• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.615-621
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• 2020

In this paper, we propose a multi-site based earthquake event classification method using graph convolution networks. In the traditional earthquake event classification methods using deep learning, they used single-site observation to estimate seismic event class. However, to achieve robust and accurate earthquake event classification on the seismic observation network, the method using the information from the multi-site observations is needed, instead of using only single-site data. Firstly, our proposed model employs convolution neural networks to extract informative embedding features from the single-site observation. Secondly, graph convolution networks are used to integrate the features from several stations. To evaluate our model, we explore the model structure and the number of stations for ablation study. Finally, our multi-site based model outperforms up to 10 % accuracy and event recall rate compared to single-site based model.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.777-784
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• 2010

In the current Korean seismic design guide, the site classification and the corresponding site coefficients were determined based on the UBC-1997 (Uniform Building Code). In order to develop the current site classification system, it is important to compare the local site conditions in Korea to other countries which have similar seismic design guides. In the eastern United States, New York City(40degrees 45minutes north latitude, 73degrees 59minutes west longitude) suggested that current design guidelines are unsuitable to shallow bedrock depth sites. So the 3-parameter methods are performed for new criteria in New York City. In this study, site response analyses were performed at 181 study sites using one-dimensional equivalent linear to evaluate the site-specific earthquake ground motions at inland areas in the Korean peninsula and reclassify the results according to similar ground motions using the 3-parameter methods. It is effective that multi-parameter methods for Korean site characteristics in comparison with single parameter method.

• Journal of the Korean housing association
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• v.21 no.3
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• pp.41-52
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• 2010

The purpose of this study is to clarify multi-habitation, a new phenomenon in which inhabitants in urban areas reside in primarily condominiums on weekdays and retreat to dwellings in rural areas on weekends, and to examine the various life styles related to multi-habitation. Through the interviews and site surveys, three major subtypes of multi-habitation were identified to support the theoretical framework: interchange style, sedentary style, and special style. Findings include that first, in order to discuss multi-habitation, the terms primary home and secondary home are introduced. Based on the concept of primary home and secondary home, a variety of multi-habitation can be described using spatial locations in urban and rural areas. Second, systematic deregulation for the second home ownership should be made to promote citizens' interchange. Also urban residents should make more efforts to integrate into rural communities. Third, for some remote areas such as Cheju Island, it is observed that multi-habitation is limited by cost, time, and lifestyle.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.708-708
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• 2002

Recent developments in sensor technologies have provided remotely sensed data with very high spatial resolution. In order to fully utilize the potential of high resolution images, new image classification strategies are necessary. Unfortunately, the high resolution images increase the spectral within-field variability, and the classification accuracy of traditional methods based on pixel-based classification algorithms such as Maximum-Likelihood method may be decreased (Schiewe 2001). Recent development in Object Oriented Classification based on image segmentation algorithms can be used for the classification of forest patches on rugged terrain of Korea. The objectives of this paper are as follows. First, to compare the pros and cons of image classification methods based on pixel-based and object oriented classification algorithm for the forest patch classification. Landsat ETM＋ data and IKONOS data will be used for the classification. Second, to investigate ways to increase classification accuracy of forest patches. Supplemental data such as DTM and Forest Type Map of 1:25,000 scale are used for topographic correction and image segmentation. Third, to propose the best classification strategy for forest patch classification in terms of accuracy and data requirement. The research site for this paper is Namhansansung Provincial Park located at the eastern suburb of Seoul Metropolitan City for its diverse forest patch types and data availability. Both Landsat ETM＋ and IKONOS data are used for the classification. Preliminary results can be summarized as follows. First, topographic correction of reflectance is essential for the classification of forest patches on rugged terrain. Second, object oriented classification of IKONOS data enables higher classification accuracy compared to Landsat ETM＋ and pixel-based classification. Third, multi-stage segmentation is very useful to investigate landscape ecological aspect of forest communities of Korea.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.104-115
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• 2019

