733 results about "Linear correlation" patented technology

The present invention provides an improved method and system for real-time monitoring, validation, optimization and predictive fault analysis in a process control system. The invention monitors process operations by continuously analyzing sensor measurements and providing predictive alarms using models of normal process operation and statistical parameters corresponding to normal process data, and generating secondary residual process models. The invention allows for the creation of a fault analyzer directly from linearly independent models of normal process operation, and provides for automatic generation from such process models of linearly dependent process models. Fuzzy logic is used in various fault situations to compute certainty factors to identify faults and / or validate underlying assumptions. In one aspect, the invention includes a real-time sensor data communications bridge module; a state transition logic module; a sensor validation and predictive fault analysis module; and a statistical process control module; wherein each of the modules operates simultaneously.

A method for recognizing an audio sample locates an audio file that most closely matches the audio sample from a database indexing a large set of original recordings. Each indexed audio file is represented in the database index by a set of landmark timepoints and associated fingerprints. Landmarks occur at reproducible locations within the file, while fingerprints represent features of the signal at or near the landmark timepoints. To perform recognition, landmarks and fingerprints are computed for the unknown sample and used to retrieve matching fingerprints from the database. For each file containing matching fingerprints, the landmarks are compared with landmarks of the sample at which the same fingerprints were computed. If a large number of corresponding landmarks are linearly related, i.e., if equivalent fingerprints of the sample and retrieved file have the same time evolution, then the file is identified with the sample. The method can be used for any type of sound or music, and is particularly effective for audio signals subject to linear and nonlinear distortion such as background noise, compression artifacts, or transmission dropouts. The sample can be identified in a time proportional to the logarithm of the number of entries in the database; given sufficient computational power, recognition can be performed in nearly real time as the sound is being sampled.

A method is disclosed for the automated synthesis of phase shifters. Phase shifters comprise circuits used to remove effects of structural dependencies featured by pseudo-random test pattern generators driving parallel scan chains. Using a concept of duality, the method relates the logical states of linear feedback shift registers (LFSRs) and circuits spacing their inputs to each of the output channels. The method generates a phase shifter network balancing the loads of successive stages of LFSRs and satisfying criteria of reduced linear dependency, channel separation and circuit complexity.

The present invention provides an improved method and system for real-time monitoring, validation, optimization and predictive fault analysis in a process control system. The invention monitors process operations by continuously analyzing sensor measurements and providing predictive alarms using models of normal process operation and statistical parameters corresponding to normal process data, and generating secondary residual process models. The invention allows for the creation of a fault analyzer directly from linearly independent models of normal process operation, and provides for automatic generation from such process models of linearly dependent process models. Fuzzy logic is used in various fault situations to compute certainty factors to identify faults and / or validate underlying assumptions. In one aspect, the invention includes a real-time sensor data communications bridge module; a state transition logic module; a sensor validation and predictive fault analysis module; and a statistical process control module; wherein each of the modules operates simultaneously.

The invention relates to a new face-iris combination identifying method-characteristic layer extraction and combination. A face-iris characteristic extraction layer combining system is established according to nerve network, evolution calculation and fuzzy theory. For structure design, full and local geometry topological structure is adopted. A particle-group optimizing arithmetic is utilized to optimize network control parameters. When the characteristics of the face and the iris image are extracted, techniques of a super-resolution image reinforcing arithmetic, an illumination compensating arithmetic based on improved spherical harmonic function, gesture estimation based on linear relevant filters, Candide model based on a three-dimensional face and expression analysis based on an ASM arithmetic, etc., are adopted to robustly extract the eigenvectors of the face and the iris, and a self-developed double face-iris collecting device is also adopted to collect images of the face and the iris image. The method not only can establish a new system which is provided with learning capability and can automatically choose optimal network topological structure and automatically regulate net control parameters, but also can overcome and reduce the bad impacts of factors of environment and physiology, etc., during the extraction process to the independent characteristics of the face and the iris, thus effectively enhancing the identifying rate of the face-iris combination identification and promoting the system performance based on the face-iris combination identification to develop towards practical, reliable and acceptable directions.

A method of evaluating a water sample for the presence or possible future presence of nitrification comprises obtaining data values of a number of parameters, processing the data values to determine correlation coefficients, to identify any linear dependencies, to standardize the scales, evaluating the data values over a plurality of proliferation time periods and neuron numbers, calculating MSEs and R2's from the evaluations, and estimating a valid likelihood of nitrification of the water sample. A method of evaluating a water sample for the presence or possible future presence of nitrification, comprises obtaining data values of a number of parameters, statistically pre-processing the data values and supplying the pre-processed data values to a neural network. Apparatus, media and processors which are used in performing such methods.

Computer simulation of the dynamics of rigid bodies interacting through collisions, stacks and joints is performed using a constraint-based system in which constraints are defined in terms of the positions of the bodies. Displacements caused by reaction forces necessary to ensure that the bodies comply with the position constraints can be calculated and can be done iteratively by updating equations defining the reaction forces and the displacements such that the computation time and memory resources required to perform the calculations is linearly dependent upon the number of bodies and the number of contacts and joints between the bodies. Computational requirements and memory requirements are reduced further by performing the calculations using vector operations.

