会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 6. 发明授权
    • Dynamic control of remelting processes
    • 重熔过程的动态控制
    • US6115404A
    • 2000-09-05
    • US244372
    • 1999-02-03
    • Lee A. BertramRodney L. WilliamsonDavid K. MelgaardJoseph J. BeamanDavid G. Evans
    • Lee A. BertramRodney L. WilliamsonDavid K. MelgaardJoseph J. BeamanDavid G. Evans
    • H05B7/152H05B7/144H05B7/148
    • H05B7/152Y02P10/256Y02P10/259
    • An apparatus and method of controlling a remelting process by providing measured process variable values to a process controller; estimating process variable values using a process model of a remelting process; and outputting estimated process variable values from the process controller. Feedback and feedforward control devices receive the estimated process variable values and adjust inputs to the remelting process. Electrode weight, electrode mass, electrode gap, process current, process voltage, electrode position, electrode temperature, electrode thermal boundary layer thickness, electrode velocity, electrode acceleration, slag temperature, melting efficiency, cooling water temperature, cooling water flow rate, crucible temperature profile, slag skin temperature, and/or drip short events are employed, as are parameters representing physical constraints of electroslag remelting or vacuum arc remelting, as applicable.
    • 一种通过向过程控制器提供测量的过程变量值来控制重熔过程的装置和方法; 使用重熔过程的过程模型估计过程变量值; 并从过程控制器输出估计的过程变量值。 反馈和前馈控制装置接收估计的过程变量值并调整重熔过程的输入。 电极重量,电极质量,电极间隙,工艺电流,工艺电压,电极位置,电极温度,电极热边界层厚度,电极速度,电极加速度,炉渣温度,熔化效率,冷却水温度,冷却水流量,坩埚温度 使用曲线,炉渣表面温度和/或滴水短路事件,以及代表电渣重熔或真空电弧重熔的物理限制的参数(如适用)。
    • 8. 发明授权
    • Augmented classical least squares multivariate spectral analysis
    • 增强经典最小二乘法多变量光谱分析
    • US06922645B2
    • 2005-07-26
    • US10963195
    • 2004-10-12
    • David M. HaalandDavid K. Melgaard
    • David M. HaalandDavid K. Melgaard
    • G01N21/27G01N21/35G01N21/64G01N21/65G01R23/16G01N23/48
    • G01N21/359G01N21/274G01N21/64G01N21/65G01N2201/1293
    • A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.
    • 称为增强经典最小二乘法(ACLS)的多变量光谱分析方法在校准样本集中包含未建模的光谱变化源时提供了改进的CLS校准模型。 在CLS校准期间,ACLS方法使用从组件或光谱残差得到的信息,以提供改进的校准增强型CLS模型。 ACLS方法基于CLS,因此它们保留了CLS的定性优势,但是它们具有PLS和其他混合技术的灵活性,因为即使没有明确地包括在光谱变化的未建模的光谱变化源中,它们也可以定义预测模型 校准模型。 未建模的光谱变化源可能是未知的成分,未知浓度的成分,非线性响应,非均匀和相关误差,或存在于校准样品光谱中的其他光谱变化源。 此外,由于各种ACLS方法都是基于CLS的,所以它们可以包含用于更新包含在预测样本集中的新的光谱变化源的预测模型的新的预测增加CLS(PACLS)方法,而不必返回到校准过程 。 ACLS方法也可以应用于交替的最小二乘模型。 ACLS方法可以应用于所有类型的多变量数据。
    • 10. 发明授权
    • Augmented classical least squares multivariate spectral analysis
    • 增强经典最小二乘法多变量光谱分析
    • US06687620B1
    • 2004-02-03
    • US10209841
    • 2002-07-31
    • David M. HaalandDavid K. Melgaard
    • David M. HaalandDavid K. Melgaard
    • G01N3348
    • G01N21/359G01N21/274G01N21/64G01N21/65G01N2201/1293
    • A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.
    • 称为增强经典最小二乘法(ACLS)的多变量光谱分析方法在校准样本集中包含未建模的光谱变化源时提供了改进的CLS校准模型。 在CLS校准期间,ACLS方法使用从组件或光谱残差得到的信息,以提供改进的校准增强型CLS模型。 ACLS方法基于CLS,因此它们保留了CLS的定性优势,但是它们具有PLS和其他混合技术的灵活性,因为即使没有明确地包括在光谱变化的未建模的光谱变化源中,它们也可以定义预测模型 校准模型。 未建模的光谱变化源可能是未知的成分,未知浓度的成分,非线性响应,非均匀和相关误差,或存在于校准样品光谱中的其他光谱变化源。 此外,由于各种ACLS方法都是基于CLS的,所以它们可以包含用于更新包含在预测样本集中的新的光谱变化源的预测模型的新的预测增加CLS(PACLS)方法,而不必返回到校准过程 。 ACLS方法也可以应用于交替的最小二乘模型。 ACLS方法可以应用于所有类型的多变量数据。