• The Korean Journal of Urological Oncology
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• 제15권3호
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• pp.131-136
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• 2017

Purpose: The aim of this study is to investigate the criteria of biochemical recurrence (BCR) and follow-up periods and methods with and without blood and imaging test of urologic oncology before established guidelines of prostate cancer in Korea. Materials and Methods: In December 2015, we sent the questionnaire to urologic oncologist in academic hospital and received the answer from 108 urologic oncologist (50%). Also, we analyzed the data of 1,141 patients underwent radical prostatectomy in 2005 from Korean Medical Insurance. Results: In follow-up, 72 physicians (66.7%) performed blood test every 3 months, 51 physicians (47.2%) performed imaging study in case of BCR. Bone scan was the most common imaging study in the follow-up (74 physicians, 68.5%). But, bone scan was only performed in case of BCR (43 physicians, 39.8%). The criteria of BCR was PSA 0.2 ng/mL (75 physician, 69.4%), 76 physicians (70.4%) was performed different follow-up according to risk of patients. In Korean Medical Insurance data analysis, PSA were performed average 2 times every year and magnetic resonance imaging, computed tomography, Bone scan were performed average 0.1, 0.2, 0.1 times every year, respectively. Conclusions: The criteria of BCR and the follow-up of prostate cancer patients in Korea were similar Korean prostate cancer guidelines. Blood and imaging test might be increased compared to 10 years ago, it is necessary to compare the Korean Medical Insurance data between 10 years ago and present.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.257-272
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• 2007

This paper analyzed relationships between various features from SAR data with multiple acquisition dates and mode (frequency, polarization and incidence angles), and land-cover classes. Two typical types of features were extracted by considering acquisition conditions of currently available SAR data. First, coherence, temporal variability and principal component transform-based features were extracted from multi-temporal and single mode SAR data. C-band ERS-1/2, ENVISAT ASAR and Radarsat-1, and L-band JERS-1 SAR data were used for those features and different characteristics of different SAR sensor data were discussed in terms of land-cover discrimination capability. Overall, tandem coherence showed the best discrimination capability among various features. Long-term coherence from C-band SAR data provided a useful information on the discrimination of urban areas from other classes. Paddy fields showed the highest temporal variability values in all SAR sensor data. Features from principal component transform contained particular information relevant to specific land-cover class. As features for multiple mode SAR data acquired at similar dates, polarization ratio and multi-channel variability were also considered. VH/VV polarization ratio was a useful feature for the discrimination of forest and dry fields in which the distributions of coherence and temporal variability were significantly overlapped. It would be expected that the case study results could be useful information on improvement of classification accuracy in land-cover classification with SAR data, provided that the main findings of this paper would be confirmed by extensive case studies based on multi-temporal SAR data with various modes and ground-based SAR experiments.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1545-1553
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• 2018

Recent eruptive activity at Kīlauea Volcano started on at the end of April in 2018 showed rapid ground deflation between May and June in 2018. On summit area Halema'uma'u lava lake continued to drop at high speed and Kīlauea's summit continued to deflate. GPS receivers and electronic tiltmeters detected the surface deformation greater than 2 meters. We explored the time-series surface deformation at Kīlauea Volcano, focusing on the early stage of eruptive activity, using multi-temporal COSMO-SkyMed SAR imagery. The observed maximum deformation in line-of-sight (LOS) direction was about -1.5 meter, and it indicates approximately -1.9 meter in subsiding direction by applying incidence angle. The results showed that summit began to deflate just after the event started and most of deformation occurred between early May and the end of June. Moreover, we confirmed that summit's deflation rarely happened since July 2018, which means volcanic activity entered a stable stage. The best-fit magma source model based on time-series surface deformation demonstrated that magma chambers were lying at depths between 2-3 km, and it showed a deepening trend in time. Along with the change of source depth, the center of each magma model moved toward the southwest according to the time. These results have a potential risk of including bias coming from single track observation. Therefore, to complement the initial results, we need to generate precise magma source model based on three-dimensional measurements in further research.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1261-1272
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• 2018

