604 results about "Moving average" patented technology

A system and method for detecting the potential of non-cathode arcing in a plasma generation apparatus, such as a physical vapor deposition chamber. The system and method involve computing a statistical parameter of cathode-arcing event data in the chamber and performing a pattern recognition technique to a moving average of the statistical parameter.

Provided are an envelope processor 103, for obtaining an envelope for a detected signal; a FFT unit 104, for converting the envelope into a frequency spectrum; a peak detector 105, for smoothing the frequency spectrum by calculating a moving average, for further performing smoothing and differentiation for the spectrum, and detecting, as peaks, frequency points at which a sign of a differential coefficient is changed from positive to negative, for extracting peaks having a predetermined threshold value or greater, and for sorting the extracted peaks and detecting upper peaks; and a diagnosis processor T, for diagnosing an abnormality based on the detected peaks.

Systems and computer-implemented methods are used for analyzing battery information. The battery information may be acquired from both passive data acquisition and active data acquisition. Active data may be used for feature extraction and parameter identification responsive to the input data relative to an electrical equivalent circuit model to develop geometric-based parameters and optimization-based parameters. These parameters can be combined with a decision fusion algorithm to develop internal battery parameters. Analysis processes including particle filter analysis, neural network analysis, and auto regressive moving average analysis can be used to analyze the internal battery parameters and develop battery health metrics. Additional decision fusion algorithms can be used to combine the internal battery parameters and the battery health metrics to develop state-of-health estimations, state-of-charge estimations, remaining-useful-life predictions, and end-of-life predictions for the battery.

Techniques are provided for detecting the onset of an episode of apnea/hypopnea substantially in real-time. A moving threshold is generated based on recent respiration cycles and differences are accumulated between amplitudes of new respiration cycles and the moving threshold. Apnea/hypopnea is then detected based upon a comparison of the accumulated differences against a fixed threshold. The technique exploits the fact that many episodes of hypopnea begin with a sharp drop in respiration and many episodes of apnea are preceded by a sharp drop in respiration. By accumulating differences between new respiration amplitudes and a short term moving average, any sharp drop in respiration is thereby promptly detected. In many cases, by the time the amplitudes of individual respiration cycles drop to levels directly indicative of apnea/hypopnea, the episode of apnea/hypopnea will have already been detected based upon the sudden sharp drop in respiration amplitude and therapy will have already been initiated.

An abnormality diagnosis system for machinery comprises an envelope processing section (103) for determining the envelope of a detection signal, an FFT section (104) for converting the envelope into a frequency spectrum, a peak detecting unit (105) in which the frequency spectrum is smoothed by the moving average method, the resultant spectrum is subjected to a smoothed differentiation, frequency points at which the sign of differential coefficient changes from positive to negative are detected as peaks, peaks higher a predetermined threshold are extracted and sorted, and the higher ones are detected as peaks, and a diagnosis section (T) for performing abnormality diagnosis on the basis of the detected peaks.

A method and apparatus for remote laser-based detection of gas at levels exceeding natural background levels preferably utilizing wavelength modulated tunable diode laser absorption spectroscopy. In a preferred embodiment, background gas and noise are estimated using statistical moving average and variance calculations. Gas concentration length measurements resulting from the spectroscopy are preferably compared in real-time or near-real-time to the sum of the background and noise estimates and an alarm limit to detect gas presence of concern. Gas levels exceeding this detection threshold are preferably indicated by a prolonged output tone with a pitch indicative of the magnitude of the gas measurement, and gas levels below the detection threshold are preferably indicated by silence.

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.

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: SAMA_t=AMAF(Q_t/√{square root over (ΔP_t)}), 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 SP_t=MA(Q_t)/SAMA_t, where the function MA is a moving average filter (MA), to obtain a desired pump speed through a PID control.

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.

A method is used in measuring data access activity. I/O data is analyzed that describes I/O activity for a slice of a logical volume. Based on the I/O data, a first value of data access activity is determined for the slice corresponding to a first time period, and a second value of data access activity is determined corresponding to a second time period. From the first and second values, an exponential moving average of data access activity is derived for the slice.

A method for adapting data retrieval polling in a mobile wireless device. The mobile wireless device is configured in an adaptive pull mode and polls a remote server when a data retrieval polling timer expires. Data retrieval information is stored in the mobile wireless device and the data retrieval polling timer is updated to a value based on the stored data retrieval information. The data retrieval information includes time intervals between successive polls or time delay intervals between data arrival at and retrieval from the remote server. Updates are based on weighted moving averages of the data retrieval information.

