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A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics

机译：一种混合深层学习架构，用于对国家点火设施激光光学分类进行分类

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Abstract >Accurately classifying microscopic damage helps automate the repair and recycling of National Ignition Facility optics and informs the study of damage initiation and growth. This complex 12‐class problem previously required human experts to distinguish and label the various damage morphologies. Finding image analysis methods to extract and calculate distinguishing features would be time consuming and challenging, so we sought to automate this task by using convolutional neural networks (CNNs) pretrained on the ImageNet database to take advantage of its automated feature discovery and extraction. We compared three model architectures on this dataset and found the one with highest overall accuracy, 99.17%, was a novel hybrid architecture, one in which we removed the final decision‐making layer of the deep learner and replaced it with an ensemble of decision trees (EDT). This combines the power of feature extraction by CNNs with the decision‐making strength of EDT. The accuracy of the hybrid architecture over the deep learning alone is shown to be significantly improved. Furthermore, we applied this novel hybrid architecture to an entirely different dataset, one containing images of repaired damage sites, and improved on the previously published findings, also with a demonstrably significant increase in accuracy over using the deep learner alone. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><Abstract Type =“Main”XML：Lang =“en”> <标题类型=“main”>抽象</ title> >准确分类显微损伤有助于自动解决国家点火设施光学器件的维修和回收，并通知研究损害启动和增长。这个复杂的12级问题以前需要人类专家来区分和标记各种损害的形态。找到提取和计算区分特征的图像分析方法将是耗时和挑战，因此我们试图通过使用ImageNet数据库上的卷积神经网络（CNNS）来自动执行此任务，以利用其自动化特征发现和提取。我们将三个模型架构进行了比较了这一数据集上的三种模型架构，并发现了最高精度的99.17％，是一种新型的混合架构，其中我们删除了深层学习者的最终决策层，并用决策树的集合替换它（美东时间）。这将CNN与EDT的决策强度相结合了特征提取的功率。仅显示了单独对深度学习的混合架构的准确性得到显着改善。此外，我们将这种新型混合架构应用于完全不同的数据集，一个包含修复的损伤网站的图像，并在先前公布的发现上改进，同时通过单独使用深学习者的准确性显着提高。</ p> </摘要> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-28614/'>《Statistical Analysis and Data Mining》</a> <b style="margin: 0 2px;">|</b><span>2019年第6期</span><b style="margin: 0 2px;">|</b><span>共9页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Amorin Connor&option=202" target="_blank" rel="nofollow">Amorin Connor;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Kegelmeyer Laura M.&option=202" target="_blank" rel="nofollow">Kegelmeyer Laura M.;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Kegelmeyer W. Philip&option=202" target="_blank" rel="nofollow">Kegelmeyer W. Philip;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Computing DivisionLawrence Livermore National LaboratoryLivermore California;</p> <p>Laser Science Engineering DivisionLawrence Livermore National LaboratoryLivermore California;</p> <p>Homeland Security and Defense Systems CenterSandia National LaboratoriesLivermore California;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/3056.html" title="经济统计学">经济统计学;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=automation&option=203" rel="nofollow">automation;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=deep learning&option=203" rel="nofollow">deep learning;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=laser optic damage&option=203" rel="nofollow">laser optic damage;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=machine learning&option=203" rel="nofollow">machine learning;</a> </p> <div class="translation"> 机译：自动化;深入学习;激光视镜损坏;机器学习; </div> </li> </ul> </div> </div> <div class="literature cardcommon"> <div class="similarity "> <h3 class="all_title" id="enpatent66">相似文献</h3> <div class="similaritytab clearfix"> <ul> <li class="active" >外文文献</li> <li >中文文献</li> <li >专利</li> </ul> </div> <div class="similarity_details"> <ul > <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/academic-journal-foreign_detail_thesis/0204121842351.html">A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics</a> <b>[J]</b> . <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Connor Amorin&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">Connor Amorin,</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Laura M. 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