• Computers and Concrete
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• v.34 no.2
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• pp.231-245
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• 2024

A 3D discrete element model integrating the rough surface contact concept with the flat-joint model is suggested to examine the mechanical characteristics of the interfacial transition zone (ITZ) in concrete. The essential components of our DEM procedure include the calculation of the actual contact area in an element contact-pair related to the bonded factor using a Gaussian probability distribution of asperity height, as well as the determination of the contact probability-relative displacement form using the least square method for further computing the force-displacement of ITZs. The present formulations are implemented in MUSEN, an open source development environment for discrete element analysis that is optimized for high performance computation. The model's meso-parameters are calibrated by using uniaxial compression and splitting tensile simulations, as well as laboratory tests of concrete from the literature. The present model's DEM predictions accord well with laboratory experimental tests of pull-out concrete specimens published in the literature.

• Journal of the Korean Society for information Management
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• v.32 no.2
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• pp.87-103
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• 2015

It is useful to find something valuable for researching through literature based discovery. Swanson's ABC model, known as literature based discovery, suggests the relationship between entities undiscovered yet. This study tries to find the valid relationship between entities by referring to citation which connects articles on similar topic. We collect citation from references in articles, and extract important concepts in titles and abstracts through text mining techniques. We reproduce the relationship between fish oil and Raynaud's disease, which is known as one of Swanson's works, and compare the results with entities identified from traditional approach.

• Geomechanics and Engineering
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• v.20 no.5
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• pp.447-459
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• 2020

Based on the literature statistical method, the paper publication status of the isolated working face and the distribution of the rockburst coal mine were obtained. The numerical simulation method is used to study the stress distribution law of working face under different mining range. In addition, based on the similar material simulation test, the overlying strata failure modes and the deformation characteristics of coal pillars during the mining process of the isolated working face with thick-hard key strata are analyzed. The research shows that, under the influence of the key strata, the overlying strata formation above the isolated working face is a long arm T-type spatial structure. With the mining of the isolated working face, a series of damages occur in the coal pillars, causing the key strata to break and inducing the rockburst occurs. Combined with the mechanism of rockburst induced by the dynamic and static combined load, the source of dynamic and static load on the isolated working face is analyzed, and the rockburst monitoring methods and the prevention and control measures are proposed. Through the above research, the occurrence probability of rockburst can be effectively reduced, which is of great significance for the safe mining of deep coal mines.

• Journal of the Korea Society of Computer and Information
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• v.21 no.6
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• pp.9-19
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• 2016

In finance literature, stock liquidity showing how stocks can be cashed out in the market has received rich attentions from both academicians and practitioners. The reasons are plenty. First, it is known that stock liquidity affects significantly asset pricing. Second, macroeconomic announcements influence liquidity in the stock market. Therefore, stock liquidity itself affects investors' decision and managers' decision as well. Though there exist a great deal of literature about stock liquidity in finance literature, it is quite clear that there are no studies attempting to investigate the stock liquidity issue as one of decision making problems. In finance literature, most of stock liquidity studies had dealt with limited views such as how much it influences stock price, which variables are associated with describing the stock liquidity significantly, etc. However, this paper posits that stock liquidity issue may become a serious decision-making problem, and then be handled by using data mining techniques to estimate its future extent with statistical validity. In this sense, we collected financial data set from a number of manufacturing companies listed in KRX (Korea Exchange) during the period of 2010 to 2013. The reason why we selected dataset from 2010 was to avoid the after-shocks of financial crisis that occurred in 2008. We used Fn-GuidPro system to gather total 5,700 financial data set. Stock liquidity measure was computed by the procedures proposed by Amihud (2002) which is known to show best metrics for showing relationship with daily return. We applied five data mining techniques (or classifiers) such as Bayesian network, support vector machine (SVM), decision tree, neural network, and ensemble method. Bayesian networks include GBN (General Bayesian Network), NBN (Naive BN), TAN (Tree Augmented NBN). Decision tree uses CART and C4.5. Regression result was used as a benchmarking performance. Ensemble method uses two types-integration of two classifiers, and three classifiers. Ensemble method is based on voting for the sake of integrating classifiers. Among the single classifiers, CART showed best performance with 48.2%, compared with 37.18% by regression. Among the ensemble methods, the result from integrating TAN, CART, and SVM was best with 49.25%. Through the additional analysis in individual industries, those relatively stabilized industries like electronic appliances, wholesale & retailing, woods, leather-bags-shoes showed better performance over 50%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.160-161
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• 2017

