• Archives of Plastic Surgery
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• v.47 no.2
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• pp.178-181
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• 2020

In this report, we present a case in which good results were achieved by treatment using a free jejunal patch graft with virtual endoscopy (VE) assistance in a patient whose swallowing had failed to improve for 40 years after he mistakenly swallowed sulfuric acid, despite pectoralis major myocutaneous flap grafting and frequent balloon dilatation surgery. During the last 20 years, virtual computed tomography imaging has improved remarkably and continues to be used to address new challenges. For reconstructive surgeons, the greatest advantage of VE is that it is a noninvasive modality capable of visualizing areas inaccessible to a flexible endoscope. Using VE findings, we were able to visualize the 3-dimensional shape beyond the stenosis. VE can also help predict the area of the defect after contracture release.

• Smart Structures and Systems
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• v.5 no.4
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• pp.317-328
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• 2009

Current maintenance operations and integrity checks on a wide array of structures require personnel entry into normally-inaccessible or hazardous areas to perform necessary nondestructive inspections. To gain access for these inspections, structure must be disassembled and removed or personnel must be transported to remote locations. The use of in-situ sensors, coupled with remote interrogation, can be employed to overcome a myriad of inspection impediments stemming from accessibility limitations, complex geometries, the location and depth of hidden damage, and the isolated location of the structure. Furthermore, prevention of unexpected flaw growth and structural failure could be improved if on-board health monitoring systems were used to more regularly assess structural integrity. A research program has been completed to develop and validate Comparative Vacuum Monitoring (CVM) Sensors for surface crack detection. Statistical methods using one-sided tolerance intervals were employed to derive Probability of Detection (POD) levels for a wide array of application scenarios. Multi-year field tests were also conducted to study the deployment and long-term operation of CVM sensors on aircraft. This paper presents the quantitative crack detection capabilities of the CVM sensor, its performance in actual flight environments, and the prospects for structural health monitoring applications on aircraft and other civil structures.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.733-741
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• 2020

A smart dust monitoring system is useful for obtaining information on rough terrain that is difficult for humans to access. One of ways to deploy sensors to gather information in smart dust environment is to use an aircraft in the Amazon rainforest to scatter an enormous amount of small and cheap sensors (or smart dust devices), or to use an unmanned spacecraft to throw the sensors on the moon's surface. However, scattering an enormous amount of smart dust devices creates the difficulty of managing such devices as they can be scattered into inaccessible areas, and also causes problems such as bottlenecks, device failure, and high/low density of devices. Of the various problems that may occur in the smart dust environment, this paper is focused on solving the bottleneck problem. To address this, we propose and construct a three-layered hierarchical smart dust monitoring system that includes relay dust devices (RDDs). An RDD is a smart dust device with relatively higher computing/communicating power than a normal smart dust device. RDDs play a crucial role in reducing traffic load for the system. To validate the proposed system, we use climate data obtained from authorized portals to compare the system with other systems (i.e., non-hierarchical system and simple hierarchical system). Through this comparison, we determined that the transmission processing time is reduced by 49%-50% compared to other systems, and the maximum number of connectable devices can be increased by 16-32 times without compromising the system's operations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.439-448
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• 2022

MV (Moving Vehicle) detection using satellite imagery is important for traffic monitoring and provides a wide range of observations. Specifically, MV detection methods utilizing the time lag in single-pass optical satellite images have been studied for detecting MVs from a single set of images. Because of limitations in detecting MVs outside of roads, most previous studies required road information to limit the moving object to cars on the road. However, it is difficult to obtain road information from inaccessible areas. Therefore, this study proposed a new method for detecting MVs regardless of their locations from single-pass optical satellite images without using additional data. WV-3 (Worldview-3) satellite images were used, and a spatial correlation coefficient map was proposed to detect spatial displacement which denotes MVs across two WV-3 MS images. Finally, evaluation was performed through quantitative metrics and visual inspection. The evaluation results revealed that the proposed method can detect MV movements from the single-pass satellite images. On the contrary, misdetected or undetected MVs due to radiometric differences between the images could be identified by visual inspection. The performance of the proposed method can be improved by minimizing radiometric variations and adding conditions that are robust to radiometric differences between the images.

• Advances in Energy Research
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• v.8 no.4
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• pp.203-211
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• 2022

The aim of this paper is to analyze the performance of commercial fuel cell (rated capacity 1000W) with the help of resistive load and output power variation with change in H₂ flow rate and calculate the maximum power point (MPP) of the proton exchange membrane (PEM) while changing AC and DC load respectively. The factors influencing the output power of a fuel cell are hydrogen flow rate, cell temperature, and membrane water content. The results show that when the H₂ flow rate is changed from 11, 13, and 15 Lpm, MPP is increased from lower to higher flow rate. The power of the fuel cell is increased at the rate of 29% by increasing the flow rate from 11 to 15 lpm. This study will allow small-scale industries and residential buildings (in remote or inaccessible areas) to characterize the performance of PEMFC. Furthermore, fuel cell helps in reducing emission in the environment compared to fossil fuels. Also, fuel cells are ecofriendly as well as cost effective and can be the best alternative way to convert energy.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.123-139
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• 2024

Forest vertical structure is vital for comprehending ecosystems and biodiversity, in addition to fundamental forest information. Currently, the forest vertical structure is predominantly assessed via an in-situ method, which is not only difficult to apply to inaccessible locations or large areas but also costly and requires substantial human resources. Therefore, mapping systems based on remote sensing data have been actively explored. Recently, research on analyzing and classifying images using machine learning techniques has been actively conducted and applied to map the vertical structure of forests accurately. In this study, Sentinel-2 and digital surface model images were obtained on two different dates separated by approximately one month, and the spectral index and tree height maps were generated separately. Furthermore, according to the acquisition time, the input data were separated into cases 1 and 2, which were then combined to generate case 3. Using these data, forest vetical structure mapping models based on random forest, support vector machine, and extreme gradient boost(XGBoost)were generated. Consequently, nine models were generated, with the XGBoost model in Case 3 performing the best, with an average precision of 0.99 and an F1 score of 0.91. We confirmed that generating a forest vertical structure mapping model utilizing bi-seasonal data and an appropriate model can result in an accuracy of 90% or higher.

