• Journal of the Korean Society for Precision Engineering
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• v.20 no.10
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• pp.31-40
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• 2003

In this paper, optimal cutting condition to minimize the form error in side wall machining with a flat end mill is studied. Cutting forces and tool deflection are calculated considering surface shape generated by the previous cutting such as roughing. Using the form error prediction method from tool deflection, optimal cutting condition considering form accuracy is investigated. Also, the effects of tool teeth number, tool geometry and cutting conditions on form error are analyzed. The characteristics and the difference of generated surface shape in up and down milling are discussed and over-cut free condition in up milling is presented. Form error reduction method through successive up and down milling is also suggested. The effectiveness and usefulness of the presented method are verified from a series of cutting experiments under various cutting conditions. It is confirmed that form error prediction from tool deflection in side wall machining can be used in optimal cutting condition selection and real time surface error simulation for CAD/CAM systems. This study also contributes to cutting process optimization for the improvement of form accuracy especially in precision die and mold manufacturing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.734-737
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• 2008

This paper presents a new automatic TFT-LCD image quality optimization system. We also have developed new algorithms using 6-point programmable matching technique with reference gamma curve, and automatic power setting sequence. It optimizes automatically gamma adjustment and power setting registers in mobile TFT-LCD driver IC to reduce gamma correction error, adjusting time, and flicker. Developed algorithms and programs are generally applicable for most of the TFT-LCD modules. The proposed optimization system contains module-under-test (MUT, TFT-LCD module), control program, multimedia display tester for measuring luminance and flicker, and control board for interface between PC and TFT-LCD module. The control board is designed with DSP, and it supports various interfaces such as RGB and CPU. Developed automatic image quality optimization system showed significantly reduced gamma adjusting time, reduced flicker, and much less average gamma error than competing system. We believe that the proposed system is very useful to provide high image quality TFT-LCD and to reduce developing process time using optimized gamma-curve setting and automatic power setting.

• Journal of Biomedical Engineering Research
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• v.31 no.4
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• pp.310-315
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• 2010

Estimation of muscle forces is important in biomechanics, therefore many researchers have tried to build a muscle model. Recently, optimization techniques for adjusting muscle parameters, i.e. EMG-driven model, have been used to estimate muscle forces and predict joint moments. In this study, an EMG-driven model based on the previous studies has been developed and isometric and isokinetic contraction movements were evaluated to validate the developed model. One healthy male participated in this study. The dynamometer tasks were performed for maximum voluntary isometric contractions (MVIC) for ankle dorsi/plantarflexors, isokinetic contraction at both $30^{\circ}/s$ and $60^{\circ}/s$. EMGs were recorded from the tibialis anterior, gastrocnemius medialis, gastrocnemius lateralis and soleus muscles at the sampling rate of 1000 Hz. The MVIC trial was used to customize the EMG-driven model to the specific subject. Once the subject's own model was developed, the model was used to predict the ankle joint moment for the other two dynamic movements. When no optimization was applied to characterize the muscle parameters, weak correlations were observed between the model prediction and the measured joint moment with large RMS error over 100% (r = 0.468 (123%) and r = 0.060 (159%) in $30^{\circ}/s$ and $60^{\circ}/s$ dynamic movements, respectively). However, once optimization was applied to adjust the muscle parameters, the predicted joint moment was highly similar to the measured joint moment with relatively small RMS error below 40% (r = 0.955 (21%) and r = 0.819 (36%) and in $30^{\circ}/s$ and $60^{\circ}/s$ dynamic movements, respectively). We expect that our EMG-driven model will be employed in our future efforts to estimate muscle forces of the elderly.

• Steel and Composite Structures
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• v.27 no.4
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• pp.427-438
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• 2018

In this paper, the single and multi-objective optimization of thin-walled conical tubes with different types of indentations under axial impact has been investigated using surrogate models called metamodels. The geometry of tapered thin-walled tubes has been studied in order to achieve maximum specific energy absorption (SEA) and minimum peak crushing force (PCF). The height, radius, thickness, tapered angle of the tube, and the radius of indentation have been considered as design variables. Based on the design of experiments (DOE) method, the generated sample points are computed using the explicit finite element code. Different surrogate models including Kriging, Feed Forward Neural Network (FNN), Radial Basis Neural Network (RNN), and Response Surface Modelling (RSM) comprised to evaluate the appropriation of such models. The comparison study between surrogate models and the exploration of indentation shapes have been provided. The obtained results show that the RNN method has the minimum mean squared error (MSE) in training points compared to the other methods. Meanwhile, optimization based on surrogate models with lower values of MSE does not provide optimum results. The RNN method demonstrates a lower crashworthiness performance (with a lower value of 125.7% for SEA and a higher value of 56.8% for PCF) in comparison to RSM with an error order of $10^{-3}$. The SEA values can be increased by 17.6% and PCF values can be decreased by 24.63% by different types of indentation. In a specific geometry, higher SEA and lower PCF require triangular and circular shapes of indentation, respectively.

