Sensors: Intelligent Multi-Sensor Modeling, Identification, and Data Fusion for Automated Manufacturing
Sensors: Intelligent Multi-Sensor Modeling, Identification, and Data Fusion for Automated Manufacturing
批准号:
0427597
负责人:
Devendra Garg
金额:
$0.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2004
资助国家:
美国
项目状态:
已结题
起止时间:
2004-10-01 至 2007-09-30
中文摘要
面向自动化制造的智能多传感器建模、识别和数据融合(美国国家科学基金会0427597号提案)摘要本研究的主要目标是开发创新的方法,用于在柔性制造机床环境中对来自多个不同信息源/传感器的数据进行建模、识别和融合。这项研究的主要贡献将包括将遗传-神经-模糊方法和贝叶斯估计等人工智能技术与信号处理和统计技术(如扩展卡尔曼滤波)以及参数估计方法(如最大似然和期望最大化)相结合。这项研究项目将集中在以下三个主要部分:1)精确的传感器建模,包括获得传感器和相关噪声的分析和概率模型,了解它们的能力和局限性;2)从多个传感器提取实时数据,并在公共处理平台上解释数据;3)开发策略,组合来自这些传感器的无噪声数据,以消除不确定性,并获得制造系统运行环境的准确模型。开发的创新理论将通过在杜克大学柔性制造车间进行的实验进行测试和验证。由于这项研究将强调在制造过程中使用各种传感器,制造业将是主要受益者。
英文摘要
Intelligent Multi-Sensor Modeling, Identification, and Data Fusion for Automated Manufacturing(NSF Proposal No. 0427597)AbstractThe primary goal of this research effort is to develop innovative methodologies for modeling, identifying and fusing data from multiple heterogeneous information sources/sensors in a flexible manufacturing workcell environment. Major contributions resulting from this research would consist of integrating artificial intelligence techniques, such as Genetic-Neuro-Fuzzy method and Bayesian estimation, with elements of signal processing and statistical techniques such as Extended Kalman filtering, and parametric estimation methods such as Maximum Likelihood, and Expectation Maximization. This research project would focus on the following three major components: 1) precise sensor modeling which would include obtaining analytical and probabilistic models of sensors and associated noises, understanding their capabilities and limitations, 2) extracting real-time data from multiple sensors and interpreting the data on a common processing platform, and 3) developing strategies to combine the noise-free data from these sensors to remove uncertainty and to obtain accurate model of the environment the manufacturing system operates in. The innovative theories developed would be tested and validated via experiments conducted at Duke University's flexible manufacturing workcell. Since the research would emphasize the use of a variety of sensors in manufacturing processes, the manufacturing industry would be a major beneficiary.
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专著(0)
科研奖励(0)
会议论文
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批准号:0228784
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项目类别:Standard Grant
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资助金额:$9.99万
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财政年份:2002
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依托单位:
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依托单位:
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批准号:7803359
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依托单位:
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项目类别:外国学者研究基金项目
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资助金额:--
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批准年份:2024
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负责人:USHARANI HAREESH GOVINDARA JAN
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依托单位: