• Wind and Structures
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• v.18 no.6
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• pp.619-631
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• 2014

This paper proposes new criteria to fix hidden neuron in Multilayer Perceptron Networks for wind speed prediction in renewable energy systems. To fix hidden neurons, 101 various criteria are examined based on the estimated mean squared error. The results show that proposed approach performs better in terms of testing mean squared errors. The convergence analysis is performed for the various proposed criteria. Mean squared error is used as an indicator for fixing neuron in hidden layer. The proposed criteria find solution to fix hidden neuron in neural networks. This approach is effective, accurate with minimal error than other approaches. The significance of increasing the number of hidden neurons in multilayer perceptron network is also analyzed using these criteria. To verify the effectiveness of the proposed method, simulations were conducted on real time wind data. Simulations infer that with minimum mean squared error the proposed approach can be used for wind speed prediction in renewable energy systems.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.2
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• pp.43-49
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• 1983

In this study, model simplification problem using singular perturbation technique is considered. The correctness and errors of simplified model which is obtained by the use of this technique, depends upon the order and the time scaling factor of the simplified model But, unfortunately, there is no explicit criteria for selections of these parameters. In this paper, error equations are derived and expanded by using the useful properties of $L_2$-norm. Then, new criteria for selecting the order of the simplified model and time scaling factor with respect to error bound are suggested. Since these criteria, newly proposed in this study, have strong concern about error bound, it can be used to choose the minimum order of the simplified model and time scaling factor with respect to given error bound. Conversely, if the order of the simplified model and time scaling factor are given, the error induced by the simplification can also be computed easily.

• Journal of Communications and Networks
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• v.9 no.4
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• pp.466-472
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• 2007

Single carrier block transmission with frequency domain equalization(SC-FDE) has been shown to be a promising candidate in ultra-wideband(UWB) communications. In this paper, we analyze the performance of SC-FDE over UWB communications with channel estimation error. The probability density functions of the frequency domain minimum mean-squared error(MMSE) equalizer taps are derived in closed form. The error probabilities of single carrier block transmission with frequency domain MMSE equalization under imperfect channel estimation are presented and evaluated numerically. Compared with the simulation results, our semi-analytical analysis yields fairly accurate bit error rate performance, thus validating the use of the Gaussian approximation method in the performance analysis of the SC-FDE system with channel estimation error.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.24-29
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• 2002

In any rapid prototyping process, the layer by layer building process introduces an area error between the staircase and the surface line specified by the computer-aided design model. This affects the dimensional accuracy as well as the surface finish for different part build orientations. This paper describes a methodology for computing the area error for any orientation of the part built by the fused deposition modelling system. This technique can be applied to determine the best build orientation of the part, based on the minimum area error. This technique is verified by comparing the results with the experimental measurements of the area error of the parts built at different orientations.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.49-55
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• 2016

In digital forensic images, there is a serious problem that is distributed with various image types. For the problem solution, this paper proposes a classification algorithm of the forensic image types. The proposed algorithm extracts the 21-dim. feature vector with the contrast and energy from GLCM (Gray Level Co-occurrence Matrix), and the entropy of each image type. The classification test of the forensic images is performed with an exhaustive combination of the image types. Through the experiments, TP (True Positive) and FN (False Negative) is detected respectively. While it is confirmed that performed class evaluation of the proposed algorithm is rated as 'Excellent(A)' because of the AUROC (Area Under Receiver Operating Characteristic Curve) is 0.9980 by the sensitivity and the 1-specificity. Also, the minimum average decision error is 0.1349. Also, at the minimum average decision error is 0.0179, the whole forensic image types which are involved then, our classification effectiveness is high.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.497-503
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• 2011

