• Journal of electromagnetic engineering and science
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• v.3 no.1
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• pp.50-56
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• 2003

This paper proposes a coding scheme of control channel for mobile packet communication to maximize the minimum Hamming distance, which is based on shifting of basis vectors of Reed Muller code with optimized dynamic puncturing and/or(partial) sequence repetition. Since the maximum likelihood decoding can be performed using the extremely simple FHT(Fast Hadamard Transformation), it is suitable for real time optimum decoding of control channel information with very little complexity. We show applications of the proposed coding method to TFCI(Transport Format Combination Indicator) code in split and normal modes of 3GPP W-CDMA system. We also discuss how this method can reduce rate indication error over AWGN(Additive White Gaussian Noise) as well as fading channels when the proposed coding scheme is applied to 1xEV-DV system of $3^{rd}$TEX> generation partnership project 2(3GPP2) to indicate the data rate transmitted on the reverse traffic channel by a Mobile Station(MS).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1364-1369
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• 2018

Speech emotion recognition, which aims to classify speaker's emotional states through speech signals, is one of the essential tasks for making Human-machine interaction (HMI) more natural and realistic. Voice expressions are one of the main information channels in interpersonal communication. However, existing speech emotion recognition technology has not achieved satisfactory performances, probably because of the lack of effective emotion-related features. This paper provides a survey on various features used for speech emotional recognition and discusses which features or which combinations of the features are valuable and meaningful for the emotional recognition classification. The main aim of this paper is to discuss and compare various approaches used for feature extraction and to propose a basis for extracting useful features in order to improve SER performance.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.735-743
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• 2016

This paper proposes a direction-of-arrival (DOA)-based beamforming approach for multi-cell massive multiple-input multiple-output systems with uniform rectangular arrays (URAs). The proposed approach utilizes the steering vectors of the URA to form a basis of the spatial space and selects the partial space for beamforming according to the DOA information. As a result, the proposed approach is of lower computational complexity than the existing methods which utilize the channel covariance matrices. Moreover, the analysis demonstrates that the proposed approach can eliminate the interference in the limit of infinite number of the URA antennas. Since the proposed approach utilizes the multipaths to enhance the signal rather than discarding them, the proposed approach is of better performance than the existing low-complexity method, which is verified by the simulation results.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1996.10a
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• pp.147-155
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• 1996

The purpose of present study is to propose a improved damage detection and assessment algorithm that has its basis on the method of system identification. In this approach, the complete sets of modes or displacements are not needed since the error response function involves only the difference between components of those vectors. The present approach also allows the use of composite data which is constitute of static displacements and eigenmodes. The effectiveness of the proposed statistical system identification method is investigated through simulated studies. A series of tests for predetermined damaged cantilever beam and bowstring truss structure have been conducted to verify the proposed method.

• Structural Engineering and Mechanics
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• v.6 no.8
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• pp.857-870
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• 1998

This paper deals with a special class of inverse problems in discrete structural plasticity involving the identification of elastic limits and hardening moduli on the basis of information on displacements. The governing equations lead naturally to a special and challenging optimization problem known as a Mathematical Program with Equilibrium Constraints (MPEC), a key feature of which is the orthogonality of two sign-constrained vectors or so-called "complementarity" condition. We investigate numerically the application of two simple algorithms, both based on the use of the general purpose nonlinear programming code CONOPT accessed via the GAMS modeling language, for solving the suitably reformulated problem. Application is illustrated by means of two numerical examples.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2307-2310
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• 2003

In this paper, a method for face recognition based on the wavelet packet decomposition is proposed. In the proposed method, the input image is decomposed by the 2-level wavelet packet transformation and then the face areas are defined by the Integral Projection technique applied to each of the 1-level subband images, HL and LH. After the defined face areas are divided into three areas, called top, bottom, and border, the mean and the variance of the three areas of the approximation image are computed, and the variance of the single predetermined face area for the rest of 15 detail images, from which the feature vectors of statistical measure are extracted. In this paper we use the wavelet packet decomposition, a generalization of the classical wavelet decomposition, to obtain its richer signal analysis features such as discontinuity in higher derivatives, self-similarity, etc. And we have shown that even with very simple statistical features such as mean values and variance we can make an excellent basis for face classification, if an appropriate probability distance is used.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.6
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• pp.581-590
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• 2014

With rapid land development, land category should be updated on a regular basis. However, manual field surveys have certain limitations. In this study, attempts were made to extract a feature vector considering spectral signature by parcel, PIMP (Percent Imperviousness), texture, and VIs (Vegetation Indices) based on RapidEye satellite image and cadastral map. A total of nine land categories in which feature vectors were significantly extracted from the images were selected and classified using SVM (Support Vector Machine). According to accuracy assessment, by comparing the cadastral map and classification result, the overall accuracy was 0.74. In the paddy-field category, in particular, PO acc. (producer's accuracy) and US acc. (user's accuracy) were highest at 0.85 and 0.86, respectively.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2930-2932
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• 2000

In this paper. a robust gesture recognition system is designed and implemented to explore the communication methods between human and computer. Hand gestures in the proposed approach are used to communicate with a computer for actions of a high degree of freedom. The user does not need to wear any cumbersome devices like cyber-gloves. No assumption is made on whether the user is wearing any ornaments and whether the user is using the left or right hand gestures. Image segmentation based upon the skin-color and a shape analysis based upon the invariant moments are combined. The features are extracted and used for input vectors to a radial basis function networks(RBFN). Our "Puppy" robot is employed as a testbed. Preliminary results on a set of gestures show recognition rates of about 87% on the a real-time implementation.

• ETRI Journal
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• v.33 no.4
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• pp.652-655
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• 2011

In this letter, we propose a new compression method for a high dimensional support vector machine (SVM). We used singular value decomposition (SVD) to compress the norm part of a radial basis function SVM. By deleting the least significant vectors that are extracted from the decomposition, we can compress each vector with minimized energy loss. We select the compressed vector dimension according to the predefined threshold which can limit the energy loss to design criteria. We verified the proposed vector compressed SVM (VCSVM) for conventional datasets. Experimental results show that VCSVM can reduce computational complexity and memory by more than 40% without reduction in accuracy when classifying a 20,958 dimension dataset.

• Journal of Intelligence and Information Systems
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• v.2 no.1
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• pp.45-58
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• 1996

This paper describes an a, pp.oach to recognizing composite features of prismatic parts. AAG (Attribute Adjacency Graph) is adopted as the basis of describing basic feature, but it is extended to enhance the expressive power of AAG by adding face type, angles between faces and normal vectors. Our a, pp.oach is called Extended AAG (EAAG). To simplify the recognition procedure, feature classification tree is built using the graph types of EEA and the number of EAD's. Algorithms to find open faces and dimensions of features are exemplified and used in decomposing composite feature. The processing sequence of recognized features is automatically determined during the decomposition process of composite features.