• Journal of Korea Water Resources Association
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• v.46 no.11
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• pp.1115-1128
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• 2013

The purpose of this study is to evaluate the suspended solids and eutrophication processes relationships in Chungju lake using CE-QUAL-W2, two-dimensional (2D) longitudinal/vertical hydrodynamic and water quality model. For water quality modeling, the lake segmentation was configured as 7 branches system according to their shape and tributary distribution. The model was calibrated (2010) and validated (2008) using 2 years of field data of water temperature, suspended solids (SS), total nitrogen (TN), total phosphorus (TP) and algae (Chl-a). The water temperature began to increase in depth from April and the stratification occurred at about 10 m early July heavy rain. The high SS concentration of the interflow density currents entering from the watershed was well simulated especially for July 2008 heavy rainfall event. The simulated concentration range of TN and TP was acceptable, but the errors might occur form the poor reflection for sedimentation velocity of nitrogen component and adsorption-sediment of phosphorus in model. The concentration of Chl-a was simulated well with the algal growth patterns in summer of 2010 and 2008, but the error of under estimation may come from the use of width-averaged velocity and concentration, not considering the actual to one side inclination by wind effect.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.561-570
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• 2010

The purpose of this study is to develop an Android smartphone app providing analysis capabilities of remote sensing images, by using mobile browsing open sources of gvSIG, open source remote sensing software of OTB and open source DBMS of PostgreSQL. In this app, five kinds of remote sensing algorithms for filtering, segmentation, or classification are implemented, and the processed results are also stored and managed in image database to retrieve. Smartphone users can easily use their functions through graphical user interfaces of app which are internally linked to application server for image analysis processing and external DBMS. As well, a practical tiling method for smartphone environments is implemented to reduce delay time between user's requests and its processing server responses. Till now, most apps for remotely sensed image data sets are mainly concerned to image visualization, distinguished from this approach providing analysis capabilities. As the smartphone apps with remote sensing analysis functions for general users and experts are widely utilizing, remote sensing images are regarded as information resources being capable of producing actual mobile contents, not potential resources. It is expected that this study could trigger off the technological progresses and other unique attempts to develop the variety of smartphone apps for remote sensing images.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.583-598
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• 2022

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system's data for deep learning model learning.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.493-503
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• 2019

This study presents a new method of estimating the size distribution of river bed gravel through image processing. The analysis was done in two steps; first the individual grain images were analyzed and then the grain particle segmentation of river-bed images were processed. In the first part of the analysis, the relationships (long axes, intermediate axes and projective areas) between grain features from images and those measured were compared. For this analysis, 240 gravel particles were collected at three river stations. All particles were measured with vernier calipers and weighed with scales. The measured data showed that river gravel had shape factors of 0.514~0.585. It was found that the weight of gravel had a stronger correlation with the projective areas than the long or intermediate axes. Using these results, we were able to establish an area-weight formula. In the second step, we calculated the projective areas of the river-bed gravels by detecting their edge lines using the ImageJ program. The projective areas of the gravels were converted to the grain-size distribution using the formula previously established. The proposed method was applied to 3 small- and medium- sized rivers in Korea. Comparisons of the analyzed size distributions with those measured showed that the proposed method could estimate the median diameter within a fair error range. However, the estimated distributions showed a slight deviation from the observed value, which is something that needs improvement in the future.

• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.376-381
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• 2019

Objective : To define optimal method that calculate the safe direction of cervical pedicle screw placement using computed tomography (CT) image based three dimensional (3D) cortical shell model of human cervical spine. Methods : Cortical shell model of cervical spine from C3 to C6 was made after segmentation of in vivo CT image data of 44 volunteers. Three dimensional Cartesian coordinate of all points constituting surface of whole vertebra, bilateral pedicle and posterior wall were acquired. The ideal trajectory of pedicle screw insertion was defined as viewing direction at which the inner area of pedicle become largest when we see through the biconcave tubular pedicle. The ideal trajectory of 352 pedicles (eight pedicles for each of 44 subjects) were calculated using custom made program and were changed from global coordinate to local coordinate according to the three dimensional position of posterior wall of each vertebral body. The transverse and sagittal angle of trajectory were defined as the angle between ideal trajectory line and perpendicular line of posterior wall in the horizontal and sagittal plane. The averages and standard deviations of all measurements were calculated. Results : The average transverse angles were $50.60^{\circ}{\pm}6.22^{\circ}$ at C3, $51.42^{\circ}{\pm}7.44^{\circ}$ at C4, $47.79^{\circ}{\pm}7.61^{\circ}$ at C5, and $41.24^{\circ}{\pm}7.76^{\circ}$ at C6. The transverse angle becomes more steep from C3 to C6. The mean sagittal angles were $9.72^{\circ}{\pm}6.73^{\circ}$ downward at C3, $5.09^{\circ}{\pm}6.39^{\circ}$ downward at C4, $0.08^{\circ}{\pm}6.06^{\circ}$ downward at C5, and $1.67^{\circ}{\pm}6.06^{\circ}$ upward at C6. The sagittal angle changes from caudad to cephalad from C3 to C6. Conclusion : The absolute values of transverse and sagittal angle in our study were not same but the trend of changes were similar to previous studies. Because we know 3D address of all points constituting cortical shell of cervical vertebrae. we can easily reconstruct 3D model and manage it freely using computer program. More creative measurement of morphological characteristics could be carried out than direct inspection of raw bone. Furthermore this concept of measurement could be used for the computing program of automated robotic screw insertion.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.337-351
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• 2022

