• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.767-772
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• 2007

A kernel machine is proposed as an estimating procedure for the linear and nonlinear Poisson regression, which is based on the penalized negative log-likelihood. The proposed kernel machine provides the estimate of the mean function of the response variable, where the canonical parameter is related to the input vector in a nonlinear form. The generalized cross validation(GCV) function of MSE-type is introduced to determine hyperparameters which affect the performance of the machine. Experimental results are then presented which indicate the performance of the proposed machine.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1141-1141
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• 2001

The aim of the study was to evaluate the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural network (ANN) on the prediction of chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with information relative to moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected with 10 different Foss NIR Systems instruments, which were either standardized or not standardized to one master instrument. The spectra were trimmed to a wavelength range between 1100 and 2498 nm. Two data sets, one standardized (IVAL) and the other not standardized (SVAL) were used as independent validation sets, but 10% of both sets were omitted and kept for later expansion of the calibration database. The remaining samples were combined into one database (n=21,696), which was split into 75% calibration (CALBASE) and 25% validation (VALBASE). The chemical components in the 3 validation data sets were predicted with each model derived from CALBASE using the calibration database before and after it was expanded with 10% of the samples from IVAL and SVAL data sets. Calibration performance was evaluated using standard error of prediction corrected for bias (SEP(C)), bias, slope and R2. None of the models appeared to be consistently better across all validation sets. VALBASE was predicted well by all models, with smaller SEP(C) and bias values than for IVAL and SVAL. This was not surprising as VALBASE was selected from the calibration database and it had a sample population similar to CALBASE, whereas IVAL and SVAL were completely independent validation sets. In most cases, Local and ANN models, but not modified PLS, showed considerable improvement in the prediction of IVAL and SVAL after the calibration database had been expanded with the 10% samples of IVAL and SVAL reserved for calibration expansion. The effects of sample processing, instrument standardization and differences in reference procedure were partially confounded in the validation sets, so it was not possible to determine which factors were most important. Further work on the development of large databases must address the problems of standardization of instruments, harmonization and standardization of laboratory procedures and even more importantly, the definition of the database population.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.461-474
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• 2020

Korea launched Geostationary Environmental Monitoring Satellite (GEMS), a UV/visible spectrometer that measure pollution gases on 18 February 2020. Because satellite retrieval is an ill-posed inverse solving process, the validation with ground-based measurements or other satellite measurements is essential to obtain reliable products. For this purpose, satellite-based OMI and OMPS total column ozone (TCO), and ground-based Pandora TCO in Busan and Seoul were selected for future GEMS validation. First of all, the goal of this study is to validate the ground ozone data using characteristics that satellite data provide coherent ozone measurements on a global basis, although satellite data have a larger error than the ground-based measurements. In the cross validation between Pandora and OMI TCO, we have found abnormal deviation in ozone time series from Pandora #29 observed in Seoul. This shows that it is possible to perform inverse validation of ground data using satellite data. Then OMPS TCO was compared with verified Pandora TCO. Both data shows a correlation coefficient of 0.97, an RMSE of less than 2 DU and the OMPS-Pandora relative mean difference of >4%. The result also shows the OMPS-Pandora relative mean difference with SZA, TCO, cross-track position and season have insignificant dependence on those variables.In addition, we showed that appropriate thresholds depending on the spatial resolution of each satellite sensor are required to eliminate the impact of the cloud on Pandora TCO.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.311-326
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• 2022

The Compact Advanced Satellite 500-4 (CAS500-4) is under development to efficiently manage and monitor forests in Korea and is scheduled to launch in 2025. The National Institute of Forest Science is developing 36 types of forestry applications to utilize the CAS500-4 efficiently. The products derived using the remote sensing method require validation with ground reference data, and the quality monitoring results for the products must be continuously reported. Due to it being the first time developing the national forestry satellite, there is no official calibration and validation site for forestry products in Korea. Accordingly, the author designed a calibration and validation site for the forestry products following international standards. In addition, to install calibration and validation sites nationwide, the authors selected appropriate sensors and evaluated the applicability of the sensors. As a result, the difference between the ground observation data and the Sentinel-2 image was observed to be within ±5%, confirming that the sensor could be used for nationwide expansion.

