• Journal of KSNVE
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• v.8 no.6
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• pp.1037-1042
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• 1998

This paper discusses the dependence of the convergence rate on the acoustic error path in these popular algorithms and introduces new algorithms which increase the convergence region regardless of the time-delay in the acoustic error path. We also Propose a novel control algorithm (AFC/CAFC) for tonal noise cancellation. The proposed algorithm estimates the magnitude and phase of the tonal noise. The algorithm uses the steepest descent method for the phase/magnitude estimation. Performances of tile CAFC algorithm are presented in comparison with those by the AFC algorithm based on computer simulations and experiments.

• Bulletin of the Korean Mathematical Society
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• v.55 no.4
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• pp.1303-1315
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• 2018

For principal component analysis (PCA) to efficiently analyze large scale matrices, it is crucial to find a few singular vectors in cheaper computational cost and under lower memory requirement. To compute those in a fast and robust way, we propose a new stochastic method. Especially, we adopt the stochastic variance reduced gradient (SVRG) method [11] to avoid asymptotically slow convergence in stochastic gradient descent methods. For that purpose, we reformulate the PCA problem as a unconstrained optimization problem using a quadratic penalty. In general, increasing the penalty parameter to infinity is needed for the equivalence of the two problems. However, in this case, exact penalization is guaranteed by applying the analysis in [24]. We establish the convergence rate of the proposed method to a stationary point and numerical experiments illustrate the validity and efficiency of the proposed method.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.10
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• pp.1335-1342
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• 2000

Three-dimensional flow analysis and numerical optimization methods are presented for the design of an axial-flow fan. Steady, incompressible, three-dimensional Reynolds-averaged Navier-Stokes equations are used as governing equations, and standard k- ${\varepsilon}$ turbulence model is chosen as a turbulence model. Governing equations are discretized using finite volume method. Steepest descent method, conjugate gradient method and BFGS method are compared to determine the searching directions. Golden section method and quadratic fit-sectioning method are tested for one dimensional search. Objective function is defined as a ratio of generation rate of the turbulent kinetic energy to pressure head. Two variables concerning sweep angle distribution are selected as the design variables. Performance of the final fan designed by the optimization was tested experimentally.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.2
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• pp.69-77
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• 2001

An artificial neural network model was developed to analyze and forecast daily steamflow flow a small watershed. Error Back propagation neural networks (EBPN) of daily rainfall and runoff data were found to have a high performance in simulating stremflow. The model adopts a gradient descent method where the momentum and adaptive learning rate concepts were employed to minimize local minima value problems and speed up the convergence of EBP method. The number of hidden nodes was optimized using Bayesian information criterion. The resulting optimal EBPN model for forecasting daily streamflow consists of three rainfall and four runoff data (Model34), and the best number of the hidden nodes were found to be 13. The proposed model simulates the daily streamflow satisfactorily by comparison compared to the observed data at the HS#3 watershed of the Baran watershed project, which is 391.8 ha and has relatively steep topography and complex land use.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4E
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• pp.21-27
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• 1996

In this paper, we propose a discriminative training algorithm for the stochastic segment model (SSM) in continuous speech recognition. As the SSM is usually trained by maximum likelihood estimation (MLE), a discriminative training algorithm is required to improve the recognition performance. Since the SSM does not assume the conditional independence of observation sequence as is done in hidden Markov models (HMMs), the search space for decoding an unknown input utterance is increased considerably. To reduce the computational complexity and starch space amount in an iterative training algorithm for discriminative SSMs, a hybrid architecture of SSMs and HMMs is programming using HMMs. Given the segment boundaries, the parameters of the SSM are discriminatively trained by the minimum error classification criterion based on a generalized probabilistic descent (GPD) method. With the discriminative training of the SSM, the word error rate is reduced by 17% compared with the MLE-trained SSM in speaker-independent continuous speech recognition.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.526-530
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• 1999

This paper presents a newly developed speed sensorless drive using Neural Network algorithm. Neural Network algorithm can be divided into three categories. In the first one, a Back Propagation-based NN algorithm is well-known to gradient descent method. In the second scheme, a Extended Kalman Filter-based NN algorithm has just the time varying learning rate. In the last scheme, a Recursive Least Square-based NN algorithm is faster and more stable than the classical back-propagation algorithm for training multilayer perceptrons. The number of iterations required to converge and the mean-squared error between the desired and actual outputs is compared with respect to each method. The theoretical analysis and experimental results are discussed.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.480-483
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• 1992

Although the back-propagation(BP) algorithm is widely used for its simple structure and easy learning method, it has a drawback of slow convergence rate. In this paper, we propose an algorithm to improve this problem by manipulating the slope parameter of the activation function. The steepest descent method is used in learning the slope parameter, as in the case of weight. The simulation shows that the learning rates of the proposed algorithm is faster than the conventional BP algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2171-2185
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• 2020

In order to judge that whether the vehicles in different images which are captured by surveillance cameras represent the same vehicle or not, we proposed a novel vehicle face recognition algorithm based on improved Nonnegative Matrix Factorization (NMF), different from traditional vehicle recognition algorithms, there are fewer effective features in vehicle face image than in whole vehicle image in general, which brings certain difficulty to recognition. The innovations mainly include the following two aspects: 1) we proposed a novel idea that the vehicle type can be determined by a few key regions of the vehicle face such as logo, grille and so on; 2) Through adding weight, sparseness and classification property constraints to the NMF model, we can acquire the effective feature bases that represent the key regions of vehicle face image. Experimental results show that the proposed algorithm not only achieve a high correct recognition rate, but also has a strong robustness to some non-cooperative factors such as illumination variation.

• Advances in aircraft and spacecraft science
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• v.2 no.4
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• pp.367-389
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• 2015

Simple solutions are obtained for the fuel required by internal combustion engine airplanes on trajectories with a constant rate of climb or descent. Three modes of flight are considered: constant speed, constant Mach number and constant angle of attack. Starting from the exact solutions of the equations of motion for the modes of motion considered, approximate solutions are obtained that are much easier to compute while still being quite precise. Simpler formulas are derived for the weight of fuel, speed, altitude, horizontal distance, time to climb, and power required. These formulas represent a new important contribution since they are fundamental for the analysis of aircraft dynamics and thus have direct applications for the analysis of aircraft performances and mission planning.

• Korean Journal of Optics and Photonics
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• v.8 no.5
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• pp.362-365
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• 1997

This paper presents a new optical phase stabilization technique for interferometric such as Mach-Zehnder interferometers or Homodyne detectors. The proposed technique can lock such interferometric systems in their maximum/minimum optical outputs without using a dither signal. The phase control scheme is a modified steepest decent algorithm, and is analyzed in terms of a delta modulation approach. It is also applicable to low-repetition rate pulsed interferometric systems in which conventionally used dithering method is difficult to be applied.