• 제목/요약/키워드: weighted normal model

검색결과 52건 처리시간 0.021초

홍수에 의한 하도변형을 고려한 물리서식처 모의 (Physical Habitat Simulation Considering Stream Morphology Change due to Flood)

  • 이성진;김승기;최성욱
    • 대한토목학회논문집
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    • 제34권3호
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    • pp.805-812
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    • 2014
  • 본 연구에서는 하상변동의 고려가 물리서식처 모의에 미치는 영향을 검토하였다. 이를 위해 수리 및 이동상 계산은 CCHE2D 모형을 이용하였으며 서식처 적합도 곡선을 사용하여 서식처 평가를 실시하였다. 적용구간은 달천 유역 괴산댐 하류 수전교에서 대수보까지 약 2.5km 구간이며, 2006년 7월 홍수기 시 실측된 유량 및 수위자료를 이용하여 이동상 모의를 수행하였다. 수치모형의 검증은 실측된 수위와의 비교를 통해 수행되었으며 하상변동의 검증은 실시 하지 않았다. 물리서식처 분석은 우점종인 성어기 피라미를 대상으로 실시하였다. 고정상과 이동상 조건에서 각각 갈수량, 저수량, 평수량, 풍수량의 유랑조건에 대한 복합서식처 적합도 지수 분포를 모의하고 가중가용면적을 산정하였다. 모의 결과, 이동상 조건에서 모의구간의 상류 및 만곡부에서 복합 서식처 적합도지수가 상승하는 결과를 확인하였다. 또한 가중가용면적은 이동상 고려 시 5.4~11.3%정도 증가하는 것을 확인하였다.

전류측정성분과 불량정보 검출을 고려한 전력계통에서의 상태추정에 관한 연구 (State Estimation Considering Current Measurement Component and Bad Data Detection)

  • 김준현;이종범
    • 대한전기학회논문지
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    • 제35권7호
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    • pp.261-271
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    • 1986
  • This paper describes a method for the state estimation considering current measurement component and detection of the bad data. The state values are estimated by weighted least square method in which measurement vector included bus injection current and line current. The bad data are detected using standardized variable of normal distribution and identified using sensitivity coefficients. When the bad data were occured by the bad measurement values. The results of the application to the model power system reveal the effectiveness of the presented algorithms.

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Exact Variance of Location Estimator in One-Way Random Effect Models with Two Distint Group Sizes

  • Lee, Young-Jo;Chung, Han-Yeong
    • Journal of the Korean Statistical Society
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    • 제18권2호
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    • pp.118-124
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    • 1989
  • In the one-way random effect model, we often estimate the variance components by the ANOVA method and then estimate the population mean. Whe there are only two distint group sizes, the conventional mean estimator is represented as a weighted average of two normal means with weights being the function of variance component estimators. In this paper, we will study a method which can compute the exact variance of the mean estimator when we set the negative variance component estimate to zero.

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Modeling pediatric tumor risks in Florida with conditional autoregressive structures and identifying hot-spots

  • Kim, Bit;Lim, Chae Young
    • Journal of the Korean Data and Information Science Society
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    • 제27권5호
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    • pp.1225-1239
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    • 2016
  • We investigate pediatric tumor incidence data collected by the Florida Association for Pediatric Tumor program using various models commonly used in disease mapping analysis. Particularly, we consider Poisson normal models with various conditional autoregressive structure for spatial dependence, a zero-in ated component to capture excess zero counts and a spatio-temporal model to capture spatial and temporal dependence, together. We found that intrinsic conditional autoregressive model provides the smallest Deviance Information Criterion (DIC) among the models when only spatial dependence is considered. On the other hand, adding an autoregressive structure over time decreases DIC over the model without time dependence component. We adopt weighted ranks squared error loss to identify high risk regions which provides similar results with other researchers who have worked on the same data set (e.g. Zhang et al., 2014; Wang and Rodriguez, 2014). Our results, thus, provide additional statistical support on those identied high risk regions discovered by the other researchers.

Evaluation by Contrast-Enhanced MR Imaging of the Lateral Border Zone in Reperfused Myocardial Infarction in a Cat Model

  • Ae Kyung Jeong;Sang Il Choi;Dong Hun Kim;Sung Bin Park;Seoung Soo Lee;Seong Hoon Choi;Tae-Hwan Lim
    • Korean Journal of Radiology
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    • 제2권1호
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    • pp.21-27
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    • 2001
  • Objective: To identify and evaluate the lateral border zone by comparing the size and distribution of the abnormal signal area demonstrated by MR imaging with the infarct area revealed by pathological examination in a reperfused myocardial infarction cat model. Materials and Methods: In eight cats, the left anterior descending coronary artery was occluded for 90 minutes, and this was followed by 90 minutes of reperfusion. ECG-triggered breath-hold turbo spin-echo T2-weighted MR images were initially obtained along the short axis of the heart before the administration of contrast media. After the injection of Gadomer-17 and Gadophrin-2, contrast-enhanced T1-weighted MR images were obtained for three hours. The size of the abnormal signal area seen on each image was compared with that of the infarct area after TTC staining. To assess ultrastructural changes in the myocardium at the infarct area, lateral border zone and normal myocardium, electron microscopic examination was performed. Results: The high signal area seen on T2-weighted images and the enhanced area seen on Gadomer-17-enhanced T1WI were larger than the enhanced area on Gadophrin-2-enhanced T1WI and the infarct area revealed by TTC staining; the difference was expressed as a percentage of the size of the total left ventricle mass (T2= 39.2 %; Gadomer-17 =37.25 % vs Gadophrin-2 = 29.6 %; TTC staining = 28.2 %; p < 0.05). The ultrastructural changes seen at the lateral border zone were compatible with reversible myocardial damage. Conclusion: In a reperfused myocardial infarction cat model, the presence and size of the lateral border zone can be determined by means of Gadomer-17- and Gadophrin-2-enhanced MR imaging.

