• Journal of Computational Design and Engineering
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• v.1 no.3
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• pp.187-193
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• 2014

One of the hottest challenges in automotive industry is related to weight reduction in sheet metal forming processes, in order to produce a high quality metal part with minimal material cost. Stamping is the most widely used sheet metal forming process; but its implementation comes with several fabrication flaws such as springback and failure. A global and simple approach to circumvent these unwanted process drawbacks consists in optimizing the initial blank shape with innovative methods. The aim of this paper is to introduce an efficient methodology to deal with complex, computationally expensive multicriteria optimization problems. Our approach is based on the combination of methods to capture the Pareto Front, approximate criteria (to save computational costs) and global optimizers. To illustrate the efficiency, we consider the stamping of an industrial workpiece as test-case. Our approach is applied to the springback and failure criteria. To optimize these two criteria, a global optimization algorithm was chosen. It is the Simulated Annealing algorithm hybridized with the Simultaneous Perturbation Stochastic Approximation in order to gain in time and in precision. The multicriteria problems amounts to the capture of the Pareto Front associated to the two criteria. Normal Boundary Intersection and Normalized Normal Constraint Method are considered for generating a set of Pareto-optimal solutions with the characteristic of uniform distribution of front points. The computational results are compared to those obtained with the well-known Non-dominated Sorting Genetic Algorithm II. The results show that our proposed approach is efficient to deal with the multicriteria shape optimization of highly non-linear mechanical systems.

• Earthquakes and Structures
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• v.5 no.1
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• pp.83-109
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• 2013

A simple damper optimization method is proposed to find optimal damper allocation for shear buildings under both target added damping ratio and interstorey drift ratio (IDR). The damping coefficients of added dampers are considered as design variables. The cost, which is defined as the sum of damping coefficient of added dampers, is minimized under a target added damping ratio and the upper and the lower constraint of the design variables. In the first stage of proposed algorithm, Simulated Annealing, Nelder Mead and Differential Evolution numerical algorithms are used to solve the proposed optimization problem. The candidate optimal design obtained in the first stage is tested in terms of the IDRs using linear time history analyses for a design earthquake in the second stage. If all IDRs are below the allowable level, iteration of the algorithm is stopped; otherwise, the iteration continues increasing the target damping ratio. By this way, a structural response IDR is also taken into consideration using a snap-back test. In this study, the effects of the selection of upper limit for added dampers, the storey mass distribution and the storey stiffness distribution are all investigated in terms of damper distributions, cost function, added damping ratio and IDRs for 6-storey shear building models. The results of the proposed method are compared with two existing methods in the literature. Optimal designs are also compared with uniform designs according to both IDRs and added damping ratios. The numerical results show that the proposed damper optimization method is easy to apply and is efficient to find optimal damper distribution for a target damping ratio and allowable IDR value.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.58-65
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• 2021

Meta-heuristic algorithms have been developed to efficiently solve difficult problems and obtain a global optimal solution. A common feature mimics phenomenon occurring in nature and reliably improves the solution through repetition. And at the same time, the probability is used to deviate from the regional optimal solution and approach the global optimal solution. This study compares the algorithm created based on the above common points with existed SA and HS to show advantages in time and accuracy of results. Existing algorithms have problems of low accuracy, high memory, long runtime, and ignorance. In a two-variable polynomial, the existing algorithms show that the memory increases and the accuracy decrease. In order to improve the accuracy, the new algorithm increases the number of initial inputs and increases the efficiency of the search by introducing a direction using vectors. And, in order to solve the optimization problem, the results of the last experiment were learned to show the learning effect in the next experiment. The new algorithm found a solution in a short time under the experimental conditions of long iteration counts using a two-variable polynomial and showed high accuracy. And, it shows that the learning effect is effective in repeated experiments.

• Investigative Magnetic Resonance Imaging
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• v.6 no.2
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• pp.137-146
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• 2002

Purpose : This paper introduces an improved classification approach which adopts a deterministic relaxation method and an agglomerative clustering technique for the classification of MRI using neural network. The proposed approach can solve the problems of convergency to local optima and computational burden caused by a large number of input patterns when a neural network is used for image classification. Materials and methods : Application of Hopfield neural network has been solving various optimization problems. However, major problem of mapping an image classification problem into a neural network is that network is opt to converge to local optima and its convergency toward the global solution with a standard stochastic relaxation spends much time. Therefore, to avoid local solutions and to achieve fast convergency toward a global optimization, we adopt MFA to a Hopfield network during the classification. MFA replaces the stochastic nature of simulated annealing method with a set of deterministic update rules that act on the average value of the variable. By minimizing averages, it is possible to converge to an equilibrium state considerably faster than standard simulated annealing method. Moreover, the proposed agglomerative clustering algorithm which determines the underlying clusters of the image provides initial input values of Hopfield neural network. Results : The proposed approach which uses agglomerative clustering and deterministic relaxation approach resolves the problem of local optimization and achieves fast convergency toward a global optimization when a neural network is used for MRI classification. Conclusion : In this paper, we introduce a new paradigm to classify MRI using clustering analysis and deterministic relaxation for neural network to improve the classification results.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.10
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• pp.192-199
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• 2000

