为了提高双直线电动机驱动的同步直接进给轴的运动精度,对该类直接进给轴的全行程热误差在线补偿方法进行了研究。分析了双直接进给轴全行程热误差的影响因素,提出一种基于核偏最小二乘法(Kernel partial least squares,KPLS)和模糊逻...为了提高双直线电动机驱动的同步直接进给轴的运动精度,对该类直接进给轴的全行程热误差在线补偿方法进行了研究。分析了双直接进给轴全行程热误差的影响因素,提出一种基于核偏最小二乘法(Kernel partial least squares,KPLS)和模糊逻辑相结合的双直接进给轴全行程热误差的在线补偿方法。应用激光干涉仪测量其热变形量,使用热电偶和红外测温仪测量进给机构关键点的温度,以时间匹配温度和变形量数据建立统计样本,在均匀离散点位置建立热误差KPLS识别模型,通过在线计算得到离散点热误差补偿量,再根据任意位置与离散点的模糊关联程度,综合计算全行程任意位置处热误差补偿量。以此理论为基础,建立补偿决策函数和补偿系统,依据补偿决策函数智能推断补偿值,通过向数控系统发送补偿码实现在线补偿。在自构建的龙门双直线电动机驱动的直接进给轴平台上,进行全行程热误差在线补偿试验研究,结果表明:混合KPLS与模糊逻辑可以有效的对双直接进给轴全行程热误差在线补偿,经过随机测试验证,补偿后的进给精度提高了50%。展开更多
A novel thickness measurement method for surface insulation coating of silicon steel based on NIR spectrometry is explored.The NIR spectra of insulation coating of silicon steel were collected by acousto-optic tunable...A novel thickness measurement method for surface insulation coating of silicon steel based on NIR spectrometry is explored.The NIR spectra of insulation coating of silicon steel were collected by acousto-optic tunable filter(AOTF) NIR spectrometer.To make full use of the effective information of NIR spectral data,discrete binary particle swarm optimization(DBPSO) algorithm was used to select the optimal wavelength variates.The new spectral data,composed of absorbance at selected wavelengths,were used to create the thickness quantitative analysis model by kernel partial least squares(KPLS) algorithm coupled with Boosting.The results of contrast experiments showed that the Boosting-KPLS model could efficiently improve the analysis accuracy and speed.It indicates that Boosting-KPLS is a more accurate and robust analysis method than KPLS for NIR spectral analysis.The maximal and minimal absolute error of 30 testing samples is respectively-0.02 μm and 0.19 μm,and the maximal relative error is 14.23%.These analysis results completely meet the practical measurement need.展开更多
Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining ...Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge.However,due to the computational resources being limited,an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms.Therefore,designing and developing a lightweight detection mechanism is crucial.To address the aforementioned challenges,a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks.The proposed anomaly-based IDS is divided into three steps:pre-processing,feature selection,and decision.In the pre-processing phase,data cleaning and normalization are performed.In the feature selection step,the proposed approach uses two data-driven kernel techniques:kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results.Therefore,in decision step,in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used.To check the efficiency of the developed detection scheme,a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results.The proposed method achieved 99.9%accuracy,99.8%specificity,100%Sensitivity,99.9 F-score.展开更多
An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partia...An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partial least squares(MPLS),are not suitable due to their intrinsic linearity when the variations are nonlinear.To address this issue,kernel partial least squares(KPLS) was used to capture the nonlinear relationship between the latent structures and predictive variables.In addition,KPLS requires only linear algebra and does not involve any nonlinear optimization.In this paper,the application of KPLS was extended to on-line monitoring of batch processes.The proposed batch monitoring method was applied to a simulation benchmark of fed-batch penicillin fermentation process.And the results demonstrate the superior monitoring performance of MKPLS in comparison to MPLS monitoring.展开更多