It is important to measure the Arctic surface air temperature because it plays a key-role in the exchange of energy between the ocean, sea ice, and the atmosphere. Although in-situ observations provide accurate measurements of air temperature, they are spatially limited to show the distribution of Arctic surface air temperature. In this study, we proposed machine learning-based models to estimate the Arctic surface air temperature in summer based on buoy data and Advanced Microwave Scanning Radiometer 2 (AMSR2)satellite data. Two machine learning approaches-random forest (RF) and support vector machine (SVM)-were used to estimate the air temperature twice a day according to AMSR2 observation time. Both RF and SVM showed $R^2$ of 0.84-0.88 and RMSE of $1.31-1.53^{\circ}C$. The results were compared to the surface air temperature and spatial distribution of the ERA-Interim reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF). They tended to underestimate the Barents Sea, the Kara Sea, and the Baffin Bay region where no IABP buoy observations exist. This study showed both possibility and limitations of the empirical estimation of Arctic surface temperature using AMSR2 data.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1025-1032
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• 2018

Snow Cover is a form of precipitation that is defined by snow on the surface and is the single largest component of the cryosphere that plays an important role in maintaining the energy balance between the earth's surface and the atmosphere. It affects the regulation of the Earth's surface temperature. However, since snow cover is mainly distributed in area where human access is difficult, snow cover detection using satellites is actively performed, and snow cover detection in forest area is an important process as well as distinguishing between cloud and snow. In this study, we applied the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to the geostationary satellites for the snow detection of forest area in existing polar orbit satellites. On the rest of the forest area, the snow cover detection using $R_{1.61{\mu}m}$ anomaly technique and NDSI was performed. As a result of the indirect validation using the snow cover data and the Visible Infrared Imaging Radiometer (VIIRS) snow cover data, the probability of detection (POD) was 99.95 % and the False Alarm Ratio (FAR) was 16.63 %. We also performed qualitative validation using the Himawari-8 Advanced Himawari Imager (AHI) RGB image. The result showed that the areas detected by the VIIRS Snow Cover miss pixel are mixed with the area detected by the research false pixel.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.401-408
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• 2021

Proton therapy using the Bragg peak is one of the radiation therapies and can deliver its maximum energy to the tumor with giving least energy for normal tissue. A cross-sectional image of the human body taken with the computed tomography (CT) has been used for radiation therapy planning. The HU values change according to the tube voltage, which lead to the change in the boundary and thickness of the anatomical structure on the CT image. This study examined the changes in the Bragg peak of the brain region according to the thickness variation in the head phantom composed of several materials using the Geant4. In the phantom composed of a single material, the Bragg peak according to the type of media and the incident energy of the proton beams were calculated, and the reliability of Geant4 code was verified by the Bragg peak. The variation of the peak in the brain region was examined when each thickness of the head phantom was changed. When the thickness of the soft tissue was changed, there was no change in the peak position, and for the skin the change in the peak was small. The change of the peak position was mainly changed when the bone thickness. In particular, when the bone was changed only or the bone was changed together with other tissues, the amount of change in the peak position was the same. It is considered that measurement of the accurate bone thickness in CT images is one of the key factors in depth-dose distribution of the radiation therapy planning.

