36results about How to "No prior knowledge required" patented technology

The invention discloses a method for monitoring Lamb wave engineering structural damage by utilizing signal decomposition, which comprises the following steps: collecting the Lamb wave healthy reference signals fhji of an engineering structure in a healthy state, wherein i and j can be 1, 2, 3,...; when the structure is damaged, collecting all the Lamb wave response signals f'ji of the damaged structure under the condition of an inspiring / sensing channel; utilizing the correlation operation to extract all the Lamb wave damage scattered signals of the damaged structure under the condition of the inspiring / sensing channel; obtaining the damage position and scope according to the characteristic parameters acquired in the steps above, thereby analyzing and judging the health condition of the monitored structure. According to the method, based on the condition of the traditional structural damage monitoring device, the Lamb wave damage scattered signals in a damage monitoring system are extracted by using the related treatment, thereby increasing the accuracy and stability of the monitoring system.

The invention discloses an inner and outer layer nesting ECMS (equivalent fuel consumption minimization strategy) multi-objective double-layer optimization method. The inner and outer layer nesting ECMS multi-objective double-layer optimization method includes steps of building multi-objective optimization models of plug-in hybrid electric vehicles; solving the multi-objective optimization modelsby the aid of inner and outer layer nesting multi-objective particle swarm algorithms to obtain multi-objective optimized Pareto solution set front edges; weighting equivalent fuel consumption per hundred kilometers and variation ranges of deviation of SOC (state of charge) final values and target values, building total evaluation functions related to the equivalent fuel consumption per hundred kilometers and SOC deviation and selecting the optimal charge and discharge equivalent factors and engine and motor power distribution modes corresponding to the optimal charge and discharge equivalentfactors. The inner and outer layer nesting ECMS multi-objective double-layer optimization method has the advantages that output power of engines and motors of the plug-in hybrid electric vehicles canbe reasonably distributed at CS (charge sustaining) stages, so that fuel consumption can be reduced as much as possible, battery SOC balance still can be effectively kept, and the fuel economy of theintegral vehicles can be improved.

The present invention relates to an image and video processing method, especially to a method for extracting and correlating video interested objects based on a two-stage interaction. In the method, a user performs an approximately positioning interaction by using a kind of interaction method which is not limited to a general mode and with a low requirement for priori knowledges, and then performs multiparameter extraction on the interested objects by using a quick and realizable extraction algorithm. Based on the most suitable interested object obtained by the reselection and interaction, the present invention performs searching and weighting by extracting various characteristics to obtain a final result, picks out corresponding images and additional information from a value-added information base, and shows the information at peripheral of the video. The method correlates the value-added information to the interested object of the user without influencing watching of the user by fully mining video information and ensuring preference of the user, thereby satisfying the requirment of the user that the area of interest needs to be looked into and further explored.

The invention discloses a passive target localization method for wireless sensor node random deployment. The passive target localization method includes the steps of first, deploying sensor nodes and base stations in a monitored area; second, acquiring RSS (Received Signal Strength) values on the condition without interference before monitoring; third, acquiring and classifying the RSS values at the monitoring stage; fourth, determining locations of links interfered by targets in the monitored area, namely locations of nodes constituting the interfered links, for accurately positioning the target location on the condition that the nodes in the monitored area are in random deployment; fifth, determining the target location in the monitored area. The passive target localization method can be implemented without priori knowledge. As meshing is omitted, the defect that location precision is affected by size of meshes in meshing-based localization methods is avoided.

The invention discloses a multi-camera dynamic scene 3D (three-dimensional) rebuilding method based on joint optimization, which can be used for partitioning a space-time consistent state, is robust and does not need prior knowledge of a background. According to the method, multi-view shooting depth recovery and static / dynamic dual-layer partitioning which participate in shooting can be solved simultaneously, free and independent movement is realized without any accurate static / dynamic camera, and dynamic scenes shot by using certain cameras can be processed. The method comprises the following steps of: I, initializing a depth map at a moment T by using a synchronous video frame which skips M cameras at each moment T; and II, performing space-time consistence depth optimization and static / dynamic dual-layer partition, and implementing space-time consistence optimization iteratively to realize high-quality dynamic 3D rebuilding. According to the method, accurate static / dynamic partition is not required; and the method has robustness specific to error partition information generated in complex scenes, and has very high application values in the fields of 3D images, 3D animations, augmented reality, motion capture and the like.

