• Title/Summary/Keyword: quantitative accuracy

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Quantitative Precipitation Estimation using High Density Rain Gauge Network in Seoul Area (고밀도 지상강우관측망을 활용한 서울지역 정량적 실황강우장 산정)

  • Yoon, Seong-sim;Lee, Byongju;Choi, Youngjean
    • Atmosphere
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    • v.25 no.2
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    • pp.283-294
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    • 2015
  • For urban flash flood simulation, we need the higher resolution radar rainfall than radar rainfall of KMA, which has 10 min time and 1km spatial resolution, because the area of subbasins is almost below $1km^2$. Moreover, we have to secure the high quantitative accuracy for considering the urban hydrological model that is sensitive to rainfall input. In this study, we developed the quantitative precipitation estimation (QPE), which has 250 m spatial resolution and high accuracy using KMA AWS and SK Planet stations with Mt. Gwangdeok radar data in Seoul area. As the results, the rainfall field using KMA AWS (QPE1) is showed high smoothing effect and the rainfall field using Mt. Gwangdeok radar is lower estimated than other rainfall fields. The rainfall field using KMA AWS and SK Planet (QPE2) and conditional merged rainfall field (QPE4) has high quantitative accuracy. In addition, they have small smoothed area and well displayed the spatial variation of rainfall distribution. In particular, the quantitative accuracy of QPE4 is slightly less than QPE2, but it has been simulated well the non-homogeneity of the spatial distribution of rainfall.

Quantitative Analysis of Automotive Radar-based Perception Algorithm for Autonomous Driving (자율주행을 위한 레이더 기반 인지 알고리즘의 정량적 분석)

  • Lee, Hojoon;Chae, HeungSeok;Seo, Hotae;Yi, Kyongsu
    • Journal of Auto-vehicle Safety Association
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    • v.10 no.2
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    • pp.29-35
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    • 2018
  • This paper presents a quantitative evaluation method and result of moving vehicle perception using automotive radar. It is also important to analyze the accuracy of the perception algorithm quantitatively as well as to accurately percept nearby moving vehicles for safe and efficient autonomous driving. In this study, accuracy of the automotive radar-based perception algorithm which is developed based on interacting multiple model (IMM) has been verified via vehicle tests on real roads. In order to obtain experimental data for quantitative evaluation, Long Range Radar (LRR) has been mounted on the front of the ego vehicle and Short Range Radar (SRR) has been mounted on the rear side of both sides. RT-range has been installed on the ego vehicle and the target vehicle to simultaneously collect reference data on the states of the two vehicles. The experimental data is acquired in various relative positions and velocity, and the accuracy of the algorithm has been analyzed according to relative position and velocity. Quantitative analysis is conducted on relative position, relative heading angle, absolute velocity, and yaw rate of each vehicle.

Evaluation of the Accuracy of Distance Measurements on 3D Volume-rendered Image of Human Skull Using Multi-detector CT: Effects of Acquisition Section Thickness and Reconstruction Section Thickness

  • Haijo Jung;Kim, Hee-Joung;Lee, Sang-Ho;Kim, Dong-Wook;Soonil Hong;Kim, Dong-Hyeon;Son, Hye-Kyung;Wonsuk Kang;Kim, Kee-Deog
    • Proceedings of the Korean Society of Medical Physics Conference
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    • 2002.09a
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    • pp.457-460
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    • 2002
  • The image quality of three-dimensional (3D) images has been widely investigated by the qualitative analysis method. A need remains for an objective and quantitative method to assess the image quality of 3D volume-rendered images. The purpose of this study was to evaluate the quantitative accuracy of distance measurements on 3D volume-rendered images of a dry human skull by using multi-detector computed tomography (MDCT). A radiologist measured five times the twenty-one direct measurement line items composed among twelve reference points on the skull surface with a digital vernier caliper. The water filled skull specimen was scanned with a MDCT according to the section thicknesses of 1.25, 2.50, 3.75, and 5.00 mm for helical (high quality; pitch 3:1) scan mode. MDCT data were reconstructed with its acquisition section thickness and with 1.25 mm section thickness for all scans. An observer also measured seven times the corresponding items on 3D volume-rendered images with measuring tools provided by volumetric analysis software. The quantitative accuracy of distance measurements on the 3D volume-rendered images was statistically evaluated (p-value < 0.05) by comparatively analyzing these measurements with the direct distance measurements. The accuracy of distance measurements on the 3D volume-rendered MDCT images acquired with 1.25, 2.50, 3,75 and 5.00 mm section thickness and reconstructed with its section thickness were 48%, 33%, 23%, and 14%, respectively. Meanwhile, there were insignificant statistical differences in accuracy of distance measurements among 3D volume-rendered images reconstructed with 1.25 mm section thickness for the each acquisition section thickness. MDCT images acquired with thick section thickness and reconstructed with thin section thickness in helical scan mode should be effectively used in medical planning of 3D volume-rendered images. The quantitative analysis of distance measurement may be a useful tool for evaluating the quantitative accuracy and the defining optimal parameters of 3D volume-rendered CT images.

