48 results about "Exponentially weighted moving average" patented technology

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.

The invention relates to a content classification based category popularity cache replacement method in an oriented content-centric networking. The content classification based category popularity cache replacement method comprises the following steps: firstly determining whether a remaining cache space of a node can accommodate new data content or not; caching the new data content if the cache space is enough; calculating the popularity of all content categories in standard computing nodes according to an exponentially weighted moving average method, and selecting the content category with the smallest popularity; removing a content item with the fewest request times in predefined time in the content category with the smallest popularity out of a node cache; extracting name character string characteristics of the new data content and classifying; storing the newly arrived data content item into corresponding content category in the node, and updating a category heat table and a log. The cache of nodes in CCN (content-centric networking) can be better managed according to the content name category, so that the content can be found and replaced from the name of the content in the communication process in the networking, the diversity of the content in the node cache is balanced, and the efficiency of cache replacement is improved.

The invention discloses a cloud computing system load predicting method capable of automatically adjusting parameters, which comprises the following steps: at the moment t, computing the actual load O(t) of a system at the moment t through system call; executing short-term prediction; computing alpha (t) and E(t) by utilizing the O(t) value and historical data; executing long-term prediction; computing alpha T(t) and ET(t) by utilizing the O(t) and the historical data; combining the short-term prediction and the long-term prediction; when t is less than T, outputting the O(t) and switching to the next step; otherwise, taking the maximum value or average value of E(t-1) and ET(t-T) as the output at the moment t; and updating the historical data, waiting the moment t+1 and switching to the first step. In the invention, the alpha (t) and the alpha T(t) are computed in real time through error functions, thereby enhancing the prediction accuracy of classic EWMA (Exponentially Weighted Moving Average); the requirement that a prediction value is slightly larger than an actual value can be met by expanding the alpha (t) and the alpha T(t) to an interval (-1, 1); and the responsiveness of the prediction to the load periodicity of a cloud computing platform is enhanced by introducing a long-term prediction module.

The invention discloses a magnetic card decoding method. The method comprises the following steps of: S1, converting a sampled analog waveform into an initial digital waveform by adopting an extreme value method or a threshold method, and modifying the pulse width of the initial digital waveform by an interpolation method to obtain a modified digital waveform; S2, judging the first pulse width of the modified digital waveform by taking the average pulse width of the pulse widths with a leading bit of 0 of a magnetic card as an initial reference pulse width, obtaining a second reference pulse width by using the initial reference pulse width according to an exponentially weighted moving average (EWMA) formula, and judging the second pulse width of the modified digital waveform by taking the second reference pulse width as a reference, repeatedly executing the processes of adjusting the reference pulse width and judging the pulse width of the modified digital waveform according to the corresponding reference pulse width until all pulse widths of the modified digital waveform are judged; S3, converting the modified digital waveform into a bit sequence according to the judgment result of the S2; and S4, decoding the bit sequence.

The invention provides a photovoltaic inverter leakage current detection method based on an exponentially weighted moving average forecasting method. The change degree of leakage current is quantified as the integral of the leakage current and the action time thereof, and by the exponentially weighted moving average forecasting method, dynamic protection on the leakage current is realized. Simulation and experiment results show that the method has effective protection on dynamic leakage current change, meeting the requirements of safety specifications, and the method can be applied to the field of dynamic protection with a time step characteristic.

The invention discloses a magcard decoding method comprising the following steps: S1. converting a sampled analog waveform to an initial digital waveform by adopting an extremum method and correcting the pulse width of the initial digital waveform by adopting a curve fitting method; S2. taking the pulse width of purported leading zero bits of a magcard as an initial reference pulse width, distinguishing a first pulse width of the corrected digital waveform by utilizing the initial reference pulse width, obtaining a second reference pulse width on the basis of the initial reference pulse width according to an EWMA (exponentially weighted moving average) formula, distinguishing a second pulse width of the corrected digital waveform by taking the second reference pulse width as a reference, and repeatedly executing the process of adjusting the reference pulse width and distinguishing the pulse width of the corrected digital waveform according to the corresponding reference pulse width until all of the pulse widths of the corrected digital waveform are distinguished; S3. converting the corrected digital waveform to a corresponding 0/1 bit sequence according to the distinguished result of the step S2; and S4. decoding the bit sequence according to magcard standards.

