• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.543-548
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• 2013

This paper presents a design of magnetorheological (MR) damper for control vibration of washing machine. This design is based on the requirements such as small dimensions with high damping force, and minimal consumed energy. The MR damper is designed using the shear mode of MR fluid, and Bingham plastic model is used for optimization process. In this design, a multi-coil design is adopted for damper to enhance damping force and reduce optimally structural parts. In optimization process, ADPL (Ansys Parametric Design Language) program is applied. Base on the optimal parameters, MR damper is manufactured and tested. In evaluation of MR damper, a modified sliding mode control is formulated and applied in both simulation and experiment. Results of experiment show that the MR damper satisfy the requirement of damping force for vibration control of washing machine.

• Journal of Applied Biological Chemistry
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• v.59 no.4
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• pp.289-298
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• 2016

FK506 hyper-yielding mutant, called the TCM8594 strain, was made from Streptomyces tsukubaensis NRRL 18488 by mutagenesis using N-methyl-N'-nitro-N-nitrosoguanidine, ultraviolet irradiation, and FK506 sequential resistance selection. FK506 production by the TCM8594 strain improved 45.1-fold ($505.4{\mu}g/mL$) compared to that of S. tsukubaensis NRRL 18488 ($11.2{\mu}g/mL$). Among the five substrates, wheat bran was selected as the best solid substrate to produce optimum quantities of FK506 ($382.7{\mu}g/g$ substrate) under solid-state fermentation, and the process parameters affecting FK506 production were optimized. Maximum FK506 yield ($897.4{\mu}g/g$ substrate) was achieved by optimizing process parameters, such as wheat bran with 5 % (w/w) dextrin and yeast extract as additional nutrients, 70 % (v/w) initial solid substrate moisture content, initial medium pH of 7.2, $30^{\circ}C$ incubation temperature, inoculum level that was 10 % (v/w) of the cell mass equivalent, and a 10 day incubation. The results showed an overall 234 % increase in FK506 production after optimizing the process parameters.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.33-38
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• 2006

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The conventional FPNN developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed advanced genetic algorithms based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.6
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• pp.109-116
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• 2017

In this study, the optimization of the overall dimensions of an automobile bumper was investigated through CAE and experiment using the Six Sigma method and design of experiment (DOE) method, respectively. Injection pressure, injection speed, injection time, cooling time, holding time, injection temperature, and holding pressure were selected as the vital parameters affecting the overall width of product through analysis of trivial many using CAE. The optimal values were determined using the DOE method, and we analyzed the improvement by applying the optimal conditions to the production process. As a result, the mean value of the overall width was close to the target value, with a deviation of 0.05mm, and the processability and I-MR control were remarkably improved. Finally, the dimension pass rate of the product improved by 20%.

• Journal of the Korean Society of Safety
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• v.21 no.1 s.73
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• pp.21-27
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• 2006

In this paper, object of experiment is to study on the effect parameters to obtain optimal surface roughness in face turning. Surface roughness is significantly important to be high quality of parts produced by turning process. For this purpose, the optimization of cutting parameters for face turning operation is investigated applying the Taguchi method. An orthogonal array, signal-to-noise, and the analysis of variance are employed to evaluate effect of cutting parameters for face turning. Also confirmation tests were performed to make a comparison between the results predicted from the mentioned correlations and the theoretical results. Cutting experiment is performed without cutting fluid using coated tungsten carbide insert about workpiece of SM45C. And regression analysis technique has been used to study the effects of the cutting parameters.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.111-116
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• 2003

In this paper, object of experiment is to study on the effect of cutting parameters to obtain optimal surface toughness in face turning. Surface roughness is significantly important to be high quality of parts produced by turning process. For this purpose, the optimization of cutting parameters for fan Owning operation is investigated applying the Taguchi method. An orthogonal array, signal-to-noise ratio, and the analysis of variance are employed to evaluate effect of cutting parameters fir face turning. Also confirmation tests were performed to make a comparison between the results predicted from the mentioned correlations and the theoretical results. Cutting experiment is performed without cutting fluid using coated tungsten carbide inserts about workpieces of SM45C.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.319-325
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• 2023

