170 results about "Spatiotemporal correlation" patented technology

The invention discloses an alarm correlation analysis method in a communication network, targeting topology research in a tree-hierarchy structure network. The method comprises the following steps: in accordance with the space-time relativity of malfunctions occurring to network nodes, defining space-time relativity of upper layer network nodes in the tree-hierarchy structure network; based on the space-time relativity of the upper layer network nodes, clustering upper layer nodes in the tree-hierarch network; based on clustering result, dividing the total alarm database into a plurality of sub alarm databases; based on the attributes of alarm items, such as alarm occurrence frequency, alarm priority and alarm failure type, determining weight of each alarm item; utilizing the weighted Apriori correlation rules algorithm, conducting correlation rules mining on each sub alarm database. The method of the invention aims at addressing the problems of alarm relativity analysis of tree-hierarchy structure, and can effectively mine alarm correlation rules of interest among a large amount of alarm information.

A motion recognition apparatus is provided. The motion recognition apparatus includes an event sensor configured to sense a portion of an object, where a motion occurs, and output events, a color sensor configured to photograph the object and output a color image, a motion area check unit configured to check a motion area which refers to an area where the motion occurs, using spatiotemporal correlations of the events, a shape recognition unit configured to recognize color information and shape information of an area corresponding to the motion area in the color image, a motion estimation unit configured to estimate a motion trajectory using the motion area, the color information, and the shape information, and an operation pattern determination unit configured to determine an operation pattern of the portion where the motion occurs, based on the estimated motion trajectory.

A method for assisting the navigation of an aircraft comprises: assembly by families of data by a processing unit of predetermined data and acquired data, including meteorological data, the families being predefined, each data value being associated with a time window of validity; formatting the data of the families to associate with each data value a type of graphical representation as text or a scalar, vector, surface, or volume; selection of families of data to be displayed; choice of a display time window for each family of data to be displayed; spatio-temporal discretization of the trajectory; spatio-temporal correlation of the discretized trajectory with each family of data as a function of the time windows of validity, in the display time windows of the family, to extract a sub-assembly from each family of data, by the processing unit; display of the sub-assemblies in a single representation on the same display screen.

The present invention describes a speech enhancement method using microphone arrays and a new iterative technique for enhancing noisy speech signals under low signal-to-noise-ratio (SNR) environments. A first embodiment involves the processing of the observed noisy speech both in the spatial- and the temporal-domains to enhance the desired signal component speech and an iterative technique to compute the generalized eigenvectors of the multichannel data derived from the microphone array. The entire processing is done on the spatio-temporal correlation coefficient sequence of the observed data in order to avoid large matrix-vector multiplications. A further embodiment relates to a speech enhancement system that is composed of two stages. In the first stage, the noise component of the observed signal is whitened, and in the second stage a spatio-temporal power method is used to extract the most dominant speech component. In both the stages, the filters are adapted using the multichannel spatio-temporal correlation coefficients of the data and hence avoid large matrix vector multiplications.

The invention discloses a dynamic probabilistic power flow (PPF) calculating method considering wind speed predication error temporal-spatial coherence. The method is to analyze the node voltage and dynamic probability distribution of branch power flow of a wind power station built power system, so as to enable operators to analyze a system state conveniently. The method comprises the steps of describing the input variable predication error process according to autocorrelation coefficient stationary process; directly fitting to obtain the predication error distribution on the basis of nonparametric kernel density estimation and according to historical predication error data; performing Nataf transformation technology on the basis of the iso-probability transformation theory to obtain an error sample of temporal-spatial coherence; performing dynamic PPF calculation by the monte carlo simulation method on the basis of latin hypercube sampling so as to obtain the node voltage amplitude value and the digital characteristics and dynamic probability distribution of the branch power flow. By adopting the method, the node voltage and the dynamic probability distribution of the branch power flow can be effectively analyzed; the method has the advantages of being accurate in result and convenient to realize.

