238 results about "Sliding time window" patented technology

Included in the invention are systems and methods of full time recording network traffic to a hierarchical data storage. Also included in the invention are systems and methods of retrieval of recorded network traffic from a hierarchically organized network data repository. Additionally included in the invention are systems and methods of efficiently filtering data in a hierarchically organized network data repository. Systems and methods of displaying recorded network data utilizing the retrieval systems are also included in the invention. Further included in the invention are systems and methods of providing sliding time window selection user interfaces. Detailed information on various example embodiments of the inventions are provided in the Detailed Description below, and the inventions are defined by the appended claims.

Included in the invention are systems and methods of full time recording network traffic to a hierarchical data storage. Also included in the invention are systems and methods of retrieval of recorded network traffic from a hierarchically organized network data repository. Additionally included in the invention are systems and methods of efficiently filtering data in a hierarchically organized network data repository. Systems and method of displaying recorded network data utilizing the retrieval systems are also included in the invention. Further included in the invention are systems and methods of providing sliding time window selection user interfaces. Detailed information on various example embodiments of the inventions are provided in the Detailed Description below, and the inventions are defined by the appended claims.

The invention discloses a prediction method and visualization method of traffic jam. The methods comprise the following steps: GPS data sent back by a taxicab is utilized for being associated to roads in an electronic map through the map matching method; the road section speed calculated in terms of the matched data is utilized for judging the traffic state of road sections; the evolution rule of the traffic jam, including formation and dissipation of the traffic jam, is extracted with the utilization of the historical data; the traffic jam prediction is performed with the utilization of the sliding time window scheme after a real-time traffic information database is associated; the congestion intensity and the influence range of congested road sections can be presented by a thermodynamic chart after the congestion intensity of the congested road sections is calculated based on the prediction result. The prediction method provided by the invention can realize highly accurate traffic jam prediction, and the visualization method of the thermodynamic chart enables the traffic jam to be visualized and understandable, so that the location and the influence range of the traffic jam can be discerned conveniently.

Included in the invention are systems and methods of full time recording network traffic to a hierarchical data storage. Also included in the invention are systems and methods of retrieval of recorded network traffic from a hierarchically organized network data repository. Additionally included in the invention are systems and methods of efficiently filtering data in a hierarchically organized network data repository. Systems and methods of displaying recorded network data utilizing the retrieval systems are also included in the invention. Further included in the invention are systems and methods of providing sliding time window selection user interfaces. Detailed information on various example embodiments of the inventions are provided in the Detailed Description below, and the inventions are defined by the appended claims.

Included in the invention are systems and methods of full time recording network traffic to a hierarchical data storage. Also included in the invention are systems and methods of retrieval of recorded network traffic from a hierarchically organized network data repository. Additionally included in the invention are systems and methods of efficiently filtering data in a hierarchically organized network data repository. Systems and methods of displaying recorded network data utilizing the retrieval systems are also included in the invention. Further included in the invention are systems and methods of providing sliding time window selection user interfaces. Detailed information on various example embodiments of the inventions are provided in the Detailed Description below, and the inventions are defined by the appended claims.

The embodiment of the invention provides a non-signalized crossing optimization control method and system in cooperative vehicle infrastructure environment. The method comprises the following steps that roadside equipment sends driving state information sent by each vehicle-mounted unit to roadside management equipment; the roadside management equipment calculates road right information and vehicle speed guide information of each vehicle by using a preset crossing signal optimization model according to the driving state information of each vehicle passing through a crossing, in addition, the road right information and the vehicle speed guide information are sent to the roadside equipment, and the roadside equipment sends the road right information and the vehicle speed guide information to corresponding vehicles. The method and the system provided by the embodiment of the invention have the advantages that the road right information and the vehicle speed guide information of each vehicle are calculated according to the preset crossing signal optimization model and the optimized green light time based on a sliding time window T, the real-time interactive guide control with vehicle individuals is realized, and the vehicles at the crossing are subjected to speed optimization guide, so that the intelligent traffic control efficiency of the crossing is improved.

A system and method are provided for creating social networks, such as text, video, or audio chat sessions, around recorded media. More specifically, a social network server operates to logically divide the recorded media into a number of time slices, which may be thought of as sliding time windows. When a user within a time slice desires to join a social network during playback of the recorded media, the social network server operates to either add the user to a pre-existing social network associated with the time slice or establish a new social network between the user and additional users within the time slice. As a result, the user may have meaningful social interactions with other users that are at similar points during playback of the recorded media.