In this study, we proposed a method for hyperparameter optimization in the building and training of a deep learning model designed to process point cloud data collected by a millimeter-wave radar system. The primary aim of this study is to facilitate the deployment of a baseline model in resource-constrained IoT devices. We evaluated a RadHAR baseline deep learning model trained on a public dataset composed of point clouds representing five distinct human activities. Additionally, we introduced a coarse-to-fine hyperparameter optimization procedure, showing substantial potential to enhance model efficiency without compromising predictive performance. Experimental results show the feasibility of significantly reducing model size without adversely impacting performance. Specifically, the optimized model demonstrated a 3.3% improvement in classification accuracy despite a 16.8% reduction in number of parameters compared th the baseline model. In conclusion, this research offers valuable insights for the development of deep learning models for resource-constrained IoT devices, underscoring the potential of hyperparameter optimization and model size reduction strategies. This work contributes to enhancing the practicality and usability of deep learning models in real-world environments, where high levels of accuracy and efficiency in data processing and classification tasks are required.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.355-368
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• 2004

In order to use Kriging, one has to estimate three parameters(nugget, sill and range) of semivariogram, which shows the relationship in the given two sites. A visual fit of the semivariogram parameters to a few standard models is widely used. But, it does not give the suitable results and not provide the automated process of Kriging. The gradient based nonlinear least squares is another choices to estimate three parameters, but it has some problems such as initial value problem. In this paper, we suggest the genetic algorithm as a compatible alternative method to solve the above mentioned problem. Finally, we estimate three parameters of semivariogram of rain-fall by adapting the genetic algorithm, compute Kriging estimate and conclude its effectiveness and compatibility.

• Food Science and Biotechnology
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• v.17 no.5
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• pp.904-911
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• 2008

Covalent cross-links between a number of proteins and peptides explain why transglutaminase may be widely used by food processing industries. The objective of this work was optimization of the fermentation process to produce transglutaminase from a new microbial source, the Streptomyces sp. P20. The strategy adopted to modify the usual literature media was: (1) fractional factorial design (FFD) to elucidate the key medium ingredients, (2) central composite design (CCD) to optimise the concentration of the key components. Optimization of the medium resulted in not only an 86% increase in microbial transglutaminase activity as compared to the media cited in the literature, but also a reduction in the production cost. Optimal fermentation conditions - namely temperature and agitation rate - were also studied, using CCD methodology. Usual conditions of $30^{\circ}C$ and 100 rpm were within the optimal area. All other parameters for enzyme production were experimentally proven to be optimum fermentation conditions.

• Journal of the Korean Society of Safety
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• v.32 no.6
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• pp.34-39
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• 2017

Vapor-liquid equilibrium calculation is required to properly design and operation of distillation process. The general calculation method is to use binary interaction parameter. Lower flash points of cyclohexanol+aniline and cyclohexanol+cyclohexanone were measured by using Seta-flash closed cup apparatus. The measured flash points were compared with those calculated by the method based on Raoult's law and the optimization method using Wilson equation. The absolute average errors(A.A.E.) of the results calculated by Raout's law are $0.25^{\circ}C$ and $1.07^{\circ}C$ for cyclohexanol+aniline and cyclohexanol+cyclohexanone, respectively. The absolute average errors of the results calculated by the optimization method are $0.22^{\circ}C$ and $0.65^{\circ}C$ for cyclohexanol+aniline and cyclohexanol+cyclohexanone, respectively. As can be seen from A.A.E., the calculated values based on the optimization method were found to be better than those based on the Raoult's law. The binary interaction parameters calculated by the optimization method are used to predict the dew points of cyclohexanol+aniline and cyclohexanol+cyclohexanone. The A.A.E. for these mixtures show that there is an acceptable agreement between experimental and calculated dew poins.