• Journal of electromagnetic engineering and science
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• v.10 no.4
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• pp.231-236
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• 2010

This study proposes a temperature compensation method of the complex permittivities of biological tissues and organs. The method is based on the temperature dependence of the Debye model of water, which has been thoroughly investigated. This method was applied to measured data at room temperature for whole blood, kidney cortex, bile, liver, and heart muscle. It is shown that our method can compensate for the Cole-Cole model using measured data at 20 $^{\circ}C$, given the Cole-Cole model based on measured data at 35 $^{\circ}C$, with a root-mean-squared deviation of 3~11 % and 2~6 % for the real and imaginary parts of the complex permittivities, respectively, among the measured tissues.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.247-264
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• 2008

This paper considers the integrated process control procedure for detecting special causes in an ARIMA(0,1,1) process that is being adjusted automatically after each observation using a minimum mean squared error adjustment policy. It is assumed that a special cause can change the process mean and the process variance. We derive expressions for the process deviation from target for a variety of different process parameter changes, and introduce a control chart, based on the generalized likelihood ratio, for detecting special causes. We also propose the integrated process control scheme bases on the future loss. The future loss denotes the cost that will be incurred in a process remaining interval from a true out-of-control signal.

• Journal of the Korean Solar Energy Society
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• v.33 no.3
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• pp.17-26
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• 2013

The aim of this study is to analyze the performance of a parabolic trough solar collector (PTC) for water heating and to validate the model performance. The simulated model was compared, calibrated and verified with the experimental results. RMSE (Root mean square error) was used to calibrate the convective heat transfer coefficient between the absorber pipe and the ambient air which was the main factor affecting the heat transfer associated with the PTC. The calibrated model was better fitted with the experimental model. The maximum, minimum and mean deviation between the measured and predicted water temperatures differed only $0.81^{\circ}C$, $0.09^{\circ}C$ and $0.31^{\circ}C$ respectively in the calibrated model. RMSE values were decreased from 0.5389 to 0.4910, 0.0134 to 0.0125 and R-squared was increased from 0.9955 to 0.9956 after calibration. The temperature of water was increased from $33.7^{\circ}C$ to $48^{\circ}C$ in 12hour test. The thermal efficiency of the collector was calculated to be 55%. The calibrated model showed good agreement with the experimental data for model validation.

• Wind and Structures
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• v.36 no.6
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• pp.367-377
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• 2023

This study proposes a few-shot learning model for extrapolating the wind pressure of scaled experiments to full-scale measurements. The proposed ML model can use scaled experimental data and a few full-scale tests to accurately predict the remaining full-scale data points (for new specimens). This model focuses on extrapolating the prediction to different scales while existing approaches are not capable of accurately extrapolating from scaled data to full-scale data in the wind engineering domain. Also, the scaling issue observed in wind tunnel tests can be partially resolved via the proposed approach. The proposed model obtained a low mean-squared error and a high coefficient of determination for the mean and standard deviation wind pressure coefficients of the full-scale dataset. A parametric study is carried out to investigate the influence of the number of selected shots. This technique is the first of its kind as it is the first time an ML model has been used in the wind engineering field to deal with extrapolation in wind performance prediction. With the advantages of the few-shot learning model, physical wind tunnel experiments can be reduced to a great extent. The few-shot learning model yields a robust, efficient, and accurate alternative to extrapolating the prediction performance of structures from various model scales to full-scale.

• Korean Journal of Psychosomatic Medicine
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• v.28 no.1
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• pp.81-88
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• 2020

Objectives : This study aimed to compare autonomic nervous system (ANS) dysregulation and differential relationships with clinical severities between anxiety disorder, depressive disorder, and trauma·stressor related disorder using heart rate variability (HRV) parameters. Methods : We conducted a retrospective chart review of outpatients from 2017 to 2018 in Stress Clinic of National Center for Mental Health. Total 473 patients were included; 166 anxiety disorder; 184 depressive disorder ; 123 trauma·stressor related disorder. Parameters of 5-min analysis of HRV were compared in three groups. Additionally, we investigated the differential association of each parameters with Clinical Global Impression-Severity Scale (CGI-S) across each group. Results : No significant differences were found in all HRV parameters between the three groups. However, significant group interactions by CGI-S were found in standard deviation of all RR intervals (SDNN) and the square root of the mean squared differences of successive normal-to-normal intervals (RMSSD) (SDNN, p=0.017 ; RMSSD, p=0.034). A negative relationship between CGI-S and SDNN, RMSSD has been found in anxiety disorder and depressive disorder. However, a positive relationship between CGI-S and SDNN, RMSSD has been found in trauma·stressor related disorder. Conclusions : Despite of no significant differences of each HRV parameter, our findings suggested the differential associations of HRV parameters with clinical severity among anxiety disorder, depressive disorder and trauma·stressor related disorder. In trauma·stressor related disorder, the clinical severity and degree of ANS dysregulation may differ, so more aggressive treatment is suggested.

