• Economic and Environmental Geology
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• v.17 no.4
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• pp.245-258
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• 1984

Geoje copper ore deposits are fissure filled copper veins which developed in late Cretaceous pyroclastics, andesite and shale. Mineral paragenesis reveals a division of the hydrothermal mineralization into three stages: Stage I, deposition of pyrite, magnetite, specularite, quartz and chlorite; Stage II, deposition of chalcopyrite, sphalerite, galena, tetrahedrite, aikinite, cosalite, electrum, quartz and chlorite; Stage III, deposition of barren calcite. Filling temperatures of fluid inclusions in quartz of stage I range from 171 to $282^{\circ}C$ whereas fluid inclusions in quartz and sphalerite of stage II range from 213 to $262^{\circ}C$ and from 186 to $301^{\circ}C$ respectively. Salinities of fluid inclusions in quartz of stage I range from 5.2 to 11.2 weight percent equivalent to NaCl. Salinities of fluid inclusions in quartz and sphalerite of stage II range from 6.6 to 10.9 and from 7.1 to 14.4 weight percent equivalent NaCl. Salinities of ore fluid during major mineralization stage in this deposits reveal nearly the same ranges as those of many copper deposits in Koseong copper mining district which located about 30km apart from Geoje mine. But filling temperatures of fluid inclusions formed during major copper mineralization stage in this deposits show slightly lower than those of copper deposits in Koseong copper mining district.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.1-9
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• 2015

In most techniques of information differentiating for data stream mining, they give larger weight to the information generated in recent compared to the old information. However, there can be important one among the old information. For example, in case of a person was a regular customer in a retail store but has not come to the store in recent, old information with the shopping record of the person can be importantly used in a target marketing for increasing sales. In this paper, highly attention itemsets(HAI) are defined, which mean the itemsets generated in the past frequently but not generated in recent. In addition, a twao-way decay mechanism and a data stream mining method for finding HAI are proposed.

• Korean Journal of Clinical Pharmacy
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• v.27 no.4
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• pp.221-227
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• 2017

Background: As personalized healthcare industry has attracted much attention, big data analysis of healthcare data is essential. Lots of healthcare data such as product labeling, biomedical literature and social media data are unstructured, extracting meaningful information from the unstructured text data are becoming important. In particular, text mining for adverse drug reactions (ADRs) reports is able to provide signal information to predict and detect adverse drug reactions. There has been no study on text analysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. Methods: Expert opinion text of KAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the whole text, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis are performed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. Results: Among total 90,522 reports, 120 insulin ADR report and 858 tramadol ADR report were analyzed. The ADRs such as dizziness, headache, vomiting, dyspepsia, and shock were ranked in order in the insulin data, while the ADR symptoms such as vomiting, 어지러움, dizziness, dyspepsia and constipation were ranked in order in the tramadol data as the most frequently used keywords. Conclusion: Using text mining of the expert opinion in KIDS-KD, frequently mentioned ADRs and medications are easily recovered. Text mining in ADRs research is able to play an important role in detecting signal information and prediction of ADRs.

• Journal of Microbiology and Biotechnology
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• v.30 no.9
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• pp.1310-1320
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• 2020

Low-quality soil for land reuse is a crucial problem in vegetation quality and especially to waste disposal sites in mining areas. It is necessary to find suitable materials to improve the soil quality and especially to increase soil microbial diversity and activity. In this study, pot experiments were conducted to investigate the effect of a mixed material of humic acid, super absorbent polymer and biochar on low-quality soil indexes and the microbial community response. The indexes included soil physicochemical properties and the corresponding plant growth. The results showed that the mixed material could improve chemical properties and physical structure of soil by increasing the bulk density, porosity, macro aggregate, and promote the mineralization of nutrient elements in soil. The best performance was achieved by adding 3 g·kg^-1 super absorbent polymer, 3 g·kg^-1 humic acid, and 10 g·kg^-1 biochar to soil with plant total nitrogen, dry weight and height increased by 85.18%, 266.41% and 74.06%, respectively. Physicochemical properties caused changes in soil microbial diversity. Acidobacteria, Bacteroidetes, Chloroflexi, Cyanobacteria, Firmicutes, Nitrospirae, Planctomycetes, and Proteobacteria were significantly positively correlated with most of the physical, chemical and plant indicators. Actinobacteria and Armatimonadetes were significantly negatively correlated with most measurement factors. Therefore, this study can contribute to improving the understanding of low-quality soil and how it affects soil microbial functions and sustainability.

• Geomechanics and Engineering
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• v.13 no.5
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• pp.861-879
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• 2017

Coal and gas outburst is a serious dynamic disaster that occurs during coal mining and threatens the lives of coal miners. Currently, coal and gas outburst is commonly predicted using single indicator and its critical value. However, single indicator is unable to fully reflect all of the factors impacting outburst risk and has poor prediction accuracy. Therefore, a more accurate prediction method is necessary. In this work, we first analyzed on-site impacting factors and precursors of coal and gas outburst; then, we constructed a Fisher discriminant analysis (FDA) index system using the gas adsorption index of drilling cutting ${\Delta}h_2$, the drilling cutting weight S, the initial velocity of gas emission from borehole q, the thickness of soft coal h, and the maximum ratio of post-blasting gas emission peak to pre-blasting gas emission $B_{max}$; finally, we studied an FDA-based multiple indicators discriminant model of coal and gas outburst, and applied the discriminant model to predict coal and gas outburst. The results showed that the discriminant model has 100% prediction accuracy, even when some conventional indexes are lower than the warning criteria. The FDA method has a broad application prospects in coal and gas outburst prediction.

