• Computers and Concrete
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• v.18 no.6
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• pp.1135-1151
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• 2016

For investigating the effect of the pre-existing joints on the initiation pattern of hydraulic fractures, the numerical simulation of circular holes under internal hydraulic pressure with a different pattern of the joint distributions are conducted by using a finite element code, FRANC2D. The pattern of hydraulic fracturing initiation are scrutinized with changing the values of the joint length, joint offset angle. The hydraulic pressures with 70% of the peak value of borehole wall breakout pressure are applied at the similar models. The simulation results suggest that the opening-mode fracture initiated from the joint tip and propagated toward the borehole for critical values of ligament angle and joint offset angle. At these critical values, the crack grow length is influenced by joint ligament length. When the ligament length is less than 3 times the borehole diameter the crack growth length increases monotonically with increasing joint length. The opening-mode fracture disappears at the joint tip as the ligament length increases.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.182-183
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• 2018

Big data analysis is the process of discovering a meaningful correlation, pattern, and trends in large data set stored in existing data warehouse management tools and creating new values. In addition, by extracts new value from structured and unstructured data set in big volume means a technology to analyze the results. Most of the methods of Big data analysis technology are data mining, machine learning, natural language processing, pattern recognition, etc. used in existing statistical computer science. Global research institutes have identified Big data as the most notable new technology since 2011.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.29-32
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• 2006

In this paper, we propose a customer personalized system that presents the web pages to users which are customized to their individuality. It analyzes the action of users who visit the shopping mall, and preferentially supplies the necessary information to them. When they actually buy some items, it forecasts the users' access pattern to web site and their following purchasable items and improves their web pare on the bases of their individuality. It reasons the relation among the web documents and among the items by using the log data of web server and the purchase information of DB. For reasoning it employs Rough Set, which is a method that searches the association rule and offers most suitable cases by reduces cases. It reasons the web pages by considering the users' access pattern and time by using the web log and reasons the users' purchase pattern by using the purchase information of DB. On the basis of the relation among them, it appends the related web pages to link of users' web pages and displays the inferred goods on users' web pages.

• Advances in concrete construction
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• v.7 no.4
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• pp.241-247
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• 2019

In this paper the effect of bedding layer on the failure mechanism of rock in direct shear test has been investigated using particle flow code, PFC. For this purpose, firstly calibration of pfc2d was performed using Brazilian tensile strength. Secondly direct shear test consisting bedding layer was simulated numerically. Thickness of layers was 10 mm and rock bridge length was 10 mm, 40 mm and 60 mm. In each rock bridge length, bedding layer angles changes from $0^{\circ}$ to $90^{\circ}$ with increment of $15^{\circ}$. Totally 21 models were simulated and tested. The results show that two types of cracks develop within the model. Shear cracks and tensile cracks. Also failure pattern is affected by bridge length while shear strength is controlled by failure pattern. It's to be noted that bedding layer has not any effect on the failure pattern because the layer interface strength is too high.

• Ocean and Polar Research
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• v.36 no.4
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• pp.383-394
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• 2014

The surface sediments from the manganese nodule exploration area of Korea in the Clarion-Clipperton fracture zone were investigated to understand the resource potential of and emplacement mechanism for rare earth elements (REEs). The sediments are categorized into three lithological units (Unit I, II and III from top to bottom), but into two groups (Unit I/II and Unit III) based on the distribution pattern of REEs. The distribution pattern of REEs in Unit I/II is similar to that of Post-Archean Australian Shale (PAAS), but shows a negative Ce anomaly and enrichment in heavy REEs (HREEs). In Unit III, the HREE enrichment and Ce anomaly is much more remarkable than Unit I/II when normalized to PAAS, which are interpreted as resulting from the absorption of REEs from seawater by Fe oxyhydroxides that were transported along the buoyant plume from remotely-located hydrothermal vents. It is supported by the PAAS-normalized REE pattern of Unit III which is similar to those of seawater and East Pacific Rise sediments. Meanwhile, the PAAS-normalized REE pattern of Unit I/II is explained by the 4:1 mixing of terrestrial eolian sediment and Unit III from each, indicating the much smaller contribution of hydrothermal origin material to Unit I/II. The studied sediments have the potentiality of a low-grade and large tonnage REE resource. However, the mining of REE-bearing sediment needs a large size extra collecting, lifting and treatment system to dress and refine low-grade sediments if the sediment is exploited with manganese nodules. It is economically infeasible to develop low-grade REE sediments at this moment in time because the exploitation of REE-bearing sediments with manganese nodules increase the mining cost.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Journal of Korean Society for Quality Management
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• v.39 no.1
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• pp.98-108
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• 2011

Clustering is an useful method to classify objects into subsets that have some meaning in the context of a particular problem and has been applied in variety of fields, customer relationship management, data mining, pattern recognition, and biotechnology etc. This paper addresses the unknown K clustering problems and presents a new approach based on a coevolutionary algorithm to solve it. Coevolutionary algorithms are known as very efficient tools to solve the integrated optimization problems with high degree of complexity compared to classical ones. The problem considered in this paper can be divided into two sub-problems; finding the number of clusters and classifying the data into these clusters. To apply to coevolutionary algorithm, the framework of algorithm and genetic elements suitable for the sub-problems are proposed. Also, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. To analyze the proposed algorithm, the experiments are performed with various test-bed problems which are grouped into several classes. The experimental results confirm the effectiveness of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.1
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• pp.77-82
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• 2002

IDS has been studied mainly in the field of the detection decision and collecting of audit data. The detection decision should decide whether successive behaviors are intrusions or not , the collecting of audit data needs ability that collects precisely data for intrusion decision. Artificial methods such as rule based system and neural network are recently introduced in order to solve this problem. However, these methods have simple host structures and defects that can't detect changed new intrusion patterns. So, we propose the method using data mining that can retrieve and estimate the patterns and retrieval of user's behavior in the distributed different hosts.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.5
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• pp.894-899
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• 2011

Traditional Korean Medicine (TKM) has been actively researched through various approaches, including computational methods. This paper aims at providing an overview of domestic studies using the computational techniques in TKM field. A literature search was conducted in Korean publications using OASIS system, and major studies of data mining in TKM were identified. A review was presented in six diagnosis fields, including sasang constitution diagnosis, eight constitution diagnosis, tongue diagnosis, pattern diagnosis for stroke, diagnosis based on ontology, diagnosis for cause of disease. They collect clinical data themselves for experiments and primarily applied a algorithm of decision tree, SVM, neural network, case-based reasoning, ontology reasoning, discriminant analysis. In the future, there needs to identify which algorithm is suitable to diagnosis or other fields of TKM.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.1910-1915
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• 2008

The load analysis for the distribution system and facilities has relied on measurement equipment. Moreover, load monitoring incurs huge costs in terms of installation and maintenance. This paper presents a new model to analyze wherein facilities load under a feeder every 15 minutes using meter reading data that can be obtained from a power consumer every 15 minute or a month even without setting up any measuring equipment. After the data warehouse is constructed by interfacing the legacy system required for the load calculation, the relationship between the distribution system and the power consumer is established. Once the load pattern is forecasted by applying clustering and classification algorithm of temporal data mining techniques for the power customer who is not involved in Automatic Meter Reading(AMR), a single-line diagram per feeder is created, and power flow calculation is executed. The calculation result is analyzed using various temporal and spatial analysis methods such as Internet Geographic Information System(GIS), single-line diagram, and Online Analytical Processing (OLAP).