A method of analysing biomedical signals, for example electrocardiograms, by using a Hidden Markov Model for subsections of the signal. In the case of an electrocardiogram two Hidden Markov Models are used to detect respectively the start and end of the QT interval. The relationship between the QT interval and heart rate can be computed and a contemporaneous value for the slope of this relationship can be obtained by calculating the QT / RR relationship for all of the beats in a sliding time window based on the current beat. Portions of electrocardiograms taken on different days can efficiently and accurately be compared by selecting time windows of the ECGs at the same time of day, and looking for similar beats in those time windows.

The invention discloses a real-time monitoring method and device for online web information. The real-time monitoring method includes: acquiring web information logs in real time; performing real-time statistical analysis of displayed log data or log click-on data in the web information logs by sliding a time window and reporting abnormal characteristic situations of the displayed log data or the log click-on log. Therefore, the online web information logs can be acquired and analyzed in real time, and real-time online web information monitoring can be realized.

A bus monitoring and debugging system operating independently without impacting the normal operation of the CPU and without adding any overhead to the application being monitored. Users are alerted to timing problems as they occur, and bus statistics that are relevant to providing insight to system operation are automatically captured. Logging of relevant events may be enabled or disabled when a sliding time window expires, or alternatively by external trigger events.

The invention relates to the processing of petroleum geophysical exploration data, in particular to a method for comparing and demarcating seismic data positions. The method comprises the following steps: carrying out pre-stack AVO processing on longitudinal wave data after data spectrum equalization to ensure that a difference attribute C and the frequency band of post-stack data of a P-SV converted wave are equivalent; carrying out large control position collection by utilizing the attribute C and the post-stack data of the P-SV converted wave processed by wavelet consistency to obtain a variation tendency of the ratio of a longitudinal wave velocity and a transverse wave velocity in a work area; scanning a sliding time window velocity ratio of the attribute C and the post-stack data of the P-SV converted wave; determining a velocity ratio on a velocity ratio spectrum; and compressing the time of the P-SV converted wave data to a longitudinal wave time scale to finish the position comparison. The invention does not need priori information restriction, avoids difficulty of position collection caused by a conventional method and can obtain a high-precision velocity ratio.

The invention discloses a high-precision pickup method of the first arrival of microseism. The high-precision pickup method comprises the steps that the signal to noise ratio of microseism data is increased through the higher moment method; according to the fact of whether the current microseism wave meets all the following constraint conditions, namely the relation between the magnitude of amplitude and space, the lineup apparent velocity and the polarization characteristics, whether the microseism wave is the first arrival of the microseism is judged; according to perforation data, actual event data and the change rule of constraint conditions, by the adoption of a statistical analysis method, the threshold value of the constraint conditions is determined; under the constraint of the amplitude and the apparent velocity, according to the temporal and spatial variation characteristics of the travel time first arrival of a microseism event, the change law of the energy, the apparent velocity and the relevant information of travel time first arrival points in a big fixed time window and a small sliding time window is utilized for automatic pickup of the first arrival of the microseism event by means of the repeated iteration method. Application effects of the actual data of different areas show that the high-precision pickup method of the first arrival of the microseism is high in adaptability and reliable in result.

A bus monitoring and debugging system operating independently without impacting the normal operation of the CPU and without adding any overhead to the application being monitored. Bus transactions to a selected slave are monitored to determine possible conflicts when multiple masters may be addressing the slave. Users are alerted to timing problems as they occur, and bus statistics that are relevant to providing insight to system operation are automatically captured. Logging of relevant events may be enabled or disabled when a sliding time window expires, by a selected address range or alternatively by external trigger events.

The invention discloses a first-arrival travel time picking method and device for a seismic signal comprising noise, and the method comprises the following steps: carrying out the decomposition of an original seismic signal s(t) comprising noise through a noise adaption complete set EMD (Empirical Mode Decomposition) method, and obtaining a series of finite IMF components from high frequency to low frequency and residual components; screening out a plurality of high-frequency IMF components according to an EIF effective index function, and carrying out the wavelet threshold denoising; carrying out the accumulation and reconstruction of the high-frequency IMF components denoised through wavelet threshold denoising and the high-frequency IMF components which are not denoised, i.e., obtaining the denoised seismic signal x(t); initially determining the first-arrival travel time t0 through an improved sliding time window energy ration method; calculating a local extreme value in a time window comprising the moment t0 through an AIC information criterion method, wherein the time corresponding to the extreme value is the precise first-arrival travel time t. According to the invention, the method can effectively improve the seismic noise inhibition effect and improve the picking precision of the first-arrival travel time, and creates the conditions for the accurate processing and explanation of the subsequent seismic data.

A method for characterizing, detecting and predicting an event of interest, a target event, based on temporal patterns useful for predicting a probable occurrence of the target event is disclosed. Measurable events and their features are defined and quantized into event classes. Temporal series of the event classes are analyzed, and preliminary prediction rules established by analyzing temporal patterns of the event classes that precede an occurrence of the target event using a sliding time window. The quality of the preliminary prediction rules is evaluated and parameters thereof are optimized by using a defined fitness function, thereby defining finalized prediction rules. The finalized prediction rules are then made available for application on temporal series of the event classes to forecast a probable occurrence of the target event.

