• Journal of The Geomorphological Association of Korea
• /
• v.26 no.2
• /
• pp.1-14
• /
• 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
• /
• v.48 no.5
• /
• pp.661-679
• /
• 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
• /
• v.24 no.1
• /
• pp.125-133
• /
• 2013

In this paper, penalized binary logistic regression models are employed as statistical models for determining the discharge of 668 patients with a chief complaint of dyspnea based on 11 blood tests results. Specifically, the ridge model based on $L^2$ penalty and the Lasso model based on $L^1$ penalty are considered in this paper. In the comparison of prediction accuracy, our models are compared with the logistic regression models with all 11 explanatory variables and the selected variables by variable selection method. The results show that the prediction accuracy of the ridge logistic regression model is the best among 4 models based on 10-fold cross-validation.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.4
• /
• pp.643-648
• /
• 2012

There are lots of analysis to determine the relation between dependent variable and explanatory variables. Often the regression analysis is used to do this, and we can analyze the how much the explanatory variable can be related with dependent variable and how much the regression model can explain the data. But the validation check of regression model is usually determined by coefficient of determination. We should check the validation of regression coefficient with different methods. This paper introduces the method for validation check the regression coefficient using the jackknife regression and bootstrap regression in clinical data.

• Healthcare Informatics Research
• /
• v.24 no.4
• /
• pp.276-282
• /
• 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
• /
• v.54 no.4
• /
• pp.1439-1448
• /
• 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.

• Steel and Composite Structures
• /
• v.52 no.1
• /
• pp.1-14
• /
• 2024

This paper introduces a system allows for seismic isolation of the pallet from the rack in the down-aisle direction, occupies minimal vertical space (5 cm) and ±7.5 cm of deformation range. A conceptual model of the isolation system is presented, leading to a constitutive equation governing its behavior. A first experimental campaign studying the response of the isolation system's components was conducted to calibrate the parameters of its constitutive equation. A second experimental campaign evaluated the response of the isolation system with mass placed on it, subjected to cyclic loading. The results of this second campaign were compared with the numerical predictions using the pre-calibrated constitutive equation, allowing a double-blind validation of the constitutive equation of the isolation system. Finally, a numerical evaluation of the isolation system subjected to a synthetic earthquake of one component. This evaluation allowed verifying attributes of the proposed isolation system, such as its self-centering capacity and its effectiveness in reducing the absolute acceleration of the isolated mass and the shear load transmitted to the supporting beams of the rack.

• The Korean Journal of Food And Nutrition
• /
• v.37 no.4
• /
• pp.197-210
• /
• 2024

This study aims to develop a deep learning model to monitor rice serving amounts in institutional foodservice, enhancing personalized nutrition management. The goal is to identify the best convolutional neural network (CNN) for detecting rice quantities on serving trays, addressing balanced dietary intake challenges. Both a vanilla CNN and 12 pre-trained CNNs were tested, using features extracted from images of varying rice quantities on white trays. Configurations included optimizers, image generation, dropout, feature extraction, and fine-tuning, with top-1 validation accuracy as the evaluation metric. The vanilla CNN achieved 60% top-1 validation accuracy, while pre-trained CNNs significantly improved performance, reaching up to 90% accuracy. MobileNetV2, suitable for mobile devices, achieved a minimum 76% accuracy. These results suggest the model can effectively monitor rice servings, with potential for improvement through ongoing data collection and training. This development represents a significant advancement in personalized nutrition management, with high validation accuracy indicating its potential utility in dietary management. Continuous improvement based on expanding datasets promises enhanced precision and reliability, contributing to better health outcomes.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.746-749
• /
• 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.

• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.8
• /
• pp.834-841
• /
• 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.