• Journal of the Korean Society for information Management
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• v.39 no.4
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• pp.75-97
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• 2022

To solve the problems of the traditional scholarly communication system, global interest in the open access paradigm continues. Nevertheless, there is still a lack of research to understand global research and growth trends in the field of open access through data-based quantitative methods. This study aims to identify which sub-fields exist in open access and analyze how long each research field will grow in the future. To this end, topic modeling and growth curve analysis were applied to global academic papers in the field of open access. This study identified 14 research topics related to open access, open data, and open collaboration, which are three key elements of open science, and foresaw that the field of open access will grow over the next 65 years. The results of this study are expected to support researchers and policymakers in understanding global research trends of open access.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.391-394
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• 2017

사물에 센서를 부착해 실시간으로 데이터를 인터넷으로 연결하는 사물인터넷 시대가 대두되고 고성능의 스마트 기기가 등장함에 따라 현대 교육 시스템에도 큰 영향을 주었다. 특히 오락과 학습이 적절한 조화를 이루는 에듀테인먼트 콘텐츠는 학습자들의 몰입(flow)과 상호작용(interaction)을 통해 흥미를 유발시킨다. 그러나 스마트교육 콘텐츠는 웹을 기반으로 한 콘텐츠가 대다수이며, 어플리케이션 역시 교육의 질 대비 유료서비스를 제공하는 것이 대부분이고 내용이 다소 부재하다는 한계가 있다. 따라서 본 논문에서는 Unity3D와 뷰포리아사의 SDK를 활용하여 증강현실의 장점을 살리고, 학습자들이 흥미를 잃지 않고 쉽게 학습할 수 있는 에듀테인먼트 콘텐츠 제작방법을 제안한다.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.7
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• pp.317-330
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• 2001

대규모 가상환경의 핵심은 사용자 PC의 성능에 영향을 받는 확장성에 있다. 기존의 접근 방식은 대규모 환경을 지원하기 위해 멀티캐스트를 주로 사용하였다. 그러나 멀티캐스트는 현재 멀티캐스트 하드웨어가 지원할 수 있는 그룹의 수가 제한된다는 문제점이 있다. 본 논문에서는 PC 클라이언트와 인터넷과 같은 대규모 네트워크 기반의 확장성 높은 가상환경 모델을 제시한다. 멀티캐스트 네트워크와 PC 클라이언트 사이에 위치하는 관심영역 관리자(AOIM)는 멀티캐스트 그룹과 함께 다중 필터링을 수행하여 정보의 흐름을 최적화한다. 또한, 관심영역 관리자는 사용자의 관심 패턴에 따라 관심영역(AOI)으로부터 PC 클라이언트로의 데이터 전송량을 조절한다. 관심영역은 정보의 정확도에 따라 세 단계로 구분되며, 각 단계의 구분은 네트워크 현황에 따라 적응적으로 수축 또는 확장될 수 있어 PC 클라이언트는 최적화된 가상환경 상태정보를 제공받는다. 결론적으로, 제안된 모델은 다양한 컴퓨팅 환경의 PC 클라이언트에게 정확한 최우선 관심영역 정보를 제공한다.

• Science of Emotion and Sensibility
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• v.2 no.1
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• pp.137-145
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• 1999

열 두 명의 피험자가 안정상태 일 때, 백색 소음에 노출되었을 때, 백색 소음 하에서 기억과제에 주의를 기울일 때, 백색 소음 하에서 기억 검사를 받을 때, 기록된 뇌파에 대해 relative power spectrum 분석을 하였다. 뇌파는 전두, 측두, 후두 영역에서 단극 유도법으로 기록되었다. 분석 결과, 백색 소음에만 노출되었을 때나, 백색 소음 하에서 기억과제에 주의를 기울일 때나 비슷한 전기피질(electrocortical) 반응이 나타났다. 즉, delta power의 증가, 알파 blocking, fast beta power의 증가, 스트레스를 일으킨다고 피험자들이 평정한 배색 소음 하에서 기억검사를 받을 때에도 동일한 뇌파 패턴이 나타났지만 그 크기가 유의하게 컸다. 정보를 지각할 때 전형적으로 나타나는 반응을 유발하는 스트레스원에 수동적으로 노출되었을 때("intaki"상황)의 생리 반응과 스트레스 상황에 적극적으로 대처할 때("rejection")의 생리 반응을 구분하는 이론 틀 아래서 데이터를 해석하였다. 스트레스 후 기간에 대부분의 뇌파 변수들이 기저선 수준으로 회복된 것으로 보아 사용한 스트레스 유발 모델은 단기적 스트레스 반응만을 유발한 것으로 보인다. 이는, 더 장기적으로 지속되는 스트레스원을 사용하게 되면, tonic상태의 전기피질 반응이 나타날 것을 시사한다.기피질 반응이 나타날 것을 시사한다.

