• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.962-968
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• 2016

The large scaled lead-acid battery is widely used for efficient operation of the photovoltaic system in many islands. However, lithium-ion battery is now being introduced to mitigate the fluctuation of wind power and to replace lead-acid battery. Therefore, hybrid ESS(Energy Storage system) that combines lithium-ion battery with lead-acid battery is being required because lithium-ion battery is costly in present stage. Under this circumstance, this paper presents the optimal algorithm to create composition rate of hybrid ESS by considering fixed and variable costs in order to maximize advantage of each battery. With minimization of total cost including fixed and variable costs, the optimal composition rate can be calculated based on the various scenarios such as load variation, life cycle and cost trend. From simulation results, it is confirmed that the proposed algorithms are an effective tool to produce a optimal composition rate.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.1
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• pp.49-54
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• 2016

The large scaled lead-acid battery is widely used for efficient operation of the photovoltaic system in many islands. However, lithium-ion battery is now being introduced to mitigate the fluctuation of wind power and to replace lead-acid battery. Therefore, hybrid ESS (Energy Storage system) that combines lithium-ion battery with lead-acid battery is being required because lithium-ion battery is costly in present stage. Under this circumstance, this paper presents the optimal algorithm to create composition rate of hybrid ESS by considering fixed and variable costs in order to maximize advantage of each battery. With minimization of total cost including fixed and variable costs, the optimal composition rate can be calculated based on the various scenarios such as load variation, life cycle and cost trend. From simulation results, it is confirmed that the proposed algorithms are an effective tool to produce a optimal composition rate.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.6
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• pp.79-89
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• 1998

In determining optimal cutting condition for face milling operation, tool wear is an important factor. For the purpose of establishing the relationship between various machining factors and tool wear, cutting tests have been performed. As a result, hardness and chemical composition of workpiece material, chemical composition and grain size of cutting tool and cutting speed have been selected as machining factors. In addition, relationship between feed rate and workpiece hardness has been observed. Prior to utilizing cutting conditions recommended by ‘Machining Data Handbook(MDH)’ as a knowledge base, an analysis for the validity of the MDH has been provided. Based on this analysis, tool life criteria applied by MDH has been modified. Finally, using MDH recommended data for neural network trainning, the results from the trained neural network for optimal cutting condition for some given workpiece and cutting tool can be used as reference cutting conditions.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.10
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• pp.1490-1495
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• 2006

The purpose of this research was to develop an optimum composition model for a new synbiotic fermented dairy product with high probiotic cell counts, and to experimentally verify this model. The optimum composition model indicated the growth promoter ratio that could provide the highest growth rate for probiotics in this fermented product. Different levels of growth promoters were first blended with milk to improve the growth rates of probiotics, and the optimum composition model was determined. The probiotic viabilities and chemical properties were analyzed for the samples made using the optimal formula. The optimal combination of the growth promoters for the synbiotic fermented milk product was 1.12% peptides, 3% fructooligosaccharides (FOS), and 1.87% isomaltooligosaccharides (IMO). A product manufactured according to the formula of the optimum model was analyzed, showing that the model was effective in improving the viability of both Lactobacillus spp. and Bifidobacterium spp.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.1038-1043
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• 1997

To adapt the neural network proess for the purpose of determination of optimal utting onditions (optimal cutting speed and feed rate), some selection strategies for the machining factors are necessary, which is considered planning cutting process. In this case, factors that have both nonlinearity and strong relationship must be selected. Although tool grade and chemical properties of workpiece material have strong effect to cutting speed, it's not easy to find a analytic relation between them. In this paper, a mathematical method for determining the optimal amount of cutting (depth of cut, feed rate) is presented by tool goemetry and heat generation during cutting process. And various tool grade and workpiece material groups ase classified based on its chemical properties. Thier chemical composition and hardness are used as input pattern for neural network learnig. The result of learning shows the relationship between tool grade and workpiece material and it is proved that it can be used as a sub-system for automatic process planning system.

