• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1594-1597
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• 2005

A rapid development and a wide use of the Internet have expanded a network environment. Further, the network environment has become more complex due to a simple and convenient network connection and various services of the Internet. However, the Internet has been constantly exposed to the danger of various network attacks such as a virus, a hacking, a system intrusion, a system manager authority acquisition, an intrusion cover-up and the like. As a result, a network security technology such as a virus vaccine, a firewall, an integrated security management, an intrusion detection system, and the like are required in order to handle the security problems of Internet. Accordingly, a router, which is a key component of the Internet, controls a data packet flow in a network and determines an optimal path thereof so as to reach an appropriate destination. An error of the router or an attack against the router can damage an entire network. This paper relates to a method for RSMS (router security management system) for secure networking based on a security policy. Security router provides functions of a packet filtering, an authentication, an access control, an intrusion analysis and an audit trail in a kernel region. Security policy has the definition of security function against a network intrusion.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.188-198
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• 2018

With recent development of high-speed wide-area wireless networks and wide spread of highperformance wireless devices, the demand on seamless video streaming services in Long Term Evolution (LTE) network environments is ever increasing. To meet the demand and provide enhanced Quality of Experience (QoE) with mobile users, the Dynamic Adaptive Streaming over HTTP (DASH) has been actively studied to achieve QoE enhanced video streaming service in dynamic network environments. However, the existing DASH algorithm to select the quality of requesting video segments is based on a procedural algorithm so that it reveals a limitation to adapt its performance to dynamic network situations. To overcome this limitation this paper proposes a novel quality selection mechanism based on a Deep Q-Network (DQN) model, the DQN-based DASH ABR($DQN_{ABR}$) mechanism. The $DQN_{ABR}$ mechanism replaces the existing DASH ABR algorithm with an intelligent deep learning model which optimizes service quality to mobile users through reinforcement learning. Compared to the existing approaches, the experimental analysis shows that the proposed solution outperforms in terms of adapting to dynamic wireless network situations and improving QoE experience of end users.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.3060-3069
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• 1999

The existed transmissions have PDH and SDH transmission system, which are based on PSTN network. But as the future ATM network is going to be constructed, it is very necessary for ATM network to interwork with PSTN for telephony service. The international standardization organizations like ITU-T, ATM Forum offered CES, VTOA1, VTOA2 and so on for interworking with PSTN service, which spend many network resources and considerable transmission delay. So in this paper, new interworking PSTN for transmission will be proposed to lessen the transmission delay and the network resources.

• Journal of Korean Academy of Nursing
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• v.52 no.3
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• pp.291-307
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• 2022

Purpose: The aims of study were to identify the main keywords, the network structure, and the main topics of press articles related to nurses that have appeared in media reports. Methods: Data were media articles related to the topic "nurse" reported in 16 central media within a one-year period spanning July 1, 2019 to June 30, 2020. Data were collected from the Big Kinds database. A total of 7,800 articles were searched, and 1,038 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. Results: The number of media reports related to nurses increased by 3.86 times after the novel coronavirus (COVID-19) outbreak compared to prior. Pre- and post-COVID-19 network characteristics were density 0.002, 0.001; average degree 4.63, 4.92; and average distance 4.25, 4.01, respectively. Four topics were derived before and after the COVID-19 outbreak, respectively. Pre-COVID-19 example topics are "a nurse who committed suicide because she could not withstand the Taewoom at work" and "a nurse as a perpetrator of a newborn abuse case," while post-COVID-19 examples are "a nurse as a victim of COVID-19," "a nurse working with the support of the people," and "a nurse as a top contributor and a warrior to protect from COVID-19." Conclusion: Topic modeling shows that topics become more positive after the COVID-19 outbreak. Individual nurses and nursing organizations should continuously monitor and conduct further research on nurses' image.

