• The Journal of Engineering Geology
• /
• v.30 no.3
• /
• pp.315-325
• /
• 2020

This study was performed to develop a model to predict landslides and determine the variable importance of landslides susceptibility factors based on the probabilistic prediction of landslides occurring on slopes along the road. Field survey data of 30,615 slopes from 2007 to 2020 in Korea were analyzed to develop a landslide prediction model. Of the total 131 variable factors, 17 topographic factors and 114 geological factors (including 89 bedrocks) were used to predict landslides. Automated machine learning (AutoML) was used to classify landslides and non-landslides. The verification results revealed that the best model, an extremely randomized tree (XRT) with excellent predictive performance, yielded 83.977% of prediction rates on test data. As a result of the analysis to determine the variable importance of the landslide susceptibility factors, it was composed of 10 topographic factors and 9 geological factors, which was presented as a percentage for each factor. This model was evaluated probabilistically and quantitatively for the likelihood of landslide occurrence by deriving the ranking of variable importance using only on-site survey data. It is considered that this model can provide a reliable basis for slope safety assessment through field surveys to decision-makers in the future.

• Proceedings of the Korean Society of Grassland Science Conference
• /
• 2002.09b
• /
• pp.5-56
• /
• 2002

The problems in the current domestic forage production were evaluated, and the prospective improvement was suggested in this paper. Grassland development in forest, production of high quality forages in upland and paddy land, efficient utilization of rice straw, development of new varieties of forages suitable for our environmental conditions and imported forages were described Among them, preferential production and utilization of forages using paddy field after rice harvest . should be enlarged for domestic supply of forages in Korea. Several studies were carried out to select the promising forage crops and barley cultivars for whole crop silage production, to determine productivity, nutritive value and production cost of forages produced in paddy field, and feeding effect of forages with Hanuwoo and milking cow for whole crop silage with forages produced in paddy field, 1999 to 2001, and also discussed restraint factors and activation plans for enlargement of forage production in paddy land. The promising forage crops in paddy field were rye and barley for Middle region, and rye, barley, early maturing Italian ryegrass and wheat for Southern region. The promising barley cultivars for whole crop silage in paddy field were Albori in Suwon, Keunalbori, Milyang 92, Saessalbori, and Naehanssalbori in Iksan, and Keunalbori, Albori, Naehanssalbori, and Saegangbori in Milyang, respectively. Silage production, quality and animal palatability of silage by trench and round bale were also compared. The production yields of whole crop barley silage(WBS) were 17,135kg as a fresh matter, and 6,011kg as a dry matter per ha, and the quality of WBS was 2∼3 grade, while that of rice straw silage was 4 grade as a farm basis. The production cost of WBS per kg was 83won as a fresh matter, and 238won as a dry matter. Feeding of WBS as forages on Hanwoo was very desirable for the improvement of live-weight gain, beef quality and farm income, particularly in growing stage of Hanwoo. Milk production and income were also increased, and feed cost was decreased by feeding of WBS. The daily voluntary intake of WBS in milking cow was 26.3kg as a fresh matter(DM 7.7kg) per head. Milk production when WBS was fed, was very similar to that of imported hay feeding such as Kentucky bluegrass or domestic corn silage. The issues to be solved in near future f3r stable forage production and supply in paddy land are sustainable livestock-forages policy, development & seed production of new varieties of barley, rye, Italian ryegrass and other promising forages, efficient demand & supply system of forages, solidification for mass production and utilization of forages, efficient application management of animal slurry on paddy field considering environmental agriculture/livestock industry, and break k development of bottleneck technique in production field. Domestic production & supply of high cost agricultural machine (round baler, wrapper, handler and so on), plastic wrapping film, and silage additives are also important.

• Journal of the Korean Wood Science and Technology
• /
• v.7 no.1
• /
• pp.8-11
• /
• 1979

The apparatus for measuring the static withdrawal resistance of nail is the AUTOGROPH S-500 static testing machine. The nails were 0.25 - 0.26 cm in diameter, 4.9-5.1cm in length, and were driven into wood for a depth of 3.0 cm. The physical properties of wood specimens are shown in table 1. Some data obtained in this experiment are as follows 1) The effects of the direction of driving and the time after driving upon static withdrawal resistance are shown in fig. 2-3. 2) The relations between static withdrawal resistance are moisture content of wood are characterized by nearly straight line as shown in fig. 4-5. The empirical formula areas follows: Y = -0.074 x + 12.73 (Pinus koraiensis Sieb & Zucc. - radial) Y = -0.073 x + 7.54 (Pinus koraiensis Sieb & Zucc. - longitudinal) Y = -0.061 x + 12.80 (Larix leptolesis Gord. - tangential) Y = -0.081 x + 7.61 (Larix leptolepsis Gord. - longitudinal) Where Y is the static withdrawal resistance ($kg{\cdot}cm$) and x is the moisture content(%).

• Journal of the Korea Convergence Society
• /
• v.9 no.5
• /
• pp.33-41
• /
• 2018

The purpose of this study is to develop more accurate and robust emotion prediction neural network (EPNN) model by combining heart rate variability (HRV) and neural network. For the sake of improving the prediction performance more reliably, the proposed EPNN model is based on various types of activation functions like hyperbolic tangent, linear, and Gaussian functions, all of which are embedded in hidden nodes to improve its performance. In order to verify the validity of the proposed EPNN model, a number of HRV metrics were calculated from 20 valid and qualified participants whose emotions were induced by using money game. To add more rigor to the experiment, the participants' valence and arousal were checked and used as output node of the EPNN. The experiment results reveal that the F-Measure for Valence and Arousal is 80% and 95%, respectively, proving that the EPNN yields very robust and well-balanced performance. The EPNN performance was compared with competing models like neural network, logistic regression, support vector machine, and random forest. The EPNN was more accurate and reliable than those of the competing models. The results of this study can be effectively applied to many types of wearable computing devices when ubiquitous digital health environment becomes feasible and permeating into our everyday lives.

