• Convergence Security Journal
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• v.1 no.1
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• pp.37-45
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• 2001

In this paper, separated and optimized pattern database model is proposed. In order to improve efficiency of Network-based IDS, pattern database is classified by proper basis. Classification basis is decided by the specific Intrusions validity on specific target. Using this model, IDS searches only valid patterns in pattern database on each captured packets. In result, IDS can reduce system resources for searching pattern database. So, IDS can analyze more packets on the network. In this paper, proper classification basis is proposed and pattern database classified by that basis is formed. And its performance is verified by experimental results.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.145-148
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• 2003

In this paper an nonlinear neuro PID controller is constructed by the control system of general PID controller using a Self-Recurrent Neural Network. And the games of the PID controller in the proposed control system are automatically adjusted by back-propagation algorithm of the neural network. Applying to the position control system, it's performance is verified through the results of computer simulation.

• Convergence Security Journal
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• v.12 no.3
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• pp.67-73
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• 2012

In this paper, in order to overcome the disadvantage of wireless-based sensor network, a hybrid sensor network using wired and wireless linkage is proposed. Proposed a wired and wireless linkage hybrid sensor network can compensate the defect of poor transmission at the indoor wireless environment, and can be free from interference between a wireless LAN and Bluetooth of the same frequency bandwidth due to an attribute of low-loss transmission at the CATV network. Also, proposed a wired and wireless linkage hybrid sensor network make use of CATV network which is well-built infrastructure, is more efficient to design network, assure a stability and high reliability of the sensor network as providing a stability for an inaccuracy and a predictable transmission link for the existing wireless network.

• Journal of KIISE:Information Networking
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• v.34 no.5
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• pp.305-315
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• 2007

Broadband Convergence Network (BcN) is the network which unifies telephone network, the Internet and broadcasting networks. Threats to each network can bring serious problems in BcN environment since the whole network can be damaged by various types of attack. The purpose of this study is to suggest the prototype of intrusion-tolerant network design of BcN to guarantee the continuous operation of BcN services against malicious attacks. First, BcN service components, selected by analysis of service time and coverage importance, are classified into three groups by their type: server type, gateway type and hybrid type. Second, the necessity of applying intrusion tolerance on BcN services is deduced by possible attack scenarios on BcN. Finally, we suggest the intrusion-tolerant network design suitable to BcN, using hardware redundancy and secure policies. Also, we present that the suggested network design can increase the intrusion tolerance of BcN.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4334-4356
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• 2017

In orthogonal frequency-division multiplexing (OFDM) systems, phase noise introduced by the local oscillators can cause bit error rate (BER) performance degradation. To solve the phase noise problem, a novel orthogonal-polarization-based phase noise self-cancellation (OP-PNSC) scheme is proposed. First, the efficiency of canceling the phase noise of the OP-PNSC scheme in the AWGN channel is investigated. Then, the OP-PNSC scheme in the polarization-dependent loss (PDL) channel is investigated due to power imbalance caused by PDL, and a PDL pre-compensated OP-PNSC (PPC -OP-PNSC) scheme is proposed to mitigate the power imbalance caused by PDL. In addition, the performance of the PPC-OP-PNSC scheme is investigated, where the signal-to-interference-plus-noise ratio (SINR) and spectral efficiency (SE) performances are analyzed. Finally, a comparison between the OP-PNSC and polarization diversity scheme is discussed. The numerical results show that the BER and SINR performances of the OP-PNSC scheme outperform the case with the phase noise compensation and phase noise self-cancellation scheme.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1236-1249
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• 2020

In a most recent object tracking research work, applying Convolutional Neural Network and Recurrent Neural Network-based strategies become relevant for resolving the noticeable challenges in it, like, occlusion, motion, object, and camera viewpoint variations, changing several targets, lighting variations. In this paper, the LSTM Network-based Tracking association method has proposed where the technique capable of real-time multi-object tracking by creating one of the useful LSTM networks that associated with tracking, which supports the long term tracking along with solving challenges. The LSTM network is a different neural network defined in Keras as a sequence of layers, where the Sequential classes would be a container for these layers. This purposing network structure builds with the integration of tracking association on Keras neural-network library. The tracking process has been associated with the LSTM Network feature learning output and obtained outstanding real-time detection and tracking performance. In this work, the main focus was learning trackable objects locations, appearance, and motion details, then predicting the feature location of objects on boxes according to their initial position. The performance of the joint object tracking system has shown that the LSTM network is more powerful and capable of working on a real-time multi-object tracking process.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.323-328
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• 2014

D-MAC protocol is used convergence network, which is designed to connect wireless link between things. This protocol is supported to local data exchange and aggregation among neighbor nodes, and distributed control packet from sink to sensor node. In this paper, we analysis about efficiency of power consumption according to whether or not security authentication of D-MAC in convergence network. If authentication scheme is applied to MAC communication, it is related to power consumption of preamble whether or not with and without authentication process. It is reduced to energy consumption against denial attack of service, when it is applied to authentication. Future work will take the effort to deal with security authentication scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.1979-1995
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• 2015

Network convergence is regarded as the development tendency of the future wireless networks, for which self-organization paradigms provide a promising solution to alleviate the upgrading capital expenditures (CAPEX) and operating expenditures (OPEX). Self-optimization, as a critical functionality of self-organization, employs a decentralized paradigm to dynamically adapt the varying environmental circumstances while without relying on centralized control or human intervention. In this paper, we present comprehensive surveys of heterogeneous networks (HetNets) and investigate the enhanced self-optimization models. Self-optimization approaches such as dynamic mobile access network selection, spectrum resource allocation and power control for HetNets, etc., are surveyed and compared, with possible methodologies to achieve self-optimization summarized. We hope this survey paper can provide the insight and the roadmap for future research efforts in the self-optimization of convergence networks.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.142-146
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• 2023

We present a method for generating 3D structures and rendering objects by combining VAE (Variational Autoencoder) and GAN (Generative Adversarial Network). This approach focuses on generating and rendering 3D models with improved quality using residual learning as the learning method for the encoder. We deep stack the encoder layers to accurately reflect the features of the image and apply residual blocks to solve the problems of deep layers to improve the encoder performance. This solves the problems of gradient vanishing and exploding, which are problems when constructing a deep neural network, and creates a 3D model of improved quality. To accurately extract image features, we construct deep layers of the encoder model and apply the residual function to learning to model with more detailed information. The generated model has more detailed voxels for more accurate representation, is rendered by adding materials and lighting, and is finally converted into a mesh model. 3D models have excellent visual quality and accuracy, making them useful in various fields such as virtual reality, game development, and metaverse.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.1
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• pp.64-71
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• 1997

Digital communication channels are imparied by linear effects such as dispersion, ISI(intersymbol Interference), fading phenomenon etc. But, the practical channel equalization system is required to design for compensating the nonlinear distortion caused by harmonic distortion etc. This paper is a study on the performance of nonlinear channel equalization using RBF(Radial Basis Funclion) network, which has the equivalent structure to the optimal Basian filter. Expecially, the variance of RBF network is modifiedby nonlinear polynomial filters to compare the convergence characteristic of nonlinear channel equalization. Experimental results show that the modified RBF network achieves the faster convergence property than conventional RBF network. Moreover, the RBF network ofhigher order variance modified represents the better performance than that of lower order variance in the bandpass channels and second/third order polynomial channels.