• Journal of Welding and Joining
• /
• v.19 no.6
• /
• pp.614-621
• /
• 2001

A study was carried out to determine the solidification cracking susceptibility of Ni-base superalloy as a function of Fe content in base metal. Three kinds of Ni-base superalloys with three different levels of Fe content were used. The solidification cracking susceptibility was evaluated by the Trans-Varestraint test at four different strain levels. Quantitative analysis of crack revealed that the solidification crack length and the temperature range in which hot cracking occurred in fusion zone (Brittle Temperature Range, BTR) decreased with a decrease in Fe content. Further, the thermo-calc data indicated that the solidification temperature range also decreased with decreasing Fe content. From these results, it was deduced that the improvement of the solidification cracking susceptibility with decreasing Fe content was attributed to the decrease of the solidification temperature range.

• Korean Journal of Veterinary Research
• /
• v.28 no.2
• /
• pp.397-404
• /
• 1988

A total of 593 mastitic pathogens were isolated from the clinical mastitic milk of dairy cattle in Gyeonggi area from March 1984 to February 1988. The mastitic pathogens were further studied bacteriologically and examined for susceptibility patterns to 10 antibiotics. The results obtained were summerized as follows: 1. One hundred and seventy three pathogens were isolated in the first year of studies, 205 pathogens in the second year, 122 pathogens in the third year and 93 pathogens in the last year. 2. The pathogens isolated from clinical mastitis were more in summer than other seasons. 3. Staphylococcus spp (50%) and Streptococcus spp (18%) were the main pathogens in the first year of studies but coliforms (15%) and other bacteria (40%) were the main pathogens in the last year of studies. 4. Coliform mastitis was occurred only in summer and autumn. 5. Antibiotic susceptibility patterns of the same genuses from clinical mastitis infections in different herds and in different years varied greatly. Therefore, treatment should be selected on the basis of susceptibility test results.

• Korean Journal Plant Pathology
• /
• v.10 no.2
• /
• pp.140-143
• /
• 1994

Comparison of susceptibility of asparagus (Asparagus officinalis L.) seedlings and plantlets to different fusarial species was made to determine whether in vitro propagated asparagus plantlets can be used as a substitute for seedlings in histopathological study on the infection processes of Fusarium species to asparagus. Fusarium oxysporum was isolated most frequently (50% of the total) from lesions of root and crown rot of asparagus cultivated in the field followed by F. moniliforme (8.8% of the total) and F. solani (2.9% of the total). Plantlets and seedlings of all asparagus were susceptible to f. moniliforme and F. oxysporum isolates, but those were not susceptible to both avirulent F. oxysporum (AVFO) and F. solani in pathogenicity tests. Overall, there were no differences between seedlings and plantlets in the susceptibility to virulent fusarial infections. In vitro propagated asparagus plantlets, therefore, could be used as a substitute for seedlings in histopathological study on the infection processes of Fuasrium species to asparagus.

• Journal of the Korean GEO-environmental Society
• /
• v.15 no.12
• /
• pp.79-85
• /
• 2014

To analyze the landslide susceptibility in Baekdu mountain area in china, we get two susceptibility maps using AcrView software through weighted overlay GIS (Geographic Information System) method in this paper. To assess the landslide susceptibility, five factors which affect the landslide occurrence were selected as: slope, aspect, soil type, geological type, and land use. The weight value and rating value of each factor were calculated by the two different methods of AHP (Analytic Hierarchy Process) and ANN (Artificial Neural Network). Then, the weight and rating value was used to obtain the susceptibility maps. Finally, the susceptibility map shows that the very dangerous areas (0.9 or higher) were mainly distributed in the mountainous areas around JiAnShi, LinJiangShi, and HeLongShi near the china-north Korea border and in the mountainous area between the WangQingXian and AnTuXian. From the contrast two susceptibility map, we also Knew that The accuracy of landslide susceptibility map drew by ANN method was better than AHP method.

• Proceedings of the KSRS Conference
• /
• 2004.10a
• /
• pp.294-297
• /
• 2004

The purpose of this study was to develop landslide susceptibility analysis techniques using artificial neural networks and then to apply these to the selected study area of Janghung in Korea. We aimed to verify the effect of data selection on training sites. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use was constructed. Thirteen landslide-related factors were extracted from the spatial database. Using these factors, landslide susceptibility was analyzed using an artificial neural network. The weights of each factor were determined by the back-propagation training method. Five different training datasets were applied to analyze and verify the effect of training. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. The results of the landslide susceptibility maps were verified and compared using landslide location data. GIS data were used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool to analyze landslide susceptibility.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.34 no.6
• /
• pp.597-607
• /
• 2016

