• Journal of the Korean Geographical Society
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• v.51 no.2
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• pp.219-233
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• 2016

In social science fields, statistical models are used almost exclusively for causal explanation, and explanatory modeling has been a mainstream until now. In contrast, predictive modeling has been rare in the fields. Hence, we focus on constructing the predictive non-parametric model, instead of the explanatory model. Gangnam-gu, Seoul was chosen as a study area and we collected single-family house sales data sold between 2011 and 2014. We applied non-parametric models proposed in machine learning area including generalized additive model(GAM), random forest, multivariate adaptive regression splines(MARS) and support vector machines(SVM). Models developed recently such as MARS and SVM were found to be superior in predictive power for house price estimation. Finally, spatial autocorrelation was accounted for in the non-parametric models additionally, and the result showed that their predictive power was enhanced further. We hope that this study will prompt methodology for property price estimation to be extended from traditional parametric models into non-parametric ones.

• Smart Structures and Systems
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• v.16 no.1
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• pp.141-162
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• 2015

The magneto-rheological visco-elastomer (MRVE) is used as a smart core to control the stochastic micro-vibration of a sandwich plate with supported mass. The micro-vibration response of the sandwich plate with MRVE core and supported mass under stochastic support motion excitations is studied and compared to evaluate the vibration suppression capability. The effects of the supported mass and localized magnetic field on the stochastic micro-vibration response of the MRVE sandwich plate are taken into account. The dynamic characteristics of the MRVE core in micro-vibration are described by a non-homogeneous complex modulus dependent on vibration frequency and controllable by applied magnetic fields. The partial differential equations for the coupled transverse and longitudinal motions of the MRVE sandwich plate with supported mass are derived from the dynamic equilibrium, constitutive and geometric relations. The simplified ordinary differential equations are obtained for the transverse vibration of the MRVE sandwich plate under localized magnetic fields. A frequency-domain solution method for the stochastic micro-vibration response of sandwich plates with supported mass is developed based on the Galerkin method and random vibration theory. The expressions of frequency-response functions, response power spectral densities and root-mean-square velocity responses of the plate in terms of the one-third octave frequency band are obtained for micro-vibration evaluation. Finally, numerical results are given to illustrate the large response reduction capacity of the MRVE sandwich plate with supported mass under stochastic support motion excitations, and the influences of MRVE parameters, supported mass and localized magnetic field placement on the micro-vibration response.

• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.1029-1035
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• 2020

The detector suffers from pulse pileup by overlapping of the signals when it was used in high radiation fields. The pulse pileup deteriorates the energy spectrum and causes count losses due to random co-incidences, which might not resolve within the resolving time of the detection system. In this study, it is aimed to propose a new pulse pileup correction method. The proposed method is to correct the start point of the pileup pulse. The parameters are obtained from the fitted exponential curve using the peak point of the previous pulse and the start point of the pileup pulse. The amplitude at the corrected start point of the pileup pulse can be estimated by the peak time of the pileup pulse. The system is composed of a NaI (Tl) scintillation crystal, a photomultiplier tube, and an oscilloscope. A 61 μCi ¹³⁷Cs check-source was placed at a distance of 3 cm, 5 cm, and 10 cm, respectively. The gamma energy spectra for the radioisotope of ¹³⁷Cs were obtained to verify the proposed method. As a result, the correction of the pulse pileup through the proposed method shows a remarkable improvement of FWHM at 662 keV by 29, 39, and 7%, respectively.

• Wind and Structures
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• v.35 no.6
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• pp.419-430
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• 2022

In wind-resistant designs, wind velocity is assumed to be a Gaussian process; however, local complex topography may result in strong non-Gaussian wind features. This study investigates the non-Gaussian wind features over complex terrain under atmospheric turbulent boundary layers by the large eddy simulation (LES) model, and the turbulent inlet of LES is generated by the consistent discretizing random flow generation (CDRFG) method. The performance of LES is validated by two different complex terrains in Changsha and Mianyang, China, and the results are compared with wind tunnel tests and onsite measurements, respectively. Furthermore, the non-Gaussian parameters, such as skewness, kurtosis, probability curves, and gust factors, are analyzed in-depth. The results show that the LES method is in good agreement with both mean and turbulent wind fields from wind tunnel tests and onsite measurements. Wind fields in complex terrain mostly exhibit a left-skewed Gaussian process, and it changes from a softening Gaussian process to a hardening Gaussian process as the height increases. A reduction in the gust factors of about 2.0%-15.0% can be found by taking into account the non-Gaussian features, except for a 4.4% increase near the ground in steep terrain. This study can provide a reference for the assessment of extreme wind loads on structures in complex terrain.