An encoder and corresponding method are disclosed for performing video rate control to meet network traffic model restrictions, including pre-encoding the sequence of pictures for each of a plurality of quantization parameter values, selecting for each picture of the sequence one of the plurality of quantization parameter values responsive to the quantization parameter values and bitrate operating points of the neighboring pictures in the sliding time window, and encoding each picture of the sequence using the quantization parameter value selected for that picture.

The invention discloses a multi-view monitoring video behavior detection and recognition method based on rarefaction, structuralization and judgment under multiple constraints, and the method is used for realizing the intelligent analysis and management of a monitored video. The method comprises the following steps of (1) preprocessing the video; (2) detecting a target; (3) tracking the target; (4) extracting the time and space characteristics; (5) sliding a time window and normalizing the characteristics; (6) detecting and recognizing the behavior in the multi-view monitored video under multiple constraints; and (7) fusing results of two adjacent time windows. The method has the advantages that the intrinsic correlation characteristics of human body behavior characteristics in a multi-view scene can be adequately analyzed, and through the excavation of rarefraction, structuralization and judgment constraints and the compilation of corresponding regular terms, a multi-view human body behavior recognition target function can be established; and meanwhile, the human body target behavior can be recognized through a coordinate descent method.

The invention provides a multivariate time series-based train brake system fault detection method. The method comprises the following steps: sample data relevant to train brake system fault detectionis collected, a multivariate time series matrix for the sample data is built, sample data for time series segments is extracted from the multivariate time series matrix via use of a sliding time window, the extracted sample data for the time series segments and characteristic data of an abnormal mode are subjected to matching detection, and train brake system fault detection results in the time series segments can be obtained according to matching detection results. The method disclosed in the invention is based on data analysis, machine learning is combined with a multivariate time series excavation algorithm, a sliding time window-based abnormal mode matching algorithm is put forward, faults in existing data can be subjected to monitoring and intelligent diagnosis via mode matching, intrinsic reasons behind exception can be accurately found, and the exception can be well positioned.

A method detects a target in a sequence of radar images, wherein each image is partitioned into a grid of cells, and wherein each cell has a corresponding position in an image coordinate system associated with a location in a world coordinate system. For each most recent image in a sliding temporal window of images, intensities of each cell are determined, and the subset of the cells having highest intensities is stored as a set of current needles. A set of hypotheses, obtained by using a state transition model and corresponding maximum limits, is determined for the current set of needles and appended to a set of queues. The hypotheses for the previous sets of needles to the corresponding set of queues are updated, and a maximum likelihood in the set of queues are selected to detect the location of targets.

The invention relates to the field of data processing, in particular to a wind power modeling and performance evaluating method based on a confidence equivalent power curve band. The wind power modeling and performance evaluating method includes the steps that preliminary screening and rejection are conducted on an abnormal data sample; wind speed is divided into three regions, and a kernel density estimation method is used for obtaining wind speed and power probability distributions in each region through statistics to obtain the Copula function in each region; the maximum likelihood estimation method is adopted to obtain confidence equivalent power boundary model in the corresponding region; a piecewise cubic Hermite interpolating polynomial method is used for reconstructing missing datato complete original data sample cleaning; the average value of the confidence bandwidth ratio is used as the model performance evaluation index, a d-fold cross validation method is used for validating the upper and lower boundary models in different regions, when the index is basically stabilized at a certain constant value, the up and lower boundary models of the different regions are determined; a sliding time window method is adopted to updating data, the deviation degree of the confidence bandwidth ratio is used as a triggering condition, and the up and lower boundary models are updatedwhen the deviation degree exceeds a certain threshold value.

The invention relates to an unsupervised human detecting and positioning method. The method includes the steps that a transmitter transmits signals, and the signals pass through a monitoring area and are received by a receiver; channel state information data are collected in real time, subcarrier amplitudes of all data in a sliding time window are averaged to obtain a vector containing the average of all the subcarrier amplitudes, and the vector serves as the signal characteristic; the signal characteristic of the detection signals is extracted from the sliding window, the correlation between the detection signal characteristic and the static standard signal characteristic is calculated, if the correlation received by all receiver antennas is larger than a set threshold value, it shows that the human body appears in the monitoring area, the position of the human body is positioned, or else signal detection is continued on the monitoring area; parameter information in a positioning model parameter file is read, the probabilities, at all positions capable of being positioned, of the detection signal characteristic are calculated, and the position with the largest probability correlation is the position where the human body is located most likely.

