• Journal of the Ergonomics Society of Korea
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• v.30 no.6
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• pp.715-725
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• 2011

Objective: This study is aimed at developing Articulated Human Models(AHM) using superquadrics to improve the geometric accuracy of the body shape. Background: The previous work presents the AHM with geometrical simplification such as ellipsoids to improve analysis efficiency. However, because of the simplicity, their physical properties such as a center of mass and moment of inertia are computed with errors compared to their actual values. Method: This paper introduces a three steps method to present the AHM with superquadrics. First, a 3D whole body scan data are divided into 17 body segments according to body joints. Second, superquadric fitting is employed to minimize the Euclidean distance between body segments and superquadrics. Finally, Fee-Form Deformation is used to improve accuracy over superquadric fitting. Results: Our computational experiment shows that the superquadric models give better accuracy of dynamic analysis than that of ellipsoid ones. Conclusion: We generate the AHM composed of 17 superquadrics and 16 joints using superquadric fitting. Application: The AHM using superquadrics can be used as the base model for dynamics and ergonomics applications with better accuracy because it presents the human motion effectively.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.6
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• pp.500-505
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• 2000

This paper deals with wafer temperature uniformity control essential in rapid thermal processing (RTP). One of the important control objectives of RTP is to keep the temperature over the wafer surface as uniformly as possible. For this, a discrete time state equation around the operating point is first identified by using the subspace fitting method, and a multivariable LQG(Linear Quadratic Gaussian) controller is designed based on the identified model. Simulation and experimental results show improvement in temperature uniformity over the conventional PID method.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.41-47
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• 2008

Cluster based fuzzy model tree has certain drawbacks to decrease performance of testinB data when over-fitting of training data exists. To reduce the sensitivity of performance due to over-fitting problem, we proposed a modified cluster based fuzzy model tree with node information. To construct model tree, cluster centers are calculated by fuzzy clustering method using all input and output attributes in advance. And then, linear models are constructed at internal nodes with fuzzy membership values between centers and input attributes. In the prediction step, membership values are calculated by using fuzzy distance between input attributes and all centers that passing the nodes from root to leaf nodes. Finally, data prediction is performed by the weighted average method with the linear models and fuzzy membership values. To show the effectiveness of the proposed method, we have applied our method to various dataset. Under various experiments, our proposed method shows better performance than conventional cluster based fuzzy model tree.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.57-65
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• 2018

PURPOSES : The study aims to predict the service life of national highway asphalt pavements through deep learning methods by using maintenance history data of the National Highway Pavement Management System. METHODS : For the configuration of a deep learning network, this study used Tensorflow 1.5, an open source program which has excellent usability among deep learning frameworks. For the analysis, nine variables of cumulative annual average daily traffic, cumulative equivalent single axle loads, maintenance layer, surface, base, subbase, anti-frost layer, structural number of pavement, and region were selected as input data, while service life was chosen to construct the input layer and output layers as output data. Additionally, for scenario analysis, in this study, a model was formed with four different numbers of 1, 2, 4, and 8 hidden layers and a simulation analysis was performed according to the applicability of the over fitting resolution algorithm. RESULTS : The results of the analysis have shown that regardless of the number of hidden layers, when an over fitting resolution algorithm, such as dropout, is applied, the prediction capability is improved as the coefficient of determination ($R^2$) of the test data increases. Furthermore, the result of the sensitivity analysis of the applicability of region variables demonstrates that estimating service life requires sufficient consideration of regional characteristics as $R^2$ had a maximum of between 0.73 and 0.84, when regional variables where taken into consideration. CONCLUSIONS : As a result, this study proposes that it is possible to precisely predict the service life of national highway pavement sections with the consideration of traffic, pavement thickness, and regional factors and concludes that the use of the prediction of service life is fundamental data in decision making within pavement management systems.

• The Journal of Information Systems
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• v.28 no.3
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• pp.249-276
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• 2019

Purpose The purpose of this study is to develop a prediction model and decision rules for the elderly's suicidal ideation based on the Korean Welfare Panel survey data. By utilizing this data, we obtained many decision rules to predict the elderly's suicide ideation. Design/methodology/approach This study used classification analysis to derive decision rules to predict on the basis of decision tree technique. Weka 3.8 is used as the data mining tool in this study. The decision tree algorithm uses J48, also known as C4.5. In addition, 66.6% of the total data was divided into learning data and verification data. We considered all possible variables based on previous studies in predicting suicidal ideation of the elderly. Finally, 99 variables including the target variable were used. Classification analysis was performed by introducing sampling technique through backward elimination and data balancing. Findings As a result, there were significant differences between the data sets. The selected data sets have different, various decision tree and several rules. Based on the decision tree method, we derived the rules for suicide prevention. The decision tree derives not only the rules for the suicidal ideation of the depressed group, but also the rules for the suicidal ideation of the non-depressed group. In addition, in developing the predictive model, the problem of over-fitting due to the data imbalance phenomenon was directly identified through the application of data balancing. We could conclude that it is necessary to balance the data on the target variables in order to perform the correct classification analysis without over-fitting. In addition, although data balancing is applied, it is shown that performance is not inferior in prediction rate when compared with a biased prediction model.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.11
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• pp.445-452
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• 2017

