• 한국지구물리탐사학회:학술대회논문집
• /
• 2005.09a
• /
• pp.37-42
• /
• 2005

Remote seismic tomography is regarded as one of the most valuable geophysical technique for the estimation of the rock mass classification in the tunnel area where hard data information such as drill logs are absent. But the results of rock mass classification based on the remote seismic tomography tend to be overestimated in practice. In this study, we propose the effective method to implement the seismic reference velocity ratio based on semblance for the improvement of rock mass classification. Also, to verify its feasibility, proposed technique was tested by using the real field data.

• Proceedings of the KSR Conference
• /
• 2005.11a
• /
• pp.599-604
• /
• 2005

As a public transportation possible to convey a large quantity, the railway is safe and keeps time, but it has hazards to cause a disaster if the accidents such as collision, derailment, and fire occur. So advanced countries carry out System Safety Plan with various program activities which have connected orders to maintain or improve safety level by finding hazards, evaluation, taking measures and practice, and improving problems. Especially they systematically manage hazards to cause railway accidents and the factors which possibly threat safety, using national classification of risk and causes with analysis of the related data such as establishing accident/incident data and safety regulations/standards. As executing railway safety regulations, domestic railway is currently trying to improve railway safety management system. The research of classification system of accidents/incidents is one thing to make railway safety management systems better. In this research, we reviewed hazardous factors of railway systems and classification of the causes as the beginning of system safety management, and we conducted study on development of railway accident classification based on findings of this research. The results are able to be used in identifying hazards and activities of systemic safety management at the step of railway accident report and investigation.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_4
• /
• pp.1945-1953
• /
• 2022

Korea Aerospace Research Institute produces mosaic images of the Korean Peninsula every year to promote the use of satellite images and provides them to users in the public sector. However, since the pan-sharpening and color balancing methodologies are applied during the mosaic image processing, the original spectral information is distorted. In addition, there is a limit to analyze using mosaic images as mosaic images provide only Red, Green and Blue bands excluding Near Infrared (NIR) band. Therefore, in order to compensate for these limitations, this study applied the Principal Component Analysis (PCA) technique and indices extracted from R, G, B bands together for image classification and compared the classification results. As a result of the analysis, the accuracy of the mosaic image classification result was about 67.51%, while the accuracy of the image classification result using both PCA and RGB indices was about 75.86%, confirming that the accuracy of the image classification result can be improved. As a result of comparing the PCA and the RGB indices, the accuracy of the image classification result was about 64.10% and 74.05% respectively. Through this, it was confirmed that the classification accuracy using the RGB indices was higher among the two techniques, and implications were derived that it was important to use high quality reference or supplementary data. In the future, additional indices and techniques are needed to improve the classification and analysis results of mosaic images, and related research is expected to increase the utilization of images that provide only R, G, B or limited spectral information.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.10
• /
• pp.43-57
• /
• 2022

In this paper, we propose a method of analyzing research papers published by researchers belonging to university departments in the field of disaster & safety for the scientometric analysis of the research status in the field of disaster safety. In order to conduct analysis research, the dataset constructed in previous studies was newly improved and utilized. In detail, for research papers of authors belonging to the disaster prevention and safety engineering type department of domestic universities, institution identification, cited journal identification of references, department type classification, disaster safety type classification, researcher major information, KSIC(Korean Standard Industrial Classification) mapping information was reflected in the experimental data. The proposed method has a difference from previous studies in the field of disaster & safety and data set based on related keyword searches. As a result of the analysis, the type and regional distribution of organizations belonging to the department of disaster prevention and safety engineering, the composition of co-authored department types, the researchers' majors, the status of disaster safety types and standard industry classification, the status of citations in academic journals, and major keywords were identified in detail. In addition, various co-occurrence networks were created and visualized for each analysis unit to identify key connections. The research results will be used to identify and recommend major organizations and information by disaster type for the establishment of an intelligent crisis warning system. In order to provide comprehensive and constant analysis information in the future, it is necessary to expand the analysis scope and automate the identification and classification process for data set construction.

• Journal of Information Technology Applications and Management
• /
• v.15 no.4
• /
• pp.21-36
• /
• 2008

As the great disturbance from abusing credit cards in Korea becomes stabilized, credit card companies need to interpret credit-card delinquents classification models from the viewpoint of profit. However, hit ratio which has been used as a measure of goodness of classification models just tells us how much correctly they classified rather than how much profits can be obtained as a result of using classification models. In this research, we tried to develop a new utility-based measure from the viewpoint of profit and then used this new measure to analyze two classification models(Neural Networks and Decision Tree models). We found that the hit ratio of neural model is higher than that of decision tree model, but the utility value of decision tree model is higher than that of neural model. This experiment shows the importance of utility based measure for credit-card delinquents classification models. We expect this new measure will contribute to increasing profits of credit card companies.