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language Opinion mining and sentiment analysis(OMSA) as a research discipline has emerged during last 15 years and provides a methodology to computationally process the unstructured data mainly to extract opinions and identify their sentiments. The relatively new but fast growing research discipline has changed a lot during these years. This paper presents a scientometric analysis of research work done on OMSA during 2007-2016. For the literature analysis, research publications indexed in Web of Science (WoS) database are used as input data. The publication data is analyzed computationally to identify year-wise publication pattern, rate of growth of publications, research areas. More detailed manual analysis of the data is also performed to identify popular approaches (machine learning and lexcon-based) used in these publications, levels (documents, sentences or aspect-level) of sentiment analysis work done and major application areass of OMSA.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.45-55
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• 2015

In this paper, we consider curriculum mining as an application of process mining in the domain of education. The basic objective of the curriculum mining is to construct a registration pattern model by using logs of registration data. However, subject registration patterns of students are very unstructured and complicated, called a spaghetti model, because it has a lot of different cases and high diversity of behaviors. In general, it is typically difficult to develop and analyze registration patterns. In the literature, there was an effort to handle this issue by using clustering based on the features of students and behaviors. However, it is not easy to obtain them in general since they are private and qualitative. Therefore, in this paper, we propose a new framework of curriculum mining applying K-means clustering based on subject attributes to solve the problems caused by unstructured process model obtained. Specifically, we divide subject's attribute data into two parts : categorical and numerical data. Categorical attribute has subject name, class classification, and research field, while numerical attribute has ABEEK goal and semester information. In case of categorical attribute, we suggest a method to quantify them by using binarization. The number of clusters used for K-means clustering, we applied Elbow method using R-squared value representing the variance ratio that can be explained by the number of clusters. The performance of the suggested method was verified by using a log of student registration data from an 'A university' in terms of the simplicity and fitness, which are the typical performance measure of obtained process model in process mining.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.344-345
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• 2017

Opinion mining and sentiment analysis(OMSA) as a research discipline has emerged during last 15 years and provides a methodology to computationally process the unstructured data mainly to extract opinions and identify their sentiments. The relatively new but fast growing research discipline has changed a lot during these years. This paper presents a scientometric analysis of research work done on OMSA during 2007-2016. For the literature analysis, research publications indexed in Web of Science (WoS) database are used as input data. The publication data is analyzed computationally to identify year-wise publication pattern, rate of growth of publications, research areas.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2002.06a
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• pp.25-33
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• 2002

Recent developments in literature data mining for biology call for the design of a common framework that can be used to assess the performance of the reported systems in a fair and objective way. In this talk, we present an on-going effort to make it possible, in the form of challenges in the extraction of biological pathways and in the ontology construction. We are currently making this effort jointly with Lynette Hirschman (MITRE), Junichi Tsujii (University of Tokyo), Limsoon Wong (KRDL), and Cathy Wu (Georgetown University)

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.214-215
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• 2022

This study analyzed the development stage and change management necessity of the construction IT system through existing research and literature review, and used WordCloud, one of the text mining techniques, to analyze current construction trends and major issues. The necessity of change management is derived by using existing research literature and construction-related social issues as analysis data.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.803-810
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• 2020

This objective of this study is to enrich the literature by the debt ratio and enterprise performance of Joint stock companies of Vietnam National Coal and Mineral Industries Holding Corporation Limited (Vinacomin). The debt ratio is an important index of capital structure, and it influences and decides the enterprise performance. Therefore, the determination of reasonable debt ratio level is beneficial to the stable operation of Vinacomin's enterprises. Based on the research conclusion about the effect on capital structure of debt ratio from domestic and foreign scholar, collecting data from 2014-2018 of Vinacomin's enterprises as a research sample, the article conducts research on the relationship between debt ratio and business performance of Vinacomin, as measured by return on total Assets. In addition, the study uses free cash flow, company size, growth opportunity, investment opportunities, operating costs to sales ratio as control variables.The study shows the debt ratio of Joint stock companies of Vietnam National Coal and Mineral Industries Holding Corporation Limited has a negative effect on the enterprise performance. Furthermore, the research results of the article are references for Vinacomin' enterprises in the course of production and business activities, determining a reasonable debt ratio, and improving the operational performance of enterprises.