The invention discloses a link-quality-based hierarchical routing method. The routing method is designed and implemented based on link quality on the premise of a hierarchical topology structure, so that the shortcomings of lower efficiency of a flat routing method and insufficient consideration of a hierarchical routing method about the link quality are overcome, and the network efficiency is further improved. A method for LQEWAL (link quality estimation based on time-window-based exponentially weighted moving average algorithm and average link quality indication) is disclosed, so that inter-node link quality information can be accurately and timely acquired; an algorithm for NIL (network initialization based on LQEWAL) is adopted for an EBCLQ (energy balanced cluster routing algorithm based on link quality), and information such as the inter-node link quality information and energy can be acquired by the algorithm for NIL; a clustering process consisting of three steps of candidate cluster head generation, formal cluster head establishment and cluster establishment is involved in the EBCLQ; an algorithm for slot allocation based on link quality and a multi-hop or one-hop algorithm based on cluster-head classification are involved in the EBCLQ.

A method of increasing a weighting factor of an exponentially weighted moving averaging (“EWMA”) filter is provided. The method includes monitoring a data stream containing raw data values, and determining an EWMA value based on the data stream by an electronic control module. The method includes determining if the EWMA value is between a predetermined maximum fault threshold value and a predetermined minimum fault threshold value. The method includes increasing the weighting factor of the EWMA filter to more heavily weigh incoming raw data values of the data stream based on the difference between the first raw data value and a previously calculated filtered value exceeding the calibration value.

Method for detecting a fault condition in a vehicular hydraulic circuit during a drive cycle using an electric pump includes monitoring an actual pump torque and monitoring a desired pump torque. A current confidence factor is determined based on the actual pump torque and the desired pump torque. An average confidence factor is iteratively calculated based on the current confidence factor and previously determined confidence factors. The average confidence factor is compared to a fault condition threshold. An absence of the fault condition in the hydraulic circuit is detected when the average confidence factor is at least the fault condition threshold, and a presence of the fault condition in the hydraulic circuit is detected when the average confidence factor is less than the fault condition threshold.

The invention discloses a monitoring method of the state of a gearbox bearing of a wind turbine generator system. The method comprises the steps of selecting variables by adopting a ReliefF feature selection algorithm, establishing an improved noise reduction self-encoding network to establish a relation model between the temperature of the gearbox bearing and influence variables thereof, reconstructing modeling variables in a monitoring stage by using the model, and predicting the temperature of the gearbox bearing; performing calculation according to a modeling variable reconstruction errorof normal operation data of the wind turbine generator system to obtain an exponentially weighted moving average control chart threshold; obtaining the fact that the unit operates normally if an EWMAcontrol chart statistic of the monitored unit is less than a threshold; and giving an alarm that the temperature of the gearbox bearing is abnormal if the temperature exceeds the threshold. The methodis used for analyzing the temperature data of the gearbox bearing, the goals of artificial intelligence monitoring and fault early warning of the temperature of the gearbox bearing of the wind turbine generator system are efficiently and accurately achieved, and the example analysis verifies the practicability and the universality of the method.

The protocol determines the quality of each node to node link(s), based on an exponentially weighted moving average of the time each packet spends within the network node. Once determined, that quality of link is then mapped into the physical QoS resources of the networking hardware in use (within the network). Packets destined for higher quality links will see an upgrade of their QoS rating, while packets destined to lower quality links will see a downgrade of their QoS rating. That adjustment permits the higher quality links to be processed before the lower quality links resulting in more efficient and complete communications in the network. Poorer quality links will no longer act as roadblocks to the higher quality packets.

A method for calibrating a linewidth measurement tool and a system for calibrating the linewidth measurement tool each employ a correction of a series of periodic actual measurements of at least one single measurement location of a topographic feature within a linewidth standard with an exponentially weighted moving average of a series of deviations of the series of periodic actual measurements from a corresponding series of regressed data points derived from mathematical regression of the series of periodic actual measurements. Such a correction provides for a more accurate calibration of the linewidth measurement tool and a more accurate measurement of linewidths within microelectronic products while employing the calibrated linewidth measurement tool.

A technique for filter-enhanced clock synchronization is disclosed. In one particular exemplary embodiment, the technique may be realized by/as a method for filter-enhanced clock synchronization. The method comprises subjecting a clock error signal to a first exponentially weighted moving average (EWMA) filter to generate a first output signal, where the first EWMA filter comprises a first gain element. And the method further comprises subjecting the first output signal to a second EWMA filter to generate a second output signal, where the second EWMA filter comprises a second gain element and the second EWMA filter is coupled with a feedback loop having a delay element and a summing junction.

