• Nuclear Engineering and Technology
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• 제48권1호
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• pp.64-71
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• 2016

Void fraction is an important parameter in the oil industry. This quantity is necessary for volume rate measurement in multiphase flows. In this study, the void fraction percentage was estimated precisely, independent of the flow regime in gas-liquid two-phase flows by using ${\gamma}-ray$ attenuation and a multilayer perceptron neural network. In all previous studies that implemented a multibeam ${\gamma}-ray$ attenuation technique to determine void fraction independent of the flow regime in two-phase flows, three or more detectors were used while in this study just two NaI detectors were used. Using fewer detectors is of advantage in industrial nuclear gauges because of reduced expense and improved simplicity. In this work, an artificial neural network is also implemented to predict the void fraction percentage independent of the flow regime. To do this, a multilayer perceptron neural network is used for developing the artificial neural network model in MATLAB. The required data for training and testing the network in three different regimes (annular, stratified, and bubbly) were obtained using an experimental setup. Using the technique developed in this work, void fraction percentages were predicted with mean relative error of <1.4%.

• 유체기계공업학회:학술대회논문집
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• 유체기계공업학회 2001년도 유체기계 연구개발 발표회 논문집
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• pp.425-432
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• 2001

Fluidic valve is adopted in APR1400 to control passively the flow rate of cooling water from the safety injection tank. It is necessary to establish independent evaluation guideline for the flow characteristics of fluidic valve in order to secure safety. Three dimensional numerical model for fluidic valve is developed and numerical results are compared with experimental data obtained at KAERI in order to verify numerical simulation. Also influence of the grid number and the turbulence model were investigated. In addition, variation of flow rate is investigated at various elapsed times after valve operating, and flow characteristics are analyzed at low and high flow rate conditions, respectively.

• Transactions of the Korean Society of Mechanical Engineers A
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• 제24권8호
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• pp.2115-2122
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• 2000

The understanding of impact-induced delamination is important in safety and reliability of composite structure. In this study, a model for arrest toughness is proposed in consideration of fracture behavior of composite materials. Also, the probabilistic model is proposed to describe the variability of arrest toughness due to the nonhomogeneity of material. For these models, experiments were conducted on the Carbon/Epoxy composite plates with various thickness using the impact hammer. The elastic work factor used in J-Integral is applicable to the evaluation of energy release rate. The fracture behavior can be described by crack arrest concept and the arrest toughness is independent of the delamination size. Additionally, a probabilistic characteristics of arrest toughness is well described by the Weibull distribution function. A variation of arrest toughness increases with specimen thickness.

• Journal of the Korean Institute of Telematics and Electronics B
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• 제31B권12호
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• pp.108-115
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• 1994

Most pattern classifiers have been designed based on the ML (Maximum Likelihood) training algorithm which is simple and relatively powerful. The ML training is an efficient algorithm to individually estimate the model parameters of each class under the assumption that all class models in a classifier are statistically independent. That assumption, however, is not valid in many real situations, which degrades the performance of the classifier. In this paper, we propose a minimum-error-rate training algorithm based on the MAP (Maximum a Posteriori) approach. The algorithm regards the normalized outputs of the classifier as estimates of the a posteriori probability, and tries to maximize those estimates. According to Bayes decision theory, the proposed algorithm satisfies the condition of minimum-error-rate classificatin. We apply this algorithm to NPM (Neural Prediction Model) for speech recognition, and derive new disrminative training algorithms. Experimental results on ten Korean digits recognition have shown the reduction of 37.5% of the number of recognition errors.

• Advances in Energy Research
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• 제1권1호
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• pp.35-52
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• 2013

A model for DMFC anode performance is developed. The model takes into account potential--independent methanol adsorption on the catalyst surface, finite rate of proton transport through the anode catalyst layer (ACL), and a potential loss due to methanol transport in the anode backing layer. An approximate analytical half--cell polarization curve is derived and equations for the anode limiting current density are obtained. The polarization curve is fitted to the curves measured by Nordlund and Lindbergh and parameters resulted from the fitting are discussed.

