• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.1022-1028
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• 2020

One of the possible options for spent-fuel management in Korea is pyroprocessing, which is a process for electrochemical recycling of spent nuclear fuel. Nuclear material accountancy is considered to be a safeguards measure of fundamental importance, for the purposes of which, the amount of nuclear material in the input and output materials should be measured as accurately as possible by means of chemical analysis and/or non-destructive assay. In the present study, a neutron measurement system based on the fast-neutron energy multiplication (FNEM) and passive neutron albedo reactivity (PNAR) techniques was designed for nuclear material accountancy of a spent-fuel assembly (i.e., the input accountancy of a pyroprocessing facility). Various parameters including inter-detector distance, source-to-detector distance, neutron-reflector material, the structure of a cadmium sleeve around the close detectors, and an air cavity in the moderator were investigated by MCNP6 Monte Carlo simulations in order to maximize its performance. Then, the detector responses with the optimized geometry were estimated for the fresh-fuel assemblies with different ²³⁵U enrichments and a spent-fuel assembly. It was found that the measurement technique investigated here has the potential to measure changes in neutron multiplication and, in turn, amount of fissile material.

• Structural Engineering and Mechanics
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• v.34 no.6
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• pp.739-753
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• 2010

Pointing to the design requirement of prestressed space grid structure being the target cable force, the pretension scheme decision analysis method is studied when there's great difference between structural actual state and the analytical model. Based on recursive formulation of cable forces, the simulative recursive system for pretension process is established from the systematic viewpoint, including four kinds of parameters, i.e., system initial value (structural initial state), system input value (tensioning control force scheme), system state parameters (influence matrix of cable forces), system output value (pretension accomplishment). The system controllability depends on the system state parameters. Based on cable force observation values, the influence matrix for system state parameters can be calculated, making the system controllable. Next, the pretension scheme decision method based on cable force observation values can be formed on the basis of iterative calculation for recursive system. In this way, the tensioning control force scheme that can meet the design requirement when next cyclic supplemental tension finished is obtained. Engineering example analysis results show that the proposed method in this paper can reduce a lot of cyclic tensioning work and meanwhile the design requirement can be met.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1062-1070
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• 2018

This paper presents a simple identification method for discrete-time low-order model of unknown delay process from pulse response. The key idea is to find the parameters of the model such that the first N moments of the unknown process and the model are equal. We first show that the k-th moment of a process can be determined by the moments of the input and output. The parameters and delay are estimated separately. It is shown that for a given delay, the parameters of the low-order model can be determined by solving linear equations in a matrix form. Delay of the model is estimated such that the integral of the absolute errors (IAE) of the candidate models with possible delays minimizes. The illustrative example shows that the proposed method can directly identify low-order models without order reduction process from a single pulse response.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3E
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• pp.140-145
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• 2002

This paper introduces the downlink Eigen-beamformer with Space-Time Block Code (STBC)[1,2] employed on the MISO (Multiple Input Multiple Output) systems. The proposed scheme is acquired both transmit diversity gain from STBC and beamforming gain from Eigen-beamformer. In general, it is well described that the diversity gain be maximized when channel parameters associated to fingers are mutually independent. Major role of utilizing Eigen-beamformer is to enforce channel parameters being uncorrelated. According to this, the proposed STBC combined with Eigen-beamformer on the downlink significantly improves its performance under the spatially correlated channel. Simulation results are accomplished under three distinct channels conditioned with varying the degree of their correlations. The result indicates that our proposed scheme is good performance in spatially correlated channel.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1098_1099
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• 2009

In this paper, we present the estimation of wind turbine power generation using Cascade Architectures of Fuzzy Neural Networks(CAFNN). The proposed model uses the wind speed average, the standard deviation and the past output power as input data. The CAFNN identification process uses a 10-min average wind speed with its standard deviation. The method for rule-based fuzzy modeling uses Gaussian membership function. It has three fuzzy variables with three modifiable parameters. The CAFNN's configuration has three Logic Processors(LP) that are constructed cascade architecture and an effective optimization method uses two-level genetic algorithm. First, The CAFNN is trained with one-day average input variables. Once the CAFNN has been trained, test data are used without any update. The main advantage of using CAFNN is having simple structure of system with many input variables. Therefore, The proposed CAFNN technique is useful to predict the wind turbine(WT) power effectively and hence that information will be helpful to decide the control strategy for the WT system operation and application.

• Computers and Concrete
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• v.15 no.1
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• pp.55-71
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• 2015

This paper presents a novel harmony search (HS)-based data-driven single input rule modules (SIRMs)-connected fuzzy inference system (FIS) for the prediction of stress in externally prestressed tendon. The proposed method attempts to extract causal relationship of a system from an input-output pairs of data even without knowing the complete physical knowledge of the system. The monotonicity property is then exploited as an additional qualitative information to obtain a meaningful SIRMs-connected FIS model. This method is then validated using results from test data of the literature. Several parameters, such as initial tendon depth to beam ratio; deviators spacing to the initial tendon depth ratio; and distance of a concentrated load from the nearest support to the effective beam span are considered. A computer simulation for estimating the stress increase in externally prestressed tendon, ${\Delta}f_{ps}$, is then reported. The contributions of this paper is two folds; (i) it contributes towards a new monotonicity-preserving data-driven FIS model in fuzzy modeling and (ii) it provides a novel solution for estimating the ${\Delta}f_{ps}$ even without a complete physical knowledge of unbonded tendons.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.123-136
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• 2005