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Comparative Study of Contrast-Enhanced Ultrasound Qualitative and Quantitative Analysis for Identifying Benign and Malignant Breast Tumor Lumps

  • Liu, Jian;Gao, Yun-Hua;Li, Ding-Dong;Gao, Yan-Chun;Hou, Ling-Mi;Xie, Ting
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.19
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    • pp.8149-8153
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    • 2014
  • Background: To compare the value of contrast-enhanced ultrasound (CEUS) qualitative and quantitative analysis in the identification of breast tumor lumps. Materials and Methods: Qualitative and quantitative indicators of CEUS for 73 cases of breast tumor lumps were retrospectively analyzed by univariate and multivariate approaches. Logistic regression was applied and ROC curves were drawn for evaluation and comparison. Results: The CEUS qualitative indicator-generated regression equation contained three indicators, namely enhanced homogeneity, diameter line expansion and peak intensity grading, which demonstrated prediction accuracy for benign and malignant breast tumor lumps of 91.8%; the quantitative indicator-generated regression equation only contained one indicator, namely the relative peak intensity, and its prediction accuracy was 61.5%. The corresponding areas under the ROC curve for qualitative and quantitative analyses were 91.3% and 75.7%, respectively, which exhibited a statistically significant difference by the Z test (P<0.05). Conclusions: The ability of CEUS qualitative analysis to identify breast tumor lumps is better than with quantitative analysis.

Quantitative Accuracy Assessment of a SPOT DEM along the Coast-Donghae City Area

  • Kim, Seung-Bum;Lee, Hae-Yeoun
    • Korean Journal of Remote Sensing
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    • v.16 no.2
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    • pp.177-188
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    • 2000
  • Quantitative accuracy assessment of a SPOT DEM (Digital Elevation Model) generated by a fully automatic software is performed along the 90km long coast around Donghae city. The theoretical requirement on the layout of the CPS (Global Positioning System) check points is derived: the Nyquist sampling. Since in practice the Nyquist frequency of a terrain is difficult to determine, the relaxed requirements are introduced and 31 check points are collected accordingly. Accuracy of the SPOT DEM is calculated to be 8.9, 11.5 and 12.0m r.m.s. in latitudinal, longitudinal and elevation directions. The bias is distinguishable from zero only for elevation and is 2.2m. The simple comparison with the world's leading commercial softwares reveals the similar accuracy level.

Randomized Response Model with Discrete Quantitative Attribute by Three-Stage Cluster Sampling

  • Lee, Gi-Sung;Hong, Ki-Hak
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.4
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    • pp.1067-1082
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    • 2003
  • In this paper, we propose a randomized response model with discrete quantitative attribute by three-stage cluster sampling for obtaining discrete quantitative data by using the Liu & Chow model(1976), when the population was made up of sensitive discrete quantitative clusters. We obtain the minimum variance by calculating the optimum number of fsu, ssu, tsu under the some given constant cost. And we obtain the minimum cost under the some given accuracy.

