• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2001년도 춘계 학술대회 논문집
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• pp.169-169
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• 2001

With the aid of fast computing power, resampling techniques are being introduced for simulation output analysis (SOA). Autocorrelation among the output from discrete-event simulation prohibit the direct application of resampling schemes (Threshold bootstrap, Binary bootstrap, Stationary bootstrap, etc) extend its usage to time-series data such as simulation output. We present a new method for inference from a regenerative process, regenerative bootstrap, that equals or exceeds the performance of classical regenerative method and approximation regeneration techniques. Regenerative bootstrap saves computation time and overcomes the problem of scarce regeneration cycles. Computational results are provided using M/M/1 model.

• 정보처리학회논문지B
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• 제16B권3호
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• pp.247-256
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• 2009

대부분의 잠정적 적합피드백기법들은 질의에 대해 검색된 상위검색문서들이 적합하다고 가정하고, 그 문서들을 질의 확장을 위한 피드백 문서로 이용하고 있다. 그러나 초기검색결과에는 상당한 양의 부적합 문서를 포함하고 있는 것이 현실이다. 이 논문에서는 보다 좋은 피드백 문서를 선택하기 위해서 중첩클러스터를 이용한 피드백문서의 재샘플링 기법을 제안한다. 주요 아이디어는 질의 중심적인 초기검색문서집합에 대해서 중첩이 허용된 문서클러스터를 이용하여 문서들 사이의 관계를 반영하여 질의에 핵심역할을 하는 지배적 문서를 찾고, 이 문서들을 반복적으로 피드백 하여 질의가 내포하는 핵심 주제를 강조하는 것이다. 대규모 실험집합인 TREC GOV2와 WT10g에 대한 실험비교에서, 최근 잠정적 적합피드백 기법들 중에서 가장 좋은 성능을 보이고 있는 적합모델보다 재샘플링기법이 우수한 성능향상을 보였다. 제안기법에 대한 검증을 위해서 피드백문서에 포함된 적합문서의 정도를 나타내는 적합밀도를 측정하였다. 재샘플링 기법이 TREC 실험집합에 대해서 적합모델에 비해 높은 적합밀도를 보였고, 이 결과 적합피드백에서 검색성능을 향상시키게 되었다. 이는 제안 기법이 잠정적 적합피드백에서 유효한 방법임을 알 수 있다.

• 치과방사선
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• 제28권2호
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• pp.387-413
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• 1998

Image resampling is of particular interest in digital radiology. When resampling an image to a new set of coordinate, there appears blocking artifacts and image changes. To enhance image quality, interpolation algorithms have been used. Resampling is used to increase the number of points in an image to improve its appearance for display. The process of interpolation is fitting a continuous function to the discrete points in the digital image. The purpose of this study was to determine the effects of the seven interpolation functions when image resampling in digital periapical images. The images were obtained by Digora, CDR and scanning of Ektaspeed plus periapical radiograms on the dry skull and human subject. The subjects were exposed to intraoral X-ray machine at 60kVp and 70 kVp with exposure time varying between 0.01 and 0.50 second. To determine which interpolation method would provide the better image, seven functions were compared; (1) nearest neighbor (2) linear (3) non-linear (4) facet model (5) cubic convolution (6) cubic spline (7) gray segment expansion. And resampled images were compared in terms of SNR(Signal to Noise Ratio) and MTF(Modulation Transfer Function) coefficient value. The obtained results were as follows ; 1. The highest SNR value(75.96dB) was obtained with cubic convolution method and the lowest SNR value(72.44dB) was obtained with facet model method among seven interpolation methods. 2. There were significant differences of SNR values among CDR, Digora and film scan(P<0.05). 3. There were significant differences of SNR values between 60kVp and 70kVp in seven interpolation methods. There were significant differences of SNR values between facet model method and those of the other methods at 60kVp(P<0.05), but there were not significant differences of SNR values among seven interpolation methods at 70kVp(P>0.05). 4. There were significant differences of MTF coefficient values between linear interpolation method and the other six interpolation methods (P< 0.05). 5. The speed of computation time was the fastest with nearest -neighbor method and the slowest with non-linear method. 6. The better image was obtained with cubic convolution, cubic spline and gray segment method in ROC analysis. 7. The better sharpness of edge was obtained with gray segment expansion method among seven interpolation methods.

