• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1125-1134
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• 2023

Out of the total 17,000 reservoirs in Korea, 13,600 small agricultural reservoirs do not have hydrological measurement facilities, making it difficult to predict water storage volume and appropriate operation. This paper examined univariate and multivariate long short-term memory (LSTM) modeling to predict the storage rate of agricultural reservoirs using remote sensing and artificial intelligence. The univariate LSTM model used only water storage rate as an explanatory variable, and the multivariate LSTM model added n-day accumulative precipitation and date of year (DOY) as explanatory variables. They were trained using eight years data (2013 to 2020) for Idong Reservoir, and the predictions of the daily water storage in 2021 were validated for accuracy assessment. The univariate showed the root-mean square error (RMSE) of 1.04%, 2.52%, and 4.18% for the one, three, and five-day predictions. The multivariate model showed the RMSE 0.98%, 1.95%, and 2.76% for the one, three, and five-day predictions. In addition to the time-series storage rate, DOY and daily and 5-day cumulative precipitation variables were more significant than others for the daily model, which means that the temporal range of the impacts of precipitation on the everyday water storage rate was approximately five days.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.663-670
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• 2023

In this study, we aimed to perform a comparative evaluation using quantitative factors between a region-growing based segmentation with noise reduction algorithms and a U-Net based segmentation. Initially, we applied median filter, median modified Wiener filter, and fast non-local means algorithm to computed tomography (CT) images, followed by region-growing based segmentation. Additionally, we trained a U-Net based segmentation model to perform segmentation. Subsequently, to compare and evaluate the segmentation performance of cases with noise reduction algorithms and cases with U-Net, we measured root mean square error (RMSE) and peak signal to noise ratio (PSNR), universal quality image index (UQI), and dice similarity coefficient (DSC). The results showed that using U-Net for segmentation yielded the most improved performance. The values of RMSE, PSNR, UQI, and DSC were measured as 0.063, 72.11, 0.841, and 0.982 respectively, which indicated improvements of 1.97, 1.09, 5.30, and 1.99 times compared to noisy images. In conclusion, U-Net proved to be effective in enhancing segmentation performance compared to noise reduction algorithms in CT images.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.182-196
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• 2023

Accurate and timely estimation of crop yields is crucial for various purposes, including global food security planning and agricultural policy development. Remote sensing techniques, particularly using vegetation indices (VIs), have show n promise in monitoring and predicting crop conditions. However, traditional VIs such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) have limitations in capturing rapid changes in vegetation photosynthesis and may not accurately represent crop productivity. An alternative vegetation index, the near-infrared reflectance of vegetation (NIRv), has been proposed as a better predictor of crop yield due to its strong correlation with gross primary productivity (GPP) and its ability to untangle confounding effects in canopies. In this study, we investigated the potential of NIRv in estimating crop yield, specifically for corn and soybean crops in major crop-producing regions in 14 states of the United States. Our results demonstrated a significant correlation between the peak value of NIRv and crop yield/area for both corn and soybean. The correlation w as slightly stronger for soybean than for corn. Moreover, most of the target states exhibited a notable relationship between NIRv peak and yield, with consistent slopes across different states. Furthermore, we observed a distinct pattern in the yearly data, where most values were closely clustered together. However, the year 2012 stood out as an outlier in several states, suggesting unique crop conditions during that period. Based on the established relationships between NIRv peak and yield, we predicted crop yield data for 2022 and evaluated the accuracy of the predictions using the Root Mean Square Percentage Error (RMSPE). Our findings indicate the potential of NIRv peak in estimating crop yield at the county level, with varying accuracy across different counties.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.209-216
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• 2024