• The Korean Journal of Mycology
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• v.26 no.4 s.87
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• pp.574-582
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• 1998

Five basidiocarps of Pholiota species have been collected from the areas of BubJu Temple for last two years, and identified to those of P. adiposa or Pholiota species. The taxonomy of these basidiocarps with the morphological aspects was compared with the analysis obtained from the polymorphisms of PCR-RAPD bands made after reacted with the random primers. The polymorphic variations were observed within the species of the basidiocarps, but not between genomic DNA's of the mycelia obtained and the basidiocarps. Several different bands made from the primers (28 and 36) in PCR-RAPD reactions were identified within the genus of Pholiota and speculated to be specific for the individual basidiocarp of P. adiposa collected. The primers employed here were considered to be very useful for distinguishing the individual isolates or basidiocarps collected from the fields. Also, the basidiospores were obtained from the sporeprints of the above basidiocarps as a simple agar and confirmed with observations of clamp connection under microscopes. The matings of them indicated the 'tetrapolar' type, being different from the 'bipolar' type reported by Japanese basidiocarps of P. adiposa or P. nameko. Based on our work, the edible fungi collected were speculated to be a new breeding resource for those of Pholiota commercialized in Japan.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.503-514
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• 2021

Rice production with adequate level of area is important for decision making of rice supply and demand policy. It is essential to grasp rice cultivation areas in advance for estimating rice production of the year. This study was carried out to classify paddy rice cultivation in Gimje-si using sentinel-1 SAR (synthetic aperture radar) and UAV imagery in early July. Time-series Sentinel-1A and 1B images acquired from early May to early July were processed to convert into sigma naught (dB) images using SNAP (SeNtinel application platform, Version 8.0) toolbox provided by European Space Agency. Farm map and parcel map, which are spatial data of vector polygon, were used to stratify paddy field population for classifying rice paddy cultivation. To distinguish paddy rice from other crops grown in the paddy fields, we used the decision tree method using threshold levels and random forest model. Random forest model, trained by mainly rice cultivation area and rice and soybean cultivation area in UAV image area, showed the best performance as overall accuracy 89.9%, Kappa coefficient 0.774. Through this, we were able to confirm the possibility of early estimation of rice cultivation area in Gimje-si using UAV image.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1357-1369
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• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.83-106
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• 2021

Interests in clean fuels have been soaring because of environmental problems such as air pollution and global warming. Unlike fossil fuels, hydrogen obtains public attention as a eco-friendly energy source because it releases only water when burned. Various policy efforts have been made to establish a hydrogen based transportation network. The station that supplies hydrogen to hydrogen-powered trucks is essential for building the hydrogen based logistics system. Thus, determining the optimal location of refueling stations is an important topic in the network. Although previous studies have mostly applied optimization based methodologies, this paper adopts machine learning to review spatial attributes of candidate locations in selecting the optimal position of the refueling stations. Machine learning shows outstanding performance in various fields. However, it has not yet applied to an optimal location selection problem of hydrogen refueling stations. Therefore, several machine learning models are applied and compared in performance by setting variables relevant to the location of highway rest areas and random points on a highway. The results show that Random Forest model is superior in terms of F1-score. We believe that this work can be a starting point to utilize machine learning based methods as the preliminary review for the optimal sites of the stations before the optimization applies.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.125-132
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• 2023

In this study, we compared the performance of machine learning models for predicting Vapor Pressure Deficits (VPD) in greenhouses that affect pore function and photosynthesis as well as plant growth due to nutrient absorption of plants. For VPD prediction, the correlation between the environmental elements in and outside the greenhouse and the temporal elements of the time series data was confirmed, and how the highly correlated elements affect VPD was confirmed. Before analyzing the performance of the prediction model, the amount and interval of analysis time series data (1 day, 3 days, 7 days) and interval (20 minutes, 1 hour) were checked to adjust the amount and interval of data. Finally, four machine learning prediction models (XGB Regressor, LGBM Regressor, Random Forest Regressor, etc.) were applied to compare the prediction performance by model. As a result of the prediction of the model, when data of 1 day at 20 minute intervals were used, the highest prediction performance was 0.008 for MAE and 0.011 for RMSE in LGBM. In addition, it was confirmed that the factor that most influences VPD prediction after 20 minutes was VPD (VPD_y__71) from the past 20 minutes rather than environmental factors. Using the results of this study, it is possible to increase crop productivity through VPD prediction, condensation of greenhouses, and prevention of disease occurrence. In the future, it can be used not only in predicting environmental data of greenhouses, but also in various fields such as production prediction and smart farm control models.

• Journal of Wetlands Research
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• v.25 no.3
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• pp.167-175
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• 2023

Precipitation data is one of the essential input datasets used in various fields such as wetland management, hydrological simulation, and water resource management. In order to efficiently manage water resources using precipitation data, it is essential to secure as much data as possible by minimizing the missing rate of data. In addition, more efficient hydrological simulation is possible if precipitation data for ungauged areas are secured. However, missing precipitation data have been estimated mainly by statistical equations. The purpose of this study is to propose a new method to restore missing precipitation data using machine learning algorithms that can predict new data based on correlations between data. Moreover, compared to existing statistical methods, the applicability of machine learning techniques for restoring missing precipitation data is evaluated. Representative machine learning algorithms, Artificial Neural Network (ANN) and Random Forest (RF), were applied. For the performance of classifying the occurrence of precipitation, the RF algorithm has higher accuracy in classifying the occurrence of precipitation than the ANN algorithm. The F1-score and Accuracy values, which are evaluation indicators of the classification model, were calculated as 0.80 and 0.77, while the ANN was calculated as 0.76 and 0.71. In addition, the performance of estimating precipitation also showed higher accuracy in RF than in ANN algorithm. The RMSE of the RF and ANN algorithms was 2.8 mm/day and 2.9 mm/day, and the values were calculated as 0.68 and 0.73.