• 제목/요약/키워드: Six-Port

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한반도 연안역 표층퇴적물 내 총 수은 분포 특성 (Distribution of Total Mercury in Korean Coastal Sediments)

  • 조동진;최만식;김찬국
    • 한국해양학회지:바다
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    • 제23권2호
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    • pp.76-90
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    • 2018
  • 한반도 연안역 표층퇴적물 내 수은의 농도 분포 특성을 파악하기 위하여 수은의 배경농도를 산정하고 오염도를 평가하였으며, 분포를 조절하는 요인을 파악하였다. 표층퇴적물 내 수은 농도는 남해연안의 진해-마산만, 동해연안의 울산-온산만, 영일만에서 상당히 높게 나타났으며, 그 외 퇴적물은 Cs와 유사한 분포를 보이며 $0.21{\sim}39.5{\mu}g/kg$ ($13.6{\pm}7.80{\mu}g/kg$) 사이의 낮은 농도를 나타내었다. 국내 해저퇴적물 해양환경기준과 비교한 결과, 전 연안의 표층퇴적물 (n=282)의 8 %가 주의기준을 초과하였으며, 동해연안의 온산항 인근 해역 (n=6)에서 관리기준을 초과하였다. Cs에 대한 선형회귀선의 잔차분석을 통해 산정한 배경농도 (2.06Cs+1.75)를 이용하여 수은 농축도를 산정하였고, 이를 이용하여 농축 정도에 따른 조절요인을 살펴보았다. 수은 농축인자 <1.69 범위에서는 퇴적물의 입도, 1.69~4.03 범위는 Fe 산화수산화물 및 유기탄소가 좋은 관계성을 보여 주요 조절요인으로 판단되었다. 4.03~74.9 범위는 다른 금속들 (Cu, Zn, Pb)과 좋은 관계성을 보였으며, 동해연안의 고성, 속초, 울진 연안에서는 유기탄소가 주요 조절요인이었고, 영일만과 울산-온산만 (n=30)에서는 주변에 위치한 중화학 공업단지의 영향으로 금속입자의 직접적인 유입에 기인한다고 판단되었다. 또한 남해연안의 진해-마산만 시료의 경우에는 상대적으로 높은 황화물 형성과 관계하여 수은 농축이 일어나는 것으로 판단되었다.

키워드 자동 생성에 대한 새로운 접근법: 역 벡터공간모델을 이용한 키워드 할당 방법 (A New Approach to Automatic Keyword Generation Using Inverse Vector Space Model)

  • 조원진;노상규;윤지영;박진수
    • Asia pacific journal of information systems
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    • 제21권1호
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    • pp.103-122
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    • 2011
  • Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.