• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.1-14
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• 2014

Both researchers and practitioners are showing an increased interested in interactive exhibition services. Interactive exhibition services are designed to directly respond to visitor responses in real time, so as to fully engage visitors' interest and enhance their satisfaction. In order to install an effective interactive exhibition service, it is essential to adopt intelligent technologies that enable accurate estimation of a visitor's emotional state from responses to exhibited stimulus. Studies undertaken so far have attempted to estimate the human emotional state, most of them doing so by gauging either facial expressions or audio responses. However, the most recent research suggests that, a multimodal approach that uses people's multiple responses simultaneously may lead to better estimation. Given this context, we propose a new multimodal emotional state estimation model that uses various responses including facial expressions, gestures, and movements measured by the Microsoft Kinect Sensor. In order to effectively handle a large amount of sensory data, we propose to use stratified sampling-based MRA (multiple regression analysis) as our estimation method. To validate the usefulness of the proposed model, we collected 602,599 responses and emotional state data with 274 variables from 15 people. When we applied our model to the data set, we found that our model estimated the levels of valence and arousal in the 10~15% error range. Since our proposed model is simple and stable, we expect that it will be applied not only in intelligent exhibition services, but also in other areas such as e-learning and personalized advertising.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.167-175
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• 2023

In the context of COVID-19, this study was conducted to study how college students' earned grades and average grade point averages changed according to face-to-face and non-face-to-face classes. For this study, grade data was extracted using an access database. For the study, 152 students during the 3rd semester were compared and analyzed the grade point average, average grade point average, midterm exam, final exam, assignment score, and attendance score of students who participated in non-face-to-face and face-to-face classes. As an analysis method, independent sample t-test statistical processing was performed. It was concluded that the face-to-face class students had better grades and average GPA. As a result, the face-to-face class students showed 4.39 points higher than the non-face-to-face class students, and the average grade value was 0.6642 points higher. As a result of the comparative analysis, it was statistically significant, and the face-to-face class averaged 21.22 and the non-face-to-face class had 16.83 points. In conclusion, it was confirmed that face-to-face students' grades were generally higher than those of non-face-to-face students, and that face-to-face students showed higher participation in class.

• Korean Journal of Remote Sensing
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• v.33 no.5_3
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• pp.809-820
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• 2017

Efficient management of large-size wastes generated from disasters etc. is always in demand. Large-size wastes are closely connected to the environment, producing adverse effects on the air quality, water quality, living environment and so on. When large-size wastes are generated, we must be able to estimate the generated amount in order to transfer them to a temporary trans-shipment site, or to properly treat them. Currently, we estimate the amount of generated large-size wastes by using satellite images or unit measure for wastes; however, the accuracy of such estimations have been constantly questioned. Therefore, the present study was performed to establish three-dimensional spatial information based on UAS, to measure the amount of waste, and to evaluate the accuracy of the measurement. A measurement was made at a waste site by using UAS, and the X, Y, Z RMSE values of the three-dimensional spatial information were found to be 0.022 m, 0.023 m, and 0.14 m, all of which show relatively high accuracy. The amount of waste measured using these values was computed to be approximately $4,273,400m^3$. In addition, the amount of waste at the same site was measured by using Terrestrial LiDAR, which is used for the precise measurement of geographical features, cultural properties and the like. The resulting value was $4,274,188m^3$, which is not significantly different from the amount of waste computed by using UAS. Thus, the possibility of measuring the amount of waste using UAS was confirmed, and UAS-based measurement is believed to be useful for environmental control with respect to disaster wastes, large-size wastes, and the like.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1479-1488
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• 2018

On September 3rd 2017, strong artificial seismic signals from North Korea were detected in KMA (Korea Meteorological Administration) seismic network. The location of the epicenter was estimated to be Punggye-ri nuclear test site and it was the most powerful to date. The event was not studied well due to accessibility and geodetic measurements. Therefore, we used remote sensing data to analyze surface changes around Mt. Mantap area. First of all, we tried to detect surface deformation using InSAR method with Advanced Land Observation Satellite-2 (ALOS-2). Even though ALOS-2 data used L-band long wavelength, it was not working well for this particular case because of decorrelation on interferogram. The main reason would be large deformation near the Mt. Mantap area. To overcome this limitation of decorrelation, we applied offset tracking method to measure deformation. However, this method is affected by window kernel size. So we applied various window sizes from 32 to 224 in 16 steps. We could retrieve 2D surface deformation of about 3 m in maximum in the west side of Mt. Mantap. Second, we used Pleiadas-A/B high resolution satellite optical images which were acquired before and after the 6th nuclear test. We detected widespread surface damage around the top of Mt. Mantap such as landslide and suspected collapse area. This phenomenon may be caused by a very strong underground nuclear explosion test. High-resolution satellite images could be used to analyze non-accessible area.