It has been discovered that system operational characteristics (e.g., power level, clock frequency, processor utilization, operating system time slice utilization, size and age of queued jobs) may be used to predict storage access requirements for the system. By predicting the storage access requirements of a system, a storage subsystem may be advantageously controlled to anticipate storage accesses. A storage device or array of such devices can be configured to operate, for example, at selected speeds no greater than that required to process the predicted storage access requirements. The storage access prediction may be based, for example, on the frequency and voltage at which a processor is running or based on other system performance indicators such as job backlog and age and size thereof. Various controllable characteristics such as the speed of a hard drive's storage media, the current applied to a read/write head, etc., can be increased or decreased continuously or to discrete values in response to moving average indicators which provide advance warning of potential processing, and therefore potential storage access, swings.

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.

The invention discloses an intensive learning based urban intersection passing method for a driverless vehicle. The method includes a step 1 of collecting vehicle continuous running state informationand position information through a photographing method, the vehicle continuous running state information and position information including speed, lateral speed and acceleration value, longitudinal speed and acceleration value, traveling track curvature value, accelerator opening degree and brake pedal pressure; a second step of obtaining characteristic motion track and the velocity quantity of actual data through clustering; a step 3 of processing original data by an exponential weighting moving average method; a step 4 of realizing the interaction passing method by utilizing an NQL algorithm. The NQL algorithm of the invention is obviously superior to a Q learning algorithm in learning ability when handling complex intersection scenes and a better training effect can be achieved in shorter training time with less training data.

A system and process for performing an exponentially weighted moving average on streaming data to establish a moving average bit rate of data units is presented. In general, the system or process computes, on a unit-by-unit basis, the product of the moving average bit rate computed for a data unit immediately prior to a unit under consideration and a first fractional weighting factor, added to the product of the instantaneous bit rate of the data unit under consideration and a second fractional weighting factor, wherein at least one fractional weighting factor is not a constant but instead based on the time between data units.

A voice-over-Internet-Protocol (VoIP) application estimates bandwidth and congestion of the reception path to the VoIP application from a sending VoIP application. Packet arrivals are timed and the inter-packet delay is compared to the voice duration of the data contained in the more recent packet. When the inter-packet delay is longer than the voice duration the network is slowing and the bandwidth estimate is reduced. The bandwidth estimate is increased when inter-packet delay is smaller than the voice duration. Packet latencies are the difference in send and receive times and are compared to a moving average latency. When the current packet's latency is longer than the moving average, congestion is detected. When the current packet's latency equals the moving average, the network has recovered from congestion and the congestion estimate is reduced. Congestion and bandwidth estimates are added to packets sent out to provide feedback to the other VoIP application.

In one embodiment, a pressure transmitter is provided which diagnoses the condition of a primary element and/or an impulse line which connects to a pressure sensor. A difference circuit coupled to the pressure sensor has a difference output which represents the sensed pressure minus a moving average. A calculate circuit receives the difference output and calculates a trained output of historical data obtained during an initial training time. The calculate circuit also calculates a monitor output of current data obtained during monitoring or normal operation of the transmitter. A diagnostic circuit receives the trained output and the monitor output and generates a diagnostic output indicating a current condition.

A method for determining the reverse link data Rate Limit for mobile stations active on the reverse link of a High Data Rate system is disclosed. In the ideal case, the Rate Limit is based on only the number of mobile stations located in a common sector that are actually active on the reverse link. Currently, the Rate Limit is determined from the total number of mobile stations in a common sector where the total includes mobiles that are transmitting and receiving. Thus, the current method includes mobile stations that are active on the forward link and may not be active on the reverse link. In this invention, a more optimum method of estimating the reverse link loading is obtained from calculations which includes only the mobile stations which are active on the reverse link. An estimate of the reverse link loading of the mobile stations in a common cell is obtained by adding together the data rates of the data sent from each mobile in a common sector during a common frame. This aggregate rate of data during the frame is filtered to minimize irregularities by using the moving average of an infinite impulse response filter and then normalized. The normalized result is a percentage of the maximum achievable aggregate reverse link rate. The final result is compared with a set of threshold values to obtain the maximum Rate Limit that is then set for each mobile station.

The invention provides a kernel correlation filtering target tracking method based on feature fusion and Bayesian classification. The method is characterized by comprising steps: firstly, the position and the scale information of an initial frame target are given; then, a standard target appearance model, a standard correlation filter and a standard color Bayesian model are built or updated; based on the target center point of a former frame, a search area is extracted; and a correlation filter of a Gaussian kernel is used for realizing target displacement estimation, the color Bayesian model is used for realizing target scale estimation, and the tracking result of the current frame is obtained further. Through sequentially processing each frame of video image, tracking of a moving target in the video is realized. The target tracking method can effectively solve the problem of precisely positioning the moving target in the video and can realize target scale estimation. In multiple challenging environments, the target can be tracked effectively, certain robustness is realized, and the tracking algorithm precision is improved.

