• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.12-17
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• 2003

Optimal determination of cluster size has an effect on the result of clustering. In K-means algorithm, the difference of clustering performance is large by initial K. But the initial cluster size is determined by prior knowledge or subjectivity in most clustering process. This subjective determination may not be optimal. In this Paper, the genetic algorithm based optimal determination approach of cluster size is proposed for automatic determination of cluster size and performance upgrading of its result. The initial population based on attribution is generated for searching optimal cluster size. The fitness value is defined the inverse of dissimilarity summation. So this is converged to upgraded total performance. The mutation operation is used for local minima problem. Finally, the re-sampling of bootstrapping is used for computational time cost.

• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.13-20
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• 2003

Based on the studies on the motion vector distributions estimated on the image sequences, we proposed constrained diamond search (DS) algorithm for fast block-matching motion estimation. By considering the fact that motion vectors are searched within the 2 pixels distance in vertically and horizontally on average, we confirmed that DS algorithm achieves close performance on error ratio and requires less computation compared with new three-step search (NTSS) algorithm. Also, by applying displaced frame difference (DFD) to DS algorithm, we reduced the computational loads needed to estimate the motion vectors within the stable block that do not have motions. And we reduced the possibilities falling into the local minima in the course of estimation of motion vectors by applying DFD to DS algorithm. So, we knew that proposed constrained DS algorithm achieved enhanced results as aspects of error ratio and the number of search points to be necessary compared with conventional DS algorithm, four step search (FSS) algorithm, and block-based gradient-descent search algorithm

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1327-1336
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• 2017

In this study, the problems in the short term stock market forecasting are analyzed and the feasibility of the ARIMA method and the backpropagation neural network is discussed. Neural network and genetic algorithm in short term stock forecasting is also examined. Since the backpropagation algorithm often falls into the local minima trap, we optimized the backpropagation neural network and established a genetic algorithm based on backpropagation neural network for forecasting model in order to achieve high forecasting accuracy. The experiments adopted the korea composite stock price index series to make prediction and provided corresponding error analysis. The results show that the genetic algorithm based on backpropagation neural network model proposed in this study has a significant improvement in stock price index series forecasting accuracy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.692-702
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• 2001

In this paper, a new block-matching algorithm for standard video encoder is proposed. The algorithm finds a motion vector using the increasing SAD transition curve for each predefined candidates, not a coarse-to-fine approach as a conventional method. To remove low-probability candidates at the early stage of accumulation, a dispersed accumulation matrix is also proposed. This matrix guarantees high-linearity to the SAD transition curve. Therefore, base on this method, we present a new fast block-matching algorithm with the slice competition technique. The Candidate Selection Step and the Candidate Competition Step makes an out-performance model that considerably reduces computational power and not to be trapped into local minima. The computational power is reduced by 10%~70% than that of the conventional BMAs. Regarding computational time, an 18%~35% reduction was achieved by the proposed algorithm. Finally, the average MAD is always low in various bit-streams. The results were also very similar to the MAD of the full search block-matching algorithm.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.15-26
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• 2014

This paper proposes a Modified Particle Swarm Optimization with Time Varying Acceleration Coefficients (MPSO-TVAC) for solving economic load dispatch (ELD) problem. Due to prohibited operating zones (POZ) and ramp rate limits of the practical generators, the ELD problems become nonlinear and nonconvex optimization problem. Furthermore, the ELD problem may be more complicated if transmission losses are considered. Particle swarm optimization (PSO) is one of the famous heuristic methods for solving nonconvex problems. However, this method may suffer to trap at local minima especially for multimodal problem. To improve the solution quality and robustness of PSO algorithm, a new best neighbour particle called 'rbest' is proposed. The rbest provides extra information for each particle that is randomly selected from other best particles in order to diversify the movement of particle and avoid premature convergence. The effectiveness of MPSO-TVAC algorithm is tested on different power systems with POZ, ramp-rate limits and transmission loss constraints. To validate the performances of the proposed algorithm, comparative studies have been carried out in terms of convergence characteristic, solution quality, computation time and robustness. Simulation results found that the proposed MPSO-TVAC algorithm has good solution quality and more robust than other methods reported in previous work.

