• Geomechanics and Engineering
• /
• v.14 no.4
• /
• pp.337-344
• /
• 2018

The study of the mining effect influenced by a normal fault has great significance concerning the prediction and prevention of fault rock burst. According to the occurrence condition of a normal fault, the stress evolution of the working face and fault plane, the movement characteristics of overlying strata, and the law of fault slipping when the working face advances from footwall to hanging wall are studied utilizing UDEC numerical simulation. Then the inducing-mechanism of fault rock burst is revealed. Results show that in pre-mining, the in situ stress distribution of two fault walls in the fault-affected zone is notably different. When the working face mines in the footwall, the abutment stress distributes in a "double peak" pattern. The ratio of shear stress to normal stress and the fault slipping have the obvious spatial and temporal characteristics because they vary gradually from the higher layer to the lower one orderly. The variation of roof subsidence is in S-shape which includes slow deformation, violent slipping, deformation induced by the hanging wall strata rotation, and movement stability. The simulation results are verified via several engineering cases of fault rock burst. Moreover, it can provide a reference for prevention and control of rock burst in a fault-affected zone under similar conditions.

• Journal of Korea Society of Industrial Information Systems
• /
• v.11 no.5
• /
• pp.18-27
• /
• 2006

Rapid increase of the amount of music data demands for a new method that allows efficient similarity retrieval of music genre using audio features in music databases. To build this similarity retrieval, an indexing techniques that support audio features as a time-series pattern and data mining technologies are needed. In this paper, we address the development of a system that retrieves similar genre music based on the indexing techniques. We first propose the structure of content-based music genre retrieval system based on the time-series pattern index file and data mining technologies. In addition, we implement the time-series pattern index file using audio features and present performance analysis of the time-series pattern index file for similar genre retrieval. The experiments are performed on real data to verify the performance of the proposed method.

• Korean Journal of Acupuncture
• /
• v.32 no.2
• /
• pp.66-74
• /
• 2015

Background : Saam acupuncture were composed by applying the elemental concepts from the Five Phase theory - the relationships between the cycles such as Saeng(Sheng, 'nourishing' or 'creating') and Geuk(Ke, 'suppressing' or 'controlling') - onto the Five Phase points and 12 channels to compensate for the imbalance in each of the 12 main energy traits. Objective : The present study is aimed to find out the characteristics of Five Phase points pattern in Saam acupuncture. Methods : We analysed the characteristics of five elements of the Five Phase points in Korean medical texts such as Saamdoinchimguyogyeol, Dongeuibogam and Chimgugyeongheombang in mid Chosun Dynasty. Using non-negative factorization(NNMF) methods, we extracted the feature matrix of five elements of Five Phase points in each classic medical text. Results : In Saam acupuncture, two characteristics were most prominent: (1) "Self" component of Five elements, (2) "Mother" and "Grandmother" component of Five elements. Conclusions : Saam acupuncture used the combination of Five-Shu acupoint based on ZangFu pattern identification. Our findings suggest that grasping the characteristics of Five Phase points combinations can improve the understanding the selection of the relevant acupoints based on the ZangFu pattern identifications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.727-730
• /
• 2016

TSC means classifying time series data based on pattern. Time series data is quite common data type and it has high potential in many fields, so data mining and machine learning have paid attention for long time. In traditional approach, distance and dictionary based methods are quite popular. but due to time scale and random noise problems, it has clear limitation. In this paper, we propose a novel approach to deal with these problems with CNN and data mutation. CNN is regarded as proven neural network model in image recognition, and could be applied to time series pattern recognition by extracting pattern. Data mutation is a way to generate mutated data with different methods to make CNN more robust and solid. The proposed method shows better performance than traditional approach.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2003.10a
• /
• pp.193-199
• /
• 2003

