• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1611-1623
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• 2021

Thisstudy demonstratessoil moisture and evapotranspiration performance using Korea Land Data Assimilation System (KLDAS) under Korea Land Information System (KLIS). Spin-up was repeated 8 times in 2018. In addition, low-resolution and high-resolution meteorological data were generated using meteorological data observed by Korea Meteorological Administration (KMA), Rural Development Administration (RDA), Korea Rural Community Corporation (KRC), Korea Hydro & Nuclear Power Co.,Ltd. (KHNP), Korea Water Resources Corporation (K-water), and Ministry of Environment (ME), and applied to KLDAS. And, to confirm the degree of accuracy improvement of Korea Low spatial resolution (hereafter, K-Low; 0.125°) and Korea High spatial resolution (hereafter, K-High; 0.01°), soil moisture and evapotranspiration to which Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and ASOS-Spatial (ASOS-S) used in the previous study were applied were evaluated together. As a result, optimization of the initial boundary condition requires 2 time (58 point), 3 time (6 point), and 6 time (3 point) spin-up for soil moisture. In the case of evapotranspiration, 1 time (58 point) and 2 time (58 point) spin-ups are required. In the case of soil moisture to which MERRA-2, ASOS-S, K-Low, and K-High were applied, the mean of R² were 0.615, 0.601, 0.594, and 0.664, respectively, and in the case of evapotranspiration, the mean of R² were 0.531, 0.495, 0.656, and 0.677, respectively, indicating the accuracy of K-High was rated as the highest. The accuracy of KLDAS can be improved by securing a large number of ground observation data through the results of this study and generating high-resolution grid-type meteorological data. However, if the meteorological condition at each point is not sufficiently taken into account when converting the point data into a grid, the accuracy is rather lowered. For a further study, it is expected that higher quality data can be produced by generating and applying grid-type meteorological data using the parameter setting of IDW or other interpolation techniques.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.587-601
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• 2020

The ocean circulation was simulated in the East Sea and Ulleungdo-Dokdo region using ROMS (Regional Ocean Modeling System) model. By adopting the East Sea 3 km model and the HYCOM 9 km data, Ulleungdo 1 km model and Ulleungdo-Dokdo 300 m model were constructed with one-way grid nesting method. During the model development, a correction method was proposed for the distortion of the open boundary data which may be caused by the bathymetry data difference between the mother and child models and the interpolation/extrapolation method. Using this model, a super-high resolution ocean circulation with a horizontal resolution of 300 m near the Ulleungdo and Dokdo region was simulated for year 2018. In spite of applying the same conditions except for the initial and boundary data, the numerical models result indicated significantly different characteristics in the study area. Therefore, these results were compared and verified by using the surface current data estimated by satellites altimeter data and temperature data from NIFS (National Institute of Fisheries Science). They suggest that in general, the improvement of the one-way grid nesting with the HYCOM data on RMSE, Mean Bias, Pattern correlation and Vector correlation is greater in 300 m model than in the 1 km model. However, the nesting results of using East Sea 3 km model showed that simulations of the 1 km model were better than 300 m model. The models better resolved distinct ridge/trough structures of isotherms in the vertical sections of water temperature when using the higher horizontal resolution. Furthermore, Karman vortex street was simulated in Ulleungdo-Dokdo 300 m model due to the terrain effect of th islands that was not shown in the Ulleungdo 1 km model.

• Journal of Soil and Groundwater Environment
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• v.13 no.1
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• pp.67-76
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• 2008

In this study, a combined model of a water-table fluctuation and a soil moisture content model is proposed for the estimation of groundwater recharge rate at a given location. To evaluate the model, groundwater level data from 4 monitoring wells (Pohang Yeonil, Pohang Kibuk, Suncheon Oeseo, Hongcheon Hongcheon) of National Groundwater Monitoring Network from 1996 to 2005 and precipitation data of corresponding years are used. From the proposed methodology, the groundwater recharge rates are estimated to be from 0.5 to 61.4% for Hongcheon Hongcheon, from 1.1 to 27.4% for Pohang Yeonil, from 5.1 to 41.4% for Pohang Kibuk, and from 1.1 to 8.3% for Suncheon Oeseo. The magnitude of variation of the estimated recharge rate depends on the soil type observed near the stations. The groundwater fluctuation model used in this study includes precipitation as a unique source of water-table perturbation and there may exist corollary limitations. To improve the applicability of the proposed method, a capillary-water content constitutive model for unsaturated fractured rock media may be considered. The proposed recharge rate delineation method is physically based and uses minimum numbers of assumptions. The method may be used as a better substitute for the previous tools for delineating recharge rate of a location using water-table fluctuation method and contribute to national groundwater management plan. Further research on the spatial interpolation of the method is under progress.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.2
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• pp.266-271
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• 2012

This study was carried out to evaluate the land suitability for southern-type garlic cultivation associated with both temperature and soil constraints. The suitability analysis was conducted with hourly temperature data from 2001 to 2010 at all fifty seven meteorological stations and the soil-based suitability map of garlic provided by Rural Development Administration. Firstly the temperature data were processed by the growth stages (germinating, bulbing, and winter vegetation season), and then were adopted to limit the irrelevant lands. Next, as a result of overlaying each soil and temperature suitability map, the total 274,339 ha of area was mapped as highly suitable or suitable for southern-type garlic cultivation and the top four of the largest classified si-guns were identified as Naju, Jeongeup, Gochang, and Jinju. On the other hand, the statistical records of KOSIS (KOrea Statistical Information Service) showed lower amount of cultivation area than the analyzed results in the major production sites, Goheung, Sinan, Haenam, and Muan. However, it should not be regarded as exceptional because farmer's preference might not correspond to potential land usability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.4D
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• pp.407-419
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• 2012

