• Journal of the Korean earth science society
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• v.36 no.7
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• pp.632-642
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• 2015

Seismograms are composed of 3 characteristics, that is, seismic source, attenuation, and site amplification. Among them, site amplification characteristics should be considered significantly to estimate seismic source and attenuation characteristics with more confidence. This purpose of this study is to estimate the site amplification characteristics at each site using horizontal to vertical (H/V) spectral ratio method. This method, originally proposed by Nakamura (1989), has been applied to study the surface waves in microtremor records. It has been recently extended to the shear wave energy of strong motion and applied to the study of site amplification. This study analyzed the H/V spectral ratio of 6 ground motions respectively using observed data from 4 sites nearby in Yedang Reservoir. And then, site amplification effects at each site, from 3 kinds of seismic energies, that is, S waves, Coda waves energy, and background noise were compared each other. The results suggested that 4 sites showed its own characteristics of site amplification property in specific resonance frequency ranges (YDS: ~11 Hz, YDU: ~4 Hz, YDD: ~7 Hz). Comparison of this study to other studies using different analysis method can give us much more information about dynamic amplification of domestic sites characteristics and site classification.

• Journal of Korean Society of Forest Science
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• v.83 no.3
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• pp.322-330
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• 1994

The Universal Soil Loss Equation (USLE) has been widely used to predict long-term soil loss by incorporating several erosion factors, such as rainfall, soil, topography, and vegetation. This study is aimed to introduce the LISLE within geographic information system(GIS) environment. The Kwangneung Experimental Forest located in Kyongki Province was selected for the study area. Initially, twelve years of hourly rainfall records that were collected from 1982 to 1993 were processed to obtain the rainfall factor(R) value for the LISLE calculation. Soil survey map and topographic map of the study area were digitized and subsequent input values(K, L, S factors) were derived. The cover type and management factor (C) values were obtained from the classification of Landsat Thematic Mapper(CM) satellite imagery. All these input values were geographically registered over a common map coordinate with $25{\times}25m^2$ ground resolution. The USLE was calculated for every grid location by selecting necessary input values from the digital base maps. Once the LISLE was calculated, the resultant soil loss values(A) were represented by both numerical values and map format. Using GIS to run the LISLE, it is possible to pent out the exact locations where soil loss potential is high. In addition, this approach can be a very effective tool to monitor possible soil loss hazard under the situations of forest changes, such as conversion of forest lands to other uses, forest road construction, timber harvesting, and forest damages caused by fire, insect, and diseases.

• Korean Journal of Remote Sensing
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• v.33 no.5_3
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• pp.889-900
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• 2017

In this study, we analyzed the paper about NIR (Near-Infrared) remote sensing data and systematically summarized the research and application fields of NIR. To do this, we conducted a case study on the use of NIR in domestic journals, and SCI journals in the field of technology development for the last 5 years. After selection, a total of 281 journals were analyzed. For the statistical analysis, the classification was divided into subclasses and the dominant research trends were examined. As a result, the researchers who wrote the papers made the highest score of about 60% or more at university. In the field of application, 50% of land, 30% of environment, and 11% of disaster were distributed on SCI journals. In Korea, on the other hand, 55% of land, 24% of environment and 10% of disasters were distributed. In addition, 17% of the national land management and 8% of the geological / natural resources. Disaster observation using NIR was used for landslide, drought, weather disaster and flood. In particular, meteorological disasters are a result of study on Asian dust. However, there were no results of forest fire detection in Korea. Considering the domestic situation, it seems necessary to carry out additional and active research on this. It is expected that this statistical analysis data will be used as basic data to help expand the NIR technology development and utilization field in Korea in the future.

• Korean Journal of Environment and Ecology
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• v.30 no.2
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• pp.228-242
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• 2016

This research was conducted for understanding the evergreen forest community structure of the Bijin Island. The island was worth of vegetational value because of the representative warm temperate species such as Castanopsis cuspidata was distribute there. According to the results of community classification by TWINSPAN and DCA, the Bijin Island evergreen forest were classified by 9 communities, such as C. cuspidata, C. cuspidata-Pinus thunbergii, Neolitsea sericea-Camellia japonica, Platycarya strobilacea, Quercus serrata-Decidus broad-leaved, Alnus firma, P. thunbergii. Due to the C. cuspidata, N. sericea, Ca. japonica etc. were still dominated at canopy and under-canopy layer, so the evergreen forest will be sustain current conditions for a while if there is no artificial disturbances that were caused by human. Pl. strobilacea community will be expected to replace N. sericea and also Q. serrata-Decidus broad-leaved community will be changing caused by mutual competition. Normally, in the way of the succession process of the warm temperate forest, P. thunbergii will be expect via deciduous broad-leaved to evergreen forest such as Machilus thunbergii, C. cuspidata var. sieboldii, and N. sericea. In case of species diversity index, P. thunbergii comm.(1.2739), Q. serrata-Decidus broad-leaved comm.(1.2325), Decidus broad-leaved comm.(1.1807), and A. firma comm.(1.0854) are relatively high, while C. cuspidata and N. sericea-Ca. japonica that was dominated by evergreen species at canopy layer and P. thunbergii that was damaged by fire are relatively low(0.7380~0.8416). Soil pH was 4.72~6.33, electric conductance was 0.035~0.128dS/m, and content of organic matter was 3.4~17.4%.

