• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.140-148
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• 2016

HEVC(High Efficient Video Coding) has twice times better compression ratio than H.264/AVC, but since the computational complexity has significantly increased in the encoder side, it may cause difficulty in real-time SW implementation in the encoder side. This paper proposes two methods about fast intra prediction. First, fast mode and prediction unit decision method using transform coefficients of the original block is proposed. and second, fast prediction unit decision method using coded block flag(cbf) is proposed. The proposed method achieves 42% encoder speed up with 0.8% bitrate increase compared with HM16.0.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.727-729
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• 2002

In this paper, we proposed quad variable rates ADPCM coding method and its implementation on C6000 DSP, which is modified from the standard ADPCM of ITU G.726 for speech quality improvement considering the environmental noise Four coding rates, 16Kbps, 24Kbps, 32Kbps and 40Kbps are used for speech window samples and the rate decision threshold is decided by the environmental noise level. The object of the proposed method is to reduce the coding rate while retaining the speech quality and the speech quality is considerably close to 40Kbps single rate coder with the coding rate close to 16Kbps single rate coder under the environmental noise. The environmental noise level affects the coding rate and the noise level is calculated per every speech window samples. At high noise level, more samples are coded at higher rates to enhance the quality, but at low noise level, only the big speech signals are coded at higher rates and more speech samples are coded at lower coding rates to reduce the coding rates. The influence of the noise on tile speech signal is considerably high for small signals and the small signal has the higher ZCR (zero crossing rate). The method is simulated in PC and to be implemented on C6000 floating point DSP board in real time operations.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.11a
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• pp.111-116
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• 2004

In this paper, parameters of a given DC motor system are estimated using the model adjustment technique and the real coded genetic algorithm(RCGA) technique. A number of tuning methods, based on experience and experiment, such as Ziegler-Nichols, Cohen-Coon, IMC, L-ITAE Method have been proposed to obtain parameters for the PID controller. This paper proposes estimating parameters of PID controller using RCGA. The performance of the proposed algorithm is demonstrated through simulations and experiences.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.2
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• pp.406-417
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• 2014

This paper presents a numerical optimization method to improve the performance of the propeller with Turbo-Ring using real-coded genetic algorithm. In the presented method, Unimodal Normal Distribution Crossover (UNDX) and Minimal Generation Gap (MGG) model are used as crossover operator and generation-alternation model, respectively. Propeller characteristics are evaluated by a simple surface panel method "SQCM" in the optimization process. Blade sections of the original Turbo-Ring and propeller are replaced by the NACA66 a = 0.8 section. However, original chord, skew, rake and maximum blade thickness distributions in the radial direction are unchanged. Pitch and maximum camber distributions in the radial direction are selected as the design variables. Optimization is conducted to maximize the efficiency of the propeller with Turbo-Ring. The experimental result shows that the efficiency of the optimized propeller with Turbo-Ring is higher than that of the original propeller with Turbo-Ring.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.1
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• pp.39-47
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• 2014

In this study, 2 degree of freedom PID controller is added to the conventional feed-forward controller for the purpose of improving its limitations such as set-point of tracking performance and disturbance suppression performance in the conventional PID controller. And the controller parameters optimization as a Real Coded Genetic Algorithm (RCGA) is used. Simulation and experiments verify the performance of the controller.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.7
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• pp.509-515
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• 2013

In this study, the real-coded adaptive range multi-objective genetic algorithm code, which represents the global multi-objective optimization algorithm, was developed for an airfoil shape design. In order to achieve the better aerodynamic characteristics than reference airfoil at landing and cruise conditions, maximum lift coefficient and lift-to-drag ratio were chosen as object functions. Futhermore, the PARSEC method reflecting geometrical properties of airfoil was adopted to generate airfoil shapes. Finally, two airfoils, which show better aerodynamic characteristics than a reference airfoil, were chosen. As a result, maximum lift coefficient and lift-to-drag ratio were increased of 4.89% and 5.38% for first candidate airfoil and 7.13% and 4.33% for second candidate airfoil.

• Journal of Navigation and Port Research
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• v.35 no.3
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• pp.227-233
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• 2011

This paper presents the tracking and stabilization problem of a night vision system for marine surveillance. Both a hardware system and software modules are developed to control azimuth and elevation axes independently with compensation for ship motion. A two degree of freedom(2DOF) PID controller is designed and its parameters are tuned using a real-coded genetic algorithm(RCGA). Simulation demonstrates the effectiveness of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.832-839
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• 2009

In this paper, we introduce an design of fuzzy relation-based fuzzy neural networks with multi-output. Fuzzy relation-based fuzzy neural networks comprise the network structure generated by dividing the entire input space. The premise part of the fuzzy rules of the network reflects the relation of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions such as constant, linear, and modified quadratic. For the multi-output structure the neurons in the output layer were connected with connection weights. The learning of fuzzy neural networks is realized by adjusting connections of the neurons both in the consequent part of the fuzzy rules and in the output layer, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, learning rate and momentum coefficient are automatically optimized by using real-coded genetic algorithm. Two examples are included to evaluate the performance of the proposed network.

• Structural Engineering and Mechanics
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• v.61 no.2
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• pp.283-293
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• 2017

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.833-839
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• 2004

This paper proposes a method that minimizes the consumed energy by searching the optimal locations of the mass centers of links composing of a biped robot using Real-Coded Genetic Algorithm. Generally, in order to utilize optimization algorithms, the system model and design variables must be defined. Firstly, the proposed model is a 6-DOF biped robot composed of seven links, since many of the essential characteristics of the human walking motion can be captured with a seven-link planar biped walking in the saggital plane. Next, Fourth order polynomials are used for basis functions to approximate the walking gait. The coefficients of the fourth order polynomials are defined as design variables. In order to use the method generating the optimal gait trajectory by searching the locations of mass centers of links, three variables are added to the total number of design variables. Real-Coded GA is used for optimization algorithm by reason of many advantages. Simulations and the comparison of three methods to generate gait trajectories including the GCIPM were performed. They show that the proposed method can decrease the consumed energy remarkably and be applied during the design phase of a robot actually.