141 results about "Nonlinear correlation" patented technology

A system for detecting subtle differences in a signal in a set of linearly and/or non-linearly related signals that characterize a sensor-instrumented machine, process or living system. The system employs an improved similarity operator for signal differentiation. Signals or data representative of several linearly and/or non-linearly related parameters that describe a machine, process or living system are input to the inventive system, which compares the input to acceptable modeled states. If one or more of the input signals or data are different than expected, given the relationships between the parameters, the inventive system will indicate that difference. The system can provide expected parameter values, as well as the differences between expected and input signals; or the system can provide raw measures of similarity between the collection of input signals and the collection of acceptable modeled states. The system can be embodied in software or in a micro-controller.

The invention discloses a vibration and audio signal-based high-speed train track damage detecting method, belongs to the field of signal detection and processing as well as safety monitoring, and solves the problems of low detection speed and single detection method in the conventional train track damage detection. The method comprises the following steps: 1, acquiring vibration signals and audio signals of a train track through sensors arranged at train track detection points; 2, respectively extracting information characteristics included in the vibration signals and the audio signals; 3, respectively obtaining a nonlinear correlation curve of the vibration signals and a nonlinear correlation curve of the audio signals by using a nonlinear correlation analysis method; 4, respectively analyzing the information of the two nonlinear correlation curves obtained in the step 3 so as to respectively obtain minimum values of the two nonlinear correlation curves; and 5, carrying out data fusion on the two minimum values and corresponding information thereof so as to obtain a damage coefficient, and looking up a table to obtain the damage degree according to the coefficient. The method is suitable for detecting the damage on railway train tracks and monitoring the safety operation of trains.

An apparatus and method for displaying content is disclosed. A particular method includes determining a viewing orientation of a user relative to a display and providing a portion of content to the display based on the viewing orientation. The portion includes at least a first viewable element of the content and does not include at least one second viewable element of the content. The method also includes determining an updated viewing orientation of the user and updating the portion of the content based on the updated viewing orientation. The updated portion includes at least the second viewable element. A display difference between the portion and the updated portion is non-linearly related to an orientation difference between the viewing orientation and the updated viewing orientation.

An LDPC unit decoder included in a signal decoding device is provided with a parity checking unit that is a multiplier for performing multiplication of a check matrix (parity check matrix) and temporary estimated values of an encoded signal, computed by a temporary estimated value computation unit. A check matrix (parity check matrix) holding unit holds the check matrix (parity check matrix). If s and t are natural numbers, s≧t≧2, among s columns extracted from this check matrix (parity check matrix), t columns or less have a linearly independent relationship. These s columns are multiplied at locations where error occurrence frequency is relatively high, with regard to the temporary estimated values. According to this mode, the matrix is composed to include t columns that are linearly independent, that is, t columns that are not linearly dependent. In this way, in a decoded signal series, when errors occur at t locations, since it is possible to avoid multiplying all the linear dependent columns at locations where errors occur, errors with a relatively high frequency of occurrence can be preferably detected.

A method for automatically characterizing non-linearities of a perpendicular read-back signal of a recording system is disclosed. The method includes using dibit extraction to obtain a read-back signal having a main pulse and a plurality of echoes where the read-back signal exhibits a baseline shift. An area under the first echo is integrated to obtain a first area where the integrating subtracts any baseline shift area within the first echo and where the first echo is associated with a first non-linearity. The method integrates an area of the read-back signal under the main pulse to obtain a second area where the integrating subtracts any baseline shift area within the main pulse. A first parameter is computed that characterizes the first non-linearity based on the first area and the second area. The method may be applied to characterize several non-linearities.

The invention provides a color image authentication method and system based on hypercomplex number encrypted domain sparse representation. The color image authentication method comprises the steps of generating a phase mask according to a Logistic chaotic sequence, carrying out double random phase encryption on a color image by adopting quaternion Fourier transform so as to acquire ciphertext data, carrying out sparse representation on a component of the ciphertext data by using a sparse matrix so as to acquire a sparse ciphertext, wherein the component comprises a real component and an imaginary component, decrypting the sparse ciphertext, calculating a nonlinear correlation coefficient of a decrypted image, and carrying out authentication on the color image according to a centralized peak value. The color image authentication method based on the hypercomplex number encrypted domain sparse representation in the embodiment of the invention has high security, and a certain degree of noises can be resisted, thereby being suitable for being applied to the fields of secret communication and authentication of images.

