• Convergence Security Journal
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• v.14 no.5
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• pp.17-24
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• 2014

This study proposed LDP (Local Directional Pattern) as a new local micro pattern for facial expression recognition to solve noise sensitive problem of LBP (Local Binary Pattern). The proposed method extracts 8-directional components using $m{\times}m$ mask to solve LBP's problem and choose biggest k components, each chosen component marked with 1 as a bit, otherwise 0. Finally, generates a pattern code with bit sequence as 8-directional components. The result shows better performance of rotation and noise adaptation. Also, a new local facial feature can be developed to present both PFF (permanent Facial Feature) and TFF (Transient Facial Feature) based on the proposed method.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.795-801
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• 2013

Recently, support vector machine has been widely used in various application fields due to its superiority of classification performance comparing with decision tree and neural network. Since support vector machine is basically designed for the binary classification problem, output coding method to analyze the classification result of multiclass binary classifier is used for the application of support vector machine into the multiclass problem. However, previous feature selection method for output coding based support vector machine found the features to improve the overall classification accuracy instead of improving each classification accuracy of each classifier. In this paper, we propose the novel feature selection method to find the features for maximizing the classification accuracy of each binary classifier in output coding based support vector machine. Experimental result showed that proposed method significantly improved the classification accuracy comparing with previous feature selection method.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.5B no.1
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• pp.70-77
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• 2005

This paper presents sensorless speed control of a cylindrical permanent magnet synchronous motor (PMSM) using the adaptive integral binary observer. In view of the composition with a main loop regulator and an auxiliary loop regulator, the normal binary observer has the feature of chattering alleviation in the constant boundary layer. However, the steady state estimation accuracy and robustness are dependent upon the thickness of the constant boundary layer. In order to improve the steady state performance of the binary observer, a new binary observer is formed by the addition of extra integral dynamics to the existing switching hyperplane equation. Also, because the parameters of the dynamic equations such as machine inertia or viscosity friction coefficient are not well known and these values can be changed during normal operations, there are many restrictions in the actual implementation. The proposed adaptive integral binary observer applies an adaptive scheme so that the observer may overcome the problems caused by using dynamic equations. The rotor speed is constructed by using the Lyapunov function. The observer structure and its design method are described. The experimental results of the proposed algorithm are presented to prove the effectiveness of the approach.

• Journal of Computing Science and Engineering
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• v.11 no.4
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• pp.130-141
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• 2017

Feature selection is one of the most challenging problems of pattern recognition and data mining. In this paper, a feature selection algorithm based on an improved version of binary differential evolution is proposed. The method simultaneously optimizes two feature selection criteria, namely, set approximation accuracy of rough set theory and relational algebra based derived score, in order to select the most relevant feature subset from an entire feature set. Superiority of the proposed method over other state-of-the-art methods is confirmed by experimental results, which is conducted over seven publicly available benchmark datasets of different characteristics such as a low number of objects with a high number of features, and a high number of objects with a low number of features.

• ETRI Journal
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• v.34 no.5
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• pp.743-752
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• 2012

Semantic interpretation of the relationship between noun compound (NC) elements has been a challenging issue due to the lack of contextual information, the unbounded number of combinations, and the absence of a universally accepted system for the categorization. The current models require a huge corpus of data to extract contextual information, which limits their usage in many situations. In this paper, a new semantic relations interpreter for NCs based on novel lightweight binary features is proposed. Some of the binary features used are novel. In addition, the interpreter uses a new feature selection method. By developing these new features and techniques, the proposed method removes the need for any huge corpuses. Implementing this method using a modular and plugin-based framework, and by training it using the largest and the most current fine-grained data set, shows that the accuracy is better than that of previously reported upon methods that utilize large corpuses. This improvement in accuracy and the provision of superior efficiency is achieved not only by improving the old features with such techniques as semantic scattering and sense collocation, but also by using various novel features and classifier max entropy. That the accuracy of the max entropy classifier is higher compared to that of other classifiers, such as a support vector machine, a Na$\ddot{i}$ve Bayes, and a decision tree, is also shown.

• MALSORI
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• no.41
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• pp.31-47
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• 2001

The purpose of this study is to explain the cause of obsturents neutralization in syllable final position. Most of the previous phonological studies did not reflect phonetic reality sufficiently because of the limited use of the binary feature system. Using binary distinctive features, we can't explain the cause of neutralization. In order to explain the cause of neutralization, I use the multi-valued phonetic feature -[vocal tract aperture]. By [vocal tract aperture] I mean the distance between articulators in the hold stage. In this study, I claim that the cause of neutralization is assimilation to [vocal tract aperture] 0 degree. The neutralized sounds become aplosives, as a consequence of assimilation to [vocal tract aperture].

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.5
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• pp.390-395
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• 2000

We present a fingerprint identification algorithm using the wavelet transform and correlation. The wavelet transform is used because of its simple operation to extract fingerprint minutiaes features for fingerprint classification. We perform the rowwise 1-D wavelet transform for a $256\times256$ fingerprint image to get a $1\times256$ column vector using the Haar wavelet and repeat 1-D wavelet transform for a 1$\times$256 column vector to get a $1\times4$ feature vector. Using PNN(Probabilistic Neural Network), we select the possible candidates from the stored feature vectors for fingerprint images. For those candidates, we compute the correlation between the input binary image and the target binary image to find the most similar fingerprint image. The proposed algorithm may be the key to a low cost fingerprint identification system that can be operated on a small computer because it does not need a large memory size and much computation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.2
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• pp.374-388
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• 2011

Biometric discretization is a process of transforming continuous biometric features of an identity into a binary bit string. This paper mainly focuses on improving the global discretization method - a discretization method that does not base on information specific to each user in bitstring extraction, which appears to be important in applications that prioritize strong security provision and strong privacy protection. In particular, we demonstrate how the actual performance of a global discretization could further be improved by embedding a global discriminative feature selection method and a Linearly Separable Subcode-based encoding technique. In addition, we examine a number of discriminative feature selection measures that can reliably be used for such discretization. Lastly, encouraging empirical results vindicate the feasibility of our approach.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.5
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• pp.11-17
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• 2010

In this parer, face recognition method which can be applied to ITCS (interactive TV control system) is proposed. We extracted ULBP(uniform local binary pattern) histogram feature from infra-red images, and we detected left-right eyes and face region by using SVM classifier. Then, We implemented face recognition system which is using Gabor transform and ULBP histogram feature and applied to personal verification for ITCS.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.857-862
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• 2004

This paper analyzes properties of the recently developed nonlinear optimization method, Dynamic Encoding Algorithm for Searches (DEAS) [1]. DEAS locates local minima with binary strings (or binary matrices for multi-dimensional problems) by iterating the two operators; bisectional search (BSS) and unidirectional search (UDS). BSS increases binary strings by one digit (i.e., zero or one), while UDS performs increment or decrement to binary strings with no change of string length. Owing to these search routines, DEAS retains the optimization capability that combines the special features of several conventional optimization methods. In this paper, a special feature of BSS and UDS in DEAS is analyzed. In addition, a effective global search strategy is established by using information of DEAS. Effectiveness of the proposed global search strategy is validated through the well-known benchmark functions.