• Journal of the Korean Society for Precision Engineering
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• 제29권7호
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• pp.745-752
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• 2012

The pivot steering of an individual wheel motor drive vehicle is an effective steering maneuver in the narrow road, but it has become a matter of concern that the torque input of each wheel is very difficult to determine. In this study, the independent yaw moment control was proposed for the smooth pivot steering control of an in-wheel drive vehicle. For this control method, the vertical forces of tires were estimated from the trailing arm dynamic model, and the yaw moments of individual wheels were calculated from the vehicle dynamic model. Dynamic simulation results showed that the independent yaw moment control was much more effective on the minimization of the instabilities of pivot steering in comparison with the conventional direct yaw moment control with yaw rate feedback.

• The Journal of the Acoustical Society of Korea
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• 제34권2호
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• pp.171-176
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• 2015

We present a speaker verification method by extracting i-vectors based on dominant state information of Hidden Markov Model (HMM) - Universal Background Model (UBM). Ergodic HMM is used for estimating UBM so that various characteristic of individual speaker can be effectively classified. Unlike Gaussian Mixture Model(GMM)-UBM based speaker verification system, the proposed system obtains i-vectors corresponding to each HMM state. Among them, the i-vector for feature is selected by extracting it from the specific state containing dominant state information. Relevant experiments are conducted for validating the proposed system performance using the National Institute of Standards and Technology (NIST) 2008 Speaker Recognition Evaluation (SRE) database. As a result, 12 % improvement is attained in terms of equal error rate.

• Journal of the Korean Society of Safety
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• 제14권2호
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• pp.155-162
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• 1999

reliability from components reliability. In this case, it assumes that components failure is mutually independent, but it may not true in real systems. In this study, the mean cost per unit time is computed as the ratio of mean life to the mean cost. The mean life is obtained by the reliability function under power rule model. The mean cost is obtained by the mathematical model based on the inspection interval. A heuristic method is proposed to determine the optimal number of redundant units and the optimal inspection interval to minimize the mean cost per unit time. The assumptions of this study are as following : First, in the load-sharing k-out-of-n:G system, total loads are applied to the system and shared by the operating components. Secondly, the number of failed components affects the failure rate of surviving components as a function of the total load applied. Finally, the relation between the load and the failure rate of surviving components is set by the power rule model. For the practical application of the above methods, numerical examples are presented.

• MALSORI
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• 제56호
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• pp.147-158
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• 2005

In this paper, we are focused to develop the simplified speaker verification algorithm without background speaker models, which will be adopted in the portable speaker verification system equipped in portable terminals such as mobile phone and PMP. According to the tolerance interval analysis, the population of someone's speaker model can be represented by a suitable number of selected independent samples of speaker model. So we can make the representative speaker model and threshold under the specified confidence level and coverage. Using proposed algorithm with the number of samples is 40, the experiments show that the false rejection rate is $3.0\%$ and the false acceptance rate $4.3\%$, worth comparing to conventional method's results, $5.4\%\;and\;5.5\%$, respectively. Next step of research will be on the suitable adaptation methods to overcome speech variation problems due to aging effect and operating environments.

• The Journal of the Acoustical Society of Korea
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• 제16권2E호
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• pp.55-61
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• 1997

This paper proposes a MSHMM(Multi-Section Hidden Markov Model) recognition method based on Fuzzy Concept, as a method on the speech recognition of speaker-independent. In this recognition method, training data are divided into several section and multi-observation sequences given proper probabilities by fuzzy rule according to order of short distance from MSVQ codebook per each section are obtained. Thereafter, the HMM per each section using this multi-observation sequences is generated, and in case of recognition, a word that has the most highest probability is selected as a recognized word. In this paper, other experiments to compare with the results of these experiments are implemented by the various conventional recognition methods(DP, MSVQ, DMS, general HMM) under the same data. Through results of all-round experiment, it is proved that the proposed MSHMM based on fuzzy concept is superior to DP method, MSVQ method, DMS model and general HMM model in recognition rate and computational time, and does not decreases recognition rate as 92.91% in spite of increment of speaker number.