• KIEE International Transaction on Systems and Control
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• v.12D no.1
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• pp.12-17
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• 2002

We propose a new category of neurofuzzy networks- Self-organizing Neural Networks(SONN) with fuzzy polynomial neurons(FPNs) and discuss a comprehensive design methodology supporting their development. Two kinds of SONN architectures, namely a basic SONN and a modified SONN architecture are dicussed. Each of them comes with two types such as the generic and the advanced type. SONN dwells on the ideas of fuzzy rule-based computing and neural networks. Simulation involves a series of synthetic as well as experimental data used across various neurofuzzy systems. A comparative analysis is included as well.

• Knowledge Management Research
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• v.19 no.4
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• pp.19-37
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• 2018

Knowledge transfer among employees integrates individual knowledge scattered within a firm, thus increases organizational effectiveness. In particular, the role of knowledge broker, which enables knowledge sharing across multiple teams or subunits, is critical for the success of knowledge management. This study classified the types of knowledge broker that facilitates knowledge flows among team, and examined the influences of various intra- and inter-team social networks. Survey responses from 128 employees of four R&D teams were gathered and analyzed using partial least square structural equation modeling. The results of analysis showed that all types of inter-team networks(i.e., emotional closeness network, frequency of interaction network, and perceived expertise network) had significant influences on related knowledge brokerage behaviors. In case of intra-team networks, only the emotional closeness network showed significant influence. These results proved the necessity of managing various types of intra- and inter-team networks to encourage knowledge brokerage behaviors within a firm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.1
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• pp.17-32
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• 1996

In this paper, a model for two-layered video traffic is proposed. The performance analysis of the proposed model and the effects of two-layer coding scehemes in ATM networks are also studied. ATM-based networks give the possibility to support image codingat variable bit rate(VBR). Two layer coding is one of the very promising methods among many proposed methods to compensate the cell loss, the major drawback in ATM networks. From the experimental data of the 2-layer coded video traffics, it is observed that traffic patterns of base layer and enhanced layer are highly correlate to each other, when constant image quality is kept. With this observation, coded two layered video traffic can be modeled as 2-dimensional Markov chain. The model well fit the real experimental data. The model was used for the analysis of the performance of statistical multiplexer with priorites in ATM networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1094-1107
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• 2013

NTRU cryptosystem has been suggested for protecting wireless body area networks, which is secure in the sense of traditional cryptanalysis. In this paper, we fulfill the first power analysis attack on the ultra-low-power environment of wireless body area networks. Specifically, two practical differential power analyses on NTRU algorithm are proposed, which can attack the existing countermeasures of NTRU. Accordingly, we suggest three countermeasures against our attacks. Meanwhile, practical experiments show that although the attacks in this paper are efficient, our countermeasures can resist them effectively.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.10 no.2
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• pp.1-17
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• 2000

We investigated dissatisfaction of elements of local exhaust system, needs for local exhaust system, and information networks for local exhaust system from June 1998 to September 1999 using the questionnaire structured. It contained questions concerning general characteristics of factory and local exhaust system, troubles and dissatisfaction of elements of local exhaust system, and information networks for local exhaust system. The collected data were analyzed by descriptive statistics analysis. Information networks for local exhaust system were analyzed by multidimensional scaling using path distance of network analysis and by graph analysis using Krackplot. Among complaints of local exhaust system, that of duct has show the highest percentage of complaint. In the information network for local exhaust system, Seoul is positioned in the center of network with mediating role.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.63.5-63
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• 2001

In this paper, moving objects tracking and dynamic characteristic analysis are studied. Kohonen´s self-organizing neural network models are used for moving objects tracking and time delay neural networks are used for dynamic characteristic analysis. Instead of objects brightness, neuron projections by Kohonen Networks are used. The motion of target objects can be analyzed by using the differential neuron image between the two projections. The differential neuron image which is made by two consecutive neuron projections is used for center detection and moving objects tracking. The two differential neuron images which are made by three consecutive neuron projections are used for the moving trajectory estimation.

• Structural Engineering and Mechanics
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• v.52 no.4
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• pp.739-753
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• 2014

There have been many packages that can be employed to analyze plane frames. However, because most structural analysis packages suffer from closeness of system, it is very difficult to integrate it with an optimization package. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrate Design, Analysis, Modeling, Definition, and Optimization phases into an integrative environment. The DAMDO methodology employs neural networks to integrate structural analysis package and optimization package so as not to need directly to integrate these two packages. The key problem of the DAMDO approach is how to generate a set of reasonable random designs in the first phase. According to the characteristics of optimized plane frames, we proposed the ratio variable approach to generate them. The empirical results show that the ratio variable approach can greatly improve the accuracy of the neural networks, and the plane frame optimization problems can be solved by the DAMDO methodology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4324-4342
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• 2014

Previous papers analyzed the throughput performance of the CDMA ALOHA system in Rayleigh fading channel, but they assume that the channel coefficient of Rayleigh fading was the same in the whole packet, which is not realistic. We recently proposed the finite-state Markov channel (FSMC) model to the throughput analysis of DS uncoded CDMA/unslotted ALOHA networks for fixed length data traffic in the mobile environment. We now propose the FSMC model to the throughput analysis of coded DS CDMA/unslotted ALOHA networks with variable length data traffic and one or two user classes in the mobile environment. The proposed DS CDMA/unslotted ALOHA wireless networks for two user classes with access control can maintain maximum throughput for the high priority user class under high message arrival per packet duration.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.575-581
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• 2011

A novel approach for analysis of criticality with respect to path and to activity in networks with fuzzy activity durations is proposed. After recalling the Yager ranking method, the relative degree of criticality of activities and paths are defined. An efficient algorithm based on path enumeration to compute the relative degree of criticality of activities and paths in networks with fuzzy durations is proposed. Examples of former researches are employed to validate the proposed approach. The proposed algorithm has been tested on real world project networks and experimental results have shown that the algorithm can calculate the relative degree of criticality of activities and paths in a reasonable time.