• 한국도서관정보학회지
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• 제36권4호
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• pp.77-98
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• 2005

이 연구의 목적은 유용한 지식포탈 구축을 위한 대안을 제시하기 위하여 판소리 도메인을 중심으로 토픽맵 시스템을 구축하고 그 유효성을 검증하기 위해서 질의유형별로 기존 사이트와 심층 분석을 수행하는 것이다. 먼저 토픽맵에 대한 간략 설명, 판소리 도메인에 대한 토픽맵 데이터 모델링, 그 모델링을 기반으로 토픽맵 기반 판소리 시스템을 구축하였다. 비교대상 사이트는 다양한 판소리 사이트를 비교한 결과, pansoree.com 사이트를 선정하였다. 보다 체계적인 성능비교를 위해서 질의유형별로 나누어서 두 사이트를 비교 분석하였다. 질의유형은 단순질의, 고급질의, 연계질의 Cross Reference 질의로 나누었고, 분석결과 토픽맵 기반 판소리 사이트가 모든 질의유형에서 기존 사이트보다 검색시간과 단계를 줄일 수 있고, 판소리 도메인을 잘 모르는 이용자의 경우에도 용이한 검색을 제공하는 것으로 판명되었다.

• 한국문헌정보학회지
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• 제41권2호
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• pp.291-316
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• 2007

기초학문자료의 특성인 복잡한 관계의 개념구조, 자료유형 및 자료간의 의미적 상관관계 등을 표현할 수 있는 기초학문자료 메타데이터 모형을 설계하였다. 설계된 메타데이터 모형의 정당성 및 효율성을 평가하기 위해 실제 구축된 자료의 분석을 토대로 실제적인 메타데이터 구성요소를 제시하였다. 메타데이터의 기반검색의 한계를 극복할 수 있는 확장된 시소러스를 설계하였으며 도메인 온톨로지를 구축하는 방안을 모색해 보았다. 또한 철학 종교학 분야의 주제분류 중심의 확장시소러스를 설계하고 토픽맵 기반 시스템으로 구현하여 주제 중심의 메타데이터 검색이 가능함을 보였다.

• 한국정보시스템학회지:정보시스템연구
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• 제18권2호
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• pp.35-59
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• 2009

The study of Information Systems(IS) is a relatively new discipline area, thus an analysis of the latest research literature could be useful to identify what the researchers are doing and what can be done to improve our discipline. With that purpose in mind, this study analyzes the total 208 articles published in "The Journal of Information Systems~ between 2001 and 2008. The classification system that comprises three key characteristics of diversity (research topic, research method, and reference discipline) was developed based on a review of prior literature. The results of this study were also compared with Kim et al.(2005)'s and Vessey et al.(2002)'s results to identify issues in current Information Systems research and 10 suggest some recommendations for future In formation Systems research. The findings identify popular research topic:s, the dominant research method, and reference discipline. The popular research topics consists of organizational concepts, problem domain-specific concepts, and systems/software management concepts. Field study was characterized as the dominant research method in the papers included in the study. Information Systems itself represents the major theoretical reference of the studies. However, many papers in this study relied on a number of reference disciplines., none of which was dominant, or they did not rely on a specific reference discipline. Finally, this study suggests more research on the disciplinary issues, more training on the research method, more accurate and specific reference discipline, and controlled diversity.

• 한국산업정보학회논문지
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• 제13권2호
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• pp.47-55
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• 2008

본 연구는 정보검색기술을 응용해서 구술문서 자료를 효율적으로 분석하는 시스템 개발을 목적으로 한다. 여기서 사용된 기술은 용어검색, 문서요약기술, 클러스터링기술 문서분류기술 주제추적기술 등이 있다. 본 연구를 위해서 전북지역에서 채록한 구술자료를 이용하였다. 구술문서 구조의 특성을 반영하면서 분석의 단위를 정하고 내용의 자동분류 및 분류체계에 따른 분류도 시도하였다. 특히 주제를 추적하면서 순서에 따라서 검색해 가는 기술은 세계적으로도 아직 연구단계에 있던 것을 실제로 구현하였다. 이러한 5가지의 검색기술이 한 시스템에서 통합적으로 처리될 수 있다는 것도 이 연구가 이룬 성과이다. 이 연구의 기대효과는 구술문서 분석의 신뢰성 타당성 효용성을 높여서 구술문화연구에도 큰 기여를 할 것으로 기대된다.