The application of finding occurrences of a pattern that contains gaps includes information retrieval, data mining, and computational biology. As the biological sequences may contain errors, it is important to find not only the exact occurrences of a pattern but also approximate ones. In this paper we present an O(mnk$_{max}$/w) time algorithm for the approximate gapped pattern matching problem, where m is the length of the text, H is the length of the pattern, w is the word size of the target machine, and k$_{max}$ is the greatest error bound for subpatterns.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.11
• /
• pp.25-30
• /
• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.161-164
• /
• 2005

Data mining techniques can be applied to identify patterns of interest in the gene expression data. One goal in mining gene expression data is to determine how the expression of any particular gene might affect the expression of other genes. To find relationships between different genes, association rules have been applied to gene expression data set [1]. A notable limitation of association rule mining method is that only the association in a single profile experiment can be detected. It cannot be used to find rules across different condition profiles or different time point profile experiments. However, with the appearance of time-series microarray data, it became possible to analyze the temporal relationship between genes. In this paper, we analyze the time-series microarray gene expression data to extract the sequential patterns which are similar to the association rules between genes among different time points in the yeast cell cycle. The sequential patterns found in our work can catch the associations between different genes which express or repress at diverse time points. We have applied sequential pattern mining method to time-series microarray gene expression data and discovered a number of sequential patterns from two groups of genes (test, control) and more sequential patterns have been discovered from test group (same CO term group) than from the control group (different GO term group). This result can be a support for the potential of sequential patterns which is capable of catching the biologically meaningful association between genes.

• The KIPS Transactions:PartD
• /
• v.9D no.2
• /
• pp.251-258
• /
• 2002

There has been much research on data mining techniques for applying more advanced applications. However, most of those techniques has focused on transaction data rather than time series data. In this paper, we introduce a approach to convert time series data into virtual transaction data for more useful data mining applications. A virtual transaction is defined to be a collection of events that occur relatively close to each other. A virtual transaction generator uses time window or event window methods. Our approach based on time series data can be used with most conventional transaction algorithms without further modification.

• Convergence Security Journal
• /
• v.11 no.6
• /
• pp.3-8
• /
• 2011

Advanced Persistent Threat (APT), aims a specific business or political targets, is rapidly growing due to fast technological advancement in hacking, malicious code, and social engineering techniques. One of the most important characteristics of APT is persistence. Attackers constantly collect information by remaining inside of the targets. Enterprise Security Management (EMS) system can misidentify APT as normal pattern of an access or an entry of a normal user as an attack. In order to analyze this misidentification, a new system development and a research are required. This study suggests the way of forecasting APT and the effective countermeasures against APT attacks by categorizing misidentified data in data-mining through threshold ratings. This proposed technique can improve the detection of future APT attacks by categorizing the data of long-term attack attempts.

• Industrial Engineering and Management Systems
• /
• v.15 no.1
• /
• pp.110-121
• /
• 2016

With the growing interest in skincare and maintenance, there are increasing numbers of studies on the classification of skin type and the factors influencing each type. This study presents a novel methodology by using data mining, for the determination of the relationships between skin type, lifestyle, and patterns of cosmetic utilization. Eight skin-specific factors, which are moisture, sebum in U-zone (both cheeks), sebum in T-zone (forehead, nose, and chin), pore, melanin, wrinkle, acne, hemoglobin, were measured in 1,246 subjects living in South Korea, in conjunction with a questionnaire survey analyzing their lifestyles and pattern of cosmetic utilization. Using various multivariate statistical methods and data mining techniques, we classified the skin types based on the skin-specific values, determined the relationship between skin type and lifestyle, and accordingly sorted the subjects into clusters. Logistic regression analysis revealed gender-related differences in the skin; therefore, separate analyses were performed for males and females. Using the Gaussian Mixture Modeling (GMM) technique, we classified the subjects based on skin type (two male and four female). Using the ANOVA and decision tree techniques, we attempted to characterize the relationship between each skin type and the lifestyles of the subjects. Menstruation, eating habits, stress, and smoking were identified as the major factors affecting the skin.