• Journal of Korean Society of Forest Science
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• v.103 no.4
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• pp.564-573
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• 2014

The objective of this study was to select a representative coastal disaster prevention forest type for each coastal area. In this study, we used cluster analysis with the results obtained from investigation for density of growing stock, tree height, DBH, and forest width and length of major coastal disaster prevention forests distributed in the west, the south, and the east coasts. The results showed that the coastal disaster prevention forests for each coast were classified into two types: a forest type with small DBH and high growing stock density (W1) or with high tree height (W2) in the west coast, a forest type with small tree height (S1) or with large DBH (S2) in the south coast, and a forest type with small growing stock density (E1) or with small tree height and low DBH (E2) in the east coast. The coastal disaster prevention forests located in Gurye beach (Hwangchon-ri, Wonbuk-myeon, Taean-gun, Chungcheongnam-do) and in Gohsapo beach (Unsna-ri, Byeonsan-myeon, Buan-gun, Jeollabuk-do) were selected as the representative forests of W1 and W2, respectively. In addition, the coastal disaster prevention forests located in Namyang beach (Namyang-ri, Seolcheon-myeon, Namhae-gun, Gyeongsangnam-do) and in Donggo beach (Donggo-ri, Sinji-myeon, Wando-gun, Jeollanam-do) were selected as the representative forests of S1 and S2, respectively. Last, the coastal disaster prevention forests located in Bonggil beach (Bonggil-ri, Yangbuk-myeon, Gyeongju-si, Gyeongsangbuk-do) and in Anmeok beach (Gyeonso-dong, Gangneung-si, Gangwon-do) were selected as the representative forests of E1 and E2, respectively. Our finding is expected to be used as baseline data in establishing the most appropriate coastal disaster prevention forest for each coast.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.79-85
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• 2022

Anomaly detection is a method to detect and block abnormal data flows in general users' data sets. The previously known method is a method of detecting and defending an attack based on a signature using the signature of an already known attack. This has the advantage of a low false positive rate, but the problem is that it is very vulnerable to a zero-day vulnerability attack or a modified attack. However, in the case of anomaly detection, there is a disadvantage that the false positive rate is high, but it has the advantage of being able to identify, detect, and block zero-day vulnerability attacks or modified attacks, so related studies are being actively conducted. In this study, we want to deal with these anomaly detection mechanisms, and we propose a new mechanism that performs both anomaly detection and classification while supplementing the high false positive rate mentioned above. In this study, the experiment was conducted with five configurations considering the characteristics of various algorithms. As a result, the model showing the best accuracy was proposed as the result of this study. After detecting an attack by applying the Extra Tree and Three-layer ANN at the same time, the attack type is classified using the Extra Tree for the classified attack data. In this study, verification was performed on the NSL-KDD data set, and the accuracy was 99.8%, 99.1%, 98.9%, 98.7%, and 97.9% for Normal, Dos, Probe, U2R, and R2L, respectively. This configuration showed superior performance compared to other models.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.10
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• pp.1369-1378
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• 2006

This is the first study in which genetic variability and relationships of a large number of Japanese chicken breeds were revealed along with those of several foreign breeds by using microsatellite DNA polymorphisms. Twenty-eight breeds (34 populations) of native Japanese chickens and seven foreign breeds or varieties were analyzed. The mean number of alleles per locus, the proportion of the polymorphic loci, and the expected average heterozygosity ranged from 1.75 to 4.70, from 0.55 to 1.00, and from 0.21 to 0.67, respectively. Microsatellite alleles being unique to a particular population were detected in some populations. The $D_A$ genetic distance between populations was obtained from allele frequency for every pair of the populations to construct a neighbor-joining tree. According to the phylogenetic tree, excluding a few exceptions, native Japanese chicken breeds and foreign breeds were clearly separated from each other. Furthermore, the tree topology divided native Japanese chickens into four main classes, which was almost in accordance with the classification based on body morphology; that is, (1) Cochin type, (2) Malay type, (3) layer type, and (4) intermediate type between Malay and layer types. This is the first finding for native Japanese chickens.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.213-219
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• 2020

In this paper, we propose a classification analysis method for diagnosing and predicting problematic smartphone use in order to provide policy data on problematic smartphone use, which is getting worse year after year. Attempts have been made to identify key variables that affect the study. For this purpose, the classification rates of Decision Tree, Random Forest, and Support Vector Machine among machine learning analysis methods, which are artificial intelligence methods, were compared. The data were from 25,465 people who responded to the '2018 Problematic Smartphone Use Survey' provided by the Korea Information Society Agency and analyzed using the R statistical package (ver. 3.6.2). As a result, the three classification techniques showed similar classification rates, and there was no problem of overfitting the model. The classification rate of the Support Vector Machine was the highest among the three classification methods, followed by Decision Tree and Random Forest. The top three variables affecting the classification rate among smartphone use types were Life Service type, Information Seeking type, and Leisure Activity Seeking type.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.1
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• pp.41-50
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• 2020

The National Geographic Information Institute of Korea takes digital aerial photograph images every two years to make and modify/renew the digital map. The cameras for aerial photogrammetry to capture these digital aerial photographs are divided into frame types and line types. Computer vision analysis of aerial photograph images was only possible for frame type. Thus, in this study, Line type aerial photograph images was intended to generate geospatial information through computer vision analysis, and forest geospatial information was created as a method for the utilization of aerial picture images. As a result, geospatial information generated by computer vision analysis of line type aerial photograph images showed that RMSE of horizontal and vertical position errors was less than quadruple that of GSD. Forest geospatial information was generated using geospatial information generated by computer vision analysis. It was confirmed that extraction of the crown of tree and calculation of tree height are possible. Through this study, it is expected that utilization of aerial photograph images will be improved.

