• Journal of Applied Biological Chemistry
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• v.56 no.2
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• pp.119-129
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• 2013

Thirteen different Streptomyces isolates were evaluated for their ability to produce keratinase using chicken feather as a sole carbon and nitrogen sources under solid state fermentation (SSF). Streptomyces sp. NRC 13S produced the highest keratinase activity [1,792 U/g fermented substrate (fs)]. The phenotypic characterization and analysis of 16S rDNA sequencing of the isolate were studied. Optimization of SSF medium for keratinase production by the local isolate, Streptomyces sp. NRC13S, was carried out using the one-variable-at-a-time and the statistical approaches. In the first optimization step, the effect of incubation period, initial moisture content, initial pH value of the fermentation medium, and supplementation of some agro-industrial by-products on keratinase production were evaluated. The strain produced about 2,310 U/gfs when it grew on chicken feather with moisture content of 75% (w/w), feather: fodder yeast ratio of 70:30 (w/w), and initial pH 7 using phosphate buffer after 8 days. Based on these results, the Box-Behnken design and response surface methodology were applied to find out the optimal conditions for the enzyme production. The corresponding maximal production of keratinase was about 2,569.38 U/gfs.

• Bulletin of the Korean Chemical Society
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• v.25 no.3
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• pp.377-381
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• 2004

Chiral separation of aryalcohols such as 1-phenyl-propanol, 1-phenyl-2-proanol, and 2-phenyl-1-propanol by capillary electrophoresis has been optimized using the overlapping resolution mapping (ORM) scheme. Three critical parameters of the electrophoretic media, i.e. phosphate concentration, sulfated ${\beta}$-cyclodextrin (CD) concentration and pH, were chosen for optimization. The working ranges were initially presumed by 7 preexperiments. Further optimization was carried out by another seven experiments within the narrow working ranges. From the final overlapping resolution mapping all peak pairs, the area of maximum separations were located. Using the conditions of a point in this area, we found that the target compounds were a baseline separated within 30 min. The maximum separation conditions of arylalcohols were a chiral selector concentration of 5.4%, a phosphate concentration of 28 mM, and a pH of 5.0.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.7
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• pp.424-433
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.7
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• pp.424-424
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.3
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• pp.143-148
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• 2018

In this paper, the optimal fuzzy logic controller(FLC) for a hybrid renewable energy system(HRES) is proposed. Generally, hybrid renewable energy systems can consist of wind power, solar power, fuel cells and storage devices. The proposed FLC can effectively control the entire HRES by determining the output power of the fuel cell or the absorption power of the electrolyzer. In general, fuzzy logic controllers can be optimized by classical optimization algorithms such as genetic algorithms(GA) or particle swarm optimization(PSO). However, these FLC have a disadvantage in that their performance varies greatly depending on the control parameters of the optimization algorithms. Therefore, we propose a method to optimize the fuzzy logic controller using the teaching-learning based optimization(TLBO) algorithm which does not have the control parameters of the algorithm. The TLBO algorithm is an optimization algorithm that mimics the knowledge transfer mechanism in a class. To verify the performance of the proposed algorithm, we modeled the hybrid system using Matlab Tool and compare and analyze the performance with other classical optimization algorithms. The simulation results show that the proposed method shows better performance than the other methods.

• Journal of the Korean Society of Food Culture
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• v.27 no.2
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• pp.202-210
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• 2012

The purpose of this study was to bake sweet rice muffins with oak mushroom ($Lentinus$ $edodes$) powder. The process included substituting sweet rice flour for cake flour and adding oak mushroom powder. This study determined the optimal mixing conditions of oak mushroom muffins by adjusting the amounts of oak mushroom powder, whole eggs, and soybean oil. The mixing conditions for the oak mushroom muffins included 3 categories: oak mushroom powder (X1), whole eggs (X2), and soybean oil (X3) by Central Composite Design (CCD) which was optimized by Response Surface Methodology (RSM). Oak mushroom muffin formulation was optimized using rheology. Yellowness (p<0.001) and redness (p<0.05) displayed a linear model pattern, whereas lightness (p<0.05) was represented by a quadratic model. Among the sensory properties, variables that appeared to show significant values such as appearance (p<0.5), texture (p<0.5), and overall quality (p<0.5) were used to identify optimums. The result of mechanical properties showed significant values in gumminess (p<0.5) and chewiness (p<0.5). The numerical and graphical methods used in this study determined that the optimum formulation for oak mushroom powder sweet rice muffins was 8.75 g of oak mushroom powder, 235.95 g of whole eggs, and 19.93 g of soybean oil.

