• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1285-1291
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• 2016

To quantify an concept we often use Likert summated rating scale of original or standardized variables in case the variables are relatively less. When variables have different scales, standardized values tends to be used rather than the original values. This is also true in evaluating systems. For example, we may use standardized values of local tax levy, population, and etc. and use the summed value of the standardized values to access the degrees of development. In this paper, we propose using a data-driven weighted sum for a scoring system and the way how to obtain the weights. We apply the proposed method to a real data set and find that proposed method is better than the usual summated rating scale.

• Korean Journal of Optics and Photonics
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• v.14 no.3
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• pp.266-271
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• 2003

The reflectance spectrum of optical films thicker than a few microns shows an intensity oscillation due to interference. Since the spectral period of the oscillation is inversely related to film thickness, the thickness of an optical film can be determined from the spectral frequency of the oscillation. For rapid data processing, the spectral frequency is obtained by use of a Fast Fourier Transformation technique. The conventional method of applying a Fast Fourier Transformation to the reflectance spectrum versus photon energy is modified so as to clear the ambiguity in choosing the proper effective refractive index value and to prevent the broadening of the Fourier transformed peak due to the refractive index dispersion. This technique of modified Fast Fourier Transformation is suggested by the authors for the first time to their knowledge. From the analysis of the calculated reflectance spectrum of a 30-${\mu}{\textrm}{m}$-thick dielectric film. it is shown to improve the accuracy in determining film thickness by a great amount. The improved accuracy of the modified Fast Fourier Transformation is also confirmed from the analysis of the reflectance spectra of a sample with 80-${\mu}{\textrm}{m}$-thick cover layer and 13-${\mu}{\textrm}{m}$-thick spacer layer on a PC substrate.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.252-252
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• 2022

Genotype-by-environment interaction (GEI) refers to the comparative response of genotypes to different environments conditions. Thus, understanding GEI is a fundamental component for selecting superior genotypes for breeding programs. The significance of utilizing early maturing cultivars not only provides flexibility in planting dates, but also serves as an effective strategy to reduce methane emission from the paddy fields. In this study, we conducted multi-environment trials (METs) to evaluate yield-related traits such as culm length, panicle length, panicle number, spikelet per plant, and thousand grain weight. A total of eighty-one Korean commercial rice cultivars categorized as early maturing cultivars, were cultivated in three regions, two planting seasons for two years. The genotype main effect plus genotype-by-environment interaction (GGE) biplot analysis of yield-related traits and grain yield explained 70.02-91.24% of genotype plus GEI variation, and exhibited various patterns of mega-environment delineation, discriminating ability, representativeness, and genotype rankings across the planting seasons and environments. Moreover, simultaneous selection using weighted average of absolute scores from the singular value decomposition (WAASB) and multi-trait stability index (MTSI) revealed six highly recommended genotypes with high stability and crop productivity. The winning genotypes under specific environment can be utilized as useful genetic materials to develop regional specialty cultivars, and recommended genotypes can be used as elite climate-resilient parents to improve yield-potential and reduce methane emission as part to accomplish carbon-neutrality.

• Industry Promotion Research
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• v.8 no.4
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• pp.29-36
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• 2023

In order to prevent industrial accidents, this study presented a methodology to ensure that risk aggravating factors are reflected in risk assessments at manufacturing sites and demonstrated it by applying it to actual manufacturing sites. As a result of a statistical analysis of all 242,906 accidents that occurred in the manufacturing industry over the past 10 years, new workers less than 6 months old, foreign workers, older workers over 55 years old, and jobs where hands and arms are exposed to risk areas, Non-routine work performed from 9 o'clock to 12 o'clock showed a significantly high accident rate. In addition, a weighted value was applied to estimate the possibility of an accident at the risk determination stage through focus group interviews. Through the results of this study, risk weighting factors can be quantitatively reflected in risk assessment, which is meaningful in preventing accidents by evaluating the size of the identified risk closer.

