• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.1001-1008
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• 2019

A high-quality pseudorandom sequence generation is an important part of many cryptographic applications, including encryption protocols. Therefore, a pseudorandom number generator (PRNG) is an essential element for generating key sequences in a cryptosystem. A PRNG must effectively generate a large, high-quality random data stream. It is well known that the bitstreams output by the CA-based PRNG are more random than the bitstreams output by the LFSR-based PRNG. In this paper, we prove that the complemented CA derived from 90/150 maximum length cellular automata(MLCA) is a MLCA to design a PRNG that can generate more secure bitstreams and extend the key space in a secret key cryptosystem. Also we give a method for calculating the cell positions outputting a nonlinear sequence with maximum period in complemented MLCA derived from a 90/150 MLCA and a complement vector.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.461-475
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• 2021

Hidden Markov model (HMM) is a statistical model in which the system consists of two elements, hidden states and observable results. HMM has been actively used in various fields, especially for time series data in the financial sector, since it has a variety of mathematical structures. Based on the HMM theory, this research is intended to apply the domestic KOSPI200 stock index as well as the prediction of global stock indexes such as NIKKEI225, HSI, S&P500 and FTSE100. In addition, we would like to compare and examine the differences in results between the HMM and support vector regression (SVR), which is frequently used to predict the stock price, due to recent developments in the artificial intelligence sector.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.296-302
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• 2021

This paper proposes an approach to the classification of respiratory states of humans based on visual information. An ultra-wide-band radar sensor acquired respiration signals, and the respiratory states were classified based on two-dimensional (2D) images instead of one-dimensional (1D) vectors. The 1D vector-based classification of respiratory states has limitations in cases of various types of normal respiration. The deep neural network model was employed for the classification, and the model learned the 2D images of respiration signals. Conventional classification methods use the value of the quantified respiration values or a variation of them based on regression or deep learning techniques. This paper used 2D images of the respiration signals, and the accuracy of the classification showed a 10% improvement compared to the method based on a 1D vector representation of the respiration signals. In the classification experiment, the respiration states were categorized into three classes, normal-1, normal-2, and abnormal respiration.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.191-197
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• 2015

In this paper, we propose the system which recognize the gaze using Random Forests in vehicular environment based on smart-phone. Proposed system is mainly composed of the following: face detection using Adaboost, face component estimation using Histograms, and gaze recognition based on Random Forests. We detect a driver based on the image information with a smart-phone camera, and the face component of driver is estimated. Next, we extract the feature vectors from the estimated face component and recognize gaze direction using Random Forest recognition algorithm. Also, we collected gaze database including a variety gaze direction in real environments for the experiment. In the experiment result, the face detection rate and the gaze recognition rate showed 82.02% and 84.77% average accuracies, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1725-1731
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• 2004

The use of gestures provides an attractive alternate to cumbersome interface devices for human-computer interaction. This has motivated a very active research area concerned with computer vision-based analysis and interpretation of hand gestures. The most important issues in gesture recognition are the simplification of algorithm and the reduction of processing time. The mathematical morphology based on geometrical set theory is best used to perform the processing. A key idea of proposed algorithm in this paper is to apply morphological shape decomposition. The primitive elements extracted to a hand gesture include in very important information on the directivity of the hand gestures. Based on this characteristic, we proposed the morphological gesture recognition algorithm using feature vectors calculated to lines connecting the center points of a main-primitive element and sub-primitive elements. Through the experiment, we demonstrated the efficiency of proposed algorithm. Coupling natural interactions such as hand gesture with an appropriately designed interface is a valuable and powerful component in the building of TV switch navigating and video contents browsing system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1047-1054
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• 2015

This paper presents an acoustic model transform method using untranscribed speech database for improved speech recognition. In the presented model transform method, an adapted GMM is obtained by employing the conventional adaptation method, and the most similar Gaussian component is selected from the adapted GMM. The bias vector between the mean vectors of the clean GMM and the adapted GMM is used for updating the mean vector of HMM. The presented GAMT combined with MAP or MLLR brings improved speech recognition performance in car noise and speech babble conditions, compared to singly-used MAP or MLLR respectively. The experimental results show that the presented model transform method effectively utilizes untranscribed speech database for acoustic model adaptation in order to increase speech recognition accuracy.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.4
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• pp.255-262
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• 2013

