• Analytical Science and Technology
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• v.25 no.2
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• pp.121-126
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• 2012

It is essential to evaluate the quality of Hanji record paper without damaging the record paper by previous destructive methods. The samples were Hanji record paper produced in the 1900s. Near-infrared (NIR) spectrometer was used as a non destructive method for evaluating the quality of record papers. Fourier transform (FT) spectrometer was used with 12,500 to 4,000 $cm^{-1}$ wavenumber range for quantitative analysis and it has high accuracy and good signal-to-noise ratio. The acidity and moisture content of Hanji record paper were measured by integrating sphere as diffuse reflectance type. The acidity (pH) of chemical factors as a quality evaluated factor of Hanji was correlated to NIR spectrum. The NIR spectrum was pretreated to obtain the coefficients of optimum correlation. Multiplicative scatter correction (MSC) and First derivative of Savitzky-Golay were used as pretreated methods. The coefficients of optimum correlation were calculated by PLSR (partial least square regression). The correlation coefficients ($R^2$) of acidity had 0.92 on NIR spectra without pretreatment. Also the standard error of prediction (SEP) of pH was 0.24. And then the NIR spectra with pretreatment would have better correlation coefficient ($R^2$ = 0.98) and 0.19 as SEP on pH. For moisture contents, the linearity correlation without pretreatment was higher than the case with pretreatment (MSC, $1^{st}$ derivative). As the best result, the $R^2$ was 0.99 and SEP was 0.45. This indicates that it is highly proper to evaluate the quality of Hanji record papers speedily with integrated sphere and FT NIR analyzer as a non-destructive method.

• Journal of Animal Science and Technology
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• v.54 no.4
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• pp.247-253
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• 2012

Ultrasound measures of back fat thickness (UBF), eye muscle area (UEMA) and marbling score (UMS) and carcass measures of carcass weight (CW), backfat thickness (BF), eye muscle area (EMA) and marbling score (MS) were available on 26,129 Hanwoo steers. Statistically significant differences by regions of the farms location and birth years-seasons for the steers taken ultrasound measures and their carcass measures (p<0.01) were found. Steers in Gyeonggi province showed the highest values in ultrasound measures and carcass traits except in BF compared to steers in the other provinces. Comparing between ultrasound and carcass measures, UBF was thicker in general than BF in all regions except in Daejeon city. UEMA was higher than EMA in all regions except in Gyeonggior in Jeju provinces. Especially, the difference in Jeonnam province between UEMA and EMA was $7cm^2$ while the differences between UMS and MS ranged from 0.9 to 2.26 depending on the regions of steers located. Steers born in spring showed greater ultrasound or carcass values than those born in autumn. However, carcass measures of steers born in autumn were greater than those born in spring, 2009 except MS. The pearson and residual correlations were 0.63 and 0.65 between UBF and BF, 0.31 and 0.32 between UEMA and EMA and 0.56 and 0.56 between UMS and MS, respectively.

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.863-874
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• 2018

The purpose of this study is to evaluate the future climate change impact on stream aquatic ecology health of Han River watershed ($34,148km^2$) using SWAT (Soil and Water Assessment Tool) and random forest. The 8 years (2008~2015) spring (April to June) Aquatic ecology Health Indices (AHI) such as Trophic Diatom Index (TDI), Benthic Macroinvertebrate Index (BMI) and Fish Assessment Index (FAI) scored (0~100) and graded (A~E) by NIER (National Institute of Environmental Research) were used. The 8 years NIER indices with the water quality (T-N, $NH_4$, $NO_3$, T-P, $PO_4$) showed that the deviation of AHI score is large when the concentration of water quality is low, and AHI score had negative correlation when the concentration is high. By using random forest, one of the Machine Learning techniques for classification analysis, the classification results for the 3 indices grade showed that all of precision, recall, and f1-score were above 0.81. The future SWAT hydrology and water quality results under HadGEM3-RA RCP 4.5 and 8.5 scenarios of Korea Meteorological Administration (KMA) showed that the future nitrogen-related water quality in watershed average increased up to 43.2% by the baseflow increase effect and the phosphorus-related water quality decreased up to 18.9% by the surface runoff decrease effect. The future FAI and BMI showed a little better Index grade while the future TDI showed a little worse index grade. We can infer that the future TDI is more sensitive to nitrogen-related water quality and the future FAI and BMI are responded to phosphorus-related water quality.

