• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2440-2443
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• 2008

This paper presents a three dimensional shape optimization procedure for a low-speed axial flow fan blade with a weighted average surrogate model. Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations. Six variables from airfoil profile and lean are selected as design variables. 3D RANS solver is used to evaluate the objective functions of total pressure efficiency. Surrogate approximation models for optimization have been employed to find the optimal design of fan blade. A search algorithm is used to find the optimal design in the design space from the constructed surrogate models for the objective function. The total pressure efficiency is increased by 0.31% with the weighted average surrogate model.

• International Journal of Fluid Machinery and Systems
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• v.10 no.3
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• pp.240-253
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• 2017

Latin hypercube sampling is widely used design-of-experiment technique to select design points for simulation which are then used to construct a surrogate model. The exploration/exploitation properties of surrogate models depend on the size and distribution of design points in the chosen design space. The present study aimed at evaluating the performance characteristics of various surrogate models depending on the Latin hypercube sampling (LHS) procedure (sample size and spatial distribution) for a diverse set of optimization problems. The analysis was carried out for two types of problems: (1) thermal-fluid design problems (optimizations of convergent-divergent micromixer coupled with pulsatile flow and boot-shaped ribs), and (2) analytical test functions (six-hump camel back, Branin-Hoo, Hartman 3, and Hartman 6 functions). The three surrogate models, namely, response surface approximation, Kriging, and radial basis neural networks were tested. The important findings are illustrated using Box-plots. The surrogate models were analyzed in terms of global exploration (accuracy over the domain space) and local exploitation (ease of finding the global optimum point). Radial basis neural networks showed the best overall performance in global exploration characteristics as well as tendency to find the approximate optimal solution for the majority of tested problems. To build a surrogate model, it is recommended to use an initial sample size equal to 15 times the number of design variables. The study will provide useful guidelines on the effect of initial sample size and distribution on surrogate construction and subsequent optimization using LHS sampling plan.

• Journal of KIISE:Information Networking
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• v.29 no.6
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• pp.685-695
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• 2002

Recently, there has been increasingly demand for connecting a embedded device to the Internet. Jini technology is interested in automatically composing a distributed network with devices But, there are some problems that the device needs high hardware requirements to adopt Jini technology for supporting Jini-enabled services. In this paper, we focused on design and implementation of surrogate system that supports non-Java devices in Jini networks. This system and protocol are implemented in Java language. The surrogate system delegates Discovery and Join processing to support a Jini service in connected networks. A Jini client can use service of the device through the surrogate system. We tested a Jini sample program to verify the implemented surrogate system. In the test result, we showed that the Jini client can use functionalities and operations of the non-Java device through the surrogate system.

• YAKHAK HOEJI
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• v.52 no.5
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• pp.331-344
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• 2008

Recently, the FDA (Food and Drug Administration) of the United States and many advanced countries remark biomarkers and surrogate endpoints as a critical path tool on model based drug development. Economic, technical and social profit on model based drug development like a reduction of the length of research and development have been achieved. Therefore we summarize previous studies about biomarkers and surrogate endpoints and suggest a development direction of therapeutic agents. In diabetes mellitus (DM) and osteoporosis, there are remarkable increases in number of patients and most of patients take medicine during their whole lifetime. For this reason, many patients with DM and osteoporosis have a tolerance on their medicine. We expect that research and development on biomarkers and surrogate endpoints will contribute to new drug development on DM and osteoporosis. Biomarkers for DM are blood levels of glucose, insulin, ${HbA}_{1c}$, CRP, alpha-glucosidase, adiponectin and DPP-4. Among these, validated surrogate endpoints for DM are blood levels of glucose, insulin and ${HbA}_{1c}$ Biomarkers for osteoporosis are BMD, BMC, trabecular volume, ICTP, DPD, osteocalcin, the activity of osteoclast and production of osteoblast. The validated surrogate endpoints for osteoporosis are BMD only. This review summarizes all suggested biomarkers and surrogate endpoints in DM and osteoporosis. The biomarkers are classified by drugs, and the method of validation for surrogate endpoints is suggested. This information would contribute to suggest a direction of DM and osteoporosis therapeutic agent development.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.422-428
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• 2013

In many cases, an $\bar{X}$ control chart which is based on the performance variable is used in industrial fields. However, if the performance variable is too costly or impossible to measure and a less expensive surrogate variable is available, the process may be more efficiently controlled using surrogate variables. In this paper, we propose a model for the economic design of a VSI (Variable Sampling Interval) $\bar{X}$ control chart using a surrogate variable that is linearly correlated with the performance variable. The total average profit model is constructed, which involves the profit per cycle time, the cost of sampling and testing, the cost of detecting and eliminating an assignable cause, and the cost associated with production during out-of-control state. The VSI $\bar{X}$ control charts using surrogate variables are expected to be superior to the Shewhart FSI (Fixed Sampling Interval) $\bar{X}$ control charts using surrogate variables with respect to the expected profit per unit cycle time from economic viewpoint.