• Annals of Occupational and Environmental Medicine
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• v.35
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• pp.35.1-35.11
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• 2023

Background: Cardio-cerebrovascular diseases (CVDs) are the most common cause of death worldwide. Various CVD risk assessment tools have been developed. In South Korea, the Korea Occupational Safety & Health Agency (KOSHA) and the National Health Insurance Service (NHIS) have provided CVD risk assessments with health checkups. Since 2018, the KOSHA guide has stated that NHIS CVD risk assessment tool could be used as an alternative of KOSHA assessment tool for evaluating CVD risk of workers. The objective of this study was to determine the correlation and agreement between the KOSHA and the NHIS CVD risk assessment tools. Methods: Subjects of this study were 17,485 examinees aged 20 to 64 years who had undergone medical examinations from January 2021 to December 2021 at a general hospital. We classified subjects into low-risk, moderate-risk, high-risk, and highest-risk groups according to KOSHA and NHIS's CVD risk assessment tools. We then compared them with cross-analysis, Spearman correlation analysis, and linearly weighted kappa coefficient. Results: The correlation between KOSHA and NHIS tools was statistically significant (p-value < 0.001), with a correlation coefficient of 0.403 and a kappa coefficient of 0.203. When we compared risk group distribution using KOSHA and NHIS tools, CVD risk of 6,498 (37.1%) participants showed a concordance. Compared to the NHIS tool, the KOSHA tool classified 9,908 (56.7%) participants into a lower risk category and 1,079 (6.2%) participants into a higher risk category. Conclusions: In this study, KOSHA and NHIS tools showed a moderate correlation with a fair agreement. The NHIS tool showed a tendency to classify participants to higher CVD risk group than the KOSHA tool. To prevent CVD more effectively, a higher estimation tool among verified CVD risk assessment methods should be selected and managements such as early intervention and treatment of risk factors should be performed targeting the high-risk group.

• Journal of radiological science and technology
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• v.34 no.4
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• pp.323-331
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• 2011

We evaluated clinical usefulness of Arterial spin labeling perfusion MR imaging on the acute ischemic cerebral infarction patients through this study. We compared 22 patients who were done with DSC imaging and ASL imaging in admitted emergency room with acute ischemic cerebral infarction, with 36 normal comparison persons (DSC image on 21persons, ASL images on 15persons). Siemens Magnetom Verio 3.0T with 12 channel head coil was used for this study. DSC image obtained 4 maps(rCBV, rCBF, rMTT, TTP) through post-processing. For qualitative analysis we compared the area of lesion macro-diagonal with the size of diffusion weighted MR image for rMTT, TTP, rCBF, rCBV, ASL maps. For Quantitative analysis we analyzed significant correlations between less than 3 cm infarction group and normal comparison group using mean relative value of flowing image with Mann-Whitney U test. TTP(95.5%) and rCBF(95.5%) maps showed high recognition rate in qualitative analysis for >3cm infarction group. The rCBF and rCBV map tests were highly related with final stage stroke areas. Mean relative value of infarction group showed a significant correlations in quantitative analysis(p<0.05). As a conclusion, arterial spin labeling image showed high lesion recognition rate in the >3cm infarction group. Mean relative values in quantitative evaluation were used for reference data. If we do more sustainable researches, ASL image will be useful for an early diagnosis of cerebral infarction, determination of the range of ischemic pneumbra and effective treatments.

• Journal of the Korean Society of Radiology
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• v.83 no.1
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• pp.112-126
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• 2022

