• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4C
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• pp.310-317
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• 2006

In this paper, we propose a new sequential message-passing decoding algorithm of low-density parity-check (LDPC) codes by partitioning check nodes. This new decoding algorithm shows better bit error rate(BER) performance than that of the conventional message-passing decoding algorithm, especially for small number of iterations. Analytical results tell us that as the number of partitioned subsets of check nodes increases, the BER performance becomes better. We also derive the recursive equations for mean values of messages at variable nodes by using density evolution with Gaussian approximation. Simulation results also confirm the analytical results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.601-606
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• 1998

In this paper, we propose a new design method of an optimal fuzzy logic controller using co-evolutionary concept. In general, it is very difficult to find optimal fuzzy rules by experience when the input and/or output variables are going to increase. Futhermore proper fuzzy partitioning is not deterministic ad there is no unique solution. So we propose a co-evolutionary method finding optimal fuzzy rules and proper fuzzy membership functions at the same time. Predator-Prey co-evolution and symbiotic co-evolution algorithms, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. Our algorithm is that after constructing two population groups made up of rule base and membership function, by co-evolving these two populations, we find optimal fuzzy logic controller. By applying the propose method to a path planning problem of autonomous mobile robots when moving objects applying the proposed method to a pa h planning problem of autonomous mobile robots when moving objects exist, we show the validity of the proposed method.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.11_12
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• pp.682-691
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• 2005

We present a two-step evolution system that produces controllable virtual creatures in physically simulated 3D environment. Previous evolutionary methods for virtual creatures did not allow any user intervention during evolution process, because they generated a creature's shape, locomotion, and high-level behaviors such as target-following and obstacle avoidance simultaneously by one-time evolution process. In this work, we divide a single system into manageable two sub-systems, and this more likely allowsuser interaction. In the first stage, a body structure and low-level motor controllers of a creature for straight movement are generated by an evolutionary algorithm. Next, a high-level control to follow a given path is achieved by a neural network. The connection weights of the neural network are optimized by a genetic algorithm. The evolved controller could follow any given path fairly well. Moreover, users can choose or abort creatures according to their taste before the entire evolution process is finished. This paper also presents a new sinusoidal controller and a simplified hydrodynamics model for a capped-cylinder, which is the basic body primitive of a creature.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.4 no.3
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• pp.156-160
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• 1992

The evolution of leading waves generated by a wavemaker in a two-dimensional tank has been studied. The front of wave trains can be described in general by the Schrodinger equation. In particular, when the slope of the carrier waves is steep, and hence nonlinearity becomes important, the cubic Schrodinger equation is proved to be an appropriate mathematical model. Computations are made by using the Crank-Nicolson algorithm and compared with experimental data. It is found that the numerical result predicts the evolution of leading waves fairly well and the evolution is significantly affected by nonlinearity for steep waves when kh＞1.36.

• Journal of Biosystems Engineering
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• v.25 no.2
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• pp.123-130
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• 2000

A forcasting scheme for daily solar irradiance on agricultural field sis proposed by application of chaos theory to a long term observation data. It was conducted by reconstruction of phase space, attractor analysis, and Lyapunov analysis. Using the methodology , it was determined whether evolution of the five climatic data such as daily air temperature , water temperature , relative humidity, solar radiation, and wind speed are chaotic or not. The climatic data were collected for three years by an automated weather station at Hwasung-gun, Kyonggi-province. The results showed that the evolution of solar radiation was chaotic , and could be predicted. The prediction of the evolution of the solar radiation data was executed by using ' local optimal linear reconstruction ' algorithm . The RMS value of the predicting for the solar radiation evolution was 4.32 MJ/$m^2$ day. Therefore, it was feasible to predict the daily solar radiation based on the chaos theory.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.310-315
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• 2005

This paper proposes a fast optimization method by combining queen-bee evolution and derivative evaluation in genetic algorithms. These two operations make it possible for genetic algorithms to focus on highly fitted individuals and rapidly evolved individuals, respectively. Even though the two operations can also increase the probability that genetic algorithms fall into premature convergence phenomenon, that can be controlled by strong mutation rates. That is, the two operations and the strong mutation strengthen exploitation and exploration of the genetic algorithms, respectively. As a result, the genetic algorithm employing queen-bee evolution and derivative evaluation finds optimum solutions more quickly than those employing one of them. This was proved by experiments with one pattern matching problem and two function optimization problems.

• Structural Engineering and Mechanics
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• v.34 no.5
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• pp.597-609
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• 2010

A problem formulation and solution methodology for design optimization of laminated thin-walled composite beams of generic section is presented. Objective functions and constraint equations are given in the form of beam stiffness. For two different problems one for open section and the other for closed section, the objective function considered is bending stiffness about x-axis. Depending upon the case, one can consider bending, torsional and axial stiffnesses. The different search and optimization algorithm, known as Evolution Strategies (ES) has been applied to find the optimal fibre orientation of composite laminates. A multi-level optimization approach is also implemented by narrowing down the size of search space for individual design variables in each successive level of optimization process. The numerical results presented demonstrate the computational advantage of the proposed method "Evolution strategies" which become pronounced to solve optimization of thin-walled composite beams of generic section.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.1014-1021
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• 1999

PID controllers with constant gains have been widely used in various control systems. But it is difficult to have uniformly good control performance in all operating conditions. In this paper, we propose a variable PID controller for robot manipulators. We divide total workspace of manipulators into several subspaces. PID controllers in each subspace are optimized using evolution strategy which is a kind of global search algorithm. In real operation, the desired trajectories may cross several subspaces and we select the corresponding gains in each subspace. The gains may have large difference on the boundary of subspaces, which may cause oscillatory motion. So we use artificial neural network to have continuous smooth gain curves to reduce the oscillatory motion. From the experimental results, although the proposed variable PID controller for robots should pay for some computational burden, we have found that the controller is more superior to the conventional constant gain PID controller.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.932-934
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• 1993

Optimum pole shape is designed to increase the levitation and propulsion force of magnetic levitation systems. Evolution Strategy is introduced as optimization method. Evolution Strategy is random based non-deterministic method, developed by combining Genetic Algorithm with Simulated Annealing. Trasnsrapid-06, which was developed in Germany, is referenced model to be analyze. Design variables are nodes which determine fields pole shape of a linear synchronous motor, and the model analyzed by F.E.M.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.146-156
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• 2020

Two High Tensile Strength Steel (EH47) plates with thickness of 70 mm were butt-welded together by multi-pass Submerged Arc Welding (SAW), also the hardness and welding residual stress were investigated experimentally. Based on Thermal-Elastic-Plastic Finite Element (TEP FE) computation, the thermal cycles during entire welding process were obtained, and the HAZ hardness of multi-pass butt welded joint was computed by the hardenability algorithm with considering microstructure evolution. Good agreement of HAZ hardness between the measurement and computational result is observed. The evolution of each phase was drawn to clarify the influence mechanism of thermal cycle on HAZ hardness. Welding residual stress was predicted with considering mechanical response, which was dominantly determined by last cap welds through analyzing its formation process.