• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.6
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• pp.982-989
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• 2010

This paper shows accuracy comparison results of reliability estimation methods for one-shot systems such as ammunitions. Quantal-response data, following a binomial distribution at each sampling time, characterizes lifetimes of one-shot systems. Various quantal-response data of different sample sizes are simulated using lifetime data randomly sampled from assumed weibull distributions with different shape parameters but the identical scale parameter in this paper. Then, reliability estimation methods in open literature are applied to the simulated quantal-response data to estimate true reliability over time. Rankings in estimation accuracy for different sample sizes are determined using t-test of SSE. Furthermore, MSE at each time, including both bias and variance of estimated reliability metrics for each method are analyzed to investigate how much both bias and variance contribute the SSE. From the MSE analysis, MSE provides reliability estimation trend for each method. Parametric estimation method provides more accurate reliability estimation results than the other methods for most of sample sizes.

• The Transactions of the Korea Information Processing Society
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• v.4 no.2
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• pp.456-464
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• 1997

We describe a random prcess model that prvides sets of patterms whth prcisely contrlolled within-class varia-bility and between-class distinctions.We used these pattems in a simulation study wity the back-propagation netwoek to chracterize its perfotmance as we varied the process-controlling parameters,the statistical differences between the processes,and the random noise on the patterns.Our results indicated that grneralized statistical difference between the processes genrating the patterns provided a good predictor of the difficulty of the clssi-fication problem. Also we analyzed the performance of the Bayes classifier whith the maximum-likeihood cri-terion and we compared the performance of the neural network to that of the Bayes classifier.We found that the performance of neural network was intermediate between that of the simulated and theoretical Bayes classifier.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.174-179
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• 2005

In the super slow speed die casting process, the casting defects due to melt flow should be controlled in order to obtain sound casting products. The casting defects that are caused by molten metal were cold shut formation, entrapment of air, gas, and inclusion. But the control of casting defects has been based on the experience of the foundry engineers. The calculation of simulation can produce very useful and important results. The calculation data of die casting process condition from the computer simulation by the Z-CAST is made to insure that the liquid metal is injected at the right velocity range and that the filling time is small enough to prevent premature solidification. The parameters of runner shape that affected on the optimized conditions that was calculated with simple equation were investigated. These die casting process control techniques of automobile valve body mid-plate have achieved good agreement with the experimental data of tensile strength, hardness test, and material structure photographies satisfactory results.

• Steel and Composite Structures
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• v.18 no.3
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• pp.547-563
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• 2015

Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage in concrete) from the elastic moments and elastic mid-span deflections (neglecting cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.

• Steel and Composite Structures
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• v.26 no.2
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• pp.197-211
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• 2018

The study presents the earthquake performance of the Bosphorus Bridge under multi-point earthquake excitation considering the spatially varying site-specific earthquake motions. The elaborate FE model of the bridge is firstly established depending on the new considerations of the used FEM software specifications, such as cable-sag effect, rigid link and gap elements. The modal analysis showed that singular modes of the deck and the tower were relatively effective in the dynamic behavior of the bridge due to higher total mass participation mass ratio of 80%. The parameters and requirements to be considered in simulation process are determined to generate the spatially varying site-specific ground motions. Total number of twelve simulated ground motions are defined for the multi-support earthquake analysis (Mp-sup). In order to easily implement multi-point earthquake excitation to the bridge, the practice-oriented procedure is summarized. The results demonstrated that the Mp-sup led to high increase in sectional forces of the critical components of the bridge, especially tower base section and tensile force of the main and back stay cables. A close relationship between the dynamic response and the behavior of the bridge under the Mp-sup was also obtained. Consequently, the outcomes from this study underscored the importance of the utilization of the multi-point earthquake analysis and the necessity of considering specifically generated earthquake motions for suspension bridges.

• Journal of the Korean Geotechnical Society
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• v.16 no.4
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• pp.5-16
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• 2000

THe purpose of this paper is to suggest the analytical method which can predict lateral nonlinear behavior in non-homogeneous soil using the coefficient of soil resistance and ultimate soil resistance. Those parameters are obtained through back analysis on the base of the results of a series of model tests.Analytical method of Chang is more or less difficult to predict nonlinear behavior in non-homogeneous sol. So, in this study, for the prediction of nonlinear behavior the compositive analytical method which apply the p - y curve to Chang model is suggested. Also, the program is developed to predict nonlinear behavior using the compositive analytical method and it can be used to calculated the deflection, bending moment and soil reaction with DFM in non-homogeneous soil. To establish applicability of the suggested analytical method, the results of model tests and field tests and Pentagon2D finite element program are compared with those of the compositive analytical method. In the analysis values of the coefficient of soil reaction and ultimate soil resistance are also applied to the case of non-homogeneous soil. Lateral defection calculated using the compositive analytical method has been found to be in good agreement with values measured in field and model load tests.

• Journal of the Korean Magnetics Society
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• v.25 no.2
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• pp.58-65
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• 2015

This paper deals with the generating characteristic analysis of permanent magnet linear generator (PMLG) with double-sided Halbach magnet array mover and three phases concentrated stator windings by using analytical method. On the basis of a magnetic vector potential and Maxwell's equations, governing equations are obtained, and magnetization modeling for Halbach magnet array is performed analytically by using the Fourier series. And then, we obtain electrical parameters such as back-EMF constant, resistance, and coil inductance based on magnetic field calculations. Finally, analytical results for generating performance are confirmed by comparing with finite element analysis results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• Journal of Korean Tunnelling and Underground Space Association
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• v.4 no.4
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• pp.277-286
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• 2002

Most of the problems in dealing with the tunnel construction are the uncertainties and complexities of the stress conditions and rock strengths in ahead of the tunnel excavation. The limitations on the investigation technology, inaccessibility of borehole test in mountain area and public hatred also restrict our knowledge on the geologic conditions on the mountainous tunneling area. Nevertheless an extensive and superior geophysical exploration data is possibly acquired deep within the mountain area, with up to the tunnel locations in the case of alternative design or turn-key base projects. An appealing claim in the use of artificial neural networks (ANN) is that they give a more trustworthy results on our data based on identifying relevant input variables such as a little geotechnical information and biological learning principles. In this study, error back-propagation algorithm that is one of the teaching techniques of ANN is applied to presupposition on Rock Mass Ratings (RMR) for unknown tunnel area. In order to verify the applicability of this model, a 4km railway tunnel's field data are verified and used as input parameters for the prediction of RMR, with the learned pattern by error back propagation logics. ANN is one of basic methods in solving the geotechnical uncertainties and helpful in solving the problems with data consistency, but needs some modification on the technical problems and we hope our study to be developed in the future design work.

• Journal of the Microelectronics and Packaging Society
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• v.23 no.1
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• pp.29-34
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• 2016

In this study, development of multichannel filter module and characteristic evaluation for bio imaging were studied. The filter module was fabricated in order to realize near infrared fluorescence imaging of 700 nm and 800 nm wavelength ranges, and contrast imaging analysis for characteristic evaluation of the filter module was studied through signal to back ground ratio (SBR), controlled by parameters such as magnification, exposure, gain. Furthermore, phantoms, which are biomimetic tissue with equal optical properties of kidney and liver, were fabricated to study characteristics of both filter module depending on thickness and exposure amount of light source for bio imaging analysis. The fabricated filter module has more than 4 of SBR difference despite changes of magnification, exposure, gain, and in the case of the kidney phantom and the liver phantom, contrast imaging of more than 4 of SBR was confirmed on 50 mA, 60 mA exposure amount of light source respectively.