• Geomechanics and Engineering
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• v.18 no.6
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• pp.567-574
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• 2019

In order to study the effect of stress and water pressure on the permeability of fractured rock mass under three-dimensional stress conditions, a single fracture triaxial stress-seepage coupling model was established; By using the stress-seepage coupling true triaxial test system, large-scale rock specimens were taken as the research object to carry out the coupling test of stress and seepage, the fitting formula of permeability coefficient was obtained. The influence of three-dimensional stress and water pressure on the permeability coefficient of fractured rock mass was discussed. The results show that the three-dimensional stress and water pressure have a significant effect on the fracture permeability coefficient, showing a negative exponential relationship. Under certain water pressure conditions, the permeability coefficient decreases with the increase of the three-dimensional stress, and the normal principal stress plays a dominant role in the permeability. Under certain stress conditions, the permeability coefficient increases when the water pressure increases. Further analysis shows that when the gob floor rock mass is changed from high stress to unloading state, the seepage characteristics of the cracked channels will be evidently strengthened.

• Journal of Korean Society for Quality Management
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• v.49 no.1
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• pp.97-112
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• 2021

Purpose: The purpose of this paper is to analyze the technology convergence and its characteristics, focusing on the defense technologies in South Korea. Methods: Patents applied by the Agency for Defense Development (ADD) during 1979~2019 were utilized in this paper. Information Entropy analysis has been conducted on the patents to analyze the usability and potential for development. To analyze the trend of technology convergence in defense technologies, Social Network Analysis(SNA) and Association Rule Mining Analysis were applied to the co-occurrence networks of International Patent Classification (IPC) codes. Results: The results show that sensor, communication, and aviation technologies played a key role in recent development of defense science and technology. The co-occurrence network analysis also showed that the convergence has gradually enhanced over time, and the convergence between different technology sectors largely emerged, showing that the convergence has been diversified. Conclusion: By analyzing the patents of the defense technologies during the last 30 years, this study presents the comprehensive perspectives on trends and characteristics of technology convergence in defense industry. The results of this study are expected to be used as a guideline for decision making in the government's R&D policies in defence industry.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.123-140
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• 2023

In recent times, the global economy has been subject to increasing volatility, which has made it considerably more difficult to accurately predict economic indicators compared to previous periods. In response to this challenge, the present study conducts an exploratory investigation that aims to predict the Business Survey Index (BSI) by leveraging data mining techniques on both structured and unstructured data sources. For the structured data, we have collected information regarding foreign, domestic, and industrial conditions, while the unstructured data consists of content extracted from newspaper articles. By employing an extensive set of 44 distinct data mining techniques, our research strives to enhance the BSI prediction accuracy and provide valuable insights. The results of our analysis demonstrate that the highest predictive power was attained when using data exclusively from the t-1 period. Interestingly, this suggests that previous timeframes play a vital role in forecasting the BSI effectively. The findings of this study hold significant implications for economic decision-makers, as they will not only facilitate better-informed decisions but also serve as a robust foundation for predicting a wide range of other economic indicators. By improving the prediction of crucial economic metrics, this study ultimately aims to contribute to the overall efficacy of economic policy-making and decision processes.

• Journal of Distribution Science
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• v.22 no.3
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• pp.71-81
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• 2024

Purpose: This study examines the impact of inclusive policies and green transformation on regional development of mining areas. Research design, data and methodology: We designed and utilized a structured questionnaire to collect data from a population of 300 individuals. The questionnaire was disseminated through Google Forms and consisted of five questions for each research variable. A total of 210 respondents completed the questionnaire, yielding a response rate of 70%. The sample was diverse in terms of gender and educational level Of the 210 respondents, 113 were female (53.8%) and 97 were male (46.2%). In terms of educational background, the sample was composed as follows: 13 individuals with a Doctorate degree (6.2%), 56 with a Master's degree (26.7%), 97 with a Bachelor's degree (46.2%), 22 with a Diploma (10.5%), and 22 with a High School education (10.5%). Results: The research outcomes highlight the significant influence of inclusive policies on driving the Distribution of green economic transformation. Emphasizing the pivotal role of inclusive distribution strategies, especially within the context of mining areas, the study sheds light on their crucial contribution to fostering regional development. Conclusion: These findings hold valuable implications for policymakers, industry stakeholders, and academics promoting environmentally conscious economic transformations.

