• Title/Summary/Keyword: Sampling interval

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디지털 검출기에서 샘플링 간격에 따른 MTF의 변화 (Change of MTF for Sampling Interval in Digital Detector)

  • 조형욱;천권수
    • 한국방사선학회논문지
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    • 제8권5호
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    • pp.225-230
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    • 2014
  • 엑스선 의료영상이 디지털 시스템으로 발전함에 따라 의료영상의 평가 방법 또한 디지털 시스템에 맞춰 새롭게 개선되었으며 그 중 MTF(Modulation Transfer Function) 측정법은 Fujita이론에 기초한 에지법으로 ISO에 규정되었다. 에지법의 기초인 Fujita이론은 MTF 측정을 위해 슬릿의 각도를 디지털 검출기의 픽셀 열에 대하여 $1^{\circ}{\sim}2^{\circ}$ 기울여 영상을 획득한 후 LSF를 합성하는 것으로 샘플링 간격은 각도에 따라 크게는 1/54배, 작게는 1/28배 감소하게 된다. 본 연구에서 사용된 장비는 Simens사의 MAMMOMAT Inspiration으로 비정질 셀레늄(amorphous selenium) 기반의 $0.085{\times}0.085mm^2$ 의 픽셀 사이즈를 갖는 검출기를 이용하여 presampling MTF를 측정하였다. 측정 방법은 Fujita의 슬릿법와 동일한 방법인 와이어법를 이용하였으며 측정된 영상에서 얻은 픽셀값을 이용하여 엑셀에서 이산 푸리에 변환을 시행하였다. 실험 결과 Fujita이론 대비 약 3배 이상의 샘플링 간격(sampling interval)의 경우 해상도 평가 지수인 10% MTF에서 약 85% 이하의 정확도를 보였으며 선예도 평가 지수인 50% MTF에서 약 93% 이하의 정확도를 나타내었다. 하지만 샘플링 간격이 Fujita 이론 대비 2배 정도 늘어난 경우는 50%와 10% MTF 모두 96% 이상의 정확도를 가진다는 것을 알 수 있었다.

가변추출간격상(假變抽出間格上)에서 분산(分散)-공분산(共分散) 행례(行例)에 대한 다변량(多變量) 기하이동평균(幾何移動平均) 처리원(處理圓) (Multivariate EWMA Control Charts for the Variance-Covariance Matrix with Variable Sampling Intervals)

  • 조교영
    • Journal of the Korean Data and Information Science Society
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    • 제4권
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    • pp.31-44
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    • 1993
  • Multivariate exponentially weighted moving average (EWMA) control charts for monitoring the variance-covariance matrix are investigated. A variable sampling interval (VSI) feature is considered in these charts. Multivariate EWMA control charts for monitoring the variance-covariance matrix are compared on the basis of their average time to signal (ATS) performances. The numerical results show that multivariate VSI EWMA control charts are more efficient than corrsponding multivariate fixed sampling interval (FSI) EWMA control charts.

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Multivariate Control Charts for Means and Variances with Variable Sampling Intervals

  • Kim, Jae-Joo;Cho, Gyo-Young;Chang, Duk-Joon
    • 품질경영학회지
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    • 제22권1호
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    • pp.66-81
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    • 1994
  • Several sample statistics to simultaneously monitor both means and variances for multivariate quality characteristics under multivariate normal process are proposed. Performances of multivariate Shewhart schemes and cumulative sum(CUSUM) schemes are evaluated for matched fixed sampling interval(FSI) and variable sampling interval(VSI) feature. Numerical results show that multivariate CUSUM charts are more efficient than Shewhart charts for small or moderate shifts and VSI feature is more efficient than FSI feature.

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Properties of variable sampling interval control charts

  • Chang, Duk-Joon;Heo, Sun-Yeong
    • Journal of the Korean Data and Information Science Society
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    • 제21권4호
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    • pp.819-829
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    • 2010
  • Properties of multivariate variable sampling interval (VSI) Shewhart and CUSUM charts for monitoring mean vector of related quality variables are investigated. To evaluate average time to signal (ATS) and average number of switches (ANSW) of the proposed charts, Markov chain approaches and simulations are applied. Performances of the proposed charts are also investigated both when the process is in-control and when it is out-of-control.

변량표본추출간격을 이용한 (equation omitted)x-R 관리도의 연구 (A study on the (equation omitted)x-R control chart with variable sampling interval scheme)

  • 이희춘
    • 산업경영시스템학회지
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    • 제18권33호
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    • pp.143-151
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    • 1995
  • In this design the sampling interval that each sampling time changes according to the valuse of the previous to sample statistics, sample mean and ranges. The VSI scheme uses large sample if the sample statistics appear near in side the control limits and smaller sample otherwise. The efficiency of the VSI scheme is compare to the FSI. It is shown that VSI control chart improves the confidence of the procedure and performens better than FSI control chart.

