• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.65-70
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• 1991

A hybrid model which uses a probabilistic model and a MLP(multi layer perceptron) model for pattern recognition of EMG(electromyogram) signals is proposed in this paper. MLP model has problems which do not guarantee global minima of error due to learning method and have different approximation grade to bayesian probabilities due to different amounts and quality of training data, the number of hidden layers and hidden nodes, etc. Especially in the case of new test data which exclude design samples, the latter problem produces quite different results. The error probability of probabilistic model is closely related to the estimation error of the parameters used in the model and fidelity of assumtion. Generally, it is impossible to introduce the bayesian classifier to the probabilistic model of EMG signals because of unknown priori probabilities and is estimated by MLE(maximum likelihood estimate). In this paper we propose the method which get the MAP(maximum a posteriori probability) in the probabilistic model by estimating the priori probability distribution which minimize the error probability using the MLP. This method minimize the error probability of the probabilistic model as long as the realization of the MLP is optimal and approximate the minimum of error probability of each class of both models selectively. Alocating the reference coordinate of EMG signal to the outside of the body make it easy to suit to the applications which it is difficult to define and seperate using internal body coordinate. Simulation results show the benefit of the proposed model compared to use the MLP and the probabilistic model seperately.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.10
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• pp.28-37
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• 2004

A two-dimensional direct numerical simulation was performed to investigate the evolution and vortical structure of a single vortex in reacting and non-reacting jet flow fields. A predictor-corrector-type numerical scheme with a low Mach number approximation was used, and a two-step global reaction mechanism was adopted as the combustion model. Through the comparisons of single vortex behaviors in reacting and non-reacting jet flow fields, it was found that the evolution characteristics and vortical structure of the single vortex were significantly influenced by a outer vortex that was generated from the buoyance effect as well as the chemical heat release. Furthermore, it was also identified that the differences of the vortical structure in reacting and non-reacting jet flow fields were mainly attributed to the thermal expansion, Baroclinic torque and buoyance effect.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.3
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• pp.245-254
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• 2000

This paper proposes a new discrete curvature error metric to generate LOD meshes. For mesh simplification, discrete curvatures are defined with geometric attributes, such as angles and areas of triangular polygonal model, and dihedral angles without any smooth approximation. They can represent characteristics of polygonal surface well. The new error metric based on them, discrete curvature error metric, increases the accuracy of simplified model by preserving the geometric information of original model and can be used as a global error metric. Also we suggest that LOD should be generated not by a simplification ratio but by an error metric. Because LOD means the degree of closeness between original and each level's simplified model. Therefore discrete curvature error metric needs relatively more computations than known other error metrics, but it can efficiently generate and control LOD meshes which preserve overall appearance of original shape and are recognizable explicitly with each level.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.2
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• pp.51-60
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• 2010

This paper proposes a new scheme to obtain enhanced 3D shape information rapidly for WLPSI(White Light Phase Shifting Interferometry). WLPSI is a convenient method to measure the height of the micro products. First we propose an effective method of limiting search interval for detecting the peak of the visibility function in order to obtain 3D shpae information rapidly. Second we propose an automatic base level decision method basad on image processing and a correction algorithm using the least square approximation method to overcome the global tilt problem of the conventional WLPSI algorithms. Third we propose an adaptive filtering method to remove the distortion known as bat-wing effect which appears near the step discontinuity. Experimental results show that the proposed overall technique is fast and provides more enhanced 3D shape information compared with the conventional WLPSI algorithms.

• Journal of Korean Association for Spatial Structures
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• v.5 no.3 s.17
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• pp.91-100
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• 2005

The paper presents a direct method for the diplacement control and stiffness redesign using displacement and response force contribution factors. At first, these two kinds of factors are derived and the relationship between them is examined. An equation to evaluate the change of displacement according to the change of each member stiffness is proposed. For the statically determinate structures, the proposed equation gives the exact solution with no approximation. But it has some error in case of statically indeterminate structures because the redistribution of response forces is neglected in the equation. However, the equation may be very useful even for statically indeterminate structures because it provides the relationship between the member stiffness and the global displacement. The proposed method is expected to be useful for the displacement control of large space or hi-rise building structures where the stiffness design governs the design result.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.633-650
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• 2013

All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes a computational intelligence based architecture for online data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing values with cluster centers, as part of the local learning strategy. Stage 2 refines the resultant approximate values using a General Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique. The offline imputation techniques employ K-Means or K-Medoids and Multi Layer Perceptron (MLP)or GRNN in Stage-1and Stage-2respectively. Several experiments were conducted on 8benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1603-1613
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• 2017

This paper proposes a method for effectively segmenting the hand region using a widely popular RGB color-based webcam. This performs the empirical preprocessing method four times to remove the noise. First, we use Gaussian smoothing to remove the overall image noise. Next, the RGB image is converted into the HSV and the YCbCr color model, and global fixed binarization is performed based on the statistical value for each color model, and the noise is removed by the bitwise-OR operation. Then, RDP and flood fill algorithms are used to perform contour approximation and inner area fill operations to remove noise. Finally, ROI (hand region) is selected by eliminating noise through morphological operation and determining a threshold value proportional to the image size. This study focuses on the noise reduction and can be used as a base technology of gesture recognition application.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.207-212
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• 2004

This paper presents a reinforcement learning framework for stock trading systems. Trading system parameters are optimized by Q-learning algorithm and neural networks are adopted for value approximation. In this framework, cooperative multiple agents are used to efficiently integrate global trend prediction and local trading strategy for obtaining better trading performance. Agents Communicate With Others Sharing training episodes and learned policies, while keeping the overall scheme of conventional Q-learning. Experimental results on KOSPI 200 show that a trading system based on the proposed framework outperforms the market average and makes appreciable profits. Furthermore, in view of risk management, the system is superior to a system trained by supervised learning.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.3
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• pp.18-25
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• 2005

Existing methods have performed the reliability analysis using nonlinear optimization techniques. This is mainly due to the fact that they directly apply Multidisciplinary Design Optimization(MDO) frameworks to the reliability analysis formulation. Accordingly, the reliability analysis and the Multidisciplinary Analysis(MDA) are tightly coupled in a single optimizer, which hampers utilizing the recursive and function-approximation based reliability analysis methods such as the Advanced First Order Reliability Method(AFORM). In order to utilize the efficient reliability analysis method under multidisciplinary analysis systems, we propose a new strategy named Sequential Approach on Reliability Analysis under Multidisciplinary analysis systems(SARAM). In this approach, the reliability analysis and the MDA are decomposed and arranged in a sequential manner, making a recursive loop. The efficiency of the SARAM method was verified using three illustrative examples taken from the literatures. Compared with existing methods, it showed the least number of subsystem analyses over other methods while maintaining accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.401-406
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• 2011

In order to solve a complex optimization problem more efficiently than traditional approaches, various meta-heuristic algorithms such as genetic algorithm, ant-colony algorithm, and harmony search algorithm have been extensively researched. Compared with other meta-heuristic algorithm, harmony search algorithm shows a better result to resolve the complex optimization issues. Harmony search algorithm is inspired by the improvision process of musician for most suitable harmony. In general, the performance of harmony search algorithm is determined by the value of harmony memory considering rate, and pitch adjust rate. In this paper, modified harmony search algorithm is proposed in order to derive best harmony. If the optimal solution of a specific problem can not be found for a certain period of time, a part of original harmony memory is updated as the selected suitable harmonies. Experimental results using test function demonstrate that the updated harmony memory can induce the approximation of reliable optimal solution in the short iteration, because of a few change of fitness.