• Journal of Gastric Cancer
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• v.5 no.2
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• pp.113-119
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• 2005

Purpose: Some controversies exist over the prognostic values of lymphatic, venous, and neural invasion in patients with gastric cancer. This study was conducted to confirm the prognostic values of these histopathologic factors in gastric cancer patients who received a gastrectomy. Materials and Methods: Data for clinicopathologic factors and clinical outcomes were collected retrospectively from the medical records of 1,018 gastric cancer patients who received a gastrectomy at Yeungnam University Medical Center between January 1995 and December 1999. A statistical analysis was done using the SPSS program for Windows (Version 10.0, SPSS Inc., USA). The Kaplan-Meier method was used for the survival analysis. Prognostic factors were analyzed by using a multivariate analysis with Cox proportional hazard regression model. Results: Ages ranged from 21 to 79 (median age, 56). A univariate analysis revealed that age, tumor size, location, gross type, depth of invasion, extent of gastrectomy or lymph node dissection, lymph node metastasis, distant metastasis, lymphatic invasion, venous invasion, neural invasion, pathologic stage, histologic type, and curability of surgery had statistical significance. Among these factors, lymph node metastasis, curability of surgery, neural invasion, lymphatic invasion, and depth of invasion were found to be independent prognostic factors by using a multivariate analysis. Venous invasion showed no prognostic value in the multivariate analysis. Conclusion: Neural invasion and lymphatic invasion are useful parameters in determining a prognosis for gastric cancer patients.

• Economic and Environmental Geology
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• v.41 no.6
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• pp.747-761
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• 2008

In order to experiment the acoustic remote classification of seabed sediment, we achieved ground-truth data(i.e. video and grab samples, etc.) and developed post-processing for automatic classification procedure on the basis of 300 kHz MultiBeam Echo Sounder(MBES) backscattering data, which was acquired using KONGBERG Simrad EM3000 at Sock-Cho Port, East Sea of South Korea. Sonar signal and its classification performance were identified with geo-referenced video imagery with the aid of GIS (Geographic Information System). The depth range of research site was from 5 m to 22.7 m, and the backscattering amplitude showed from -36dB to -15dB. The mean grain sizes of sediment from equi-distanced sampling site(50 m interval) varied from 2.86$(\phi)$ to 0.88(\phi). To acquire the main feature for the seabed classification from backscattering amplitude of MBES, we evaluated the correlation factors between the backscattering amplitude and properties of sediment samples. The performance of seabed remote classification proposed was evaluated with comparing the correlation of human expert segmentation to automatic algorithm results. The cross-model perception error ratio on automatic classification algorithm shows 8.95% at rocky bottoms, and 2.06% at the area representing low mean grain size.

• Economic and Environmental Geology
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• v.52 no.4
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• pp.313-322
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• 2019

SAR (Synthetic Aperture Radar) remote sensing data is a very useful tool for near-real-time identification of landslide affected areas that can occur over a large area due to heavy rains or typhoons. This study aims to develop an effective algorithm for automatically delineating landslide areas from the polarimetric SAR data acquired after the landslide event. To detect landslides from SAR observations, reduction of the speckle effects in the estimation of polarimetric SAR parameters and the orthorectification of geometric distortions on sloping terrain are essential processing steps. Based on the experimental analysis, it was found that the IDAN filter can provide a better estimation of the polarimetric parameters. In addition, it was appropriate to apply orthorectification process after estimating polarimetric parameters in the slant range domain. Furthermore, it was found that the polarimetric entropy is the most appropriate parameters among various polarimetric parameters. Based on those analyses, we proposed an automatic landslide detection algorithm using the histogram thresholding of the polarimetric parameters with the aid of terrain slope information. The landslide detection algorithm was applied to the ALOS-2 PALSAR-2 data which observed landslide areas in Japan triggered by Typhoon in September 2011. Experimental results showed that the landslide areas were successfully identified by using the proposed algorithm with a detection rate of about 82% and a false alarm rate of about 3%.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.699-708
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• 2017

