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1474 results about "Moving average" patented technology

In statistics, a moving average (rolling average or running average) is a calculation to analyze data points by creating a series of averages of different subsets of the full data set. It is also called a moving mean (MM) or rolling mean and is a type of finite impulse response filter. Variations include: simple, and cumulative, or weighted forms (described below).

Method and device for enhanced capture tracking by discrimination of fusion beats

InactiveUS7006869B2Improving stimulation device performanceElectrotherapyTemplate matchingMoving average
An apparatus and a method for identifying fusion beats as part of an automatic capture routine in a cardiac stimulation device are disclosed. By requiring each post-pulse electrical response to be closely correlated with an expected morphology for either a capture condition or a loss-of-capture condition before classification of the post-pulse electrical response as either capture or loss-of-capture respectively, the present invention is able to identify fusion beats. In various embodiments, this fusion identification is performed using both template matching and assessment of particular detection features. Moreover, in one embodiment, a moving average and a standard deviation are maintained for each detection feature, thereby creating an automatically adjusting evoked response detection threshold.
Owner:PACESETTER INC

Method and Apparatus for Pump Control Using Varying Equivalent System Characteristic Curve, AKA an Adaptive Control Curve

The present invention provides, e.g., apparatus comprising at least one processor; at least one memory including computer program code; the at least one memory and computer program code being configured, with at least one processor, to cause the apparatus at least to: respond to signaling containing information about an instant pressure and a flow rate of fluid being pumped in a pumping system, and obtain an adaptive control curve based at least partly on the instant pressure and flow rate using an adaptive moving average filter. The adaptive moving average filter may be based at least partly on a system flow equation: SAMAt=AMAF(Qt/√{square root over (ΔPt)}), where the function AMAF is an adaptive moving average filter (AMAF), and the parameters Q and ΔP are a system flow rate and differential pressure respectively. The at least one memory and computer program code may be configured to, with the at least one processor, to cause the apparatus at least to obtain an optimal control pressure set point from the adaptive control curve with respect to an instant flow rate or a moving average flow rate as SPt=MA(Qt)/SAMAt, where the function MA is a moving average filter (MA), to obtain a desired pump speed through a PID control.
Owner:FLUID HANDLING

Method and apparatus for touch sensor with interference rejection

A touch sensitive control system for controlling a device includes a touch sensitive interface and a controller configured to communicate with the touch sensitive interface. The control system detects user manipulation of the touch sensitive interface with a touch detection sequence executed by the controller. The touch detection sequence determines a moving average of baseline signal level readings of the touch sensitive interface over time. The touch detection sequence compares a current baseline signal level reading to the moving average of baseline signal level readings, thereby detecting an interference event associated with an unexpectedly high current baseline signal level reading which could otherwise lead to a false touch detection.
Owner:TE CONNECTIVITY CORP

A CNN and LSTM-based rolling bearing residual service life prediction method

The invention discloses a CNN and LSTM-based rolling bearing residual service life prediction method, and relates to the field of rolling bearing life prediction. The method aims to solve the problemthat residual service life (RUL) prediction of a rolling bearing is difficult in two modes of performance degradation gradual change faults and sudden faults. The method comprises the following stepsof: firstly, carrying out FFT (Fast Fourier Transform) on an original vibration signal of the rolling bearing, then carrying out normalization processing on a frequency domain amplitude signal obtained by preprocessing, and taking the frequency domain amplitude signal as the input of a CNN (Convolutional Neural Network); The CNN is used for automatically extracting data local abstract informationto mine deep features, and the problem that a traditional feature extraction method depends too much on expert experience is avoided. the deep features are input into an LSTM network, a trend quantitative health index is constructed, and a failure threshold value is determined at the same time; And finally, smoothing processing is carried out by using a moving average method, eliminating local oscillation, and a future failure moment is predicted by using polynomial curve fitting to realize rolling bearing RUL prediction. And the prediction result can be well close to the real life value.
Owner:HARBIN UNIV OF SCI & TECH
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