• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.357-367
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• 2018

Recent earthquakes in Gyeongju and Pohang have raised interest in liquefaction in South Korea. Liquefaction, which is a phenomenon that excessive pore pressure is generated and the shear strength of soil is decreased by repeated loads such as earthquakes, causes severe problems such as ground subsidence and overturning of structures. Therefore, it is necessary to identify and prepare for the possibility of liquefaction in advance. In general, the possibility of liquefaction is quantitatively assessed using the Liquefaction Potential Index (LPI), but it takes a lot of time and effort for performing site response analysis which is essential for the liquefaction evaluation. In this study, a simple method to evaluate the liquefaction potential without executing the site response analysis in a downtown area with a lot of borehole data was proposed. In this simple method, the correlation between the average shear wave velocity of the target location ground and the LPI divided by thickness of liquefiable layer was established. And the applicable correlation equation for various rock outcrop accelerations were derived. Using the 104 boreholes information in Seoul, the correlation equation between LPI and the shear wave velocity (ground water level: 0m, 1m, 2m, 3m) is obtained and the possibility of liquefaction occurrence in Seoul and Gyeongju is evaluated. The applicability of the proposed simple method was verified by comparing the LPI values calculated from the correlation equation and the LPI values derived using the existing site response analysis. Finally, the distribution map of LPI calculated from the correlation was drawn using Kriging, a geostatistical technique.

• Economic and Environmental Geology
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• v.12 no.2
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• pp.95-104
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• 1979

The position of any point on the earth's surface can be. represented in the spherical coordinates by surface spherical harmonics. Since geomagnetic field is a function of position on the earth, it can be also expressed by spherical harmonic analysis as spherical harmonics of trigonometric series of $a_m({\theta})$ cos $m{\phi}$ and $b_m({\theta})$ sin $m{\phi}$. Coefficients of surface spherical harmonics, $a_m({\theta})$ and $b_m({\theta})$, can be drawn from the components of the geomagnetic field, declination and inclination, and vice versa. In this paper, components of geomagnetic field, declination and inclination in the Korean peninsula are obtained by spherical harmonic analysis using the Gauss coefficients calculated from the world-wide magnetic charts of 1960. These components correspond to the values of normal geomagnetic field having no disturbances of subsurface mass, structure, and so on. The vertical and total components offer the zero level for the interpretation of geomagnetic data obtained by magnetic measurement in the Korean peninsula. Using this zero level, magnetic anomaly map is obtained from the data of airborne magnetic. prospecting carried out during 1958 to 1960. The conclusions of this study are as follows; (1) The intensity of horizontal component of normal geomagnetic field in Korean peninsula ranges from $2{\times}10^4$ gammas to $2.45{\times}10^4$ gammas. It decreases about 500 with the increment of $1^{\circ}$ in latitude. Along the same. latitude, it increases 250 gammas with the increment of $1^{\circ}$ in longitude. (2) Intensity of vertical component ranges from $3.85{\times}10^4$ gammas to $5.15{\times}10^4$ gammas. It increases. about 1000 gammas with the increment of $1^{\circ}$ in latitude. Along the same latitude, it decreases. 150~240 gammas with the increment of $1^{\circ}$ in longitude. Decreasing rate is considerably larger in higher latitude than in lower latitude. (3) Total intensity ranges from $4.55{\times}10^4$ gammas to $5.15{\times}10^4$ gammas. It increases 600~700 gammas with the increament of $1^{\circ}$ in latitude. Along the same latitude, it decreases 10~90 gammas. with the increment of $1^{\circ}$ in longitude. Decreasing rate is considerably larger in higher latitude as the case of vertical component. (4) The declination ranges from $-3.8^{\circ}$ to $-11.5^{\circ}$. It increases $0.6^{\circ}$ with the increment of $1^{\circ}$ in latitude. Along the same latutude, it increases $0.6^{\circ}$ with the increment of l O in longitude. Unlike the cases of vertical and total component, the rate of change is considerably larger in lower latitude than in higher latitude. (5) The inclination ranges from $57.8^{\circ}$ to $66.8^{\circ}$. It increases about $1^{\circ}$ with 'the increment of $1^{\circ}$ in latitude Along the same latitude, it dereases $0.4^{\circ}$ with the increment of $1^{\circ}$ in longitude. (6) The Boundaries of 5 anomaly zones classified on the basis of the trend and shape of anomaly curves correspond to the geologic boundaries. (7) The trend of anomaly curves in each anomaly zone is closely related to the geologic structure developed in the corresponding zone. That is, it relates to the fault in the 3rd zone, the intrusion. of granite in the 1st and 5th zones, and mountains in the 2nd and 4th zones.