• Earthquakes and Structures
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• v.20 no.2
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• pp.149-160
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• 2021

Buildings made using the locally available clay materials are amongst the least expensive forms of construction in many developing countries, and therefore, widely popular in remote areas. It is despite the fact that these low-strength masonry structures are vulnerable to seismic forces. Since transporting imported materials like cement and steel in areas inaccessible by motorable roads is challenging and financially unviable. This paper presents, and experimentally investigates, adobe masonry structures that utilize the abundantly available local clay materials with moderate use of imported materials like cement, aggregates, and steel. Shake-table tests were performed on two 1:3 reduce-scaled adobe masonry models for experimental seismic testing and verification. The model AM1 was confined with vertical lightly reinforced concrete columns provided at all corners and reinforced concrete horizontal bands (i.e., tie beams) provided at sill, lintel, and eave levels. The model AM2 was confined only with the horizontal bands provided at sill, lintel, and eave levels. The models were subjected to sinusoidal base motions for studying the damage evolution and response of the model under dynamic lateral loading. The lateral forcedeformation capacity curves for both models were developed and bi-linearized to compute the seismic response parameters: stiffness, strength, ductility, and response modification factor R. Seismic performance levels, story-drift, base shear coefficient, and the expected structural damages, were defined for both the models. Seismic performance assessment of the selected models was carried out using the lateral seismic force procedure to evaluate their safety in different seismic zones. The use of vertical columns in AM1 has shown a considerable increase in the lateral strength of the model in comparison to AM2. Although an R factor equal to 2.0 is recommended for both the models, AM1 has exhibited better seismic performance in all seismic zones due to its relatively high lateral strength in comparison to AM2.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.317-325
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• 2021

Pine wilt disease first appeared in Busan in 1998; it is a serious disease that causes enormous damage to pine trees. The Korean government enacted a special law on the control of pine wilt disease in 2005, which controls and prohibits the movement of pine trees in affected areas. However, existing forecasting and control methods have physical and economic challenges in reducing pine wilt disease that occurs simultaneously and radically in mountainous terrain. In this study, the authors present the use of a deep learning object recognition and prediction method based on visual materials using an unmanned aerial vehicle (UAV) to effectively detect trees suspected of being infected with pine wilt disease. In order to observe pine wilt disease, an orthomosaic was produced using image data acquired through aerial shots. As a result, 198 damaged trees were identified, while 84 damaged trees were identified in field surveys that excluded areas with inaccessible steep slopes and cliffs. Analysis using image segmentation (SegNet) and image detection (YOLOv2) obtained a performance value of 0.57 and 0.77, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.533-540
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• 2021

High-resolution satellite images have high potential to acquire geospatial information over inaccessible areas such as Antarctica. Reference data are often required to increase the positional accuracy of the satellite data but the data are not available in many inland areas in Antarctica. Therefore this paper presents a multi-image RPCs (Rational Polynomial Coefficients) sensor modeling without any ground controls or reference data. Conjugate points between multi-images are extracted and used for the multi-image sensor modeling. The experiment was carried out for Kompsat-3A and showed that the significant accuracy increase was not observed but the approach has potential to suppress the maximum errors, especially the vertical errors.

• Journal of The Geomorphological Association of Korea
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• v.28 no.1
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• pp.83-99
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• 2021

Burn severity analysis using satellite imagery has high capabilities for research and management in inaccessible areas. We extracted the forest fire area of the DMZ (Demilitarized Zone) in the western Imjin Estuary which is restricted to access due to the confrontation between South and North Korea. Then we analyzed the forest fire severity and recoverability using atmospheric corrected Surface Reflectance Level-2 data collected from Landsat-8 OLI (Operational Land Imagery) / TIRS (Thermal Infrared Sensor). Normalized Burn Ratio (NBR), differenced NBR (dNBR), and Relative dNBR (RdNBR) were analyzed based on changes in the spectral pattern of satellite images to estimate burn severity area and intensity. Also, we evaluated the recoverability after a forest fire using a land cover map which is constructed from the NBR, dNBR, and RdNBR analyzed results. The results of dNBR and RdNBR analysis for the six years (during May 30, 2014 - May 30, 2020) showed that the intensity of monthly burn severity was affected by seasonal changes after the outbreak and the intensity of annual burn severity gradually decreased after the fire events. The regrowth of vegetation was detected in most of the affected areas for three years (until May 2020) after the forest fire reoccurred in May 2017. The monthly recoverability (from April 2014 to December 2015) of forests and grass fields was increased and decreased per month depending on the vegetation growth rate of each season. In the case of annual recoverability, the growth of forest and grass field was reset caused by the recurrence of a forest fire in 2017, then gradually recovered with grass fields from 2017 to 2020. We confirmed that remote sensing was effectively applied to research of the burn severity and recoverability in the DMZ. This study would also provide implications for the management and construction statistics database of the forest fire in the DMZ.