• Journal of Cadastre & Land InformatiX
• /
• v.50 no.1
• /
• pp.75-85
• /
• 2020

This study examines machine learning cross-validation to utilized create ROI for classification of land cover. The study area located in Sejong and one KOMPSAT-3A image was used in this analysis: procedure on October 28, 2019. We used four bands(Red, Green, Blue, Near infra-red) for learning cross validation process. In this study, we used K-fold method in cross validation and used SVM kernel type with cross validation result. In addition, we used 4 kernels of SVM(Linear, Polynomial, RBF, Sigmoid) for supervised classification land cover map using extracted ROI. During the cross validation process, 1,813 data extracted from 3,500 data, and the most of the building, road and grass class data were removed about 60% during cross validation process. Based on this, the supervised SVM linear technique showed the highest classification accuracy of 91.77% compared to other kernel methods. The grass' producer accuracy showed 79.43% and identified a large mis-classification in forests. Depending on the results of the study, extraction ROI using cross validation may be effective in forest, water and agriculture areas, but it is deemed necessary to improve the distinction of built-up, grass and bare-soil area.

• The Journal of Bigdata
• /
• v.6 no.1
• /
• pp.91-113
• /
• 2021

Despite the influence of real estate in the Korean asset market, it is not easy to predict market trends, and among them, apartments are not easy to predict because they are both residential spaces and contain investment properties. Factors affecting apartment prices vary and regional characteristics should also be considered. This study was conducted to compare the factors and characteristics that affect apartment prices in Seoul as a whole, 3 Gangnam districts, Nowon, Dobong, Gangbuk, Geumcheon, Gwanak and Guro districts and to understand the possibility of price prediction based on this. The analysis used machine learning algorithms such as neural networks, CHAID, linear regression, and random forests. The most important factor affecting the average selling price of all apartments in Seoul was the government's policy element, and easing policies such as easing transaction regulations and easing financial regulations were highly influential. In the case of the three Gangnam districts, the policy influence was low, and in the case of Gangnam-gu District, housing supply was the most important factor. On the other hand, 6 mid-lower-level districts saw government policies act as important variables and were commonly influenced by financial regulatory policies.

• Korean Journal of Remote Sensing
• /
• v.35 no.2
• /
• pp.299-316
• /
• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.

• Korean Journal of Environment and Ecology
• /
• v.35 no.2
• /
• pp.193-203
• /
• 2021

As endangered species are gradually increasing due to land development by humans, it is essential to secure sufficient protected areas (PAs) proactively. Therefore, this study checked priority conservation areas to select candidate PAs when considering the impact of land development. We determined the conservation priorities by analyzing four scenarios based on existing conservation areas and reflecting the development impact using MARXAN, the decision-making support software for the conservation plan. The development impact was derived using the developed area ratio, population density, road network system, and traffic volume. The conservation areas of endangered species were derived using the data of the appearance points of birds, mammals, and herptiles from the 3rd National Ecosystem Survey. These two factors were used as input data to map conservation priority areas with the machine learning-based optimization methodology. The result identified many non-PAs areas that were expected to play an important role conserving endangered species. When considering the land development impact, it was found that the areas with priority for conservation were fragmented. Even when both the development impact and existing PAs were considered, the priority was higher in areas from the current PAs because many road developments had already been completed around the current PAs. Therefore, it is necessary to consider areas other than the current PAs to protect endangered species and seek alternative measures to fragmented conservation priority areas.

• Journal of the Microelectronics and Packaging Society
• /
• v.30 no.4
• /
• pp.44-49
• /
• 2023

In the global pursuit of carbon neutrality, the rapid increase in the adoption of electric vehicles (EVs) has led to a corresponding surge in the demand for batteries. To achieve high efficiency in electric vehicles, considerations of weight reduction and battery safety have become crucial factors. Copper and aluminum, both recognized as lightweight materials, can be effectively joined through laser welding. However, due to the distinct physical characteristics of these two materials, the process of joining them poses technical challenges. This study focuses on conducting simulations to identify the optimal laser parameters for welding copper and aluminum, with the aim of streamlining the welding process. Additionally, a Graphic User Interface (GUI) program has been developed using the Python language to visually present the results. Using machine learning image data, this program is anticipated to predict joint success and serve as a guide for safe and efficient laser welding. It is expected to contribute to the safety and efficiency of the electric vehicle battery assembly process.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.27 no.1
• /
• pp.81-98
• /
• 2024

This study analyzed the impact of urban spatial factors on the thermal environment. The personal thermal sensation was set as the unit of thermal environment to analyze its correlation with environmental factors. To collect data on personal thermal sensation, Living Lab was applied, allowing citizens to record their thermal sensation and measure the temperature. Based on the input points of the collected personal thermal sensation, nearby urban spatial elements were collected to build a dataset for statistical analysis. Logistic regression analysis was conducted to analyze the impact of each factor on personal thermal sensation. The analysis results indicate that the temperature is influenced by the surrounding spatial environment, showing a negative correlation with building height, greenery rate, and road rate, and a positive correlation with sky view factor. Furthermore, the road rate, sky view factor, and greenery rate, in that order, had a strong impact on perceived heat. The results of this study are expected to be utilized as basic data for assessing the thermal environment to prepare local thermal environment measures in response to climate change.