• Korean Journal of Radiology
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• v.23 no.11
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• pp.1078-1088
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• 2022

Objective: To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC). Materials and Methods: This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets. Results: Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62-0.89) and 0.77 (95% CI, 0.65-0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40-0.67], p = 0.006), CE-T1WI (0.53 [95% CI, 0.40-0.67], p = 0.012), and clinical parameters (0.53 [95% CI, 0.39-0.67], p = 0.021) in the external validation set. Conclusion: The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.

• Korean Journal of Radiology
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• v.20 no.5
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• pp.854-861
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• 2019

Objective: To evaluate quantitative magnetic resonance imaging (MRI) parameters for differentiation of cysts from and solid masses in the anterior mediastinum. Materials and Methods: The development dataset included 18 patients from two institutions with pathologically-proven cysts (n = 6) and solid masses (n = 12) in the anterior mediastinum. We measured the maximum diameter, normalized T1 and T2 signal intensity (nT1 and nT2), normalized apparent diffusion coefficient (nADC), and relative enhancement ratio (RER) of each lesion. RERs were obtained by non-rigid registration and subtraction of precontrast and postcontrast T1-weighted images. Differentiation criteria between cysts and solid masses were identified based on receiver operating characteristics analysis. For validation, two separate datasets were utilized: 15 patients with 8 cysts and 7 solid masses from another institution (validation dataset 1); and 11 patients with clinically diagnosed cysts stable for more than two years (validation dataset 2). Sensitivity and specificity were calculated from the validation datasets. Results: nT2, nADC, and RER significantly differed between cysts and solid masses (p = 0.032, 0.013, and < 0.001, respectively). The following criteria differentiated cysts from solid masses: RER < 26.1%; nADC > 0.63; nT2 > 0.39. In validation dataset 1, the sensitivity of the RER, nADC, and nT2 criteria was 87.5%, 100%, and 75.0%, and the specificity was 100%, 40.0%, and 57.4%, respectively. In validation dataset 2, the sensitivity of the RER, nADC, and nT2 criteria was 90.9%, 90.9%, and 72.7%, respectively. Conclusion: Quantitative MRI criteria using nT2, nADC, and particularly RER can assist differentiation of cysts from solid masses in the anterior mediastinum.

• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.205-211
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• 2008

This paper deals with the estimations of kernel ridge regression when the responses are subject to randomly right censoring. The iterative reweighted least squares(IRWLS) procedure is employed to treat censored observations. The hyperparameters of model which affect the performance of the proposed procedure are selected by a generalized cross validation(GCV) function. Experimental results are then presented which indicate the performance of the proposed procedure.

• 한국연소학회:학술대회논문집
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• 2004.11a
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• pp.62-69
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• 2004

Zone conditional formulations for the Reynolds average reaction progress variable are used to derive an asymptotic expression for turbulent burning velocity. New DNS runs are performed for validation in a statistically one dimensional steady state configuration. Parametric study is performed with respect to turbulent intensity, integral length scale, density ratio and laminar flame speed. Results show good agreement between DNS results and the asymptotic expression in terms of measured maximum flame surface density and estimated turbulent diffusivity in unburned gas.

• Communications for Statistical Applications and Methods
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• v.21 no.1
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• pp.105-114
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• 2014

In this paper we suggest an efficient method to estimate the distribution function using the Bezier curve, and compare it with existing methods by simulation studies. In addition, we suggest a robust version of cross-validation criterion to estimate the number of Bezier points, and showed that the proposed method is better than the existing methods based on simulation studies.

• 한국생물공학회:학술대회논문집
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• 2003.04a
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• pp.512-512
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• 2003