Computer Simulation-Based Designs for Industrial Engineering Experiments

doi:10.11648/J.SJAMS.20180603.12

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Journal Article•DOI•

Research on effective recognition of alarm signals in a human–machine system based on cognitive neural experiments

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Yun Teng, Yuwei Sun, Xinlin Chen, Mei Zhang

06 Jun 2022-International Journal of Occupational Safety and Ergonomics

TL;DR: Experimental results showed that: three kinds of alarm signals all induce visual mismatch waves, and the effective response of human to the alarm signal is color + orientation, color + shape, color from small to large, which provides a reference for the design of the alarms of the man-machine system.

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Abstract: ABSTRACT The rational design of the alarm signal in the man-machine system is an important factor in determining the occurrence of safety accidents. Neuroergonomics provides a new perspective for the study of the cognitive process of alarm signals, which can reveal the mechanism of human perception of visual alarm signals from the cognitive level of the brain, thereby identifying the effectiveness of alarm signals. This study simulates the new energy vehicle cooling man-machine system, uses the automatic control interface of the test cooling water system as the stimulation material, and uses the event-related potential technology in cognitive neuroscience to conduct experimental verification. The experimental results showed that: three kinds of alarm signals (color , color+shape, color+orientation) all induce visual mismatch waves, and the effective response of human to the alarm signal is color+orientation, color+shape, color from small to large, which provides a reference for the design of alarm signal.

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Journal Article•DOI•

Design and analysis of computer experiments

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Sonja Kuhnt¹, David M. Steinberg²•Institutions (2)

Technical University of Dortmund¹, Tel Aviv University²

01 Dec 2010-AStA Advances in Statistical Analysis

TL;DR: The included papers present an interesting mixture of recent developments in the field as they cover fundamental research on the design of experiments, models and analysis methods as well as more applied research connected to real-life applications.

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Abstract: The design and analysis of computer experiments as a relatively young research field is not only of high importance for many industrial areas but also presents new challenges and open questions for statisticians. This editorial introduces a special issue devoted to the topic. The included papers present an interesting mixture of recent developments in the field as they cover fundamental research on the design of experiments, models and analysis methods as well as more applied research connected to real-life applications.

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2,583 citations

"Computer Simulation-Based Designs f..." refers background or methods in this paper

...detailed treatment of this topic, please refer to references [6, 7]....
[...]
...LHDs are the most popular designs for computer-based industrial engineering experiments and have enjoyed lots of development (see, for example, references [6, 7, 9-12])....
[...]
...In this paper the most commonly-used model, the Gaussian process model (see references [6, 7, 16]) is considered....
[...]
...Computer experiments are becoming widely used in industrial engineering where computational methods are used to simulate the real-world phenomena (see, for example, references [1-7])....
[...]

Journal Article•DOI•

Predicting the output from a complex computer code when fast approximations are available

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Marc C. Kennedy¹, Anthony O'Hagan•Institutions (1)

University of Sheffield¹

01 Mar 2000-Biometrika

TL;DR: The purpose of the current paper is to explore ways in which runs from several levels of a code can be used to make inference about the output from the most complex code.

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Abstract: S We consider prediction and uncertainty analysis for complex computer codes which can be run at different levels of sophistication. In particular, we wish to improve efficiency by combining expensive runs of the most complex versions of the code with relatively cheap runs from one or more simpler approximations. A Bayesian approach is described in which prior beliefs about the codes are represented in terms of Gaussian processes. An example is presented using two versions of an oil reservoir simulator. 1. C  Complex mathematical models, implemented in large computer codes, have been used to study real systems in many areas of scientific research (Sacks et al., 1989), usually because physical experimentation is too costly and sometimes impossible, as in the case of large environmental systems. A ‘computer experiment’ involves running the code with various input values for the purpose of learning something about the real system. Often a simulator can be run at different levels of complexity, with versions ranging from the most sophisticated high level code to the most basic. For example, in § 4 we consider two codes which simulate oil pressure at a well of a hydrocarbon reservoir. Both codes use finite element analysis, in which the rocks comprising the reservoir are represented by small interacting grid blocks. The flow of oil within the reservoir can be simulated by considering the interaction between the blocks. The two codes differ in the resolution of the grid, so that we have a very accurate, slow version using many small blocks and a crude approximation using large blocks which runs much faster. Alternatively, a mathematical model could be expanded to include more of the scientific laws underlying the physical processes. Simple, fast versions of the code may well include the most important features, and are useful for preliminary investigations. In real-time applications the number of runs from a high level simulator may be limited by expense. Then there is a need to trade-off the complexity of the expensive code with the availability of the simpler approximations. The purpose of the current paper is to explore ways in which runs from several levels of a code can be used to make inference about the output from the most complex code. We may also have uncertainty about values for the input parameters which apply in any given application. Uncertainty analysis of computer codes describes how this uncertainty on the inputs affects our uncertainty about the output.

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1,260 citations

"Computer Simulation-Based Designs f..." refers methods in this paper

...Currin, Mitchell, Morris and Ylvisaker [13] applied Bayesian prediction to analyze the outputs of computer experiments; Joseph, Hung and Sudjianto [14] proposed blind kriging for developing surrogate models; Kennedy and O'Hagan [15] gave a method for predicting the output from a computer experiment when fast approximations are available....
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Book•

Design and Modeling for Computer Experiments

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Kai-Tang Fang, Runze Li, Agus Sudjianto

14 Oct 2005

TL;DR: This book discusses models for computer experiments, design techniques, and some concepts in Experimental Design Computer Experiments.

