• Journal of the Korean Data and Information Science Society
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• v.14 no.1
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• pp.131-141
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• 2003

In this paper, we prove that multi-variate fuzzy polynomials are universal approximators for multi-variate fuzzy functions which are the extension principle of continuous real-valued function under $T_W-based$ fuzzy arithmetic operations for a distance measure that Buckley et al.(1999) used. We also consider a class of fuzzy polynomial regression model. A mixed non-linear programming approach is used to derive the satisfying solution.

• Journal of Korean Tunnelling and Underground Space Association
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• v.13 no.6
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• pp.531-555
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• 2011

This study aims to derive the statistical models for the estimation of the required specifications of a rock TBM as well as for its cutterhead design suitable for a given rock mass condition. From a series of multi-variate regression analysis of 871 TBM driving data and 51 linear rock cutting test results, the optimum models were newly proposed to consider a variety of rock properties and mechanical cutting conditions. When the derived models were applied to two domestic shield tunnels, their predictions of cutter penetration depth, cutter acting forces and cutter spacing were very close to real TBM driving data, showing their high applicability.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.2
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• pp.108-113
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• 2016

The real-world problems usually show nonlinear and multi-variate characteristics, so it is difficult to establish concrete mathematical models for them. Thus, it is common to practice data-driven modeling techniques in these cases. Among them, most widely adopted techniques are regression model and intelligent model such as neural networks. Regression model has drawback showing lower performance when much non-linearity exists between input and output data. Intelligent model has been shown its superiority to the linear model due to ability capable of effectively estimate desired output in cases of both linear and nonlinear problem. This paper proposes modeling method of daily photovoltaic power systems using ELM(Extreme Learning Machine) based modular networks. The proposed method uses sub-model by fuzzy clustering rather than using a single model. Each sub-model is implemented by ELM. To show the effectiveness of the proposed method, we performed various experiments by dataset acquired during 2014 in real-plant.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.581-593
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• 2006

Various geological thematic maps have been generated by interpolating sparsely sampled ground survey data and geostatistical kriging that can consider spatial correlation between neighboring data has widely been used. This paper applies multi-variate geostatistical algorithms to integrate secondary information with sparsely sampled ground survey data for geological thematic mapping. Simple kriging with local means and kriging with an external drift are applied among several multi-variate geostatistical algorithms. Two case studies for spatial mapping of groundwater level and grain size have been carried out to illustrate the effectiveness of multi-variate geostatistical algorithms. A digital elevation model and IKONOS remote sensing imagery were used as secondary information in two case studies. Two multi-variate geostatistical algorithms, which can account for both spatial correlation of neighboring data and secondary data, showed smaller prediction errors and more local variations than those of ordinary kriging and linear regression. The benefit of applying the multi-variate geostatistical algorithms, however, depends on sampling density, magnitudes of correlation between primary and secondary data, and spatial correlation of primary data. As a result, the experiment for spatial mapping of grain size in which the effects of those factors were dominant showed that the effect of using the secondary data was relatively small than the experiment for spatial mapping of groundwater level.

• Journal of Environmental Science International
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• v.15 no.9
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• pp.887-895
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• 2006

Through this research of the analysis on the frequency flood discharges regarding basin property factors, a linear regression system was introduced, and as a result, the item with the highest correlation with the frequency flood discharges from Nakdong river basin is the basin area, and the second highest is the average width of basin and the river length. The following results were obtained after looking at the multi correlation between the flood discharge and the collected basin property factors using the data from the established river maintenance master plan of the one hundred twenty-five rivers in the Nakdong river basin. The result of analysis on multivariate correlation between the flood discharges and the most basic data in determining the flood discharges as basin area, river length, basin slope, river slope, average width of basin, shape factor and probability precipitation showed more than 0.9 of correlation in terms of the multi correlation coefficient and more than 0.85 for the determination coefficient. The model which induced a regression system through multi correlation analysis using basin property factors is concluded to be a good reference in estimating the design flood discharge of unmeasured basin.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.265-267
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• 2020

본 논문에서는 열간압연 공정에 있어 효율적인 제품 생산 스케줄링에 필수적인 제품단위 에너지 사용 모델링 기법을 제안한다. 제안된 모델은 시스템 자원을 효율적 혹은 최소화하여 사용하여 실시간 처리량을 최대화함으로써 생산 예정 리스트로부터의 예측 작업 수행시간을 최소화할 수 있도록 한다. 제안된 기법은 다변량 선형 모델 방식으로 구성됨으로써 인공 지능 혹은 신경망 학습 방식에 비교하여 그 처리 속도가 빠르다는 장점을 가지고 있다. 본 논문에서는 서두에서 대상 응용처인 철강 산업과 열간 압연 공정 및 에너지 스케줄링에 대하여 간략히 언급한 후 본문에서 모델링을 위한 사전 데이터 수집, 모델링 기법을 자세히 설명하고 결론에서 모델의 정확도 성능을 최신 신경망 기법과 비교하여 검증하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.73-82
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• 2017