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딥러닝 알고리즘을 이용한 매설 배관 피복 결함의 간접 검사 신호 진단에 관한 연구 (Indirect Inspection Signal Diagnosis of Buried Pipe Coating Flaws Using Deep Learning Algorithm)

  • 조상진;오영진;신수용
    • 한국압력기기공학회 논문집
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    • 제19권2호
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    • pp.93-101
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    • 2023
  • In this study, a deep learning algorithm was used to diagnose electric potential signals obtained through CIPS and DCVG, used indirect inspection methods to confirm the soundness of buried pipes. The deep learning algorithm consisted of CNN(Convolutional Neural Network) model for diagnosing the electric potential signal and Grad CAM(Gradient-weighted Class Activation Mapping) for showing the flaw prediction point. The CNN model for diagnosing electric potential signals classifies input data as normal/abnormal according to the presence or absence of flaw in the buried pipe, and for abnormal data, Grad CAM generates a heat map that visualizes the flaw prediction part of the buried pipe. The CIPS/DCVG signal and piping layout obtained from the 3D finite element model were used as input data for learning the CNN. The trained CNN classified the normal/abnormal data with 93% accuracy, and the Grad-CAM predicted flaws point with an average error of 2m. As a result, it confirmed that the electric potential signal of buried pipe can be diagnosed using a CNN-based deep learning algorithm.

Experimental Cats Model for Research of the Blood Ocular Barrier

  • Park Byung-Rae
    • 대한의생명과학회지
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    • 제11권4호
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    • pp.555-559
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    • 2005
  • Evaluate the triolein emulsion could disrupt the barriers and to suggest as an experimental model in blood-ocular barrier studies. Triolein emulsion was infused into the carotid artery in the experimental group ten cats. Normal saline was used in another the control group ten cats. Pre contrast and postcontrast T1-weighted MR images were obtained at 30 minutes and 3 hours after embolization. Signal intensities were evaluated in the anterior, posterior chamber and in the vitreus qualitatively and quantitatively. Postembolization 30 minutes MR images were not different from those of the control group. Postembolization 3 hour MR images demonstrated delayed contrast enhancement in the anterior chamber of the ipsilateral and contralateral eyeballs and in the posterior chamber of the ipsilateral eyeball. Delayed contrast enhancement of the posterior chamber of the ipsilateral eyeball was statistically significant (P<0.05). The present study demonstrated significant contrast enhancement in the posterior chamber with infusion of triolein emulsion and can be a model in blood-aqueous barrier studies.

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Optimizing the maximum reported cluster size for normal-based spatial scan statistics

  • Yoo, Haerin;Jung, Inkyung
    • Communications for Statistical Applications and Methods
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    • 제25권4호
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    • pp.373-383
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    • 2018
  • The spatial scan statistic is a widely used method to detect spatial clusters. The method imposes a large number of scanning windows with pre-defined shapes and varying sizes on the entire study region. The likelihood ratio test statistic comparing inside versus outside each window is then calculated and the window with the maximum value of test statistic becomes the most likely cluster. The results of cluster detection respond sensitively to the shape and the maximum size of scanning windows. The shape of scanning window has been extensively studied; however, there has been relatively little attention on the maximum scanning window size (MSWS) or maximum reported cluster size (MRCS). The Gini coefficient has recently been proposed by Han et al. (International Journal of Health Geographics, 15, 27, 2016) as a powerful tool to determine the optimal value of MRCS for the Poisson-based spatial scan statistic. In this paper, we apply the Gini coefficient to normal-based spatial scan statistics. Through a simulation study, we evaluate the performance of the proposed method. We illustrate the method using a real data example of female colorectal cancer incidence rates in South Korea for the year 2009.

계단적 충격 생명검사에 관한 연구 (A study on the step stress life testing)

  • 이석훈
    • 응용통계연구
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    • 제2권2호
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    • pp.61-78
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    • 1989
  • 정상조건에서 수명이 상당히 긴 개체의 생명검사(Life Test)를 현실적으로 수행하기 위하여 제안된 충격생명검사에 관하여 고찰하였다. 생명검사의 결과로 얻는 자료의 통계적 분석을 위하여 이미 제안된 모형의 검토와 이들을 일면 포함하는 모형을 제시하고 그에 따르는 통계적 추론 과정을 최대우도추정법과 가중최소자승법을 사용하여 토의하였다. 한편 검사를 계획할 때 발생하는 실험설계의 문제를 검토하고 단순 계단적 충격검사에서 잘려진 자료(Consored Data)를 포함한 경우를 연구하였다.

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2단계 사례-대조자료를 위한 로지스틱 회귀모형의 추론 (Estimation of Logistic Regression for Two-Stage Case-Control Data)

  • 신미영;신은순
    • 응용통계연구
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    • 제13권2호
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    • pp.237-245
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    • 2000
  • 이 논문에서는 2단계 계획 하에서의 사례-대조 자료를 로지스틱 회귀 모형에 적합시키고 WESML방법으로 모수를 추정하며 추정량의 점근분포를 찾는다. 또한 WESML,방법과 CML 방법으로 얻은 모수의 추정량과 표준오차를 실제 자료를 이용하여 비교한다.

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