• Transactions of Materials Processing
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• v.29 no.2
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• pp.89-96
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• 2020

The finite element method has been widely applied in the sheet metal forming process. However, the finite element method is computationally expensive and time consuming. In order to tackle this problem, surrogate modeling methods have been proposed. An artificial neural network (ANN) is one such surrogate model and has been well studied over the past decades. However, when it comes to ANN with two or more layers, so called deep neural networks (DNN), there is distinct a lack of research. We chose to use DNNs our surrogate model to predict the behavior of sheet metal in the deep drawing process. Thickness variation is selected as an output of the DNN in order to evaluate workpiece feasibility. Input variables of the DNN are radius of die, die corner and blank holder force. Finite element analysis was conducted to obtain data for surrogate model construction and testing. Sampling points were determined by full factorial, latin hyper cube and monte carlo methods. We investigated the performance of the DNN according to its structure, number of nodes and number of layers, then it was compared with a radial basis function surrogate model using various sampling methods and numbers. The results show that our DNN could be used as an efficient surrogate model for the deep drawing process.

• Korean Journal of Computational Design and Engineering
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• v.17 no.5
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• pp.375-386
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• 2012

Most engineering design problems require analyses or simulations to evaluate objective functions. However, a single simulation can take many hours or even days to finish for many real world problems. As a result, design optimization becomes impossible since they require hundreds or thousands of simulation evaluations. The surrogate-based optimization (SBO) strategy became a remedy for such computationally expensive analyses and simulations. A surrogate-based optimization strategy has been developed in this study in order to improve global optimization performance. The strategy is a heuristic algorithm and it exploits not only multiple surrogates, but also multiple optimizers. Multiple optimizations of multiple surrogate models yield multiple candidate design points of optima. During the sequential sampling process, the algorithm ranks candidate design points, selects the points as many as specified, and builds the improved surrogate model. Various mathematical functions with different numbers of design variables are chosen to compare the proposed method with the other most recent algorithm, MSEGO. The proposed method shows superior performance to the other method.

• Smart Structures and Systems
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• v.17 no.4
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• pp.611-629
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• 2016

This study presents a new approach of surrogate modeling for time-consuming finite element analysis. A surrogate model is widely used to reduce the computational cost under an iterative computational analysis. Although a variety of the methods have been widely investigated, there are still difficulties in surrogate modeling from a practical point of view: (1) How to derive optimal design of experiments (i.e., the number of training samples and their locations); and (2) diagnostics of the surrogate model. To overcome these difficulties, we propose a sequential surrogate modeling based on Gaussian process model (GPM) with self-adaptive sampling. The proposed approach not only enables further sampling to make GPM more accurate, but also evaluates the model adequacy within a sequential framework. The applicability of the proposed approach is first demonstrated by using mathematical test functions. Then, it is applied as a substitute of the iterative finite element analysis to Monte Carlo simulation for a response uncertainty analysis under correlated input uncertainties. In all numerical studies, it is successful to build GPM automatically with the minimal user intervention. The proposed approach can be customized for the various response surfaces and help a less experienced user save his/her efforts.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.5 s.164
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• pp.670-679
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• 2007

Contemporary consumers interested in fashion develop global tastes regarding consumption and senses on how much certain products cost in the global market place. Demand for foreign brands and products produced a new type of e-tailor called surrogate Internet shopping malls. Due to the unfamiliarity of such retailers, consumers may perceive different types of risks and may show different styles of seeking informations. The research interest of this study was to investigate the differences of risk perception and information search between surrogate e-mall shoppers and general e-mall shoppers. In addition, we examined the influence of these two variables on consumer satisfaction. A survey questionnaire was developed. Measures of three types of e-shopping risks (delivery, transaction, service), information search and satisfaction were included. Data from surrogate e-mall consumers and general e-mall consumers were statistically analyzed. Surrogate e-mall shoppers showed a higher level of product delivery risk and customer service risk than general e-mall shoppers. They also spend more time in seeking information before making purchases. Regression analysis showed that perceived risk had significant influence on information search and consumer satisfaction for surrogate e-mall shoppers, whereas for general e-mall shoppers, no significant influence was detected. The findings should assist marketers and academics in their understanding of the surrogate e-shopping malls.

• Smart Structures and Systems
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• v.26 no.2
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• pp.175-184
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• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.