• 디지털산업정보학회논문지
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• 제15권1호
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• pp.1-7
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• 2019

The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.

• 영어어문교육
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• 제18권3호
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• pp.135-148
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• 2012

This is a retrospective, quantitative review of the English Teachers Association in Korea, namely the ETAK and its journals during the period of 18 years ever since the establishment in August 1994. It examines the history of the association, its domestic and international conferences, and most importantly, its articles. The purpose was to learn how it has emerged into a full-fledged organization, what the preferred language of the article has been, how the volume size has changed, and how many foreign scholars' articles have been contributed. It also looked into the number of authors each article was written by to examine the trend of cooperative work in the field of English education. Classification of the research topic was focused on the 4 skills of the language, grammar and vocabulary, literature, linguistics and all the rest areas were categorized into others. From the results of the study, suggestions for the future ETAK in the Korean English teaching were to be given.

• 디지털산업정보학회논문지
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• 제15권2호
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• pp.19-28
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• 2019

The purpose of this study is to examine the trends on machine learning and deep learning research in the published journals from the Web of Science Database. To achieve the study purpose, we used the abstracts of 20,664 articles published between 1990 and 2017, which include the word 'machine learning', 'deep learning', and 'artificial neural network' in their titles. Twenty major research topics were identified from topic modeling analysis and they were inclusive of classification accuracy, machine learning, optimization problem, time series model, temperature flow, engine variable, neuron layer, spectrum sample, image feature, strength property, extreme machine learning, control system, energy power, cancer patient, descriptor compound, fault diagnosis, soil map, concentration removal, protein gene, and job problem. The analysis of the time-series linear regression showed that all identified topics in machine learning research were 'hot' ones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.392-412
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• 2018

Due to the semantic gap problem across different modalities, automatically retrieval from multimedia information still faces a main challenge. It is desirable to provide an effective joint model to bridge the gap and organize the relationships between them. In this work, we develop a deep image annotation and classification by fusing multi-modal semantic topics (DAC_mmst) model, which has the capacity for finding visual and non-visual topics by jointly modeling the image and loosely related text for deep image annotation while simultaneously learning and predicting the class label. More specifically, DAC_mmst depends on a non-parametric Bayesian model for estimating the best number of visual topics that can perfectly explain the image. To evaluate the effectiveness of our proposed algorithm, we collect a real-world dataset to conduct various experiments. The experimental results show our proposed DAC_mmst performs favorably in perplexity, image annotation and classification accuracy, comparing to several state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1284-1297
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• 2019

The network environment is presently becoming very increased. Accordingly, the study of traffic classification for network management is becoming difficult. Automatic signature extraction system is a hot topic in the field of traffic classification research. However, existing automatic payload signature generation systems suffer problems such as semi-automatic system, generating of disposable signatures, generating of false-positive signatures and signatures are not kept up to date. Therefore, we provide a fully automatic signature update system that automatically performs all the processes, such as traffic collection, signature generation, signature management and signature verification. The step of traffic collection automatically collects ground-truth traffic through the traffic measurement agent (TMA) and traffic management server (TMS). The step of signature management removes unnecessary signatures. The step of signature generation generates new signatures. Finally, the step of signature verification removes the false-positive signatures. The proposed system can solve the problems of existing systems. The result of this system to a campus network showed that, in the case of four applications, high recall values and low false-positive rates can be maintained.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.213-219
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• 2023

In recent years, popularity of deep learning (DL) is increased due to its ability to extract features from Hyperspectral images. A lack of discrimination power in the features produced by traditional machine learning algorithms has resulted in poor classification results. It's also a study topic to find out how to get excellent classification results with limited samples without getting overfitting issues in hyperspectral images (HSIs). These issues can be addressed by utilising a new learning network structure developed in this study.EfficientNet-B4-Based Convolutional network (EN-B4), which is why it is critical to maintain a constant ratio between the dimensions of network resolution, width, and depth in order to achieve a balance. The weight of the proposed model is optimized by Search and Rescue Operations (SRO), which is inspired by the explorations carried out by humans during search and rescue processes. Tests were conducted on two datasets to verify the efficacy of EN-B4, with Indian Pines (IP) and the University of Pavia (UP) dataset. Experiments show that EN-B4 outperforms other state-of-the-art approaches in terms of classification accuracy.