• Food Science of Animal Resources
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• v.37 no.3
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• pp.449-455
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• 2017

Chocolate milk is one of the most commonly used non-fermentative dairy products, which, due to high level of sucrose, could lead to diabetes and tooth decay among children. Therefore, it is important to replace sucrose with other types of sweeteners, especially, natural ones. In this research, response surface methodology (RSM) was used to optimize the ingredients formulation of prebiotic chocolate milk, date syrup as sweetener (4-10%w/w), inulin as prebiotic texturizer (0-0.5%w/w) and carrageenan as thickening agent (0-0.04%w/w) in the formulation of chocolate milk. The fitted models to predict the variables of selected responses such as pH, viscosity, total solid, sedimentation and overall acceptability of chocolate milk showed a high coefficient of determination. The independent effect of carrageenan was the most effective parameter which led to pH and sedimentation decrease but increased viscosity. Moreover, in most treatments, date syrup and inulin variables had significant effects which had a mutual impact. Optimization of the variables, based on the responses surface 3D plots showed that the sample containing 0.48% (w/w) of inulin, 0.04% (w/w) of carrageenan, and 10% of date syrup was selected as the optimum condition.

• Wind and Structures
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• v.27 no.1
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• pp.41-57
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• 2018

Concentrated Solar Photovoltaic (CPV) is a promising alternative to conventional solar structures. These solar tracking structures need to be optimized to be competitive against other types of energy production. In particular, the selection of the structural parameters needs to be optimized with regards to the dynamic wind response. This study aims to evaluate the effect of the main structural parameters, as selected in the preliminary design phase, on the wind response and then on the weight of the steel support structure. A parametric study has been performed where parameters influencing dynamic wind response are varied. The study is performed using a semi-deterministic time-domain wind analysis method. Unsteady aerodynamic model is applied for the shape of the CPV structure collector at different configurations in conjunction with a consistent mass-spring-damper model with the corresponding degrees of freedom to describe the dynamic response of the system. It is shown that, unlike the static response analysis, the variation of the peak wind response with many structural parameters is highly nonlinear because of the dynamic wind action. A steel structural optimization process reveals that close attention to structural and site wind parameters could lead to optimal design of CPV steel support structure.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.629-634
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• 2000

Generally, the accuracy of motion control systems is strongly influenced by both the mechanical characteristics and servo characteristics of feed drive systems. In the fed drive systems of machine tools that consist of mechanical parts and electrical parts, a torsional vibration is often generated because of its elastic elements in torque transmission. Especially, a torsional vibration caused by the elasticity of mechanical elements might deteriorate the quick movement of system and lead to shorten the life time of the mechanical transmission elements. So it is necessary to analyze the electromechanical system mathematically to optimize the dynamic characteristics of the feed drive system. In this paper, based on the simplifies feed drive system model, radius errors due to position gain mismatch and servo response characteristic have been developed and an optimal criterion for tuning the gain of speed controller is discussed. The proportional and integral parameter gain of the feed drive controller are optimal design variables for the gain tuning of PI speed controller. Through the optimization problem formulation, both proportional and integral parameter are optimally tuned so as to compensate the radius errors by using the genetic algorithm. As a result, higher performance on circular profile tests has been achieved than the one with standard parameters.

• Journal of the Korea Society for Simulation
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• v.22 no.2
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• pp.41-51
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• 2013

Tendency for small changes in end-consumer demand to be amplified as one moves further up the supply chain is known as bullwhip effect (BE). BE is usually defined as variance(order)/variance(demand). Since such distorted information throughout the supply chain can lead to inefficiencies, many studies to reduce variance(order) have been performed. However, in this study, we show that minimization of BE may increase inefficiencies of the supply chain. We introduce a new objective function to increase system efficiency using smoothed ordering policies. Simulation optimization is utilized to find optimal smoothed ordering policies.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.4
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• pp.557-566
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• 2002

This paper presents an optimum deck and girder system design for minimizing the life-cycle cost(LCC) of steel box girder bridges. The problem of optimum LCC design of steel box girder bridges is formulated as that of minimization of the expected total LCC that consists of initial cost, maintenance cost and expected retrofit costs for strength, deflection and crack. To demonstrate the cost effectiveness of LCC design of steel box girder bridges, the LCC optimum design is compared with conventional design method for steel box girder bridges. From the numerical investigations, it may be positively stated that the optimum design of steel box girder bridges based on LCC will lead to mote rational, economical and safer design.

