• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.95-100
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• 2024

The public survey performance review conducted by the Spatial Information Quality Management Institute is conducted at the screening rate in accordance with the regulations, and the examiner directly judges the overall trend of the submitted performance based on the extracted sample. However, the evaluation of the Ministry of Land, Infrastructure and Transport, the evaluation trustee shall be specified by random extraction (Random Collection) is specified by the sample. In this study, it analyzed the details of the actual site and analyzed through securing actual performance review data. In addition, we analyzed considerations according to various field conditions and studied ways to apply the public survey performance review sampling algorithm. Therefore, detailed sampling criteria analysis by performance reviewers is necessary. A relative comparison was made feasible by comparing the data for which the real performance evaluation was performed with the outcomes of the Python automation program. This automation program is expected to be employed as a foundation program for the automated application of public survey performance evaluation sampling in the future.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.1-8
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• 2006

This paper describes the procedures developing the algorithm for analyzing signals acquired from the Bridge Weigh-in-Motion (BWIM) system installed in Seohae Bridge as a part of the bridge monitoring system. Through the analysis procedure, information about heavy traffics such as weight, speed, and number of axles are attempted to be extracted from time domain strain data of the BWIM system. One of numerous pattern recognition techniques, artificial neural network (ANN) is employed since it can effectively include dynamic effects, bridge-vehicle interaction, etc. A number of vehicle running experiments with sufficient load cases are executed to acquire training and/or test set of ANN. Extracted traffic information can be utilized for developing quantitative database of loading effect. Also, it can contribute to estimate fatigue lift or current health condition, and design truck can be revised based on the database reflecting recent trend of traffic.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1487-1501
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• 2011

Recently, fast-growing up railway transportations. Because, regional traffic congestion problem solving and a period of rapid expansion to meet the demand of industries. In addition the government also suggest to new paradigm for the future 'Low Carbon, Green Growth' is presented as a new national vision. To meet the social needs and the time demands, Last of the railway increase very long tunnels and huge deep tunnels. Especially this trend accelerated high speed up in the tunnel, the revision of design criteria and research challenges are being actively improved. Mainly in the tunnel cross-section was under the control of the vehicle train speed 150km/hr by the construction of the vehicle cross-section of the tunnel. More than 200km/hr rail tunnel depending on the vehicle's speed caused the tunnel to the pressure fluctuations will be governed by the aerodynamic changes. Considering the economy to ensure the optimum cross-section of the railway tunnel to the description scheme is selected cross-section of the railway tunnel to determine the size domestic or international railway tunnel for the elements((based on fast Algorithm design criteria, the center line spacing, streetcar line, cross-sectional shape, sectoral issues, such as interface and aerodynamics) based on design practices and to review results. In this study, to propose guidelines depending on the size of a railway tunnel cross section for the size of the determining reasonable factors when designing the railway tunnel and cost-effective standards guidelines.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.1-9
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• 2016

In accordance with the increased political and social utilization of social media, demands on online trend analysis and monitoring technologies based on social bigdata are also increasing rapidly. In this paper, we define 'risk' as issues which have probability of turn to negative public opinion among big social issues and classify their types in details. To define risk types, we conduct a complete survey on news documents and analyzed characteristics according to issue domains. We also investigate cross-medias analysis to find out how different public media and personalized social media. At the result, we define 58 risk types for 6 domains and developed automatic classification model based on machine learning algorithm. Based on empirical experiments, we prove the possibility of automatic detection for social issue risk in social media.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.885-894
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• 2015

It is necessary to forecast the electricity demand for reliable and effective operation of the power system. In this study, we try to categorize a functional data, the mean curve in accordance with the time of daily power demand pattern. The data were collected between January 1, 2009 and December 31, 2011. And it were converted to time series data consisting of seasonal components and error component through log transformation and removing trend. Functional clustering by Ma et al. (2006) are applied and parameters are estimated using EM algorithm and generalized cross validation. The number of clusters is determined by classifying holidays or weekdays. Monday, weekday (Tuesday to Friday), Saturday, Sunday or holiday and season are described the mean curve of daily power demand pattern.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1399-1410
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• 2015

Gartner is requiring companies to considerably change their survival paradigms insisting that companies need to understand and provide again the upcoming era of data competition. With the revealing of successful business cases through statistic algorithm-based predictive analytics, also, the conversion into preemptive countermeasure through predictive analysis from follow-up action through data analysis in the past is becoming a necessity of leading enterprises. This trend is influencing security analysis and log analysis and in reality, the cases regarding the application of the big data analysis framework to large-scale log analysis and intelligent and long-term security analysis are being reported file by file. But all the functions and techniques required for a big data log analysis system cannot be accommodated in a Hadoop-based big data platform, so independent platform-based big data log analysis products are still being provided to the market. This paper aims to suggest a framework, which is equipped with a real-time and non-real-time predictive analysis engine for these independent big data log analysis systems and can cope with cyber attack preemptively.

