• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5116-5134
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• 2015

In this paper, due to the network security defense is mainly static defense, a dynamic classification network security defense strategy model is proposed by analyzing the security situation of complex computer network. According to the network security impact parameters, eight security elements and classification standard are obtained. At the same time, the dynamic classification algorithm based on fuzzy theory is also presented. The experimental analysis results show that the proposed model and algorithm are feasible and effective. The model is a good way to solve a safety problem that the static defense cannot cope with tactics and lack of dynamic change.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.2
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• pp.236-243
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• 2016

This paper presents a threat list acquisition method in an engagement area using the support vector machines (SVM). The proposed method consists of track creation, track estimation, track feature extraction, and threat list classification. To classify the threat track robustly, dynamic track estimation and pattern recognition algorithms are used. Dynamic tracks are estimated accurately by approximating a track movement using position, velocity and time. After track estimation, track features are extracted from the track information, and used to classify threat list. Experimental results showed that the threat list acquisition method in the engagement area achieved about 95 % accuracy rate for whole test tracks when using the SVM classifier. In case of improving the real-time process through further studies, it can be expected to apply the fire control systems.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.540-549
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• 2018

Classifying the front body of the missile and debris of a high-speed missile in intercepting a high-speed missile is an important issue. The motion of the front body of the missile is characterized by precession, but the motion of the debris in the boosting part separation phase is characterized by tumbling. There are periodic patterns caused by the precession or tumbling motion on the dynamic radar cross section (RCS). In addition, there are statistical properties caused by the change pattern of the dynamic RCS. A method is proposed to classify the front body of the missile and debris using periodic and statistical properties of the dynamic RCS. Three kinds of feature vector are extracted from the periodic and statistical properties of the dynamic RCS. The front body of the missiles and debris was classified using a support vector machine.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.201-208
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• 2014

In this paper, we propose the classifier structure design algorithm using DTW. Proposed algorithm uses DTW result to design the binary tree architecture based on the SVM which classify the multi-class data. Design the binary tree architecture for Support Vector Machine(SVM-BTA) using the threshold criterion calculated by the sum columns in square matrix which components are the reference data from each class. For comparison the performance of the proposed algorithm, compare the results of classifiers which binary tree structure are designed based on database and k-means algorithm. The data used for classification is 333 signals from 18 classes of underwater transient noise. The proposed classifier has been improved classification performance compared with classifier designed by database system, and probability of detection for non-biological transient signal has improved compare with classifiers using k-means algorithm. The proposed SVM-BTA classified 68.77% of biological sound(BO), 92.86% chain(CHAN) the mechanical sound, and 100% of the 6 kinds of the other classes.

• Journal of Internet Technology
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• v.20 no.5
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• pp.1575-1588
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• 2019

Recently, to design dynamic networks without existing infrastructure, wireless ad hoc networks have been proposed to establish self-organizing networks. In this type of network, to resolve the primary research challenge of establishing a stable path between source and destination, several metrics or utility values have been proposed to meet the specific objectives, as well as improve packet delivery ratio when developing communication protocols or addressing technical issues. Notably, most existing studies use the Multi Attribute Decision Making (MADM) algorithm to balance weights between relevant metrics to realize the above objective. However, despite their significant efforts, a comprehensive survey paper analyzing them together has not been published. Thus, in this paper, we describe the recent research and development efforts to employ MADM in ad hoc networks. First, we provide an overview of MADM and explain the well-known algorithms. After categorizing the current work according to the algorithms, the existing schemes are further divided by the type of networks. Based on this classification, we then detail the procedures with their research objectives. Furthermore, we present other research challenges and apparent problems in this research area.

• Journal of Software Engineering Society
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• v.29 no.1
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• pp.7-12
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• 2020

As interest in functional safety has recently increased, application of functional safety standards has been required in various industrial fields. A functional safety standard is a document that defines functional safety-related activities required to prevent system malfunctions. All activities defined in this standard are required differentially according to the classification results calculated through the risk analysis and assessment of the system. In the field of domestic weapon systems, there is a manual for the development and management of weapon system software issued by the Defense Acquisition Program Administration (DAPA ). This manual requires static and dynamic analysis of software for functional safety related activities. However, the manual does not specifically address the classification activity through risk analysis and assessment as required for the preceding activities. Therefore, in this study, we analyze the problems of the manual based on the representative functional safety standards, and propose improvement plans.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.4
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• pp.193-205
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• 2003

There has been so many research activities about robot soccer system in the many research fields, for example, intelligent control, communication, computer technology, sensor technology, image processing, mechatronics. Especially researchers research strategy for attacking in the field of strategy, and develop intelligent strategy. Then, soccer robots cannot defense completely and efficiently by using simple defense strategy. Therefore, intention extraction of attacker is needed for efficient defense. In this thesis, intention extractor of soccer robots is designed and developed based on FMMNN(Fuzzy Min-Max Neural networks ). First, intention for soccer robot system is defined, and intention extraction for soccer robot system is explained.. Next, FMMNN based intention extractor for soccer robot system is determined. FMMNN is one of the pattern classification method and have several advantages: on-line adaptation, short training time, soft decision. Therefore, FMMNN is suitable for soccer robot system having dynamic environment. Observer extracts attack intention of opponents by using this intention exactor, and this intention extractor is also used for analyzing strategy of opponent team. The capability of developed intention extractor is verified by simulation of 3 vs. 3 robot succor simulator. It was confirmed that the rates of intention extraction each experiment increase.