The invention relates to a transformer winding looseness fault detection method based on an average displacement method, and belongs to the technical field of power transformer safety monitoring. The method comprises the steps that surface vibration data of a transformer with an unloosened winding are measured at preset time intervals within preset time range, the average displacement of the transformer is calculated, and a first average displacement sequence is obtained; surface vibration data of a transformer to be measured are obtained at the same time intervals within the same time range, the average displacement of the transformer is calculated, and a second average displacement sequence is obtained; difference values between the average displacements at the same time delay are calculated, the difference values are added, and evaluation factors for evaluating the winding looseness degree are obtained. The transformer winding looseness fault detection method is easy to implement, easy and convenient to operate and accurate in diagnosis, and the number of needed devices is small.

The invention provides a method for classifying pixel blocks of JPEG images and image falsification detecting and falsified area positioning methods based on the same. The method comprises the following steps of: dividing to-be-detected JPEG images into continuous and non-overlapped 8*8 pixel blocks; taking one pixel block as the center and selecting a square area combined by (2n+1)*(2n+1) pixel blocks as a sub-image; utilizing a first number distribution probability feature of AC coefficients in the front i AC coefficient regions of the sub-image to divide the central pixel block into singlecompression or double compression; and confirming a classifying result as the double compression and a connected region with an area more than M 8*8 pixel blocks as a falsified area. According to themethod provided by the invention, the falsified area can be accurately detected and positioned; the falsified areas of three different falsifying technologies can be detected according to the method;the method is wide in application scope; the method is an automatic blind detecting method for JPEG falsified images and does not require any priori knowledge; and the detection coverage rate is higher and the error detection rate is low.

The invention discloses a spacecraft attitude anomaly detection method and system. The method comprises the following steps: taking normal telemetry parameter sample data as a training set; establishing a PCA-LPP fusion dimension reduction reconstruction model through a fusion matrix; performing dimensionality reduction reconstruction on the training set based on the fusion model to obtain a fusion matrix, and obtaining the training model and dimensionality reduction reconstruction data according to the fusion matrix; calculating the variable quantity and the control limit of the training setin the dimension reduction reconstruction process according to the dimension reduction reconstruction data; performing dimensionality reduction reconstruction on a test set formed by the online telemetry data to be detected through the training model to obtain dimensionality reduction reconstruction data, and calculating the variable quantity of the test set in dimensionality reduction reconstruction; calculating the contribution rate of each telemetry parameter variable to the variable quantity of the test set when the variable quantity exceeds the control limit, and outputting the variable with the maximum contribution rate or exceeding the threshold value as an abnormal variable. The method is used for solving the problems of high calculation complexity, low detection accuracy and the like when facing high-dimensional multivariate complex spacecraft parameters in the prior art, reducing the calculation complexity and improving the detection accuracy.

The invention discloses a strip mine slope deformation online monitoring method based on ground trinocular video frame matching. The method comprises the steps: firstly, completing parameter calibration of trinocular video cameras by uniformly arranging targets and adopting a post-orientation method; secondly, matching and tracking targets and homonymy features of trinocular video frames at different moments, performing homonymy feature deviation estimation, and automatically identifying deformation features; and then, calculating the three-dimensional deformation quantity of the slope deformation characteristics by utilizing forward intersection of the three groups of video frames, and carrying out time sequence analysis of the three-dimensional deformation characteristics to complete early warning analysis of side slope deformation. Hardware equipment used in the method is low in cost, deformation characteristic capturing and three-dimensional deformation quantity estimation efficiency is high, slope deformation characteristics can be automatically captured, potential landslide areas can be determined in an auxiliary mode, online high-precision three-dimensional deformation monitoring is completed, and the safety requirement for three-dimensional deformation monitoring of the high and steep side slope of the strip mine is met.