• Conservation Science in Museum
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• v.19
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• pp.1-18
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• 2018

The Stele for National Preceptor Hongbeop from the Jeongtosa Temple site in Chungju is one of the most important stone cultural heritage items for exemplifying the style of the Goryeo era. Despite its obvious value, this relic has been stored in a weathered condition at the National Museum of Korea. It had suffered various dismantling and displacements during the Japanese colonial period and had long been exposed in the open air. The stele was selected as a subject for the Stone Monuments Restoration Project launched by the National Museum of Korea in 2015. In preparation for its outdoor exhibition as part of the restoration project, this study investigated the characteristics of its materials, produced a map of its deterioration from weathering, and carried out ultrasonic analysis of the materials to provide findings useful for conservation treatment. The materials analysis revealed that the turtle-shaped pedestal of the stele was made from two-mica granite consisting of medium-grained quartz, plagioclase, alkali feldspar, biotite, and muscovite. Its body stone is crystalline marble, the rock-forming mineral in which is medium-grained calcite in a rose-pink color with dark grey spots. The dragon top of the stele is made of crystalline marble, the major component of which is medium-grained calcite of a light-grey color. The deterioration consists of 21.5% abrasion on the stone body, with its south face most damaged, and 18.6% granular disintegration, with the north face most damaged. The ultrasonic material characterization conducted for mapping the general condition of weathering shows low values on the parts-assembly area of the turtle-shaped pedestal and on the upper portion of the stone body. It is considered that there is dislocation due to partial blistering and fracturing as well as to the differences in surface treatment. Prior to the outdoor exhibition of the stele, the surface was cleaned of contaminants and was consolidated based on the scientific investigation in order to prevent weathering from the external environment.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.126-138
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• 2017

Recently, the national interest in environment is increasing and for dealing with water environment-related issues swiftly and accurately, the demand to facilitate the analysis of water environment data using a GIS is growing. To meet such growing demands, a spatial network data-based stream network analysis map(Korean Reach File; KRF) supporting spatial analysis of water environment data was developed and is being provided. However, there is a difficulty in delineating catchment areas, which are the basis of supplying spatial data including relevant information frequently required by the users such as establishing remediation measures against water pollution accidents. Therefore, in this study, the development of a computer program was made. The development process included steps such as designing a delineation method, and developing an algorithm and modules. DEM(Digital Elevation Model) and FDR(Flow Direction) were used as the major data to automatically delineate catchment areas. The algorithm for the delineation of catchment areas was developed through three stages; catchment area grid extraction, boundary point extraction, and boundary line division. Also, an add-in catchment area delineation module, based on ArcGIS from ESRI, was developed in the consideration of productivity and utility of the program. Using the developed program, the catchment areas were delineated and they were compared to the catchment areas currently used by the government. The results showed that the catchment areas were delineated efficiently using the digital elevation data. Especially, in the regions with clear topographical slopes, they were delineated accurately and swiftly. Although in some regions with flat fields of paddles and downtowns or well-organized drainage facilities, the catchment areas were not segmented accurately, the program definitely reduce the processing time to delineate existing catchment areas. In the future, more efforts should be made to enhance current algorithm to facilitate the use of the higher precision of digital elevation data, and furthermore reducing the calculation time for processing large data volume.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.10
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• pp.916-927
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• 2010

In general water treatment process, the disinfection process by chlorine is used to prevent water borne disease and microbial regrowth in water distribution system. Because chlorines were reacted with organic matter, carcinogens such as disinfection by-products (DBPs) were produced in drinking water. Therefore, a suitable injection of chlorine is need to decrease DBPs. Rechlorination in water pipelines or reservoirs are recently increased to secure the residual chlorine in the end of water pipelines. EPANET 2.0 developed by the U.S. Environmental Protection Agency (EPA) is used to compute the optimal chlorine injection in water treatment plant and to predict the dosage of rechlorination into water distribution system. The bulk decay constant ($k_{bulk}$) was drawn by bottle test and the wall decay constant ($k_{wall}$) was derived from using systermatic analysis method for water quality modeling in target region. In order to predict water quality based on hydraulic analysis model, residual chlorine concentration was forecasted in water distribution system. The formation of DBPs such as trihalomethanes (THMs) was verified with chlorine dosage in lab-scale test. The bulk decay constant ($k_{bulk}$) was rapidly decreased with increasing temperature in the early time. In the case of 25 degrees celsius, the bulk decay constant ($k_{bulk}$) decreased over half after 25 hours later. In this study, there were able to calculate about optimal rechlorine dosage and select on profitable sites in the network map.

