• 기술혁신연구
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• 제24권4호
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• pp.101-126
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• 2016

본 연구는 동시출현단어(co-word) 분석을 이용하여 기술경영 분야의 연구 주제 네트워크를 구축하고, 핵심 연구 주제 및 연구 주제 간 상호연관관계를 도출한다. 동시출현 빈도수의 정규화를 통해 키워드 간 유사성을 도출하여 무방향 네트워크를 분석하는 기존 연구들과는 달리 본 연구는 연관규칙분석(association rule)을 통해 키워드 간 신뢰도(confidence)를 도출하여 유방향 네트워크 분석을 수행한다. 2011~2014년 기술경영 분야 9개 국제 학술지에 게재된 2,456개의 논문의 저자키워드를 대상으로 빈도수 상위 200개 키워드를 추출하고, 주제(THEME), 방법(METHOD), 분야(FIELD)의 세 가지 유형으로 키워드를 분류한다. 각 유형별 일원(one-mode) 네트워크를 구축하여, 함께 많이 연구가 이루어진 키워드들을 찾아내고, 핵심 키워드를 도출한다. 또한 두 가지 유형의 키워드 간의 이원(two-mode) 네트워크를 구축하여, 연구 주제별로 함께 많이 활용된 방법 및 대상 분야를 탐색한다. 본 연구 결과는 최근 성숙기에 접어든 기술경영 분야의 연구 흐름 및 지식 구조를 키워드 수준에서 구체적으로 제시함으로써, 기술경영 분야 연구자들의 연구 주제 탐색 및 연구방향 설계에 활용될 수 있을 것으로 기대된다.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.21-32
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• 2024

This study analyzes the global literature on the black open-access phenomenon from 2011 to 2023. A bibliometric analysis was conducted using the Scopus database. The search strategy employed advanced queries with multiple synonymous terms to ensure exhaustive retrieval of relevant documents. The VOSviewer software was employed to visualize the co-occurrence networks. The findings reported 90 papers published during the study period. An evolving scholarly landscape was revealed, with heightened attention from 2016 onwards, peaking in 2017, 2021, and 2023. Articles constitute 83.3% of the total published documents. Singh and Srichandan are prolific authors, with 11.2% of the total publications. The United States contributes 18.9% of the papers, followed by India and Spain. Information Development and Scientometrics are pivotal journals in scholarly discussions about this scope, contributing 4.4% of publications. Co-occurrence network visualization revealed "Sci-Hub" and "open access" as the most used keywords in the global literature. The findings underscore the need for additional research to discover innovative business models to safeguard intellectual property rights while meeting researchers' evolving needs. The importance of this paper comes from being the first bibliometric study analyzing international literature related to this phenomenon, which provides a basis for future research efforts and policymaking.

• 한국항해항만학회지
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• 제46권3호
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• pp.243-250
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• 2022

본 연구에서는 인공지능을 활용한 영상분석 기술을 통해 영상 내의 월파를 실시간으로 감지하고 처오름 높이를 산정하는 기술을 제안하였다. 본 연구에서 제안한 월파 감지 시스템은 실시간으로 악기상 및 야간에도 월파를 감지할 수 있음을 확인하였다. 특히, 합성곱 신경망을 적용하여 실시간으로 CCTV 영상에서 파랑의 처오름을 감지하고 월파 여부를 판단하는 여과 알고리즘을 적용하여 월파의 발생 감지에 대한 정확성을 향상시켰다. AP50을 통해 월파 감지 결과의 정확도는 59.6%로 산정되었으며, 월파 감지 모델의 속도는 GPU 기준 70fps로 실시간 감지에 적합한 정확도와 속도를 보임을 확인하였다.

• Journal of Contemporary Eastern Asia
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• 제13권2호
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• pp.15-39
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• 2014

This research conducted a before/after naturalistic field experiment, with the early Arab Spring as the treatment. Compared to before the early Arab Spring, after the observation period the associations became stronger among the Web terms: 'Jihad, Sharia, innovation, democracy and civil society.' The Western concept of civil society transformed into a central Islamist ideological component. At another level, the inter-nation network based on Jihad-weighted Web hyperlinks between pairs of 46 Muslim Majority (MM) nations found Iran in one of the top two positions of flow betweenness centrality, a measure of network power, both before and after early Arab Spring. In contrast, Somalia, UAE, Egypt, Libya, and Sudan increased most in network flow betweenness centrality. The MM 'Jihad'-centric word co-occurrence network more than tripled in size, and the semantic structure more became entropic. This media "cloud" perhaps billowed as Islamist groups changed their material-level relationships and the corresponding media representations of Jihad among them changed after early Arab Spring. Future research could investigate various rival explanations for this naturalistic field experiment's findings.

• International Journal of Internet, Broadcasting and Communication
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• 제14권4호
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• pp.173-180
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• 2022

In the naked eye observation, the health of livestock can be controlled by the range of activity, temperature, pulse, cough, snot, eye excrement, ears and feces. In order to confirm the health of livestock, this paper uses calf face image data to classify the health status by image shape, color and texture. A series of images that have been processed in advance and can judge the health status of calves were used in the study, including 177 images of normal calves and 130 images of abnormal calves. We used GLCM calculation and Convolutional Neural Networks to extract 6 texture attributes of GLCM from the dataset containing the health status of calves by detecting the image of calves and learning the composite image of Convolutional Neural Networks. In the research, the classification ability of GLCM-CNN shows a classification rate of 91.3%, and the subsequent research will be further applied to the texture attributes of GLCM. It is hoped that this study can help us master the health status of livestock that cannot be observed by the naked eye.