The present invention belongs to the field of biological characteristic distinction, and is especially the method of extracting geometric characteristic of iris based on characteristic edge distribution. The method includes first defining geometric characteristic of iris and describing its reliability and stability; subsequent extracting geometric characteristic edge of iris in antisymmetric double orthogonal wavelet process and constituting overall edge distribution characteristic in unequal interval grading according to the inhomogeneous distribution of geometric characteristic of iris; and finally introducing characteristic distance expression and proposing legal user judging method. The method has high distinction precision and less outer factor influence.

The invention discloses a convolutional neural network fault diagnosis method based on a multi-channel attention module. The method comprises the following steps of: 1, collecting vibration signals ofto-be-diagnosed equipment under different working conditions; 2, performing data enhancement processing and fault labeling on the vibration signals acquired in the step 1; 3, establishing a convolutional neural network fault diagnosis model based on a multi-channel attention module; 4, inputting a vibration signal under a certain working condition into the convolutional neural network fault diagnosis model based on a multi-channel attention module established in the step 3, and training the model; and 5, inputting a vibration signal under another working condition into the fault diagnosis model with the trained network parameters, carrying out fault state identification, and outputting a fault label of the to-be-diagnosed equipment to obtain a fault type. According to the method, higher fault identification capability and generalization capability can be obtained under variable working conditions, and the problem of poor generalization performance of a traditional convolutional neuralnetwork model under variable working conditions is solved.

The invention provides a shrimp meat freshness detection method based on a spatial migration Raman spectrum, and belongs to the technical field of food nondestructive testing. The method, based on a Raman scattering point light source image detection system, comprises the steps: collecting and obtaining Raman images of unhousinged shrimp samples at different positions and under 1024 wave bands; converting the Raman image intensity wavelength domain into an intensity space domain, and extracting an interested region and waveband as sub-images of the corresponding Raman image; selecting the optimal spatial offset distance to make the internal signal relatively strongest and the signal-to-noise ratio highest; removing abnormal point spectral data, and inputting the abnormal point spectral data into random forest features for feature band selection; and inputting the spectral data of the selected characteristic wave band into a support vector regression model for prediction. The method ishigh in prediction precision and high in speed for the freshness of the unhousinged shrimps, and field detection can be achieved; and meanwhile, a deep detection method is provided, and the method isexpected to be applied to deep detection of unhousinged or packaged or multi-layer samples.

The invention discloses a hyperspectral image suspicious target detection method based on low-rank sparse representation. The method comprises the following steps: reading a hyperspectral image data cube and related projection parameters; clustering spectrums of all pixels in the input hyperspectral image to obtain a plurality of clustering centers and a category to which each pixel belongs; counting an intra-class clustering result, and determining the distance between each pixel and the clustering center of the class to which the pixel belongs; selecting s pixels with the minimum distance asvarious description sample points, and adding the description sample points into a final dictionary to form a reconstruction dictionary of the scene remote sensing data; performing low-rank sparse constraint matrix decomposition on the hyperspectral image data by using the reconstructed dictionary; carrying out residual error statistics on the decomposed residual error; and outputting the T pixels with the maximum residual values and the ground coordinates corresponding to the T pixels as suspicious targets. The detection method provided by the invention has good adaptability to hyperspectralimages collected by different sensors, has a low false alarm rate, and still has a high detection capability for small targets.

The invention relates to a handwriting form identification method and device. The method comprises the steps of detecting a form line; determining a longest vertical form line and a longest horizontal form line which are crossed; calculating a boundary rectangle just covering all form lines; for each form line, extending the form line on the boundary rectangle to obtain a corresponding virtual form line; calculating the position of a central point of a frame defined by the virtual form line and the form line; dividing each central point into an internal point and an external point, wherein the internal point is the central point surrounded by the form line; grouping internal points, and if a path between the adjacent internal points is not blocked by the form line, determining that the internal points belong to a same group; combining frames corresponding to the internal points of the same group to form a cell; and identifying handwriting contents in each cell. According to the handwriting form identification method and device provided by embodiments of the invention, a handwriting form can be identified with high efficiency and high accuracy.

