• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.38 no.2
• /
• pp.16-26
• /
• 2001

This paper develops the fast dynamic programming technique to construct the subband structure yielding the maximum coding gain for given filter bases and a given limit of implementation complexity. We first derive the unified coding gain which can be applied to non-orthogonal filter basis as well as orthogonal filter basis and to arbitrary subband decompositions. Then, we verify that the unified coding gains in real systems are monotonically increasing function for the implementation complexities which are proportional to the number of subbands. By using this phenomenon, the implementation complexity and the coding gain are treated in the same way as the rate and distortion function. This makes it possible to use the Lagrangian multiplier method for finding the optimal subband decomposition producing the maximum coding gain [or a given limit of implementation complexity.

• 제어로봇시스템학회:학술대회논문집
• /
• 1998.10a
• /
• pp.285-288
• /
• 1998

A least-squares identification method is studied that estimates a finite number of coefficients in the series expansion of a transfer function, where the expansion is in terms of recently introduced generalized basis functions, We will expand and generalize the orthogonal functions as basis functions for dynamical system representations. To this end, use is made of balanced realizations as inner transfer functions. The orthogonal functions can be considered as generalizations of, for example, the pulse functions, Laguerre functions, and Kautz functions, and give rise to an alternative series expansion of rational transfer functions. We show that the Laplace transform of the expansion for some sets$\Psi_{\kappa}(Z)$ is equivalent to a series expansion . Techniques based on this result are presented for obtaining the coefficients $c_{n}$ as those of a series. One of their important properties is that, if chosen properly, they can substantially increase the speed of convergence of the series expansion. This leads to accurate approximate models with only a few coefficients to be estimated. The set of Kautz functions is discussed in detail and, using the power-series equivalence, the truncation error is obtained.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.34 no.4
• /
• pp.487-493
• /
• 2010

Most structural analyses are concerned with the deformations and stresses in a body subjected to external loads. However, in many fields, inverse problems have to be interpreted to determine surface tractions or internal stresses from displacements measured on a remote surface. In this study, the inverse processes are studied by using the finite element method for the evaluation of internal stresses. Small errors in the measured displacements often result in a substantial loss of stability of an inverse system. In order to improve the stability of the inverse system, the displacements on a section near the region of the unknown tractions are predicted by using orthogonal basis functions. We use the Gram-Schmidt orthogonal technique to determine two bases for the displacements on a section near the region of the unknown tractions. Advantages over previous methods are discussed by using numerical examples.

• Transactions of Materials Processing
• /
• v.27 no.1
• /
• pp.28-36
• /
• 2018

Electromagnetic forming is a type of high-speed forming process to deform a workpiece through a Lorentz force. As the high strain rate in an electromagnetic-forming simulation causes infeasibility in determining constitutive parameters, we employed inverse parameter estimation in the previous study. However, the inverse parameter estimation process required us to spend considerable time, which leads to an increase in computational cost. To overcome the computational obstacle, in this research, we applied two types of surrogate modeling methods and compared them to each other to evaluate which model is best for the electromagnetic-forming simulation. We exploited an artificial neural network and we reduced-order modeling methods. During the construction of a reduced-order model, we extracted orthogonal bases with proper orthogonal decomposition and predicted basis coefficients by utilizing an artificial neural network. After the construction of the surrogate models, we verified the artificial neural network and reduced-order models through training and testing samples. As a result, we determined the artificial neural network model is slightly more accurate than the reduced-order model. However, the construction of the artificial neural network model requires a considerably larger amount of time than that of the reduced-order model. Thus, a reduced order modeling method is more efficient than an artificial neural network for estimating the electromagnetic forming and for the rapid approximation of structural simulations which needs repetitive runs.

