• Journal of applied mathematics & informatics
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• v.5 no.3
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• pp.701-716
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• 1998

In this paper we develop algorithms in programming lan-guage SCHEME for implementation of the main first order gradient techniques for unconstrained optimization. Implementation of the de-scent techniques which use non-optimal descent steps as well as imple-mentation of the optimal descent techniques are described. Also we investigate implementation of the global problem called optimization along a line. Developed programs are effective and simpler with re-spect to the corresponding in the procedural programming languages. Several numerical examples are reported.

• Investigative Magnetic Resonance Imaging
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• v.4 no.1
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• pp.20-26
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• 2000

Gradient moment nulling techniques require the introduction of an additional gradient on each axis for each order of motion correction to be applied. The additional gradients introduce new constraints on the sequence design and increase the demands on the gradient system. The purpose of this paper is to demonstrate techniques for optimization of gradient echo gradient moment nulling sequences within the constraints of the gradient hardware. Flow compensated pulse sequences were designed and implemented on a clinical magnetic resonance imaging system. The design of the gradient moment nulling sequences requires the solution of a linear system of equations. A Mathematica package was developed that interactively solves the gradient moment nulling problem. The package allows the physicist to specify the desired order of motion compensation and the duration of the gradients in the sequence with different gradient envelopes. The gradient echo sequences with first, second, and third order motion compensation were implemented with minimum echo time. The sequences were optimized to take full advantage of the capabilities of the gradient hardware. The sequences were used to generate images of phantoms and human brains. The optimized sequences were found to have better motion compensation than comparable standard sequences.

• 한국전산유체공학회:학술대회논문집
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• 1997.10a
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• pp.126-130
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• 1997

A new and efficient method is presented for design optimization, which is based on a computational fluid dynamics (CFD). The method is applied to design an airfoil configuration. The Navier-Stokes equations are solved for the viscous analysis of the flow, which provides the object function. The CFD analysis is then coupled with the optimization procedure that used a conjugate gradient method. During the one-dimensional search of the optimization procedure, an approximate flow analysis based on a first-order Taylor series expansion is used to reduce the computational cost, (This study is supported by Korean Ministry of Education through Research Fund)

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.200-206
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• 2021

Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.4
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• pp.292-297
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• 2016

The first-order plus dead time(FOPDT) and second-order plus dead time(SOPDT), which describes a linear monotonic process quite well in most chemical and industrial processes and is often sufficient for PID and IMC controller tuning. This paper presents an application of heuristic harmony search(HS) optimization algorithm to the identification of linear continuous time-delay systems from step response. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. The effectiveness of the proposed identification method has been demonstrated through a number of simulation examples.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.636-642
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• 2015

This paper presents an application of heuristic harmony search (HS) optimization algorithm for the identification of linear continuous time-delay system from step response. Identification model is first-order plus dead time (FOPDT), which describes a linear monotonic process quite well in most chemical processes and HAVC process and is often sufficient for PID controller tuning. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. The effectiveness of the identification method has been demonstrated through a number of simulation examples.

• International Journal of Contents
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• v.12 no.3
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• pp.22-28
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• 2016

Tensor with missing or incomplete values is a ubiquitous problem in various fields such as biomedical signal processing, image processing, and social network analysis. In this paper, we considered how to reconstruct a dataset with missing values by using tensor form which is called tensor completion process. We applied Tucker factorization to solve tensor completion which was built base on optimization problem. We formulated the optimization objective function using components of Tucker model after decomposing. The weighted least square matric contained only known values of the tensor with low rank in its modes. A first order optimization method, namely Nonlinear Conjugated Gradient, was applied to solve the optimization problem. We demonstrated the effectiveness of the proposed method in EEG signals with about 70% missing entries compared to other algorithms. The relative error was proposed to compare the difference between original tensor and the process output.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.12
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• pp.3053-3060
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• 2000

Alternative formulation is presented for robust optimization problems and an efficient computational scheme for reliability estimation is proposed. Both design variables and design parameters considered as random variables about their nominal values. To ensure the robustness of objective performance a new cost function bounding the performance and a new constraint limiting the performance variation are introduced. The constraint variations are regulated by considering the probability of feasibility. Each probability constraint is transformed into a sub-optimization problem and then is resolved with the modified advanced first order second moment(AFOSM) method for computational efficiency. The proposed robust optimization method has advantages that the mean value and the variation of the performance function are controlled simultaneously and the second order sensitivity information is not required even in case of gradient based optimization. The suggested method is examined by solving three examples and the results are compared with those for deterministic case and those available in literature.

• Computers and Concrete
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• v.24 no.4
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• pp.313-327
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• 2019

The purpose of the present study is to present a new approach to designing and selecting the details of multidimensional continuous RC beam by applying all strength, serviceability, ductility and other constraints based on ACI318-14 using Teaching Learning Based Optimization (TLBO) algorithm. The optimum reinforcement detailing of longitudinal bars is done in two steps. in the first stage, only the dimensions of the beam in each span are considered as the variables of the optimization algorithm. in the second stage, the optimal design of the longitudinal bars of the beam is made according to the first step inputs. In the optimum shear reinforcement, using gradient-based methods, the most optimal possible mode is selected based on the existing assumptions. The objective function in this study is a cost function that includes the cost of concrete, formwork and reinforcing steel bars. The steel used in the objective function is the sum of longitudinal and shear bars. The use of a catalog list consisting of all existing patterns of longitudinal bars based on the minimum rules of the regulation in the second stage, leads to a sharp reduction in the volume of calculations and the achievement of the best solution. Three example with varying degrees of complexity, have been selected in order to investigate the optimal design of the longitudinal and shear reinforcement of continuous beam.

• The KSFM Journal of Fluid Machinery
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• v.16 no.1
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• pp.11-16
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• 2013

A numerical study was conducted to improve the performance of an impeller of centrifugal compressor. Nine design variables were chosen with constraints. Only meridional contours and blade profile were adjusted. ANN (Artificial Neural Net) was adopted as a main optimization algorithm with PSO (Particle Swarm Optimization) in order to reduce the optimization time. At first, ANN was learned and trained with the design variable sets which were obtained using DOE (Design of Experiment). This ANN was continuously improved its accuracy for each generation of which population was one hundred. New design variable set in each generation was selected using a non-gradient based method of PSO in order to obtain the global optimized result. After $7^{th}$ generation, the prediction difference of efficiency and pressure ratio between ANN and CFD was less than 0.6%. From more than 1,200 design variable sets, a pareto of efficiency versus pressure ratio was obtained and an optimized result was selected based on the multi-objective function. On this optimized impeller, the efficiency and pressure ratio were improved by 1% and 9.3%, respectively.