• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.1177-1180
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• 2004

최근 침입의 형태는 기존 공격자의 직접적인 시스템 침입 및 악의적 행위들의 행사와는 달리 침입 자동화 도구들을 사용하는 형태로 변모해 가고 있다. 알려지지 않은 공격의 유형 또한 변형된 이들 도구들의 사용이 대부분이다. 이들 공격도구들 대부분은 기존 형태에서 크게 벗어나지 않으며, 침입 도구의 산출물 또한 공통적인 형태로 존재한다. 본 논문에서는 알려지지 않은 다양한 공격 유형 또한 기존 유사한 공격군으로 분류하기 위한 침입 분석 알고리즘으로 SOM(self-Organizing Maps)을 적용하고, 침입 구체화 분석 단계에서 공격도구들의 패턴을 정형화한 지식베이스를 기반으로 분석하는 시스템을 제안한다.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.31-36
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• 2021

RNA sequencing (RNA-Seq) is a technology that facilitates transcriptome analysis using next-generation sequencing (NSG) tools. Information on the quantity and sequences of RNA is vital to relate our genomes to functional protein expression. RNA-Seq data are characterized as being high-dimensional in that the number of variables (i.e., transcripts) far exceeds the number of observations (e.g., experiments). Given the wide range of dimensionality reduction techniques, it is not clear which is best for RNA-Seq data analysis. In this paper, we study the effect of three dimensionality reduction techniques to improve the classification of the RNA-Seq dataset. In particular, we use PCA, SVD, and SOM to obtain a reduced feature space. We built nine classification models for a cancer dataset and compared their performance. Our experimental results indicate that better classification performance is obtained with PCA and SOM. Overall, the combinations PCA+KNN, SOM+RF, and SOM+KNN produce preferred results.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.1-4
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• 2022

The growth in global population and industrialization has led to an increasing demand for electricity. Accordingly, the electricity providers need to increase the electricity generation. Due to the economical and environmental concerns associated with the generation of electricity from fossil fuels. Alternative power recourses that can potentially mitigate the economical and environmental are of interest. Renewable energy resources are promising recourses that can participate in producing power. Among renewable power resources, solar energy is an abundant resource and is currently a field of research interest. Photovoltaic solar power is a promising renewable energy resource. The power output of PV systems is mainly affected by the solar irradiation and ambient temperature. this paper investigates the utilization of machine learning unsupervised neural network techniques that potentially improves the reliability of PV solar power systems during integration into the electrical grid.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.572-587
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• 2008

The Self-organizing Feature Map(SOFM) that is one of unsupervised neural networks is a very powerful tool for data clustering and visualization in high-dimensional data sets. Although the SOFM has been applied in many engineering problems, it needs to cluster similar weights into one class on the trained SOFM as a post-processing, which is manually performed in many cases. The traditional clustering algorithms, such as t-means, on the trained SOFM however do not yield satisfactory results, especially when clusters have arbitrary shapes. This paper proposes automatic clustering on trained SOFM, which can deal with arbitrary cluster shapes and be globally optimized by graph cuts. When using the graph cuts, the graph must have two additional vertices, called terminals, and weights between the terminals and vertices of the graph are generally set based on data manually obtained by users. The Proposed method automatically sets the weights based on mode-seeking on a distance matrix. Experimental results demonstrated the effectiveness of the proposed method in texture segmentation. In the experimental results, the proposed method improved precision rates compared with previous traditional clustering algorithm, as the method can deal with arbitrary cluster shapes based on the graph-theoretic clustering.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.141-154
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• 2014

The adoption of International Financial Reporting Standards (IFRS) is the one of important issues in the recent accounting research because the change from local GAAP (Generally Accepted Accounting Principles) to IFRS has a substantial effect on accounting information. Over 100 countries including Australia, China, Canada and the European Union member countries adopt IFRS (International Financial Reporting Standards) for financial reporting purposes, and several more including the United States and Japan are considering the adoption of IFRS (International Financial Reporting Standards). In Korea, 61 firms voluntarily adopted Korean International Financial Reporting Standard (K-IFRS) in 2009 and 2010 and all listed firms mandatorily adopted K-IFRS (Korea-International Financial Reporting Standards) in 2011. The adoption of IFRS is expected to increase financial statement comparability, improve corporate transparency, increase the quality of financial reporting, and hence, provide benefits to investors This study investigates whether recognized accounts receivable discounting (AR discounting) under Korean International Financial Reporting Standard (K-IFRS) is more value relevant than disclosed AR discounting under Korean Generally Accepted Accounting Principles (K-GAAP). Because more rigorous standards are applied to the derecognition of AR discounting under K-IFRS(Korea-International Financial Reporting Standards), most AR discounting is recognized as a short term debt instead of being disclosed as a contingent liability unless all risks and rewards are transferred. In this research, I try to figure out industrial responses to the changes in accounting rules for the treatment of accounts receivable toward more strict standards in the recognition of sales which occurs with the adoption of Korea International Financial Reporting Standard. This study examines whether accounting information is more value-relevant, especially information on accounts receivable discounting (hereinafter, AR discounting) is value-relevant under K-IFRS (Korea-International Financial Reporting Standards). First, note that AR discounting involves the transfer of financial assets. Under Korean Generally Accepted Accounting Principles (K-GAAP), when firms discount AR to banks before the AR maturity, firms conventionally remove AR from the balance-sheet and report losses from AR discounting and disclose and explain the transactions in the footnotes. Under K-IFRS (Korea-International Financial Reporting Standards), however, most firms keep AR and add a short-term debt as same as discounted AR. This process increases the firms' leverage ratio and raises the concern to the firms about investors' reactions to worsening capital structures. Investors may experience the change in perceived risk of the firm. In the study sample, the average of AR discounting is 75.3 billion won (maximum 3.6 trillion won and minimum 18 million won), which is, on average 7.0% of assets (maximum 38.6% and minimum 0.002%), 26.2% of firms' accounts receivable (maximum 92.5% and minimum 0.003%) and 13.5% of total liabilities (maximum 69.5% and minimum 0.004%). After the adoption of K-IFRS (Korea-International Financial Reporting Standards), total liabilities increase by 13%p on average (maximum 103%p and minimum 0.004%p) attributable to AR discounting. The leverage ratio (total liabilities/total assets) increases by an average 2.4%p (maximum 16%p and minimum 0.001%p) and debt-to-equity ratio increases by average 14.6%p (maximum 134%p and minimum 0.006%) attributable to the recognition of AR discounting as a short-term debt. The structure of debts and equities of the companies engaging in factoring transactions are likely to be affected in the changes of accounting rule. I suggest that the changes in accounting provisions subsequent to Korea International Financial Reporting Standard adoption caused significant influence on the structure of firm's asset and liabilities. Due to this changes, the treatment of account receivable discounting have become critical. This paper proposes an intelligent diagnostic system for estimating negative impact on stock value with self-organizing maps and case based reasoning. To validate the usefulness of this proposed model, real data was analyzed. In order to get the significance of this proposed model, several models were compared to the research model. I found out that this proposed model provides satisfactory results with compared models.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.3
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• pp.235-245
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• 2005