In this research, we tried to check the applicability of a Chow test to the linear models which are generated in the process of transportation planning or traffic flow analyses. The Chow test is a very popular statistical method which is being used to see if the coefficients from two separate linear regression models are equal or not. In order to prove the effectiveness of the Chow test, we found the linear relationships between speed and density under the situations such as driving in daytime and in nighttime on a rainy day. Based on the two months of Joong-Bu Expressway traffic data, we proved that the Chow test is useful to testify the similarity between two linear regression models. And this statistical tool seems to be able to have a very important role in traffic flow analysis or in transportation planning process. Finally, we expect the Chow test be implemented even to the non-linear regression models or to the multi-variate models.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.1
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• pp.29-35
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• 2009

The aim of the present study was to update the calibration that is used for the measurement of the total nitrogen content in the rice plant samples by using the visible and near infrared spectrum. Before the equation merge, correlation coefficient of calibration equation for nitrogen content on each rice parts was 0.945 (Leaf), 0.928 (Stem), and 0.864 (Whole plant), respectively. In the calibration models created by each part in the rice plant under the various regression method, the calibration model for the leaf was recorded with relatively high accuracy. Among of those, the calibration equation developed by Partial least squares (PLS) method was more accurate than the Multiple linear regression (MLR) method. The calibration equation was sensitive based on variety and location variations. However, we have merged and enlarged various of the samples that made not only to measure the nitrogen content more accurately, but also later sampling populations became more diversified. After merging, $R^2$ value becomes more accurate and significantly to 0.950 (L.), 0.974 (S.), 0.940 (W.). Also, after removal of outlier, R2 values increased into 0.998, 0.995, and 0.997. In view of the results so far achieved, Standard error of prediction (SEP) and SEP (C) were reduced in the stem and whole plant. Biases were reduced in the leaf, stem as well as whole plant. Slopes were high in the stem. Standard deviation reduced in the stem but $R^2$ was high in the stem and whole plant. Result was indicated that calibration equation make update, and updating robust calibration equation from merge function and multi-variate calibration.

• Korea Journal of Hospital Management
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• v.12 no.3
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• pp.47-67
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• 2007

This paper aims at suggesting several ways lo change financial vulnerability and to improve managerial capability of local public hospitals (LPHs) in Korea through the identification of factors affecting profitability. Several findings of the research are as follows: To begin with, LPHs exhibited a statistically significant difference in their profitability from one another, according to tile analyses of their profitable margins from tile general characteristics. It depends on the number of hospitals in the area, the population of the hospital-built area, the number of competing hospitals, the number of staff per 100 beds, the opening of special clinic, the educational function, and the capacity of rooms. However, there was no variable in the managerial characteristics, presenting a significant difference, in contrast with hospitals which have been managed by private companies and made a great amount of profits. Second, according to the analyses of profit differences in behavioral effort-characteristics, a statistically significant difference was revealed upon the basis of the efforts to improve the clinic service, invite special patients, and shorten the period of being hospitalized. Third, the result of analyses about the difference of profitability from medical care and finance is statistically significant in the rate of labor cost, the rate of management cost, bed-occupancy rate, and the period of being hospitalized. Fourth, according to the analyses of the factors influencing the net profit ratio of the entire capital, Adjusted explanatory power(Adjusted $R^2$) was shown up to 65.2%, which is high. To compare the adjusted explanatory power stage by stage, the first stage model applying only two variables such as structural and strategic characteristics exhibited 23.8%, and the second stage model adding financial characteristics showed 51.5%. The explanatory power was much improved up to 65.2% when the third stage model incorporated the outcome of medical care performance. When the return on investment(ROI) was examined by using the multi-variate linear regression analysis at the final model of third stage, it was found that ROI had a positive relationship with the increase rate of patients, labor costs per doctor, and medical care rate of socially protected inpatients. However, it revealed that ROI had a negative relationship with the ratio of labor costs, the number of patients per managerial staff, and occupancy rate of rooms, respectively. The research suggests that in order for LPHs to increase profitability, LPH, should make efforts not only to attract patients to the hospitals without any discrimination of the patients depending on their financial status, but also to develop efficient management methods to reduce labor costs.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.133-149
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• 2022

Existing domestic studies on estimating rice yield were mainly implemented at the level of cities and counties in the entire nation using MODIS satellite images with low spatial resolution. Unlike previous studies, this study tried to estimate rice yield at the level of eup-myon-dong in Gimje-si, Jeollabuk-do using Sentinel-2 satellite images with medium spatial resolution, rainfall and soil data, and then to evaluate its accuracy. Five vegetation indices such as NDVI, LAI, EVI2, MCARI1 and MCARI2 derived from Sentinel-2 images of August 1, 2018 for Gimje-si, Jeollabuk-do, rainfall and paddy soil-type data were aggregated by the level of eup-myon-dong and then rice yield was estimated with gamma generalized linear model, an expanded variant of multi-variate regression analysis to solve the non-normality problem of dependent variable. In the rice yield model finally developed, EVI2, rainfall days in September, and saline soils ratio were used as significant independent variables. The coefficient of determination representing the model fit was 0.68 and the RMSE for showing the model accuracy was 62.29kg/10a. This model estimated the total rice production in Gimje-si in 2018 to be 96,914.6M/T, which was very close to 94,470.3M/T the actual amount specified in the Statistical Yearbook with an error of 0.46%. Also, the rice production per unit area of Gimje-si was amounted to 552kg/10a, which was almost consistent with 550kg/10a of the statistical data. This result is similar to that of the previous studies and it demonstrated that the rice yield can be estimated using Sentinel-2 satellite images at the level of cities and counties or smaller districts in Korea.