The goal of this study was to analyze landslide susceptibility using two different models and compare the results. For this purpose, a landslide inventory map was produced from a field survey, and the inventory was divided into two groups for training and validation, respectively. Sixteen landslide conditioning factors were considered. The relationships between landslide occurrence and landslide conditioning factors were analyzed using the FR (Frequency Ratio) and EBF (Evidential Belief Function) models. The LSI (Landslide Susceptibility Index) maps that were produced were validated using the ROC (Relative Operating Characteristics) curve and the SCAI (Seed Cell Area Index). The AUC (Area under the ROC Curve) values of the FR and EBF LSI maps were 80.6% and 79.5%, with prediction accuracies of 72.7% and 71.8%, respectively. Additionally, in the low and very low susceptibility zones, the FR LSI map had higher SCAI values compared to the EBF LSI map, as high as 0.47%p. These results indicate that both models were reasonably accurate, however that the FR LSI map had a slightly higher accuracy for landslide susceptibility mapping in the study area.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2008.03a
• /
• pp.926-931
• /
• 2008

This paper show two different standardized test methods(Japanese Geotechnical Society; JGS 2003). One test is a "Test Method for Frost Heave Prediction Test, JGS 0171-2003", and the other test is a "Test Method for Frost Susceptibility, JGS 0172-2003". The purpose of this test is to obtain the freezing rate(freezing speed), frost heave ratio(heave to sample height), frost heave rate(heaving speed), and other parameters to be used for frost heave prediction and determine the frost susceptibility by freezing test with water intake. This method shall be used to predict the frost heave in frozen ground and evaluate the frost susceptibility of natural and replacement materials.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
• /
• v.26 no.1
• /
• pp.40-44
• /
• 2000

The most proper antibiotic must be selected after antimicrobial susceptibility test. If difference in antimicrobial susceptibility was significant between oral sampling sites, it is rationale to use the most susceptible antibiotic agent respond to dental procedure and object of treatment. This study examined sampling site variation from saliva, supragingival plaque and subgingival plaque of 16 children's oral microbes. The cultured bacterial isolates, which were Streptococcus viridans and Neisseria, were examined for 10 antimicribial drugs with the Bauer-Kirby agar disk diffusion method. The used drugs were Penicillin, Ampicillin, Oxacillin, Cephalothin, Imipenem, Gentamicin, Erythromycin, Vancomycin, Ciprofloxacin, Clindamycin. There was no significant difference between three sampling sites for antimicrobial susceptibility test of S. viridans and Neisseria and the sequence of susceptibility was agreed among them. In conclusion, it was suggested that antimicrobial susceptibility test from saliva, supragingival plaque and subgingival plaque of children have no significant sampling site variation.

• Journal of The Geomorphological Association of Korea
• /
• v.23 no.2
• /
• pp.109-125
• /
• 2016

This study aims to identify the landslide susceptible zones of Boeun area and provide reliable landslide susceptibility maps by applying different modeling methods. Aerial photographs and field survey on the Boeun area identified landslide inventory map that consists of 388 landslide locations. A total ofseven landslide causative factors (elevation, slope angle, slope aspect, geology, soil, forest and land-use) were extracted from the database and then converted into raster. Landslide causative factors were provided to investigate about the spatial relationship between each factor and landslide occurrence by using fuzzy set and logistic regression model. Fuzzy membership value and logistic regression coefficient were employed to determine each factor's rating for landslide susceptibility mapping. Then, the landslide susceptibility maps were compared and validated by cross validation technique. In the cross validation process, 50% of observed landslides were selected randomly by Excel and two success rate curves (SRC) were generated for each landslide susceptibility map. The result demonstrates the 84.34% and 83.29% accuracy ratio for logistic regression model and fuzzy set model respectively. It means that both models were very reliable and reasonable methods for landslide susceptibility analysis.

• Economic and Environmental Geology
• /
• v.52 no.2
• /
• pp.199-212
• /
• 2019

In the machine learning techniques, the sampling strategy of the training data affects a performance of the prediction model such as generalizing ability as well as prediction accuracy. Especially, in landslide susceptibility analysis, the data sampling procedure is the essential step for setting the training data because the number of non-landslide points is much bigger than the number of landslide points. However, the previous researches did not consider the various sampling methods for the training data. That is, the previous studies selected the training data randomly. Therefore, in this study the authors proposed several different sampling methods and assessed the effect of the sampling strategies of the training data in landslide susceptibility analysis. For that, total six different scenarios were set up based on the sampling strategies of landslide points and non-landslide points. Then Random Forest technique was trained on the basis of six different scenarios and the attribute importance for each input variable was evaluated. Subsequently, the landslide susceptibility maps were produced using the input variables and their attribute importances. In the analysis results, the AUC values of the landslide susceptibility maps, obtained from six different sampling strategies, showed high prediction rates, ranges from 70 % to 80 %. It means that the Random Forest technique shows appropriate predictive performance and the attribute importance for the input variables obtained from Random Forest can be used as the weight of landslide conditioning factors in the susceptibility analysis. In addition, the analysis results obtained using specific sampling strategies for training data show higher prediction accuracy than the analysis results using the previous random sampling method.