• ETRI Journal
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• v.32 no.3
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• pp.395-405
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• 2010

A novel multiclassifier system (MCS) strategy is proposed and applied to a text-dependent speaker verification task. The presented scheme optimizes the linear combination of classifiers on an on-line basis. In contrast to ordinary MCS approaches, neither a priori distributions nor pre-tuned parameters are required. The idea is to improve the most accurate classifier by making use of the incremental information provided by the second classifier. The on-line multiclassifier optimization approach is applicable to any pattern recognition problem. The proposed method needs neither a priori distributions nor pre-estimated weights, and does not make use of any consideration about training/testing matching conditions. Results with Yoho database show that the presented approach can lead to reductions in equal error rate as high as 28%, when compared with the most accurate classifier, and 11% against a standard method for the optimization of linear combination of classifiers.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.516-525
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• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.6
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• pp.965-973
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• 2011

Small unmanned ground vehicles(SUGVs) are typically operational on unstructured environments such as crashed building, mountain area, caves, and so on. On those terrains, driving control can suffer from the unexpected ground disturbances which occasionally lead turnover situation. In this paper, we have proposed an algorithm which sustains driving stability of a SUGV as preventing from turnover. The algorithm exploits potential field method in order to determine the stability of the robot. Then, the flipper and manipulator posture of the SUGV is optimized from local optimization algorithm known as gradient descent method. The proposed algorithm is verified using 3D dynamic simulation, and results showed that the proposed algorithm contributes to driving stability of SUGV.

• Nuclear Engineering and Technology
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• v.48 no.2
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• pp.360-367
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• 2016

A windowless spallation target can provide a neutron source and maintain neutron chain reaction for a subcritical reactor, and is a key component of China's nuclear waste transmutation of coupling accelerator and subcritical reactor. The main issue of the windowless target design is to form a stable and controllable free surface that can ensure that energy spectrum distribution is acquired for the neutron physical design when the high energy proton beam beats the lead-bismuth eutectic in the spallation target area. In this study, morphology and flow characteristics of the free surface of the windowless target were analyzed through the volume of fluid model using computational fluid dynamics simulation, and the results show that the outlet cross section size of the target is the key to form a stable and controllable free surface, as well as the outlet with an arc transition. The optimization parameter of the target design, in which the radius of outlet cross section is $60{\pm}1mm$, is verified to form a stable and controllable free surface and to reduce the formation of air bubbles. This work can function as a reference for carrying out engineering design of windowless target and for verification experiments.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.494-496
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• 2013

Cost-Benefit Analysis (CBA) is a systematic optimization process that allows users to compare different alternatives and to determine if a project is a solid investment. Many state DOTs have included CBA in their pavement management systems (PMSs) to help allocate state funds for maintenance, rehabilitation, resurfacing, and reconstruction of pavements. In a typical CBA, each pavement type has an assigned weight factor which represents the level of importance of this pavement type. To conduct an accurate CBA, it is essential to select appropriate weight factors. Arbitrarily assigning weights factors to pavements can lead to biased and inaccurate funding allocation decisions. The purpose for this paper is to outline a method to develop an ideal set of weight factors that can be utilized to conduct more accurate CBA. To this end, a matrix of all possible weight factors sets was developed. CBA was conducted for each set of weight factors to obtain a population of possible optimization solutions. Then a regression analysis was performed to establish the relationship between benefit and weight factors. Finally, a multi-objective genetic algorithm was applied to select the optimal set of weight factors. The findings from this study can be used by state DOTs to strategically manage their roadway systems in a cost effective manner.