With the full spread and rapid development of social media, the trend of decentralization of social media information propagation is becoming clearer day by day, and the segmentation of time by audiences using social media information is clearly progressing. Therefore, this study aims to study the influence relationship between social media attitudes toward fake news, social capital, risk perception, and discriminant intentions based on existing studies. Accordingly, the research model presented related research questions and organized a questionnaire to collect a total of 500 valid surveys. The SPSS 26.0 program and the AMOS 24.0 program were used to analyze the data. The research results are as follows. First, the more positive the user's attitude towards the fake news identification intention of social media, the more they want to use various methods or tools to identify the authenticity of online information. Second, the more positive the user's attitude towards social media fake news, the more aware of the potential threats social media fake news poses to their own physical, psychological, financial and so on. At the same time, by raising one's own awareness of the dangers, counterintelligence intentions against fake news on social media will also increase. Third, the richer the social capital the user has, the stronger the information literacy, and therefore the stronger the identification intention of social media fake news. Fourth, the higher the value of social capital Chinese users have, the greater the damage they have suffered from fake news, and the higher the risk awareness of fake news to protect their interests. Fifth, it means that Chinese users recognized information suspected of social media and took corresponding measures.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1871-1877
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• 2022

In the past, satellite development required enormous budget and time, so only some developed countries possessed satellites. However, with the recent emergence of low-budget satellites such as micro-satellites, many countries around the world are participating in satellite development. Low-orbit and geostationary-orbit satellites are used in various fields such as environment and weather monitoring, precise change detection, and disasters. Recently, it has been actively used for monitoring through deep learning-based object-of-interest detection. Until now, Korea has developed satellites for national demand according to the space development plan, and the satellite image obtained through this is used for various purpose in the public and private sectors. Interest in satellite image is continuously increasing in Korea, and various contests are being held to discover ideas for satellite image application and promote technology development. In this special issue, we would like to introduce the topics that participated in the recently held 2022 Satellite Information Application Contest and research on the processing and utilization of KOMPSAT image data.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.597-608
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• 2022

Remotely sensed data have been used in various fields, such as disasters, agriculture, urban planning, and the military. Recently, the demand for the multitemporal dataset with the high-spatial-resolution has increased. This manuscript proposed an automatic image matching algorithm using a deep learning technique to utilize a multitemporal remotely sensed dataset. The proposed deep learning model was based on High Resolution Net (HRNet), widely used in image segmentation. In this manuscript, denseblock was added to calculate the correlation map between images effectively and to increase learning efficiency. The training of the proposed model was performed using the multitemporal orthophotos of the National Geographic Information Institute (NGII). In order to evaluate the performance of image matching using a deep learning model, a comparative evaluation was performed. As a result of the experiment, the average horizontal error of the proposed algorithm based on 80% of the image matching rate was 3 pixels. At the same time, that of the Zero Normalized Cross-Correlation (ZNCC) was 25 pixels. In particular, it was confirmed that the proposed method is effective even in mountainous and farmland areas where the image changes according to vegetation growth. Therefore, it is expected that the proposed deep learning algorithm can perform relative image registration and image matching of a multitemporal remote sensed dataset.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.535-543
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• 2022

A drone is used to diagnose crop growth and to provide information through images in the agriculture field. In the case of using high spatial resolution drone images, growth information for each object can be produced. However, accurate object detection is required and adjacent objects should be efficiently classified. The purpose of this study is to develop a Chinese cabbage object detection algorithm using multispectral reflectance images observed from drone and computer vision techniques. Drone images were captured between 7 and 15 days after planting a Chinese cabbage from 2018 to 2020 years. The thresholds of object detection algorithm were set based on 2019 year, and the algorithm was evaluated based on images in 2018 and 2019 years. The vegetation area was classified using the characteristics of spectral reflectance. Then, morphology techniques such as dilatation, erosion, and image segmentation by considering the size of the object were applied to improve the object detection accuracy in the vegetation area. The precision of the developed object detection algorithm was over 95.19%, and the recall and accuracy were over 95.4% and 93.68%, respectively. The F1-Score of the algorithm was over 0.967 for 2 years. The location information about the center of the Chinese cabbage object extracted using the developed algorithm will be used as data to provide decision-making information during the growing season of crops.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.63-80
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• 2022

As a technology to analyze and predict an issue has been developed by constructing real space into virtual space, it is becoming more important to acquire precise spatial information in complex cities. In this study, images were acquired using an unmanned aerial vehicle for urban area with complex landscapes, and land cover classification was performed object-based image analysis and semantic segmentation techniques, which were image classification technique suitable for high-resolution imagery. In addition, based on the imagery collected at the same time, the replicability of land cover classification of each artificial intelligence (AI) model was examined for areas that AI model did not learn. When the AI models are trained on the training site, the land cover classification accuracy is analyzed to be 89.3% for OBIA-RF, 85.0% for OBIA-DNN, and 95.3% for U-Net. When the AI models are applied to the replicability assessment site to evaluate replicability, the accuracy of OBIA-RF decreased by 7%, OBIA-DNN by 2.1% and U-Net by 2.3%. It is found that U-Net, which considers both morphological and spectroscopic characteristics, performs well in land cover classification accuracy and replicability evaluation. As precise spatial information becomes important, the results of this study are expected to contribute to urban environment research as a basic data generation method.