• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2001년도 봄 학술발표회 논문집
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• pp.351-358
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• 2001

In this study, for the investigation of effects of several parameters, such as fluid mesh boundary size, cylinder or block shape, dimensions of depth, breadth and length at free suface, and fluid mesh element size to the depth direction on a reliable shock response of finite element model under underwater explosion with consideration of the bulk cavitation analysis of a simplified surface ship was carried out using the LS-DYNA3D/USA code. The shock responses were not much affected by the fluid mesh parameters. The computational time was greatly dependent on the number of DAA boundary segments. It is desirable to reduce the DAA boundary segments in the fluid mesh model, and it is not necessary to cover the fluid mesh boundary to or beyond the bulk cavitation zone just for the concerns about an initial shock wave response. It is also the better way to prefer cylinder type of the fluid mesh model to the block one.

• Journal of Dairy Science and Biotechnology
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• 제41권2호
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• pp.45-56
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• 2023

This review serves two purposes: first, to promote the use of improved optimization techniques in response surface methodology (RSM); and second, to enhance the optimum conditions for the fermentation of probiotics. According to research in dairy science, Lactiplantibacillus plantarum K79 is a candidate probiotic that has beneficial health effects, such as lowering blood pressure. The optimum conditions for L. plantarumK79 to produce peptides with angiotensin-converting enzyme (ACE) inhibitory activity were proposed, through modeling each of ACE inhibitory activity and pH as a function of the four factors that are skim milk concentration (%), incubation temperature (℃), incubation time (hours), and starter added amount (%). To estimate optimum conditions, the researchers employed a desirability-based multi-response optimization approach, utilizing third-order models with a nonsignificant lack of fit. The estimated optimum fermentation conditions for L. plantarum K79 were as follows: a skim milk concentration of 10.76%, an incubation temperature of 36.9℃, an incubation time of 23.76 hours, and a starter added amount of 0.098%. Under these conditions, the predicted ACE inhibitory activity was 91.047%, and the predicted pH was 4.6. These predicted values achieved the objectives of the multi-response optimization in this study.

• Journal of Dairy Science and Biotechnology
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• 제26권2호
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• pp.51-56
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• 2008

Using central composite design, we have designed optimization of the manufacturing of processed butter. And response surface analysis by least-square regression was used Statistical Analysis System(SAS). Central composite design can be achieved by response surface techniques that allow flexibility in modeling and analysis. Response surface methodology(RSM) was used to optimize hardness(%) using as independent variables; the content of butter($X_1$), ranging from 50 to 90(%), the content of soybean oil($X_2$), from 0 to 20(%), and the hydrogenated soybean oil($X_3$) from 0 to 4(%). The results on the regression coefficients calculated for overrun by response surface by least-square regression(RSREG) were followed. It was considered that the linear regression was significant(p<0.01). As for the processed butter, the regression model equation for the hardness(Y, %) to the change of an independent variable could be predicted as follow: $Y=60.88-8.92X_2-{29.3X_2}^2$. The optimal for the manufacturing of processed butter were determined at the content of butter of 88.22%, soybean oil of 6.71% and hydrogenated soybean oil of 2.36%, respectively. Optimum compositions were resulted in hardness of 65.78 N. Finally the reference sample(Butter in the morning, Seoul Dairy Co-op.) and processed butter manufacturing under the optimal conditions were compared with spreadability test. The spreadability scores result from reference sample and butter under optimal conditions was not found a significant difference.

• 한국기계가공학회지
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• 제12권2호
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• pp.68-74
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• 2013

In this paper, an average surface roughness, $R_a$, was measured by optical measurement and its mathematical model according to spindle speed and feedrate was obtained by least square method. Also, its result is compared and investigated with real measured average surface roughness. The optical measurement of surface roughness is performed by CLSM(confocal laser scanning microscope) and the captured HEI(height encoded image) data is used as an original data for the generation of average surface roughness and its mathematical plane or contour surface of surface roughness. Using this polynomial model with two independent variables, the behavior of an average surface roughness is investigated and analyzed with an experimental modeling of least square algorithm. And it can be used for the prediction of $R_a$ in different condition of machining.

• Steel and Composite Structures
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• 제42권1호
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• pp.59-74
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• 2022

This study introduces a general reliability-based, performance-based design framework to design frames regarding their uncertainties and user-defined design goals. The Iterative-R method extracted from the main framework can designate a proper R (i.e., response modification factor) satisfying the design goal regarding target reliability index and pre-defined probability of collapse. The proposed methodology is based on FEMA P-695 and can be used for all systems that FEMA P-695 applies. To exemplify the method, multiple three-dimensional, four-story steel special moment-resisting frames are considered. Closed-form relationships are fitted between frames' responses and the modeling parameters. Those fits are used to construct limit state functions to apply reliability analysis methods for design safety assessment and the selection of proper R. The frameworks' unique feature is to consider arbitrarily defined probability density functions of frames' modeling parameters with an insignificant analysis burden. This characteristic enables the alteration in those parameters' distributions to meet the design goal. Furthermore, with sensitivity analysis, the most impactful parameters are identifiable for possible improvements to meet the design goal. In the studied examples, it is revealed that a proper R for frames with different levels of uncertainties could be significantly different from suggested values in design codes, alarming the importance of considering the stochastic behavior of elements' nonlinear behavior.