The invention discloses a data filtering method and system for an active power distribution network. The method comprises the steps that active power distribution network data is cleaned; property reduction is performed on the cleaned active power distribution network data, and training data and to-be-filtered data containing condition properties in a minimum relative property set after reductionare determined and obtained; classified filtering is performed on the to-be-filtered data obtained after reduction, a genetic expression algorithm is utilized to perform data mining on the training data, and a classification function relation between the condition properties and known sensitive properties is obtained; and the condition properties in the to-be-filtered data are substituted into theobtained classification function relation, a function actual value is obtained through calculation, whether the properties are sensitive properties is judged according to the calculated function actual value, and sensitive data with the sensitive properties is filtered out. Through the data filtering method and system, classified filtering can be performed on all kinds of service sensitive data of the active power distribution network, so that proactive protection is performed on the sensitive data, and a data transmission problem of the active power distribution network is solved.

The invention discloses a hyperspectral image water area automatic extraction method, and belongs to the technical field of remote sensing image processing. The method comprises the following steps: firstly, reading a hyperspectral image, analyzing waveband information of data, and calculating a water body index value of each pixel; then, an improved OTSU method is used for carrying out water areaautomatic segmentation threshold calculation, and a water area primary extraction result is obtained; then, on the basis of a water area primary extraction result, extracting a spectrum of water, carrying out spectrum consistency analysis, and obtaining an Euclidean distance between each pixel and a water body spectrum; taking the Euclidean distance as a weighted value, carrying out covariance matrix calculation, and carrying out water area extraction of a constraint energy minimization method on the basis; obtaining a water area secondary extraction result by utilizing an improved OTSU method; and finally, comprehensively analyzing the water area primary extraction result and the water area secondary extraction result to obtain a final water area extraction result. The method can be carried out in a full-automatic mode, priori knowledge and any parameter setting are not needed, and high precision is achieved.

The invention discloses a CT image beam hardening artifact correction system based on adjustable double factors, and particularly relates to the technical field of X-ray CT imaging. According to the system, in the correction process, a high-density region in a CT image is segmented to obtain edge information; projection data of the high-density area is calculated; the attenuation coefficient of the substance to the ray is approximately calculated to obtain artifact data caused by a high-density region; and finally, artifact data is reconstructed by using a classical filtering back projection algorithm, and the reconstructed artifact image is substracted from the original image to obtain a corrected CT image. The system can provide high-quality images for industrial CT and medical CT detection, and compared with a previous CT image beam hardening artifact correction method, the system can quickly and effectively eliminate hardening artifacts without priori knowledge.

The invention provides a reductive-rule-based anti-interference decision-making method of a wireless communication system. According to a rough set theory, a decision-making space is simplified to obtain a reduced decision-making rule; screening of an initial population generated by a genetic algorithm randomly is carried out according to the decision-making rule to keep an individual meeting the decision-making rule; and on the step of the genetic algorithm, optimization seeking is carried out on a communication parameter. In order to improve practicability of the algorithm, a maximum transmission rate that is obtained on the condition of a minimum transmitting power under a designated error rate is used as a target function of communication anti-interference decision making, thereby avoiding the dependence on an experience weighted value by the traditional algorithm. The simulation result demonstrates that the convergence speed of the genetic algorithm is substantially improved on the premise that calculation accuracy is guaranteed; and the practicability is high.

The invention provides a method for classifying pixel blocks of JPEG images and image falsification detecting and falsified area positioning methods based on the same. The method comprises the following steps of: dividing to-be-detected JPEG images into continuous and non-overlapped 8*8 pixel blocks; taking one pixel block as the center and selecting a square area combined by (2n+1)*(2n+1) pixel blocks as a sub-image; utilizing a first number distribution probability feature of AC coefficients in the front i AC coefficient regions of the sub-image to divide the central pixel block into single compression or double compression; and confirming a classifying result as the double compression and a connected region with an area more than M 8*8 pixel blocks as a falsified area. According to the method provided by the invention, the falsified area can be accurately detected and positioned; the falsified areas of three different falsifying technologies can be detected according to the method; the method is wide in application scope; the method is an automatic blind detecting method for JPEG falsified images and does not require any priori knowledge; and the detection coverage rate is higher and the error detection rate is low.