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.99-108
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• 2017

Open Science is evolving not only to share research results, but also to open the research process. We are developing the EDISON platform for the spread of open science in computational science and engineering. The EDISON platform provides online simulation services developed by computational science and engineering researchers. It also provides an environment for sharing source code, data, and related research publications. An effective simulation software registration management system is required for successful service on the EDISON platform. In this paper, we proposes a simulation software management system to provide online simulation service through EDISON platform. The proposed system allows the developer to register the simulation software on the EDISON platform without administrator intervention and effectively build a web-based simulation environment.

• Journal of KIISE:Information Networking
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• v.28 no.3
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• pp.389-405
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• 2001

In these days, we have increasing demands on the real-time services, especially for the multimedia data transmission in both of wired and wireless environments and thus efficient and stable ways of transmitting realtime data are needs. Although RTP is widely used for internet-based realtime applications, it cannot avoid packet losses, due to the use of UDP stack and its underlying layers. In the case of mobile computing applications, the packet losses are more frequent and consecutive because of the limited bandwidth. In this paper, we first statistically analyze the characteristics of packet losses in the wired and wireless communications, based on Gilbert model, and a new packet recovery scheme for realtime data transmission is presented. To reflect the transmission characteristics of the present network environment, our scheme makes the sender to dynamically adjust the amount of redundant information, using the current packet loss characteristic parameters reported by the receiver. Additionally, we use relatively large and discontinuous offset values, which enables us to recover from both of the random and consecutive packet losses. Due to these characteristics, our scheme is suitable for the mobile computing environment where packet loss rates are relatively high and varies rapidly in a wide range. Since our scheme is based on the analytic model form statistics, it can also be used for other network environments. We have implemented the scheme with Mobile IP and RTP/RTCP protocols to experimentally verify its efficiency.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.793-799
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• 2009

Esterel is an imperative synchronous language that is well-adopted to specify reactive systems. Programmers sometimes want simple validations that can be applied while the system is under development. Since a reactive system reacts to environment changes, a test data is a sequence of input events. Generating proper test data by hand is complex and error-prone. Although several test data generators exist, they are hard to learn and use. Mostly, system designers need test data to reach a specific status of a target program. In this paper, we develop a test data generator to generate test input sequences for debugging Esterel programs. Our tool is focused on easy usage; users can describe test data properties with simple specifications. We show a case study in which the test data generator is used for a practical development process.

• Journal of KIISE
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• v.42 no.7
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• pp.840-845
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• 2015

In recent years, Big Data has been a topic of great interest for the production and operation work of offshore plants as well as for enterprise resource planning. The ability to predict future equipment performance based on historical results can be useful to shuttling assets to more productive areas. Specifically, a centrifugal compressor is one of the major piece of equipment in offshore plants. This machinery is very dangerous because it can explode due to failure, so it is necessary to monitor its performance in real time. In this paper, we present stream data processing architecture that can be used to compute the performance of the centrifugal compressor. Our system consists of two major components: a virtual tag stream generator and a real-time data stream manager. In order to provide scalability for our system, we exploit a parallel programming approach to use multi-core CPUs to process the massive amount of stream data. In addition, we provide experimental evidence that demonstrates improvements in the stream data processing for the centrifugal compressor.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Journal of Technology Innovation
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• v.32 no.2
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• pp.23-58
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• 2024

Despite the important roles of institutions and their collaboration in producing knowledge for innovation, the lack of accurate methods for identifying such knowledge-producing institutions has restricted empirical research on the role of institutions in innovation. This study explores methods to enhance the accuracy of identifying institutions involved in innovation process. To this end, we propose ways to improve accuracy in both aspects of information - data and algorithms - using bibliographic information in the digital health field. Specifically, in the data processing stage before applying algorithms, we address contextual inaccuracies of bibliographic information; in the algorithm application stage, we propose methods to improve the ambiguity of institution names (IND). When compared with the PKG dataset, which is publicly available datasets based on the same bibliographic information, our methods doubled the number of cases available for subsequent analysis. We also discovered that the contribution of Korean institutions in the digital health field is either underestimated or overestimated. The method presented in this study is expected to contribute to empirically researching the role of knowledge-producing institutions in innovation process and ecosystem.