• Journal of Korean Society of Forest Science
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• v.95 no.4
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• pp.501-506
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• 2006

To study the regeneration process of tree species after clear cutting, we investigated the density of seedling occurred after 1 year in the coniferous forest of Sikotuko Hokkaido, northern Japan that was clearcut after windthrow damage due to typhoon. As the results, 25 species of tree seedlings were growing and the density of seedlings and sprouts was $9.8trees/m^2$ ($1.25tres/m^2{\sim}54.44trees/m^2$) in the area of clear cutting. The 87% ($8.6/m^2$) of all seedlings was current seedlings and non-animal dispersal seedlings (average density $7.2tree/m^2$) were about 5 times more than animal-dispersal seedlings ($1.4tree/m^2$). The seedling density was beyond $6tree/m^2$ within 10m and $2{\sim}9tree/m^2$ in 150~250m from a natural forest. Number of non-animal dispersal seedlings were decreased along the distance from a natural forest but there was not such a tendency in animal dispersal seedlings. The variation in seedling density was higher in non-animal dispersal seedling than in animal dispersal seedling. In natural regeneration of tree species after clear-cutting, the possibility that pioneer species like Betula spp. etc. will be composed of the major species is high. Therefore, in order to maintain the species diversity, the nurture work for reducing competition among the individuals is necessary.

• 한국ITS학회:학술대회논문집
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• 2010.05a
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• pp.196-203
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• 2010

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1139-1149
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• 2017

Measuring tree's volume is very important input data of various environmental analysis modeling However, It's difficult to use economical and equipment to measure a fragmented small green space in the city. In addition, Trees are sensitive to seasons, so we need new and easier equipment and quantification methods for measuring trees than lidar for high frequency monitoring. In particular, the tree's size in a city affect management costs, ecosystem services, safety, and so need to be managed and informed on the individual tree-based. In this study, we aim to acquire image data with UAV(Unmanned Aerial Vehicle), which can be operated at low cost and frequently, and quickly and easily quantify a single tree using SfM-MVS(Structure from Motion-Multi View Stereo), and we evaluate the impact of reducing number of images on the point density of point clouds generated from SfM-MVS and the quantification of single trees. Also, We used the Watertight model to estimate the volume of a single tree and to shape it into a 3D structure and compare it with the quantification results of 3 different type of 3D models. The results of the analysis show that UAV, SfM-MVS and solid model can quantify and shape a single tree with low cost and high time resolution easily. This study is only for a single tree, Therefore, in order to apply it to a larger scale, it is necessary to follow up research to develop it, such as convergence with various spatial information data, improvement of quantification technique and flight plan for enlarging green space.

• The Korean Journal of Ecology
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• v.7 no.4
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• pp.185-193
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• 1984

Regneration process of Abies koreana-Pinus koraiensis community consisted of a subalpione coniferous forest in Mt. Jiri, Korea, was studied in relation to age structure, especially to gap formation. The tall-tree layer (ca. 6.5m) is dominated by Abies koreana and Pinus koraiensis, while lower layer by the sapling and juveniles of A. koreana and Picea jezoensis below 2m tall. The ranges of DBH in A. koreana, P. koraiensis and P. jezonesis were 11.8cm~26.4m, 11.7cm~24.5 cm and 18.2cm~21.7 cm, respectively. The trees below 130 cm tall had contagious distribution, while tall and subtall trees had uniform distribution. The average tree ages of A. koreana, P. koraieniensis and P. jezoensis were 60~70 years, 70~80 years and 70~90 years, respectively. The saplings and juveniles below 20 years in tree ages were occupied over 80% of total trees. The coniferous trees in the gaps or around dead trees were composed of sapligs and juveniles which had emerged or invaded about 20 years before and after the gap formation. The Betula type regeneration of the coniferous forest took place in gaps which orginated from the failing down of a single or a few trees by longevity. Accordingly, it is clear that the subalpine coniferous forest composed of A. koreana of A. koreana, P. koraiensis and P. jezoensis in Mt. jiri was supporting by the regeneration pattern of Betula type.

• Safety and Health at Work
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• v.1 no.2
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• pp.140-148
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• 2010

Objectives: Determining the work-relatedness of lung cancer developed through occupational exposures is very difficult. Aims of the present study are to develop a decision tree of occupational lung cancer. Methods: 153 cases of lung cancer surveyed by the Occupational Safety and Health Research Institute (OSHRI) from 1992-2007 were included. The target variable was whether the case was approved as work-related lung cancer, and independent variables were age, sex, pack-years of smoking, histological type, type of industry, latency, working period and exposure material in the workplace. The Classification and Regression Test (CART) model was used in searching for predictors of occupational lung cancer. Results: In the CART model, the best predictor was exposure to known lung carcinogens. The second best predictor was 8.6 years or higher latency and the third best predictor was smoking history of less than 11.25 pack-years. The CART model must be used sparingly in deciding the work-relatedness of lung cancer because it is not absolute. Conclusion: We found that exposure to lung carcinogens, latency and smoking history were predictive factors of approval for occupational lung cancer. Further studies for work-relatedness of occupational disease are needed.