We propose a recognition system for superimposed patterns based on selective attention model and SVM which produces better performance than artificial neural network. The proposed selective attention model includes attention layer prior to SVM which affects SVM's input parameters. It also behaves as selective filter. The philosophy behind selective attention model is to find the stopping criteria to stop training and also defines the confidence measure of the selective attention's outcome. Support vector represents the other surrounding sample vectors. The support vector closest to the initial input vector in consideration is chosen. Minimal euclidean distance between the modified input vector based on selective attention and the chosen support vector defines the stopping criteria. It is difficult to define the confidence measure of selective attention if we apply common selective attention model, A new way of doffing the confidence measure can be set under the constraint that each modified input pixel does not cross over the boundary of original input pixel, thus the range of applicable information get increased. This method uses the following information; the Euclidean distance between an input pattern and modified pattern, the output of SVM, the support vector output of hidden neuron that is the closest to the initial input pattern. For the recognition experiment, 45 different combinations of USPS digit data are used. Better recognition performance is seen when selective attention is applied along with SVM than SVM only. Also, the proposed selective attention shows better performance than common selective attention.

• Computers and Concrete
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• v.32 no.1
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• pp.99-117
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• 2023

The crack under the influence of the higher intensities of the stresses grows and the structure gets collapsed with the time when the crack length reaches to critical value. Therefore, the fracture behavior of a structure in terms of stress intensity factors (SIF) becomes important to determine the remaining fracture strength and capacity of material and structure for avoiding catastrophic failure, increasing safety and further improvement in the design. The robustness of the method has been demonstrated by comparing the numerical results with analytical and experimental results of some problems. XFEM is used to model cracks and holes in structures and predict their strength and reliability under service conditions. Further, XFEM is extended with a stochastic method for predicting the sensitivity in terms of output COVs and fracture strength in terms of mean values of stress intensity factors (SIFs) of a structure with discontinuities (cracks and holes) under tensile loading condition with input individual and combined randomness in different system parameters. In stochastic technique, the second order perturbation technique (SOPT) has been used for the predicting the fracture behavior of the structures. The stochastic/perturbation technique is also known as Taylor series expansion method and it provides the reliable results if the input randomness is less than twenty percentage. From the present numerical analysis it is observed that, the crack tip near to the hole is under the influence of the stress concentration and the variational effect of the input random parameters on the crack tip in terms of the SIFs are lesser so the COVs are the less sensitive. The COVs of mixed mode SIFs are the most sensitive for the crack angles (α=45° to 90°) for all the values of c₁ and d₁. The plate with the shorter distance between hole and crack is the most sensitive with all the crack angles but the crack tip which is much nearer to the hole has the highest sensitivity.

• Journal of Biosystems Engineering
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• v.17 no.1
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• pp.65-78
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• 1992

When using a computer vision system for a measurement, the geometrically distorted input image usually restricts the site and size of the measuring window. A geometrically distorted image caused by the image sensing and processing hardware degrades the accuracy of the visual measurement and prohibits the arbitrary selection of the measuring scope. Therefore, an image calibration is inevitable to improve the measuring accuracy. A calibration process is usually done via four steps such as measurement, modeling, parameter estimation, and compensation. In this paper, the efficient error calibration technique of a geometrically distorted input image was developed using a neural network. After calibrating a unit pixel, the distorted image was compensated by training CMLAN(Cerebellar Model Linear Associator Network) without modeling the behavior of any system element. The input/output training pairs for the network was obtained by processing the image of the devised sampled pattern. The generalization property of the network successfully compensates the distortion errors of the untrained arbitrary pixel points on the image space. The error convergence of the trained network with respect to the network control parameters were also presented. The compensated image through the network was then post processed using a simple DDA(Digital Differential Analyzer) to avoid the pixel disconnectivity. The compensation effect was verified using known sized geometric primitives. A way to extract directly a real scaled geometric quantity of the object from the 8-directional chain coding was also devised and coded. Since the developed calibration algorithm does not require any knowledge of modeling system elements and estimating parameters, it can be applied simply to any image processing system. Furthermore, it efficiently enhances the measurement accuracy and allows the arbitrary sizing and locating of the measuring window. The applied and developed algorithms were coded as a menu driven way using MS-C language Ver. 6.0, PC VISION PLUS library functions, and VGA graphic functions.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.61.1-61.1
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• 2017

As the application of deep-learning methods has been succeeded in various fields, they have a high potential to be applied to space weather forecasting. Convolutional neural network, one of deep learning methods, is specialized in image recognition. In this study, we apply the AlexNet architecture, which is a winner of Imagenet Large Scale Virtual Recognition Challenge (ILSVRC) 2012, to the forecast of daily solar flare occurrence using the MatConvNet software of MATLAB. Our input images are SOHO/MDI, EIT $195{\AA}$, and $304{\AA}$ from January 1996 to December 2010, and output ones are yes or no of flare occurrence. We consider other input images which consist of last two images and their difference image. We select training dataset from Jan 1996 to Dec 2000 and from Jan 2003 to Dec 2008. Testing dataset is chosen from Jan 2001 to Dec 2002 and from Jan 2009 to Dec 2010 in order to consider the solar cycle effect. In training dataset, we randomly select one fifth of training data for validation dataset to avoid the over-fitting problem. Our model successfully forecasts the flare occurrence with about 0.90 probability of detection (POD) for common flares (C-, M-, and X-class). While POD of major flares (M- and X-class) forecasting is 0.96, false alarm rate (FAR) also scores relatively high(0.60). We also present several statistical parameters such as critical success index (CSI) and true skill statistics (TSS). All statistical parameters do not strongly depend on the number of input data sets. Our model can immediately be applied to automatic forecasting service when image data are available.