• 한국수학교육학회지시리즈B:순수및응용수학
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• 제3권1호
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• pp.73-81
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• 1996

Multiple objective programming has been a popular research area since 1970. The pervasiveness of multiple objective in decision problems have led to explosive growth during the 1980's. Several approaches (interactive methods, feasible direction methods, criterion weight space methods, Lagrange multiplies methods, etc) have been developed for solving decision problems having multiple objectives. However there are still many mathematically challengings including multiple objective integer, nonlinear optimization problems which require further mathematically oriented research. (omitted)

• 대기
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• 제25권4호
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• pp.669-683
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• 2015

In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don't constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.

• Journal of Information Processing Systems
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• 제19권5호
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• pp.652-662
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• 2023

Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 추계 학술발표회 논문집
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• pp.13-17
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• 2005

In recent years, many people use R as a statistics system. R is frequently updated by many R project teams. We are interested in the method of multiple outlier detection and know that R is not supplied the method of multiple outlier detection. In this talk, we review these procedures for detecting multiple outliers and provide more efficient procedures combined with direct methods and indirect methods using R.

• Communications for Statistical Applications and Methods
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• 제3권2호
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• pp.1-11
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• 1996

The detection of multiple outliers in nonlinear regression models can be computationally not feasible. As a compromise approach, we consider the use of simulated annealing algorithm, an approximate approach to combinatorial optimization. We show that this method ensures convergence and works well in locating multiple outliers while reducing computational time.

• 한국멀티미디어학회논문지
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• 제20권6호
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• pp.893-910
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• 2017

In this paper, we propose a simple online multiple object (human) tracking method, LKDeep (Lucas-Kanade feature and Detection based Simple Online Multiple Object Tracker), which can run in fast online enough on CPU core only with acceptable tracking performance for embedded surveillance purpose. The proposed LKDeep is a pragmatic hybrid approach which tracks multiple objects (humans) mainly based on LK features but is compensated by detection on periodic times or on necessity times. Compared to other state-of-the-art multiple object tracking methods based on 'Tracking-By-Detection (TBD)' approach, the proposed LKDeep is faster since it does not have to detect object on every frame and it utilizes simple association rule, but it shows a good object tracking performance. Through experiments in comparison with other multiple object tracking (MOT) methods using the public DPM detector among online state-of-the-art MOT methods reported in MOT challenge [1], it is shown that the proposed simple online MOT method, LKDeep runs faster but with good tracking performance for surveillance purpose. It is further observed through single object tracking (SOT) visual tracker benchmark experiment [2] that LKDeep with an optimized deep learning detector can run in online fast with comparable tracking performance to other state-of-the-art SOT methods.

• Communications for Statistical Applications and Methods
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• 제3권2호
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• pp.53-67
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• 1996

Numerous classical as well as robust methods have been proposed in the literature for the detection of multiple outlier in multivariate data. The effectiveness and power of each of these methods have not been thoroughly investigated. In this paper we first reduce the vast number of outlier detection methods to a small number of viable ones. This reduction is based on previous work of other researches and on some theoretical arguments. Then we design and implement a Monte Carlo experiment for comparing these methods. The main goal of our study is to determine which methods are most powerful in the detection of multiple outlier and in dealing with the masking and swamping problems. The results of the Monte Carlo study indicate that two of the methods seem to hace better performances than the others for the detection of multiple outlier in multivariate data.

• 한국정보전자통신기술학회논문지
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• 제4권3호
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• pp.189-196
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• 2011

본 논문에서는 다중입출력 (MIMO : multiple-input multiple-output) 시스템에서 순차적 간섭 제거 기반 (SIC : successive interference cancellation) 신호 검출 기법의 성능을 분석한다. 고려되는 신호검출 기법들은 SIC 기법와 LR-SIC 기법이며, 이러한 신호 검출 기법들의 블록오류확률 (BLER; block error ratio) 성능을 나타내는 식을 유도 하고, 모의실험 결과를 통해 유도된 식과 성공적으로 일치함을 확인한다.

• 한국초등수학교육학회지
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• 제22권3호
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• pp.283-308
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• 2018

약수와 배수는 초 중등 교육과정에서 모두 다루어지는 주제이지만, 초등 수준에서 약수와 배수를 다룬 연구가 많지 않다. 특히 학생들이 최대공약수와 최소공배수를 구하는 방법의 의미를 제대로 알지 못한 채, 그 방법을 기계적으로 적용한다는 연구는 있는 반면, 구체적으로 어떻게 지도해야 하는가에 대한 연구는 찾아보기 어렵다. 이에 본 연구에서는 시각적 모델을 토대로 최대공약수와 최소공배수를 구하는 과정에서 의미를 강조한 지도방안을 도출한 후 4학년 1개 학급을 대상으로 수업을 실시한 결과를 분석하였다. 구체적으로 검사지와 면담을 바탕으로 학생들의 사고과정을 분석하였고, 추가적으로 현행 수학교과서로 약수와 배수를 학습한 5학년 학생들과의 차이를 살펴보았다. 분석 결과 최대공약수와 최소공배수를 구하는 과정에서 의미를 강조한 지도 방안은 초등학교 4학년 학생들이 최대공약수와 최소공배수를 구하는 방법을 개념적으로 이해하는데 긍정적인 영향을 주었다. 이와 같은 결과를 토대로 최대공약수와 최소공배수를 구하는 방법의 의미를 강조한 지도 방안에 대하여 시사점을 논의하였다.

• 한국정보과학회논문지:시스템및이론
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• 제27권3호
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• pp.337-344
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• 2000

본 논문에서는 다중자원(multiple resource)을 사용하는 시스템의 명세와 검증을 위한 프로세스 대수 ACSMR(Algebra of Communicating Shared Multiple Resources)을 정의한다. ACSMR은 프로세스 대수 기반의 정형기법(formal methods)인 ACSR에 다중자원의 개념을 확장한 것이다. 명세와 검증의 예로 실시간 시스템의 스케줄링 기법의 하나인 Earliest-Deadline-First(EDF)를 멀티프로세서하에서의 시스템의 행동 명세와 다중 포트를 가진 레지스터를 이용한 수퍼스칼라 프로세서의 타이밍 특성과 자원 제한을 묘사하기 위한 명세방법을 제시한다.