• Journal of Contemporary Eastern Asia
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• 제18권1호
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• pp.7-29
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• 2019

This study describes an approach for comparative bibliometric analysis of scientific publications related to (i) individual or several departments comprising a university, and (ii) broader integrated subject areas using multiple disciplinary schemes. It uses a custom dataset of scientific publications (ca. 15,000 articles and reviews, published during 2009-2013, and recorded in the Web of Science Core Collections) with author affiliations to the research departments, dedicated to science, technology, engineering, mathematics, and medicine (STEMM), of a comprehensive university. The dataset was subjected, at first, to the department level and discipline level analyses using the newly available KAKEN-L3 classification (based on MEXT/JSPS Grants-in-Aid system), hierarchical clustering, correspondence analysis to decipher the major departmental and disciplinary clusters, and visualization of the department-discipline relationships using two-dimensional stacked bar diagrams. The next step involved the creation of subsets covering integrated subject areas and a comparative analysis of departmental contributions to a specific area (medical, health and life science) using several disciplinary schemes: Essential Science Indicators (ESI) 22 research fields, SCOPUS 27 subject areas, OECD Frascati 38 subordinate research fields, and KAKEN-L3 66 subject categories. To illustrate the effective use of the science mapping techniques, the same subset for medical, health and life science area was subjected to network analyses for co-occurrences of keywords, bibliographic coupling of the publication sources, and co-citation of sources in the reference lists. The science mapping approach demonstrates the ways to extract information on the prolific research themes, the most frequently used journals for publishing research findings, and the knowledge base underlying the research activities covered by the publications concerned.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.214-222
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• 2022

Convolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.

• 과학교육연구지
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• 제46권3호
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• pp.293-311
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• 2022

본 연구는 분자생물학 기초 소양 함양을 위한 제한 효소 지도 작성의 탐구실험을 구성하였고, 일반계 고등학교 2학년을 대상으로 학생 중심의 제한 효소 지도 작성 탐구실험 수업을 통해 탐구실험 능력과 과학긍정경험에 미치는 영향을 분석하였다. 우선, 탐구실험 수업이 일반계 고등학생의 과학긍정경험에 있어 '그렇다' 이상으로 응답한 비율이 사전에 비해 사후에서 높은 것으로 보아 유의미하게 효과가 있는 것으로 나타났다. 제한 효소 지도 작성을 위한 연속의 5단계로 구성하여 적용한 결과 학생들의 과학 분야 학업에 대한 흥미 유발뿐만 아니라 학생들의 수업 참여도가 높아졌고, 구체적인 과학 학습 동기, 과학 진로 포부 및 체험 자료로도 효과적인 것으로 나타났다. 탐구실험 수업의 과학 환경이 학생들의 학습 태도와 과학긍정경험의 향상으로 이어져 수업의 집중과 수업의 질적 향상, 학생간의 적극적인 의사 소통과 상호 협력의 중요성에 긍정적인 영향을 끼쳤다. 또 탐구실험 수업이 진로 체험의 기회를 제공함으로써 분자생물학의 기초 소양 함양과 이공계 진학의 토대가 될 것이다.

• Journal of Smart Tourism
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• 제2권1호
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• pp.5-19
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• 2022

Smart tourism is seen as a revolution in the tourism industry, involving innovative and transformative theoretical-practical approaches for the sector. As a result of its application in the tourist context, benefits can be seen such as more sustainable practices, greater mobility and better accessibility in destinations, evolution of processes and experiences of tourists. Much of this is achieved through the support of technological solutions. However, despite the immense expectations, and the many researches carried out on it, a literature summary regarding the dimensions that can be observed in each application of this smart tourism has not yet been proposed. Therefore, supported by the PRISMA recommendation, this research proposed to carry out an integrative review of the literature on smart tourism (in its different levels of application, such as the city, the destination and the smart tourism region), with the objective of mapping the dimensions that underlie it. Thus, from an initial scope of 833 intellectual productions obtained, inputs were found for the dimensions in 363 of them after a thorough analysis. The compilation of data obtained from these productions supported the proposition of 14 operational dimensions of smart tourism, namely: collaboration, technology, sustainability, experience, accessibility, knowledge management, innovation management, human capital, marketing, customized services, transparency, safety, governance and mobility. With this set of dimensions, it is envisaged that the implementation of smart tourism projects can present more comprehensive and assertive results. In addition, shortcomings and opportunities for new research that support the evolution of the theory and practice of smart tourism are highlighted.

• 대한의용생체공학회:의공학회지
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• 제29권4호
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• pp.329-336
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• 2008

Recently, causality analysis of source time series extracted from EEG or MEG signals is becoming of great importance in human brain mapping studies and noninvasive diagnosis of various brain diseases. Two approaches have been widely used for the analyses: one is independent component analysis (ICA), and the other is multiple signal classification (MUSIC). To the best of our knowledge, however, any comparison studies to reveal the difference of the two approaches have not been reported. In the present study, we compared the performance of the two different techniques, ICA and MUSIC, especially focusing on how accurately they can estimate and separate various brain electrical signals such as linear, nonlinear, and chaotic signals without a priori knowledge. Results of the realistic simulation studies, adopting directed transfer function (DTF) and Granger causality (GC) as measures of the accurate extraction of source time series, demonstrated that the MUSIC-based approach is more reliable than the ICA-based approach.