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SPATIAL AND TEMPORAL INFLUENCES ON SOIL MOISTURE ESTIMATION

  • Kim, Gwang-seob
    • Water Engineering Research
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    • 제3권1호
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    • pp.31-44
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    • 2002
  • The effect of diurnal cycle, intermittent visit of observation satellite, sensor installation, partial coverage of remote sensing, heterogeneity of soil properties and precipitation to the soil moisture estimation error were analyzed to present the global sampling strategy of soil moisture. Three models, the theoretical soil moisture model, WGR model proposed Waymire of at. (1984) to generate rainfall, and Turning Band Method to generate two dimensional soil porosity, active soil depth and loss coefficient field were used to construct sufficient two-dimensional soil moisture data based on different scenarios. The sampling error is dominated by sampling interval and design scheme. The effect of heterogeneity of soil properties and rainfall to sampling error is smaller than that of temporal gap and spatial gap. Selecting a small sampling interval can dramatically reduce the sampling error generated by other factors such as heterogeneity of rainfall, soil properties, topography, and climatic conditions. If the annual mean of coverage portion is about 90%, the effect of partial coverage to sampling error can be disregarded. The water retention capacity of fields is very important in the sampling error. The smaller the water retention capacity of the field (small soil porosity and thin active soil depth), the greater the sampling error. These results indicate that the sampling error is very sensitive to water retention capacity. Block random installation gets more accurate data than random installation of soil moisture gages. The Walnut Gulch soil moisture data show that the diurnal variation of soil moisture causes sampling error between 1 and 4 % in daily estimation.

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Multistage Point and Confidence Interval Estimation of the Shape Parameter of Pareto Distribution

  • Hamdy, H.I.;Son, M.S.;Gharraph, M.K.;Rashad, A.M.
    • Communications for Statistical Applications and Methods
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    • 제10권3호
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    • pp.1069-1086
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    • 2003
  • This article presents the asymptotic theory of triple sampling procedure as pertain to estimating the shape parameter of Pareto distribution. Both point and confidence interval estimation are considered within the same inference unified framework. We show that this group sampling technique possesses the efficiency of Anscome (1953), Chow and Robbins (1965) purely sequential procedure as well as reduce the number of sampling operations by utilizing Stein (1945) two stages procedure. The analysis reveals that the technique performs excellent as far as the accuracy is concerned. The present problem differs from those considered by many authors, in multistage sampling, in that the final stage sample size and the parameter's estimate become highly correlated and therefore we adopted different approach.

고정표본채취시점을 갖는 가변표본채취간격 다변량 $T^2$ 관리도 (A Variable Sampling Interval $T^2$ Control Chart with Sampling at Fixed Times)

  • 서종현;장영순
    • 산업경영시스템학회지
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    • 제34권2호
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    • pp.1-8
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    • 2011
  • This paper proposes a variable sampling interval multivariate $T^2$ control chart with sampling at fixed times, where samples are taken at specified equally spaced fixed time points and additional samples are allowed between these fixed times when indicated by the preceding $T^2$ statistics. At fixed sampling points, the $T^2$ statistics are composed of all quality characteristics and a part of quality characteristics are selected to obtain $T^2$ statistics at additional sampling points. A Markov chain approach is used to evaluate the performance of the proposed chart. Numerical studies for the performance of the proposed chart show that the proposed chart reduces the observations obtained from a process and detects the assignable cause of a process with low correlated quality characteristics quickly.

Fast Volume Visualization Techniques for Ultrasound Data

  • Kwon Koo-Joo;Shin Byeong-Seok
    • 대한의용생체공학회:의공학회지
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    • 제27권1호
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    • pp.6-13
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    • 2006
  • Ultrasound visualization is a typical diagnosis method to examine organs, soft tissues and fetus data. It is difficult to visualize ultrasound data because the quality of the data might be degraded by artifact and speckle noise, and gathered with non-linear sampling. Rendering speed is too slow since we can not use additional data structures or procedures in rendering stage. In this paper, we use several visualization methods for fast rendering of ultrasound data. First method, denoted as adaptive ray sampling, is to reduce the number of samples by adjusting sampling interval in empty space. Secondly, we use early ray termination scheme with sufficiently wide sampling interval and low threshold value of opacity during color compositing. Lastly, we use bilinear interpolation instead of trilinear interpolation for sampling in transparent region. We conclude that our method reduces the rendering time without loss of image quality in comparison to the conventional methods.

변량표본크기와 변량표본추출구간을 이용한$\bar{X}$관리도의 경제적 설계 (An Economic Design of $\bar{X}$ Control Charts with Variable Sample Size and Sampling Interval)

  • 김계완;윤덕균
    • 품질경영학회지
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    • 제28권3호
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    • pp.18-30
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    • 2000
  • Recent studies have shown that the $\bar{X}$ chart with variable sampling intervals(VSI) and the $\bar{X}$ chart with variable sample size(VSS) are much quicker than Shewhart $\bar{X}$ chart in detecting shiks in the process. Shewhart $\bar{X}$ chart has been beneficial to detect large shifts but it is hard to apply Shewhart $\bar{X}$ chart in detecting moderate shifts in the process mean. In this article the $\bar{X}$ chart using variable sample size(VSS) and variable sampling Intervals(VSI) has been proposed to supplement the weak point mentioned above. So the purpose of this paper is to consider finding the design parameters which minimize expected loss costs for unit process time and measure the performance of VSSI(variable sample size and sampling interval) $\bar{X}$ chart. It is important that assignable causes be detected to maintain the process controlled. This paper has been studied under the assumption that one cycle is from starting of the process to eliminating the assignable causes in the process. The other purpose of this article is to represent the expected loss costs in one cycle with three process parameters(sample size, sampling interval and control limits) function and find the three parameters.

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