The rapid development of computer network technology has brought the Internet as the major infrastructure to our society. But the rapid increase in malicious computer intrusions using such technology causes urgent problems of protecting our information society. The recent trends of the intrusions reflect that the intruders do not break into victim host directly and do some malicious behaviors. Rather, they tend to use some automated intrusion tools to penetrate systems. Most of the unknown types of the intrusions are caused by using such tools, with some minor modifications. These tools are mostly similar to the Previous ones, and the results of using such tools remain the same as in common patterns. In this paper, we are describing design and implementation of attacker-traceback system, which traces the intruder based on the multi-agent architecture. The system first applied SOM to classify the unknown types of the intrusion into previous similar intrusion classes. And during the intrusion analysis stage, we formalized the patterns of the tools as a knowledge base. Based on the patterns, the agent system gets activated, and the automatic tracing of the intrusion routes begins through the previous attacked host, by finding some intrusion evidences on the attacked system.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.4
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• pp.58-65
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• 2003

This paper studies a steering control method using a neural network algorithm for an intelligent autonomous driving robot. Previous horizontal steering control methods were made by various possible situation on the road. However, it isn't possible to make out algorithms that consider all sudden variances on the road. In this paper, an intelligent steering control algorithm for an autonomous driving robot system is presented. The algorithm is based on Self Organizing Maps(SOM) and the feature points on the road are used as training datum. In a simulation test, it is available to handle a steering control using SOM for an autonomous steering control. The algorithm is evaluated on an autonomous driving robot. The algorithm is available to control a steering for an autonomous driving robot with better performance at the experiments.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2108-2115
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• 2000

The conventional Boolean retrieval systems based on vector spae model can provide the results of retrieval fast, they can't reflect exactly user's retrieval purpose including semantic information. Consequently, the results of retrieval process are very different from those users expected. This fact forces users to waste much time for finding expected documents among retrieved documents. In his paper, we designed a bayesian SOM(Self-Organizing feature Maps) in combination with bayesian statistical method and Kohonen network as a kind of unsupervised learning, then perform classifying documents depending on the semantic similarity to user query in real time. If it is difficult to observe statistical characteristics as there are less than 30 documents for clustering, the number of documents must be increased to at least 50. Also, to give high rank to the documents which is most similar to user query semantically among generalized classifications for generalized clusters, we find the similarity by means of Kohonen centroid of each document classification and adjust the secondary rank depending on the similarity.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.10
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• pp.799-807
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• 2002

The vibration signal can give an indication of the condition of rotating machinery, highlighting potential faults such as unbalance, misalignment and bearing defects. The features in the vibration signal provide an important source of information for the faults diagnosis of rotating machinery. When additional training data become available after the initial training is completed, the conventional neural networks (NNs) must be retrained by applying total data including additional training data. This paper proposes the fault diagnostics algorithm using the ART-Kohonen network which does not destroy the initial training and can adapt additional training data that is suitable for the classification of machine condition. The results of the experiments confirm that the proposed algorithm performs better than other NNs as the self-organizing feature maps (SOFM) , learning vector quantization (LYQ) and radial basis function (RBF) NNs with respect to classification quality. The classification success rate for the ART-Kohonen network was 94 o/o and for the SOFM, LYQ and RBF network were 93 %, 93 % and 89 % respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1125-1132
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• 2010

WiBro(Wireless Broadband Internet) is the standard of high-speed portable internet based on OFDMA/TDD (Orthogonal frequency division multiple access / Time division duplexing) techniques, and the subset of consolidated version of IEEE802.16e Wireless MAN standard. In this paper, we propose performance improvements of WiBro system using the 64QAM SOFM(Self-Organizing Feature Maps)prefiltering. Proposed method used the prefiltering SOFM neural network blind equalization in the Broadband 64 QAM WiBro system receiver. The prefiltering SOFM neural network constellates 64QAM that is transmitter data shape and the blind equalization removes ICI(Inter Carrier Interference). To verificate the proposed method usability, the MSE and the BER are simulated. The simulation results shown that is improved the performances of the proposed WiBro system using the 64QAM SOFM Prefiltering than the existing WiBro system.