• Journal of Power Electronics
• /
• v.15 no.1
• /
• pp.216-226
• /
• 2015

Grid synchronization is one of the key techniques for the grid-connected power converters used in distributed power generation systems. In order to achieve fast and accurate grid synchronization, a new phase locked loop (PLL) is proposed on the basis of the complex filter matrixes (CFM) orthogonal signal generator (OSG) crossing-decoupling method. By combining first-order complex filters with relation matrixes of positive and negative sequence voltage components, the OSG is designed to extract specific frequency orthogonal signals. Then, the OSG mathematical model is built in the frequency-domain and time-domain to analyze the spectral characteristics. Moreover, a crossing-decoupling method is suggested to decouple the fundamental voltage. From the eigenvalue analysis point of view, the stability and dynamic performance of the new PLL method is evaluated. Meanwhile, the digital implementation method is also provided. Finally, the effectiveness of the proposed method is verified by experiments under unbalanced and distorted grid voltage conditions.

• Journal of Satellite, Information and Communications
• /
• v.7 no.1
• /
• pp.21-26
• /
• 2012

For the last 20 years, our country has been pointing to a great power for digital information technology, but quantum information technology which is already researched in many forefront nations lags significantly behind other countries. Recently, quantum information management, quantum computing and quantum communication based on the quantum mechanics have been researching actively in many fields such as cryptology. On the basis of these background, in this paper, to efficient data transmission and detection for quantum data, we apply the orthogonal sequence to quantum communication system. The performance of proposed scheme is analyzed in terms of auto and cross correlation performance.

• Current Optics and Photonics
• /
• v.5 no.6
• /
• pp.686-698
• /
• 2021

A camouflaged encryption scheme based on Hadamard matrix and ghost imaging is proposed. In the process of the encryption, an orthogonal matrix is used as the projection pattern of ghost imaging to improve the definition of the reconstructed images. The ciphertext of the secret image is constrained to the camouflaged image. The key of the camouflaged image is obtained by the method of sparse decomposition by principal component orthogonal basis and the constrained ciphertext. The information of the secret image is hidden into the information of the camouflaged image which can improve the security of the system. In the decryption process, the authorized user needs to extract the key of the secret image according to the obtained random sequences. The real encrypted information can be obtained. Otherwise, the obtained image is the camouflaged image. In order to verify the feasibility, security and robustness of the encryption system, binary images and gray-scale images are selected for simulation and experiment. The results show that the proposed encryption system simplifies the calculation process, and also improves the definition of the reconstructed images and the security of the encryption system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2000.06a
• /
• pp.1654-1660
• /
• 2000

Recently, the regulations from the government and the concerns of people give rise to the interest in exhaust and intake noise of passenger car as much as other vehicles. In these demands, performance prediction software was developed in the previous study. In this study, Robust design was used for improving the noise reduction capacity of intake system with the performance prediction software. On the basis of the existing design, length and radius of each component that was thought to effect to capacity of intake system was selected. At first factors are arranged by using $L_{18}$ table of orthogonal array and then optimum value can be obtained by $L_{16}$ table of orthogonal array.

• Journal of electromagnetic engineering and science
• /
• v.17 no.1
• /
• pp.14-19
• /
• 2017

In this paper, a planar directional beam-switchable antenna with four orthogonal beam directions is proposed. The proposed antenna is designed with two crossed active elements and two parasitic elements for each direction. The design methodology is described on the basis of the Yagi-Uda method for the active and parasitic elements, respectively. By adjusting the effective electric lengths of the parasitic elements, the roles of a director and a reflector are exchanged with each other. The planar four-way beam-switchable Yagi-Uda antenna is implemented. From the experimental results. The proposed design method is verified for orthogonal radiation beam switching.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 1999.11a
• /
• pp.214-217
• /
• 1999

In this paper we proposed a heterogeneous hidden layer consisting of both sigmoid functions and RBFs(Radial Basis Function) in multi-layered neural networks. Focusing on the orthogonal relationship between the sigmoid function and its derivative, a derived RBF that is a derivative of the sigmoid function is used as the RBF in the neural network. so the proposed neural network is called ONN's feasibility Neural Network). Identification results using a nonlinear. function confirm both the ONN's feasibility and characteristics by comparing with those obtained using a conventional neural network which has sigmoid function or RBF in hidden layer.