• Smart Structures and Systems
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• v.31 no.4
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• pp.351-363
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• 2023

Visual structural inspections are an inseparable part of post-earthquake damage assessments. With unmanned aerial vehicles (UAVs) establishing a new frontier in visual inspections, there are major computational challenges in processing the collected massive amounts of high-resolution visual data. We propose twin deep learning models that can provide accurate high-resolution structural components and damage segmentation masks efficiently. The traditional approach to cope with high memory computational demands is to either uniformly downsample the raw images at the price of losing fine local details or cropping smaller parts of the images leading to a loss of global contextual information. Therefore, our twin models comprising Trainable Resizing for high-resolution Segmentation Network (TRS-Net) and DmgFormer approaches the global and local semantics from different perspectives. TRS-Net is a compound, high-resolution segmentation architecture equipped with learnable downsampler and upsampler modules to minimize information loss for optimal performance and efficiency. DmgFormer utilizes a transformer backbone and a convolutional decoder head with skip connections on a grid of crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost performance further and reduce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on the 3D physics-based graphics models (PBGMs) synthetic environments in the QuakeCity dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling). The models were developed as part of the 2^nd International Competition for Structural Health Monitoring.

• Journal of The Geomorphological Association of Korea
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• v.27 no.3
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• pp.87-103
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• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.

• Journal of The Geomorphological Association of Korea
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• v.26 no.3
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• pp.53-67
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• 2019

The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.

• Journal of The Geomorphological Association of Korea
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• v.27 no.2
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• pp.65-80
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• 2020

This paper deals with risk assessment of life in a landslide-prone area by a GIS-based modeling method. Landslide susceptibility maps can provide a probability of landslide prone areas to mitigate or proper control this problems and to take any development plan and disaster management. A landslide inventory map of the study area was prepared based on past historical information and aerial photography analysis. A total of 550 landslides have been counted at the whole study area. The extracted landslides were randomly selected and divided into two different groups, 50% of the landslides were used for model calibration and the other were used for validation purpose. Eleven causative factors (continuous and thematic) such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in hazard analysis. The correlation between landslides and these factors, pixels were divided into several classes and frequency ratio was also extracted. Eventually, a landslide susceptibility map was constructed using a logistic regression model based on entire events. Moreover, the landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract a success rate curve. Based on the results, logistic regression produced an 85.18% accuracy, so we believed that the model was reliable and acceptable for the landslide susceptibility analysis on the study area. In addition, for risk assessment, vulnerability scale were added for social thematic data layer. The study area predictive landslide affected pixels 2,000 and 5,000 were also calculated for making a probability table. In final calculation, the 2,000 predictive landslide affected pixels were assumed to run. The total population causalities were estimated as 7.75 person that was relatively close to the actual number published in Korean Annual Disaster Report, 2006.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.9-18
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• 2024

Small streams, despite their rich ecosystems, face challenges in vegetation assessment due to the limitations of traditional, time-consuming methods. This study presents a groundbreaking approach, combining unmanned aerial vehicles(UAVs), convolutional neural networks(CNNs), and the vegetation differential vegetation index (VDVI), to revolutionize both assessment and management of stream vegetation. Focusing on Idong Stream in South Korea (2.7 km long, 2.34 km² basin area)with eight diverse revetment methods, we leveraged high-resolution RGB images captured by UAVs across five dates (July-December). These images trained a ResNeXt101 CNN model, achieving an impressive 89% accuracy in classifying vegetation cover(soil,water, and vegetation). This enabled detailed spatial and temporal analysis of vegetation distribution. Further, VDVI calculations on classified vegetation areas allowed assessment of vegetation vitality. Our key findings showcase the power of this approach:(a) TheCNN model generated highly accurate cover maps, facilitating precise monitoring of vegetation changes overtime and space. (b) August displayed the highest average VDVI(0.24), indicating peak vegetation growth crucial for stabilizing streambanks and resisting flow. (c) Different revetment methods impacted vegetation vitality. Fieldstone sections exhibited initial high vitality followed by decline due to leaf browning. Block-type sections and the control group showed a gradual decline after peak growth. Interestingly, the "H environment block" exhibited minimal change, suggesting potential benefits for specific ecological functions.(d) Despite initial differences, all sections converged in vegetation distribution trends after 15 years due to the influence of surrounding vegetation. This study demonstrates the immense potential of UAV-based remote sensing and CNNs for revolutionizing small-stream vegetation assessment and management. By providing high-resolution, temporally detailed data, this approach offers distinct advantages over traditional methods, ultimately benefiting both the environment and surrounding communities through informed decision-making for improved stream health and ecological conservation.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.151-166
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• 2024

