• Journal of KIISE:Information Networking
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• v.31 no.4
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• pp.363-374
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• 2004

In this paper, we propose an in-line mode NIDS using network processors(NPs) that achieve performance comparable to ASIC and flexibility comparable to general-purpose processors. Even if many networking applications using NPs have been proposed, we cannot find any NP applications to NIDS in the literature. The proposed NIDS supports packet payload inspection detecting attacks, as well as packet filtering and traffic metering. In particular, we separate the filtering and metering functions from the complicated and time-consuming operations of the deep packet inspection function using two-level searching scheme, thus we can improve the performance, stability, and scalability of In-line mode system. We also implement a proto-type based on a PC platform and the Agere PayloadPlus (APP) 2.5G NP solution, and present a payload inspection algorithm to apply APP NP.

• Knowledge Management Research
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• v.24 no.4
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• pp.177-193
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• 2023

This study explores the role of organizational culture, job characteristics, and work values and orientation in shaping the competitiveness of a multinational company (MNC) based in Korea. The purpose of the study was to examine the impact of these variables on the competitiveness attributes of the organizational culture profile through MR-QAP analysis. Data were collected from 161 employees in 15 different teams at a Korean automotive company headquartered in Seoul. The results of the study revealed the impact of network characteristics associated with competitive organizational culture on competitiveness. 'found to have a negative effect on competitiveness. Among the organizational culture profiles, social responsibility, supportiveness, innovation, and performance orientation have a significant positive effect on competitive organizational culture, while emphasis on rewards and stability have no significant effect. These findings provide practical implications for understanding the complex dynamics of organizational culture and promoting strategic approaches to enhance organizational competitiveness.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.857-860
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• 1991

This paper describes a High-Speed 8-bit Current-Mode BiCMOS A/D Converter. The characteristics of this A/D Converter are as fellows. First, as ADC is operating in current-mode we can obtain the properties of increase of converting speed, low noise, and wideband. Second, the properties of high switching speed in bipolar transistor and of high packing density, low power consumption in MOS trnsistor are combined. Finally we reduce chip area by designing it with subranging mode and improve the converting speed by performing subtraction directly, which doesn't need D/A convertings, using current switching element. This converter is composed of two 4-bit ADC, current soure array which provides signal and reference current, current comparator and encoding network.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.447-452
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• 1998

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model non linear systems. In this paper, we proposes an FDI system for non linear systems using ANFIS. The proposed diagnositc system consists of two ANFISs which operate in two different modes (parallel-and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis function) network to identify the faults. The proposed FDI scheme has been tested by simultation on a two-tank system

• Journal of Information Technology Services
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• v.16 no.3
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.1
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• pp.136-142
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• 2001

In this paper, a method of canceling and suppressing differential mode crosstalk noise signals caused by non-uniform coupling between two transmission lines in UTP (unshielded twisted pair) modular jacks is discussed. Differential mode crosstalk noise signals in balanced transmission lines with UTP modular jacks were suppressed, by applying field cancellation techniques to this modular jack. To verify an effectiveness of the field cancellation techniques, 8 pin modular jacks were made, and the NEXT (Near End Crosstalk) losses were measured to prove its applicability by the network analyzer(HP8720C) at 100 Mb/s.

• Journal of Communications and Networks
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• v.11 no.3
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• pp.211-228
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• 2009

After clarifying the underlying problems in a secure network storage, we introduce two important requirements, leakageresilience and availability in higher levels respectively, for data keys that are used to protect remotely-stored data. As a main contribution of this paper, we give a new security layer for overlay networks by proposing a leakage-resilient authentication and data management system. In this system, we specifically propose a single mode and a cluster mode where the latter provides a higher level of both leakage-resilience and availability for the data key.

• Journal of Satellite, Information and Communications
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• v.2 no.2
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• pp.85-91
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• 2007

The satellite networks provide global coverage. The integration of terrestrial networks with a satellite network is the most attractive approach to develop a global communication system. The IP Multimedia Subsystem (IMS) is intended to be the system that will merge the internet with the telecom world. A user with a dual-mode terminal can access both the satellite network and terrestrial network. The seamless handoff between two networks and a user's QoS level is the major issue concerning this integration. In this paper, we propose IMS-based satellite/terrestrial integrated network architecture for a global communication system. Based on the proposed architecture, an inter-network handoff and end-to-end soft QoS procedure is discussed. Our proposed soft QoS scheme is also analyzed to calculate the number of rejected calls.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.4
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• pp.510-519
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• 2015

Concerning the issue of high-dimensions, hybrid uncertainties of randomness and intervals including implicit and highly nonlinear limit state function, reliability analysis based on the hybrid uncertainty reliability mode combining with back propagation neural network (HU-BP neural network) is proposed in this paper. Random variables and interval variables are as input layer of the neural network, after the training and approximation of the neural network, the response variables are obtained through the output layer. Reliability index is calculated by solving the optimization model of the most probable point (MPP) searching in the limit state band. Two numerical cases are used to demonstrate the method proposed in this paper, and finally the method is employed to solving an engineering problem of the aerospace friction plate. For this high nonlinear, small failure probability problem with interval variables, this method could achieve a good analysis result.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.123-134
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• 2018

Recently, due to the rising incidence of disasters in the nation, there has been a growing interest in the relevance and role of science and technology in solving disaster and safety related issues. In addition, the necessities of securing the human rights of all citizens in disaster risk reduction, identifying fields of technology development for effective disaster response, and improving the efficiency of R&D investment for disaster and safety are becoming more important as the different types of disasters and stages of disaster and safety management process have been considered. In this study, we analyzed bipartite or two-mode networks constructed from an expert survey dataset of technology development for disaster and safety management. The results reveal that earthquake and fire are the two disasters affecting an individual and society at large and demonstrate that AI and big data analytics are effective supports in managing disaster and safety. We believe that such a network analytic approach can be used to explore some important implications exist for the national science and technology effort and successful disaster and safety management practices in Korea.