• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6B
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• pp.600-607
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• 2011

As network traffic is dramatically increasing due to the popularization of Internet, the need for application traffic identification becomes important for the effective use of network resources. In this paper, we present an Internet application traffic identification method based on flow correlation to overcome limitation of signature-based identification methods and to improve performance (completeness) of it. The proposed method can identify unidentified flows from signature-based method using flow correlation between identified and unidentified flows. We propose four separate correlation methods such as Server-Client, Time, Host-Host, and Statistic correlation and describe a flow correlation-based identification system architecture which incorporates the four separate methods. Also we prove the feasibility and applicability of our proposed method by an acceptable experimental result.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1752-1758
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• 2015

This paper presents partial discharge localization in stator winding of generators using multi-conductor transmission line (MTL) and RLC ladder network models. The high-voltage (HV) winding of a 6kV/250kW generator has been modeled by MATLAB software. The simulation results of the MTL and the RLC ladder network models have been evaluated with the measurements results in the frequency domain by applying of the Pearson’s correlation coefficients. Two PD generated calibrator signals in kHz and MHz frequency range were injected into different points of generator winding and the signals simulated/measured at the both ends of the winding. For partial discharge localization in stator winding of generators is necessary to calculate the frequency spectrum of the PD current signals and then estimate the poles of the system from the calculated frequency spectrum. Finally, the location of PD can be estimated. This theory applied for the above generator and the simulation/measured results show the good correlation for PD Location for RLC ladder network and MTL models in the frequency range of kHz (10kHz<f<1MHz) and MHz (1MHz<f<5MHz) respectively.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.139-148
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• 2022

Research into wireless sensor networks (WSNs) is a trendy issue with a wide range of applications. With hundreds to thousands of nodes, most wireless sensor networks interact with each other through radio waves. Limited computational power, storage, battery, and transmission bandwidth are some of the obstacles in designing WSNs. Clustering and routing procedures have been proposed to address these concerns. The wireless sensor network's most complex and vital duty is routing. With the Greedy Perimeter Stateless Routing method (GPSR), an efficient and responsive routing protocol is built. In packet forwarding, the nodes' locations are taken into account while making choices. In order to send a message, the GPSR always takes the shortest route between the source and destination nodes. Weighted directed graphs may be constructed utilising four distinct distance metrics, such as Euclidean, city block, cosine, and correlation distances, in this study. NS-2 has been used for a thorough simulation. Additionally, the GPSR's performance with various distance metrics is evaluated and verified. When compared to alternative distance measures, the proposed GPSR with correlation distance performs better in terms of packet delivery ratio, throughput, routing overhead and average stability time of the cluster head.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3093-3115
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• 2020

Network anomaly detection system plays an essential role in detecting network anomaly and ensuring network security. Anomaly detection system based machine learning has become an increasingly popular solution. However, due to the unbalance and high-dimension characteristics of network traffic, the existing methods unable to achieve the excellent performance of high accuracy and low false alarm rate. To address this problem, a new network anomaly detection method based on data balancing and recursive feature addition is proposed. Firstly, data balancing algorithm based on improved KNN outlier detection is designed to select part respective data on each category. Combination optimization about parameters of improved KNN outlier detection is implemented by genetic algorithm. Next, recursive feature addition algorithm based on correlation analysis is proposed to select effective features, in which a cross contingency test is utilized to analyze correlation and obtain a features subset with a strong correlation. Then, random forests model is as the classification model to detection anomaly. Finally, the proposed algorithm is evaluated on benchmark datasets KDD Cup 1999 and UNSW_NB15. The result illustrates the proposed strategies enhance accuracy and recall, and decrease the false alarm rate. Compared with other algorithms, this algorithm still achieves significant effects, especially recall in the small category.

• The Korea Journal of Herbology
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• v.38 no.1
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• pp.45-53
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• 2023