Real estate market prices are determined by various factors, including macroeconomic variables, as well as the influence of a variety of unstructured text data such as news articles and social media. News articles are a crucial factor in predicting real estate transaction prices as they reflect the economic sentiment of the public. This study utilizes sentiment analysis on news articles to generate a News Sentiment Index score, which is then seamlessly integrated into a real estate price prediction model. To calculate the sentiment index, the content of the articles is first summarized. Then, using AI, the summaries are categorized into positive, negative, and neutral sentiments, and a total score is calculated. This score is then applied to the real estate price prediction model. The models used for real estate price prediction include the Multi-head attention LSTM model and the Vector Auto Regression model. The LSTM prediction model, without applying the News Sentiment Index (NSI), showed Root Mean Square Error (RMSE) values of 0.60, 0.872, and 1.117 for the 1-month, 2-month, and 3-month forecasts, respectively. With the NSI applied, the RMSE values were reduced to 0.40, 0.724, and 1.03 for the same forecast periods. Similarly, the VAR prediction model without the NSI showed RMSE values of 1.6484, 0.6254, and 0.9220 for the 1-month, 2-month, and 3-month forecasts, respectively, while applying the NSI led to RMSE values of 1.1315, 0.3413, and 1.6227 for these periods. These results demonstrate the effectiveness of the proposed model in predicting apartment transaction price index and its ability to forecast real estate market price fluctuations that reflect socio-economic trends.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.4 no.3
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• pp.299-303
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• 2018

Generally, when predicting the accuracy of the anti-air artillery system, the error is classified as fixed bias, variable bias, and random error. Then the standard deviation on the target is expressed as the square root of the squared sum of each error value which comes from the random error and variable bias and in the case of fixed bias, the mean value is shifted as the sum of errors from the fixed bias. At this time, the variables indicating the displacement of the direction of azimuth and elevation direction with regard to the change of the unit value of each error are weighted. These errors are then used to predict the system's delivery accuracy through a normally distributed integral. This paper presents a method of predicting system accuracy by considering the correlation of errors. This approach shows that it helps to predict the delivery accuracy of the system, precisely.

• Progress in Medical Physics
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• v.30 no.4
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• pp.128-138
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• 2019

Purpose: Segmental analysis of volumetric modulated arc therapy (VMAT) is not clinically used for compositional error source evaluation. Instead, dose verification is routinely used for plan-specific quality assurance (QA). While this approach identifies the resultant error, it does not specify which machine parameter was responsible for the error. In this research study, we adopted an approach for the segmental analysis of VMAT as a part of machine QA of linear accelerator (LINAC). Methods: Two portal dose QA plans were generated for VMAT QA: a) for full arc and b) for the arc, which was segmented in 12 subsegments. We investigated the multileaf collimator (MLC) position and dosimetric accuracy in the full and segmented arc delivery schemes. A MATLAB program was used to calculate the MLC position error from the data in the dynalog file. The Gamma passing rate (GPR) and the measured to planned dose difference (DD) in each pixel of the electronic portal imaging device was the measurement for dosimetric accuracy. The eclipse treatment planning system and a MATLAB program were used to calculate the dosimetric accuracy. Results: The maximum root-mean-square error of the MLC positions were <1 mm. The GPR was within the range of 98%-99.7% and was similar in both types of VMAT delivery. In general, the DD was <5 calibration units in both full arcs. A similar DD distribution was found for continuous arc and segmented arcs sums. Exceedingly high DD were not observed in any of the arc segment delivery schemes. The LINAC performance was acceptable regarding the execution of the VMAT QA plan. Conclusions: The segmental analysis proposed in this study is expected to be useful for the prediction of the delivery of the VMAT in relation to the gantry angle. We thus recommend the use of segmental analysis of VMAT as part of the regular QA.