The invention relates to a survival analysis method for predicting machine damage time, which decomposes a survival analysis problem for predicting the machine damage time into sub-problems of a timeslice, and greatly reduces the difficulty of using a neural network to model a long-time sequence prediction problem after decomposing a time sequence prediction problem on the whole time length; therisk probability of each time slice is modeled by using the same neural network, and the final survival probability is obtained through a conditional probability rule. On the premise of not carrying out any assumption on the time distribution of the damage time of the machine, a prediction model can be trained by combining big data. The invention not only can be used for predicting the survival probability of discrete time slices, but also can play a role in predicting the survival probability of continuous time. Experiments prove that the prediction accuracy of the survival analysis model trained through the deep neural network is far higher than that of a traditional method. And through parallel calculation, the algorithm can perform long-distance survival probability prediction under the condition of not increasing the operation time.

A survival analysis method for predicting machine damage time. The survival analysis problem of predicting machine damage time is decomposed into sub-problems of component time slices, and the time series prediction problem over the entire time length is decomposed to greatly reduce the problem. The use of neural network modeling The difficulty of long-term series prediction problem, by using the same neural network to model the risk probability of each time slice, the final survival probability is obtained through the conditional probability rule. Without making any assumptions about the temporal distribution of the damage time of the machine, a prediction model can be trained in combination with big data. Not only can it be used in the prediction of survival probability in discrete time slices, but also in prediction of survival probability in continuous time. Experiments show that the prediction accuracy of survival analysis model trained by deep neural network is far better than traditional methods. And through parallel computing, the algorithm can perform long-distance survival probability prediction without increasing the computing time.

The invention discloses an inner and outer layer nesting ECMS (equivalent fuel consumption minimization strategy) multi-objective double-layer optimization method. The inner and outer layer nesting ECMS multi-objective double-layer optimization method includes steps of building multi-objective optimization models of plug-in hybrid electric vehicles; solving the multi-objective optimization modelsby the aid of inner and outer layer nesting multi-objective particle swarm algorithms to obtain multi-objective optimized Pareto solution set front edges; weighting equivalent fuel consumption per hundred kilometers and variation ranges of deviation of SOC (state of charge) final values and target values, building total evaluation functions related to the equivalent fuel consumption per hundred kilometers and SOC deviation and selecting the optimal charge and discharge equivalent factors and engine and motor power distribution modes corresponding to the optimal charge and discharge equivalentfactors. The inner and outer layer nesting ECMS multi-objective double-layer optimization method has the advantages that output power of engines and motors of the plug-in hybrid electric vehicles canbe reasonably distributed at CS (charge sustaining) stages, so that fuel consumption can be reduced as much as possible, battery SOC balance still can be effectively kept, and the fuel economy of theintegral vehicles can be improved.

The present application discloses a health monitoring method of a positioning and speed measuring system, including: obtaining the full width at half maximum of the autocorrelation curve of the variables representing the state of the positioning and speed measuring system; determining each subdivision when subdividing the original matrix according to the full width at half maximum and the number of variables The number of columns of the matrix, where the original matrix is ​​composed of historical data of variables; the original matrix is ​​subdivided into multiple subdivision matrices according to the number of columns, and the multiple subdivision matrices are reconstructed to obtain a random matrix; the positioning is determined according to the random matrix The average spectral radius of the speed measurement system; according to the relationship between the average spectral radius and the preset threshold, determine whether the positioning speed measurement system is healthy. This health monitoring method does not need to know the failure mode, degradation mode and internal mechanism of the positioning speed measurement system, but only needs the historical data of the variable and the full width at half maximum. It has the advantages of small amount of calculation and no prior knowledge. In addition, the present application also provides a health monitoring device, equipment and computer-readable storage medium having the above-mentioned advantages.