A method of processing a raw acceleration signal, measured by an accelerometer-based compression monitor, to produce an accurate and precise estimated actual depth of chest compressions. The raw acceleration signal is filtered during integration and then a moving average of past starting points estimates the actual current starting point. An estimated actual peak of the compression is then determined in a similar fashion. The estimated actual starting point is subtracted from the estimated actual peak to calculate the estimated actual depth of chest compressions. In addition, one or more reference sensors (such as an ECG noise sensor) may be used to help establish the starting points of compressions. The reference sensors may be used, either alone or in combination with other signal processing techniques, to enhance the accuracy and precision of the estimated actual depth of compressions.

A system and method directed to an investment strategy that focuses on buying the worst-performing stocks in terms of percentage price change over the last twelve months in the Dow Jones Industrial Average. Specifically, an investor in one embodiment buys one or more of the worst performers, holds the portfolio of stocks for twelve months and one day, re-balances the portfolio to include new worst-to-first performers, holds the portfolio for twelve months and one day, and so on. Other embodiments include variations in the strategy, such as shorting the best performing stocks, using stock options and exchange-traded funds instead of common stocks to create the portfolios, and using a ratio of stock price to two-hundred-day moving average price instead of the twelve-month price change in order to determine the best and worst performers.

The invention relates to the technical field of image processing and particularly relates to a target tracking method and system based on image block characteristics, and the method and the system can be used for increasing the accuracy in tracking a moving target. The tracking method comprises the steps of: establishing a reference target template based on the characteristics of a reference target image block and a background image block; performing similarity comparison on the characteristics of an undetermined target and the characteristics of the reference target image block in the reference target template; and determining the position of the undetermined target according to a similarity comparison result. The tracking system comprises a module for establishing the reference target template according to the characteristics of the reference target image block and the background image block; a module for performing similarity comparison on the characteristics of the undetermined target and the characteristics of the reference target image block in the reference target template; and a module for determining the position of the undetermined target according to the similarity comparison result.

Congestion within a communication is controlled by rate limiting packet transmissions over selected communication links within the network and modulating the rate limiting according to buffer occupancies at control nodes within the network. Preferably, though not necessarily, the rate limiting of the packet transmissions is performed at an aggregate level for all traffic streams utilizing the selected communication links. The rate limiting may also be performed dynamically in response to measured network performance metrics; such as the throughput of the selected communication links input to the control points and/or the buffer occupancy level at the control points. The network performance metrics may be measured according to at least one of: a moving average of the measured quantity, a standard average of the measured quantity, or another filtered average of the measured quantity. The rate limiting may be achieved by varying an inter-packet delay time over the selected communication links at the control points. The control points themselves may be located upstream or even downstream (or both) of congested nodes within the network and need only be located on only a few of a number of communication links that are coupled to a congested node within the network. More generally, the control points need only be associated with a fraction of the total number of traffic streams applied to a congested node within the network.

The invention relates to the field of elastic computing of cloud computing, and discloses an agile elastic telescoping method in a cloud environment. The agile elastic telescoping method includes the specific steps: forecasting the load of a next time slice according to historical load data of a data center through an ARIMA (autoregressive integrated moving average) model and an ARMA (autoregressive moving average) model by taking the time slice as a cycle; performing saving operation and restoring operation on a virtual machine, saving the memory state of the virtual machine by the saving operation to hang up the virtual machine, and then restoring the memory state of the virtual machine by the restoring operation to restore use of the virtual machine; hanging up one or a plurality of application-ready virtual machines or rapidly placing the virtual machines into service through the forecasted load of the data center obtained by the load forecasting step and by the aid of the rapid supply step of the virtual machines to dynamically adjust resources of application clusters of the data center. The agile elastic telescoping method has the advantages that the sizes of the clusters are adjusted in real time according to current conditions of the application clusters, and energy consumption of the data center is reduced.

Described are computer-based methods and apparatuses, including computer program products, for dynamic deduplication. Data is processed using an algorithm that deduplicates the data based on a first set of parameters. A first moving average of a first deduplication performance metric is calculated for the algorithm over a time period. A second moving average of a second deduplication performance metric is calculated for the algorithm over the time period. It is determined that the first moving average satisfies a first criterion, the second moving average satisfies a second criterion, or both. The algorithm is reconfigured based on a second set of parameters to deduplicate data.

