• Journal of Digital Contents Society
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• v.13 no.1
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• pp.21-29
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• 2012

In ubiquitous computing environment, rising large scale data exchange through sensor network with sharply growing the internet, the processing of the continuous stream data is required. Therefore there are some mining researches related to the extracting of frequent structures and the efficient query processing of XML stream data. In this paper, we propose a mining method to extract frequent structures of XML stream data in recent window based on the active window sliding using trigger rule. The proposed method is a basic research to control the stream data flow for data mining and continuous query by trigger rules.

• Journal of Microbiology and Biotechnology
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• v.30 no.6
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• pp.848-855
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• 2020

Land subsidence induced by underground coal mining leads to severe ecological and environmental problems. Arbuscular mycorrhizal fungi (AMF) have the potential to improve plant growth and soil properties. We aimed to assess the effects of AMF on the growth and soil properties of sea buckthorn under field conditions at different reclamation times. Inoculation with AMF significantly promoted the survival rate of sea buckthorn over a 50-month period, while also increasing plant height after 14, 26, and 50 months. Crown width after 14 months and ground diameter after 50 months of inoculation treatment were significantly higher than in the uninoculated treatment. AMF inoculation significantly improved plant mycorrhizal colonization rate and promoted an increase in mycelial density in the rhizosphere soil. The pH and electrical conductivity of rhizosphere soil also increased after inoculation. Moreover, after 26 and 50 months the soil organic matter in the inoculation treatment was significantly higher than in the control. The number of inoculated soil rhizosphere microorganisms, as well as acid phosphatase activity, also increased. AMF inoculation may play an active role in promoting plant growth and improving soil quality in the long term and is conducive to the rapid ecological restoration of damaged mining areas.

• 한국지구물리탐사학회:학술대회논문집
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• 1999.08a
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• pp.21-36
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• 1999

Recently resistivity survey is widely used for site investigations in the field of civil engineering. Since such application area requires accurate interpretation tools especially in the area of complicated geology and rough terrain topography, we developed a three-dimensional (3-D) resistivity inversion code, which can reconstruct real earth structures. Furthermore, the inversion code gives resolution-enhanced images by applying the ACB(Active Constraint Balancing) method. With the help of this inversion code, 3-D resistivity survey is now used as new techniques for site investigations in civil engineering problem. By imaging the 3-D resistivity distribution, we could get useful informations such as depth distribution of basement rock, distribution of weak zone, fractures and cavities which is crucial to civil engineers.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.221-226
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• 2024

This study used text mining, a big data analysis technique, to explore XR trends in South Korea. For this research, I utilized a big data platform called BigKinds. I collected data focusing on the keyword 'XR', spanning approximately 14 years from 2010 to 2024. The gathered data underwent a cleansing process and was analyzed in three ways: keyword trend analysis, relational analysis, and word cloud. The analysis identified the emergence and most active discussion periods of XR, with XR devices and manufacturers emerging as key keywords.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.575-588
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• 2012

Exploiting unlabeled text data with a relatively small labeled corpus has been an active and challenging research topic in text mining, due to the recent growth of the amount of biomedical literature. Biomedical named-entity recognition is an essential prerequisite task before effective text mining of biomedical literature can begin. This paper proposes an Active Co-Training (ACT) algorithm for biomedical named-entity recognition. ACT is a semi-supervised learning method in which two classifiers based on two different feature sets iteratively learn from informative examples that have been queried from the unlabeled data. We design a new classification problem to measure the informativeness of an example in unlabeled data. In this classification problem, the examples are classified based on a joint view of a feature set to be informative/non-informative to both classifiers. To form the training data for the classification problem, we adopt a query-by-committee method. Therefore, in the ACT, both classifiers are considered to be one committee, which is used on the labeled data to give the informativeness label to each example. The ACT method outperforms the traditional co-training algorithm in terms of f-measure as well as the number of training iterations performed to build a good classification model. The proposed method tends to efficiently exploit a large amount of unlabeled data by selecting a small number of examples having not only useful information but also a comprehensive pattern.

