• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.7
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• pp.805-811
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• 2012

The reliability based design optimization (RBDO) approach requires high computing cost to consider uncertainties. In order to reduce the design cost, the single loop single vector (SLSV) approach has been developed for RBDO. This method can reduce the cost in calculating deign sensitivity by elimination of the nested optimization process. However, this process causes the increment of the instability or inaccuracy of the method according to the problem characteristics. Therefore, the method may not give accurate solution or the robustness of the solution is not guaranteed. Especially, when the function is concave, the process frequently diverges. In this research, the concept of the conjugate gradient method for unconstrained optimization is utilized to develop a new single loop single vector method. The conjugate gradient is calculated with gradient directions at the most probable points (MPP) of previous cycles. Mathematical examples are solved for the verification of the proposed method. The numeri cal performances of the obtained results are compared to those of other RBDO methods. The SLSV approach using conjugate gradient is not greatly influenced by the problem characteristics and improves its convergence capability.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.12
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• pp.986-993
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• 2007

The microchannel heat sink is promising heat dissipation method far high density electronic devices. The cross-sectional shape of MEMS based microchannel heat sink is limited to triangular, trapezoidal, and rectangular due to their fabrication method. And heat is added to one side surface of heat source. Therefore, those specific conditions make some complexity of heat transfer in microchannel heat sink. Though many previous research of conjugate heat transfer in microchannel was conducted, most of them did not consider heat loss. In this study, numerical investigation of conjugate heat transfer in rectangular microchannel was conducted. The method of heat loss evaluation was verified numerically. Heat distribution was different for each wall of rectangular microchannel due to thermal conductivity and distance from heat source. However, the ratio of heat from each channel wall was correlated. Therefore, the effective area correction factor could be proposed to evaluate accurate heat flux in one side heating condition.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.9
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• pp.1124-1132
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• 2004

The inverse problem of determining unknown inlet temperature in thermally developing, hydrodynamically developed two phase laminar flow in a parallel plate duct is considered. The inlet temperature profile is determined by measuring temperature in the flow field. No prior information is needed for the functional form of the inlet temperature profile. The inverse convection problem is solved by minimizing the objective function with regularization method. The conjugate gradient method as iterative method and the Tikhonov regularization method are employed. The effects of the functional form of inlet temperature, the number of measurement points and the measurement errors are investigated. The accuracy and efficiency of these two methods are compared and discussed.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.666-671
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• 2010

Multi-layer perceptron neural networks have been constructed to classify four types of defects in steam generator tubes. Three features are extracted from the signals of the eddy current testing method. These include maximum impedance, phase angle at the point of maximum impedance, and an angle between the point of maximum impedance and the point of half the maximum impedance. Two hundred sets of these features are used for training and assessing the networks. Two approaches are involved to train the networks and to classify the defect type. One is the conjugate gradient method and the other is the Broydon-Fletcher-Goldfarb-Shanno method which is recognized as the most popular algorithm of quasi-Newton methods. It is found from the computation results that the training time of the Broydon-Fletcher-Goldfarb-Shanno method is much faster than that of the conjugate gradient method in most cases. On the other hand, no significant difference of the classification performance between the two methods is observed.

• The Korean Journal of Nuclear Medicine
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• v.39 no.3
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• pp.191-199
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• 2005

