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公开(公告)号：US08762080B2
公开(公告)日：2014-06-24
申请号：US12971346
申请日：2010-12-17
CPC分类号： G06F11/3051 , G06F11/3058
摘要： A method for identifying missing components of a computer system may include receiving telemetry signals characterizing a current configuration of the computer system and determining a cross power spectral density signature of at least some of the telemetry signals. The method may further include comparing information about the determined cross power spectral density signature with information about a predetermined cross power spectral density signature to determine whether a component is missing within the computer system.
摘要翻译： 用于识别计算机系统的缺失组件的方法可以包括接收表征计算机系统的当前配置的遥测信号，并确定至少一些遥测信号的交叉功率谱密度签名。 该方法还可以包括将关于所确定的交叉功率谱密度签名的信息与关于预定交叉功率谱密度签名的信息进行比较，以确定组件是否在计算机系统内丢失。
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2.发明申请公开(公告)号：US20070179727A1
公开(公告)日：2007-08-02
申请号：US11342057
申请日：2006-01-27
CPC分类号： H03M1/20
摘要： One embodiment of the present invention provides a system that reconstructs a high-resolution signal from a set of low-resolution quantized samples. During operation, the system receives a time series containing low-resolution quantized signal values which are sampled from the high-resolution signal. Next, the system performs a spectral analysis on the time series to obtain a frequency series for the low-resolution quantized signal values. The system next selects a subset of frequency terms from the frequency series which have the largest amplitudes. The system then reconstructs the high-resolution signal by performing an inverse spectral analysis on the subset of the frequency terms.
摘要翻译： 本发明的一个实施例提供一种从一组低分辨率量化样本重构高分辨率信号的系统。 在操作期间，系统接收包含从高分辨率信号中采样的低分辨率量化信号值的时间序列。 接下来，系统对时间序列执行频谱分析，以获得低分辨率量化信号值的频率序列。 该系统接下来从具有最大幅度的频率序列中选择频率项的子集。 然后，该系统通过对频率项的子集执行反频谱分析来重构高分辨率信号。
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公开(公告)号：US20070131779A1
公开(公告)日：2007-06-14
申请号：US11298959
申请日：2005-12-09
CPC分类号： G06K19/06 , G06K2017/009
摘要： A method of tagging a manufactured product with a passive tag includes processing a subset of a plurality of unique combinations of at least two axis ratios, where the subset is determinable by a plurality of parameters that define a portion of a coordinate space, to determine a first particular unique combination of the at least two axis ratios. A gas having the determined particular one unique combination of at least two axis ratios is incorporated into the manufactured product. The product to be tagged may be a first product, characterized by a first particular characteristic, and a second product is characterized by a second particular characteristic different from the first particular characteristic. The unique combination of at least two axis ratios is a first unique combination. The plurality of unique combinations of at least two axis ratios is processed to determine a second particular unique combination of the at least two axis ratios, and a gas having the determined second particular unique combination of at least two axis ratios is incorporated into the second product.
摘要翻译： 用无源标签标记制造产品的方法包括处理至少两个轴比的多个唯一组合的子集，其中该子集可由定义坐标空间的一部分的多个参数确定，以确定 所述至少两个轴比的第一特定独特组合。 具有确定的至少两个轴比的特定一个独特组合的气体被并入制造的产品中。 要标记的产品可以是第一产品，其特征在于第一特定特征，第二产品的特征在于与第一特定特性不同的第二特定特征。 至少两个轴比的独特组合是第一个独特的组合。 处理至少两个轴比的多个独特组合以确定至少两个轴比的第二特定独特组合，并且将具有至少两个轴比的所确定的第二特定独特组合的气体并入第二产品 。
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公开(公告)号：US20070040582A1
公开(公告)日：2007-02-22
申请号：US11205924
申请日：2005-08-17
IPC分类号： H03K19/094 , B60L1/00 , G08C19/16
CPC分类号： G06F1/28 , G06F1/3209 , G06F11/3409 , G06F11/3452 , Y02D10/34
摘要： A system that facilitates estimating power consumption in a computer system by inferring the power consumption from instrumentation signals. During operation, the system monitors instrumentation signals within the computer system, wherein the instrumentation signals do not include corresponding current and voltage signals that can be used to directly compute power consumption. The system then estimates the power consumption for the computer system by inferring the power consumption from the instrumentation signals and from an inferential power model generated during a training phase.
摘要翻译： 通过推断仪表信号的功耗，有助于估算计算机系统的功耗。 在操作期间，系统监视计算机系统内的仪表信号，其中仪器信号不包括可用于直接计算功耗的相应的电流和电压信号。 系统然后通过推断来自仪器信号的功率消耗和在训练阶段产生的推理功率模型来估计计算机系统的功耗。
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公开(公告)号：US20120158326A1
公开(公告)日：2012-06-21
申请号：US12971346
申请日：2010-12-17
CPC分类号： G06F11/3051 , G06F11/3058
摘要： A method for identifying missing components of a computer system may include receiving telemetry signals characterizing a current configuration of the computer system and determining a cross power spectral density signature of at least some of the telemetry signals. The method may further include comparing information about the determined cross power spectral density signature with information about a predetermined cross power spectral density signature to determine whether a component is missing within the computer system.
摘要翻译： 用于识别计算机系统的缺失组件的方法可以包括接收表征计算机系统的当前配置的遥测信号，并确定至少一些遥测信号的交叉功率谱密度签名。 该方法还可以包括将关于所确定的交叉功率谱密度签名的信息与关于预定交叉功率谱密度签名的信息进行比较，以确定组件是否在计算机系统内丢失。
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6.发明申请Using a genetic technique to optimize a regression model used for proactive fault monitoring 有权使用遗传技术优化用于主动故障监测的回归模型公开(公告)号：US20070220340A1
公开(公告)日：2007-09-20
申请号：US11359672
申请日：2006-02-22
IPC分类号： G06F11/00
CPC分类号： G06F11/0751 , G06F11/0748
摘要： One embodiment of the present invention provides a system that optimizes a regression model which predicts a signal as a function of a set of available signals. During operation, the system receives training data for the set of available signals from a computer system during normal fault-free operation. The system also receives an objective function which can be used to evaluate how well a regression model predicts the signal. Next, the system initializes a pool of candidate regression models which includes at least two candidate regression models, wherein each candidate regression model in the pool includes a subset of the set of available signals. The system then optimizes the regression model by iteratively: (1) selecting two regression models U and V from the pool of candidate regression models, wherein regression models U and V best predict the signal based on the training data and the objective function; (2) using a genetic technique to create an offspring regression model W from U and V by combining parts of the two regression models U and V; and (3) adding W to the pool of candidate regression models.
摘要翻译： 本发明的一个实施例提供了一种优化回归模型的系统，该回归模型预测作为一组可用信号的函数的信号。 在运行期间，在正常无故障运行期间，系统接收来自计算机系统的一组可用信号的训练数据。 该系统还接收到一个目标函数，可用于评估回归模型预测信号的良好程度。 接下来，系统初始化包括至少两个候选回归模型的候选回归模型池，其中池中的每个候选回归模型包括该组可用信号的子集。 该系统通过迭代优化回归模型：（1）从候选回归模型池中选择两个回归模型U和V，其中回归模型U和V根据训练数据和目标函数最佳地预测信号; （2）使用遗传技术通过组合两个回归模型U和V的部分来从U和V创建后代回归模型W; 和（3）在候选回归模型池中加入W。
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