perception factors.

• Nuclear Engineering and Technology
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• 제54권4호
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• pp.1439-1448
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• 2022

Background: We investigated the feasibility of in vitro radiosensitivity prediction with gene expression using deep learning. Methods: A microarray gene expression of the National Cancer Institute-60 (NCI-60) panel was acquired from the Gene Expression Omnibus. The clonogenic surviving fractions at an absorbed dose of 2 Gy (SF2) from previous publications were used to measure in vitro radiosensitivity. The radiosensitivity prediction model was based on the convolutional neural network. The 6-fold cross-validation (CV) was applied to train and validate the model. Then, the leave-one-out cross-validation (LOOCV) was applied by using the large-errored samples as a validation set, to determine whether the error was from the high bias of the folded CV. The criteria for correct prediction were defined as an absolute error<0.01 or a relative error<10%. Results: Of the 174 triplicated samples of NCI-60, 171 samples were correctly predicted with the folded CV. Through an additional LOOCV, one more sample was correctly predicted, representing a prediction accuracy of 98.85% (172 out of 174 samples). The average relative error and absolute errors of 172 correctly predicted samples were 1.351±1.875% and 0.00596±0.00638, respectively. Conclusion: We demonstrated the feasibility of a deep learning-based in vitro radiosensitivity prediction using gene expression.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.746-749
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• 2002

This paper is introducing a radio control helicopter as a new platform of ground truth measurement. This helicopter is normally used for spraying an agricultural chemical. It can do pinpoint hovering and programing flight using DGPS etc., A spectrometer with dual port can measure ground surface and white reference plate at the same time. And it can also take digital images by digital camera. It is needed to collect ground reflectance information as satellite sensor footprint size for satellite data validation. Generally it is possible to get such ground reflectance by an airplane measurement. But it is high cost and not so easy to make a measurement by airplane. Developed validation system can provide such ground reflectance in low cost and easy.

• 제어로봇시스템학회논문지
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• 제12권8호
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• pp.834-841
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• 2006

Recently, many enterprises are installing AMSs(Automated Manufacturing Systems) for their competitive advantages. As the level of automation increases, proper design and validation of control logic is a imperative task for the successful operation of AMSs. However, current discrete event simulation methods mainly focus on the performance evaluation. As a result, they lack the modeling capabilities for the detail logic of automated manufacturing system controller. Proposed in this paper is a method of validation of the controller logic for automated material handling system using an object-oriented design and simulation. Using this method, FA engineers can validate the controller logic easily in earlier stage of system design, so they can reduce the time for correcting the logic errors and enhance the productivity of control program development Generated simulation model can also be used as a communication tool among FA engineers who have different experiences and disciplines.

• International Journal of Aeronautical and Space Sciences
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• 제18권3호
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• pp.498-511
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• 2017

This work conducts a validation study for loads analysis of the UH-60A slowed rotor at high advance ratios. The nonlinear flexible multibody dynamics analysis code, DYMORE II, is used with a freewake model for the rotorcraft comprehensive analysis. Wind tunnel test data of airloads and structural loads of a full-scale UH-60A slowed rotor are used for this validation study. This analysis predicts well the thrust reversal phenomenon at the advance ratio of 1.0. The section airloads such as normal forces and pitching moments and the oscillatory blade structural moments in this analysis are compared well or moderately with the measured data, although the higher harmonics components of blade torsion moments are not captured well. This validation study assesses the prediction accuracy and investigates the unique aeromechanics characteristics of a slowed rotor at high advance ratio.

• Journal of the Korean Data and Information Science Society
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• 제27권6호
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• pp.1653-1660
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• 2016

Smoothly clipped absolute deviation (SCAD) penalty is known to satisfy the desirable properties for penalty functions like as unbiasedness, sparsity and continuity. In this paper, we deal with the regression function estimation and variable selection based on SCAD penalized censored regression model. We use the local linear approximation and the iteratively reweighted least squares algorithm to solve SCAD penalized log likelihood function. The proposed method provides an efficient method for variable selection and regression function estimation. The generalized cross validation function is presented for the model selection. Applications of the proposed method are illustrated through the simulated and a real example.