• Title/Summary/Keyword: 기술 분류

Search Result 6,587, Processing Time 0.044 seconds

Taxonomy of XML Document Types (XML 문서 타입의 분류)

  • Lee Jung-Won;Park Seung-Soo
    • Journal of KIISE:Databases
    • /
    • v.32 no.2
    • /
    • pp.161-176
    • /
    • 2005
  • oping and applying XML techniques. One key aspect of our taxonomy is the support of the credibility of the result by evaluating which XML document types can be processed by a method. Another key aspect is to provide a basis for determining which is the best for target XML document types. Application with preparations for XML document mining shows that our taxonomy may present XML document types to be able to consider during the preparation process and target XML document types to be used for experiments.

Recommendation System for Research Field of R&D Project Using Machine Learning (머신러닝을 이용한 R&D과제의 연구분야 추천 서비스)

  • Kim, Yunjeong;Shin, Donggu;Jung, Hoekyung
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.25 no.12
    • /
    • pp.1809-1816
    • /
    • 2021
  • In order to identify the latest research trends using data related to national R&D projects and to produce and utilize meaningful information, the application of automatic classification technology was also required in the national R&D information service, so we conducted research to automatically classify and recommend research field. About 450,000 cases of national R&D project data from 2013 to 2020 were collected and used for learning and evaluation. A model was selected after data pre-processing, analysis, and performance analysis for valid data among collected data. The performance of Word2vec, GloVe, and fastText was compared for the purpose of deriving the optimal model combination. As a result of the experiment, the accuracy of only the subcategories used as essential items of task information is 90.11%. This model is expected to be applicable to the automatic classification study of other classification systems with a hierarchical structure similar to that of the national science and technology standard classification research field.

Research on creating information map for water-friendly facilities based on RS/GIS (RS/GIS 기반 친수시설 정보맵 작성 연구)

  • Kim, Seong Jun;Kim, Chang Sung
    • Proceedings of the Korea Water Resources Association Conference
    • /
    • 2021.06a
    • /
    • pp.222-222
    • /
    • 2021
  • 도시 내 하천 친수공간은 레저 및 여가를 위한 공간과 더불어 자연경관 및 생태체험 등의 다목적으로 활용되어 지역사회에 있어 중요한 공간으로 활용되고 있다. 과거 4대강 사업으로 국가하천 내 휴식공간을 조성하였다. 그 후 친수지구 중에서 이용도가 저조한 곳을 해제하였으며, 현재 297개의 친수지구를 중점으로 관리하고 있다. 이러한 친수지구를 유지하는데 필요한 보수 비용들을 지자체에서 담당하고 있으나, 상당한 비용이 소요되므로 친수지구 지정 후 운영단계에서 지역 주민들의 특성 및 요구를 정확히 파악할 필요가 있다. 하천 친수공간에 대한 정보구축은 조사원 조사, 유지관리 기관 조사 등 인적 조사 방식을 통한 데이터 수집으로 많은 비용이 필요할 뿐만아니라 DB 갱신 부분에도 한계가 있다. 그러므로 본 연구에서는 RS/GIS를 기반으로 친수시설에 대한 정보맵 작성 방안 연구와 친수시설 뿐만 아니라 유역조사 시 하천공간에서 수행할 수 있는 기술에 대한 연구를 수행하였다. 연구대상지역은 대저생태공원과 삼락생태공원을 대상으로 선정하였다. 해당 지역 항공영상의 정합 및 전처리를 실시한 후 QGIS를 활용하여 LSMS(Large-Scale Mean Shift) 기법으로 시설물 분류를 실시하였다. 공원 내 친수시설 분류를 위해 공간 반경(Spatial radius)를 10 ~ 25까지 변화시키면서 최적 분류 결과를 도출하는 공간 반경을 찾았으며 친수시설 규모와 시설의 특성에 따라 공간 반경을 조절하여 친수시설 분류맵을 작성하였다. 친수지구 내 친수시설 분류맵과 친수지구 내 친수시설 현황 및 친수시설별 코드와, 위치정보(위도, 경도 및 표고), 면적 및 관리현황으로 분류하여 입력 할 수 있도록 하였다. 본 연구에서 구축한 친수시설 자동분류 알고리즘을 통해 전국 단위 통합 하천관리체계 구축 및 친수시설에 대한 정보맵을 작성할 수 있는 기반 마련이 가능할 것이다.