The present invention proposes a wireless communication system anti-interference decision-making method based on reduction rules. First, the decision space is simplified according to the rough set theory to obtain reduced decision rules; The individual of the decision-making rule; finally optimize the communication parameters according to the genetic algorithm steps; in order to improve the practicability of the algorithm, the present invention also adopts the maximum transmission rate under the condition of the minimum transmission power under the specified bit error rate as the communication anti-interference The objective function of the decision-making avoids the dependence on the experience weight value in the traditional algorithm. The simulation results show that the algorithm significantly improves the convergence speed of the genetic algorithm on the premise of ensuring the calculation accuracy, and has stronger practicability.

The invention discloses a passive target positioning method for random deployment of wireless sensor nodes, which specifically includes the following steps: Step 1: Deploy sensor nodes and base stations in the monitoring area; Step 2: Collect RSS values ​​without interference before monitoring; Step 3: RSS value collection and classification in the monitoring stage; Step 4: Since the nodes in the monitoring area are randomly deployed, in order to accurately locate the target location, it is first necessary to determine the location of the link interfered by the target in the monitoring area, that is, to form The locations of the nodes of these interfered links; Step 5: Determination of target locations in the monitoring area. The method of the present invention can be performed without prior knowledge. In addition, the method does not require grid division, thereby avoiding the shortcomings of grid size affecting positioning accuracy in positioning methods based on grid division.

The invention discloses a method for monitoring Lamb wave engineering structural damage by utilizing signal decomposition, which comprises the following steps: collecting the Lamb wave healthy reference signals fhji of an engineering structure in a healthy state, wherein i and j can be 1, 2, 3,...; when the structure is damaged, collecting all the Lamb wave response signals f'ji of the damaged structure under the condition of an inspiring / sensing channel; utilizing the correlation operation to extract all the Lamb wave damage scattered signals of the damaged structure under the condition of the inspiring / sensing channel; obtaining the damage position and scope according to the characteristic parameters acquired in the steps above, thereby analyzing and judging the health condition of the monitored structure. According to the method, based on the condition of the traditional structural damage monitoring device, the Lamb wave damage scattered signals in a damage monitoring system are extracted by using the related treatment, thereby increasing the accuracy and stability of the monitoring system.

The invention discloses a multi-AUV communication delay active compensation method based on a data-driven predictor, comprising the following steps: establishing an autoregressive model; determining the undetermined order of the autoregressive model; designing an online update law of a parameter vector; The data-driven state predictor of j AUVs; realizes active compensation of communication delay. The present invention estimates the current actual motion state of the neighboring AUVs online in real time by designing a state predictor, and applies the estimated value to the design of the formation control law, so as to realize the active compensation of the AUV communication delay. The active compensation method for multi-AUV communication delay proposed by the present invention has no restriction on the design method of the AUV formation control law, and can flexibly apply various advanced formation control theoretical methods. The active compensation method for multi-AUV communication delay proposed by the present invention is data-driven and does not depend on the AUV motion mathematical model, therefore, no prior knowledge of the parameters and structure of the AUV motion mathematical model is required.

An embodiment of the invention discloses a scheduling method based on a co-current flow. The method comprises the steps of 1, expanding each co-current flow according to a preset condition, and obtaining corresponding expanded co-current flows, wherein at least two expanded co-current flows in the expanded co-current flows comprise same network flows; 2, performing priority sequencing on the network flows in the expanded co-current flow; 3, performing priority sequencing on the expanded co-current flows; and 4, performing scheduling according to the sequence of the expanded co-current flows after sequencing, wherein the network flow which is included in a plurality of expanded co-current flows is not scheduled along with other expanded co-current flows in the plurality of expanded co-current flows after being scheduled along with the expanded co-current flow with highest priority in the plurality of expanded co-current flows. Correspondingly, the embodiment of the invention further discloses a server. Through application of the scheduling method and the server, an effect caused by an identification error in a co-current flow clustering process can be reduced.