In this paper, we proposed the MCE as a GMM training method to improve the performance of environmental sounds recognition. We model the environmental sounds data with newly defined misclassification function using the log likelihood of the corresponding class and the log likelihood of the rest classes for discriminative training. The model parameters are estimated with the loss function using GPD(generalized probabilistic descent). For recognition performance comparison, we extracted the 12 degrees features using preprocessing and MFCC(mel-frequency cepstral coefficients) of the 9 kinds of environmental sounds and carry out GMM classification experiments. According to the experimental results, MCE training method showed the best performance by an average of 87.06% with 19 mixtures. This result confirmed us that MCE training method could be effectively used as a GMM training method in environmental sounds recognition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12A
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• pp.1185-1190
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• 2008

In this paper, we propose an simplified successive minimum mean square error ($S^{2}MMSE$) algorithm that can simplify the computational complexity for precoding matrix generation in the successive minimum mean square error (SMMSE) precoding method, which is adopted as a multiuser multiple-input multiple-output (MU-MIMO) precoding technique in the IST (information society technologies)-WINNER (wireless world initiative new radio) project. The original algorithm generates the precoding matrix by calculating all individual precoding vectors with each requiring its own MMSE nulling matrix, over all receive antennas for all users. In contrast, this proposed algorithm first calculates the MMSE nulling matrix for each user, and then calculates all precoding vectors for respective receive antennas of the corresponding user by using the identical MMSE nulling matrix, in which only a simple matrix-vector multiplication is required for each vector. Consequently, it can simplify significantly the computational complexity to generate a precoding matrix for SMMSE precoding.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.465-478
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• 2003

This paper presents two methods for automatic estimation of threshold values in unsupervised change detection of multi-temporal remote sensing images. The proposed methods consist of two analytical steps. The first step is to compute the parameters of a 3-component Gaussian mixture model from difference or ratio images. The second step is to determine a threshold value using Bayesian rule for minimum error. The first method which is an extended version of Bruzzone and Prieto' method (2000) is to apply an Expectation-Maximization algorithm for estimation of the parameters of the Gaussian mixture model. The second method is based on an iterative thresholding algorithm that successively employs thresholding and estimation of the model parameters. The effectiveness and applicability of the methods proposed here were illustrated by two experiments and one case study including the synthetic data sets and KOMPSAT-1 EOC images. The experiments demonstrate that the proposed methods can effectively estimate the model parameters and the threshold value determined shows the minimum overall error.

• The Journal of the Acoustical Society of Korea
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• v.27 no.6
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• pp.319-324
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• 2008

In this paper, we propose a novel approach to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the discriminative weight training which is based on the minimum classification error (MCE) algorithm. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then proposed the speech/music decision rule is expressed as the geometric mean of optimally weighted features which are selected from the SMV. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme of the SMV.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.229-241
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• 1997

We consider discrimination curve and minimum dwell time for Poisson distribution and Poisson-power function distribution. Let the random variable X has Poisson distribution with mean .lambda.. For the hypothesis testing H$\_$0/:.lambda. = t vs. H$\_$1/:.lambda. = d (d$\_$0/ if X.leq.c. Since a critical value c can not be determined to satisfy both types of errors .alpha. and .beta., we considered discrimination curve that gives the maximum d such that it can be discriminated from t for a given .alpha. and .beta.. We also considered an algorithm to compute the minimum dwell time which is needed to discriminate at the given .alpha. and .beta. for the Poisson counts and proved its convergence property. For the Poisson-power function distribution, we reject H$\_$0/ if X.leq..'{c}.. Since a critical value .'{c}. can not be determined to satisfy both .alpha. and .beta., similar to the Poisson case we considered discrimination curve and computation algorithm to find the minimum dwell time for the Poisson-power function distribution. We prosent this algorithm and an example of computation. It is found that the minimum dwell time algorithm fails for the Poisson-power function distribution if the aiming error variance .sigma.$\^$2/$\_$2/ is too large relative to the variance .sigma.$\^$2/$\_$1/ of the Gaussian distribution of intensity. In other words, if .ell. is too small, we can not find the minimum dwell time for a given .alpha. and .beta..