• Applied Chemistry for Engineering
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• v.32 no.5
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• pp.574-580
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• 2021

The main goal of this study was to find an optimal surfactant mixture composition for the development of the best performing cleansing products. Three different surfactants including sodium cocoyl alaninate (SCoA), cocamidopropyl betaine (CPB), and decyl glucoside (DG) were selected, which showed excellent properties in detergency, foaming height, and contamination rate through preliminary experiments. The experiments by simplex centroid design matrix for surfactant mixtures were performed, and the regression analysis was conducted with the experimental data. Surface response model equations, which is statistically significant (p < 0.05), were obtained. The optimal composition of the surfactant mixture was also determined as SCoA (0.22), CPB (0.78), and DG(0.00) from simultaneous optimization of three response variables.

• Fisheries and Aquatic Sciences
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• v.26 no.9
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• pp.525-534
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• 2023

Vitamin C plays an important role for fish survival, growth and disease resistance. However, the optimal vitamin C for rearing red drum Sciaenops ocellatus juveniles in Vietnam is not known. To address this issue, a 70-day feeding trial was conducted to evaluate the optimal dietary vitamin C requirements for red drum juveniles. Seven isonitrogenous (55.35% protein) and isolipidic (9.07% lipid) diets were formulated to include graded vitamin C concentrations of 23.2, 124.5, 235.2, 423.8, 626.7, 824.6, and 1,027.3 mg/kg, respectively. The results showed that fish fed on 423.8 mg/kg vitamin C diet had the highest growth rate, which can be linked to the increased feed utilization. Broken-line analysis indicated that the optimal dietary vitamin C requirements of red drum juveniles were 342.92 and 405.80 mg/kg for growth parameters, feed utilization, body composition and biochemical parameters of serum. Based on these parameters the optimal vitamin C supplementation level for red drum juveniles was estimated in the range of 342.92-405.80 mg/kg vitamin C in the diets with direct applications in producing artificial feed for rearing juveniles of this species in Vietnam.

• Journal of the Korean institute of surface engineering
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• v.32 no.4
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• pp.487-495
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• 1999

In order to form metallic electrodes on PZT (Pb (Zr, Ti)O$_3$) ceramics, plating conditions for optimal electroless Ni deposition were investigated. Pb in PZT is the major component to inhibit the electroless deposition, because it plays a active role of catalytic poison in plating solution. Adhesion of the electroless Ni deposits is measured by push-pull scale test and peel test. Results such as deposition ability, deposition rate, and thickness of deposits showed in terms of concentration of etchant, composition of catalyzing solution, and composition and pH of electroless bath solution.

• Proceedings of the KIEE Conference
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• 1994.07b
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• pp.1435-1438
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• 1994

The effects of Ge composition variation in $a-Si_{1-x}Ge_x:H$ alloy p-i-n solar cells on the physical properties and current density-voltage characteristics are analyzed by a new simulation modelling based on the update published experimental datas. The simulation modelling includes newly formulated density of gap density spectrum corresponding to Ge composition variation and utilizes the newly derived generation rate formulars which include the reflection coefficients and can apply to multijunction structures as well as single junction structure. The effects in $a-Si_{1-x}Ge_x:H$ single junction are analyzed through the efficiency, fill factor, open circuit voltage, short circuit current density, free carriers, trap carriers, electric field, generation rate and recombination rate. Based on the results analyzed in single junction structure, the applications to multiple junction structures are discussed and the optimal conditions reaching to a high performance are investigated.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.2
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• pp.228-231
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• 2015

In power system research fields, one of current key issues is the construction and commercialization of micro grid site which is called green island, carbon zero island, energy independent island, building micro grid, etc. and various affiliated technologies have been being vigorously developed to realize. In addition, various researches about electric vehicles (EVs) are in progress and it is expected to penetrate rapidly with the next a few years. Some new load models should be developed integrating with electric vehicle loads because the EVs' deployment could cause the change of load composition rate on power system planning and operations. EVs are also resources for micro grid as well as distributed generation and demand response so that various supply and demand side resources should be considered for micro grid researches. In this paper, the load composition rate of residential sectors is prospected considering the deployment of EVs and the resource configuration of micro grid is optimized based on net present cost. In the optimization, the load patten of case studies includes EV's charging characteristics and various cases are simulated comparing micro grid environment and normal condition. HOMER is used to compare various cases and economic effects.