The invention discloses a small-fault detection method and device based on multiple moving average. The method includes the steps that sample data under normal working conditions are collected, a PCA model is built through the PCA method, and a load matrix P is acquired; multivariate statistics SPE and T2 of a first sliding time window are acquired based on the sample data of process variables under normal working conditions of the first sliding time window at each sampling moment; first statistics characteristics of the SPE and T2 of the first sliding time window are extracted; multiple sliding average treatment is carried out in terms of first statistics characteristics of the SPE and T2 of a second sliding time window, and second statistics of the SPE and T2 are acquired; a fault judgment interval for small fault detection is determined, and fault detection rules are defined; sample data of process variables of a working site are collected, the second statistics characteristics of the multivariate statistics SPE and T2 are acquired in the working site according to the load matrix P, and whether small faults occur or not is judged according to the fault detection rules.

The invention discloses a network manage system, and an alarm counting filter method between network unit manage systems, wherein the invention arranges a sliding time window sliding front and back on a time axis, when receives a new repeat alarm and judges each time, counting the total number of repeated alarms in present sliding window, as the present value of the repeat alarm counter, the value of the repeat alarm counter is increased or decreased along with the sliding of the sliding time window, without timed clear condition and lost alarm, to effectively filter repeated alarm, reduce interface load between NMS and EMS, avoid useful alarm immerged by lost of repeated alarms, and support quick position and resolution on the accident.

The invention discloses a method and monitoring system for detecting and separating a micro fault in an industrial process. The method comprises steps of: acquiring and using sensor data in a normal condition of the industrial process as training data and establishing a principal component analysis model of the training data; giving an appropriate sliding time window width and computing an improvement reconstruction contribution value of each variable of each sample in the training data; determining the control limit of the improvement reconstruction contribution of each variable; acquiring and using sensor data in a real-time condition as test data; computing the improvement reconstruction contribution of each variable in the test data and comparing the improvement reconstruction contribution with the corresponding control limit in order to analyze the fault of the test data; and if a fault analysis result indicates that a fault happens, identifying the variable with a maximum improvement reconstruction contribution value as a fault variable in order to achieve fault separation. Compared with a method in the prior art, the method does not require a mathematic model of the industrial process and may detect and separate the micro fault in the industrial process.

Requests to an operational module are received at a monitoring device, where an estimated amount of energy expected to be consumed by the operational module while executing operations related to requests is accumulated for a sliding time window. A throttle signal is generated to limit the amount of energy consumed by the operation module is generated when the accumulated amount exceeds a maximum amount

The health of a tool is predicted based on temporally ordered input data representing parameters indicative of tool health. A sliding time window is used to partition input data into temporally displaced data sets. Non-linear regression models determine, based on the data sets, a set of predictive values relating to tool health at a future time. A tool-health metric is then determined based on one or more of the predictive values.

The invention discloses a sliding time window based WLAN (Wireless Local Area Network) indoor WKNN (Weighted K Nearest Neighbors) tracking method, relating to the field of mode identification and solving the problem of unsmooth estimated track, i.e. fierce shake of an estimated position coordinate, caused by elements of limited terminal sampling rate and difficult motion state obtain in the traditional WLAN indoor tracking method. The method comprises the steps of: firstly, acquiring signal samples and establishing a corresponding position fingerprint database; obtaining the pre-estimated position coordinate, motion rate and confidence region at different time of the terminal in real time with a WKNN positioning method according to a signal sample newly acquired; rejecting fault pre-estimated position points with abrupt signal intensity change in a pre-estimated position set compared with adjacent time position points according to a forward sliding time window threshold, a backward sliding time window threshold and a corresponding confidence region; and finally, connecting final estimated position points in chronological sequence to obtain the smooth estimated motion track for the terminal. The invention is suitable for indoor tracking and positioning.

The invention provides an abnormal behavior detection method based on user behavior analysis. User behavior data are collected by a sliding time window that can be fed back and adjusted, a multi-angle behavior library and a multi-angle behavior analysis model are constructed to analyze the behavior data of a user, the analysis result data group is analyzed and compared with a set acceptable confidence value, and the parameter of the time window of data collection can be fed back and adjusted and the behavior library can be updated according to an analysis and comparison result so as to obtain realize comprehensive analysis and accurate judgment, the analysis result of the user behavior can be obtained by computational complexity as small as possible on the premise of improving the detection accuracy.

A bus monitoring and debugging system operating independently without impacting the normal operation of the CPU and without adding any overhead to the application being monitored. The bus is monitored for discarded speculative read and for merged write transactions in order to determine the true bus throughputs. Bus statistics that are relevant to providing insight to system operation are automatically captured. Logging of relevant events may be enabled or disabled when a sliding time window expires, or alternatively by external trigger events.