• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.1
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• pp.147-155
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• 2010

Landcover information of access-denied area was extracted from low-medium and high resolution satellite image. Training for supervised classification was performed to refer visually by landcover map which is made and distributed from The Ministry of Environment. The classification result was compared by relating data of FACC land classification system. As we rasterize digital military map with same pixel size of satellite classification, the accuracy test was performed by image to image method. In vegetation case, ancillary data such as NDVI and image for seasons are going to improve accuracy. FACC code of FDB need to recognize the properties which can be automated.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.192-197
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• 2020

In this study, we try to find ways to recognize face recognition more stably and to improve the effectiveness and reliability of face recognition. In order to improve the face recognition rate, a lot of data must be used, but that does not necessarily mean that the recognition rate is improved. Another criterion for improving the recognition rate can be seen that the top/bottom of the recognition rate is determined depending on how accurately or precisely the degree of classification of the data to be used is made. There are various methods for classification analysis, but in this study, classification analysis is performed using a support vector machine (SVM). In this study, feature information is extracted using a normalized image with rotation information, and then projected onto the eigenspace to investigate the relationship between the feature values through the classification analysis of SVM. Verification through classification analysis can improve the effectiveness and reliability of various recognition fields such as object recognition as well as face recognition, and will be of great help in improving recognition rates.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.43-51
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• 2013

Classification result by comprehensive analysis of rhythm section and heartbeat unit makes a reliable diagnosis of heart disease possible. In this paper, based on feature-points of ECG signals, rhythm analysis for constant section and heartbeat unit is conducted using rule-based classification and SVM-based classification respectively. Rhythm types are classified using a rule base deduced from clinical materials for features of rhythm section in rule-based classification, and monotonic rhythm or major abnormality heartbeats are classified using multiple SVMs trained previously for features of heartbeat unit in SVM-based classification. Experimental results for the MIT-BIH arrhythmia database show classification ratios of 68.52% by rule-based method alone and 87.04% by fusion method of rule-based and SVM-based for 11 rhythm types. The proposed fusion method is improved by about 19% through misclassification improvement for monotonic and arrangement rhythms by SVM-based method.

• Journal of the Korean Society for information Management
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• v.28 no.4
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• pp.33-48
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• 2011

This study investigated and analyzed various library classification systems and related literature in order to suggest some modifications and expansion of the Dewey Decimal Classification, the 23rd edition (DDC 23) in the area of immigration policy - an interdisciplinary subject - for the best information organization and services. First of all, definitions and scopes of the immigration policy were dealt with and then primary subject areas of it were selected. And then, DDC, Library of Congress Classification, Korean Decimal Classification, and Universal Decimal Classification were compared and analyzed according to the structures, headings and characteristics. Finally, modified classification schedules in immigration policy of the DDC 23 - the most frequently used one with an regular revision was proposed with their principles and main schedules with an auxiliary table. It can be used for an effective information organization in immigration policy area and it will be useful for many libraries and research institutes on immigration policy.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.403-407
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• 2007

Korean Government conducted "a research for establishing the integrated construction information classification system" and suggested the classification system for the first and second level in terms of the complexity in May, 2001. The Ministry of Construction and Transportation announced a standard of the integrated construction information classification system using previous research in August, 2001. Since 2005, many BTL projects have been constructed for the educational facility. However, there is mixed official standard of the system for the educational facility. Moreover, it is difficult for the SPC to use the current "integrated construction information classification system", since the item in the educational facility cannot be easily located in the current system. Thus, this research suggests an efficient maintenance system for the educational facility. Two classification systems, space-oriented and element-oriented systems, are suggested to increase efficiency of the classification system.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.1
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• pp.173-191
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• 2014

The purpose of this study is to analyze and identify the characteristics in the current library classification research, and to provide suggestions for the future in this area. This study covers the published studies in library classification from 2004 to 2013. They include the research on Korean Decimal Classification (KDC), Dewey Decimal Classification and others. The results show that the main purpose in those studies is to improve current KDC system. A suggestion is provided in this study to concentrate on specific classification systems for school libraries and small libraries, for digital collections.

• Journal of Korean Library and Information Science Society
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• v.41 no.4
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• pp.5-33
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• 2010

This study is intended to present methods improving the classification system of KDC education fields after comparing and analyzing the academic system of education, classification system of KDC, NDC, DDC and LCC, and that of the research field classification system of National Research Foundation of Korea. The results of the analysis have revealed that it is required to improve and correct the KDC 5th edition of education including the addition of classification items that reflect the trend of academic development, proper development in the rank classification terms of education detailed fields, addition of detailed subjects, errors of classification symbols and omission of correlative indexes in the classification items. This study has proposed improved methods to solve those problems.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.2 s.16
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• pp.47-60
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• 2000

On this study, Among the classification methods of land cover using satellite imagery, we compared the classification accuracy of Neural Network Classifier and that of Maximum Likelihood Classifier which has the characteristics of parametric and non-parametric classification method. In the assessment of classification accuracy, we analyzed the classification accuracy about testing area as well as training area that many analysts use generally when assess the classification accuracy. As a result, Neural Network Classifier is superior to Maximum Likelihood Classifier as much as 3% in the classification of training data. When ground reference data is used, we could get poor result from both of classification methods, but we could reach conclusion that the classification result of Neural Network Classifier is superior to the classification result of Maximum Likelihood Classifier as much as 10%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4246-4267
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• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.4
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• pp.428-436
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• 2014

This paper proposes a statistical regression method for classifying pillars and vehicles in parking area using a single ultrasonic sensor. There are three types of information provided by the ultrasonic sensor: TOF, the peak and the width of a pulse, from which 67 different features are extracted through segmentation and data preprocessing. The classification using the multiple SVM and the multinomial logistic regression are applied to the set of extracted features, and has achieved the accuracy of 85% and 89.67%, respectively, over a set of real-world data. The experimental result proves that the proposed feature extraction and classification scheme is applicable to the object classification using an ultrasonic sensor.