• 품질경영학회지
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• 제46권1호
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• pp.39-74
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• 2018

Purpose: For more than 30 years, robust parameter design (RPD), which attempts to minimize the process bias (i.e., deviation between the mean and the target) and its variability simultaneously, has received consistent attention from researchers in academia and industry. Based on Taguchi's philosophy, a number of RPD methodologies have been developed to improve the quality of products and processes. The primary purpose of this paper is to review and discuss existing RPD methodologies in terms of the three sequential RPD procedures of experimental design, parameter estimation, and optimization. Methods: This literature study composes three review aspects including experimental design, estimation modeling, and optimization methods. Results: To analyze the benefits and weaknesses of conventional RPD methods and investigate the requirements of future research, we first analyze a variety of experimental formats associated with input control and noise factors, output responses and replication, and estimation approaches. Secondly, existing estimation methods are categorized according to their implementation of least-squares, maximum likelihood estimation, generalized linear models, Bayesian techniques, or the response surface methodology. Thirdly, optimization models for single and multiple responses problems are analyzed within their historical and functional framework. Conclusion: This study identifies the current RPD foundations and unresolved problems, including ample discussion of further directions of study.

• 한국축산식품학회지
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• 제32권3호
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• pp.274-284
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• 2012

In this study, the effects of physical parameters (30-270 MPa of pressure, 3-57 min of time, and 1-$49^{\circ}C$ of temperature) on pork quality were investigated. Response surface methodology was used in order to monitor and model the changes in pork quality under varied pressure conditions. As quality characteristics, shear force, water holding capacity (WHC) and the CIE color of pork were measured, and optimum pressure conditions were evaluated by statistical modeling. Pressure improved the WHC of pork at relatively low temperature ($<25^{\circ}C$); however, the opposite occurred with increasing temperature. Although pressure and temperature affected the tenderness of the meat, interaction effects among variations were not observed. At pressure levels higher than 200 MPa, the color of pork differed markedly from that of the untreated controls. In particular, differential scanning calorimetry (DSC) revealed marked evidence of myosin denaturation. The present study demonstrates that pork quality varies depending on pressure conditions.

• 품질경영학회지
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• 제30권4호
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• pp.58-67
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• 2002

Since the inception of off-line quality control, it has drawn a particular attention from research community and it has been implemented in a wide variety of industries mainly due to its extensive applicability to numerous real situations. Emphasizing design issues rather than control issues related to manufacturing processes, off-line quality control has been recognized as a cost-effective approach to quality improvement. It mainly consists of three design stages: system design, parameter design, and tolerance design which are implemented in a sequential manner. Utilizing experimental designs and optimization techniques, off-line quality control is aimed at achieving product performance insensitive to external noises by reducing process variability. In spite of its conceptual soundness and practical significance, however, off-line quality control has also been criticized to a great extent due to its heuristic nature of investigation. In addition, it has also been pointed out that the process optimization procedures are inefficient. To enhance the current practice of off-line quality control, this study proposes an integrated optimization model by utilizing a well-established statistical tool, so called response surface methodology (RSM), and a tolerance - cost relationship.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1994년도 추계학술발표회 및 정기총회
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• pp.1-2
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• 1994

With the prevalence of computers in modern organizations, simulation is receiving more atention as an effectvie decision -making tool. Simualtion is a computer-based numerical technique which uses mathmatical and logical models to approximate the behaviror of a real-world system. However, iptimization of synamic stochastic systems often defy analytical and algorithmic soluions. Although a simulation approach is often free fo the liminting assumption s of mathematical modeling, cost and time consiceration s make simulation the henayst's last resort. Therefore, whenever possible, analytical and algorithmica solutions are favored over simulation. This paper discussed the issues and procedrues for using simulation as a tool for optimization of stochastic complex systems that are dmodeled by computer simulation . Its emphasis is mostly on issues that are speicific to simulation optimization instead of consentrating on the general optimizationand mathematical programming techniques . A simulation optimization problem is an optimization problem where the objective function. constraints, or both are response that can only be evauated by computer simulation. As such, these functions are only implicit functions of decision parameters of the system, and often stochastic in nature as well. Most of optimization techniqes can be classified as single or multiple-resoneses techniques . The optimization of single response functins has been researched extensively and consists of many techniques. In the single response category, these strategies are gradient based search techniques, stochastic approximate techniques, response surface techniques, and heuristic search techniques. In the multiple response categroy, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphica techniqes, direct search techniques, constrained optimization techniques, unconstrained optimization techniques, and goal programming techniques. The choice of theprocedreu to employ in simulation optimization depends on the analyst and the problem to be solved. For many practival and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computersimulation is one of the most effective means of studying such complex systems. In this paper, after discussion of simulation optmization techniques, the applications of above techniques will be presented in the modeling process of many flexible manufacturing systems.

• 한국정밀공학회지
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• 제9권3호
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• pp.29-41
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• 1992

According to the recent increase of demands for multi-function and economics on hydraulic excavator, it is required that excavator should have simple operation, higher and operational efficiency, however the modeling of engine/pump system of excavator is not prescribed by the paper. So, in this paper the modeling of engine/pump system of excavator is suggested by identification method from step response and verified effectiveness of identification system by comparing with experimental results which was conducted using PID controller. To improve the problem of parameter variation and modeling error in the system, sliding mode control was introduced and new switching surface was designed. This control algorithm was applied to a hydraulic excavator by simulation, and its effectiveness was verified, and the results of variable structure system for the excavator system using a output component was compared with that of full state feedback when load disturbances and system paramenter variation exist.