• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1831-1836
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• 2014

Probabilistic liquefaction hazard map is now widely needed for engineering practice. Based on the Liquefaction Potential Index (LPI) calculated from liquefied and non-liquefied cases, we attempted to estimate probabilities of liquefaction induced ground failures using logistic regression. We then applied this approach for the regional area. LPIs were calculated based on 273 Standard Penetration Tests in the floodplains in the St. Louis area, USA and then interpolated using cokriging with the covariable of peak ground acceleration. Our result shows that some areas of $LPI{\geq}5$, due to soft soil layers and shallow groundwater table, appear probabilities of ground $failure{\geq}0.5$.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2822-2843
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• 2023

All aspects of human life have become increasingly dependent on data in the last few decades. The development of several applications causes an enormous issue on data volume in current years. This information must be safeguarded and kept in safe locations. Massive volumes of data have been safely stored with cloud computing. This technology is developing rapidly because of its immense potentials. As a result, protecting data and the procedures to be handled from attackers has become a top priority in order to maintain its integrity, confidentiality, protection, and privacy. Therefore, it is important to implement the appropriate security measures in order to prevent security breaches and vulnerabilities. An improved version of Modular Encryption Standard (IMES) based on layered modelling of safety mechanisms is the major focus of this paper's research work. Key generation in IMES is done using a logistic map, which estimates the values of the input data. The performance analysis demonstrates that proposed work performs better than commonly used algorithms against cloud security in terms of higher performance and additional qualitative security features. The results prove that the proposed IMES has 0.015s of processing time, where existing models have 0.017s to 0.022s of processing time for a file size of 256KB.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.2
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• pp.105-127
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• 2003

In this paper, four different data mining techniques, two neural networks and two statistical modeling techniques, are compared in terms of prediction accuracy in the context of bankruptcy prediction. In business setting, how to accurately detect the condition of a firm has been an important event in the literature. In neural networks, Backpropagation (BP) network and the Kohonen self-organizing feature map, are selected and compared each other while in statistical modeling techniques, discriminant analysis and logistic regression are also performed to provide performance benchmarks for the neural network experiment. The findings suggest that the BP network is a better choice among the data mining tools compared. This paper also identified some distinctive characteristics of Kohonen self-organizing feature map.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.1064-1079
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• 2019

Information security has become increasingly important with the rapid development of mobile devices and internet. An efficient encryption system is a key to this end. In this paper, we propose an image encryption method based on the cross diffusion of two chaotic maps. We use two chaotic sequences, namely the Logistic map and the Chebyshev map, for key generation which has larger security key space than single one. Moreover, we use these two sequences for further image encryption diffusion which decreases the correlation of neighboring pixels significantly. We conduct extensive experiments on several well-known images like Lena, Baboon, Koala, etc. Experimental results show that our algorithm has the characteristics of large key space, fast, robust to statistic attack, etc.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.755-768
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• 2017

As Big Data becomes the core of the fourth industrial revolution, big data-based processing and analysis capabilities are expected to influence the company's future competitiveness. Comparative studies of RHadoop and RHIPE that integrate R and Hadoop environment, have not been discussed by many researchers although RHadoop and RHIPE have been discussed separately. In this paper, we constructed big data platforms such as RHadoop and RHIPE applicable to large scale data and implemented the machine learning algorithms such as multiple regression and logistic regression based on MapReduce framework. We conducted a study on performance and scalability with those implementations for various sample sizes of actual data and simulated data. The experiments demonstrated that our RHadoop and RHIPE can scale well and efficiently process large data sets on commodity hardware. We showed RHIPE is faster than RHadoop in almost all the data generally.

