Chang-Kyu Song

TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization

Since the fuzzy time series forecasting methods provide a powerful framework to cope with vague or ambiguous problems, they have been widely used in real applications. The forecasting accuracy of these methods usually, however, depend on their universe of discourse and the length of intervals. So, we present a new forecasting method using two-factors high-order fuzzy time series and particle swarm optimization (PSO) for increasing the forecasting accuracy. To show the effectiveness of the proposed method, we applied our method for the Taiwan futures exchange (TAIFEX) forecasting and the Korea composite price index (KOSPI) 200 forecasting. The results show better forecasting accuracy than previous methods.

Neuro-Fuzzy Rule Generation for Backing up Navigation of Car-like Mobile Robots

An automatic neuro-fuzzy rule generation scheme is proposed for backing up navigation of car-like mobile robots. The proposed method is based on the Conditional Fuzzy C-Means (CFCM) and Fuzzy Equalization (FE) methods. The CFCM is adopted to render clusters, which can represent the homogeneous properties of the given input and output fuzzy data, and also the FE method is used to systematically construct the fuzzy membership functions for the ANFIS. From these, a compact size of fuzzy rules can be automatically obtained, which satisfy the given goal. The proposed method has been applied to a truck, and also to a truck-trailer backing up navigation problem, and good results have been achieved in comparison to previous work.

PAPR analysis in OFDM systems with frequency diversity

This letter proposes a modified orthogonal frequency division multiplexing (OFDM) signal with low peak-to-average power ratio (PAPR). As the case of previous works [9], [10], OFDM system exploits a frequency diversity by using a simple symbol repetition. From the presented results, we can see that three modified OFDM signals using one transmit antenna can be implemented with low PAPR, still maintaining the same diversity gain at the receiver as in [9], [10].

Real Fuzzy Vault for Protecting Face Template

Face recognition techniques have been widely used for various areas including criminal identification due to their capability of easy implementing and user friendly interface. However, they have some drawbacks related to individual`s privacy in case that his or her face information is divulged to illegal users. So, this paper proposed a novel method for protecting face template based on the real fuzzy vault. This proposed method has some advantages of regenerating a new face template when a registered face template is disclosed. Through implementing and testing the proposed method, we showed its validity and usefulness.

Gait Recognition and Person Identification for Surveillance Robots

The surveillance robot has been an important component in the field of service robot industry. In the surveillance robot technology, one of the most important technology is to identify a person. In this paper, we propose a gait recognition method based on contourlet and fuzzy LDA (Linear Discriminant Analysis) for surveillance robots. After decomposing a gait image into directional subband images by contourlet, features are obtained in each subband by the fuzzy LDA. The final gait recognition is performed by a fusion technique that effectively combines similarities calculated respectively in each local subband. To show the effectiveness of the proposed algorithm, various experiments are performed for CBNU and NLPR DB datasets. From these, we obtained better classification rates in comparison with the result produced by previous methods.

Feature Selection by Genetic Algorithm and Information Theory

In the pattern classification problem, feature selection is an important technique to improve performance of the classifiers. Particularly, in the case of classifying with a large number of features or variables, the accuracy of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. In this paper we propose a feature selection method using genetic algorithm and information theory. Experimental results show that this method can achieve better performance for pattern recognition problems than conventional ones.

Improved PAPR Statistics in Multiband OFDM Systems

This letter introduces a modified multiband orthogonal frequency division multiplexing (MB-OFDM) signal with low peak-to-average power ratio (PAPR). From the presented results, we can see that the modified MB-OFDM signal can be implemented with low PAPR. When MB-OFDM signals is equipped with a partial transmit sequence (PTS) approach, the PAPR of the modified MB-OFDM signals using two partial transmit sequences is almost the same to that of the ordinary MB-OFDM signals using four partial transmit sequences.

Feature Selection Method by Information Theory and Particle S warm Optimization

In this paper, we proposed a feature selection method using Binary Particle Swarm Optimization(BPSO) and Mutual information. This proposed method consists of the feature selection part for selecting candidate feature subset by mutual information and the optimal feature selection part for choosing optimal feature subset by BPSO in the candidate feature subsets. In the candidate feature selection part, we computed the mutual information of all features, respectively and selected a candidate feature subset by the ranking of mutual information. In the optimal feature selection part, optimal feature subset can be found by BPSO in the candidate feature subset. In the BPSO process, we used multi-object function to optimize both accuracy of classifier and selected feature subset size. DNA expression dataset are used for estimating the performance of the proposed method. Experimental results show that this method can achieve better performance for pattern recognition problems than conventional ones.

Face Recognition using Contourlet Transform and PCA

Contourlet transform is an extention of the wavelet transform in two dimensions using the multiscale and directional fillet banks. The contourlet transform has the advantages of multiscale and time-frequency-localization properties of wavelets, but also provides a high degree of directionality. In this paper, we propose a face recognition system based on fusion methods using contourlet transform and PCA. After decomposing a face image into directional subband images by contourlet, features are obtained in each subband by PCA. Finally, face recognition is performed by fusion technique that effectively combines similarities calculated respectively In each local subband. To show the effectiveness of the proposed method, we performed experiments for ORL and CBNU dataset, and then we obtained better recognition performance in comparison with the results produced by conventional methods.

Evaluation of SIR statistics in a DS/CDMA system with signal‐level‐based power control and multipath dispersion

The statistical evaluation of the estimated short-term signal-to-interference ratio (SIR) for power control is presented in many-to-one reverse link. As mentioned in other previous works, the statistical evaluation shows that the estimated short-term SIR can be approximated by a log-normal distribution. The analysis has applications to a cellular system employing direct-sequence spread-spectrum code-division multiple access (CDMA) with M-ary orthogonal modulation on the uplink. Copyright