• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.4
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• pp.183-190
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• 2018

Predicting accurate electricity prices is an important task in the electricity trading market. To address the electricity price forecasting problem, various approaches have been proposed so far and it is known that linear regression-based approaches are the best. However, the use of such linear regression-based methods is limited due to low accuracy and performance. In traditional linear regression methods, it is not practical to find a nonlinear regression model that explains the training data well. If the training data is complex (i.e., small-sized individual data and large-sized features), it is difficult to find the polynomial function with n terms as the model that fits to the training data. On the other hand, as a linear regression model approximating a nonlinear regression model is used, the accuracy of the model drops considerably because it does not accurately reflect the characteristics of the training data. To cope with this problem, we propose a new electricity price forecasting method that divides the entire dataset to multiple split datasets and find the best linear regression models, each of which is the optimal model in each dataset. Meanwhile, to improve the performance of the proposed method, we modify the proposed localized linear regression method in the map and reduce way that is a framework for parallel processing data stored in a Hadoop distributed file system. Our experimental results show that the proposed model outperforms the existing linear regression model. Specifically, the accuracy of the proposed method is improved by 45% and the performance is faster 5 times than the existing linear regression-based model.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1435-1447
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• 2020

The seasonal variations in weather parameters can significantly affect the atmospheric transmission characteristics. Herein, we propose a novel augmented multiple regression algorithm for the accurate estimation of atmospheric transmittance and solar irradiance over highly localized areas. The algorithm employs 1) adaptive atmospheric model selection using measured meteorological data and 2) multiple linear regression computation augmented with the conventional application of MODerate resolution atmospheric TRANsmission (MODTRAN). In this study, the proposed algorithm was employed to estimate the solar irradiance over Taean coastal area using the 2018 clear days' meteorological data of the area, and the results were compared with the measurement data. The difference between the measured and computed solar irradiance significantly improved from 89.27 ± 48.08σ W/㎡ (with standard MODTRAN) to 21.35 ± 16.54σ W/㎡ (with augmented multiple regression algorithm). The novel method proposed herein can be a useful tool for the accurate estimation of solar irradiance and atmospheric transmission characteristics of highly localized areas with various weather conditions; it can also be used to correct remotely sensed atmospheric data of such areas.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.5
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• pp.561-568
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• 2003

Surface ozone concentrations are highly sensitive to meteorological variability. Therefore, in order to reveal the long-term changes in ozone due to the changes in precursor emissions, we need to remove the effects of meteorological fluctuations on the annual distribution of surface ozone. In this paper, the meteorologically adjusted trends of daily maximum surface ozone concentrations in two major Korean cities (Seoul and Busan) are investigated based on ozone data from 11 (Seoul) and 6 (Busan) sites over the period 1992 ∼ 2000. The original time series consisting of the logarithm of daily maximum ozone concentrations are splitted into long-term, seasonal and short-term component using Kolmogorov-Zurbenko (KZ) filter. Meteorological effects are removed from filtered ozone series using multiple linear regression based on meteorologcial variables. The long-term evolution of ozone forming capability due to changes in precursor emission can be obtained applying the KZ filter to the residuals of the regression. The results indicated that meteorologically adjusted long-term daily maximum ozone concentrations had a significant upward trend (Seoul: ＋ 3.02% yr$^{-1}$ , Busan: ＋ 3.45% yr$^{-1}$ ). These changes of meteorologically adjusted ozone concentrations represent the effects of changing background ozone concentrations as well as the more localized changes in emissions.

