• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.265-277
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• 1999

Classical and inverse estimation methods are two well known methods in statistical calibration problems. When there are outliers, both methods have large MSE's and could not estimate the input value correctly. We suggest median calibration estimation based on the LD-statistics. To investigate the robust performances, the influence function of the median calibration estimator is calculated and compared with other methods. When there are outliers in the response variables, the influence function is found to be bounded. In simulation studies, the MSE's for each calibration methods are compared. The estimated inputs as well as the performance of the influence functions are calculated.

• Journal of Mechanical Science and Technology
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• v.14 no.1
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• pp.113-120
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• 2000

This study has developed a new liquid crystal calibration technique using Neural networks with median filtering and applied this technique to heat transfer measurements. To verify the validity of this new measurement technique, the local Nusselt numbers on a flat plate surface subjected to an axisymmetric impinging jet were measured and compared with the results by the conventional Hue-temperature calibration technique under the same conditions. Because the Neural networks predict the non-linear relations between temperatures and corresponding R, G, B values, Neural networks-median filtering calibration technique can utilize a much wider color band in the experiment than the Hue-temperature calibration technique, resulting in a significant reduction in the experimental time.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.224-229
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• 2007

This paper presents a camera calibration method using several images for three dimensional measurement applications such as stereo systems, mobile robots, and visual inspection systems in factories. Conventional calibration methods that use single image suffer from errors related to reference point extraction in image, lens distortion, and numerical analysis of nonlinear optimization. The camera parameter values obtained from images of same camera is not same even though we use same calibration method. The camera parameters that are obtained from several images of different view for a calibration target is usaully not same with large error values and we can not assume a special probabilistic distribution when we estimate the parameter values. In this paper, the median value of camera parameters from several images is used to improve estimation of the camera values in an iterative step with nonlinear optimization. The proposed method is proved by experiments using real images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1711-1717
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• 2017

In this paper, we propose a fast and accurate method of extracting lens array lattice in integral imaging by using an appropriate calibration pattern image and fast median filtering. In order to extract the lattice of a lens array, vertical and horizontal edge images are required. To extract edge images, the well-known previous method used separable median filters. However, this method is slow and difficult to determine the median filter size. In order to overcome this problem, we try to improve speed by calculating median value through binary counting method. In addition, we propose a calibration pattern image that detects edges well and improves the accuracy. Experimental results indicate that the proposed method is superior to the existing method in extracting the lattice of a lens array in integral imaging.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.995-1006
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• 2008

This article deals with the one-sided hypothesis testing problem in inverse Gaussian distribution. We propose Bayesian hypothesis testing procedures for the one-sided hypotheses of the shape parameter under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian hypothesis testing procedures based on the fractional Bayes factor, the median intrinsic Bayes factor and the encompassing intrinsic Bayes factor under the reference prior. Simulation study and a real data example are provided.

• Korean Journal of Agricultural Science
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• v.39 no.3
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• pp.413-420
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• 2012

Spectroscopic measurement method based on visible and near-infrared wavelengths was prominent technology for rapid and non-destructive evaluation of internal quality of fruits. Reflectance measurement was performed to evaluate firmness, soluble solid content, and acid content of truss tomatoes by hyperspectral reflectance imaging system. The Vis/NIR reflectance spectra was acquired from truss tomatoes sorted by 6 ripening stages. The multivariable analysis based on partial least square (PLS) was used to develop regression models with several preporcessing methods, such as smoothing, normalization, multiplicative scatter correction (MSC), and standard normal variate (SNV). The best model was selected in terms of coefficient of determination of calibration ($R_c^2$) and full cross validation ($R_{cv}^2$), and root mean standard error of calibration (RMSEC) and full cross validation (RMSECV). The results of selected models were 0.8976 ($R_p^2$), 6.0207 kgf (RMSEP) with gaussian filter of smoothing, 0.8379 ($R_p^2$), $0.2674^{\circ}Bx$ (RMSEP) with the mean of normalization, and 0.7779 ($R_p^2$), 0.1033% (RMSEP) with median filter of smoothing for firmness, soluble solid content (SSC), and acid content, respectively. Results show that Vis / NIR hyperspectral reflectance imaging technique has good potential for the measurement of internal quality of truss tomato.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.249-257
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• 2017