A channel estimation method and system using linear correlation based interference cancellation combined with decision-feedback-equalization (LCIC-DFE) are provided. The channel estimation method includes generating a first correlation sequence by calculating a linear correlation between a baseband sampled complex signal and a locally stored pseudo-noise signal and obtaining a second correlation sequence by iteratively removing inter-path interference from the first correlation sequence and generating a first channel impulse response (CIR) sequence based on the second correlation sequence. And, obtaining a third correlation sequence by removing random-data interference from the second correlation sequence based on the first CIR sequence and a feedback signal and generating a second CIR sequence based on the third correlation sequence.

The invention relates to a quality evaluation model for natural ageing of redried strips and a building method thereof and belongs to the technical field of tobacco processing and quality evaluation. The building method includes: according to factors influencing quality changes during natural ageing of tobacco, determining a major quality evaluation index, performing screening by means of quantitative characteristic analysis, factor analysis, linear correlation and canonical correlation analysis to obtain quality evaluation indexes highly related to tobacco sensory evaluation quality, subjecting the quality evaluation indexes to factor analysis to obtain factor score coefficient matrixes, subjecting scores of common factors and the sensory evaluation quality to principal component regression to obtain a standard equation, substituting the score coefficient matrixes obtained by factor analysis, to the standard equation so as to obtain the equality evaluation model for the natural ageing of the redried strips. The method and the model are scientific and effective and good in stability, and index detection related is simple and fast and has small errors.

Computer simulation of the dynamics of rigid bodies interacting through collisions, stacks and joints is performed using a constraint-based system in which constraints are defined in terms of the positions of the bodies. Displacements caused by reaction forces necessary to ensure that the bodies comply with the position constraints can be calculated and can be done iteratively by updating equations defining the reaction forces and the displacements such that the computation time and memory resources required to perform the calculations is linearly dependent upon the number of bodies and the number of contacts and joints between the bodies.

The embodiment of the present invention provides a method and device of constructing a building energy consumption predication model. The method comprises the steps of obtaining an energy consumptioninfluence factor set; dividing the energy consumption influence factor set into a linear correlation influence factor set and a nonlinear correlation influence factor set; separately constructing thecorresponding Bayes network models; based on the corresponding Bayes network models, grouping into the first primary influence factors, the first secondary influence factors, the second primary influence factors and the second secondary influence factors; constructing the BP neural network training models; based on the training sample data, training the BP neural network training models separately; based on the preset test sample data, separately predicting and verifying the trained BP neural network training models, and outputting the prediction result values; if an error of the prediction result values is within a preset error range, outputting an energy consumption predication model of the linear correlation influence factors and an energy consumption predication model of the nonlinearcorrelation influence factors.

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 holding unit holds the check matrix. If s and t are natural numbers, s≧t≧2, among s columns extracted from this 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.

A monitoring device may include logic to receive arterial blood pressure data associated with a patient and receive tissue hemoglobin data associated with the patient. The logic may also calculate a linear correlation between the arterial blood pressure data and the tissue hemoglobin data. The correlation may be used to assess vascular reactivity.

The method is suitable for the technical field of computer application, and provides a cloud platform resource utilization prediction method, terminal equipment and a computer readable storage medium.The method comprises the steps of obtaining historical use data of a cloud platform, determining the historical resource usage amount and the correlation between the usage moments; according to the correlation, adjusting the weight of a preset linear regression model and the weight of a preset neural network model; filing the acquired historical use data according to the monthly maximum value; determining whether the time and the usage amount are in linear correlation or not according to the calculated linear correlation coefficient; adjusting The weight of the two prediction models accordingto related conditions, and finally, predicting the usage amount of future preset time according to the two prediction models and the adjusted weight, so that the number of values participating in prediction and the number and types of the predicted values are simplified, and the efficiency and accuracy of the cloud platform resource usage amount prediction process are improved.

The invention discloses a device for monitoring and testing the seepage speed of a porous medium structural body. The device comprises a heating system (1), a distributed optical fiber temperature sensing system (2), a porous medium structural body model slot (3) and a water outlet system (4), wherein a monitoring optical fiber is buried in the porous medium structural body model slot (3), and the heating system (1), the distributed optical fiber temperature sensing system (2) and the water outlet system (4) are connected with the porous medium structural body model slot (3) respectively. The invention further discloses a method for monitoring and testing the seepage speed of the porous medium structural body. The method breaks the mode that in a conventional method for monitoring optical fiber seepage, the motoring period is the heating temperature rise period of the whole optical fiber, the linear dependence of the seepage speed and the average heat conduction coefficient is accurately located, and monitoring accuracy is reliably guaranteed.

The invention relates to a method for judging abnormal electricity consumption behaviors of users based on an EEMD method. The method comprises the following steps of S1, carrying out cluster on electrical loads of the users by adopting a self-organizing map neural network method; S2, selecting one cluster from the clustering results of the step S1 at random and selecting any user in the cluster at random to obtain load data on any two identical time periods; S3, decomposing data in the step S2 into an IMF component and residual trend terms by adopting an empirical mode decomposition method; S4, carrying out linear correlation analysis on load residual terms of the two time periods respectively to obtain rho1 and rho2; S5, analyzing the rho1 and the rho2. The method for judging the abnormal electricity consumption behaviors of the users based on the EEMD method enables power supply enterprises to detect the abnormal behaviors in the electricity consumption process of the users, corresponding measures can be taken, and better services can be supplied to the users.