• Journal of Korean Society of Forest Science
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• v.34 no.1
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• pp.21-30
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• 1977

Japanese larch (Larix leptolepis) is one of main timber species in Korea that could find much plantation and growing stands on all over the country. It is thought to be in meaningful that a guiding diagram for density control of Japanese larch stands is made to estimate easily the density conditions in the quantitaive, ecological and economic viewpoint. Sample plots for this study are selected from the stands that have not been thinned in recent years, and mean height, mean diameter, dominant height, tree numbers per hectare and stem volume of mean tree are calculated from the each sample plots among total 165 plots In this study, especially, the theory of slenderness of mean tree are applied, that have been identified through the results of the spacing trial. Relative growth characteristics of this species are calculated from the general logistic curve and its formula is $Y=ax^b$. Relatwion between the measured items are found out as follows: 1. Relation between the mean height and tree numbers per hectare by slender class is showing the high correlation as table 1 and fig. 2, and between mean diameter and tree numbers per hectare is also high correlation as table 1 and fig 3. 2. The stem volume can be correctly estimated from height in case that slender class may be known, as showing in table 3 and fig. 4. 3. The stem volume can be more correctly estimated from the relation with $D^2H$ as formula, $Log_e\;V=0.9569\;Log_eD^2H-9.8431$, and relation between stem volume of single tree or volume per hectare and tree numbers per hectare are as following formulas: $Log_e$ stem volume=9.5026-1.6800 $Log_e$ tree numbers per hectare $Log_e$ stem volume per hectare=9.4911-0.6784 $Log_e$ tree numbers per hectare. Stem volume of mean tree, tree numbers per hectare and stem volume per hectare correspond to the mean tree height are calculated to slender class as table 5, 6, 7. Through the above steps, the diagram for density control of Japanese larch are produced as fig. 9. It is thought that this diagram could be applied to control the density of Japanese larch stands.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1691-1707
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• 2022

Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Land and Housing Review
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• v.12 no.2
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• pp.33-45
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• 2021

Due to recent changes in government policy, officetels have received attention as alternative assets, along with the uplift of office and apartment prices in Seoul. However, the current officetel price indexes use small-size samples and, thus, there is a critique on their accuracy. They rely on valuation prices which lag the market trend and do not properly reflect the volatile nature of the property market, resulting in 'smoothing'. Therefore, the purpose of this paper is to create the officetel price index using transaction data. The data, provided by the Ministry of Land, Infrastructure and Transport from 2005 to 2020, includes sales prices and rental prices - Jeonsei and monthly rent (and their combinations). This study employed a repeat sales model for sales, jeonsei, and monthly rent indexes. It also contributes to improving conversion rates (between deposit and monthly rent) as a supplementary indicator. The main findings are as follows. First, the officetel price index and jeonsei index reached 132.5P and 163.9P, respectively, in Q4 2020 (1Q 2011=100.0P). However, the rent index was approximately below 100.0. Sales prices and jeonsei continued to rise due to high demand while monthly rent was largely unchanged due to vacancy risk. Second, the increase in the officetel sales price was lower than other housing types such as apartments and villas. Third, the employed approach has seen a potential to produce more reliable officetel price indexes reflecting high volatility compared to those indexes produced by other institutions, contributing to resolving 'smoothing'. As seen in the application in Seoul, this approach can enhance accuracy and, therefore, better assist market players to understand the market trend, which is much valuable under great uncertainties such as COVID-19 environments.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1125-1135
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• 2022

Damages by heavy snow does not occur very often, but when it does, it causes damage to a wide area. To mitigate snow damage, it is necessary to know, in advance, the depth of snow that causes damage in each region. However, snow depths are measured at observatory locations, and it is difficult to understand the spatial distribution of snow depth that causes damage in a region. To understand the spatial distribution of snow depth, the point measurements are interpolated. However, estimating spatial distribution of snow depth is not easy when the number of measured snow depth is small and topographical characteristics such as altitude are not similar. To overcome this limit, satellite images such as Synthetic Aperture Radar (SAR) can be analyzed using Differential Interferometric SAR (DInSAR) method. DInSAR uses two different SAR images measured at two different times, and is generally used to track minor changes in topography. In this study, the spatial distribution of snow depth was estimated by DInSAR analysis using dual polarimetric IW mode C-band SAR data of Sentinel-1B satellite operated by the European Space Agency (ESA). In addition, snow depth was estimated using geostationary satellite Chollian-2 (GK-2A) to compare with the snow depth from DInSAR method. As a result, the accuracy of snow cover estimation in terms with grids was about 0.92% for DInSAR and about 0.71% for GK-2A, indicating high applicability of DInSAR method. Although there were cases of overestimation of the snow depth, sufficient information was provided for estimating the spatial distribution of the snow depth. And this will be helpful in understanding regional damage-causing snow depth.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.