• Journal of the Korean earth science society
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• v.38 no.2
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• pp.161-171
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• 2017

Observed ground motions are composed of three factors such as, seismic source, attenuation, and site amplification effect. Among them, the site amplification characteristics should be considered significantly when estimating seismic source and attenuation characteristics with more confidence. The site effect is also necessary when estimating not only seismic hazard in seismic design engineering but also rock mechanical properties. This study uses the method of H/V spectral ratio of observed ground motion between target site and reference site called a reference site method. In addition to using the vertical Fourier spectrum of the reference site, we try out the horizontal Fourier spectrum as a new method in this study. We analyze H/V spectral ratio of six ground motions respectively, observed at four sites close to Yedang Reservoir. We then compare site amplification effects at each site using 3 kinds of seismic energies including S waves, Coda waves energy, and background noise. The results suggest that each site showed similar site amplification patterns in S waves and Coda waves energy. However, the site amplification of background noise shows much different characteristics from those of S waves and Coda wave energy, which suggests that the background noises at each site have their own developing mechanism. Each station shows its own characteristics of specific resonance frequency and site amplification properties in low, high and specific resonance frequency ranges. Comparison of the method used in this study to the others that used different methods can provide us with more information about the dynamic amplification of a site characteristics and site classification.

• Journal of Korean Society of Forest Science
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• v.89 no.3
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• pp.287-296
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• 2000

This study was carried out to examine and compare the changes of inhabitation of soil microarthropoda after forest fire between different types of forest; i.e. the coniferous forest (Mt. Chocdae) and the broad-leaved forest (Samsinbong in Mt. Chiri). The individuals of soil microarthropoda found at the burned and unburned areas of Samsinbong and Mt. Chocdae were 12 orders in 5 classes, and individuals of Insecta and Arachnida 98% of them. In respect of classification groups, Collembola order was high at the burned and unburned areas of coniferous forest, while Acari order was high at the broad-leaved area. When classified by soil depth, the total number of soil microarthropoda individuals inhabiting at Samsinbong, the broad-leaved forest, was 25,342 and 37,350 at 1~5cm depth of burned and unburned areas respectively, while at 5~10cm depth the number turned out 9,722 and 15,906. Soil microarthropoda individuals of unburned area was 1.6 times higher than for the burned area. At the coniferous forest, the number marked 31,665 and 51,431, respectively for 1~5cm depth of burned and unburned area, and 10,189 and 13,202 for 5~10cm depth. Here also, the number for the unburned area was examined to be 1.4 times higher than for the burned area.

• Convergence Security Journal
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• v.21 no.1
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• pp.191-200
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• 2021

Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.113-126
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• 2021

This study was conducted to analyze the forest status and deforestation area changes of the DMZ region in North Korea based on satellite images. Using growing and non-growing season's RapidEye satellite images, land cover of the North Korean DMZ was classified into stocking land(conifer, deciduous, mixed), deforested land(unstocked mountain, cultivated mountain, bare mountain), and non-forest areas. Deforestation rates in the Yeonan-baecheon, Beopdong-Pyeonggang, Heoyang-Geumgang and Tongcheon-Goseong district were calculated as 14.24%, 16.75%, 5.98%, and 16.63% respectively. Forest fire and land use change of forest were considered as the main causes of deforestation of DMZ. Changes in deforestation area were analyzed through Google Earth images. As a results, it was shown that the area of deforestation was on a decreasing trend. This study can be used as basic data for establishing inter-Korean border region's forest cooperation strategies by providing forest spatial information on the North Korea's DMZ.

• Resources Recycling
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• v.31 no.4
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• pp.3-11
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• 2022

Owing to the increasing demand for electric vehicles (EVs), appropriate management of their waste batteries is required urgently for scrapped vehicles or for addressing battery aging. With respect to technological developments, data-driven diagnosis of waste EV batteries and management technologies have drawn increasing attention. Moreover, robot-based automatic dismantling technologies, which are seemingly interesting, require industrial verifications and linkages with future battery-related database systems. Among these, it is critical to develop and disseminate various advanced battery diagnosis and assessment techniques to improve the efficiency and safety/environment of the recirculation of waste batteries. Incorporation of lithium-related chemical substances in the public pollutant release and transfer register (PRTR) database as well as in-depth risk assessment of gas emissions in waste EV battery combustion and their relevant fire safety are some of the necessary steps. Further research and development thus are needed for optimizing the lifecycle management of waste batteries from various aspects related to data-based diagnosis/classification/disassembly processes as well as reuse/recycling and final disposal. The idea here is that the data should contribute to clean design and manufacturing to reduce the environmental burden and facilitate reuse/recycling in future production of EV batteries. Such optimization should also consider the future technological and market trends.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.225-227
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• 2022

Now In Seoul, about 75,000 CCTVs are installed in 25 district offices. Each ward office in Seoul has built a control center for CCTV control and is building information such as people, vehicle types, license plate recognition and color classification into big data through 24-hour artificial intelligence intelligent image analysis. Seoul Metropolitan Government has signed MOUs with the Ministry of Land, Infrastructure and Transport, the National Police Agency, the Fire Service, the Ministry of Justice, and the military base to enable rapid response to emergency/emergency situations. In other words, we are building a smart city that is safe and can prevent disasters by providing CCTV images of each ward office. In this paper, the CCTV image is designed to extract the characteristics of the vehicle and personnel when an incident occurs through artificial intelligence, and based on this, predict the escape route and enable continuous tracking. It is designed so that the AI automatically selects and displays the CCTV image of the route. It is designed to expand the smart city integration platform by providing image information and extracted information to the adjacent ward office when the escape route of a person or vehicle related to an incident is expected to an area other than the relevant jurisdiction. This paper will contribute as basic data to the development of smart city integrated platform research.