• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.33-40
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• 2012

This paper proposes a method to implement biological early warning systems(BEWS). This system generates periodically data event using a monitoring daemon and it extracts the feature parameters from this data sets. The feature parameters are derived with 6 variables, x/y coordinates, distance, absolute distance, angle, and fractal dimension. Specially by using the fractal dimension theory, the proposed algorithm define the input features represent the organism characteristics in non-toxic or toxic environment. And to find a moderate algorithm for learning the extracted feature data, the system uses an extended learning algorithm(UChoo) popularly used in machine learning. And this algorithm includes a learning method with the extended data expression to overcome the BEWS environment which the feature sets added periodically by a monitoring daemon. In this algorithm, decision tree classifier define class distribution information using the weight parameter in the extended data expression. Experimental results show that the proposed BEWS is available for environmental toxicity detection.

• Journal of Biomedical Engineering Research
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• v.37 no.2
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• pp.61-67
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• 2016

This paper presented a method for random forest based the arrhythmia classification using both heart rate (HR) and heart rate variability (HRV) features. We analyzed the MIT-BIH arrhythmia database which contains half-hour ECG recorded from 48 subjects. This study included not only the linear features but also non-linear features for the improvement of classification performance. We classified abnormal ECG using mean_NN (mean of heart rate), SD1/SD2 (geometrical feature of poincare HRV plot), SE (spectral entropy), pNN100 (percentage of a heart rate longer than 100 ms) affecting accurate classification among combined of linear and nonlinear features. We compared our proposed method with Neural Networks to evaluate the accuracy of the algorithm. When we used the features extracted from the HRV as an input variable for classifier, random forest used only the most contributed variable for classification unlike the neural networks. The characteristics of random forest enable the dimensionality reduction of the input variables, increase a efficiency of classifier and can be obtained faster, 11.1% higher accuracy than the neural networks.

• Health Policy and Management
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• v.14 no.1
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• pp.89-120
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• 2004

The purpose of this empirical study is to test hypotheses in order to identify the cost drivers that drive indirect costs in general hospitals in Korea. In various cases' studies, it has been suggested that overhead costs are driven by volume and complexity variables, how they are structurally related and how the cost impacts of these variables can be A unique feature of the research is the treatment of complexity as an endogenous variable. It is hypothesized that level of hospital complexity in terms of the number of services provided(i.e., “breath" complexity) and the intensity of individual estimated in practice. overhead services(ie., “depth" complexity) are simultaneous determined with the level of costs needed to support the complexity. Data used in this study were obtained from the Database of Korean Health Industry Development Institute, Health Insurance Review Agency and analyzed using simultaneous equation model, path model. The results found those volume and complexity variables are all statistically signi-ficance drivers of general hospital overhead costs. This study has documented that the level of service complexity is a significant determinant of hospital overhead costs, caution should be exercised in interpreting this as supportive of the cost accounting procedures associated with ABC. with ABC.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.793-808
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• 2017

We compare various variable screening methods on multiclass classification problems when the data is ultrahigh-dimensional. Two different approaches were considered: (1) pairwise extension from binary classification via one versus one or one versus rest comparisons and (2) direct classification of multiclass responses. We conducted extensive simulation studies under different conditions: heavy tailed explanatory variables, correlated signal and noise variables, correlated joint distributions but uncorrelated marginals, and unbalanced response variables. We then analyzed real data to examine the performance of the methods. The results showed that model-free methods perform better for multiclass classification problems as well as binary ones.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.898-901
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• 1988

Up to now, using adaptive control method, Identification deals with system whose entire state variables and prameters are accessible for measurement. In practical situations, all the state variables cannot be measured and it is impossible to directly apply since the parameters of the system are unknown. Therefore, in this paper, using only input-output data, such a model of the system is not available since the parameters of the system are unknown. this leads to the concept of an adptive observer in which both the parameters and the state variable of the system are identified simultaniously. Lyapunov's direct method and Kalman-Yakubovich (K-Y) lemma are employed to ensure the stability of this schemes. The feature is that the signal and adaptive gain which is generated from filter is imposed upon feedback vector and then state variables and the unknown parameters can be identified. To show the usefulness of the proposed schemes, computer simulation result of unknown second-order system shows the effectiveness of the proposed schems.