It is difficult to obtain the accurate and homogeneous height information over the whole Korea due to the effect of different vertical datums have been divided into land and sea part. In this study, we tried to unify the different vertical datums using the precise spirit leveling between TBM (tidal bench mark) and BM (bench mark) in order to solve the problems caused by different vertical datums. For this, the vertical datum offsets at observed points which were calculate from leveling results and then transformation model of vertical datum will be modelled using calculated offsets along the coastal line. For suggesting the precise modelling method to vertical datum transformation, we analyzed results from various interpolation methods such as Spline and LSC method. As the results from analysis, the LSC method combined with 4-parameters trend model is more suitable for modelling the offsets between vertical datums. The final transformation model of vertical datum using the combination of LSC and 4-parameter model which provides the transformation accuracies of ${\pm}10.4cm{\sim}14.8cm$ level. And, the software for vertical datum transformation that was also developed using the final model in order to convert the height information included in various spatial data effectively. Therefore, the transformation model between vertical datums of land and sea part, which is developed in this study, is expected to minimize the confusion caused by mismatch of height information in the use of spatial data, and it also can be minimize economic and time losses in various application fields such as coastal development project, coastal disaster prevention, etc.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.2
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• pp.79-86
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• 1999

This study was carried out to estimate the area of damaged forest and the volume of stand tree in burned area, Mt. Weol-A in eastern Chinju, Korea using digital maps derived from supervised classification of Landsat thematic mapper(TM) imagery as reference data. Criterion laser estimator and WinDENDRO$^{tm}$(v. 6.3b) system as a computer-aided tree ring measuring system were used to measure a volume and age of sampled tree. The sample site had been chosen in unburned areas having the same terrain condition and forest type of burned areas. The tree age, diameter at breast height, tree height and volume of the sample tree selected from sample site in unburned area were 27years, 20.9cm, 9.7m and $0.1396m^3$ respectively. Total stand volume of sample site was estimated $2.9316m^3$/0.04ha, Damaged stand volume evaluated to about $16,007m^3$ in the burned area of 218.4ha.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.33-43
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• 2011

This study is to make snowfall distribution map for the 4 heavy snowfall events of January 2001, March of 2004, December of 2005 and January of 2010, and compare the results for three cases of construction methods. The cases are to generate the map by applying IDW(Inverse Distance Weighting) interpolation to 76 ground measured snowfall point data (Snow Depth Map; SDM), mask out the SDM with the MODIS snow cover area (MODIS SCA) of Terra MODIS (MODerate resolution Imaging Spectroradiometer) (SDM+MODIS SCA; SDM_M), and consider the snowdepth lapse rate of snowfall by elevation (Digital Elevation Model; DEM) to the second case (SDM_M+DEM; SDM_MD). By applying the MODIS SCA, the SCA of 4 events was 62.9%, 44.1%, 52.0%, and 69.0% for the area of South Korea. For the average snow depth, the SDM_M decreased 0.9cm, 1.9cm, 0.8cm, and 1.5cm compared to SDM and the SDM_MD increased 1.3cm, 0.9cm, 0.4cm, and 1.2cm respectively.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.47-58
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• 2016

The purpose of this study was to select the sectoral vulnerable areas index in welfare field and analyze the socially vulnerable areas from comprehensive analysis. For this study, preceding research and theoretical background were reviewed. Through this, we selected for the analysis index and criteria, and data corresponding to the index are collected. Based on the index and criteria, the data analysis was performed in Seoul Metropolitan City selected as the spatial extent of this study. From the results of analysis, the sectoral lower rank 10% of social vulnerable areas was determined. In addition, Junggu, Yongsangu, and Seodaemungu from the comprehensive analysis of individual vulnerable areas were derived as a final vulnerable areas. In particular, Junggu was weak in all sectors; Yongsangu was in the medical sector; and Seodaemungu was poor in housing and education. Lower vulnerability index of all sectors (energy, housing, medical, transportation, and education) in 1st, 2nd, and 3rd residential areas by examining use zoning was showed. From the results of this study, we can expect time and labor saving of policy support in public sector.

• Archives of design research
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• v.18 no.4 s.62
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• pp.139-146
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• 2005

Animation works have been peformed by the objective sensitivity and experience so far. Software designs have been also manufactured based on intelligent data because they are easy to objectify and digitalize. In contrast, there are many elements, which human senses are hard to objectify and digitalize. This study investigates how to digitalize and objectify human senses and how to use them as the quantitative data and its subject is a flying path. In the experiment, this study collects some sensitive words for how human beings express the living path. The evaluation words for sensitivity through the collected sensitive words are extracted and the sketch images for the flying path are collected from the extracted evaluation words for sensitivity. Based on the collected sketch images, the samples of real moving image, which are the core of this study, are manufactured. Then, quantification theory III and I are used in order to analyze the correlation between the sensitive words representing the flying path and the samples of moving image. As a result, this study can figure out the structure of sensitive words and the samples of moving image and analyze the physical stimulating elements for the flying path. The flying path corresponds to the path that the object has passed. Some unique sensitive words are expressed by means of interacting some sensitive stimulating elements after looking at such a path. There are some elements that stimulate the senses and they include the physical elements such as speed, rotation, pattern and length of arc. The purpose of this study is to objectify and quantify the animation works that are created by animators' subjective thought and experience and to use them in animation works in the future.