• Journal of Korea Multimedia Society
• /
• v.20 no.10
• /
• pp.1637-1645
• /
• 2017

Target/Nontarget classification can be divided into the study of shape estimation of the target analysing reflected echo signal and of type classification of the target using acoustical features. In active sonar system, the feature vectors are extracted from the signal reflected from the target, and an classification algorithm is applied to determine whether the received signal is a target or not. However, received sonar signals can be distorted in the underwater environments, and the spatio-temporal characteristics of active sonar signals change according to the aspect of the target. In addition, it is very difficult to collect real sea-trial data for research. In this paper, target/non-target classification were performed using real sea-trial data. Feature vectors are extracted using MFCC(Mel-Frequency Cepstral Coefficients), filterbank energy in the Fourier spectrum and wavelet domain. For the performance verification, classification experiments were performed using backpropagation neural network classifiers.

• Journal of the Korean Society for information Management
• /
• v.18 no.4
• /
• pp.201-215
• /
• 2001

The purpose of this study is to identify how to construct an internet shopping mall classification system used on the library classification theories. To aid in identifying classification system, this study focused on the Ranganathan’s classification canons; canons for characteristics, canons for terms. The study shows six priniciples for an internet shopping mall classification system construct: products’characteristics, inclusiveness, various access points, category sequence and term consistency, term currency and obviousness, no term duplication. For future research, product’s search patterns and relationship to interface are suggested.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.3 no.3
• /
• pp.285-307
• /
• 2009

This paper introduces DICE, a Domain-Independent text Classification Engine. DICE is robust, efficient, and domain-independent in terms of software and architecture. Each module of the system is clearly modularized and encapsulated for extensibility. The clear modular architecture allows for simple and continuous verification and facilitates changes in multiple cycles, even after its major development period is complete. Those who want to make use of DICE can easily implement their ideas on this test bed and optimize it for a particular domain by simply adjusting the configuration file. Unlike other publically available tool kits or development environments targeted at general purpose classification models, DICE specializes in text classification with a number of useful functions specific to it. This paper focuses on the ways to locate the optimal states of a practical text classification framework by using various adaptation methods provided by the system such as feature selection, lemmatization, and classification models.

• Journal of Korea Multimedia Society
• /
• v.25 no.11
• /
• pp.1653-1671
• /
• 2022

Crop diseases affect crop production, more than 30 billion USD globally. We proposed a classification study of crop species and diseases using deep learning algorithms for corn, cucumber, pepper, and strawberry. Our study has three steps of species classification, disease detection, and disease classification, which is noteworthy for using captured images without additional processes. We designed deep learning approach of deep learning convolutional neural networks based on Mask R-CNN model to classify crop species. Inception and Resnet models were presented for disease detection and classification sequentially. For classification, we trained Mask R-CNN network and achieved loss value of 0.72 for crop species classification and segmentation. For disease detection, InceptionV3 and ResNet101-V2 models were trained for nodes of crop species on 1,500 images of normal and diseased labels, resulting in the accuracies of 0.984, 0.969, 0.956, and 0.962 for corn, cucumber, pepper, and strawberry by InceptionV3 model with higher accuracy and AUC. For disease classification, InceptionV3 and ResNet 101-V2 models were trained for nodes of crop species on 1,500 images of diseased label, resulting in the accuracies of 0.995 and 0.992 for corn and cucumber by ResNet101 with higher accuracy and AUC whereas 0.940 and 0.988 for pepper and strawberry by Inception.

• Journal of the Korean Society for Library and Information Science
• /
• v.40 no.1
• /
• pp.39-57
• /
• 2006

Cadastral Science has not been evaluated as an independent discipline according to Code of Research Fields of Korea Research Foundation. The purpose of this study is to find the problems of knowledge classification of Cadastral Science and suggest method of improvement. For the studying, analyzes the definition, objects of research, educational system and curriculum of Cadastral Science. Besides, investigates the condition of Cadastre classification on Code of Research Fields, DDC and KDC. This paper suggests that Cadastral Science can be restructured to the new Interdisciplinary Studies and moved to the upper division. The items of division and subdivision can be also added.