Coastal debris presents a significant environmental threat globally. This research sought to improve the monitoring methods for coastal debris by employing deep learning and remote sensing technologies. To achieve this, an object detection approach utilizing the You Only Look Once (YOLO)v8 model was implemented to develop a comprehensive image dataset for 11 primary types of coastal debris in our country, proposing a protocol for the real-time detection and analysis of debris. Drone imagery was collected over Sinja Island, situated at the estuary of the Nakdong River, and analyzed using our custom YOLOv8-based analysis program to identify type-specific hotspots of coastal debris. The deployment of these mapping and analysis methodologies is anticipated to be effectively utilized in managing coastal debris.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.3
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• pp.74-83
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• 2005

The Riparian Buffer Zone has many potential values including the preservation of water quality as well as being ecologically friendly. This study aims to quantitatively analyze the landscape structure index of the Riparian Buffer Zone in the Kyoung-an stream and to produce base information necessary for proper management. The study used aerial images that were applied to geometric corrections for a time series from 1966 to 2000 for land data and also used FRAGSTATS, which is a type of ARCVIEW extension module, as an analysis tool. An analysis of land use change and the Landscape Index revealed that the area of farm land has decreased and that the area of residential property has increased. In addition, there was a slight change for land used for purposes other than farming or for residence. The results of analyzing the Landscape Structure Index, revealed that the NP has increased from 437 in 1966 to 695 in 2000. This data reveals that the change of land use is influenced by various artificial factors. The NPS, which represents the declining degree of patch, decreased from 9.441 to 5.934, revealing that the change of land use has been progressing considerably. In regard to forest areas, land use reduced somewhat but did not indicate a significant change. Therefore, an analysis of the total index reveals that the edge of patch has become more complicated and that the variation index of patch has increased significantly. However, this study reveals that barriers to block pollution have weakened as a result and that there is a need to concentrate on the implementation and the management of the Riparian Buffer Zone. Consequently, this study reveals that substantial research is necessary in order to carry out the proper management of the Riparian Buffer Zone, especially in light of the distribution type of each patch and the change in conditions regarding them.

• Strategy21
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• s.32
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• pp.123-159
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• 2013