Purpose: In order to obtain better quantitation of kidney uptake, this study is to evaluate a conjugate view method (CVM) using a geometric mean attenuation correction for kidney uptake and to compare it to Gate's method. Materials & Methods: We used a Monte Carlo code, SIMIND and a Zubal phantom, to simulate kidney uptake. SIMIND was both simulated with or without scatter for the Zubal phantom. Also, a real phantom test was carried out using a dual-head gamma camera. The activity of 0.5 mCi was infused into two small cylinder phantoms of 5 cm diameter, and then, they were inserted into a cylinder phantom of 20 cm diameter. The results by the CVM method were compared with ideal data without both of attenuation and scatter and with Gate's method. The CVM was performed with or without scatter correction. The Gate's method was performed without scatter correction and it was evaluated with regards to $0.12cm^{-1}\;and\;0.15cm^{-1}$ attenuation coefficients. Data were analyzed with comparisons of mean counts in the legions of interest (ROI), profiles drawn over kidney images and linear regression. Correlation coefficients were calculated with ideal data, as well. Results: In the case of the computer simulation, mean counts measured from ideal data, the CVM and the Gate's method were (right $998{\pm}209$, left: $896{\pm}249$), (right: $911{\pm}207$, left: $815{\pm}265$), and (right: $1065{\pm}267$, left: $1546{\pm}267$), respectively. The ideal data showed good correlation with the CVM and the correlation coefficients of the CVM, Gate's method were (right: 0.91, left: 0.93) and (right: 0.85, left: 0.90), respectively. Conclusion: The conjugate view method using geometric mean attenuation correction resulted in better accuracy than the Gate's method. In conclusion, the conjugate view method independent of renal depths may provide more accurate kidney uptake.

• Journal of computational fluids engineering
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• v.3 no.1
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• pp.37-45
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• 1998

A new and efficient inverse design method based on the numerical optimization technique has been developed. The 2-D incompressible Navier-Stokes equations are solved for obtaining the objective functions and coupled with the optimization procedure to perform the inverse design. The steepest descent and the conjugate gradient method have been applied to find the searching direction. The golden section method was applied to compute the design variable intervals. It has been found that the airfoil and the pump impellers are well converged to their targeting shapes.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.697-699
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• 2004

Stochastic Neighbor Embedding(SNE) is a probabilistic method of mapping high-dimensional data space into a low-dimensional representation with preserving neighbor identities. Even though SNE shows several useful properties, the gradient-based naive SNE algorithm has a critical limitation that it is very slow to converge. To overcome this limitation, faster optimization methods should be considered by using trust region method we call this method fast TR SNE. Moreover, this paper presents a couple of useful optimization methods(i.e. conjugate gradient method and Newton's method) to embody fast SNE algorithm. We compared above three methods and conclude that TR-SNE is the best algorithm among them considering speed and stability. Finally, we show several visualizing experiments of TR-SNE to confirm its stability by experiments.

• Computers and Concrete
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• v.12 no.2
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• pp.229-241
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• 2013

The objective of this study is to reveal the sufficiency of neural networks (NN) as a securer, quicker, more robust and reliable method to be used in seismic assessment of existing reinforced concrete buildings. The NN based approach is applied as an alternative method to determine the seismic performance of each existing RC buildings, in terms of damage level. In the application of the NN, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN based model wasd eveloped, trained and tested through a based MATLAB program. The database of this model was developed by using a statistical procedure called P25 method. The NN based model was also proved by verification set constituting of real existing RC buildings exposed to 2003 Bingol earthquake. It is demonstrated that the NN based approach is highly successful and can be used as an alternative method to determine the seismic performance of each existing RC buildings.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.8 s.239
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• pp.903-910
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• 2005

Inverse radiation problems are solved for estimating the boundary conditions such as temperature distribution and wall emissivity in axisymmetric absorbing, emitting and scattering medium, given the measured incident radiative heat fluxes. Various regularization methods, such as hybrid genetic algorithm, conjugate-gradient method and finite-difference Newton method, were adopted to solve the inverse problem, while discussing their features in terms of estimation accuracy and computational efficiency. Additionally, we propose a new combined approach that adopts the hybrid genetic algorithm as an initial value selector and uses the finite-difference Newton method as an optimization procedure.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.1
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• pp.1-13
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• 2018

Boosting, one of the most successful algorithms for supervised learning, searches the most accurate weighted sum of weak classifiers. The search corresponds to a convex programming with non-negativity and affine constraint. In this article, we propose a novel Conjugate Gradient algorithm with the Modified Polak-Ribiera-Polyak conjugate direction. The convergence of the algorithm is proved and we report its successful applications to boosting.