• The Journal of Korean Society for School & Community Health Education
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• v.25 no.1
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• pp.29-41
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• 2024

Objectives: This study used an efficient data mining algorithm to explore association rules between the lifestyle risk behaviors and multimorbidity (having more than one chronic disease) in Korean adults. Methods: We used data from the 8th Korean National Health and Nutrition Examination Survey(2019-2020) for 7,609 adults aged ≥19 years. This study was undertaken where 6 lifestyle risk behaviors and 11 morbidities were analyzed using R and Rstudio for the ARM. Results: Among 117 association rules, combinations of hypertension, dyslipidemia and diabetes, hypertension were important role in inadequate sleep, physical inactivity and inadequate weight. Conclusion: The findings of this study are significant because they demonstrate the importance of lifestyle risk factors and the role of multiple chronic diseases using big data analytics such as association rule mining. We recommend developing selective and focused health education programs, such as exercise programs to address physical inactivity, dietary interventions to address inadequate weight, and mental health education programs to address inadequate sleep.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.113-122
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• 2009

Since the traditional pattern mining methods only probe unspecified moving patterns that seem to satisfy users' requests among diverse patterns within the limited scopes of time and space, they are not applicable to problems involving the mining of optimal moving patterns, which contain complex time and space constraints, such as 1) searching the optimal path between two specific points, and 2) scheduling a path within the specified time. Therefore, in this paper, we illustrate some problems on mining the optimal moving patterns with complex time and space constraints from a vast set of historical data of numerous moving objects, and suggest a new moving pattern mining method that can be used to search patterns of an optimal moving path as a location-based service. The proposed method, which determines the optimal path(most frequently used path) using pattern frequency retrieved from historical data of moving objects between two specific points, can efficiently carry out pattern mining tasks using by space generalization at the minimum level on the moving object's location attribute in consideration of topological relationship between the object's location and spatial scope. Testing the efficiency of this algorithm was done by comparing the operation processing time with Dijkstra algorithm and $A^*$ algorithm which are generally used for searching the optimal path. As a result, although there were some differences according to heuristic weight on $A^*$ algorithm, it showed that the proposed method is more efficient than the other methods mentioned.

• Geomechanics and Engineering
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• v.8 no.4
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• pp.477-493
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• 2015

In rock drilling, the most important characteristic to clarify is the wear of the drill bits. The reason that the rock drill bits fail with time is wear. In dry sliding contact adhesive wear deteriorates the materials in contact, quickly, and is the result of shear fracture in the momentary contact joins between the surfaces. This paper aims at presenting an overview of the assessment of WC/Co cemented carbide (CC) tricone bit in rotary drilling. To study wear of these bits, two approaches have been used in this research. Firstly, the new bits were weighted before they mounted on the drill rigs and also after completion their useful life to obtain bit weight loss percentage. The characteristics of the rock types drilled by using such this bit were measured, simultaneously. Alternatively, to measure contact wear, namely, matrix wear a micrometer has been used with a resolution of 0.02 mm at different direction on the tricone bits. Equivalent quartz content (EQC), net quartz content (QC), muscovite content (Mu), coarseness index (CI) of drill cuttings and compressive strength of rocks (UCS) were obtained along with thin sections to investigate mineralogical properties in detail. The correlation between effective parameters and bit wear were obtained as result of this study. It was observed that UCS shows no significant correlation with bit wear. By increasing CI and cutting size of rocks wear of bit increases.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• Geomechanics and Engineering
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• v.24 no.3
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• pp.281-293
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• 2021

Uncertainties in geomechanical input parameters which mainly related to inappropriate data acquisition and estimation due to lack of sufficient calibration information, have led wellbore instability not yet to be fully understood or addressed. This paper demonstrates a workflow of employing Quantitative Risk Assessment technique, considering these uncertainties in terms of rock properties, pore pressure and in-situ stresses to makes it possible to survey not just the likelihood of accomplishing a desired level of wellbore stability at a specific mud pressure, but also the influence of the uncertainty in each input parameter on the wellbore stability. This probabilistic methodology in conjunction with Monte Carlo numerical modeling techniques was applied to a case study of a well. The response surfaces analysis provides a measure of the effects of uncertainties in each input parameter on the predicted mud pressure from three widely used failure criteria, thereby provides a key measurement for data acquisition in the future wells to reduce the uncertainty. The results pointed out that the mud pressure is tremendously sensitive to UCS and SHmax which emphasize the significance of reliable determinations of these two parameters for safe drilling. On the other hand, the predicted safe mud window from Mogi-Coulomb is the widest while the Hoek-Brown is the narrowest and comparing the anticipated collapse failures from the failure criteria and breakouts observations from caliper data, indicates that Hoek-Brown overestimate the minimum mud weight to avoid breakouts while Mogi-Coulomb criterion give better forecast according to real observations.