• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.149-160
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• 2014

Recently studies on application development and utilization using sensors and devices embedded in smartphones have flourished at home and abroad. This study aimed to analyze the accuracy of the images of smartphone to determine three-dimension position of close objects prior to the development of photogrammetric system applying smartphone and evaluate the feasibility to use. First of all, camera calibration was conducted on autofocus and infinite focus. Regarding camera calibration distortion model with balance system and unbalance system was used for the decision of lens distortion coefficient, the results of calibration on 16 types of projects showed that all cases were in RMS error by less than 1 mm from bundle adjustment. Also in terms of autofocus and infinite focus on S and S2 model, the pattern of distorted curve was almost the same, so it could be judged that change in distortion pattern according to focus mode is very little. The result comparison according to autofocus and infinite focus and the result comparison according to a software used for multi-image processing showed that all cases were in standard deviation less than ${\pm}3$ mm. It is judged that there is little result difference between focus mode and determination of three-dimension position by distortion model. Lastly the checkpoint performance by total station was fixed as most probable value and the checkpoint performance determined by each project was fixed as observed value to calculate statistics on residual of individual methods. The result showed that all projects had relatively large errors in the direction of Y, the direction of object distance compared to the direction of X and Z. Like above, in terms of accuracy for determination of three-dimension position for a close object, the feasibility to use smartphone camera would be enough.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.281-290
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• 2007

Linear estimator, a weighted sum of the sample observation, is commonly adopted to estimate the finite population parameters such as population totals in survey sampling. The weight for a sampled unit is often constructed by multiplying the base weight, which is the inverse of the first-order inclusion probability, by an adjustment term that takes into account of the auxiliary information obtained throughout the population. The linear estimator using the weight adjustment is often more efficient than the one using only the bare weight, but its valiance estimation is more complicated. We discuss variance estimation for a general class of weight-adjusted estimator. By identifying that the weight-adjusted estimator can be viewed as a function of estimated nuisance parameters, where the nuisance parameters were used to incorporate the auxiliary information, we derive a linearization of the weight-adjusted estimator using a Taylor expansion. The method proposed here is quite general and can be applied to wide class of the weight-adjusted estimators. Some examples and results from a simulation study are presented.

• Ocean and Polar Research
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• v.39 no.2
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• pp.159-168
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• 2017

To evaluate the accuracy of UAV photogrammetry for Hyangho coast, eastern coast of Korea, we conducted a field experiment wherein UAV photogrammetry test was repeated three times. Since the Haygho coast is located within a military reservation zone, it was necessary to obtain permission to gain access to the beach and to have sensitive aerial photographs showing military facilities inspected and cropped. The standard deviation of the UAV shooting position between the three tests was less than 1 m, but repeatability of footprint on the ground was low due to wind-driven variability of the UAV pose. Self-calibrating bundle adjustment(SCBA) of implementing non-metric camera calibration was failed in one test. In two tests, the vertical error was twice as large as the pixel size except for those areas that were subject to security inspection and cropping. Given the problems that can arise with regard to the repeatability of the shooting area as well as the possibility of failure with regard to SCBA, we strongly recommend that UAV photogrammetry in coastal areas needs to be repeated at least twice.

• Safety and Health at Work
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• v.10 no.2
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• pp.196-204
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• 2019

