• Journal of Broadcast Engineering
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• v.16 no.2
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• pp.360-371
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• 2011

T-DMB hybrid data service provides advanced data services while maintaining backward compatibility with legacy T-DMB receivers by using hybrid BIFS technology enabling distributed delivery of scene description information and object description information. This paper presents the hybrid service authoring framework implementing hybrid BIFS technology for creating contents for distributed delivery, and the results of experiments by using it. Hybrid service authoring framework is comprised of service creation system, service management system, and contents offering system. It enables the creation of combined hybrid data service and the splitting of the contents into two parts for ecah delivery network, data for broadcasting network and the data for mobile network. It also enables the managements of the contents. The feasibility of advanced data services while maintaining backward compatibility with the legacy T-DMB receiver has been proved by the contents created by using the hybrid authoring framework presented in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.473-493
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• 2019

5G vehicular communication is one of key enablers in next generation intelligent transportation system (ITS), that require ultra-reliable and low latency communication (URLLC). To meet this requirement, a new hybrid vehicular network structure which supports both centralized network structure and distributed structure is proposed in this paper. Based on the proposed network structure, a new vehicular network utility model considering the latency and reliability in vehicular networks is developed based on Euclidean norm theory. Building on the Pareto improvement theory in economics, a vehicular network uplink optimization algorithm is proposed to optimize the uplink utility of vehicles on the roads. Simulation results show that the proposed scheme can significantly improve the uplink vehicular network utility in vehicular networks to meet the URLLC requirements.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.780-791
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• 1996

A hybrid image processing system which automatically distinguished lean tissues in the image of a complex beef cut surface and generated the lean tissue contour has been developed. Because of the in homegeneous distribution and fuzzy pattern of fat and lean tissue on the beef cut, conventional image segmentation and contour generation algorithm suffer from a heavy computing requirement, algorithm complexity and poor robustness. The proposed system utilizes an artificial neural network enhance the robustness of processing. The system is composed of pre-network , network and post-network processing stages. At the pre-network stage, gray level images of beef cuts were segmented and resized to be adequate to the network input. Features such as fat and bone were enhanced and the enhanced input image was converted tot he grid pattern image, whose grid was formed as 4 X4 pixel size. at the network stage, the normalized gray value of each grid image was taken as the network input. Th pre-trained network generated the grid image output of the isolated lean tissue. A training scheme of the network and the separating performance were presented and analyzed. The developed hybrid system showed the feasibility of the human like robust object segmentation and contour generation for the complex , fuzzy and irregular image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8B
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• pp.812-821
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• 2009

This paper proposes a novel dynamically reconfigurable switch for various multimedia applications in hybrid NoC systems. Current NoC systems, which adopt hybrid NoC structure with fixed switch and job distribution algorithms, require designers to precisely predict the property of applications to be processed. This paper proposes a reconfigurable switch which minimizes buffer overflow in various multimedia applications running on an NoC system. To verify the performance of the proposed system, we performed experiments on various multimedia applications running on embedded systems, such as MPEG4 and MP3 decoder, GPS positioning system, and OFDM demodulator. Experimental results show that buffer overflow has been decreased by 41.8% and 29.0%, respectively, when compared with NoC systems having sub-clusters with mesh or star topology. Power usage has been increased by 2.3% compared with hybrid NoC systems using fixed switches, and chip area has been increased from -0.6% to 5.7% depending on sub-cluster topology.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.287-289
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• 2005

This paper presents a hybrid neural network system which used a Self-Organizing Map and Multilayer Perceptron for the problem of Malay syllables speech recognition. The novel idea in this system is the usage of a two-dimension Self-organizing feature map as a sequential mapping function which transform the phonetic similarities or acoustic vector sequences of the speech frame into trajectories in a square matrix where elements take on binary values. This property simplifies the classification task. An MLP is then used to classify the trajectories that each syllable in the vocabulary corresponds to. The system performance was evaluated for recognition of 15 Malay common syllables. The overall performance of the recognizer showed to be 91.8%.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.11
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• pp.1081-1087
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• 2006

Generally, biped walking is difficult to control because a biped robot is a nonlinear system with various uncertainties. In this paper, we propose a hybrid sliding-mode control method using a WNN uncertainty observer for stable walking of the 5-link biped robot with model uncertainties and the external disturbance. In our control system, the sliding mode control is used as main controller for the stable walking and a wavelet neural network(WNN) is used as an uncertainty observe. to estimate uncertainties of a biped robot model, and the error compensator is designed to compensate the reconstruction error of the WNN. The weights of WNN are trained by adaptation laws that are induced from the Lyapunov stability theorem. Finally, the effectiveness of the proposed control system is verified through computer simulations.

• Proceedings of the Optical Society of Korea Conference
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• 2006.02a
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• pp.221-222
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• 2006

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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• pp.690-696
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• 2004

This paper is proposed a hybrid intelligent controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neural network-fuzzy(NNF) control and speed estimation using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.10
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• pp.1832-1837
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• 2011

The fault current has increased due to the large power demand in power distribution system and network distribution system. To protect the power system effectively from the increased fault current, the superconducting fault current limiter (SFCL) has been notified. However, the conventional SFCL has some problems such as cost, operation, recovery, loss. To solve some problems, the hybrid superconducting fault current limiter using the fast switch was proposed. However, hybrid SFCL also has a problem that is protection coordination in power distribution system with hybrid SFCL. In this paper, the fault current limiting characteristics of hybrid SFCL with first half cycle non-limiting operation according to the fault angle, the resistance of superconducting element, and the magnitude of Current Limit Resistor (CLR) which are the components of hybrid SFCL were analyzed through the experiments.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.882-885
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• 2003

In this paper, nonlinear echo cancellation using a correlation LMS (CLMS) algorithm is proposed to cancel the undesired nonlinear echo signals generated in the hybrid system of the telephone network. In the telephone network, the echo signals may result the degradation of the network performance. Furthermore, digital to analog converter (DAC) and analog to digital converter (ADC) may be the source of the nonlinear distortion in the hybrid system. The adaptive filtering technique based on the nonlinear Volterra filter has been the general technique to cancel such a nonlinear echo signals in the telephone network. But in the presence of the double-talk situation, the error signal for tap adaptations will be greatly larger, and the near-end signal can cause any fluctuation of tap coefficients, and they may diverge greatly. To solve a such problem, the correlation LMS (CLMS) algorithm can be applied as the nonlinear adaptive echo cancellation algorithm. The CLMS algorithm utilizes the fact that the far-end signal is not correlated with a near-end signal. Accordingly, the residual error for the tap adaptation is relatively small, when compared to that of the conventional normalized LMS algorithm. To demonstrate the performance of the proposed algorithm, the DAC of hybrid system of the telephone network is considered. The simulation results show that the proposed algorithm can cancel the nonlinear echo signals effectively and show robustness under the double-talk situations.