  • PDF

Improving learning outcome prediction method by applying Markov Chain (Markov Chain을 응용한 학습 성과 예측 방법 개선)

  • Chul-Hyun Hwang
    • The Journal of the Convergence on Culture Technology
    • /
    • v.10 no.4
    • /
    • pp.595-600
    • /
    • 2024
  • As the use of artificial intelligence technologies such as machine learning increases in research fields that predict learning outcomes or optimize learning pathways, the use of artificial intelligence in education is gradually making progress. This research is gradually evolving into more advanced artificial intelligence methods such as deep learning and reinforcement learning. This study aims to improve the method of predicting future learning performance based on the learner's past learning performance-history data. Therefore, to improve prediction performance, we propose conditional probability applying the Markov Chain method. This method is used to improve the prediction performance of the classifier by allowing the learner to add learning history data to the classification prediction in addition to classification prediction by machine learning. In order to confirm the effectiveness of the proposed method, a total of more than 30 experiments were conducted per algorithm and indicator using empirical data, 'Teaching aid-based early childhood education learning performance data'. As a result of the experiment, higher performance indicators were confirmed in cases using the proposed method than in cases where only the classification algorithm was used in all cases.

An Industry-Service Classification Development of 5G-based Autonomous Vehicle Applications (5G 기반 자율주행차 활용 산업-서비스 분류체계 개발)

  • Kim, Dong Ha;Park, Seon Jeong;Leem, Choon Seong
    • The Journal of Society for e-Business Studies
    • /
    • v.24 no.2
    • /
    • pp.91-112
    • /
    • 2019
  • In accordance with the advent of the 5th generation (5G) communication technology, we are having a change in various communication services which converge with high technologies related to the 4th Industrial Revolution. To utilize the upcoming 5G technology effectively and practically, we analyzed the technologies which have the most potential in convergence under the introduction of 5G technology and as a result, it is a autonomous vehicle that we'll discuss the core technologies of the 4th Industrial Revolution, which can lead to service activation by being combined with 5G technology. In addition, we developed an industry-service classification of 5G-based autonomous vehicle, we provided a basis for supporting a new business and its new business model converged with 5G communication technology. Furthermore, we will create a linkage matrix with the industry-service classification system of a new autonomous vehicles. This matrix will service as a guideline for industry-service development where autonomous vehicles can be utilized actively in the next generation.

Accuracy Evaluation of Supervised Classification about IKONOS Imagery using Mixed Pixels (혼합화소를 이용한 IKONOS 영상의 감독분류정확도 평가)

  • Lee, Jong-Sin;Kim, Min-Gyu;Park, Joon-Kyu
    • Journal of the Korea Academia-Industrial cooperation Society
    • /
    • v.13 no.6
    • /
    • pp.2751-2756
    • /
    • 2012
  • Selection of training set influences the classification accuracy in supervised classification using satellite imagery. Generally, if pure pixels which character of training set is clear were selected, whole accuracy is high while if mixed pixels were selected, accuracy is decreased because of low-resolution imagery or unclear distinguishment. However, it is too difficult to choose the pure pixels as training set actually. Accordingly, this study should be suggested the suitable classification method in case of mixed pixels choice. To achieve this, a few pure pixels were chosen as training set and classification accuracy was calculated which was compared with classification result using an equal number of mixed pixels. As a result, accuracy of SVM was the highest among the classification method using mixed pixels and it was a relatively small difference with the result of classification using pure pixels. Therefore, imagery classification using SVM is most suitable in the mixed area of construction and green because it is high possibility to choose mixed pixels as training set.

An Automatic Web Page Classification System Using Meta-Tag (메타 태그를 이용한 자동 웹페이지 분류 시스템)

  • Kim, Sang-Il;Kim, Hwa-Sung
    • The Journal of Korean Institute of Communications and Information Sciences
    • /
    • v.38B no.4
    • /
    • pp.291-297
    • /
    • 2013
  • Recently, the amount of web pages, which include various information, has been drastically increased according to the explosive increase of WWW usage. Therefore, the need for web page classification arose in order to make it easier to access web pages and to make it possible to search the web pages through the grouping. Web page classification means the classification of various web pages that are scattered on the web according to the similarity of documents or the keywords contained in the documents. Web page classification method can be applied to various areas such as web page searching, group searching and e-mail filtering. However, it is impossible to handle the tremendous amount of web pages on the web by using the manual classification. Also, the automatic web page classification has the accuracy problem in that it fails to distinguish the different web pages written in different forms without classification errors. In this paper, we propose the automatic web page classification system using meta-tag that can be obtained from the web pages in order to solve the inaccurate web page retrieval problem.