Since the Fukushima Daiichi accident, there has been an emphasis on the risk resulting from multi-unit accidents. Human reliability analysis (HRA) is one of the important issues in multi-unit probabilistic safety assessment (MUPSA). Hence, there is a need to properly identify all the human and organizational factors relevant to a multi-unit incident scenario in a nuclear power plant (NPP). This study identifies and categorizes the human and organizational factors relevant to a multi-unit incident scenario of NPPs based on a review of relevant literature. These factors are then analyzed to ascertain all possible unit-to-unit interactions that need to be considered in the multi-unit HRA and the pattern of interactions. The human and organizational factors are classified into five categories: organization, work device, task, performance shaping factors, and environmental factors. The identification and classification of these factors will significantly contribute to the development of adequate strategies and guidelines for managing multi-unit accidents. This study is a necessary initial step in developing an effective HRA method for multiple NPP units in a site.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1346-1348
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• 2003

In this paper, the interferometric coherence was estimated and the amplitude intensity was extracted using the repeat-pass interferometric data, acquired by European Remote Sensing Satellite 1 and 2. Then discrimination and classification of surface land types in Zhangjiakou test site, Hebei Province were carried out based on the coherence estimation and the intensity extraction. Seven types of land were discriminated and classified, including in two different types of meadows, woodland, dry land, grassland, steppe and water body. The backscatter and coherence characteristics of these land types on the multi-temporal images were analyzed, and the change of surface features with time series was also discussed.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.4
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• pp.10-27
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• 2014

HAZUS-MH is a GIS-based computer program that estimates potential losses from multi-hazard phenomena: earthquakes, floods and hurricanes. With respect to seismic disaster, characteristics of a hypothetical or actual earthquake are entered into HAZUS. Then HAZUS estimates the intensity of ground shaking and calculates the correspondent losses. In this study, HAZUS was used as a part of the preparations of the future seismic events at a coastal plant facility area. To reliably characterize the target facility area, many geotechnical characteristics data were synthesized from the existing site investigation reports. And the buildings and facilities were sorted by analyzing their material and structural characteristics. In particular, the study area was divided into 17 blocks taking into account the situation of both land development and facility distribution. The ground conditions of blocks were categorized according to the site classification scheme for earthquake-resistant design. Moreover, seismic fragility curves of a main facilities were derived based on the numerical modeling and were incorporated into the database in HAZUS. The results estimated in the study area using HAZUS showed various seismic damage and loss potentials depending on site conditions and structural categories. This case study verified the usefulness of the HAZUS for estimating earthquake losses in coastal facility areas.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of the Korean Institute of Landscape Architecture
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• v.32 no.4
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• pp.28-38
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• 2004

Korean goral (Nemorhaedus caudatus raddeanus) is one of the endangered species in Korea, and the rugged terrain of the Soraksan National Park (373㎢) is a critical habitat for the species. But the goral population is threatened by habitat fragmentation caused by roads and hiking trails. The objective of this study was to develop a potential habitat suitability model for Korean goral in the park, and the model was based on the concepts of fuzzy set theory and multi-criteria evaluation. The process of the suitability modeling could be divided into three steps. First, data for the modeling was collected by using field work and a literature survey. Collected data included 204 points of GPS data obtained through a goral trace survey and through the number of daily visitors to each hiking trail during the peak season of the park. Second, fuzzy set theory was employed for building a GIS data base related to environmental factors affecting the suitability of the goral habitat. Finally, a multiple-criteria evaluation was performed as the final step towards a goral habitat suitability model. The results of the study were as follows. First, characteristics of suitable habitats were the proximity to rock cliffs, scattered pine (Pinus densiflora) patches, ridges, the elevation of 700∼800m, and the aspect of south and southeast. Second, the habitat suitability model had a high classification accuracy of 93.9％ for the analysis site, and 95.7％ for the validation site at a cut off value of 0.5. Finally, 11.7％ of habitatwith more than 0.5 of habitat suitability index was affected by roads and hiking trails in the park.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.433-440
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• 2009

As information technology of constructional area develops recently, web-based on-line system is rapidly increasing to provide information on diverse constructional materials so as to enhance productivity of constructional business and to reduce cost. Since the constructional materials information provided by these systems, i.e., quality, specification, etc are not standardized, however, the staffs on the constructional site suffer considerable difficulties in using materials information when acquiring information on specific materials, e.g., using diverse information systems or repeating similar jobs. Thus, this research typified information items of constructional materials on the basis of GDAS and designed multi system model to control integrated information on constructional materials. This system can efficiently control and utilize materials information by supporting automatic classification of constructional materials to which OmniClass Part-22 and UNSPSC are applied, conditional complex retrieval of materials information, real-time automatic embodiment of electronic catalog and retrieving/controlling RFID.