• Journal of the Korean Data and Information Science Society
• /
• v.19 no.3
• /
• pp.967-977
• /
• 2008

We consider densities of quotient Y/X and ratio X/(X+Y), and estimation of reliability P(Y

• Journal of the Korean Data and Information Science Society
• /
• v.7 no.2
• /
• pp.219-226
• /
• 1996

Basic objective in cluster analysis is to discover natural groupings of items or variables. In general, variable clustering was conducted based on some similarity measures between variables which have binary characteristics. We propose a variable clustering method when variables have more categories ordered in some sense. We also consider some measures of association as a similarity between variables. Numerical example is included.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.3
• /
• pp.555-563
• /
• 2004

In classification and discrimination, we often face with incomplete group variable arising typically from many missing values and/or incredible cases. This paper suggests the use of K-means clustering for pre-adjusting incompleteness and in turn classification based on generalized statistical distance is performed. For illustrating the proposed procedure, simulation study is conducted comparatively with CART in data mining and traditional techniques which are ignoring incompleteness of group variable. Simulation study manifests that our methodology out-performs.

• Journal of Korean Society of Transportation
• /
• v.27 no.5
• /
• pp.51-62
• /
• 2009

Variable message signs (VMS) are a useful tool for providing real-time traffic information to drivers. In particular, effective warning information provision leading to safer driving would be an important countermeasure to prevent traffic accidents. The purpose of this study was to identify users' preferences for traffic safety warning information formats. A variety of warning information scenarios using text and pictograms were devised and investigated for the purpose of selecting more effective methods to provide warning information. A portable variable message sign (PVMS) was used to evaluate users' preferences. The results of this study can be used for designing better warning information for the enhancement of traffic safety.

• The Korean Journal of Applied Statistics
• /
• v.21 no.2
• /
• pp.301-312
• /
• 2008

Mutual information is a measure of association of explanatory variable for predicting target variable. It is used for variable ranking and variable subset selection. This study is about the Sample-spacing approach which can be used for the estimation of mutual information from data consisting of continuous explanation variables and categorical target variable without estimating a joint probability density function. The results of Monte-Carlo simulation and experiments with real-world data show that m = 1 is preferable in using Sample-spacing.

• Communications for Statistical Applications and Methods
• /
• v.29 no.2
• /
• pp.251-261
• /
• 2022

As AI has a wide range of influence on human social life, issues of transparency and ethics of AI are emerging. In particular, it is widely known that due to the existence of historical bias in data against ethics or regulatory frameworks for fairness, trained AI models based on such biased data could also impose bias or unfairness against a certain sensitive group (e.g., non-white, women). Demographic disparities due to AI, which refer to socially unacceptable bias that an AI model favors certain groups (e.g., white, men) over other groups (e.g., black, women), have been observed frequently in many applications of AI and many studies have been done recently to develop AI algorithms which remove or alleviate such demographic disparities in trained AI models. In this paper, we consider a problem of using the information in the sensitive variable for fair prediction when using the sensitive variable as a part of input variables is prohibitive by laws or regulations to avoid unfairness. As a way of reflecting the information in the sensitive variable to prediction, we consider a two-stage procedure. First, the sensitive variable is fully included in the learning phase to have a prediction model depending on the sensitive variable, and then an imputed sensitive variable is used in the prediction phase. The aim of this paper is to evaluate this procedure by analyzing several benchmark datasets. We illustrate that using an imputed sensitive variable is helpful to improve prediction accuracies without hampering the degree of fairness much.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.7 no.4
• /
• pp.301-306
• /
• 2007

The people information is distributed in various forms such as database, web page, text, and so on, where the world wide web is one of the main sources of publicly-available people information. It has a characteristic that the information on people is intrinsically temporal. Therefore, the reconstruction of the information is needed for an individual or a company to use it efficiently. In order to maintain or manage the temporal people information, it must distinguish the variable information from invariable information of people. In this paper, we propose a method that constructs an ontology based on events to manage the variable people information efficiently. In addition, we present a system which reconstructs people information that satisfies the users' demand with the ontology.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.31A no.6
• /
• pp.149-158
• /
• 1994

Through high level synthesis, designers can obtain the precious information on the area and speed trade-offs as well as synthesized datapaths from behavioral design descriptions. While previous researches were concentrated on the synthesis of pipelined, datapaths with fixed DII (Data Initiation Interval) by inserting delay elements where needed, we propose a novel methodology of synthesizing pipeline structures with variable DIIs. Determining the time-overlapping of pipeline stages with variable DIIs, the proosed algorithm performs scheduling and module allocation using the time-overlapping information. Experimental results show that significant improvement can be achieved both in speed and in area.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.6
• /
• pp.1653-1660
• /
• 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.

• Journal of Power Electronics
• /
• v.14 no.6
• /
• pp.1243-1253
• /
• 2014

The phase inductance of a switch reluctance motor (SRM) is significantly nonlinear. With different saturation conditions, the phase inductance shape is clearly changed. This study focuses on the relationship between coefficient and current in an inductance model with ignored harmonics above the order of 3. A position estimation method based on the variable coefficient inductance model is proposed in this paper. A four-quadrant sensorless control system of the SRM drive is constructed based on the relationship between variable coefficient inductance and rotor position. The proposed algorithms are implemented in an experimental SRM test setup. Experimental results show that the proposed method estimates position accurately in operating two/four-quadrants. The entire system also has good static and dynamic performance.