Collaborative Research: Variable Selection for Remedying the Effects of Uncontrolled Variation in Data Driven Predictions

协作研究:用于补救数据驱动预测中不受控制的变化影响的变量选择

基本信息

  • 批准号:
    1506915
  • 负责人:
  • 金额:
    $ 26万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2015
  • 资助国家:
    美国
  • 起止时间:
    2015-08-15 至 2019-01-31
  • 项目状态:
    已结题

项目摘要

In a project funded by the Chemical Measurement and Imaging Program of the Chemistry Division, Professors Steven Brown and Karl Booksh, both from the University of Delaware, and Professor Barry Lavine of Oklahoma State University, seek to improve mathematical and statistical modeling of real-time measurement of chemical species. This research is aimed at reducing the cost-of-ownership of calibration and classification models that have become increasingly common in process monitoring, quality assurance and quality-by-design applications by studying the ways in which these models fail and by searching for ways to stabilize the modeling. The broader impacts of this project include the improvements in efficiency and performance from improved and more reliable chemical models for production and quality assurance, as well as the training of graduate and undergraduate students in data analysis, a skill in very high demand.This project is a collaborative effort aimed at investigating fundamental issues important to chemical modeling in modern measurement science: (1) stabilizing calibration and classification relationships in the presence of unexpected contributions, (2) making calibrations and classifications as robust as possible to additional sources of background, and (3) improving classification and calibration models measured over multiple instruments. Methods for automated identification of the portions of the chemical responses that best model the system are being investigated.
在一个由化学系化学测量和成像计划资助的项目中,特拉华大学的史蒂文·布朗和卡尔·布克什教授以及俄克拉荷马州立大学的巴里·拉文教授试图改进化学物种实时测量的数学和统计建模。这项研究的目的是通过研究校准和分类模型失败的方式并寻找稳定建模的方法来降低这些模型的拥有成本,这些模型在过程监控、质量保证和按设计质量的应用中已变得越来越普遍。该项目的广泛影响包括改进和更可靠的生产和质量保证化学模型的效率和性能的提高,以及对研究生和本科生进行数据分析方面的培训,这是一项非常高要求的技能。该项目是一个合作努力,旨在调查现代测量科学中对化学建模重要的基本问题:(1)在存在意外贡献的情况下稳定校准和分类关系,(2)使校准和分类对其他背景来源尽可能稳健,以及(3)改进在多台仪器上测量的分类和校准模型。目前正在研究自动识别对系统进行最佳模拟的化学反应部分的方法。

项目成果

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Barry Lavine其他文献

Barry Lavine的其他文献

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{{ truncateString('Barry Lavine', 18)}}的其他基金

D3SC: Collaborative Research: Overcoming Challenges in Classification Near the Limit of Determination
D3SC:协作研究:克服接近确定极限的分类挑战
  • 批准号:
    2003867
  • 财政年份:
    2020
  • 资助金额:
    $ 26万
  • 项目类别:
    Standard Grant
REU Site: Research Experience for Undergraduates in Integrated Nanoscience
REU 网站:综合纳米科学本科生的研究经验
  • 批准号:
    0649162
  • 财政年份:
    2007
  • 资助金额:
    $ 26万
  • 项目类别:
    Continuing Grant
Research Experiences for Undergraduates in Chemistry at Clarkson University
克拉克森大学化学本科生的研究经历
  • 批准号:
    9101219
  • 财政年份:
    1991
  • 资助金额:
    $ 26万
  • 项目类别:
    Standard Grant

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