Collaborative Research: Transfer Learning for Chemical Analyses from Laser-Induced Spectroscopy

合作研究：激光诱导光谱化学分析的迁移学习

• 批准号: 1307179
• 负责人: Sridhar Mahadevan
• 金额: $ 15.89万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1307179&HistoricalAwards=false
• 关键词: Collaborative; Research; Transfer; Learning; Chemical

With support from the Chemical Measurements and Imaging program, Professors Melinda Dyar of Mt. Holyoke College and Sridhar Mahadevan of University of Massachusetts at Amherst and their students will use laser-induced breakdown spectroscopy (LIBS) measurements, including laboratory investigations of standard materials at varying experimental conditions, to develop numerical methods that will address limitations to the broad application of LIBS imposed by matrix effects and plasma variability. State-of-the-art dimensionality reduction and transfer learning methods from machine learning and statistics will be used to build innovative LIBS-based predictive models. These investigations will extend classical methods in statistics for dealing with multiple paired data sets, such as canonical correlational analysis, to deal with unlabeled data, and extract nonlinear low-dimensional regularities in the data. The project includes the design of a suite of model-building tools that can deal with a range of problems and optimization objectives, including different types of correspondence information available across datasets, diversity of global objectives ranging from preserving local to global geometry, and producing linear or nonlinear mappings to lower-dimensional factors. Laser-induced breakdown spectroscopy (LIBS) is a chemical analysis tool that uses the light emitted by a sample when a focused laser pulse generates a plasma at the sample surface. LIBS has a number of features that make it particularly useful for field use, including rapid analysis, minimal sample preparation and suitability for stand-off, that is remote, detection. Moreover, LIBS can detect and quantify light elements that are not always measured using other methods. Consequently, LIBS is well-suited to many applications including, defense interests (e.g., military explosive detection, illegal drug detection, airport security), in-situ analysis of archeological sites, field work at hazardous waste sites, and geological resource exploration. However, utilization of LIBS measurements is limited by signal variability with measurement and sample conditions. This project launches an integrated research program to couple state of the art LIBS instrumentation at Mount Holyoke College to equally state of the art numerical methodology in artificial intelligence and machine learning at the nearby University of Massachusetts to increase the utility of LIBS measurements. This project will provide an interdisciplinary training environment that includes undergraduate, graduate and post-doctoral researchers.

在化学测量和成像项目的支持下，霍利奥克山学院的Melinda Dyar教授和马萨诸塞大学阿默斯特分校的Sridhar Mahadevan教授及其学生将使用激光诱导击穿光谱（LIBS）测量，包括在不同实验条件下对标准材料的实验室研究，开发数值方法，以解决矩阵效应和等离子体变变性对LIBS广泛应用的限制。来自机器学习和统计学的最先进的降维和迁移学习方法将用于构建创新的基于lib的预测模型。这些研究将扩展统计学中用于处理多成对数据集的经典方法，如典型相关分析，以处理未标记数据，并提取数据中的非线性低维规律。该项目包括设计一套模型构建工具，这些工具可以处理一系列问题和优化目标，包括跨数据集可用的不同类型的对应信息，从保留局部到全局几何形状的全局目标的多样性，以及生成到低维因素的线性或非线性映射。激光诱导击穿光谱（LIBS）是一种化学分析工具，它利用聚焦激光脉冲在样品表面产生等离子体时样品发出的光。LIBS具有许多功能，使其对现场使用特别有用，包括快速分析，最少的样品制备和适合远距离检测。此外，LIBS可以检测和量化其他方法无法测量的轻元素。因此，LIBS非常适合许多应用，包括国防利益（例如，军事爆炸探测，非法毒品探测，机场安全），考古遗址的现场分析，危险废物现场工作和地质资源勘探。然而，LIBS测量的利用受到测量和样品条件下信号可变性的限制。该项目启动了一个综合研究计划，将Mount Holyoke学院最先进的LIBS仪器与附近马萨诸塞大学人工智能和机器学习领域同样先进的数值方法结合起来，以提高LIBS测量的效用。该项目将提供一个跨学科的培训环境，包括本科生、研究生和博士后研究人员。