• Steel and Composite Structures
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• v.8 no.5
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• pp.343-359
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• 2008

In the present study, an efficient method for the optimum design of three-dimensional (3D) steel framed structures is proposed. In this method, in addition to choosing the best position of columns based on architectural requirements, the optimum cross-sectional dimensions of elements are determined. The preliminary design variables are considered as the number of columns in structural plan, which are determined by a direct optimization method suitable for discrete variables, without requiring the evaluation of derivatives. After forming the geometry of structure, the main variables of the cross-sectional dimensions are evaluated, which satisfy the design constraints and also achieve the least-weight of the structure. To reduce the number of finite element analyses and the overall computational time, a new third order approximate function is introduced which employs only the diagonal elements of the higher order derivatives matrices. This function produces a high quality approximation and also, a robust optimization process. The main feature of the proposed techniques that the higher order derivatives are established by the first order exact derivatives. Several examples are solved and efficiency of the new approximation method and also, the proposed method for the best position of columns in 3D steel framed structures is discussed.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.38-50
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• 2018

Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.

• Journal of Distribution Science
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• v.22 no.7
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• pp.1-11
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• 2024

Purpose: Shopee, Indonesia's most frequently visited marketplace in November 2023, had 427.2 million visits. Supported by the live streaming feature, Shopee has become the most widely used online shopping platform, with an 83.4% market share in 2022. Several factors, such as brand trust and promotions, have significantly influenced Shopee's dominance and consumer purchasing decisions. This research aims to investigate the effect of cosmetic product distribution, brand trust, and promotions on purchasing decisions, considering gender and age as control variables. Research design, data and methodology: A quantitative approach using a survey research method was employed with a sample of 150 respondents, who were followers of the Shopee ESQA Cosmetics account, obtained through the Yamane formula. Data was collected via an online questionnaire. The data analysis technique used in this study was PLS-SEM with Smart PLS software. The results of this research indicate a significant effect of the distribution of cosmetics products, brand trust, promotions, gender, and age as control variables on the purchase decision variable. Conclusions: The distribution of cosmetic products, brand trust and promotions have a positive and significant impact on purchase decisions during live streaming on Shopee, and control variables (gender and age 36-45) have a positive and significant influence on purchase decisions.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.671-681
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• 2019

Studies on predicting arrhythmia using machine learning have been actively conducted with increasing number of arrhythmia patients. Existing studies have predicted arrhythmia based on multivariate data of feature variables extracted from RR interval data at a specific time point. In this study, we consider that the pattern of the heart state changes with time can be important information for the arrhythmia prediction. Therefore, we investigate the usefulness of predicting the arrhythmia with multivariate time series data obtained by extracting and accumulating the multivariate vectors of the feature variables at various time points. When considering 1-nearest neighbor classification method and its ensemble for comparison, it is confirmed that the multivariate time series data based method can have better classification performance than the multivariate data based method if we select an appropriate time series distance function.

• Journal of Physiology & Pathology in Korean Medicine
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• v.26 no.1
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• pp.128-132
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• 2012

In this paper, we performed a quantitative analysis of face color according to the health status of four constitution types. 205 Korean male in age from 65 to 80 were participated in this study and 85 subjects were finally selected for the analysis. Imaging process techniques were employed to extract feature variables associated with face color from a frontal facial image. Using the extracted feature variables, the correlations between face color and health status, face color and health status in each constitution type, and face color and four constitution types in heath status group were investigated. As the result, it was observed that the face color of healthy group contained more red component and less blue component than unhealthy group. For each constitution type, the face parts showing a significant difference according to health status were different. This is the first work which reports the correlation between the face color and health status of four constitution types with a objective method, and the numerical data for the face color according to the health status of four constitution types will be an objective standard to diagnose a patient's health status.