Background: Liquid chromatography-tandem mass spectrometry (LC-MSMS) for simultaneous analysis of multiple microbial secondary metabolites (MSMs) is potentially subject to interference by matrix components. Methods: We examined potential matrix effects (MEs) in analyses of 31 MSMs using ultraperformance LC-MSMS. Twenty-one dust aliquots from three buildings (seven aliquots/building) were spiked with seven concentrations of each of the MSMs ($6.2pg/{\mu}l-900pg/{\mu}l$) and then extracted. Another set of 21 aliquots were first extracted and then, the extract was spiked with the same concentrations. We added deepoxy-deoxynivalenol (DOM) to all aliquots as a universal internal standard. Ten microliters of the extract was injected into the ultraperformance LC-MSMS. ME was calculated by subtracting the percentage of the response of analyte in spiked extract to that in neat standard from 100. Spiked extract results were used to create a matrix-matched calibration (MMC) curve for estimating MSM concentration in dust spiked before extraction. Results: Analysis of variance was used to examine effects of compound (MSM), building and concentration on response. MEs (range: 63.4%-99.97%) significantly differed by MSM (p < 0.01) and building (p < 0.05). Mean percent recoveries adjusted with DOM and the MMC method were 246.3% (SD = 226.0) and 86.3% (SD = 70.7), respectively. Conclusion: We found that dust MEs resulted in substantial underestimation in quantifying MSMs and that DOM was not an optimal universal internal standard for the adjustment but that the MMC method resulted in more accurate and precise recovery compared with DOM. More research on adjustment methods for dust MEs in the simultaneous analyses of multiple MSMs using LC-MSMS is warranted.

• Journal of Preventive Medicine and Public Health
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• v.46 no.2
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• pp.74-81
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• 2013

Objectives: The objective of this study was to evaluate the performance of risk-adjusted mortality models for colorectal cancer surgery. Methods: We investigated patients (n=652) who had undergone colorectal cancer surgery (colectomy, colectomy of the rectum and sigmoid colon, total colectomy, total proctectomy) at five teaching hospitals during 2008. Mortality was defined as 30-day or in-hospital surgical mortality. Risk-adjusted mortality models were constructed using claims data (basic model) with the addition of TNM staging (TNM model), physiological data (physiological model), surgical data (surgical model), or all clinical data (composite model). Multiple logistic regression analysis was performed to develop the risk-adjustment models. To compare the performance of the models, both c-statistics using Hanley-McNeil pair-wise testing and the ratio of the observed to the expected mortality within quartiles of mortality risk were evaluated to assess the abilities of discrimination and calibration. Results: The physiological model (c=0.92), surgical model (c=0.92), and composite model (c=0.93) displayed a similar improvement in discrimination, whereas the TNM model (c=0.87) displayed little improvement over the basic model (c=0.86). The discriminatory power of the models did not differ by the Hanley-McNeil test (p>0.05). Within each quartile of mortality, the composite and surgical models displayed an expected mortality ratio close to 1. Conclusions: The addition of clinical data to claims data efficiently enhances the performance of the risk-adjusted postoperative mortality models in colorectal cancer surgery. We recommended that the performance of models should be evaluated through both discrimination and calibration.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.4
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• pp.157-165
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• 1992

IMS, a precision coordinate measuring system using theodolites, is being used to survey and align precision mechanical structures. Compared to conventional mechanical devices for precision measurement, such as CMM (Coordinate Measuring Machine), the target objects of IMS have little limitations in their sizes and shapes, and can be measured in place. Also since IMS displays the coordinate values in real-time, it is possible to perform measurement and alignment of the objects simultaneously. In this paper, the elements and functions of IMS are introduced and a mathematical model of the new software, which utilizes an altered version of the 'Bundle' adjustment algorithm of analytical photogrammetry for the specific use of IMS, is demonstrated. Differences of the mathematical model of IMS from that of analytical photogrammetry are discussed by following the steps of the 'Measurement' option in the 'Main Menu' of the software. A new IMS calibration method is proposed to calculate better first approximations for the 4 unknown theodolite parameters and the coordinates of target objects. The software provides the 'Bundle' procedure for the first approximations of the unknowns before the real-time measurement. It also provides an opportunity of 'bundling' to re-adjust the collected positional data at the end of the measurement.