• Toxicological Research
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• v.29 no.1
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• pp.69-79
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• 2013

The purpose of this study was to investigate the current status of metal pollution in the sediment from rivers, lakes, and streams in active gold mining districts in Ghana. Two hundred and fifty surface sediment samples from 99 locations were collected and analyzed for concentrations of As, Hg, Cr, Co, Cu, Fe, Zn, Pb, Cd, Ni, and Mn using inductively coupled plasma-mass spectroscopy (ICP-MS). Metal concentrations were then used to assess the human health risks to resident children and adults in central tendency exposure (CTE) and reasonable maximum exposure (RME) scenarios. The concentrations of Pb, Cd, and As were almost twice the threshold values established by the Hong Kong Interim Sediment Quality Guidelines (ISQG). Hg, Cu, and Cr concentrations in sediment were 14, 20, and 26 times higher than the Canadian Freshwater Sediment Guidelines for these elements. Also, the concentrations of Pb, Cu, Cr, and Hg were 3, 11, 12, and 16 times more than the Australian and New Zealand Environment and Conservation Council (ANZECC) sediment guideline values. The results of the human health risk assessment indicate that for ingestion of sediment under the central tendency exposure (CTE) scenario, the cancer risks for child and adult residents from exposure to As were $4.18{\times}10^{-6}$ and $1.84{\times}10^{-7}$, respectively. This suggests that up to 4 children out of one million equally exposed children would contract cancer if exposed continuously to As over 70 years (the assumed lifetime). The hazard index for child residents following exposure to Cr(VI) in the RME scenario was 4.2. This is greater than the United States Environmental Protection Agency (USEPA) threshold of 1, indicating that adverse health effects to children from exposure to Cr(VI) are possible. This study demonstrates the urgent need to control industrial emissions and the severe heavy metal pollution in gold mining environments.

• Journal of the Korean earth science society
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• v.30 no.7
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• pp.921-925
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• 2009

It is introduced briefly to understand the overall state of mineral resources of Argentina profile of 30 major ore deposits and mining projects. Prospecting deposits are mostly concentrated on the Northwestern and Midwestern area in Argentina and this fact implies that deposit formation is strongly related to Andean Orogeny. Argentina is important mineral exporting country for copper, gold, silver, lead, zinc, lithium and boron. For a long-term strategy of fuel energy and mineral supply active cooperation of geological research and mine business between Korea and Argentina is needed.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1317-1326
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• 2017

The cascaded H-bridge (CHB) multilevel converter is a promising topology for large-scale photovoltaic (PV) systems. The output voltage over-modulation derived by the inter-module active power imbalance is one of the key issues for CHB PV systems. This paper proposed a dynamic power distribution strategy to eliminate the over-modulation in a CHB PV system by suitably redistributing the reactive power among the inverter modules of the CHB PV system. The proposed strategy can effectively extend the operating region of the CHB PV system with a simple control algorithm and easy implementation. Simulation and experimental results carried out on a seven-level CHB grid-connected PV system are shown to validate the proposed strategy.

• Journal of KIISE:Databases
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• v.29 no.1
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• pp.1-14
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• 2002

Association rule discovery is a method of mining for the associated item set on large databases based on support and confidence threshold. The discovered association rules can be applied to the marketing pattern analysis in E-commerce, large shopping mall and so on. The association rule discovery makes multiple scan over the database storing large transaction data, thus, the algorithm requiring very high overhead might not be useful in real-time association rule discovery in dynamic environment. Therefore this paper proposes an active candidate set management model based on trigger and incremental update mechanism to overcome non-realtime limitation of association rule discovery. In order to implement the proposed model, we not only describe an implementation model for incremental updating operation, but also evaluate the performance characteristics of this model through the experiment.

• Food Science of Animal Resources
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• v.33 no.4
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• pp.463-473
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• 2013

Through the development of efficient data collecting technologies like RFID, and inter-enterprise collaboration platforms such as web services, companies which participate in supply chains can acquire visibility over the whole supply chain, and can make decisions to optimize the overall supply chain networks and processes, based on the extracted knowledge from historical data collected by the visibility system. Although not currently active, the MeatWatch system has been developed, and is used in part for this purpose, in the imported beef distribution network in Korea. However, the imported beef distribution network is too complicated to analyze its various aspects using ordinary process analysis approaches. In this paper, we suggest a novel approach, called RFID-based supply chain process mining, to automatically discover and analyze the overall supply chain processes from the distributed RFID event data, without any prior knowledge. The proposed approach was implemented and validated, by using a case study of the imported beef distribution network in Korea. Specifically we demonstrated that the proposed approach can be successfully applied to discover supply chain networks from the distributed event data, to simplify the supply chain networks, and to analyze anomaly of the distribution networks. Such novel process mining functionalities can reinforce the capability of traceability services like MeatWatch in the future.