• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4A
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• pp.330-335
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• 2002

In the direct sequence code division multiple access system, the problem of multiple access interference due to multiple access is generated. A interference cancelling receiver is used to solve this problem. The conventional interference cancelling receiver is structure of successive interference canceller using antenna array. In this structure, the difference of between method I and method II depends on updating weight vector. In this paper, the adaptive blind CMA array interference cancelling receiver using cost function of constant modulus algorithms is proposed to update weight vector at conventional structure. The simulation compared the proposed interference cancelling receiver with two conventional interference cancelling receivers by signal to interference ratio and bit error rate curve under additive white Gaussian noise environment. The simulation results show that the proposed receiver has about the gain of SIR of 1.5[dB] more than method I which is conventional receiver at SIR curve, and about the gain of SIR of 0.5(dB) more than method II. In BER curve, the proposed IC receiver about the gain of SNR of 2[dB] more than method I and about the gain of SNR of 0.5[dB] more than method If, Thus, the proposed interference cancelling receiver has the higher performance than conventional interference cancelling receivers.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.22-27
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• 2014

Blocking and ringing artifacts continue to be the most serious defects that occur in images and video streams compressed to low bit rates using block-based discrete cosine transform(DCT) compression standards. These artifacts contain the high frequency components near the block and the edge boundaries. Usually the lowpass filter can remove them. However, simple lowpass filter results into blur by removing important information such as edges at the same time. To overcome these problems, we propose a novel postfiltering algorithm that calculate the weight value based on the intensity similarity in the neighboring pixels and multiply this weight to the Gaussian lowpass filter coefficient. Experimental results show that the proposed technique provides satisfactory performance in both objective and subjective image quality.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.57-67
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• 2018

The ensemble is a unified approach used for getting better performance by using multiple algorithms in machine learning. In this paper, we introduce boosting and bagging, which have been widely used in ensemble techniques, and design a method using support vector regression, radial basis function network, Gaussian process, and multilayer perceptron. In addition, our experiment was performed by adding a recurrent neural network and MOHID numerical model. The drifter data used for our experimental verification consist of 683 observations in seven regions. The performance of our ensemble technique is verified by comparison with four algorithms each. As verification, mean absolute error was adapted. The presented methods are based on ensemble models using bagging, boosting, and machine learning. The error rate was calculated by assigning the equal weight value and different weight value to each unit model in ensemble. The ensemble model using machine learning showed 61.7% improvement compared to the average of four machine learning technique.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2419-2424
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• 2013

In a conventional MOG algorithm, a small threshold for background decision causes the background recognition delay in a periodic background and a large threshold makes it recognize passing objects as background in a stationary background. This paper proposes the improved MOG algorithm using adaptive threshold. The proposed algorithm estimates changes of weight in the dominant model of the MOG algorithm both in the short and long terms, classifies backgrounds into the stationary and periodic ones, and assigns proper thresholds to them. The simulation results show that the proposed algorithm decreases the maximum number of frame in background recognition delay from 137 to 4 in the periodic background keeping the equal performance with the conventional algorithm in the stationary background.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2390-2395
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• 2011

For intersymbol interference (ISI) compensation from communication channels with multi-path fading and impulsive noise, a decision feedback equalizer algorithm that minimizes Euclidean distance of error probability is proposed. The Euclidean distance of error probability is defined as the quadratic distance between the probability error signal and Dirac-delta function. By minimizing the distance with respect to equalizer weight based on decision feedback structures, the proposed decision feedback algorithm has shown to have significant effect of residual ISI cancellation on severe multipath channels as well as robustness against impulsive noise.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.767-778
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• 2003

This paper presents a method for generating optimal fuzzy rules using a genetic algorithm. Fuzzy rules are generated from the training data in the first stage. In this stage, fuzzy c-Means clustering method and cluster validity are used to determine the structure and initial parameters of the fuzzy inference system. A cluster validity is used to determine the number of clusters, which can be the number of fuzzy rules. Once the structure is figured out in the first stage, parameters relating the fuzzy rules are optimized in the second stage. Weights and variance parameters are tuned using genetic algorithms. Variance parameters are also managed with left and right for asymmetrical Gaussian membership function. The method ensures convergence toward a global minimum by using genetic algorithms in weight and variance spaces.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.854-867
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• 2014

This paper deals with the performance comparison of a PSO algorithm inspired in the process of simulating the behavior pattern of the organisms. The PSO algorithm finds the optimal solution (fitness value) of the objective function based on a stochastic process. Generally, the stochastic process, a random function, is used with the expression related to the velocity included in the PSO algorithm. In this case, the random function of the normal distribution (Gaussian) or uniform distribution are mainly used as the random function in a PSO algorithm. However, in this paper, because the probability distribution which is various with 2 shape parameters can be expressed, the performance comparison of a PSO algorithm using the beta probability distribution function, that is a random function which has a high degree of freedom, is introduced. For performance comparison, 3 functions (Rastrigin, Rosenbrock, Schwefel) were selected among the benchmark Set. And the convergence property was compared and analyzed using PSO-FIW to find the optimal solution.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1064-1073
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• 2015

In this study, we introduce the pedestrian detection system by using the feature of HOG-PCA and RBFNNs pattern classifier. HOG(Histogram of Oriented Gradient) feature is extracted from input image to identify and recognize a object. And a dimension is reduced for improving performance as well as processing speed by using PCA which is a typical dimensional reduction algorithm. So, the feature of HOG-PCA through the dimensional reduction by using PCA leads to the improvement of the detection rate. FCM clustering algorithm is used instead of gaussian function to apply the characteristic of input data as well and connection weight is used by polynomial expression such as constant, linear, quadratic and modified quadratic. Finally, INRIA person database known as one of the benchmark dataset used for pedestrian detection is applied for the performance evaluation of the proposed classifier. The experimental result of the proposed classifier are compared with those studied by Dalal.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.877-879
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• 2013

Imaging device such as digital TV is being popular in a modern society based on communication technology. However, because of internal and external cause of system in the process of transmission, storage and acquisition, image is degraded by noise. Therefore, the importance of denoising technology is being increased, and a research for that is being actively made. In this paper, a weighted filter algorithm that considers different pixels of masks and estimated noise variance was proposed. in order to remove AWGN. And, PSNR(peak signal to noise ratio) was used to represent the excellence of proposed algorithm.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.52-59
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• 2007

This paper presents a greedy method for user-steered mesh segmentation, which is based on the merging priority metric defined for representing the geometric properties of meaningful parts. The priority metric is a weighted function composed of five geometric parameters: distribution of Gaussian map, boundary path concavity, boundary path length, cardinality, and segmentation resolution. This scheme can be extended without any modification only by defining more geometric parameters and adding them. Our experimental results show that the shapes of segmented parts can be controlled by setting up the weight values of geometric parameters.