• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
• /
• 1998.11a
• /
• pp.119-122
• /
• 1998

Chemical analysis, measurement of pumping rates of 60 production wells and depth to water tables of 57 monitoring wells were carried to protect depletion of water resources and deterioration of water quality for the commercial portable ground-water. Borehole depth of production well averages 149m(31 boreholes), casing depth is 28m(29 boreholes), production rate is 70 $m^3$/day and depth to water table of monitoring well is 23.26m, respectively. The geology of 60 wells can be divided into Daebo granite(20), Okchun metarmorphic complex(18), Precambrian granitic gneiss(15), Bulguksa granite(4), Cheju volcanics(2), Cretaceous sedimentary rock(1). Average electrical conductivity and pH are 152$\mu$S/cm, and 7.35, respectively. The contents of major cation and anion predominantly $Ca^{2+}$>N $a^{+}$>M $g^{2+}$> $K^{+}$ and HC $O_{3}$$^{－}$ >S $O_{4}$$^{2-}$>Cl￣>F￣. Water type is predominantly $Ca^{2+}$-HC $O_{3}$$^{－}$(81.7%). It's possible that water chemistry of some wells were affected not only by the geology of boreholes penetrated but by inflows of surface water or shallow ground-water. Therefore, it is strongly necessary to steadily monitor the water quality and hydrogeologic conditins of production wells.ells.ls.ells.

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

• Journal of Korean Society of Environmental Engineers
• /
• v.32 no.12
• /
• pp.1102-1110
• /
• 2010

Tidal mudflat is a reservoir for diverse microbial resources. Microbial diversity in tidal mudflat sediment can be easily influenced by various human activities. It is necessary to take representative samples to monitor microbial diversity in tidal mudflat sediments. In this study, we analyzed the microbial diversity and chemical characteristics of vegetation and non-vegetation tidal mudflat regions in the Kangwha tidal mudflat using geo-statistics and data-mining. According to the geo-statistical analysis, most correlation range values for the vegetation region were smaller than those for the non-vegetation region, which suggested that the shorter number and interval of sampling are required for the vegetation tidal mudflat environment due to its higher degree of chemical and biological complexity and heterogeneity. The data-mining analysis suggested that the organic content and nitrate were the major environmental factors influencing microbial diversity in the vegetation region while pH and sulfate were the major influencing factors in the non-vegetation region. Using the geo-statistical and data-mining integration approach, we proposed a guideline for determining the sampling interval and number to monitor microbial diversity in tidal mudflat.

• Tunnel and Underground Space
• /
• v.6 no.1
• /
• pp.48-56
• /
• 1996

Presently, mobile diesel equipments contribute a lot to improve the economic feasibility of underground mining and tunneling operations. Even in Korea, a lot of diesel equipments are being applied to the undergroud workings already, but the technology of management and control of them is not sufficient yet. This study handled the production procedure, characteristics and evaluation technology on diesel particulate matte(DPM) which is known as a carcinogen. For easy measurement of DPM using laser dust monitor, conversion known as a carcinogen. For easy measurement of DPM using laser dust monitor, conversion factor(k) to gravimetric concentration has been acquired. It is appeared that the critical material among the diesel exhaust pollutants is becoming DPM instead of NOx from this year of 1996 according to the Government regulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.550-552
• /
• 2022

A video wall controller has a special type of multi-monitor that displays multiple monitors on a single large screen by arranging them consecutively. Operator maps and stores the video and monitor in advance. In a small system the mapping task of videos and monitors is simple. But as the number of monitors increases, the number of mapping cases increases, and thus work efficiency decreases. In this paper, we propose a association rule-based recommender system which help improve the efficiency of mapping task.

• Journal of Information Technology Services
• /
• v.18 no.1
• /
• pp.153-171
• /
• 2019

Competition among cities has become fierce with decentralization and globalization, and each city tries to establish a brand image of the city to build its competitiveness and implement its policies based on it. At this time, surveys, expert interviews, etc. are commonly used to establish city brands. These methods are difficult to establish as sampling methods an empirical component, the biggest component of a city brand. In this paper, therefore, based on the precedent research's urban brand measurement and components, the words representing each city image property were extracted and relocated to five indicators to form the evaluation index. The constructed indicators have been validated through the review of three experts. Through the index, we analyzed the brands of four cities, Ulsan, Incheon, Yeosu, and Gyeongju, and identified the factors by using Topic Modeling and Word Cloud. This methodology is expected to reduce costs and monitor timely in identifying and analyzing urban brand images in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.9
• /
• pp.2564-2588
• /
• 2024