The invention discloses a bearing performance degradation state detection method and system. The method comprises the steps: S1, collecting vibration signals in the full-life service process of a bearing; S2, constructing a high-dimensional degradation trend feature set, and performing smoothing processing by using exponentially weighted moving average; S3, designing a feature sensitivity evaluation criterion, and screening out a bearing performance degradation state sensitive feature set; and S4, fusing the sensitive feature set by using a consistent manifold approximation algorithm, and further smoothing the fusion index by using exponential weighted moving average to form a bearing performance degradation state curve. According to the method, noise in characterization indexes can be removed, and fluctuation in the performance degradation process of the bearing is slowed down; monotonicity and correlation in the bearing performance degradation process are integrated in the characterization index screening criterion, and effective characterization indexes can be selected more reasonably; and consistent manifold approximation algorithm is used for fusing selected effective indexes, the global structure and the local structure of data can be considered, and the defects of a traditional data fusion method are overcome.

The invention discloses an energy acquisition type wireless sensor network monitoring frequency maximization method, comprising the following steps: setting up a virtual source node, establishing a connection relationship between the virtual source node and a monitored target node, predicting the energy acquired by monitoring nodes according to an exponentially weighted moving average process, and setting link weights between monitoring network nodes based on energy acquisition; performing monitoring network topology decomposition, performing topology decomposition by adopting a node splitting operation, and establishing a decomposed monitoring network; sequentially calculating maximum energy flow paths from the virtual source node to receiving nodes according to the decomposed monitoring network, performing reverse operation on each path, and subtracting energy values required for the monitoring of the nodes on the paths; updating the link weights of the monitoring network; repeating the above calculations until no maximum energy flow exists; and calculating the number of maximum energy paths to obtain the target monitoring frequency. According to the method disclosed by the invention, the network monitoring frequency and energy utilization performance can be improved, the maximization of the monitoring frequency can be achieved, and the throughput of the monitoring network also can be effectively improved.

The invention discloses a method for detecting tiny faults of a satellite attitude control system and based on locally linear embedding (LLE). The method is based on a locally linear embedding method,and comprises the steps that firstly, historical data is enabled to have a zero mean value and a unified variance, then the number of neighborhood points is determined by a dynamic locally linear embedding (DLLE) method, a weight matrix W is reconstructed dynamically, then low-dimensional embedding Y of a sample set is found through the obtained weight matrix W, and two statistics of T<2> and SPEare further acquired; then an exponentially weighted moving average (EWMA) is solved by using an EWMA algorithm, an EWMA statistic range of normal data is used as a threshold value for judging whether to-be-detected data has faults or not, a mapping matrix A is further obtained and used for calculating the statistics of T<2> and SPE of online data, and the corresponding EWMA statistics is calculated; and finally whether the EWMA statistic of the online data is greater than a control limit or not is judged, if the EWMA statistic is greater than the control limit, the system faults are generated, and if not, the system is normal. According to the method for detecting the tiny faults of the satellite attitude control system and based on LLE, deficiencies of an original algorithm are overcome, and the detecting performance of the algorithm towards the tiny faults is improved.

A lightweight Learning mechanism combining a linear function approximation based Reinforcement Learning and an adaptive learning rate method is provided for energy management of Internet of Things (IoT) nodes and other energy constrained electrical systems, especially for nodes with harvested energy and wireless transmitters. The adaptive learning rate method may be based on an exponentially weighted moving average (EWMA), or Adam, which incorporated EWMA. Optimal decay coefficient ranges outside the usual range in Neural Network contexts have been found to be effective in implementations based on this linear function approach.

An embodiment of the present invention provides a method and system for detecting the working state of an aerator. The method comprises a step of acquiring a corner point corresponding to a corner point in a target area in each frame and a reference frame according to an optical flow method for each frame in an aerator video to be detected, a step of acquiring an average displacement of the corresponding corner point in the frame and obtaining an exponentially weighted moving average displacement according to the average displacement, a step of inputting the average displacement of the corresponding corner point in the frame and the exponentially weighted moving average displacement into a preset support vector machine to obtain the working state of the aerator in the frame. According to the method and system for detecting the working state of an aerator provided by the embodiment of the present invention, the reference frame is used as a comparison object, the matched corner point isobtained by corner point detection and the optical flow method, the working state of the aerator is detected according to the average displacement of the matched corner point and the exponentially weighted moving average displacement by using the support vector machine, very obvious classification features can be extracted, the possibility of interference is little, and the accuracy of detecting the working state of the aerator can be improved.