• Atmosphere
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• v.20 no.3
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• pp.287-306
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• 2010

An ensemble Kalman filter (EnKF) with Weather Research and Forecasting (WRF) Model is applied for Typhoon Wukong (200610) to investigate the performance of ensemble forecasts depending on experimental configurations of the EnKF. In addition, the ensemble sensitivity analysis is applied to the forecast and analysis ensembles generated in EnKF, to investigate the possibility of using the ensemble sensitivity analysis as the adaptive observation guidance. Various experimental configurations are tested by changing model error, ensemble size, assimilation time window, covariance relaxation, and covariance localization in EnKF. First of all, experiments using different physical parameterization scheme for each ensemble member show less root mean square error compared to those using single physics for all the forecast ensemble members, which implies that considering the model error is beneficial to get better forecasts. A larger number of ensembles are also beneficial than a smaller number of ensembles. For the assimilation time window, the experiment using less frequent window shows better results than that using more frequent window, which is associated with the availability of observational data in this study. Therefore, incorporating model error, larger ensemble size, and less frequent assimilation window into the EnKF is beneficial to get better prediction of Typhoon Wukong (200610). The covariance relaxation and localization are relatively less beneficial to the forecasts compared to those factors mentioned above. The ensemble sensitivity analysis shows that the sensitive regions for adaptive observations can be determined by the sensitivity of the forecast measure of interest to the initial ensembles. In addition, the sensitivities calculated by the ensemble sensitivity analysis can be explained by dynamical relationships established among wind, temperature, and pressure.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.2
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• pp.161-168
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• 2015

In this paper, we present an inertial sensor-based gait distance measurement system using accelerometer, gyroscope, and magnetometer. To minimize offset and gain error of inertial sensors, we performed the calibration using the self-made calibration jig with 9 degrees of freedom. For measuring accurate gait distance, we used gradient descent algorithm to remove gravity error and used analysis of gait pattern to remove drift error. Finally, we measured a gait distance by double-integration of the error-removed acceleration data. To evaluate the performance of our system, we walked 10m in a straight line indoors to observe the improvement of removing error which compared un-calibrated to calibrated data. Also, the gait distance measured by the system was compared to the measurement of the Vicon motion capture system. The evaluation resulted in the improvement of $31.4{\pm}14.38%$(mean${\pm}$S.D.), $78.64{\pm}10.84%$ and $69.71{\pm}26.25%$ for x, y and z axis, respectively when walked in a straight line, and a root mean square error of 0.10m, 0.16m, and 0.12m for x, y and z axis, respectively when compared to the Vicon motion capture system.

• Journal of the Korean Magnetics Society
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• v.26 no.3
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• pp.92-98
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• 2016

To aim of this study was to assess the full scan and half scan of imaging with half scan factor. Patients without a cerebral vascular disease (n = 30) and were subject to the full scan half scan, and set a region of interest in the cerebral artery from the three regions (C1, C2, C3) in the range of 7 to 8 mm. MIP (maximum intensity projection) to reconstruct the images in signal strength SNR (signal to noise ration), PSNR (peak signal noise to ratio), RMSE (root mean square error), MAE (mean absolute error) and calculated by paired t-test for use by statistics were analyzed. Scan time was half scan (4 minutes 53 seconds), the full scan (6 minutes 04 seconds). The mean measurement range (7.21 mm) of all the ROI in the brain blood vessel, was the SNR of the first C1 is completely scanned (58.66 dB), half-scan (62.10 dB), a positive correlation ($r^2=0.503$), for the second C2 SNR is completely scanned (70.30 dB), half-scan (74.67 dB) the amount of correlation ($r^2=0.575$), third C3 of a complete scan SNR (70.33 dB), half scan SNR (74.64 dB) in the amount of correlation between the It was analyzed with ($r^2=0.523$). Comparative full scan with half of SNR ($4.75{\pm}0.26dB$), PSNR ($21.87{\pm}0.28dB$), RMSE ($48.88{\pm}1.61$), was calculated as MAE ($25.56{\pm}2.2$). SNR is also applied to examine the half-scans are not many differences in the quality of the two scan methods were not statistically significant in the scan (p-value > .05) image takes less time than a full scan was used.