Objectives : Network pharmacology-based research is one of useful tool to predict the possible efficacy and molecular mechanisms of natural materials with multi compounds-multi targeting effects. In this study, we investigated the functional underlying mechanisms of Astragalus membranaceus Bunge (AM) on its anti-obesity effects using a network pharmacology analysis. Methods : The constituents of AM were collected from public databases and its target genes were gathered from PubChem database. The target genes of AM were compared with the gene set of obesity to find the correlation. Then, the network was constructed by Cytoscape 3.9.1. and functional enrichment analysis was conducted to predict the most relevant pathway of AM. Results : The result showed that AM network contained the 707 nodes and 6867 edges, and 525 intersecting genes were exhibited between AM and obesity gene set, indicating that high correlation with the effects of AM on obesity. Based on GO biological process and KEGG Pathway, 'Response to lipid', 'Cellular response to lipid', 'Lipid metabolic process', 'Regulation of chemokine production', 'Regulation of lipase activity', 'Chemokine signaling pathway', 'Regulation of lipolysis in adipocytes' and 'PPAR signaling pathway' were predicted as functional pathways of AM on obesity. Conclusions : AM showed high relevance with the lipid metabolism related with the chemokine production and lipolysis pathways. This study could be a basis that AM has promising effects on obesity via network pharmacology analysis.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.745-748
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• 1996

Korea Composite Stock Price Index (KOSPI) was predicted using Cascade Correlation Network (CCN) model. CCN was suggested, by Fahlman and Lebiere [1990], to overcome the limitations of backpropagation algorithm such as step size problem and moving target problem. To test the applicability of CCN as a function approximator to the stock price movements, CCN was used as a tool for univariate time series analysis. The fitting and forecasting performance fo CCN on the KOSPI was compared with those of Multi-Layer Perceptron (MLP).

• Proceedings of the Korea Society of Design Studies Conference
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• 2001.05a
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• pp.118-119
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• 2001

• The Journal of the Korea Contents Association
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• v.14 no.4
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• pp.407-417
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• 2014

An arirang is the most famous Korean folk song and was registered in UNESCO(Unitied Nations Educational, Scientific and cultural Organization) as an intangible cultural heritage in 2012. Most arirangs are composed of text and refrain parts. Genealogy of the arirang was classified in refrain patterns by using multiple sequence alignment algorithm. There are two different refrain patterns, slow and fast melodies. Of 106 arirangs, 38 and 68 arirangs contain fast and slow melodies, respectively. 73 arirangs and 104 their key words were extracted from bipartate arirang network that composed of arirangs, text works, and their relationships. The correlation among the arirangs was analyzed from the selected arirangs and key words by using pairwise comparison matrix. Also, analysis of correlation among the arirnags was performed by stepwise removal of the single degree nodes from the bipartate arirang network In this study, arirangs were analyzed in genealogy and correlation among arirangs by using informatic algorithm and network technology, in which arirang research will be constructed a stepping stone for the popularization and globalization of the arirangs.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.309-317
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• 2022

In this paper, we propose a machine learning-based copy-move forgery detection network with dual branches. Because the rotation or scaling operation is frequently involved in copy-move forger, the conventional convolutional neural network is not effectively applied in detecting copy-move tampering. Therefore, we divide the input into rotation-invariant and scaling-invariant features based on the wavelet coefficients. Each of the features is input to different branches having the same structure, and is fused in the combination module. Each branch comprises feature extraction, correlation, and mask decoder modules. In the proposed network, VGG16 is used for the feature extraction module. To check similarity of features generated by the feature extraction module, the conventional correlation module used. Finally, the mask decoder model is applied to develop a pixel-level localization map. We perform experiments on test dataset and compare the proposed method with state-of-the-art tampering localization methods. The results demonstrate that the proposed scheme outperforms the existing approaches.

• Journal of the Korean Applied Science and Technology
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• v.38 no.4
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• pp.1070-1080
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• 2021

This study is a descriptive survey research on social network service addiction tendency and interpersonal relationships on college life adaptation among nursing students. The study period was from October 1 to 4, 2019, the survey was conducted for 228 first and third year nursing students located in G city. The collected data were analyzed with the SPSS 23.0 program using descriptive statistics, ANOVA, Pearson correlation coefficients, and multiple regression. The results of the study showed that there were differences in the subjects' college life adaptation depending on their gender (t=5.26, p<.001), daily average duration of using social network service (F=8.07, p<.001), and friends in real life (F=2.87, p=.037). College life adaptation had a significant correlation with social network service addiction tendency (r=-.31, p<.001) and interpersonal relationships (r=.52, p<.001), and social network service addiction tendency with interpersonal relationships had a significant correlation (r=-.17, p=.011). Factors that affected college life adaptation included interpersonal relationships (𝛽=.477, p<.001), gender (𝛽=-.198, p=.001), and daily average duration of using social network service (𝛽=-.177, p=.003), and the explanatory power of these factors was 37.8%. Based on the results of this study, it is thought that it is necessary to develop a program for a positive interpersonal relationship formation using social network service in order to improve the college life adaptation of nursing students.