Purpose To determine the value of 3 Tesla (T) MRI texture analysis for predicting tumor margin infiltration in soft tissue sarcomas. Materials and Methods Thirty-one patients who underwent 3T MRI and had a pathologically confirmed diagnosis of soft tissue sarcoma were included in this study. Margin infiltration on pathology was used as the gold standard. Texture analysis of soft tissue sarcomas was performed on axial T1-weighted images (WI) and T2WI, fat-suppressed contrast-enhanced (CE) T1WI, diffusion-weighted images (DWI) with b-value of 800 s/mm², and apparent diffusion coefficient (ADC) was mapped. Quantitative parameters were compared between sarcomas with infiltrative margins and those with circumscribed margins. Results Among the 31 patients with soft tissue sarcomas, 23 showed tumor margin infiltration on pathology. There were significant differences in kurtosis with the spatial scaling factor (SSF) of 0 and 6 on T1WI, kurtosis (SSF, 0) on CE-T1WI, skewness (SSF, 0) on DWI, and skewness (SSF, 2, 4) on ADC between sarcomas with infiltrative margins and those with circumscribed margins (p ≤ 0.046). The area under the receiver operating characteristic curve based on MR texture features for identification of infiltrative tumor margins was 0.951 (p < 0.001). Conclusion MR texture analysis is reliable and accurate for the prediction of infiltrative margins of soft tissue sarcomas.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• Investigative Magnetic Resonance Imaging
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• v.21 no.1
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• pp.9-19
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• 2017

Background: Normalized cerebral blood volume (nCBV) can be measured using manual or semiautomatic segmentation method. However, the difference in diagnostic performance on brain tumor differentiation between differently measured nCBV has not been evaluated. Purpose: To compare the diagnostic performance of manually obtained nCBV to that of semiautomatically obtained nCBV on glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) differentiation. Materials and Methods: Histopathologically confirmed forty GBM and eleven PCNSL patients underwent 3T MR imaging with dynamic susceptibility contrast-enhanced perfusion MR imaging before any treatment or biopsy. Based on the contrast-enhanced T1-weighted imaging, the mean nCBV (mCBV) was measured using the manual method (manual mCBV), random regions of interest (ROIs) placement by the observer, or the semiautomatic segmentation method (semiautomatic mCBV). The volume of enhancing portion of the tumor was also measured during semiautomatic segmentation process. T-test, ROC curve analysis, Fisher's exact test and multivariate regression analysis were performed to compare the value and evaluate the diagnostic performance of each parameter. Results: GBM showed a higher enhancing volume (P = 0.0307), a higher manual mCBV (P = 0.018) and a higher semiautomatic mCBV (P = 0.0111) than that of the PCNSL. Semiautomatic mCBV had the highest value (0.815) for the area under the curve (AUC), however, the AUCs of the three parameters were not significantly different from each other. The semiautomatic mCBV was the best independent predictor for the GBM and PCNSL differential diagnosis according to the stepwise multiple regression analysis. Conclusion: We found that the semiautomatic mCBV could be a better predictor than the manual mCBV for the GBM and PCNSL differentiation. We believe that the semiautomatic segmentation method can contribute to the advancement of perfusion based brain tumor evaluation.

• Korean Journal of Plant Resources
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• v.35 no.4
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• pp.471-501
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• 2022

This study was carried out on 455 forest wetlands of south Korea for which an inventory was established through value evaluation and grade. Correlation analysis was conducted to find out the correlation between the types and grades of forest wetlands and 23 evaluation factors in four categories: vegetation and landscape, material circulation and hydraulics·hydrology, humanities and social landscape, and disturbance level. Through the improvement of types and grades of forest wetlands, it is possible to secure basic data that can be used in setting up conservation measures by preparing standards necessary for future forest wetland conservation and restoration, and to found a systematic monitoring system. First, between the type of forest wetland and size and accessibility showed a positive correlation, but the remaining items were analyzed to have negative or no correlation. In particular, it was found that there was no negative correlation or no correlation with the grades of forest wetland. Moreover, it was found that there was a very strong negative correlation with the weighted four category items. Thus, it is judged that improvement is necessary because there is an error in the weight or adjust the evaluation criteria of the value evaluation item, add an item that can increase objectivity. Especially, in the case of forest wetlands, the ecosystem service function due to biodiversity is the largest, so evaluation items should be improved in consideration of this. Therefore, it can be divided into five categories: uniqueness and rarity (15%), wildlife habitat (15%), vegetation and landscape (35%), material cycle·hydraulic hydrology (30%), and humanities and social landscape (5%). It will be possible to propose weights that can increase effectiveness.