The invention discloses a target identification device and method based on ghost imaging calculation. The device is formed by a mode-locked laser, a laser beam expander, a diaphragm, a high-speed spatial light modulator, an imaging lens, a high-speed photodiode, a digital acquisition module and the like. The method comprises the steps of utilizing a random speckle generated by the high-speed spatial light modulator for sampling a target to be identified, adopting the ghost imaging calculation for rebuilding the target, and realizing target identification under extra-low sampling ratio through nonlinear correlation. According to the target identification device and the method based on the ghost imaging calculation provided by the invention, an information acquisition method of the ghost imaging calculation is adopted, and a nonlinear correlation test is combined, so that not only is target identification with single pixel and low sampling ratio realized and is the spectral region of target identification expanded, but also the size of a detector is reduced, and the cost of an imaging system is reduced.

The invention discloses a reservoir physical property parameter prediction method combined with deep learning, and the method comprises the steps: introducing the nonlinear correlation between an MICquantitative measurement physical property parameter and a logging curve, and selecting the logging curve which is obvious in response to the physical property parameter; introducing CEEMDAN to decompose the physical property parameter data sequence to obtain an IMF component and a residual RES component of an intrinsic mode function, and subjecting the physical property parameter data sequence tostationary processing; introducing SE to evaluate the complexity of each IMF component and RES margin, and recombining component sequences with similar entropy values to obtain a new intrinsic mode component; carrying out normalization processing on the new intrinsic mode component data and then dividing the new intrinsic mode component data into a training set and a test set; introducing an LSTMrecurrent neural network to establish a prediction model for the reconstructed new component, and obtaining a prediction value of each new intrinsic mode component; and carrying out inverse normalization on the prediction value of each new intrinsic mode component, and carrying out superposition reconstruction to obtain a physical parameter prediction result. According to the method, the modelingnumber of redundant information and prediction components is reduced, and the prediction precision and the prediction speed are improved.

The invention discloses an electric power system static security assessment method considering wind speed correlation. According to wind speed historical data, cut-off pair copula is used to realize wind speed modeling with non-linear correlation; then according to fan power characteristics, electric power system quick static security assessment is realized. The electric power system static security assessment method can be used for processing wind speeds with any probability characteristics in an electric power system and is suitable for quickly assessing the influence of connected wind power plants on the static security of the electric power system in medium-term and long-term plans.

The invention discloses a comprehensive monitoring method for a large coal-fired unit combustion system based on dynamic characteristic and static characteristic synergistic analysis of. According toan idea of system distribution, a combustion system is used as an upper integrated system, and is physically divided into various lower-level sub-devices according to system structural characteristics. For the lower-level sub-devices, dynamic information and static information of each sub-device are extracted by using slow feature analysis, and independent monitoring of each sub-device is realized. In the upper layer, process characteristics extracted from all lower-layer sub-devices are synthesized, nonlinear relations between different device variable groups are taken into account, and the dynamic and static information of the whole combustion system and the nonlinear correlations between sub-device variable groups are extracted by using kernel slow feature analysis, so that the monitoring of the overall process state of the entire combustion system is realized. Through the comprehensive monitoring method for the large coal-fired unit combustion system based on the dynamic characteristic and static characteristic synergistic analysis, the synergistic comprehensive detection of multiple devices is realized, The normal working condition switching and process faults of a system andthe sub-devices can be effectively distinguished through the extracted dynamic information and the static information, and the accuracy of the industrial large-scale system state monitoring is improved.

The invention belongs to the technical field of wind speed prediction, and discloses a wind speed prediction method and system based on a neighborhood gate long-short-term memory network. The Pearsoncorrelation coefficient and the maximum information coefficient are respectively adopted to explore the linear and nonlinear correlation among variables to screen the wind speed correlation factors. On the basis of correlation analysis, the Granger causality test is used to explore the statistical causality of wind speed and wind speed factors. The structure of causality is divided into five types, and all types of causality are unified into an equivalent tree causality structure by the method of 'decomposition-dummy variable-pruning'. Aiming at the causality structure of equivalent tree, a long-term and short-term memory network model based on neighborhood gates is proposed to predict wind speed. The prediction method (NLSTM) of the invention accurately considers the causality between thewind speed and the wind speed factors, effectively improves the prediction accuracy of the wind speed, and plays a vital role in the application of the wind power and the dispatching of the power grid.

The invention belongs to the technical field of runoff prediction, and discloses a runoff probability prediction method and system based on deep learning, and the method comprises the steps: employinga maximum information coefficient to analyze the linear and nonlinear correlation between variables, so as to screen a runoff correlation factor; building an extreme gradient boosting tree model on the basis of correlation analysis, and inputting runoff correlation factors into a trained XGB model to complete runoff point prediction; inputting a point prediction result obtained by the XGB model into a GPR model, and performing secondary prediction to obtain a runoff probability prediction result; selecting confidence and acquiring a runoff interval prediction result under the corresponding confidence through Gaussian distribution; and optimizing hyper-parameters in the XGB model and the GPR model by adopting a Bayesian optimization algorithm. A high-precision runoff point prediction result, an appropriate runoff prediction interval and reliable runoff probability prediction distribution can be obtained, and the prediction method plays a crucial role in utilization of water resourcesand reservoir scheduling.