The invention relates to a spare part assembling demand forecasting information processing method applied to inventory management, comprising the following steps:1) take one part of historical sales data as method learning data and take the other part of the historical sales data as weight building data; 2) use the method learning data to practice an ARMA (Autoregression and Moving Average) method, a multiple linear regression method and a BP (Back Propagation) neural network approach respectively for each spare part; 3) apply the above three methods respectively by using the weight building data, so as to obtain forecasting relative error values for different methods; 4) calculate the weights of the above methods; 5)construct an assembling demand forecasting model; 6) forecast the future sales demands of all spare parts. Compared with the prior art, the method is suitable for the spare parts with different demand features, and has the advantages of strong objectivity, high forecasting precision, and can greatly reduce the inventory cost on the premise of having a certain service level and improves the supply chain management efficiency and customer satisfaction.

The invention discloses an indoor passive positioning method based on channel state information and a support vector machine. The method comprises the following steps: firstly preprocessing the acquired channel state information data, performing de-noising and smoothness through the adoption of a density-based spatial clustering of applications with noise and a weight-based moving average algorithm, and then using the principal component analysis algorithm to extract the features. The data after the preprocessing and feature-extracting can reflect the signal change more accurately and the dimension is greatly reduced. The passive positioning adopts two-stage positioning. In the training stage, the large positioning space is divided into sub-regions, the support vector machine classification and regression model is established for each sub-region so as to acquire a statistic model for accurately representing the nonlinear relationship between the position and the signal. The two-stage positioning firstly determines the sub-regions through the classification of the support vector machine, and the precision position is determined in the sub-region through the regression of the support vector machine. The method disclosed by the invention has the beneficial effects that the passive positioning can be performed in the absence of the active participation of the target, and the indoor positioning precision is improved to sub-meter level.

A first positional information of a wafer stage is measured using an interferometer system such as, for example, a Z interferometer. At the same time, a second positional information of the wafer stage is measured using a surface position measurement system such as, for example, two Z heads. Moving average is applied to a difference between the first positional information and the second positional information for a predetermined measurement time to set a coordinate offset, and the coordinate offset is used to inspect the reliability of output signals of the surface position measurement system. When the output signals are confirmed to be normal, servo control of the wafer stage is performed using a sum of the first positional information and the coordinate offset. According to the servo control by this hybrid method, drive control of the wafer stage which has the stability of the interferometer and the precision of the Z heads becomes possible.

The invention relates to a microgrid energy optimization method based hybrid energy storage dispatching under different time scales. Microgrid optimized dispatching is divided into day-ahead dispatching and real-time dispatching according to different time scales; for the day-ahead dispatching, the operation plan of future 24 hours is provided one day in advance, the time granularity is 1 hour, the connecting line interaction power and the fuel cell (FC) output are optimized according to the difference between the electricity prices in the peak and valley periods, and a storage battery (SB) is dispatched to achieve low-storage high-output interest arbitrage; the real-time dispatching coordinates with the day-ahead dispatching, the microsource output is allocated according to the day-ahead plan, the time granularity is 1 minute, the power fluctuation in the microgrid is smoothened by use of a first-order low-pass filtering algorithm, the filtered fluctuating power is distributed reasonably between storage battery energy accumulation and a super capacitor by use of a moving average filtering algorithm and then reference is provided for the optimized dispatching of hybrid energy storage.

The invention discloses a real-time navigation system and a real-time navigation method for an underwater structure detection robot. The navigation system comprises a magnetic compass, a gyroscope, an accelerometer, a depth meter and a navigation microprocessor, wherein the magnetic compass, the gyroscope, the accelerometer and the depth meter are used for respectively collecting magnetic field intensity, an angular speed, a linear speed and submerged depth data and transmitting the magnetic field intensity, the angular speed, the linear speed and the submerged depth data to the navigation microprocessor; the navigation microprocessor is used for calculating attitude and position of the underwater robot according to the collected data. The navigation method comprises an attitude algorithm, a speed algorithm and a depth algorithm; according to the attitude algorithm, a complementary filtering method, a quaternion gradient descent method and a Kalman algorithm are combined for obtaining an attitude matrix and an attitude angle; the speed algorithm is used for calculating the speed and the position of the robot by using a three-order upwind scheme with rotary compensation; the depth algorithm is used for processing the data of the depth meter by using a moving average filter algorithm so as to obtain the submerged depth. By virtue of the real-time navigation system for the underwater structure detection robot and the method thereof, the navigation cost is reduced and a relatively good navigation precision is achieved.