• Journal of the Korean Geotechnical Society
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• v.38 no.5
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• pp.47-59
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• 2022

A series of experiments were performed to investigate the design and application of a rubble-ground modification method with grout injection. A small-sized injection machine was designed, and the grouts with various mix proportions were injected into 25 mm aggregate using the designed small-sized injection machine. With the compressive strength of the grout ranging from 20 to 80 MPa, the uniaxial compressive strength of the grout-filling bodies with clean gravels was higher than 1/6th of the strength of grouts themselves. However, this fraction may reduce depending on the interface conditions. The erosion resistance of the hardened grout was evaluated, and it was determined that the grout with a strength greater than 15 MPa did not require erosion consideration. Moreover, a full-scale injection test was performed for 25 cm-sized rubbles in cages with a diameter greater than 1 m and a height of 1.2 m to evaluate the filling characteristics of the grout. Results from this test indicated that the grout flowability sensitively influenced the filling characteristics.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.57-64
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• 2022

If the inside of a building collapses due to a disaster such as fire, collapse, or natural disaster, the physical security inside the building is likely to become ineffective. Here, physical security is needed to minimize the human casualties and physical damages in the collapsed building. Therefore, this paper proposes an algorithm to minimize the damage in a disaster situation by fusing existing research that detects obstacles and collapsed areas in the building and a deep learning-based object detection algorithm that minimizes human casualties. The existing research uses a single camera to determine whether the corridor environment in which the robot is currently located has collapsed and detects obstacles that interfere with the search and rescue operation. Here, objects inside the collapsed building have irregular shapes due to the debris or collapse of the building, and they are classified and detected as obstacles. We also propose a method to detect rescue requesters-the most important resource in the disaster situation-and minimize human casualties. To this end, we collected open-source disaster images and image data of disaster situations and calculated the accuracy of detecting rescue requesters in disaster situations through various deep learning-based object detection algorithms. In this study, as a result of analyzing the algorithms that detect rescue requesters in disaster situations, we have found that the YOLOv4 algorithm has an accuracy of 0.94, proving that it is most suitable for use in actual disaster situations. This paper will be helpful for performing efficient search and rescue in disaster situations and achieving a high level of physical security, even in collapsed buildings.

• Korean Journal of Heritage: History & Science
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• v.55 no.3
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• pp.6-20
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• 2022

The restoration of cultural heritage materials is an important research theme. This study improved the existing cultural heritage restoration method and attempted to establish a restoration system for cultural heritage data based on historical documents and visual materials. Recognizing the limitations of existing studies, this paper attempted to restore cultural heritage data through interdisciplinary research. In addition, 3D restoration was carried out after restoration in 2D form based on literature documents rather than existing visual sources. The object of restoration that was selected was "Jeol," which represents the power of the king of Koguryo. Koguryo's Jeol is a type of flag. Jeol appears in the mural in Anak Tomb No. 3. Rather than using only photographic materials of murals, the restoration was carried out through cross-validation of literature data and materials on archaeological art history. This is important in that the restoration carried out in this study is an accurate restoration with a historical understanding based on the literature of the relevant cultural heritage. In this study, a restoration process based on historical records was established. A 3D restoration process was performed by adding and applying visual materials after the object was first shaped based on the literature data. Restoration based on literature and visual materials was carried out based on interdisciplinary research. Therefore, this study aims to build a digital restoration system for cultural heritages and to contribute to spreading the 3D digital restoration research of cultural heritages that can be applied to various platforms.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.407-418
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• 2023

The Multi-Object Goal Visual Navigation(MultiOn) is a visual navigation task in which an agent must visit to multiple object goals in an unknown indoor environment in a given order. Existing models for the MultiOn task suffer from the limitation that they cannot utilize an integrated view of multimodal context because use only a unimodal context map. To overcome this limitation, in this paper, we propose a novel deep neural network-based agent model for MultiOn task. The proposed model, MCFMO, uses a multimodal context map, containing visual appearance features, semantic features of environmental objects, and goal object features. Moreover, the proposed model effectively fuses these three heterogeneous features into a global multimodal context map by using a point-wise convolutional neural network module. Lastly, the proposed model adopts an auxiliary task learning module to predict the observation status, goal direction and the goal distance, which can guide to learn the navigational policy efficiently. Conducting various quantitative and qualitative experiments using the Habitat-Matterport3D simulation environment and scene dataset, we demonstrate the superiority of the proposed model.