• Communications for Statistical Applications and Methods
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• 제29권2호
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• pp.251-261
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• 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.

• Communications for Statistical Applications and Methods
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• 제26권2호
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• pp.175-189
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• 2019

The problem of the estimation of quantitative sensitive variable using the item sum technique (IST) on successive occasions has been discussed. IST difference, IST regression, and IST general class of estimators have been proposed to estimate quantitative sensitive variable at the current occasion in two occasion successive sampling. The proposed new estimators have been elaborated under Trappmann et al. (Journal of Survey Statistics and Methodology, 2, 58-77, 2014) as well as Perri et al. (Biometrical Journal, 60, 155-173, 2018) allocation designs to allocate long list and short list samples of IST. The properties of all proposed estimators have been derived including optimum replacement policy. The proposed estimators have been mutually compared under the above mentioned allocation designs. The comparison has also been conducted with a direct method. Numerical applications through empirical as well as simplistic simulation has been used to show how the illustrated IST on successive occasions may venture in practical situations.

• Communications for Statistical Applications and Methods
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• 제8권3호
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• pp.753-765
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• 2001

We suggest a quantitative conditional unrelated question model that can be used in obtaining more sensitive information. For whom say "yes" about the less 7han sensitive question .B we ask only about the more sensitive variable X. We extend our model to two sample case when there is no information about the true mean of the unrelated variable Y. Finally we compare the efficiency of our model with that of Greenberg et al.′s.

• 대한전자공학회논문지
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• 제25권12호
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• pp.1586-1593
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• 1988

A new control scheme for the variable-structure control system using new time-varying switching variable is presented in this paper. It is proposed to have new algorithm for reducing the reaching time on a switching hyperplane by modifying the Morgan's algorithm. From the results of the simulation, it is concluded the proposed control algorithm yields smaller control inputs (without disturbance) and ripples (with disturbance) than that obtained by Morgan's algorithm in the steady-state. This control algorithm can be applied to proper control systems having sensitive effects on disturbances, due to the robustness.

• 대한전기학회논문지
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• 제34권10호
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• pp.389-398
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• 1985

Recently maximum power demand of our country has become to be under the great in fluence of electric cooling and air conditioning demand which are sensitive to weather conditions. This paper presents the technique and algorithm to forecast the long-term maximum power demand considering the characteristics of electric power and weather variable. By introducing a weather load model for forecasting long-term maximum power demand with the recent statistic data of power demand, annual maximum power demand is separated into two parts such as the base load component, affected little by weather, and the weather sensitive load component by means of multi-regression analysis method. And we derive the growth trend regression equations of above two components and their individual coefficients, the maximum power demand of each forecasting year can be forecasted with the sum of above two components. In this case we use the coincident dry bulb temperature as the weather variable at the occurence of one-day maximum power demand. As the growth trend regression equation we choose an exponential trend curve for the base load component, and real quadratic curve for the weather sensitive load component. The validity of the forecasting technique and algorithm proposed in this paper is proved by the case study for the present Korean power system.

• Communications for Statistical Applications and Methods
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• 제16권1호
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• pp.169-183
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• 2009

The simplicity and wide application of Greenberg et al. (1971) prompts to propose a set of alternative estimators of population total for multi-character surveys that elicit simultaneous information on many. sensitive study variables. The proposed estimators take into account the already known rough value of the correlation coefficient between Y(the characteristic under study) and p(the measure of size). These estimators are biased, but it is expected that the extent of bias will be smaller, since the proposed estimators are suitable for situations in between those optimum for the usual estimators and the estimators based on multi-characters for no correlation. The relative efficiency of the proposed estimators has been studied under a super population model through empirical study. It has been found through simulation study that a choice of an unrelated variable in the Greenberg et al. (1971) model could be made based on its correlation with the auxiliary variable used at estimation stage in multi-character surveys.

• Journal of the Optical Society of Korea
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• 제8권2호
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• pp.83-89
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• 2004

A variable optical attenuator with a bending-sensitive fiber (BSF) that can be used in optical networks is developed. The refractive index profile of the BSF is divided into four regions which are inner core, center dip of inner core, outer core and clad. The 3-dimensional finite difference beam propagation method (3D FD-BPM) is utilized to find the characteristics of the BSF, so the mode profile of the BSF and optical power attenuation according to the bending are investigated, and the equivalent model of the BSF is made. By using this equivalent model of the BSF, the BSF is fabricated, and the refractive index profile of the BSF is measured, which is similar to refractive index profile of the proposed BSF. The fabricated variable optical fiber attenuator (VOFA) consists of the BSF in a rectangular rubber ring with a fixed bend radius (BR) in a steady state. The VOFA using the proposed BSF was able to attenuate the optical power by more than about －38 ㏈ at certain wavelengths (1540∼1560 nm) based on adjusting the mechanical pressure applied to the upper surface of the rectangular rubber ring with the bent BSF. In addition, the proposed VOFA produced an insertion loss of 0.68 ㏈, polarization dependent loss (PDL) of about 0.5 ㏈, and return loss of less than －60 ㏈.

• 한국시뮬레이션학회논문지
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• 제22권3호
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• pp.81-87
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• 2013

본 논문은 컴퓨터 해석프로그램인 SimulationX를 이용하여 굴삭기 주행모터의 내부에서 발생하는 서지압력의 저감 방법에 대하여 분석하는 연구이다. 설계민감도 해석을 통하여 설계상의 문제점을 파악하고 해결책에 접근하는 방법을 다룬다. 진행순서는 다음과 같다. 우선 현재 설계 된 주행모터에 장착된 밸브들의 동적거동을 분석하여 설계의 문제점을 찾아낸다. 그 후 많은 설계변수들 중 동적성능향상에 민감하다고 판단되는 변수를 도출하고 설계치수를 조정하여 동적성능 안정화의 경향을 살펴본다. 마지막으로 민감한 변수가 다수 일 경우 변수조합을 통해 효과적인 튜닝방법을 제안한다.

• 한국산학기술학회논문지
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• 제8권6호
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• pp.1325-1331
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• 2007

본 연구에서는 냉방운전 시 가변용량방식의 압축기를 적용한 시스템 에어컨의 냉방능력과 증발기 및 응축기의 온도특성을 조사하기 위해 압축기 운전율(10가지)과 실내외 온도(16가지)의 변화에 따른 시스템의 운전특성을 실험적으로 조사하였다. 시스템의 운전특성은 칼로리미터를 이용하여 측정되었다. 냉방능력은 실외온도가 낮아질수록 실내온도가 증가할수록 더 큰 값을 그리고 압축기 운전율이 증가할수록 냉방능력은 선형적으로 증가하였다. 응축온도는 실외온도 변화에 증발온도는 실내온도 변화에 더 민감하였다. 또한 압력-엔탈피선도를 이용하여 사이클의 운전특성을 분석하였다.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2009년도 정보통신설비 학술대회
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• pp.382-386
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• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.