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Accuracy Improvement of Lattice Parameters Measured from Electron Diffraction Data (전자회절을 이용한 격자상수의 측정 정확도 향상)

  • Lee, Sang-Gil;Song, Kyung;Kim, Jin-Gyu
    • Applied Microscopy
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    • v.41 no.1
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    • pp.75-79
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    • 2011
  • For quantitative analysis of nano-crystal structure, we reported the accuracy improvement method of lattice parameters measured from electron diffraction. For calculation of Au lattice parameters used as a standard crystal structure, it was considered two different acquisition methods (detector and enegy-filter) and three different calculation methods (conventional, least-square and regression fit). As a result, the measurement reliability could be enhanced by using CCD camera which gives higher performance, while energy-filtering did not affect the improvement the camera constant accuracy. Also, the accuracy of lattice parameters could be improved up to $10^{-4}$ order by regression fitting with correction formula. Finally, it is expected that the combination of regression fitting and intensity extraction from energy-filtered precession electron diffraction gives a solution of quantitative structure analysis for unknown nano-crystals.

A Study on the Application of Interpolation and Terrain Classification for Accuracy Improvement of Digital Elevation Model (수지표고지형의 정확도 향상을 위한 지형의 분류와 보간법의 상용에 관한 연구)

  • 문두열
    • Journal of Ocean Engineering and Technology
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    • v.8 no.2
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    • pp.64-79
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    • 1994
  • In this study, terrain classification, which was done by using the quantitative classification parameters and suitable interpolation method was applied to improve the accuracy of digital elevation models, and to increase its practical use of aerial photogrammetry. A terrain area was classified into three groups using the quantitative classification parameters to the ratio of horizontal, inclined area, magnitude of harmonic vectors, deviation of vector, the number of breakline and proposed the suitable interpolation. Also, the accuracy of digital elevation models was improved in case of large grid intervals by applying combined interpolation suitable for each terrain group. As a result of this study, I have an algorithm to perform the classification of the topography in the area of interest objectively and decided optimal data interpolation scheme for given topography.

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A Study on the Quantitative Evaluation Method of Quality Control using Ultrasound Phantom in Ultrasound Imaging System based on Artificial Intelligence (인공지능을 활용한 초음파영상진단장치에서 초음파 팬텀 영상을 이용한 정도관리의 정량적 평가방법 연구)

  • Yeon Jin, Im;Ho Seong, Hwang;Dong Hyun, Kim;Ho Chul, Kim
    • Journal of Biomedical Engineering Research
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    • v.43 no.6
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    • pp.390-398
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    • 2022
  • Ultrasound examination using ultrasound equipment is an ultrasound device that images human organs using sound waves and is used in various areas such as diagnosis, follow-up, and treatment of diseases. However, if the quality of ultrasound equipment is not guaranteed, the possibility of misdiagnosis increases, and the diagnosis rate decreases. Accordingly, The Korean Society of Radiology and Korea society of Ultrasound in Medicine presented guidelines for quality management of ultrasound equipment using ATS-539 phantom. The DenseNet201 classification algorithm shows 99.25% accuracy and 5.17% loss in the Dead Zone, 97.52% loss in Axial/Lateral Resolution, 96.98% accuracy and 20.64% loss in Sensitivity, 93.44% accuracy and 22.07% loss in the Gray scale and Dynamic Range. As a result, it is the best and is judged to be an algorithm that can be used for quantitative evaluation. Through this study, it can be seen that if quantitative evaluation using artificial intelligence is conducted in the qualitative evaluation item of ultrasonic equipment, the reliability of ultrasonic equipment can be increased with high accuracy.

Structural monitoring and maintenance by quantitative forecast model via gray models

  • C.C. Hung;T. Nguyen
    • Structural Monitoring and Maintenance
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    • v.10 no.2
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    • pp.175-190
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    • 2023
  • This article aims to quantitatively predict the snowmelt in extreme cold regions, considering a combination of grayscale and neural models. The traditional non-equidistant GM(1,1) prediction model is optimized by adjusting the time-distance weight matrix, optimizing the background value of the differential equation and optimizing the initial value of the model, and using the BP neural network for the first. The adjusted ice forecast model has an accuracy of 0.984 and posterior variance and the average forecast error value is 1.46%. Compared with the GM(1,1) and BP network models, the accuracy of the prediction results has been significantly improved, and the quantitative prediction of the ice sheet is more accurate. The monitoring and maintenance of the structure by quantitative prediction model by gray models was clearly demonstrated in the model.