• Spatial Information Research
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• v.2 no.1
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• pp.3-14
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• 1994

The external diseconomy has been accelerated by the megaspatial structure of metropolis such as Seoul Capital Region(below SCR), Korea in which the more than 10 million populations inhabit. The main course for It could be elaborated by the overconcentration of the urban and regional function of various kinds. The study is performed to analyze quantitatively the status quo of the region as described above and proceed into forecasting the future population trend, the land use at location for the increment of regional population and to set the location of new towns in Seoul Capital Region System projected by the methods in computer algorithm of descriptive models such as the simple and multiple regress ion analysis models, the gravity model and the facility location on a plane model analysis. The goal and object ive of the metropolitan planning are to decentralize the regional growth management to the optimum degree, which will not hinder the economic growth of the region, but the result of the study is that we can not discourage the functional concentration of Seoul Capital Region and, we have to provide the region with the appropriate new towns.

• The Journal of Bigdata
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• v.1 no.2
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• pp.9-19
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• 2016

Since ICT convergence became a major issue, German government has carried forward a policy 'Industry 4.0' that triggered ICT convergence with manufacturing. Now this trend gets into our stride. From this facts, we can expect great leap up to quality perfection in low cost. Recently Korean government also enforces policy with 'Manufacturing 3.0' for upgrading Korean manufacturing industry with being accelerated by many related technologies. We, in the paper, developed a custom-made operational big data analysis platform for the implementation of operational intelligence to improve industry capability. Our platform is designed based on spring framework and web. In addition, HDFS and spark architectures helps our system analyze massive data on the field with streamed data processed by process mining algorithm. Extracted knowledge from data will support enhancement of manufacturing performance.

• The Korean Journal of Nuclear Medicine Technology
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• v.20 no.2
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• pp.69-74
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• 2016

Purpose Using a regression formula of the trendline near the coefficient of determination (R2) "1" by substituting the dependent variable of the standard curve to calculate the values of the independent variable. To determine the suitability of a regression equation by comparing the difference between the independent variables of the standard curve and the predicted independent variables. Materials and Methods Perkin Elmer Gamma-Counter machine was used for Standard curve of regression methods. TSH. TG-Ag (Thyroglobulin Antigen), Insulin that used materials and method test to compare the result from the Excel trendline of the regression formula. Results Each of the value of coefficient of determination ($R^2$) and Trendline $R^2=1$, Polynomial Trendline for TSH, $R^2=1$, Polynomial Trendline for TG-Ag, $R^2=0.9994$, Polynomial Trendline for Insulin. Conclusion We confirmed that IRMA immune method is found to the nearest trends elected a standard curve using polynomial trendline. The independent variables to predict the trend by using a polynomial trendline formula containing the error was a limitation.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.637-651
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• 2011

This study presents a rule-based urban image classification method for time series analysis of changes in the vicinity of Asan-si and Cheonan-si in Chungcheongnam-do, using Landsat satellite images (1991-2006). The area has been highly developed through the relocation of industrial facilities, land development, construction of a high-speed railroad, and an extension of the subway. To determine the yearly changing pattern of the urban area, eleven classes were made depending on the trend of development. An algorithm was generalized for the rules to be applied as an unsupervised classification, without the need of training area. The analysis results show that the urban zone of the research area has increased by about 1.53 times, and each correlation graph confirmed the distribution of the Built Up Index (BUI) values for each class. To evaluate the rule-based classification, coverage and accuracy were assessed. When Optimal allowable factor=0.36, the coverage of the rule was 98.4%, and for the test using ground data from 1991 to 2006, overall accuracy was 99.49%. It was confirmed that the method suggested to determine the maximum allowable factor correlates to the accuracy test results using ground data. Among the multiple images, available data was used as best as possible and classification accuracy could be improved since optimal classification to suit objectives was possible. The rule-based urban image classification method is expected to be applied to time series image analyses such as thematic mapping for urban development, urban development, and monitoring of environmental changes.