• Journal of the Korean Chemical Society
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• v.45 no.5
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• pp.401-412
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• 2001

The geometrical parameters, vibrational frequencies, and dissociation energies for $H_{2n+1}^+$ (n=1~6) clusters have been investigated using high level ab initio quantum mechanical techniques with large basis sets. The equilibrium geometries have been optimized at the self-consistent field (SCF), the single and double excitation configuration interaction (CISD), the coupled cluster with single and double excitation (CCSD), and the CCSD with connected triple excitations [CCSD(T)] levels of theory. The highest levels of theory employed in this study are TZ2P+d CCSD(T) up to $H^+_g$ and TZ2P CCSD(T) for $H_{11}^+$ and $H_{13}^+$. Harmonic vibrational frequencies are also determined at the SCF level of theory with various basis sets and confirm that all the optimized geometries are true minima. The dissociation energies, $D_e$, for $H_{2n+1}^+$ (n=26) have been predicted using energy differences at each optimized geometry and zero-point vibrational energies(ZPVEs) have been considered to compare with experimental dissociation energies, $D_0$.

• Journal of Sensor Science and Technology
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• v.21 no.1
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• pp.59-67
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• 2012

It was reported that the latest sensor technology allow an 65536 conductive polymer sensor array to be made with broad but overlapping selectivity to different families of chemicals emulating the characteristics found in biological olfaction. However, the supernumerary redundancy always accompanies great error and risk as well as an inordinate amount of computation time and local minima in signal processing, e.g. neural networks. In this paper, we propose a new method to reduce the number of sensor for analysis by reducing redundancy between sensors and by removing unstable sensors using the cosine similarity method and to decide on representative sensor using FCM(Fuzzy C-Means) algorithm. The representative sensors can be just used in analyzing. And, we introduce DWT(Discrete Wavelet Transform) for data compression in the time domain as preprocessing. Throughout experimental trials, we have done a comparative analysis between gas sensor data with and without reduced redundancy. The possibility and superiority of the proposed methods are confirmed through experiments.

• Journal of Korea Water Resources Association
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• v.31 no.1
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• pp.45-57
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• 1998

The purpose of this paper is to develop a neural network model in order to forecast flood inflow into the reservoir that has the nature of uncertainty and nonlinearity. The model has the features of multi-layered structure and parallel multi-connections. To develop the model. backpropagation learning algorithm was used with the Momentum and Levenberg-Marquardt techniques. The former technique uses gradient descent method and the later uses gradient descent and Gauss-Newton method respectively to solve the problems of local minima and for the speed of convergency. Used data for learning are continuous fixed real values of input as well as output to emulate the real physical aspects. after learning process. a reservoir inflows forecasting model at flood period was constructed. The data for learning were used to calibrate the developed model and the results were very satisfactory. applicability of the model to the Chungju Mlultipurpose Reservoir proved the availability of the developed model.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.7
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• pp.81-91
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• 2006

Recently, G.A studies have studied and demonstrated that artificial intelligence like G.A networks, G.A PI controller. The design techniques of PI controller using G.A with the newly proposed teaming algorithm was presented, and the designed controller with AC servo motor system. The goal of this paper is to design the AC servo motor using genetic algorithm and to control drive robot. And in this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for genetic algorithm PI controller. Our experimental results show that this approach increases overall classification accuracy rate significantly. Finally, we executed for the implementation of high performance speed control system. It is used a 16-bit DSP, IMS320LF2407, which is capable of the high speed and floating point calculation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.2
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• pp.1-10
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• 2003

This paper presents an efficient method for image segmentation based on a multiresolution application of a wavelet transform and watershed segmentation algorithm. The procedure toward complete segmentation consists of four steps: pyramid representation, image segmentation, region merging and region projection. First, pyramid representation creates multiresolution images using a wavelet transform. Second, image segmentation segments the lowest-resolution image of the pyramid using a watershed segmentation algorithm. Third, region merging merges the segmented regions using the third-order moment values of the wavelet coefficients. Finally, the segmented low-resolution image with label is projected into a full-resolution image (original image) by inverse wavelet transform. Experimental results of the presented method can be applied to the segmentation of noise or degraded images as well as reduce over-segmentation.