• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.6 s.261
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• pp.651-658
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• 2007

A new tree-structured neural network classifier is proposed for the automatic real-time inspection of cold-rolled steel strip surface defects. The defects are classified into 3 groups such as area type, disk type, area & line type in the first stage of the tree-structured neural network. The defects are classified in more detail into 11 major defect types which are considered as serious defects in the second stage of neural network. The tree-structured neural network classifier consists of 4 different neural networks and optimum features are selected for each neural network classifier by using SFFS algorithm and correlation test. The developed classifier demonstrates very plausible result which is compatible with commercial products having high world-wide market shares.

• Journal of KIISE:Information Networking
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• v.35 no.2
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• pp.139-148
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• 2008

Many types of sensor data exhibit strong correlation in both space and time. Suppression, both temporal and spatial, provides opportunities for reducing the energy cost of sensor data collection. Unfortunately, existing clustering algorithms are difficult to utilize the spatial or temporal opportunities, because they just organize clusters based on the distribution of sensor nodes or the network topology but not correlation of sensor data. In this paper, we propose a novel clustering algorithm with suppression techniques. To guarantee independent communication among clusters, we allocate multiple channels based on sensor data. Also, we propose a spatio-temporal suppression technique to reduce the network traffic. In order to show the superiority of our clustering algorithm, we compare it with the existing suppression algorithms in terms of the lifetime of the sensor network and the site of data which have been collected in the base-station. As a result, our experimental results show that the size of data was reduced by $4{\sim}40%$, and whole network lifetime was prolonged by $20{\sim}30%$.

• Journal of the Korean Geotechnical Society
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• v.19 no.4
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• pp.287-298
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• 2003

The feasibility of using neural networks to model the complex relationship between piezocone measurements and the undrained shear strength of clays has been investigated. A three layered back propagation neural network model was developed based on actual undrained shear strengths, which were obtained from the isotrpoically and anisotrpoically consolidated triaxial compression test(CIUC and CAUC), and piezocone measurements compiled from various locations around the world. It was validated by comparing model predictions with measured values about new piezocone data, which were not previously employed during development of model. Performance of the neural network model was compared with conventional empirical method, direct correlation method, and theoretical method. It was found that the neural network model is not only capable of inferring a complex relationship between piezocone measurements and the undrained shear strength of clays but also gives a more precise and reliable undrained shear strength than theoretical and empirical approaches. Furthermore, neural network model has a possibility to be a generalized relationship between piezocone measurements and undrained shear strength over the various places and countries, while the present empirical correlations present the site specific relationship.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.48-58
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• 2013

The stock market is constantly changing and sometimes a slump or a sudden rising in stocks happens without any special reason. So the stock market is recognized as a complex system and it is hard to predict the change on stock prices. In this paper we consider the stock market to a network consisting of stocks. We analyzed the dynamics of the Korean stock market network and evaluated the changing of the correlation between shares consisting of the time series data of 137 companies belong to KOSPI200. Our analysis shows that the stock prices tend to plummet when the correlation between stocks is very high. We propose a method for recommending the stock portfolio based on the analysis of the stock market network. To show the effectiveness of the recommended portfolio, we conducted the simulated stock investment and compared the recommended portfolio with the efficient portfolio proposed Markowitz. According to the experiment results, the rate of return of the portfolio is about 10.6% which is about 3.7% and 5.6% higher than the average rate of return of the efficient portfolio and KOSPI200 respectively.

• Convergence Security Journal
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• v.17 no.5
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• pp.27-34
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• 2017

Network security visualization technique using security alerts provide the administrator with intuitive network security situation by efficiently visualizing a large number of security alerts occurring from the security devices. However, most of these visualization techniques represent events using overlap the timelines of the alerts or Top-N analysis by their frequencies resulting in failing to provide information such as the attack trend, the relationship between attacks, the point of occurrence of attack, and the continuity of the attack. In this paper, we propose an effective visualization technique which intuitively explains the transition of the whole attack and the continuity of individual attacks by arranging the events spirally according to timeline and marking occurrence point and attack type. Furthermore, the relationship between attackers and victims is provided through a single screen view, so that it is possible to comprehensively monitor not only the entire attack situation but also attack type and attack point.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.51-57
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• 2021

In this paper, we propose a method to find the DoF(Depth of field) that is blurred in an image by focusing and out-focusing the camera through a efficient convolutional neural network. Our approach uses the RGB channel-based cross-correlation filter to efficiently classify the DoF region from the image and build data for learning in the convolutional neural network. A data pair of the training data is established between the image and the DoF weighted map. Data used for learning uses DoF weight maps extracted by cross-correlation filters, and uses the result of applying the smoothing process to increase the convergence rate in the network learning stage. The DoF weighted image obtained as the test result stably finds the DoF region in the input image. As a result, the proposed method can be used in various places such as NPR(Non-photorealistic rendering) rendering and object detection by using the DoF area as the user's ROI(Region of interest).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3C
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• pp.97-104
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• 2009