The purpose of this research paper is to re-valuate the factors that affected the Royal Navy's rearmament and preparation for war by conducting analysis on the discussion held in the Britain on the strategic priorities and Navy's coping measures adopted during the interwar period. After the end of the WWI, each of the military arms of the Britain faced significant difficulty in securing budget and increasing their military power all throughout the interwar period, and the Navy was not an exception. The WWII that got started on September 1939 was the turning point in which this difficulty led to full-fledged crisis. Immensely many criticisms followed after the war and problems were identified when it comes to the Royal Navy's performance during the war. This type of effort to identify problem led to the attempt to analyze whether Royal Navy's preparation for war and rearmament policy during interwar period were adequate, and to identify the root causes of failure. Existing studies sought to find the root cause of failed rearmament from external factors such as the deterioration of the Britain itself or pressure from the Treasury Department to cut the budget for national defense, or sought to detect problems from the development of wrong strategies by the Navy. However, Royal Navy's failed preparation for the war during interwar period is not the result of one or two separate factors. Instead, it resulted due to the diverse factors and situations that the Britain was facing at the time, and due to intricate and complex interaction of these factors. Meanwhile, this research paper focused on the context characterized by 'strategic selection and setting up of priorities' among the various factors to conduct analysis on the Navy's rearmament by linking it with the discussion held at the time on setting up strategic priorities, and sought to demonstrate that the Navy Department's inadequate counter-measures developed during this process waned Royal Navy's position. After the end of WWI, each of the military arms continued to compete for the limited resources and budget all throughout the interwar period, and this type of competition amidst the situation in which the economic situation of Britain was still unstable, made prioritization when it comes to the allocation of resources and setting up of the priorities when it comes to the military power build-up, inevitable. Amidst this situation, the RAF was able to secure resources first and foremost, encouraged by the conviction of some politicians who were affected by the 'theory of aerial threat' and who believed that curtailing potential attack with the Air Force would be means to secure national security at comparatively lower cost. In response, Navy successfully defended the need for the existence of Navy despite the advancement of the aerial power, by emphasizing that the Britain's livelihood depends on trade and on the maintenance of maritime traffic. Despite this counter-measuring logic, however, Navy's role was still limited to the defense of overseas territory and to the fleet run-off instead of sea traffic route production when it comes to the specific power build-up plan, and did not understand the situation in which financial and economic factors gained greater importance when it comes to the setting up of strategic priorities. As a result, Navy's plan to build its powers was met with continual resistance of the Treasury Department, and lost the opportunity to re-gain the status of 'senior service' that it had enjoyed in the past during the competition for strategic prioritization. Given that the strategic and economic situation that Korea faces today is not very different from that of the Britain during the interwar period, our Navy too should leverage the lessons learned from the Royal Navy to make the effort to secure viable position when it comes to the setting of priorities in case of national defense strategy by presenting the basis on why maritime coping should be prioritized among the numerous other threats, and by developing the measures for securing the powers needed effectively amidst the limited resources.

• Applied Biological Chemistry
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• v.51 no.3
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• pp.223-227
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• 2008

The aerial parts of Sajabalssuk (Artemisia princeps PAMPANINI, Sajabalssuk) was extracted with 80% aqueous MeOH, and the concentrated extract was partitioned with EtOAc, n-BuOH and $H_2O$, successively. From the EtOAc fraction, three cycloartane-type triterpnoids and one ursane-type triterpenoid were isolated through the repeated silica gel, ODS and Sephadex LH-20 column chromatographies. From the results of physico-chemical data including NMR, MS and IR, the chemical structures of the triterpenoids were determined as wrightial (1), wrightial acetate (2), 27-norcycloart-20(21)-ene-25-al-3${\beta}$-ol acetate (3) and ursolic acid (4). No report has been found for isolation of compound 3 in the literature so far, and compounds 1, 2 and 3 were the first to be isolated from Sajabalssuk (Artemisia princeps PAMPANINI, Sajabalssuk). Also, compound 1 showed Acyl-CoA:Cholesterol acyltransferase (hACAT-1) and hACAT-2 inhibitory activity with the $IC_{50}$ values of 33.0 and 45.0 ${\mu}g/ml$, respectively. Compounds 2 and 3 inhibited hACAT-1 activity with the $IC_{50}$ values of 12.0 and 16.0 ${\mu}g/ml$, respectively.

• Journal of agriculture & life science
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• v.50 no.2
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• pp.33-43
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• 2016

Pinellia tripartita(Blume) Schott is a herbaceous perennial plant belonging to the Araceae and distributed on Asia including of Korea, Japan, and China. P. tripartita is often used for gardening but has not been developed mass-breeding methods. In this study, we compared the tuber growth in different combinations of mixed soils used six compositions. Tubers used to study was cultured in vitro and divided into two groups. Type I was diameter more than 1cm and the group of Type II was diameter below than 1cm. Enlargement of tubers and growth of aerial parts were measuring the plant height, number of fresh leaves and dead leaves, number of bullets, tuber size, and fresh / dry weight. The size/weight and numbers of tubers from the mixed soil B (coir 68.0%, peat moss 14.7%, perlite 3.0%, vermiculite 7.0% and zeolite 7.0%) were the best grown up for eight weeks. In case of Type I, GI (Growth index) of tuber size and weight were 45% and 101%, respectively. The difference of growth was doubled compared to the bad growth treatment as the mixed soil E(Coir 14.3%, peat moss 14.3%, perlite 42.9%, vermiculite 14.3%, and zeolite 14.3%). These results could be used as the basic information for the similar experimental design for the P. ternata.