Electric pole is a supporting beam used for power transmission/distribution which accelerometer are used for measuring a external force. The meteorological condition has various effects on the external forces of electric pole. One of them is the elasticity change of the aerial wire. It is very important to perform modelling. The acceleration sensor is converted into a pitch and a roll angle. The meteorological condition has a high correlation between variables, and selecting significant explanatory variables for modeling may result in the problem of over-fitting. We constructed high deviance explained model considering multicollinearity using the Generalized Additive Model which is one of the machine learning methods. As a result of the Variation Inflation Factor Test, we selected and fitted the significant variable as temperature, precipitation, wind speed, wind direction, air pressure, dewpoint, hours of daylight and cloud cover. It was noted that the Hours of daylight, cloud cover and air pressure has high explained value in explonatory variable. The average coefficient of determination (R-Squared) of the Generalized Additive Model was 0.69. The constructed model can help to predict the influence on the external forces of electric pole, and contribute to the purpose of securing safety on utility pole.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.12 s.354
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• pp.33-39
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• 2006

An empirical nonlinear model with intrinsic nonlinear elements has been newly developed to predict the RF nonlinear characteristics of nano-scale bulk MOSFET accurately over the wide bias range. Using an extraction method suitable for nano-scale MOSFET, the bias-dependent data of intrinsic model parameters have been accurately obtained from measured S-parameters. The intrinsic nonlinear capacitance and drain current equations have been empirically obtained through 3-dimensional curve-fitting to their bias-dependent curves. The modeled S-parameters of 60nm MOSFET have good agreements with measured ones up to 20GHz in the wide bias range, verifying the accuracy of the nano-scale MOSFET model.

• Journal of Fashion Business
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• v.22 no.3
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• pp.122-142
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• 2018

In the virtual fitting system, the use of a 3D avatar is not a simple garment model, but it should be able to reproduce the size and shape of the customer using a fitting system. Although various virtual fitting systems have their own 3D avatar sizing systems and provide 3D avatars that match the size of the customer, there are limitations in realizing the actual body shape in actual use by the consumer. The purpose of this study is to realize a 3D avatar with excellent size and conformity for customer use. Therefore, this study aims to provide basic data for the formation of a 3D standard avatar of Korean women aged in their 20's, by comparing and analyzing the degree of the consumer user friendly system change of a body type, and the consumer's ability in selecting a consumer representative body type. Based on the survey data of 'Size Korea' conducted from 2004 to 2015 at three times, we examined the change of body shape over 10 years. Then, based on the results of 6th and 7th data, 4 factors of the concurrent body shape change of women of the consumer demographic studied were selected through the use of a factor analysis. Following this analysis, the 4 extracted factors were clustered again and finally released 7 representative body types, which were obtained based on height and weight. The size of each representative figure is derived by the use of a regression analysis, and it is used as a basic data for 3D avatar formation of the virtual fitting system.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.21 no.2
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• pp.63-73
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• 2017

Segmenting the image into multiple regions is at the core of image processing. Many segmentation formulations of an images with multiple regions have been suggested over the years. We consider segmentation algorithm based on the multi-phase level set method in this work. Proposed method gives the best result upon other methods found in the references. Moreover it can segment images with intensity inhomogeneity and have multiple junction. We extend our method (GLIF) in [T. Dultuya, and M. Kang, Segmentation with shape prior using global and local image fitting energy, J.KSIAM Vol.18, No.3, 225-244, 2014.] using a multiphase level set formulation to segment images with multiple regions and junction. We test our method on different images and compare the method to other existing methods.

• Journal of Korean Society of Water and Wastewater
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• v.16 no.6
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• pp.710-716
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• 2002

A General Pipe Break Prediction Model that incorporates linear and exponential models in its form is developed. The model is capable of fitting pipe break trends that have linear, exponential or in between of linear and exponential trend by using a weighting factor. The weighting factor is adjusted to obtain a best model that minimizes the sum of squared errors of the model. The model essentially plots a best curve (or a line) passing through "cumulative number of pipe breaks" versus "break times since installation of a pipe" data points. Therefore, it prevents over-predicting future number of pipe breaks compared to the conventional exponential model. The optimal replacement time equation is derived by using the Threshold Break Rate equation by Loganathan et al. (2002).