• Horticultural Science & Technology
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• v.28 no.6
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• pp.1066-1071
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• 2010

To evaluate the effect of horticultural therapy (HT) on the emotional improvement of depressed patients, computer-based heart rate variability (HRV) was compared with self-report scale (SRS) known as existing subjective evaluation method. SRS included four test areas: mental stress scale (MSS), physical stress scale (PSS), Beck anxiety inventory (BAI), and Beck depression inventory (BDI). HRV was itemized into four parameters: standard deviation of the N-N intervals (SDNN), square root of mean squared difference of successive N-N intervals (RMSSD), total power (TP), and low-frequency/high-frequency ratio (LF/HF ratio). Thirty patients with depression at the same mental hospital participated in this study. 15 patients of the treatment group received HT once a week for three months, but the control group did not during the same period. As a result, the emotional improvement in treatment group was clearly identified through HRV as well as SRS. The significant difference was shown at three test areas (MSS, BAI, and BDI, $p$ < 0.001) in SRS and at one parameter (total power, $p$ < 0.05) in HRV. There was noticeable increase in SDNN, RMSSD, and LF/HF ratio in treatment group after HT activity, but no significant difference. Although all parameters of HRV did not show significance, the possibility of HRV as an objective evaluation method to HT was recognized in this study. These results also implied that HT was efficient in the mental and physical regeneration of the depressed patients in both subjective and objective evaluation methods.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.903-911
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• 2022

Groundwater, one of the resources for supplying water, fluctuates in water level due to various natural factors. Recently, research has been conducted to predict fluctuations in groundwater levels using Artificial Neural Network (ANN). Previously, among operators in ANN, Gradient Descent (GD)-based Optimizers were used as Optimizer that affect learning. GD-based Optimizers have disadvantages of initial correlation dependence and absence of solution comparison and storage structure. This study developed Gradient Descent combined with Harmony Search (GDHS), a new Optimizer that combined GD and Harmony Search (HS) to improve the shortcomings of GD-based Optimizers. To evaluate the performance of GDHS, groundwater level at Icheon Yullhyeon observation station were learned and predicted using Multi Layer Perceptron (MLP). Mean Squared Error (MSE) and Mean Absolute Error (MAE) were used to compare the performance of MLP using GD and GDHS. Comparing the learning results, GDHS had lower maximum, minimum, average and Standard Deviation (SD) of MSE than GD. Comparing the prediction results, GDHS was evaluated to have a lower error in all of the evaluation index than GD.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.695-708
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• 2011

Image fusion is the process of combining multiple images of the same scene into a single fused image with application to many fields, such as remote sensing, computer vision, robotics, medical imaging and military affairs. The widely used image fusion rules that use wavelet transform have been based on a simple comparison with the activity measures of local windows such as mean and standard deviation. In this case, information features from the original images are excluded in the fusion image and distorted fusion images are obtained for noisy images. In this paper, we propose the use of a nonparametric squared ranks test on the quality of variance for two samples in order to overcome the influence of the noise and guarantee the homogeneity of the fused image. We evaluate the method both quantitatively and qualitatively for image fusion as well as compare it to some existing fusion methods. Experimental results indicate that the proposed method is effective and provides satisfactory fusion results.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.757-764
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• 2012

This research is for comparison of test statistics for homogeneity when the data is collected based on complex sample design. The survey data based on complex sample design does not satisfy the condition of independency which is required for the standard Pearson multinomial-based chi-squared test. Today, lots of data sets ara collected by complex sample designs, but the tests for categorical data are conducted using the standard Pearson chi-squared test. In this study, we compared the performance of three test statistics for homogeneity between two populations using data from the 2009 customer satisfaction evaluation survey to the service from Gyeongsangnam-do regional offices of education: the standard Pearson test, the unbiasedWald test, and the Pearsontype test with survey-based point estimates. Through empirical analyses, we fist showed that the standard Pearson test inflates the values of test statistics very much and the results are not reliable. Second, in the comparison of Wald test and Pearson-type test, we find that the test results are affected by the number of categories, the mean and standard deviation of the eigenvalues of design matrix.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.262-276
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• 2018

Models to predict Leaf Wetness Duration (LWD) were evaluated using the observed meteorological and dew data at the 11 citrus orchards in Jeju, South Korea from 2016 to 2017. The sensitivity and the prediction accuracy were evaluated with four models (i.e., Number of Hours of Relative Humidity (NHRH), Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD), Penman-Monteith (PM), Deep-learning Neural Network (DNN)). The sensitivity of models was evaluated with rainfall and seasonal changes. When the data in rainy days were excluded from the whole data set, the LWD models had smaller average error (Root Mean Square Error (RMSE) about 1.5hours). The seasonal error of the DNN model had the similar magnitude (RMSE about 3 hours) among all seasons excluding winter. The other models had the greatest error in summer (RMSE about 9.6 hours) and the lowest error in winter (RMSE about 3.3 hours). These models were also evaluated by the statistical error analysis method and the regression analysis method of mean squared deviation. The DNN model had the best performance by statistical error whereas the CART/SLD model had the worst prediction accuracy. The Mean Square Deviation (MSD) is a method of analyzing the linearity of a model with three components: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). Better model performance was determined by lower SB and LC and higher NU. The results of MSD analysis indicated that the DNN model would provide the best performance and followed by the PM, the NHRH and the CART/SLD in order. This result suggested that the machine learning model would be useful to improve the accuracy of agricultural information using meteorological data.