The design of multi-stage gear drives is a time-consuming process because it includes more complicated problems, which are not considered in the design of single-stage gear drives. The designer has no determine the number of reduction stages and the gear ratios of each reduction stage. In addition, the design problems include not only dimensional design but also configuration design of gear drive elements. There is no definite rule or principle for these types of design problems. Thus the design practices largely depend on the sense and the experiences of the designer, and consequently result in undesirable design solution. A new and generalized design algorithm has been proposed to support the designer at the preliminary phase of the design of multi-stage gear drives. The proposed design algorithm automates the design process by integrating the dimensional design and the configuration design process. The algorithm consists of four steps. In the first step, the user determines the number of reduction stages. In the second step, gear ratios of every stage are chosen using the random search method. The values of the basic design parameters of a gear are chose in the third step by using the generate and test method. Then the values of the dimensions, such as pitch diameter, outer diameter and face width, are calculated for the configuration design in the next step. The strength and durability of each gear is guaranteed by the bending strength and the pitting resistance rating practices by using AGMA rating formulas. In the final step, the configuration design is carried out using simulated annealing algorithm. The positions of gears and shafts are determined to minimize the geometrical volume (size) of a gearbox while avoiding interferences between them. These steps are carried out iteratively until a desirable solution is acquired. The proposed design algorithm is applied to the preliminary design of four-stage gear drives in order to validate the availability. The design solution has considerably good results in both aspects of the dimensional and the configuration design.

• Journal of Biomedical Engineering Research
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• v.34 no.4
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• pp.182-188
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• 2013

This study aims to develop a practical algorithm which can detect freezing of gait(FOG) in patients with Parkinson's disease(PD). Eighteen PD patients($68.8{\pm}11.1yrs.$) participated in this study, and three($68.7{\pm}4.0yrs.$) of them showed FOG. We suggested two time-domain algorithms(with 1-axis or 3-axes acceleration signals) and compared them with the frequency-domain algorithm in the literature. We measured the acceleration of left foot with a 3-axis accelerometer inserted at the insole of a shoe. In the time-domain method, the root-mean-square(RMS) acceleration was calculated in a moving window of 4s and FOG was defined as the periods during which RMS accelerations located within FOG range. The parameters in each algorithm were optimized for each subject using the simulated annealing method. The sensitivity and specificity were same, i.e., $89{\pm}8%$ for the time-domain method with 1-axis acceleration and were $91{\pm}7%$ and $90{\pm}8%$ for the time-domain method with 3-axes acceleration, respectively. Both performances were better in the time-domain methods than in the frequency-domain method although the results were statistically insignificant. The amount of calculation in the time-domain method was much smaller than in the frequency-domain method. Therefore it is expected that the suggested time domain algorithm would be advantageous in the systematic implementation of FOG detection.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.1
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• pp.121-135
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• 2013

This paper propose an efficient algorithm for selecting electric bus charging facility location. In nature, the optimal charging facility location problem is similar to Set Covering Problem. Set Covering Problem is the problem of covering all the rows of an $m{\times}n$ matrix of ones and zeros by a subset of columns with a minimal cost. It has many practical applications of modeling of real world problems. The Set Covering Problem has been proven to be NP-Complete. In order to overcome the computational complexity involved in seeking optimal solutions, this paper present an enhanced greedy algorithm and simulated annealing algorithm. In this paper, we apply the developed algorithm to Seoul's public bus system.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.562-572
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• 2003

A matched-target model inversion method was developed for a passive sonar to estimate the position of moving targets. Based on the well known matched-field processing in underwater acoustics, the method finds target position by matching the measured target directions and frequencies with the corresponding values of the proposed target model. For the efficient and accurate estimations, the parameter searching was accomplished using a hybrid optimizing method, which first starts with a global optimization such as generic algorithm or simulated annealing then applies a local optimization of a simple down hill algorithm. The suggested method was testified using simulations for three different moving scenarios. The simulation results showed that the method is robust in convergence, even under the situation of over 5 times standard deviation of Gaussian distribution of measured error, and is practical in calculation time as well.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.107-112
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• 2022

In an embedded system such as a drone, it is difficult to store, transfer and analyze the entire hyper-spectral image to a server in real time because it takes a lot of power and time. Therefore, the hyper-spectral image data is transmitted to the server through dimension reduction or compression pre-processing. Feature selection method are used to send only the bands for analysis purpose, and these algorithms usually take a lot of processing time depending on the size of the image, even though the efficiency is high. In this paper, by improving the temporal disadvantage of the band selection algorithm, the time taken 24 hours was reduced to around 60-180 seconds based on the 40000*682 image resolution of 8GB data, and the use of 7.6GB RAM was significantly reduced to 2.3GB using 45 out of 150 bands. However, in terms of pixel classification performance, more than 98% of analysis results were derived similarly to the previous one.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.3
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• pp.17-24
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• 2008

Rail clamps are mechanical components installed to fix the container crane to its lower members against wind blast or slip. According to rail clamps should be designed to survive harsh wind loading conditions. In this study, a jaw structure, which is a part of a wedge-typed rail clamp, is optimized with respect to its strength under a severe wind loading condition. According to the classification of structural optimization, the structural optimization of a jaw is included in the category of shape optimization. Conventional structural optimization methods have difficulties in defining complex shape design variables and preventing mesh distortions. To overcome the difficulties, the metamodel using Kriging interpolation method is introduced to replace the true response by an approximate one. This research presents the shape optimization of a jaw using iterative Kriging interpolation models and a simulated annealing algorithm. The new Kriging models are iteratively constructed by refining the former Kriging models. This process is continued until the convergence criteria are satisfied. The optimum results obtained by the suggested method are compared with those obtained by the DOE (design of experiments) and VT (variation technology) methods built in ANSYS WORKBENCH.