Filtering the data for travel time records obtained from DSRC probes is essential for a better estimation of the link travel time. This study addresses the major deficiency in the performance of TRANSGUIDE in removing anomalous data. This algorithm is unable to handle unstable traffic flow conditions for certain time intervals, where fluctuations are observed. In this regard, this study proposes an algorithm that is capable of overcoming the weaknesses of TRANSGUIDE. If TRANSGUIDE fails to validate sufficient number of observations inside one time interval, another process specifies a new validity range based on the median absolute deviation (MAD), a common statistical approach. The proposed algorithm suggests the parameters, ${\alpha}$ and ${\beta}$, to consider the maximum allowed outlier within a one-time interval to respond to certain traffic flow conditions. The parameter estimation relies on historical data because it needs to be updated frequently. To test the proposed algorithm, the DSRC probe travel time data were collected from a multilane highway road section. Calibration of the model was performed by statistical data analysis through using cumulative relative frequency. The qualitative evaluation shows satisfactory performance. The proposed model overcomes the deficiency associated with the rapid change in travel time.

• 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.

• Radiation Oncology Journal
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• v.36 no.2
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• pp.139-146
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• 2018

Purpose: IBTR! 2.0 nomogram is web-based nomogram that predicts ipsilateral breast tumor recurrence (IBTR). We aimed to validate the IBTR! 2.0 using an external data set. Materials and Methods: The cohort consisted of 2,206 patients, who received breast conserving surgery and radiation therapy from 1992 to 2012 at our institution, where wide surgical excision is been routinely performed. Discrimination and calibration were used for assessing model performance. Patients with predicted 10-year IBTR risk based on an IBTR! 2.0 nomogram score of <3%, 3%-5%, 5%-10%, and >10% were assigned to groups 1, 2, 3, and 4, respectively. We also plotted calibration values to observe the actual IBTR rate against the nomogram-derived 10-year IBTR probabilities. Results: The median follow-up period was 73 months (range, 6 to 277 months). The area under the receiver operating characteristic curve was 0.607, showing poor accordance between the estimated and observed recurrence rate. Calibration plot confirmed that the IBTR! 2.0 nomogram predicted the 10-year IBTR risk higher than the observed IBTR rates in all groups. High discrepancies between nomogram IBTR predictions and observed IBTR rates were observed in overall risk groups. Compared with the original development dataset, our patients had fewer high grade tumors, less margin positivity, and less lymphovascular invasion, and more use of modern systemic therapies. Conclusions: IBTR! 2.0 nomogram seems to have the moderate discriminative ability with a tendency to over-estimating risk rate. Continued efforts are needed to ensure external applicability of published nomograms by validating the program using an external patient population.

• Journal of Korean Society on Water Environment
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• v.35 no.6
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• pp.520-538
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• 2019

In this study, it was proposed that a method of setting the target water quality for TOC using the watershed model and the load duration curves to manage non-biodegradable organics in the total water load management system. To simulate runoff and water quality of the watershed, the HSPF model is used which is appropriate for urban and rural areas. Additionally, the load duration curve is used to reflect the variable water quality correlated with various river flow rates in preparing the TMDL plans in the U.S. First, the model was constructed by inputting the loads calculated from the pollutant sources in 2015. After the calibration and verification process, the water quality by flow conditions was analyzed from the BOD and TOC simulation results. When the BOD achieved the target water quality by inputting the target year loads for 2020, the median and average values of TOC were proposed for the target water quality. The provisional method of TOC target water quality for the management of non-biodegradable organics, which is one of the challenges of the total water load management system, was considered. In the future, it is expected to be used as basic data for the conversion of BOD into TOC in the total water load management system.