• The Journal of Engineering Geology
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• v.21 no.3
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• pp.247-257
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• 2011

In this study the infinite slope model, one of the physical landslide models has been suggested to evaluate the susceptibility of the landslide. However, applying the infinite slope model in regional study area can be difficult or impossible because of the difficulties in obtaining and processing of large spatial data sets. With limited site investigation data, uncertainties were inevitably involved with. Therefore, the probabilistic analysis method such as Monte Carlo simulation and the GIS based infinite slope stability model have been used to evaluate the probability of failure. The proposed approach has been applied to practical example. The study area in Boeun area been selected since the area has been experienced tremendous amount of landslide occurrence. The geometric characteristics of the slope and the mechanical properties of soils like to friction angle and cohesion were obtained. In addition, coefficient of variation (COV) values in the uncertain parameters were varied from 10% to 30% in order to evaluate the effect of the uncertainty. The analysis results showed that the probabilistic analysis method can reduce the effect of uncertainty involved in input parameters.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.69-80
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• 2007

The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences. In this study, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques with better performance.

• Journal of Digital Convergence
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• v.11 no.6
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• pp.103-112
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• 2013

Traditional method of establishing prediction model is usually using formal data stored in Data Base. However, nowadays advent of "smart" era brought by ground-breaking development of communication system makes informal data to dominate overall data, such 80% in total. Therefore, conventional method using formal data as establishing predicting model would be untrustworthy means in present. In other words, it is indispensible to make prediction model credible including informal data(SNS, image, video) and semi-formal data(log data). In this study, we increase credibility of predicting model adapting Bigdata method and comparing reliability of conventional measurement to real-data.

• Journal of the Economic Geographical Society of Korea
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• v.15 no.4
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• pp.615-627
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• 2012

This study analyzed the travel pattern with respect to use of public transportation by using transportation card data and presented the measures that can be used in a traffic policy. Transportation card data targeted Gyeonggi-Do area and as a utilization plan, a scenario that when a traffic policy decision maker improves bus stop facilities, the person selects a target site by using several variables that can be obtained from transportation card data was set and analyzed. The analysis result showed that K means cluster analysis which is decision making methodology and CHAID(Chi-squared automatic interaction detection) were used and it can be used usefully in policies in significance level of p <0.01. Also, based on these results, this study presented policy implications to be improved to actually use transportation card data in policies.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.735-740
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• 2012

In this paper, we introduce design methodologies of polynomial radial basis function neural network classifier with the aid of Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA). By minimizing the information loss of given data, Feature data is obtained through preprocessing of PCA and LDA and then this data is used as input data of RBFNNs. The hidden layer of RBFNNs is built up by Fuzzy C-Mean(FCM) clustering algorithm instead of receptive fields and linear polynomial function is used as connection weights between hidden and output layer. In order to design optimized classifier, the structural and parametric values such as the number of eigenvectors of PCA and LDA, and fuzzification coefficient of FCM algorithm are optimized by Artificial Bee Colony(ABC) optimization algorithm. The proposed classifier is applied to some machine learning datasets and its result is compared with some other classifiers.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.261-267
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• 2006

In order to perform a probabilistic safety assessment (PSA), it requires a large number of data for various fields. And the quality of a PSA results have become more important thing of the risk assessment. As part of enhancing the PSA qualify, Korea Atomic Energy Research Institute is developing a full power Human Reliability Analysis (HRA) calculator to manage human failure events (HFEs) and to calculate the diagnosis human error probabilities and execution human error probabilities. This paper introduces the development process and an overview of a standard HRA method for nuclear power plants. The study was carried out in three stages; 1) development of the procedures and rules for a standard HRA method. 2) design of a system structure, 3) development of the HRA calculator.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.685-694
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• 2008

Recently, construction projects are being gradually changed to large projects with complicated work processes. As a result, claims are increasing as well. Of these, the claims by delayed construction duration are given much weight in the general construction claims. To solve this problem, researches to estimate damages due to delay are carried out in research institutes by analyzing the causes of schedule delay. A system to easily estimate responsible days by analyzing the complicated processes needs for project manager. This study suggests a methodology based on the time impact analysis method, which can utilize the consequential analytical method with the consideration of reduced construction period. The suggested methodology is verified by comparing with actual data of case study by the computerized system for estimating the responsible days.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.23-29
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• 2015

In this study, the spatial relevance between the regional housing price data and the spatial distribution of the location-based social media data is explored. The spatial analysis with rasterization was applied to this study, because the both data have a different form to analyze. The geo-tagged Twitter data had been collected for a month and the regional housing price index about sales and lease were used. The spatial range of both data includes Seoul and the some parts of the metropolitan area. 2,000m grid was constructed to consider the different spatial measure between two data, and they were combined into the constructed grids. The Hotspot Analysis was operated using the combined dataset to see the comparison of spatial distribution, and the bivariate spatial correlation coefficients between two data were measured for the quantitative analysis. The result of this study shows that Seocho-gu area is detected as a common hotspot of tweet and housing sales price index data. though the spatial relevance is not detected between tweet and housing lease price index data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.115-127
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• 2024

The recent increase in digital audio media has greatly expanded the size and diversity of sound data, which has increased the importance of sound data analysis in the digital forensics process. However, the lack of standardized procedures and guidelines for sound data analysis has caused problems with the consistency and reliability of analysis results. The digital environment includes a wide variety of audio formats and recording conditions, but current audio forensic methodologies do not adequately reflect this diversity. Therefore, this study identifies Life-Cycle-based sound data elemental technologies and provides overall guidelines for sound data analysis so that effective analysis can be performed in all situations. Furthermore, the identified elemental technologies were analyzed for use in the development of digital forensic techniques for sound data. To demonstrate the effectiveness of the life-cycle-based sound data elemental technology identification system presented in this study, a case study on the process of developing an emergency retrieval technology based on sound data is presented. Through this case study, we confirmed that the elemental technologies identified based on the Life-Cycle in the process of developing digital forensic technology for sound data ensure the quality and consistency of data analysis and enable efficient sound data analysis.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.427-441
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• 2021

Purpose: This study proposes a method for developing a model that predicts the probability of traffic accidents in advance to prevent the most frequent traffic accidents in the military. Method: For this purpose, CRISP-DM (Cross Industry Standard Process for Data Mining) was applied in this study. The CRISP-DM process consists of 6 stages, and each stage is not unidirectional like the Waterfall Model, but improves the level of completeness through feedback between stages. Results: As a result of modeling the same data set as the previously constructed accident investigation data for the entire group, when the classification criterion was 0.5, Significant results were derived from the accuracy, specificity, sensitivity, and AUC of the model for predicting traffic accidents. Conclusion: In the process of designing the prediction model, it was confirmed that it was difficult to obtain a meaningful prediction value due to the lack of data. The methodology for designing a predictive model using the data set was proposed by reorganizing and expanding a data set capable of rational inference to solve the data shortage.