• Journal of Korean Society of Forest Science
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• v.101 no.1
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• pp.1-6
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• 2012

To achieve the forest fire management goals such as early detection and quick suppression, fire resources should be allocated at high probability area where forest fires occur. The objective of this study was to develop and validate models to estimate spatially distributed probabilities of occurrence of forest fire. The models were builded by exploring relationships between fire ignition location and forest, terrain and anthropogenic factors using logistic regression. Distance to forest, cemetery, fire history, forest type, elevation, slope were chosen as the significant factors to the model. The model constructed had a good fit and classification accuracy of the model was 63%. This model and map can support the allocation optimization of forest fire resources and increase effectiveness in fire prevention and planning.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.8
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• pp.629-637
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• 2013

Chaos communication systems have the characteristics such as non-periodic, wide-band, non-predictability of signals and easy implementation. There have been many studies about chaos communication systems because of these advantages. But, chaos communication systems have low BER(Bit Error Rate) compare to general digital communication system. Existing researches on chaos communication systems only analyze BER performance according to various chaos maps. There are no studies on analysis of BER performance according to PDF(Probability Density Function) of chaos maps. In this paper, we analyze the BER performance according to changing parameter, equation, and initial values of chaos map's PDF. In addition, we propose new chaos map to improve BER performance. Simulation results show that BER performance of CDSK(Correlation Delay Shift Keying) is changed when PDF of chaos map changed. And the proposed chaos map has a better BER performance compare to previous chaos maps such as Tent map, Logistic map, and Henon map.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.121-129
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• 2020

The increase of impervious surface and development along the river due to urbanization not only causes an increase in the number of associated flood risk factors but also exacerbates flood damage, leading to difficulties in flood management. Flood control measures should be prioritized based on various geographical information in urban areas. In this study, a probabilistic flood hazard assessment was applied to flood-prone areas near an urban river. Flood hazard maps were alternatively considered and used to describe the expected inundation areas for a given set of predictors such as elevation, slope, runoff curve number, and distance to river. This study proposes a Bayesian logistic regression-based flood risk model that aims to provide a probabilistic risk metric such as population-at-risk (PAR). Finally, the logistic regression model demonstrates the probabilistic flood hazard maps for the entire area.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.6
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• pp.77-87
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• 2019

The first grade zones in Ecology and Nature Map are important regions for the conservation of the ecosystem, but it would be degraded by various anthropogenic factors. This study analyzes the relationship between potential land cover change and degradation of the first grade zones using land cover transition probability. As a result, it was shown that most of the first grade zones with degraded were converted from forest to urban(5.1%), cropland(27.2%), barren(11.0%) and grass(27.5%) in Gangwon and forest to urban(18.0%), cropland(15.3%), grass(28.4%), barren(12.3%) in Gyeonggi. The result of the logistic regression analysis showed that the probability of degradation of first grade zone was higher in area where was expected the higher probability of urban, cropland, barren, grass transition. The barren transition probability was the most influential and grass was the next highest. There were regional differences in the probability of urban transition and cropland transition, and the urban transition probability was more influential in Gyeonggi-do. This is because development pressure such as housing site development is high in Gyeonggi-do. Due to the limitations of the Act on Mountain Districts Management, even in the first grade zones, the grade may be degraded. Therefore, if Ecology and Nature Map are used to prevent deforestation or conversion of mountainous districts, it may contribute to the preservation of the ecosystem.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.281-292
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• 2014

This study examined the accuracy that produced using various types and combinations of landslide-related factors from landslide susceptibility index maps. A database of landslide-related factors was adopted by the landslide locations that obtained from aerial photographs, and the topographic factors that derived from airborne LiDAR observations and digital maps, and various soil, forest, and land cover. Landslide susceptibility index maps were calculated by logistic regression and frequency ratio from the landslide susceptibility index. The correlation between airborne LiDAR data and digital map was shown strong similarities with one another. Landslide susceptibility index maps indicated the existence of a strong correlation and high prediction accuracy, especially when the frequency ratio and airborne LiDAR were used. Therefore, we concluded that the Airborne LiDAR will contribute to the development of effective landslide prediction methods and damage reduction measures.