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Abstract: PART I AN OVERVIEW INTRODUCTION Experiments and Their Statistical Designs Some Concepts in Experimental Design Computer Experiments Examples of Computer Experiments Space-Filling Designs Modeling Techniques Sensitivity Analysis Strategies for Computer Experiments and An Illustration Case Study Remarks on Computer Experiments Guidance of Reading This Book PART II DESIGNS FOR COMPUTER EXPERIMENTS Latin Hypercube Sampling and its Modifications Uniform Experimental Design Optimization in Construction of Designs for Computer Experiments PART III MODELING FOR COMPUTER EXPERIMENTS METAMODELING Model Interpretation Functional Response APPENDIX Abbreviation References Index Author Index

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942 citations

"Computer Simulation-Based Designs f..." refers background or methods in this paper

...A popular method for analyzing the model in (2) is through kriging [7]....
[...]
...In this paper the most commonly-used model, the Gaussian process model (see references [6, 7, 16]) is considered....
[...]
...detailed treatment of this topic, please refer to references [6, 7]....
[...]
...Computer experiments are becoming widely used in industrial engineering where computational methods are used to simulate the real-world phenomena (see, for example, references [1-7])....
[...]
...LHDs are the most popular designs for computer-based industrial engineering experiments and have enjoyed lots of development (see, for example, references [6, 7, 9-12])....
[...]

Journal Article•DOI•

Bayesian Prediction of Deterministic Functions, with Applications to the Design and Analysis of Computer Experiments

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Carla Currin¹, Toby J. Mitchell², Max D. Morris², Don Ylvisaker³•Institutions (3)

Ohio State University¹, Oak Ridge National Laboratory², University of California, Los Angeles³

01 Dec 1991-Journal of the American Statistical Association

TL;DR: This article is concerned with prediction of a function y(t) over a (multidimensional) domain T, given the function values at a set of “sites” in T, and with the design, that is, with the selection of those sites.

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Abstract: This article is concerned with prediction of a function y(t) over a (multidimensional) domain T, given the function values at a set of “sites” {t (1), t (2), …, t (n)} in T, and with the design, that is, with the selection of those sites. The motivating application is the design and analysis of computer experiments, where t determines the input to a computer model of a physical or behavioral system, and y(t) is a response that is part of the output or is calculated from it. Following a Bayesian formulation, prior uncertainty about the function y is expressed by means of a random function Y, which is taken here to be a Gaussian stochastic process. The mean of the posterior process can be used as the prediction function ŷ(t), and the variance can be used as a measure of uncertainty. This kind of approach has been used previously in Bayesian interpolation and is strongly related to the kriging methods used in geostatistics. Here emphasis is placed on product linear and product cubic correlation func...

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789 citations

"Computer Simulation-Based Designs f..." refers methods in this paper

...Currin, Mitchell, Morris and Ylvisaker [13] applied Bayesian prediction to analyze the outputs of computer experiments; Joseph, Hung and Sudjianto [14] proposed blind kriging for developing surrogate models; Kennedy and O'Hagan [15] gave a method for predicting the output from a computer experiment when fast approximations are available....
[...]
...Consider a function from Currin, Mitchell, Morris and Ylvisaker (1991) which is given by 3 2 1 1 1 3 2 2 1 1 1 2300 1900 2092 601 [1 exp( )] ....
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Journal Article•DOI•

Blind Kriging: A New Method for Developing Metamodels

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V. Roshan Joseph¹, Ying Hung¹, Agus Sudjianto²•Institutions (2)

Georgia Institute of Technology¹, Bank of America²

01 Mar 2008-Journal of Mechanical Design

TL;DR: A modified kriging method is proposed, which has an unknown mean model, called blind kriged, which is identified from experimental data using a Bayesian variable selection technique.

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Abstract: Kriging is a useful method for developing metamodels for product design optimization. The most popular kriging method, known as ordinary kriging, uses a constant mean in the model. In this article, a modified kriging method is proposed, which has an unknown mean model. Therefore, it is called blind kriging. The unknown mean model is identified from experimental data using a Bayesian variable selection technique. Many examples are presented, which show remarkable improvement in prediction using blind kriging over ordinary kriging. Moreover, a blind kriging predictor is easier to interpret and seems to be more robust against mis-specification in the correlation parameters.

...read moreread less

285 citations

"Computer Simulation-Based Designs f..." refers methods in this paper

...Currin, Mitchell, Morris and Ylvisaker [13] applied Bayesian prediction to analyze the outputs of computer experiments; Joseph, Hung and Sudjianto [14] proposed blind kriging for developing surrogate models; Kennedy and O'Hagan [15] gave a method for predicting the output from a computer experiment when fast approximations are available....
[...]
...Plug the MLEs of β and 2σ into formula (4), the MLE of Θ is then given by 2arg min ( log( ) log(| |)).n Rσ ∧∧ ΘΘ = + (7) There are several optimization algorithms available to solve the problem in (7), see Fang, Li and Sudjianto [1] for details....
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Computer Simulation-Based Designs for Industrial Engineering Experiments

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"Computer Simulation-Based Designs f..." refers methods in this paper

"Computer Simulation-Based Designs f..." refers background or methods in this paper

"Computer Simulation-Based Designs f..." refers methods in this paper

"Computer Simulation-Based Designs f..." refers methods in this paper

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