Optimal Experimental Designs and Response Surface Optimization

最佳实验设计和响应面优化

基本信息

  • 批准号:
    RGPIN-2020-06745
  • 负责人:
  • 金额:
    $ 1.31万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2021
  • 资助国家:
    加拿大
  • 起止时间:
    2021-01-01 至 2022-12-31
  • 项目状态:
    已结题

项目摘要

Design of experiments is a popular tool in new product development and in process improvement and optimization. Optimal design provides interpretable and accurate inference at minimal costs. Methodology and computation issues are two main challenges in optimal design theory. The goal of the proposed research is to address various optimality problems in follow-up design, model-robust design, and response surface optimization. Fractional factorial designs are cost-efficient experimental plans for identifying and estimating important effects of multiple variables on a response. A fundamental problem for fractional factorial designs is that some effects are aliased and cannot be estimated. Follow-up design is one of the methods for resolving the problem. Alphabetical criteria are popular optimality criteria used to select optimal designs. The drawback of the criteria is that they depend on a pre-specified model. This is sometimes impossible since the true model is unknown in advance in practice. Thus the optimal designs obtained by these criteria may not be robust. Response surface optimization aims to select optimal operating settings to optimize the response. The challenge in this area is that it involves complicated statistical modelling and inference procedures. The application of such optimization includes manufacturing processes, such as assembly, machining, and welding, and service operations, such as banking systems and public transportation. The proposed research will develop new theory and methods to handle these issues in follow-up design, model-robust design, and response surface optimization. New optimality criteria will be proposed to construct optimal model-robust designs. My research program will involve extensive training of highly qualified personnel (HQP) in preparing them for future positions in both academia and industry. The proposed research will advance design theory and methodology and provide new economical experimental plans for experimenters in industrial, agricultural, pharmaceutical, manufacturing, engineering, and chemical sciences.
实验设计是新产品开发和工艺改进与优化的常用工具。优化设计以最小的成本提供可解释和准确的推理。方法和计算问题是优化设计理论的两个主要挑战。本研究的目标是解决后续设计、模型稳健设计和响应面优化中的各种优化问题。部分因子设计是一种经济有效的实验方案,用于识别和估计多个变量对响应的重要影响。部分析因设计的一个基本问题是某些效应是混叠的,无法估计。后续设计是解决这一问题的方法之一。字母顺序准则是用于选择最优设计的常用最优性准则。这些标准的缺点是它们依赖于预先指定的模型。这有时是不可能的,因为在实践中,真正的模型事先是未知的。因此,由这些准则得到的最优设计可能不是稳健的。响应面优化旨在选择最佳操作设置以优化响应。这一领域的挑战在于,它涉及复杂的统计建模和推断程序。这种优化的应用包括制造过程,如装配、机加工和焊接,以及服务操作,如银行系统和公共交通。本文的研究将为后续设计、模型稳健设计和响应面优化等问题的解决提供新的理论和方法。将提出新的最优性准则来构造最优模型-稳健设计。 我的研究计划将包括对高素质人才(HQP)的广泛培训,为他们将来在学术界和工业界的职位做好准备。该研究将推进设计理论和方法,并为工业,农业,制药,制造,工程和化学科学的实验者提供新的经济实验方案。

项目成果

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Yang, Po其他文献

Growth of cucumber seedlings in different varieties as affected by light environment
A Hybrid Hierarchical Framework for Gym Physical Activity Recognition and Measurement Using Wearable Sensors
  • DOI:
    10.1109/jiot.2018.2846359
  • 发表时间:
    2019-04-01
  • 期刊:
  • 影响因子:
    10.6
  • 作者:
    Qi, Jun;Yang, Po;Zhou, Bo
  • 通讯作者:
    Zhou, Bo
Effectively Measuring Respiratory Flow With Portable Pressure Data Using Back Propagation Neural Network
使用反向传播神经网络通过便携式压力数据有效测量呼吸流量
Clinical study of transcatheter arterial chemoembolization combined with microwave ablation in the treatment of advanced hepatocellular carcinoma
Lifelogging Data Validation Model for Internet of Things Enabled Personalized Healthcare

Yang, Po的其他文献

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{{ truncateString('Yang, Po', 18)}}的其他基金

Optimal Experimental Designs and Response Surface Optimization
最佳实验设计和响应面优化
  • 批准号:
    RGPIN-2020-06745
  • 财政年份:
    2022
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Optimal Experimental Designs and Response Surface Optimization
最佳实验设计和响应面优化
  • 批准号:
    RGPIN-2020-06745
  • 财政年份:
    2020
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Optimization Problems in Factorial and Response Adaptive Designs
因子和响应自适应设计中的优化问题
  • 批准号:
    RGPIN-2015-06079
  • 财政年份:
    2019
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Optimization Problems in Factorial and Response Adaptive Designs
因子和响应自适应设计中的优化问题
  • 批准号:
    RGPIN-2015-06079
  • 财政年份:
    2018
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Optimization Problems in Factorial and Response Adaptive Designs
因子和响应自适应设计中的优化问题
  • 批准号:
    RGPIN-2015-06079
  • 财政年份:
    2017
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Optimization Problems in Factorial and Response Adaptive Designs
因子和响应自适应设计中的优化问题
  • 批准号:
    RGPIN-2015-06079
  • 财政年份:
    2016
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Optimization Problems in Factorial and Response Adaptive Designs
因子和响应自适应设计中的优化问题
  • 批准号:
    RGPIN-2015-06079
  • 财政年份:
    2015
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual

相似海外基金

Optimal experimental designs and response-adaptive designs
最佳实验设计和响应自适应设计
  • 批准号:
    RGPIN-2018-06452
  • 财政年份:
    2022
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Optimal Experimental Designs and Response Surface Optimization
最佳实验设计和响应面优化
  • 批准号:
    RGPIN-2020-06745
  • 财政年份:
    2022
  • 资助金额:
    $ 1.31万
  • 项目类别:
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    2021
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    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Optimal Experimental Designs and Response Surface Optimization
最佳实验设计和响应面优化
  • 批准号:
    RGPIN-2020-06745
  • 财政年份:
    2020
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Optimal experimental designs and response-adaptive designs
最佳实验设计和响应自适应设计
  • 批准号:
    RGPIN-2018-06452
  • 财政年份:
    2020
  • 资助金额:
    $ 1.31万
  • 项目类别:
    Discovery Grants Program - Individual
Optimal experimental designs and response-adaptive designs
最佳实验设计和响应自适应设计
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  • 财政年份:
    2019
  • 资助金额:
    $ 1.31万
  • 项目类别:
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Optimal experimental designs and response-adaptive designs
最佳实验设计和响应自适应设计
  • 批准号:
    RGPIN-2018-06452
  • 财政年份:
    2018
  • 资助金额:
    $ 1.31万
  • 项目类别:
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Study on cubature formulas, Hilbert identities, optimal experimental designs
体积公式、希尔伯特恒等式、最优实验设计的研究
  • 批准号:
    26870259
  • 财政年份:
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  • 资助金额:
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  • 项目类别:
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  • 批准号:
    8700945
  • 财政年份:
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  • 资助金额:
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  • 项目类别:
    Standard Grant
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数学科学:最优实验设计
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  • 财政年份:
    1984
  • 资助金额:
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  • 项目类别:
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