• Geomechanics and Engineering
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• v.32 no.4
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• pp.375-385
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• 2023

There are many joint fissures distributed in the engineering rock mass. In the process of geological history, the underground rock mass undergoes strong geological processes, and undergoes complex geological processes such as fracture breeding, expansion, recementation, and re-expansion. In this paper, the damage-stick-slip process (DSSP), an analysis model used for rock mass failure slip, was established to examine the master control and time-dependent mechanical properties of the new and primary fractures of a multi-fractured rock mass under the action of stress loading. The experimental system for the recemented multi-fractured rock mass was developed to validate the above theory. First, a rock mass failure test was conducted. Then, the failure stress state was kept constant, and the fractured rock mass was grouted and cemented. A secondary loading was applied until the grouted mass reached the intended strength to investigate the bearing capacity of the recemented multi-fractured rock mass, and an acoustic emission (AE) system was used to monitor AE events and the update of damage energy. The results show that the initial fracture angle and direction had a significant effect on the re-failure process of the cement rock mass; Compared with the monitoring results of the acoustic emission (AE) measurements, the master control surface, key blocks and other control factors in the multi-fractured rock mass were obtained; The triangular shaped block in rock mass plays an important role in the stress and displacement change of multi-fracture rock mass and the long fissure and the fractures with close fracture tip are easier to activate, and the position where the longer fractures intersect with the smaller fractures is easier to generate new fractures. The results are of great significance to a multi-block structure, which affects the safety of underground coal mining.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.06a
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• pp.91-100
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• 2003

In this paper we investigate the role that warfare played In the formation of the network of alliances between sites that are associated with the formation of the state in the Valley of Oaxaca, Mexico. A model of state formation proposed by Marcos and Flannery (1996) is used as the basis for an agent-based simulation model. Agents reside in sites and their actions are constrained by knowledge extracted from the Oaxaca Surface Archaeological Survey (Kowalewski 1989). The simulation is run with two different sets of constraint rules for the agents. The first set is based upon the raw data collected in the surface survey. This represents a total of 79 sites and constitutes a minimal level of warfare (raiding) in the Valley. The other site represents the generalization of these constraints to sites with similar locational characteristics. This set corresponds to 987 sites and represents a much more active role for warfare in the Valley. The rules were produced by a data mining technique, Decision Trees, guided by Genetic Algorithms. Simulations were run using the two different rule sets and compared with each other and the archaeological data for the Valley. The results strongly suggest that warfare was a necessary process in the aggregations of resources needed to support the emergence of the state in the Valley.

• 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.

• 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.

• Economic and Environmental Geology
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• v.28 no.3
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• pp.231-241
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• 1995

It is well known that hydroxyapatite [$Ca_{10}(PO_4)_6(OH)_2$] have an exchangeability for various heavy metal ions in aqueous solution. To evalute the feasibility of employing the synthetic hydroxyapatites as an eliminatable exchanger for environmentally noxious caions in waste water, the adsorption properties, the removal capacities and the selectivity of the apatites for various cations were investigated in more detailed. The heavy metal cations have been exchanged in calcium part of hydroxyapatite. The order of the degree of amount exchanged of the investigated cations is $Pb^{2+}>Cd^{2+}>Zn^{2+}>Ba^{2+}$. The molar ratios between released $Cd^{2+}$ ions and remeved divalent metal cations in the reacted solution with hydroxyapatite are roughly close to an integer 1.0, suggesting that an ion-exchange reaction could have played a major role in the removal of heavy metals rather then an adsorption effect. The exchangeability of the hydroxyapatite powder of Ca/P molar ratio 1.67, which have specipic surface area of $104.5m^2g^{-1}$, appeared to be better then $237.6{\mu}g$ per g for $Pb^{2+}$ ions. The removal capacity of the heavy metal ions varies directly as particle size of hydroxyapatites. All evidences obtained indicate that the synthesized hydroxyapatite powders by precipitation reaction method can be employed as an effective cation exchanger for eliminating noxious ions in waste water even in some improvemental.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.541-546
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• 2006

Data Mining is the process of finding hidden patterns inside a large data set. Cluster analysis has been used as a popular technique for data mining. It is a fundamental process of data analysis and it has been Playing an important role in solving many problems in pattern recognition and image processing. If fuzzy cluster analysis is to make a significant contribution to engineering applications, much more attention must be paid to fundamental decision on the number of clusters in data. It is related to cluster validity problem which is how well it has identified the structure that Is present in the data. In this paper, we design an adaptive data mining model using fuzzy performance measures. It discovers clusters through an unsupervised neural network model based on a fuzzy objective function and evaluates clustering results by a fuzzy performance measure. We also present the experimental results on newsgroup data. They show that the proposed model can be used as a document classifier.