In this study, artificial neural network was performed using the data of soils characteristic value, standard penetration test, and field permeability test of the 12 embankment that are located in the near Nak-dong and Kum-ho river to estimate the coefficient of field permeability of river embankment. The 89 data of total 108, 82% was used in learning step, and the other 19 data was used in estimation step. Also the results of generally used empirical equations were compared with those of artificial neural network for evaluation of application. As results, all of the coefficient of field permeability by empirical equation showed below 0.4 in terms of the coefficient of correlation with the measured values, but the coefficient of correlation of the predicted results by artificial neural network was up 0.8 in the all case. Therefore artificial neural network could predict more the precise field permeability well than the empirical equations.

• The Korea Journal of Herbology
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• v.38 no.6
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• pp.73-91
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• 2023

Objectives : This study used a network pharmacology approach to elucidate the efficacy and molecular mechanisms of Daehwangmokdanpitang (DHMDPT) on Psoriasis. Methods : Using OASIS databases and PubChem database, compounds of DHMDPT and their target genes were collected. The putative target genes of DHMDPT and known target genes of psoriasis were compared and found the correlation. Then, the network was constructed using Cytoscape 3.10.1. The key target genes were screened by Analyzer network and their functional enrichment analysis was conducted based on the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathways to predict the mechanisms. Results : The result showed that total 30 compounds and 439 related genes were gathered from DHMDPT. 264 genes were interacted with psoriasis gene set, suggesting that the effects of DHMDPT are closely related to psoriasis. Based on GO enrichment analysis and KEGG pathways, 'Binding', 'Cytokine Activity', 'Receptor Ligand Activity' 'HIF-1 signaling pathway', 'IL-17 signaling pathway', 'Toll-like receptor signaling pathway', and 'TNF signaling pathway' were predicted as functional pathways of 16 key target genes of DHMDPT on psoriasis. Among the target genes, IL6, IL1B, TNF, AKT1 showed high correlation with the results of KEGG pathways. Additionally, Emodin, Acetovanillone, Gallic acid, and Ferulic acid showed a high relevance with key genes and their mechanisms. Conclusion : Through a network pharmacological method, DHMDPT was predicted to have high relevance with psoriasis. This study could be used as a basis for studying therapeutic effects of DHMDPT on psoriasis.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.323-329
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• 2015

In this paper we applied pattern recognition approach to detect audio forgery. Classification of the microphone types and models can help determining the authenticity of the recordings. Canonical correlation analysis was applied to extract feature for microphone classification. We utilized the linear dependence between two near-silence regions. To utilize the advantage of multi-feature based canonical correlation analysis, we selected three commonly used features to capture the temporal and spectral characteristics. Using three different microphones, we tested the usefulness of multi-feature based characteristics of canonical correlation analysis and compared the results with single feature based method. The performance of classification rate was carried out using the backpropagation neural network. Experimental results show the promise of canonical correlation features for microphone classification.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.791-800
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• 2023

This study was conducted to lay the foundation for creating a society safe from radiation by investigating the establishment of a radiation social safety net and the establishment of safety awareness and safety culture among citizens living in Busan. Data was collected through an online survey, and 200 copies of the survey were analyzed. Data were analyzed using SPSS Window Ver 28.0. To verify differences between groups, t-test and one way ANOVA were performed, and correlation analysis was performed to confirm the relationship between variables. In addition, multiple linear regression analysis was conducted to confirm the influence between variables. As a result, first, in terms of building a social safety net, citizens' safety awareness, and establishing a safety culture, the scores of the group with male gender, age in 20s, and high school graduation were found to be high. Among them, there was a statistical difference in gender at the significance level of .01 for building a social safety network and at the significance level of .05 for establishing a safety culture. In terms of occupation, there was a statistical difference between professionals and service workers at the significance level of .05 regarding the building of a radiation social safety network. Second, as a result of multiple regression analysis, it was found that 'local government radiation safety education', a subordinate factor in building a radiation social safety network, had a positive effect on citizens' safety awareness and establishment of a safety culture. Third, the results of the correlation analysis between the building of a social safety network, citizens' safety awareness, and establishment of a safety culture showed a positive correlation. Therefore, it is believed that a good radiation social safety network will have a positive impact on citizens' safety awareness and the establishment of a safety culture.