• Asian Pacific Journal of Cancer Prevention
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• 제13권10호
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• pp.5171-5176
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• 2012

We here performed a systematic review of PBIC literature using terms analysis in a hope of both identifying potential trends and patterns and exploring methods leveraging traditional literature reviews in this specific area. Articles meeting inclusion criteria were retrieved from PUBMED and translated into dichotomized article records representing presence or non-presence of MeSH terms and a metric consisting of numbers of times of co-occurrence between all pairs of terms identified using a self-designed program. The occurrence of and relations among the terms were calculated and visualized using Excel2007 and UCINET respectively. A total of 1,742 terms were identified from 997 articles retrieved. Put in a descending order, the lines representing the times of term occurrence formed a typical hyperbolic curve; when plotted along the x-axis of whole MESH terms, the lines clustered within four specific regions. Comparison of term occurrence between 2002 and 2011 revealed priority changes in population and subjects (from general groups to priority groups), intervention approaches (from medicine to exercise and psychotherapy), methodology and techniques (from cohort studies to randomized controlled trials) and outcomes (from health and mental health to quality of life, depression etc.). Networks of the terms featured a number of closely linked groups of topics including method and questionnaires, therapy and outcomes, survival management, psychological assessment and intervention, behavioral intervention (individual and community oriented). Terms analysis revealed interesting trends and patterns about PBIC publications and both the analysis methods and findings have implications for future research and literature reviews.

• 인지과학
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• 제33권1호
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• pp.23-50
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• 2022

출처기억은 사물에 대한 일화기억 경험의 맥락을 표상하는 중요 인지기제이다. 출처기억에 대한 그 동안의 연구는 일상의 일화기억과 밀접한 뇌신경, 행동학적 중요 연구들의 기초가 되어 왔고, 특히 집행기능이나 연합기제와 같은 인지기제를 강조하여 왔다. 본 연구에서는 계량서지학적 방법론을 통해 1989년에서 2020년 사이 출간된 출처기억 연구논문들을 분석하였고 핵심어 공동출현 연결네트워크와 저자 인용 연결망을 기반으로 출처기억 연구의 발전 흐름에 대한 깊이 있는 개관을 제시한다. 계량서지학적 분석을 통해 출처기억 연구의 추세를 확인한 결과, 2010년을 기준으로 이전 연구들에서는 출처기억의 인지적 기제와 관련한 개별 특성을 살핀 반면, 최근의 연구들은 뇌신경영역 간 연결성 특징 분석을 통한 임상적 특징연구를 비롯해 사회신경과학적 주제에 이르는 영향을 탐색하였다. 핵심어 연결성 분석을 통해 노화, 집행기능이 주요 핵심 주제어로서 연구되었음을 확인하였고, 최근 아동발달심리학과 메타기억 등의 관점에서 연구되는 추세로 나아가고 있음을 보았다. 관련된 출처기억의 이론과 연구모델을 기반으로 심리과학분야 내외에서 인지적 향상의 발달과 관련된 연구가 지속될 가능성을 제안하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권4호
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• pp.238-246
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• 2011

This paper proposes a new semantic representation and its associated similarity measure. The representation expresses textual context observed in a context of a certain term as a network where nodes are terms and edges are the number of cooccurrences between connected terms. To compare terms represented in networks, a graph kernel is adopted as a similarity measure. The proposed representation has two notable merits compared with previous semantic representations. First, it can process polysemous words in a better way than a vector representation. A network of a polysemous term is regarded as a combination of sub-networks that represent senses and the appropriate sub-network is identified by context before compared by the kernel. Second, the representation permits not only words but also senses or contexts to be represented directly from corresponding set of terms. The validity of the representation and its similarity measure is evaluated with two tasks: synonym test and unsupervised word sense disambiguation. The method performed well and could compete with the state-of-the-art unsupervised methods.

• 대한간호학회지
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• 제41권5호
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• pp.623-632
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• 2011

Purpose: This study was done to explore the knowledge structure of Korean Nursing Science. Methods: The main variables were key words from the research papers that were presented in the Journal of Korean Academy of Nursing and journals of the seven branches of the Korean Academy of Nursing. English titles and abstracts of the papers (n=5,936) published from 1995 through 2009 were included. Noun phrases were extracted from the corpora using an in-house program (BiKE Text Analyzer), and their co-occurrence networks were generated via a cosine similarity measure, and then the networks were analyzed and visualized using Pajek, a Social Network Analysis program. Results: With the hub and authority measures, the most important research topics in Korean Nursing Science were identified. Newly emerging topics by three-year period units were observed as research trends. Conclusion: This study provides a systematic overview on the knowledge structure of Korean Nursing Science. The Social Network Analysis for this study will be useful for identifying the knowledge structure in Nursing Science.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.505-509
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• 2020

Twitter is a useful medium to grasp various damage situations that have occurred in society. However, it is a laborious task to spot damage-related topics according to time in the environment where information is constantly produced. This paper proposes a methodology of constructing a knowledge structure by combining the BERT-based classifier and the community detection techniques to discover the topics underlain in the damage information. The methodology consists of two steps. In the first step, the tweets are classified into the classes that are related to human damage, infrastructure damage, and industrial activity damage by a BERT-based transfer learning approach. In the second step, networks of the words that appear in the damage-related tweets are constructed based on the co-occurrence matrix. The derived networks are partitioned by maximizing the modularity to reveal the hidden topics. Five keywords with high values of degree centrality are selected to interpret the topics. The proposed methodology is validated with the Hurricane Harvey test data.