文摘为了提高双直线电动机驱动的同步直接进给轴的运动精度,对该类直接进给轴的全行程热误差在线补偿方法进行了研究。分析了双直接进给轴全行程热误差的影响因素,提出一种基于核偏最小二乘法(Kernel partial least squares,KPLS)和模糊逻辑相结合的双直接进给轴全行程热误差的在线补偿方法。应用激光干涉仪测量其热变形量,使用热电偶和红外测温仪测量进给机构关键点的温度,以时间匹配温度和变形量数据建立统计样本,在均匀离散点位置建立热误差KPLS识别模型,通过在线计算得到离散点热误差补偿量,再根据任意位置与离散点的模糊关联程度,综合计算全行程任意位置处热误差补偿量。以此理论为基础,建立补偿决策函数和补偿系统,依据补偿决策函数智能推断补偿值,通过向数控系统发送补偿码实现在线补偿。在自构建的龙门双直线电动机驱动的直接进给轴平台上,进行全行程热误差在线补偿试验研究,结果表明:混合KPLS与模糊逻辑可以有效的对双直接进给轴全行程热误差在线补偿,经过随机测试验证,补偿后的进给精度提高了50%。
基金National High Technology Research and Development Program of China(2009AA04Z131)Natural Science Foundation of China (50877056)
文摘A novel thickness measurement method for surface insulation coating of silicon steel based on NIR spectrometry is explored.The NIR spectra of insulation coating of silicon steel were collected by acousto-optic tunable filter(AOTF) NIR spectrometer.To make full use of the effective information of NIR spectral data,discrete binary particle swarm optimization(DBPSO) algorithm was used to select the optimal wavelength variates.The new spectral data,composed of absorbance at selected wavelengths,were used to create the thickness quantitative analysis model by kernel partial least squares(KPLS) algorithm coupled with Boosting.The results of contrast experiments showed that the Boosting-KPLS model could efficiently improve the analysis accuracy and speed.It indicates that Boosting-KPLS is a more accurate and robust analysis method than KPLS for NIR spectral analysis.The maximal and minimal absolute error of 30 testing samples is respectively-0.02 μm and 0.19 μm,and the maximal relative error is 14.23%.These analysis results completely meet the practical measurement need.
基金supported by the Deanship of Scientific Research at the University of Tabuk through Research No.S-1443-0111.
文摘Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge.However,due to the computational resources being limited,an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms.Therefore,designing and developing a lightweight detection mechanism is crucial.To address the aforementioned challenges,a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks.The proposed anomaly-based IDS is divided into three steps:pre-processing,feature selection,and decision.In the pre-processing phase,data cleaning and normalization are performed.In the feature selection step,the proposed approach uses two data-driven kernel techniques:kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results.Therefore,in decision step,in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used.To check the efficiency of the developed detection scheme,a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results.The proposed method achieved 99.9%accuracy,99.8%specificity,100%Sensitivity,99.9 F-score.
基金National Natural Science Foundation of China (No. 61074079)Shanghai Leading Academic Discipline Project,China (No.B504)
文摘An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partial least squares(MPLS),are not suitable due to their intrinsic linearity when the variations are nonlinear.To address this issue,kernel partial least squares(KPLS) was used to capture the nonlinear relationship between the latent structures and predictive variables.In addition,KPLS requires only linear algebra and does not involve any nonlinear optimization.In this paper,the application of KPLS was extended to on-line monitoring of batch processes.The proposed batch monitoring method was applied to a simulation benchmark of fed-batch penicillin fermentation process.And the results demonstrate the superior monitoring performance of MKPLS in comparison to MPLS monitoring.
基金Supported by the National Natural Science Foundation of China(61573364,61703089)the State Key Laboratory of Synthetical Automation for Process Industries(PAL–N201504)the State Key Laboratory of Process Automation in Mining&Metallurgy and Beijing Key Laboratory of Process Automation in Mining&Metallurgy(BGRIMM–KZSKL–2018–06)