The invention discloses a short-time traffic flow prediction method based on spatial-temporal correlativity. The method comprises the following steps: determining an intraday traffic flow data matrixcontaining a target prediction point and a monitoring point; performing convolution processing and gating circular unit processing on the intraday traffic flow data matrix to acquire spatial-temporalcharacteristics of the traffic flow data; determining the previous day traffic flow data matrix and a last week traffic flow data matrix based on the traffic flow data of the target prediction point and the monitoring point at the same moment of the previous day and the same moment of the same day of the last week, and processing by adopting a bidirectional gating circular unit to acquire periodiccharacteristics of the traffic flow data; performing fusion on the spatial-temporal characteristics of the traffic flow data and the periodic characteristics of the traffic flow data, inputting intoa predictor, thereby predicting the traffic flow of the target prediction point. By applying the method disclosed by the invention, the problem that the short-time traffic flow prediction is complex in model and slow in prediction speed can be solved.

The disclosure includes a method for traffic flow prediction based on data mining on spatio-temporal correlations. The method includes establishing a prediction model, data mining on spatio-temporal correlations, and traffic flow prediction based on spatio-temporal correlated data. The prediction model can be a linear regression model with multiple variables. The data mining on spatio-temporal correlations is based on a multi-factor linear regression model and by means of the optimization method in terms of sparse representation. The data from the spatio-temporal correlated sensors that are relevant to the prediction task are determined automatically. The traffic flow prediction based on spatio-temporal correlated data refers to that the prediction is performed with the input to the prediction model to be the data from the spatio-temporal correlated sensors.

The invention discloses a crowd sensing excitation method for random participation based on a crowd sensing system. The crowd sensing system comprises a control platform and a plurality of mobile users. According to the method, first, the control platform calculates task returns according to relative heat of sensing tasks; second, the control platform evaluates data quality of all the sensing tasks, measurement times needed by the sensing tasks are calculated according to a data quality increment in single measurement, decreasing steps of measurement returns are determined, and returns to participants in next measurement are further calculated; and last, the control platform publishes tasks information, the mobile users select the sensing tasks, execute measurement and upload sensing data, and the control platform sends corresponding returns to the mobile users. Through the method, limitations on the crowd sensing system by random participation and time-space correlation are better solved, the excitation demand for participating crowd sensing of the mobile users is met, the degree of balance of user participation is increased while the degree of completion of the crowd sensing tasks is increased, and meanwhile the total income of each participant is maximized.

The invention discloses an intra-frame quick selection method based on a non-separable secondary transform mode. By means of the spatial-temporal correlativity of coding units (CU) which are located at the same location and different depths in a video sequence, an index value of an optimal NSST mode is predicted in advance, the unnecessary index cycling process is skipped, the time-consuming NSSTmode selection process in the coding process is avoided, and on the condition that it is guaranteed that the video subjective quality reduction is negligible, the computing complexity of a coder is reduced, the coding time is shortened, and the coding efficiency is improved. Meanwhile, the scheme is simple, easy to operate and beneficial to industrial popularization of a new-generation video coding standard.

The invention discloses a two-stage robust unit combination method considering spatiotemporal correlation of uncertainty prediction error. The method comprises the following steps that firstly, basedon complete historical data, a polyhedron uncertainty set capable of describing the spatiotemporal correlation linearity of the uncertain amount (wind power and load) is established, and an analysis relation between the confidence probability and the uncertainty set is given; then based on the polyhedron set, a two-stage robust safety constraint unit combination model with the lowest operating cost in a prediction scene as a target is established, and a generalized Benders decomposition method is adopted for solving. Finally, a large amount of historical wind power and load data are counted toconstruct an uncertainty set taking the spatiotemporal correlation into consideration, and testing is carried out on the basis of an improved IEEE-30 and IEEE-118 node system. The result shows that the polyhedron set considering the spatiotemporal correlation can effectively reduce the conservativeness of robust optimization, and meanwhile, the combination safety and economical efficiency of therobust safety constraint unit are ensured.

The invention discloses a traffic jam prediction method based on deep learning and fuzzy clustering. The method comprises the following steps: (1) obtaining three-parameter original data of road traffic flow detected by a detector; (2) removing abnormal data from the data by using a threshold method, replacing the abnormal data by using a moving average method, and complementing missing data to obtain complete traffic time series data; aggregating the data to obtain a proper time interval, and finally normalizing the aggregated data; (3) mining spatial-temporal correlation according to historical data, performing training by using a CNN and a GRU to extract traffic flow spatial-temporal features, and performing future moment parameter prediction by using the trained network; (4) performingfuzzy clustering by using the historical data in the step (2), and calculating a membership degree to obtain a clustering center; and (4) judging the traffic state of the prediction time period according to the membership degree of the traffic data predicted in the step (3), thereby achieving the purpose of predicting traffic congestion. According to the invention, the traffic jam can be predicted more accurately.