• 로봇학회논문지
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• 제7권2호
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• pp.92-100
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• 2012

A map of complex environment can be generated using a robot carrying sensors. However, representation of environments directly using the integration of sensor data tells only spatial existence. In order to execute high-level applications, robots need semantic knowledge of the environments. This research investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The proposed system is decomposed into five steps: sequential LIDAR scan, point classification, ground detection and elimination, segmentation, and object classification. This method could classify the various objects in urban environment, such as cars, trees, buildings, posts, etc. The simple methods minimizing time-consuming process are developed to guarantee real-time performance and to perform data classification on-the-fly as data is being acquired. To evaluate performance of the proposed methods, computation time and recognition rate are analyzed. Experimental results demonstrate that the proposed algorithm has efficiency in fast understanding the semantic knowledge of a dynamic urban environment.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제15권5호
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• pp.385-389
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• 2009

최근 블로그 사용이 급증하면서 방대한 양의 블로그 자원으로부터 적합한 자원을 추출하여 사용자에게 제공하기 위한 연구들이 시도되었다. 이들 연구 중 OSEM(Ontology-based Semantic Blog Model)은 블로그 공간(Blogosphere) 내 지식베이스를 효과적으로 모델링하기 위해 이를 온톨로지로 정의한 모델이다. OSEM을 시맨틱 웹 환경에서 활용하기 위해 본 논문에서는 매핑 기법을 적용하여 OSEM의 지식베이스를 OWL로 변환하고, OWL로 변환된 온톨로지에 SWRL 추론과 SPARQL 질의를 적용하여 블로그 사용자에게 유용한 관심 커뮤니티를 추천하는 기법을 제안한다. 제안 기법은 OSEM 내 지식베이스를 OWL로 변환함으로써 시맨틱 웹 환경에서의 공유와 재사용을 가능하게 하고, 제안한 추론 기법을 적용하여 사용자 관점의 관심 블로그 커뮤니티 추천을 가능하도록 한다.

• 한국멀티미디어학회논문지
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• 제10권3호
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• pp.289-302
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• 2007

XML 문서 구조를 모르고서도 질의할 수 있는 CXQuery라는 질의 언어에 대한 기존의 질의 처리 기법은 관계형 데이터베이스를 사용하기 때문에 XML 문서 구조를 관계형 테이블에 매핑하는 문제와, 질의 처리시나 결과를 반환하기 위하여 테이블간의 조인 때문에 운영 상에 어려움을 지니고 있다. 본 논문에서는, 표준화가 진행 중인 XQuery 질의 처리 기법을 이용하기 위하여 CXQuery를 XQuery로 변환하는 변환기를 개발하였다. 이 변환기의 변환 속도는 질의 처리하는 전체 시간에 비해 무시할 정도의 짧은 시간이 걸린다. 또한 기존의 관계형 데이터베이스와 관계없이 XML 문서에 대해 직접적으로 질의 처리가 가능하도록 하며, 사용자는 CXQuery를 이용하여 문서 구조를 모르고서도 질의할 수 있도록 하는 장점을 갖는다.

• International Journal of Knowledge Content Development & Technology
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• 제11권3호
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• pp.29-44
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• 2021

The study provides a quantitative and qualitative description of global research in the domain of quantum machine learning (QML) as a way to understand the status of global research in the subject at the global, national, institutional, and individual author level. The data for the study was sourced from the Scopus database for the period 1999-2020. The study analyzed global research output (1374 publications) and global citations (22434 citations) to measure research productivity and performance on metrics. In addition, the study carried out bibliometric mapping of the literature to visually represent network relationship between key countries, institutions, authors, and significant keyword in QML research. The study finds that the USA and China lead the world ranking in QML research, accounting for 32.46% and 22.56% share respectively in the global output. The top 25 global organizations and authors lead with 35.52% and 16.59% global share respectively. The study also tracks key research areas, key global players, most significant keywords, and most productive source journals. The study observes that QML research is gradually emerging as an interdisciplinary area of research in computer science, but the body of its literature that has appeared so far is very small and insignificant even though 22 years have passed since the appearance of its first publication. Certainly, QML as a research subject at present is at a nascent stage of its development.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2009년도 추계학술발표대회
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• pp.801-802
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• 2009

Currently, XML and its schema language have become the standard for data representation and information exchange format on the current web. Unfortunately, problems happen when integrating different data sources since XML mainly supports the document structure but lack consideration on sharing knowledge of data. Meanwhile, Semantic Web technologies, such as Web Ontology Language (OWL), can include the structure as well as the semantics of the data. Therefore, finding a way to integrate XML data as OWL ontology receives a high interest nowadays. In this paper we present a mapping notation to convert XML Schema to OWL domain knowledge and an effective method to transform XML instances into OWL individuals. While keeping the XML original structure, our work also adds more semantics for the XML document. Moreover, whole of the transformation processes are done automatically without any user interference. Further, our transforming approach provides the solution for duplicate element names in XML document which has not mentioned in the previous work. Our results in existing OWL syntaxes can be loaded immediately by OWL editors and Semantic Web applications.