• Korean Journal of Heritage: History & Science
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• v.43 no.3
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• pp.306-333
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• 2010

Previous studies on Wolseong fortress have focused on capital system of Silla Dynasty and on the recreation of Wolseong fortress due to the excavations in and around Wolseong moat. Since the report on the Geographical Survey of Wolseong fortress was published and GPR survey in Wolseong fortress was executed as a trial test in 2004, the academic interest in the site has now expanded to the inside of the fortress. From such context, the preliminary research on the fortress including geophysical survey had been commenced. GPR survey had been conducted for a year from March, 2007. The principal purpose of the recent 3D GPR survey was to provide visualization of subsurface images of the entire Wolseong fortress area. In order to obtain 3D GPR data, dense profile lines were laid in grid-form. The total area surveyed was $112,535m^2$. Depth slice was applied to analyse each level to examine how the layers of the remains had changed and overlapped over time. In addition, slice overlay analysis methodology was used to gather reflects of each depth on a single map. Isolated surface visualization, which is one of 3D analysis methods, was also employed to gain more in-depth understanding and more accurate interpretations of the remain The GPR survey has confirmed that there are building sites whose archaeological features can be classified into 14 different groups. Three interesting areas with huge public building arrangement have been found in Zone 2 in the far west, Zone 9 in the middle, and Zone 14 in the far east. It is recognized that such areas must had been used for important public functions. This research has displayed that 3D GPR survey can be effective for a vast area of archaeological remains and that slice overlay images can provide clearer image with high contrast for objects and remains buried the site.

• Korean Journal of Breeding Science
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• v.50 no.4
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• pp.424-433
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• 2018

Watermelon (Citrullus lanatus) is an economically important vegetable crop all over the world, which has functional compounds such as lycopene and citrulline. Gummy stem blight caused by Didymella bryoniae is one of the most devastative diseases in watermelon. Single nucleotide polymorphisms (SNPs), which are genetic variations occurring between individuals with respect to a single base, were often used to construct genetic linkage maps and develop molecular markers linked to a variety of horticultural traits and resistance to several diseases. In this study, we developed high-resolution melting (HRM) markers based on SNPs generated from NGS resequencing of two parents in watermelon. Plant materials were C. lanatus '920533' (female and susceptible parent), C. amarus 'PI 189225' (male and resistant parent), and their $F_1$ and $F_2$ progenies. A total of 13.6 Gbp ('920533') and 13.1 Gbp ('PI 189225') of genomic sequences were obtained using NGS analysis. A total of 6.09 million SNPs between '920533' and 'PI 189225' were detected, and 354,860 SNPs were identified as potential HRM primer sets. From these, a total of 330 primer sets for HRM analysis were designed. As a result, a total of 61 HRM markers that have polymorphic melting curves were developed. These HRM markers can be used for the construction of SNP-based linkage maps and for the analysis of quantitative trait loci (QTLs) related to gummy stem blight resistance.

• Korean Journal of Breeding Science
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• v.50 no.4
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• pp.387-395
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• 2018

This study was conducted to identify DNA markers related to resistance to herbicide containing mesotrione in Tongil type rice. Two Tongil type elite lines; Milyang154 and Suweon382, showed resistance to mesotrione, whereas the others were susceptible at 20 days after mesotrione application, and severe growth inhibition was observed in the remaining 13 lines. As a result of analysis of mesotrione resistance using 190 $F_2$ populations derived from a cross of Hanareum2 (susceptible) and Milyang154 (resistant), the mesotrione resistance locus was shown to be a single dominant gene with a 3:1 segregation ratio ($X^2=1.19$, P=0.31). To identify a DNA marker closely linked to the mesotrione resistance gene, bulked segregant analysis (BSA) was adopted. The DNA marker RM3501 was identified on chromosome 2 with a recombinant value of 0.53 to the mesotrione resistance gene. Mst1(t) was located between SSR (simple sequence repeat) markers RM3501 and RM324 with a physical map distance of 10.2 Mb-11.4 Mb on chromosome 2. The band pattern of agarose gel electrophoresis of the SSR marker RM3501 showed the same segregation pattern with respect to mesotrione treatment in 20 Tongil type varieties and a $BC_2F_2$ segregation population derived from a cross between Unkwang (resistant) and Hanareum2 (susceptible). Thus, the RM3501 DNA marker could be used in breeding programs for Marker Assisted Selection in mesotrione resistant rice breeding.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.