• 한국정보과학회논문지:시스템및이론
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• 제27권8호
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• pp.691-700
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• 2000

볼륨 데이타에 대한 팔진트리와 같은 계층 자료구조를 사용하는 광선 추적법은 모든 광선이 계층구조를 순회하는 것으로 인한 중복된 계산을 포함하고 있으며, 좋은 화질의 영상을 얻기 위한 3차원 보간으로 인하여 많은 계산 비용을 요구한다. 본 논문은 볼륨 데이타의 계층구조에 대한 중복된 방문을 피하고， 오직 한 번만 계층구조를 방문하면서 효과적으로 광선의 리샘플링 지점을 결정하여 색상과 투병도를 구하는 볼륨 렌더링 알고리듬을 제안한다. 이 방법은 물체 순서로 광선 추적법을 수행하면서, 각 복셀 주위에서의 리샘플링 지점을 점진적으로 찾아가면서 각 슬라이스 상에서의 2차원 보간에 기반을 둔 리샘플링을 수행한다. 또한 물체 순서 렌더링에서는 조기 광선 종결과 같은 최적화 기법을 구현하기 힘든데, 영상공간에서의 동적 자료구조를 이용하여 이를 효과적으로 해결하였다 본 논문이 제안한 방법은 구현하기 쉽고 속도 향상을 위하여 추가적으로 요구되는 메모리가 매우 적기 때문에 광선 추적법과 쉬어 와핑 방법 사이의 성능 차이를 메워주는 효과적인 방법으로 사용될 수 있을 것이다.

• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.92-102
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• 2017

In this paper, we consider a typical health care system via the help of Wireless Sensor Network (WSN) for wireless patient tracking. The wireless patient tracking module of this system performs localization out of samples of Received Signal Strength (RSS) variations and tracking through a Particle Filter (PF) for WSN assisted by multiple transmit-power information. We propose a modified PF, Kullback-Leibler Distance (KLD)-resampling PF, to ameliorate the effect of RSS variations by generating a sample set near the high-likelihood region for improving the wireless patient tracking. The key idea of this method is to approximate a discrete distribution with an upper bound error on the KLD for reducing both location error and the number of particles used. To determine this bound error, an optimal algorithm is proposed based on the maximum gap error between the proposal and Sampling Important Resampling (SIR) algorithms. By setting up these values, a number of simulations using the health care system's data sets which contains the real RSSI measurements to evaluate the location error in term of various power levels and density nodes for all methods. Finally, we point out the effect of different power levels vs. different density nodes for the wireless patient tracking.

• 한국수산과학회지
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• 제45권5호
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• pp.499-506
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• 2012

We estimated the population size and fishing mortality of Pacific cod Gadus macrocephalus during the spawning season in waters off Woipo, Geoje Island, Korea, using a mark-recapture method. We marked and released 51 cod>50 cm in total length; six were recaptured by local fishermen during the period from December 15 to 31, 2009. The estimated population size was ca. 180,000 and the fishing mortality of the exploitable cod was 26%. Although we could assume a closed population due to the short survey period, we evaluated the uncertainty in the estimates by applying bootstrap resampling because the sample size was small. The estimated 95% confidence interval was 94,000-568,000 for the population size and 8-49% for fishing mortality. Our study demonstrated that the application of mark-recapture methods and bootstrap resampling can be useful in stock assessment for fisheries management in Korea, but requires a larger sample size, spatially extensive coverage, and sophisticated mark-recapture models based on a refined sampling design for reliable stock assessment and biological reference points in sustainable cod management.