• Journal of The Korean Society of Grassland and Forage Science
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• v.32 no.4
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• pp.361-368
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• 2012

The present study was conducted to develop persimmon peel, a by-product of dried persimmon manufacturing, as a feed additive via lactic acid bacteria fermentation. Pediococcus pentosaceus, Lactobacillus plantarum, and three strains of Leuconostoc mesenteroides were used as a starter culture in the solid state fermentation of persimmon peel, and antioxidant activity and total polyphenol content were assessed. Leuconostoc mesenteroides KCTC 3100 showed high antioxidant activity (p<0.05), whereas Pediococcus pentosaceus showed high total polyphenol content (p<0.05). These two strains were thus selected as starter culture strains. Glucose, sucrose and molasses were used as variables for optimization and a total 15 experimental runs were produced according to Box-Behnken design. Regarding significant effects of variables, molasses showed linear and square effects on antioxidant activity of persimmon peel fermentation (p<0.05). In conclusion, optimum concentrations of glucose, sucrose, and molasses were determined to be 4.2, 3.9 and 5.3 g/L, respectively, using a response surface model. Antioxidant activity was also improved 2.5 fold after optimization.

• Advances in aircraft and spacecraft science
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• v.6 no.5
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• pp.349-369
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• 2019

In the design of flight control systems there are issues that deserve special consideration and attention such as external perturbations or systems failures. A Simple Adaptive Controller (SAC) that does not require a-priori knowledge of the faults is proposed in this paper with the aim of realizing a fault tolerant flight control system capable of leading the pitch motion of an aircraft. The main condition for obtaining a stable adaptive controller is the passivity of the plant; however, since real systems generally do not satisfy such requirement, a properly defined Parallel Feedforward Compensator (PFC) is used to let the augmented system meet the passivity condition. The design approach used in this paper to synthesize the PFC and to tune the invariant gains of the SAC is the Population Decline Swarm Optimization ($P_DSO$). It is a modification of the Particle Swarm Optimization (PSO) technique that takes into account a decline demographic model to speed up the optimization procedure. Tuning and flight mechanics results are presented to show both the effectiveness of the proposed $P_DSO$ and the fault tolerant capability of the proposed scheme to control the aircraft pitch motion even in presence of elevator failures.

• Journal of Electrochemical Science and Technology
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• v.14 no.2
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• pp.174-183
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• 2023

The dye wastewater discharge from the textile industries mainly affects the aquatic environment. Hence, the treatment of this wastewater is essential for a pollutant-free environment. The purpose of this research is to optimize the dye removal efficiency for process influencing independent variables such as pH, electrolysis time (ET), and current density (CD) by using Box-Behnken design (BBD) optimization and Genetic Algorithm (GA) modelling. The electrocoagulation treatment technique was used to treat the synthetically prepared Reactive Black dye solution under batch mode potentiometric operations. The percentage of error for the BBD optimization was significantly greater than for the GA modelling results. The optimum factors determined by GA modelling were CD-59.11 mA/cm², ET-24.17 minutes, and pH-8.4. At this moment, the experimental and predicted dye removal efficiencies were found to be 96.25% and 98.26%, respectively. The most and least predominant factors found by the beta coefficient were ET and pH respectively. The outcome of this research shows GA modeling is a better tool for predicting dye removal efficiencies as well as process influencing factors.

• Journal of the Korean Applied Science and Technology
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• v.33 no.1
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• pp.176-185
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• 2016

Yeonsan Ogae has been known as supporting health and high efficacy of treatment. In recent days, as the efficacy of functional peptides has known, the optimization of oligo peptides production and its characteristics from Ogae legs has been performed. Response surface method was used to perform the optimizaion of enzyme hydrolysis. The range of processes was temperature ( 40, 50 and $60^{\circ}C$), pH( pH 6.0, 7.0 and 8.0 ), and enzyme( 1, 2 and 3% ). The degree of hydrolysis, amino acids, molecular weight of products were analyzed. The optimum process of enzyme hydrolysis were determined as temperature $58^{\circ}C$, pH 7.5, and enzyme concentration 3%. At optimum conditions, the degree of hydrolysis after 2 h reaction was 75-80%. The amino acid and were 168.131 mg/100 g, respectively. The molecular weight of products by using MALDI-TOF was ranged from 300 to 1,000 Da.