The invention provides an internet-of-things tampering invasion detection method in combination with abnormal monitoring. Aiming at a periodic environment monitoring network, the technical problem that the real perception data is tampered by malicious node and the credibility of the data is damaged is solved. The method comprises a local abnormal monitoring step, a neighbor invasion detection step and a global invasion detection step. The space-time relativity of the data in the network is considered, and mutual supervision is performed by using the openness of a channel usable for a node and a neighbor node, so invasion detection between neighbors is realized, and the space relativity is detected by using the global invasion detection with a suspicious abnormal event node as the center; by using the result, on one hand, the monitoring and the positioning of the abnormal event are realized, and on the other hand, the invasion node close to the abnormal event can be reversely detected.

The invention relates to a traffic data tensor filling method based on spatiotemporal constraints. The method comprises the steps of obtaining incomplete traffic data, and constructing a traffic flowdata tensor; building a tensor filling model based on factor decomposition; constructing a spatiotemporal constraint tensor by analyzing spatiotemporal characteristics of the data, and optimizing thetensor filling model based on the factor decomposition; and performing spatiotemporal constraint tensor filling, and restoring original traffic data. According to the traffic data tensor filling method based on the spatiotemporal constraints, provided by the invention, the tensor filling method based on the factor decomposition is applied to the field of traffic data restoration, and the spatiotemporal correlation and the low-rank characteristic of the traffic data are fully excavated, so that the precision of restoring the complete traffic data is improved.

The invention discloses a spatio-temporal clustering method for compressive data gathering. The spatio-temporal clustering method comprises the steps of: 1, an initialization stage; 2, spatio-temporal correlation measurement; 3, network clustering; 4, compressive data gathering; 5, data reconstruction. In the network clustering step, aggregation nodes obtain clustering information according to a calculated value of spatio-temporal correlation and broadcast the information to the whole network, and intra-cluster nodes select cluster head nodes dynamically. By adopting the steps, the spatio-temporal clustering method for compressive data gathering reduces data transmission times under the condition of same reconfiguration error, balances network energy consumption, prevents a certain node from dying too early due to energy consumption, and prolongs network lifetime.

The invention provides a spatio-temporal correlation day-ahead plan load flow analysis method with uncertainty being taken into consideration. The spatio-temporal correlation day-ahead plan load flow analysis method comprises the following steps: obtaining next-day forecast wind speed of wind power plants in 96 time period within 24 hours; obtaining next-day output of the wind power plants; obtaining forecast error distribution of the next-day output of the wind power plants; carrying out independence conversion on random variables having spatio-temporal correlation; calculating semi-invariant of a load flow in each line; and determining relevant information for dispatching. The method carries out engineering algorithm processing on the forecast error distribution having correlation, thereby solving the problem that probability distribution having correlation cannot be obtained by utilizing a series expansion method directly. Analysis is carried out on the next-day load flow of each line in each time period through a Gram-Charlier series expansion method ; through such analysis method, probabilistic load flow problem can be solved conveniently and effectively; the method has a practical value; and the present series expansion method commonly used in medium/ long-term probabilistic load flow analysis is expanded to short-term plan load flow analysis, thereby providing more data support for economic dispatching.

Provided are a method and apparatus for detecting an abnormal movement. The apparatus includes a feature tracing unit configured to extract features of a moving object in an input image, trace a variation in position of the extracted features according to time, and ascertain trajectories of the extracted features; a topic online learning unit configured to classify the input image in units of documents which are bundles of the trajectories, and ascertain probability distribution states of topics, which constitute the classified document, by using an online learning method which is a probabilistic topic model; and a movement pattern online learning unit configured to learn a velocity and a direction for each of the ascertained topics, and learn a movement pattern by inferring a spatiotemporal correlation between the ascertained topics.