• 대한공간정보학회지
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• 제1권1호
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• pp.181-192
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• 1993

영상은 어떤 현상이나 대상물의 종류와 현상조건에 대한 정보를 포함하고 있는 화소값으로 구성되며, 화소값은 전처리 과정인 기하보정으로 변화된다. 이러한 화소값의 변화는 토지피복 분류 결과에 영향을 미친다. 본 논문에서는 기하보정으로 재구성된 영상을 이용하여 토지피복 분류를 함으로서 재배열의 영향을 알고자 한다. 연구대상 지역은 북한강 수계 중에서 가장 다양한 피복형태를 가지고 있는 춘천시를 중심으로 하는 춘천유역을 선정하였고, 전처리과정에서 재배열된 RESTEC 데이타가 이용되었다. 토지피복 분류는 최대우도법을 사용하여 LEVEL I 수준인 여섯개의 분류항목으로 분류되었다. 본 연구에서 두 가지 방법으로 재배열된 영상을 이용하여 토지피복 분류를 실시하였다. 각각의 분류항목을 지형도와 비교한 결과 Bilinear Interpolation법이 나지(BARE-LAND)를 제외한 다섯개의 분류항목에서 정확도가 좋았다. 결론적으로 기하보정의 영상 재배열은 어떤 분류항목에 중점을 두고 분류를 행하느냐에 따라서 재배열 방법을 선택해야 하며 논과 밭의 경우와 같은 분류항목간의 혼돈은 사계절 영상을 이용하면 더욱 더 정확하게 분류할 수 있을 것이다.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.89-95
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• 2023

Analyzing breast cancer patient files is becoming an exciting area of medical information analysis, especially with the increasing number of patient files. In this paper, breast cancer data is collected from Khartoum state hospital, and the dataset is classified into recurrence and no recurrence. The data is imbalanced, meaning that one of the two classes have more sample than the other. Many pre-processing techniques are applied to classify this imbalanced data, resampling, attribute selection, and handling missing values, and then different classifiers models are built. In the first experiment, five classifiers (ANN, REP TREE, SVM, and J48) are used, and in the second experiment, meta-learning algorithms (Bagging, Boosting, and Random subspace). Finally, the ensemble model is used. The best result was obtained from the ensemble model (Boosting with J48) with the highest accuracy 95.2797% among all the algorithms, followed by Bagging with J48(90.559%) and random subspace with J48(84.2657%). The breast cancer imbalanced dataset was classified into recurrence, and no recurrence with different classified algorithms and the best result was obtained from the ensemble model.

• Journal of Power Electronics
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• 제12권6호
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• pp.982-991
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• 2012

In this study, a new method has been presented for the detection of broken rotor bar (BRB) faults in inverter driven induction motors controlled via Field Oriented Control (FOC). To this end, a FOC controlled induction motor with a BRB fault was modeled using the Matlab/Simulink program. Experiments were carried out using the prepared simulation model at various loads and operating speeds. The motor current and speeds were monitored for healthy, 1, 2 and 3 BRB faults. The Resampling Based Order Tracking Analysis (RB-OTA) method was applied to the monitored signals. The obtained results were compared by using the classic Fast Fourier Transform (FFT) method. When the obtained results were analyzed via the FFT method no information regarding any faults was determined in the run up or run down regions of the motor and the presented method gave very good results. The reliability of the proposed method was validated with experimental results. The main innovative part of this study is that the RB-OTA method was implemented on the induction motor current signal for detecting BRB faults.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1079-1086
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• 2005

Most discussions of single imputation methods and the EM algorithm concern point estimation of population quantities with missing values. A second concern is how to get standard errors of the point estimates obtained from the filled-in data by single imputation methods and EM algorithm. Now we focus on how to estimate standard errors with incorporating the additional uncertainty due to nonresponse. There are some approaches to account for the additional uncertainty. The general two possible approaches are considered. One is the jackknife method of resampling methods. The other is multiple imputation(MI). These two approaches are reviewed and compared through simulation studies.