An electronic circuit for adjusting a property of an input signal of a non-linear element is disclosed. A detector detects a strength of the input signal. An analog controller forms a control signal as a piecewise approximation of a non-linear dependency between the property and the strength of the input signal detected by the detector. An adjusting circuit adjusts the property of the input signal according to the control signal from a controller.

The invention discloses a line transformation relation abnormity judgment method based on correlation between electric quantity and line loss. The line transformation relation abnormity judgment method includes a data acquisition module which is used for acquiring electric quantity information, a data processing module which is used for analyzing correlation between electric quantity and line loss, and a data display module which is used for displaying abnormal line transformation relations, wherein the data acquisition module, the data processing module and the data display module are sequentially connected. The line transformation relation abnormity judgment method utilizes power consumption, voltage, current and other data of a line and a transformer provided by a power consumption information acquisition system, and utilizes a big data analysis technology and a transformer area line loss non-linear correlation analysis method to construct the line transformation relation error correction model independent of the local communication relation and find out the line transformation relation with statistical errors, so as to improve the accuracy of topological information of the power transmission line and provide technical guidance for conducting line transformation relation on-site troubleshooting, so that the calculation precision of line loss is improved, and technical support is provided for lean marketing management.

The invention discloses a behavior recognition method of nuclear covariance descriptors based on dense tracks. The objective of the invention is to solve a problem of low behavior recognition accuracy caused by nonlinear correlation between different characteristics which are not considered in the prior art. The method comprises steps of 1), extracting dense tracks, extracting characteristics of each pixel point in a track cuboid and acquiring a base layer characteristic matrix; 2), calculating a nuclear covariance matrix of the base layer characteristic matrix, mapping the nuclear covariance matrix to the euclidean space and acquiring vectorization characteristic representation; 3), by use of all characteristic representation in the track cuboid and constructing nuclear covariance descriptors based on dense tracks; and 4), using a BOW model to code the nuclear covariance matrix descriptors, acquiring a codon histogram, training a SVM by use of the codon histogram of a training set, testing the codon histogram of the training set in the trained SVM and acquiring a behavior recognition result. According to the invention, description ability of behaviors is further improved and the method can be used in complex environment like video monitoring.

The invention belongs to the field of ultrasonic imaging, and discloses a synthetic aperture ultrasonic imaging system for calculating delay time based on non-linear correlation. The synthetic aperture ultrasonic imaging system for calculating the delay time based on the non-linear correlation comprises a channel receiving and amplifying module, a channel amplifying and driving module, an analog/digital (A/D) conversion module, a digital/analog (D/A) conversion module, a sampling control module, a wave beam synthesizing module, a wave beam forming module, an emission control module, a universal serial bus (USB) subsidiary communication module and a USB communication module. The wave beam synthesizing module calculates the delay time of reconstruction of a synthetic aperture based on the non-linear correlation, and can solve the problem that large deviation exists between a synthetic aperture focusing algorithm based on the delay superposition and reality when the delay time of a transducer and theoretical calculation time differ so much.

The invention provides a dimension reduction model training method and device and electronic equipment, and belongs to the technical field of artificial intelligence. The embodiment of the invention provides a dimension reduction model training method, the device and electronic equipment. First, feature data is acquired, the feature data is input into a dimension reduction algorithm; a preset hierarchical number of feature results is output, the feature data are input into a to-be-trained dimension reduction model; training data of which the number is the same as that of the feature results ata preset level is obtained; a loss function value is determined according to the training data and the feature result; training data, the feature result and the feature data are used for training a to-be-trained dimension reduction model; ending training until the loss function value converges, obtaining a trained dimension reduction model. The training model can carry out dimension reduction ondata of the non-linear correlation feature, and carry out dimension reduction by the trained dimension reduction model when new feature data is obtained, so as to predict the dimension of the new feature data.

The invention discloses a method for recognizing a nonlinear correlation between equipment failures and electric quantity information. The method comprises the steps as follows: selecting equipment operation related information of multiple groups of transformers with same models in different time periods as samples; establishing an information base containing equipment electric information amounts of each group of samples and current equipment states corresponding to the equipment electric information amounts; calculating a correlation coefficient between the electric information amounts of each group of samples and the current equipment states corresponding to the electric information amounts through a nonlinear correlation recognition algorithm; and analyzing the difference between different samples through matlab software according to the correlation coefficients obtained through calculation. The method has the benefits as follows: the correlation between the equipment failures and the operating state quantities of the transformers can be analyzed rapidly and effectively, the accurate extraction of failure information is facilitated, the failure diagnostic accuracy is improved, meanwhile, the validity and unbiasedness of the nonlinear correlation recognition algorithm are verified, and the method is high in practicability.