• Journal of Radiation Protection and Research
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• v.22 no.3
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• pp.143-151
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• 1997

As the pulse heights from a NaI(Tl) detector vary with the temperature of the measuring environment a significant change in temperature may affect the energy calibration of the spectrometer. The auto-adjustment of the channel corresponding to a pulse heights can be achieved by introducing an external reference source to compensate the temperature dependency of pulse heights, but unfavorable increases of the Compton continuum are caused due to the external source. In this study, the total absorption peaks dominant in the typical environmental gamma spectrum-239 keV from $^{212}Pb$, 351 keV from $^{214}Pb$, 1460 keV from $^{40}K$ and 2614 keV from $^{208}Tl$ for examples - were used as reference in the correction of energy calibration. With these peaks, the program to calibrate the energy of the s spectrum was developed using Microsoft Visual Basic language. The program developed here was applied to the environmental spectra measured at intervals of 30 minutes in the temperature range of from $-20^{\circ}C$ to $10^{\circ}C$ to demonstrate the validity and applicability. As a result of the test, the correction scheme appeared to be effective in the temperature changes encountered in the usual environment.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.5
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• pp.1111-1121
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• 2015

Roundabouts face a congestion problem when there are unbalanced traffic conditions, especially during peak times. The congestion at roundabouts results in long delay times and low level of service. Some research has addressed congestion problems of the metering roundabout, however, few studies have focused on the congestion issues of the double signals metering roundabout. This research bridges this gap through analyzing the congestion issues of the Old Belair double signals roundabout in Adelaide. The research proposes a new analysis method based on a four-nodes intersection in order for double signals roundabout analysis to be carried out as follows: firstly, at the modeling stage, a connection of four individual intersections, adjustment of lane geometry, input of directional traffic volumes, priority and phase time set up are proposed. Secondly, for calibration, PFF, critical gap, follow-up headway and extra bunching values have been applied. The results from the analysis show that the new method is able to analyze the delay time, level of service, degree of saturation and allow for further analysis.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.895-905
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• 2019

Commercial Unmanned Aerial Vehicle (UAV) image processing software products currently used in the industry provides camera calibration information and block bundle adjustment accuracy. However, they provide mapping accuracy achievable out of input UAV images. In this paper, the quality of mapping is calculated by using orientation parameters from UAV image processing software. We apply the orientation parameters to the digital photogrammetric workstation (DPW) for verifying the reliability of the mapping quality calculated. The quality of mapping accuracy was defined as three types of accuracy: Y-parallax, relative model and absolute model accuracy. The Y-parallax is an accuracy capable of determining stereo viewing between stereo pairs. The Relative model accuracy is the relative bundle adjustment accuracy between stereo pairs on the model coordinates system. The absolute model accuracy is the bundle adjustment accuracy on the absolute coordinate system. For the experimental data, we used 723 images of GSD 5 cm obtained from the rotary wing UAV over an urban area and analyzed the accuracy of mapping quality. The quality of the relative model accuracy predicted by the proposed technique and the maximum error observed from the DPW showed precise results with less than 0.11 m. Similarly, the maximum error of the absolute model accuracy predicted by the proposed technique was less than 0.16 m.

• Journal of the Korea Computer Graphics Society
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• v.23 no.3
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• pp.31-38
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• 2017

In this paper, we propose a system which estimates Manhattan coordinate systems for urban scene images using a convolutional neural network (CNN). Estimating the Manhattan coordinate system from an image under the Manhattan world assumption is the basis for solving computer graphics and vision problems such as image adjustment and 3D scene reconstruction. We construct a CNN that estimates Manhattan coordinate systems based on GoogLeNet [1]. To train the CNN, we collect about 155,000 images under the Manhattan world assumption by using the Google Street View APIs and calculate Manhattan coordinate systems using existing calibration methods to generate dataset. In contrast to PoseNet [2] that trains per-scene CNNs, our method learns from images under the Manhattan world assumption and thus estimates Manhattan coordinate systems for new images that have not been learned. Experimental results show that our method estimates Manhattan coordinate systems with the median error of $3.157^{\circ}$ for the Google Street View images of non-trained scenes, as test set. In addition, compared to an existing calibration method [3], the proposed method shows lower intermediate errors for the test set.