• Steel and Composite Structures
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• v.45 no.2
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• pp.205-218
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• 2022

Proper calculation of splitting tensile strength (STS) of concrete has been a crucial task, due to the wide use of concrete in the construction sector. Following many recent studies that have proposed various predictive models for this aim, this study suggests and tests the functionality of three hybrid models in predicting the STS from the characteristics of the mixture components including cement compressive strength, cement tensile strength, curing age, the maximum size of the crushed stone, stone powder content, sand fine modulus, water to binder ratio, and the ratio of sand. A multi-layer perceptron (MLP) neural network incorporates invasive weed optimization (IWO), cuttlefish optimization algorithm (CFOA), and electrostatic discharge algorithm (ESDA) which are among the newest optimization techniques. A dataset from the earlier literature is used for exploring and extrapolating the STS behavior. The results acquired from several accuracy criteria demonstrated a nice learning capability for all three hybrid models viz. IWO-MLP, CFOA-MLP, and ESDA-MLP. Also in the prediction phase, the prediction products were in a promising agreement (above 88%) with experimental results. However, a comparative look revealed the ESDA-MLP as the most accurate predictor. Considering mean absolute percentage error (MAPE) index, the error of ESDA-MLP was 9.05%, while the corresponding value for IWO-MLP and CFOA-MLP was 9.17 and 13.97%, respectively. Since the combination of MLP and ESDA can be an effective tool for optimizing the concrete mixture toward a desirable STS, the last part of this study is dedicated to extracting a predictive formula from this model.

• Journal of Microbiology and Biotechnology
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• v.33 no.9
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• pp.1238-1249
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• 2023

In this study, we sought to investigate the production and optimization of biosurfactants by soil fungi isolated from petroleum oil-contaminated soil in Saudi Arabia. Forty-four fungal isolates were isolated from ten petroleum oil-contaminated soil samples. All isolates were identified using the internal transcribed spacer (ITS) region, and biosurfactant screening showed that thirty-nine of the isolates were positive. Aspergillus niger SA1 was the highest biosurfactant producer, demonstrating surface tension, drop collapsing, oil displacement, and an emulsification index (E₂₄) of 35.8 mN/m, 0.55 cm, 6.7 cm, and 70%, respectively. This isolate was therefore selected for biosurfactant optimization using the Fit Group model. The biosurfactant yield was increased 1.22 times higher than in the nonoptimized medium (8.02 g/l) under conditions of pH 6, temperature 35℃, waste frying oil (5.5 g), agitation rate of 200 rpm, and an incubation period of 7 days. Model significance and fitness analysis had an RMSE score of 0.852 and a p-value of 0.0016. The biosurfactant activities were surface tension (35.8 mN/m), drop collapsing (0.7 cm), oil displacement (4.5 cm), and E₂₄ (65.0%). The time course of biosurfactant production was a growth-associated phase. The main outputs of the mathematical model for biomass yield were Yx/s (1.18), and µ_max (0.0306) for biosurfactant yield was Y_p/s (1.87) and Y_p/x (2.51); for waste frying oil consumption the So was 55 g/l, and Ke was 2.56. To verify the model's accuracy, percentage errors between biomass and biosurfactant yields were determined by experimental work and calculated using model equations. The average error of biomass yield was 2.68%, and the average error percentage of biosurfactant yield was 3.39%.

• Structural Engineering and Mechanics
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• v.65 no.2
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• pp.173-181
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• 2018

In this paper, finite element (FE) model updating based on multi-objective optimization with the surrogate model for a steel plate girder bridge is investigated. Conventionally, FE model updating for bridge structures uses single-objective optimization with finite element analysis (FEA). In the case of the conventional method, computational burden occurs considerably because a lot of iteration are performed during the updating process. This issue can be addressed by replacing FEA with the surrogate model. The other problem is that the updating result from single-objective optimization depends on the condition of the weighting factors. Previous studies have used the trial-and-error strategy, genetic algorithm, or user's preference to obtain the most preferred model; but it needs considerable computation cost. In this study, the FE model updating method consisting of the surrogate model and multi-objective optimization, which can construct the Pareto-optimal front through a single run without considering the weighting factors, is proposed to overcome the limitations of the single-objective optimization. To verify the proposed method, the results of the proposed method are compared with those of the single-objective optimization. The comparison shows that the updated model from the multi-objective optimization is superior to the result of single-objective optimization in calculation time as well as the relative errors between the updated model and measurement.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.4
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• pp.407-417
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• 2003

Enen though several GA-based optimization algorithms have been successfully applied to complex optimization problems in various engineering fields, GA-based optimization methods are computationally too expensive for practical use in the field of structural optimization, particularly for large- scale problems. Furthermore, a successful implementation of GA-based optimization algorithm requires a cumbersome and trial-and-error routine related to setting of parameters dependent on a optimization problem. Therefore, to overcome these disadvantages, a high-performance GA is developed in the form of distributed hybrid genetic algorithm for structural optimization on a cluster of personal computers. The distributed hybrid genetic algorithm proposed in this paper consist of a simple GA running on a master computer and multiple μ-GAs running on slave computers. The algorithm is implemented on a PC cluster and applied to the minimum weight design of steel structures. The results show that the computational time required for structural optimization process can be drastically reduced and the dependency on the parameters can be avoided.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.7061-7070
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• 2015

Multi-Response Surface Optimization aims at finding the optimal setting of input variables considering multiple responses simultaneously. The Weighted Mean Squared Error (WMSE) minimization approach, which imposes a different weight on the two components of mean squared error, squared bias and variance, first obtains WMSE for each response and then minimizes all the WMSEs at once. Most of the methods proposed for the WMSE minimization approach to date are classified into the prior preference articulation approach, which requires that a decision maker (DM) provides his/her preference information a priori. However, it is quite difficult for the DM to provide such information in advance, because he/she cannot experience the relationships or conflicts among the responses. To overcome this limitation, this paper proposes a posterior preference articulation method to the WMSE minimization approach. The proposed method first generates all (or most) of the nondominated solutions without the DM's preference information. Then, the DM selects the best one from the set of nondominated solutions a posteriori. Its advantage is that it provides an opportunity for the DM to understand the tradeoffs in the entire set of nondominated solutions and effectively obtains the most preferred solution suitable for his/her preference structure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1111-1130
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• 2016

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle Swarm Ant Colony Optimization) based on PSACO (Particle Swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.