• RED RIBBON
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• s.55
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• pp.12-13
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• 2003

에이즈환자라고 하면 간병인을 구하기도 어려웠다. 감염인이라는 사실을 알리지 않고 간병인을 구했다가도 감염인인 것을 알고는 그만 두는 것도 문제였다. 비감염인이면서 감염인을 간병하겠다고 나서는 사람이 간혹 있을라치면 그 때는 간병비가 비감염인 간병보다 $50\%$정도 더 요구되었다.

• KOREAN POULTRY JOURNAL
• /
• v.24 no.6 s.272
• /
• pp.131-135
• /
• 1992

칼슘과 인은 동물체내에 존재하는 광물질로 칼슘이 대략 $1.6{\~}2.0{\%}$, 인이$0.9{\~}1.1{\%}$ 함유되어 있다. 칼슘과 인은 주로 뼈에 많이 함유되어 있는데 주로 $Ca_3(PO_4)_2$의 형태로 존재한다.

• Environmental engineer
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• s.58
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• pp.24-27
• /
• 1991

• Environmental engineer
• /
• s.171
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• pp.26-28
• /
• 2000

• Environmental engineer
• /
• s.36
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• pp.32-33
• /
• 1989

• Environmental engineer
• /
• s.5
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• pp.3-5
• /
• 1986

• Journal of the Korean GEO-environmental Society
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• v.2 no.4
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• pp.29-38
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• 2001

In this study, the fractional composition and the releasing of nutrients and metals from sediments of Boryung reservoir were investigated. Based on present study, the forms of sediment phosphorus of the four sites in reservoir were classified as adsorbed phosphorus, non apatite inorganic phosphorus(NAI-P), apatite phosphorus and residual phosphorus. Residual phosphorus showed the highest fration, NAI-P second, apatite phosphorus third and absorbed phosphorus fourth. The sedimentation rate of T-P and T-N in aerobic condition were $0.31mg/m^2{\cdot}day$ and $29mg/m^2{\cdot}day$. The sedimentation rate of T-P and T-N in anaerobic condition were $0.12mg/m^2{\cdot}day$ and $13.6mg/m^2{\cdot}day$. The releasing rate of T-P and T-N in anaerobic condition were $0.23mg/m^2{\cdot}day$ and $10.5mg/m^2{\cdot}day$.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.386-390
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• 2007

In this paper we present an Ontology Instance Management Tool. OntoManager is a user-friendly interactive ontology Instance management tool with webpage annotation tool and an image annotation tool. It supports the user with the task of creating and maintaining ontology-based OWL-markup, creating of OWL-instances, attributes and relationships. It include an ontology browser for the exploration of the ontology and instances and a HTML browser that will display the annotated parts of the text. And OntoManager is an image annotation tool that allows users to markup regions of an image with respect to concepts in an ontology. It provides the functionality to import images, ontologies, instance bases, perform markup, and export the resulting annotations to disk or the Web.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.857-868
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• 2014

Data mining is the representative analysis methodology in the era of big data, and is the process to analyze a massive volume database and summarize it into meaningful information. Association rule technique finds the relationship among several items in huge database using the interestingness measures such as support, confidence, lift, etc. But these interestingness measures cannot be used to establish a causality relationship between antecedent and consequent item sets. Moreover, we can not know association direction by them. This paper propose causally confirmed association thresholds to compensate for these problems, and then check the three conditions of interestingness measures. The comparative studies with basic association thresholds, causal association thresholds, and causally confirmed association thresholds are shown by simulation studies. The results show that causally confirmed association thresholds are better than basic and causal association thresholds.

• Journal of KIISE:Software and Applications
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• v.35 no.5
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• pp.335-346
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• 2008

Causal relations in ontology should be defined based on the inference types necessary to solve problems specific to application as well as domain. In this paper, we present a model to define and extract causal relations for application ontology for Question-Answering (QA) on fault-diagnosis of electronic devices. Causal categories are defined by analyzing generic patterns of QA application; the relations between concepts in the corpus belonging to the causal categories are defined as causal relations. Instances of casual relations are extracted using lexical patterns in the concept definitions of domain, and extended incrementally with information from thesaurus. On the evaluation by domain specialists, our model shows precision of 92.3% in classification of relations and precision of 80.7% in identifying causal relations at the extraction phase.