Due to COVID19 pandemic, most of educational institutions and schools changed the traditional way of teaching to online teaching and learning using well-known Learning Management Systems (LMS) such as Moodle, Canvas, Blackboard, etc. Accordingly, LMS started to generate a large data related to students' characteristics and achievements and other course-related information. This makes it difficult to teachers to monitor students' behaviour and performance. Therefore, a need to support teachers with a tool alerting student who might be in risk based on their recorded activities and achievements in adopted LMS in the school. This paper focuses on the benefits of using recorded data in LMS platforms, specifically Moodle, to predict students' performance by analysing their behavioural data and engagement activities using data mining techniques. As part of the overall process, this study encountered the task of extracting and selecting relevant data features for predicting performance, along with designing the framework and choosing appropriate machine learning techniques. The collected data underwent pre-processing operations to remove random partitions, empty values, duplicates, and code the data. Different machine learning techniques, including k-NN, TREE, Ensembled Tree, SVM, and MLPNNs were applied to the processed data. The results showed that the MLPNNs technique outperformed other classification techniques, achieving a classification accuracy of 93%, while SVM and k-NN achieved 90% and 87% respectively. This indicates the possibility for future research to investigate incorporating other neural network methods for categorizing students using data from LMS.

• Quality Improvement in Health Care
• /
• v.8 no.2
• /
• pp.186-207
• /
• 2001

Background : This study presented an analysis of healthcare quality indicators using data mining and a development of decision support system for quality improvement. Method : Specifically, important factors influencing the key quality indicators were identified using a decision tree method for data mining based on 8,405 patients who discharged from a medical center during the period between December 1, 2000 and January 31, 2001. In addition, a decision support system was developed to analyze and monitor trends of these quality indicators using a Visual Basic 6.0. Guidelines and tutorial for quality improvement activities were also included in the system. Result : Among 12 selected quality indicators, decision tree analysis was performed for 3 indicators ; unscheduled readmission due to the same or related condition, unscheduled return to intensive care unit, and inpatient mortality which have a volume bigger than 100 cases during the period. The optimum range of target group in healthcare quality indicators were identified from the gain chart. Important influencing factors for these 3 indicators were: diagnosis, attribute of the disease, and age of the patient in unscheduled returns to ICU group ; and length of stay, diagnosis, and belonging department in inpatient mortality group. Conclusion : We developed a decision support system through analysis of healthcare quality indicators and data mining technique which can be effectively implemented for utilization review and quality management in a healthcare organization. In the future, further number of quality indicators should be developed to effectively support a hospital-wide Continuous Quality Improvement activity. Through these endevours, a decision support system can be developed and the newly developed decision support system should be well integrated with the hospital Order Communication System to support concurrent review, utilization review, quality and risk management.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2005.02a
• /
• pp.73-116
• /
• 2005

[ $\blacksquare$ ] The integration of radar interferometry(InSAR), GIS and GPS can be used as an operational technology to monitor ground deformation due to underground mining, earthquakes, and so on, at sub-centimetre of mm level accuracy; $\blacksquare$ Operational procedures and tools have been developed and tested at UNSW; and $\blacksquare$ We are very keen to promote the technology together with you all.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.3
• /
• pp.639-652
• /
• 2015

In today's manufacturing environment, the process data can be easily measured and transferred to a computer for analysis in a real-time mode. As a result, it is possible to monitor several correlated quality variables simultaneously. Various multivariate statistical process control (MSPC) procedures have been presented to detect an out-of-control event. Although the classical MSPC procedures give the out-of-control signal, it is difficult to determine which variable has caused the signal. In order to solve this problem, data mining and machine learning techniques can be considered. In this paper, we applied the technique of decision tree learning to the MSPC, and we did simulation for MSPC procedures to monitor the bivariate normal process means. The results of simulation show that the overall performance of the MSPC procedure using decision tree learning technique is similar for several values of correlation coefficient, and the accurate classification rates for out-of-control are different depending on the values of correlation coefficient and the shift magnitude. The introduced procedure has the advantage that it provides the information about assignable causes, which can be required by practitioners.