为保障医院信息网络的安全管理,避免医疗信息泄露,提出了基于深度生成模型的医院网络异常信息入侵检测算法。采用二进制小波变换方法,多尺度分解医院网络运行数据,结合自适应软门限去噪系数提取有效数据。运用最优运输理论中的Wasserst...为保障医院信息网络的安全管理,避免医疗信息泄露,提出了基于深度生成模型的医院网络异常信息入侵检测算法。采用二进制小波变换方法,多尺度分解医院网络运行数据,结合自适应软门限去噪系数提取有效数据。运用最优运输理论中的Wasserstein距离算法与MMD(Maximun Mean Discrepancy)距离算法,在深度生成模型中,对医院网络数据展开降维处理。向异常检测模型中输入降维后网络正常运行数据样本,并提取样本特征。利用深度学习策略中的Adam算法,生成异常信息判别函数,通过待测网络运行数据与正常网络运行数据的特征对比,实现医院网络异常信息入侵检测。实验结果表明,算法能实现对医院网络异常信息入侵的高效检测,精准检测多类型网络入侵行为,为医疗机构网络运行提供安全保障。展开更多
The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the s...The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.展开更多
卷积神经网络性能的快速提升是以不断堆叠的网络层数以及成倍增长的参数量和存储空间为代价,这不仅会使模型在训练过程中出现过拟合等问题,也不利于模型在资源受限的嵌入式设备上运行,因而提出模型压缩技术来解决上述问题,主要对模型压...卷积神经网络性能的快速提升是以不断堆叠的网络层数以及成倍增长的参数量和存储空间为代价,这不仅会使模型在训练过程中出现过拟合等问题,也不利于模型在资源受限的嵌入式设备上运行,因而提出模型压缩技术来解决上述问题,主要对模型压缩技术中的特征蒸馏算法进行了研究。针对特征蒸馏中利用教师网络特征图指导学生网络并不能很好地锻炼学生网络特征拟合能力的问题,提出基于特征分布蒸馏算法。该算法利用条件互信息的概念构建模型特征空间的概率分布,并引入最大平均差异(maximum mean discrepancy,MMD)设计损失函数以最小化教师网络和学生网络特征分布间的距离。在知识蒸馏的基础上利用toeplitz矩阵对学生网络进行权重共享操作,进一步节省了模型的存储空间。为验证在特征分布蒸馏算法训练下学生网络的特征拟合能力,在图像分类、目标检测和语义分割三种图像处理任务上进行了实验验证,实验表明所提算法在以上三种学习任务中的表现均优于对比算法且实现了不同网络架构间的蒸馏。展开更多
针对领域适应学习(Domain adaptation learning,DAL)问题,提出一种核分布一致局部领域适应学习机(Kernel distribution consistency based local domaina daptation classifier,KDC-LDAC),在某个通用再生核Hilbert空间(Universally repr...针对领域适应学习(Domain adaptation learning,DAL)问题,提出一种核分布一致局部领域适应学习机(Kernel distribution consistency based local domaina daptation classifier,KDC-LDAC),在某个通用再生核Hilbert空间(Universally reproduced kernel Hilbert space,URKHS),基于结构风险最小化模型,KDC-LDAC首先学习一个核分布一致正则化支持向量机(Support vector machine,SVM),对目标数据进行初始划分;然后,基于核局部学习思想,对目标数据类别信息进行局部回归重构;最后,利用学习获得的类别信息,在目标领域训练学习一个适于目标判别的分类器.人造和实际数据集实验结果显示,所提方法具有优化或可比较的领域适应学习性能.展开更多
文摘为保障医院信息网络的安全管理,避免医疗信息泄露,提出了基于深度生成模型的医院网络异常信息入侵检测算法。采用二进制小波变换方法,多尺度分解医院网络运行数据,结合自适应软门限去噪系数提取有效数据。运用最优运输理论中的Wasserstein距离算法与MMD(Maximun Mean Discrepancy)距离算法,在深度生成模型中,对医院网络数据展开降维处理。向异常检测模型中输入降维后网络正常运行数据样本,并提取样本特征。利用深度学习策略中的Adam算法,生成异常信息判别函数,通过待测网络运行数据与正常网络运行数据的特征对比,实现医院网络异常信息入侵检测。实验结果表明,算法能实现对医院网络异常信息入侵的高效检测,精准检测多类型网络入侵行为,为医疗机构网络运行提供安全保障。
基金the National Key Research and Development Program of China(No.2020YFB1713500)the Natural Science Basic Research Program of Shaanxi(Grant No.2023JCYB289)+1 种基金the National Natural Science Foundation of China(Grant No.52175112)the Fundamental Research Funds for the Central Universities(Grant No.ZYTS23102).
文摘The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.
文摘卷积神经网络性能的快速提升是以不断堆叠的网络层数以及成倍增长的参数量和存储空间为代价,这不仅会使模型在训练过程中出现过拟合等问题,也不利于模型在资源受限的嵌入式设备上运行,因而提出模型压缩技术来解决上述问题,主要对模型压缩技术中的特征蒸馏算法进行了研究。针对特征蒸馏中利用教师网络特征图指导学生网络并不能很好地锻炼学生网络特征拟合能力的问题,提出基于特征分布蒸馏算法。该算法利用条件互信息的概念构建模型特征空间的概率分布,并引入最大平均差异(maximum mean discrepancy,MMD)设计损失函数以最小化教师网络和学生网络特征分布间的距离。在知识蒸馏的基础上利用toeplitz矩阵对学生网络进行权重共享操作,进一步节省了模型的存储空间。为验证在特征分布蒸馏算法训练下学生网络的特征拟合能力,在图像分类、目标检测和语义分割三种图像处理任务上进行了实验验证,实验表明所提算法在以上三种学习任务中的表现均优于对比算法且实现了不同网络架构间的蒸馏。
基金Supported by the National Natural Science Foundation of China(61972261)the Major Statistic Project of National Bureau of Statistics(2020ZX14)+1 种基金the National Training Program of Innovation and Entrepreneurship for Undergraduates(S202010590028)the Scientific Research Foundation of Shenzhen University for Newly-introduced Teachers(2018060)。