In this paper, a shape design sensitivity analysis(DSA) method is presented for Mindlin plates using an isogeometric approach. The isogeometric method possesses desirable advantages; the representation of exact geometry and the higher order inter-element continuity, which lead to the fast convergence of solution as well as accurate sensitivity results. Unlike the finite element methods using linear shape functions, the isogeometric method considers the exact normal vector and curvature of the CAD geometry, taking advantages of higher order NURBS basis functions. A selective reduced integration(SRI) technique is incorporated to overcome the difficulty of 'shear locking' phenomenon. This simple technique is surprisingly helpful for the accuracy of the isogeometric shape sensitivity without complicated formulation. Through the numerical examples of plate bending problems, the accuracy of the proposed isogeometric analysis method is compared with that of finite element one. Also, the isogeometric shape sensitivity turns out to be very accurate when compared with finite difference sensitivity.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.28-35
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• 2013

The Krylov-Schur algorithm has been applied to reveal the eigen-properties of the wave guide having the square cross section. The eigen-matrix equation has been constructed from FEM with the basis function of the tangential edge-vectors of the triangular element. This equation has been treated firstly with Arnoldi decomposition to obtain a upper Hessenberg matrix. The QR algorithm has been carried out to transform it into Schur form. The several eigen values satisfying the convergent condition have appeared in the diagonal components. The eigen-modes for them have been calculated from the inverse iteration method. The wanted eigen-pairs have been reordered in the leading principle sub-matrix of the Schur matrix. This sub-matrix has been deflated from the eigen-matrix equation for the subsequent search of other eigen-pairs. These processes have been conducted several times repeatedly. As a result, a few primary eigen-pairs of TE and TM modes have been obtained with sufficient reliability.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.12
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• pp.889-898
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• 2022

In this paper, parallel computing method on the three-dimensional electromagnetic field is proposed. The present electromagnetic scattering analysis is conducted based on the time-harmonic vector wave equation and the finite element method. The edge-based element and 2^nd -order absorbing boundary condition are used. Parallelization of the elemental numerical integration and the matrix assemblage is accomplished by allocating the partitioned finite element subdomain for each processor. The graph partitioning library, METIS, is employed for the subdomain generation. The large sparse matrix computation is conducted by MUMPS, which is the parallel computing library based on the multi-frontal method. The accuracy of the present program is validated by the comparison against the Mie-series analytical solution and the results by ANSYS HFSS. In addition, the scalability is verified by measuring the speed-up in terms of the number of processors used. The present electromagnetic scattering analysis is performed for a perfect electric conductor sphere, isotropic/anisotropic dielectric sphere, and the missile configuration. The algorithm of the present program will be applied to the finite element and tearing method, aiming for the further extended parallel computing performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1845-1852
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• 2008

The key elements of inter prediction are motion estimation(ME) and motion compensation(MC). Motion estimation is to find the optimum motion vectors, not only by using a distance criteria like the SAD, but also by taking into account the resulting number of 비트s in the 비트 stream. Motion compensation is compensate for movement of blocks of current frame. Inter-prediction Encoding is always the main bottleneck in high-quality streaming applications. Therefore, in real-time streaming applications, dedicated hardware for executing Inter-prediction is required. In this paper, we studied a motion estimator(ME) for H.264/AVC. The designed motion estimator is based on 2-D systolic array and it connects processing elements for fast SAD(Sum of Absolute Difference) calculation in parallel. By providing different path for the upper and lower lesion of each reference data and adjusting the input sequence, consecutive calculation for motion estimation is executed without pipeline stall. With data reuse technique, it reduces memory access, and there is no extra delay for finding optimal partitions and motion vectors. The motion estimator supports variable-block size and takes 328 cycles for macro-block calculation. The proposed architecture is local memory-free different from paper [6] using local memory. This motion estimation encoder can be applicable to real-time video processing.