The present study was to develop an approach for predicting soybean yield using a crop growth simulation model at the regional level where the detailed and site-specific information on cultivation management practices is not easily accessible for model input. CROPGRO-Soybean model included in Decision Support System for Agrotechnology Transfer (DSSAT) was employed for this study, and Illinois which is a major soybean production region of USA was selected as a study region. As a first step to predict soybean yield of Illinois using CROPGRO-Soybean model, genetic coefficients representative for each soybean maturity group (MG I~VI) were estimated through sowing date experiments using domestic and foreign cultivars with diverse maturity in Seoul National University Farm ($37.27^{\circ}N$, $126.99^{\circ}E$) for two years. The model using the representative genetic coefficients simulated the developmental stages of cultivars within each maturity group fairly well. Soybean yields for the grids of $10km{\times}10km$ in Illinois state were simulated from 2,000 to 2,011 with weather data under 18 simulation conditions including the combinations of three maturity groups, three seeding dates and two irrigation regimes. Planting dates and maturity groups were assigned differently to the three sub-regions divided longitudinally. The yearly state yields that were estimated by averaging all the grid yields simulated under non-irrigated and fully-Irrigated conditions showed a big difference from the statistical yields and did not explain the annual trend of yield increase due to the improved cultivation technologies. Using the grain yield data of 9 agricultural districts in Illinois observed and estimated from the simulated grid yield under 18 simulation conditions, a multiple regression model was constructed to estimate soybean yield at agricultural district level. In this model a year variable was also added to reflect the yearly yield trend. This model explained the yearly and district yield variation fairly well with a determination coefficients of $R^2=0.61$ (n = 108). Yearly state yields which were calculated by weighting the model-estimated yearly average agricultural district yield by the cultivation area of each agricultural district showed very close correspondence ($R^2=0.80$) to the yearly statistical state yields. Furthermore, the model predicted state yield fairly well in 2012 in which data were not used for the model construction and severe yield reduction was recorded due to drought.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.363-373
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• 2020

During last decades numerous studies generating orthoimage have been carried out. Traditional methods require exterior orientation parameters of aerial images and precise 3D object modeling data and DTM (Digital Terrain Model) to detect and recover occlusion areas. Furthermore, it is challenging task to automate the complicated process. In this paper, we proposed a new concept of true orthoimage generation using DL (Deep Learning). DL is rapidly used in wide range of fields. In particular, GAN (Generative Adversarial Network) is one of the DL models for various tasks in imaging processing and computer vision. The generator tries to produce results similar to the real images, while discriminator judges fake and real images until the results are satisfied. Such mutually adversarial mechanism improves quality of the results. Experiments were performed using GAN-based Pix2Pix model by utilizing IR (Infrared) orthoimages, intensity from LiDAR data provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the ISPRS (International Society for Photogrammetry and Remote Sensing). Two approaches were implemented: (1) One-step training with intensity data and high resolution orthoimages, (2) Recursive training with intensity data and color-coded low resolution intensity images for progressive enhancement of the results. Two methods provided similar quality based on FID (Fréchet Inception Distance) measures. However, if quality of the input data is close to the target image, better results could be obtained by increasing epoch. This paper is an early experimental study for feasibility of DL-based true orthoimage generation and further improvement would be necessary.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1373-1387
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• 2021

Forest fire poses a significant threat to the environment and society, affecting carbon cycle and surface energy balance, and resulting in socioeconomic losses. Widely used multi-spectral satellite image-based approaches for burned area detection have a problem in that they do not work under cloudy conditions. Therefore, in this study, Sentinel-1 Synthetic Aperture Radar (SAR) data from Europe Space Agency, which can be collected in all weather conditions, were used to identify forest fire damaged area based on a series of processes including Principal Component Analysis (PCA) and K-means clustering. Four forest fire cases, which occurred in Gangneung·Donghae and Goseong·Sokcho in Gangwon-do of South Korea and two areas in North Korea on April 4, 2019, were examined. The estimated burned areas were evaluated using fire reference data provided by the National Institute of Forest Science (NIFOS) for two forest fire cases in South Korea, and differenced normalized burn ratio (dNBR) for all four cases. The average accuracy using the NIFOS reference data was 86% for the Gangneung·Donghae and Goseong·Sokcho fires. Evaluation using dNBR showed an average accuracy of 84% for all four forest fire cases. It was also confirmed that the stronger the burned intensity, the higher detection the accuracy, and vice versa. Given the advantage of SAR remote sensing, the proposed statistical processing and K-means clustering-based approach can be used to quickly identify forest fire damaged area across the Korean Peninsula, where a cloud cover rate is high and small-scale forest fires frequently occur.