• Imaging Science in Dentistry
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• v.50 no.2
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• pp.117-124
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• 2020

Purpose: The purpose of this study was to evaluate possible differences in the location of the inferior alveolar canal in male and female Egyptians. Materials and Methods: This cross-sectional retrospective study involved the evaluation of 210 CBCT scans of Egyptian individuals (18-70 years old). The inferior alveolar canal was localized by measuring 8 linear dimensions: 2 for the vertical localization of the mental foramen (superior and inferior to the mental foramen), 4 at the first molar bifurcation for the vertical and horizontal localization of the inferior alveolar canal (superior, inferior, buccal, and lingual to the inferior alveolar canal), and 2 for the horizontal localization of the mandibular foramen (anterior and posterior to the mandibular foramen). The measurements were statistically analyzed via comparative analysis, stepwise logistic regression, and receiver operating characteristic (ROC) curve analysis. Results: Six of the 8 measured distances differed to a statistically significant extent between the sexes. Regression analysis suggested a logistic function with a concordance index of 84%. The diagnostic accuracy capabilities of the linear measurements as sex predictors were calculated using ROC analysis, and the 6 best predictors for sex determination were selected and ranked from highest to lowest predictive power. Moreover, combining these 6 predictors increased the predictive power to 84%. Conclusion: The location of the inferior alveolar canal in the Egyptian population varies significantly by sex; accordingly, this anatomic landmark could be used as a reliable indicator of sexual dimorphism.

• Journal of radiological science and technology
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• v.46 no.2
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• pp.131-139
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• 2023

High-dose I-131 used for the treatment of thyroid cancer causes localized exposure among radiology technologists handling it. There is a delay between the calibration date and when the dose of I-131 is administered to a patient. Therefore, it is necessary to directly measure the radioactivity of the administered dose using a dose calibrator. In this study, we attempted to apply machine learning modeling to measured external dose rates from shielded I-131 in order to predict their radioactivity. External dose rates were measured at 1 m, 0.3 m, and 0.1 m distances from a shielded container with the I-131, with a total of 868 sets of measurements taken. For the modeling process, we utilized the hold-out method to partition the data with a 7:3 ratio (609 for the training set:259 for the test set). For the machine learning algorithms, we chose linear regression, decision tree, random forest and XGBoost. To evaluate the models, we calculated root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) to evaluate accuracy and R² to evaluate explanatory power. Evaluation results are as follows. Linear regression (RMSE 268.15, MSE 71901.87, MAE 231.68, R² 0.92), decision tree (RMSE 108.89, MSE 11856.92, MAE 19.24, R² 0.99), random forest (RMSE 8.89, MSE 79.10, MAE 6.55, R² 0.99), XGBoost (RMSE 10.21, MSE 104.22, MAE 7.68, R² 0.99). The random forest model achieved the highest predictive ability. Improving the model's performance in the future is expected to contribute to lowering exposure among radiology technologists.

• Physical Therapy Korea
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• v.8 no.2
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• pp.41-54
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• 2001

Fatigue is the decline in force produced as a result of prolonged muscle activity. Localized muscle fatigue can be identified by a shift toward low in the frequency components of the EMG signal, typically represented by a fall in the median frequency. Previous studies show that a shortened muscle develops a higher fatigue than elongated muscles. The purpose of this study was to investigate the time-related change of median frequency and torque during maximal isometric back extension exercises at different exercise angles ($0^{\circ}$, $12^{\circ}$, $36^{\circ}$, $72^{\circ}$). Twenty healthy subjects (mean age = $24.35{\pm}2.70$) were evaluated in this study. Median frequency was extracted from the EMG signals by fast Fourier transform (FFT). Initial median frequency and the slope of median frequency change over time were computed from linear regression analysis. Pearson's product moment correlation was used to quantify the relationship between slope of median frequency and torque. The results were as follows: 1) Significant differences in y-intercepts of torque regression equation with respect to exercise angle were shown. However, there were no differences in the slopes of the median frequency and torque, and y intercept of the median frequency among exercise angles. 2) There was no significant correlation between slope of median frequency and torque. 3) But there was moderate correlation between median frequency and torque at each exercise angle. In conclusion, the exercise angle during maximal isometric back extension exercise is not a direct effect on slope of median frequency and torque. But results showed that median frequency and torque shift were highly correlated in all subjects.