• Smart Structures and Systems
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• v.29 no.2
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• pp.361-374
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• 2022

As civil infrastructure has continued to age worldwide, its structural integrity has been threatened owing to material deteriorations and continual loadings from the external environment. Structural Health Monitoring (SHM) has emerged as a cost-efficient method for ensuring structural safety and durability. As SHM research has gradually addressed an increasing number of structure-related problems, it has become difficult to understand the changing research topic trends. Although previous review papers have analyzed research trends on specific SHM topics, these studies have faced challenges in providing (1) consistent insights regarding macroscopic SHM research trends, (2) empirical evidence for research topic changes in overall SHM fields, and (3) methodological validations for the insights. To overcome these challenges, this study proposes a framework tailored to capturing the trends of research topics in SHM through a bibliometric and network analysis. The framework is applied to track SHM research topics over 15 years by identifying both quantitative and relational changes in the author keywords provided from representative SHM journals. The results of this study confirm that overall SHM research has become diversified and multi-disciplinary. Especially, the rapidly growing research topics are tightly related to applying machine learning and computer vision techniques to solve SHM-related issues. In addition, the research topic network indicates that damage detection and vibration control have been both steadily and actively studied in SHM research.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.117-133
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• 2021

This study aimed to identify its research trends using social network analysis(SNA). The results of the analysis showed that, for degree centrality, Busan Port(0.223) was the keyword that had the highest centrality, followed by DEA(0.060), AHP(0.056), and container terminal and port competitiveness(0.049). Busan Port(0.245) also had the highest betweenness centrality, followed by DEA(0.048), container terminal(0.044), AHP(0.039), and Busan New Port(0.032). The trend analysis inferred that efficiency analysis(DEA), strategy selection, and competition analysis(AHP) were the keywords with a high centrality for Busan Port to gain a competitive edge with global ports. However, research on the Fourth Industrial Revolution, which is emerging as a key issue, was insufficient. In the future, research using social data, such as mass media and social networks, is necessary.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• ETRI Journal
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• v.32 no.5
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• pp.713-721
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• 2010

E-textile technology has earned a great deal of interest in many fields; however, existing wearable network protocols are not optimized for use with conductive yarn. In this paper, some of the basic properties of conductive textiles and requirements on wearable personal area networks (PANs) are reviewed. Then, we present a wearable personal network (WPN), which is a four-layered wearable PAN using bus topology. We have designed the WPN to be a lightweight protocol to work with a variety of microcontrollers. The profile layer is provided to make the application development process easy. The data link layer exchanges frames in a master-slave manner in either the reliable or best-effort mode. The lower part of the data link layer and the physical layer of WPN are made of a fabric serial-bus interface which is capable of measuring bus signal properties and adapting to medium variation. After a formal verification of operation and performances of WPN, we implemented WPN communication modules (WCMs) on small flexible printed circuit boards. In order to demonstrate the behavior of our WPN on a textile, we designed a WPN tutorial shirt prototype using implemented WCMs and conductive yarn.

• IMMUNE NETWORK
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• v.9 no.2
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• pp.46-52
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• 2009

Although tuberculosis poses a significant health threat to the global population, it is a challenge to develop new and effective therapeutic strategies. Nitric oxide (NO) and inducible NO synthase (iNOS) are important in innate immune responses to various intracellular bacterial infections, including mycobacterial infections. It is generally recognized that reactive nitrogen intermediates play an effective role in host defense mechanisms against tuberculosis. In a murine model of tuberculosis, NO plays a crucial role in antimycobacterial activity; however, it is controversial whether NO is critically involved in host defense against Mycobacterium tuberculosis in humans. Here, we review the roles of NO in host defense against murine and human tuberculosis. We also discuss the specific roles of NO in the central nervous system and lung epithelial cells during mycobacterial infection. A greater understanding of these defense mechanisms in human tuberculosis will aid in the development of new strategies for the treatment of disease.

• Imaging Science in Dentistry
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• v.49 no.1
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• pp.1-7
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• 2019

Purpose: Artificial intelligence (AI), represented by deep learning, can be used for real-life problems and is applied across all sectors of society including medical and dental field. The purpose of this study is to review articles about deep learning that were applied to the field of oral and maxillofacial radiology. Materials and Methods: A systematic review was performed using Pubmed, Scopus, and IEEE explore databases to identify articles using deep learning in English literature. The variables from 25 articles included network architecture, number of training data, evaluation result, pros and cons, study object and imaging modality. Results: Convolutional Neural network (CNN) was used as a main network component. The number of published paper and training datasets tended to increase, dealing with various field of dentistry. Conclusion: Dental public datasets need to be constructed and data standardization is necessary for clinical application of deep learning in dental field.