82 results about "Augmented matrix" patented technology

The invention discloses a fault moment determining and fault region location method based on a random matrix theory, and the method comprises the steps: obtaining the PMU data and signal to noise ratio of each node in a power system in a time period T, obtaining an original data matrix according to the PMU data of each node, carrying out the standardization processing of the original data matrix,and then obtaining the mean spectral radius at each moment of the time period T through a monocyclic theorem; obtaining an augmented matrix and a reference augmented matrix of each node according to the signal to noise ratio and the original data matrix, obtaining the mean spectral radius difference and mean spectral radius integral of the augmented matrix and the reference augmented matrix of each node in the time period T through the monocyclic theorem, wherein the moment when the mean spectral radius at each moment of the time period T is less than a normal operation value of the mean spectral radius is determined as a fault moment, and a node with the largest mean spectral radius in the nodes with the difference of the mean spectral radiuses of all nodes in the time period T being greater than a critical value is determined as a fault region. The method cannot be affected by bad data.

The invention provides a machine learning method and a machine learning device. The machine learning method includes the steps: constructing an original input matrix and an original output matrix according to an acquired training sample set; constructing a mapping feature node matrix based on the original input matrix by using a first random weight and a first random number; constructing an enhanced node matrix based on the mapping feature node matrix by using a second random weight and a second random number; and according to an augmented matrix composed of the mapping feature node matrix andthe enhanced node matrix and the original output matrix, determining to connect the weight matrix. In the machine learning method and the machine learning device of the present invention, not only the factor of direct connection between the input layer and the output layer in the neural network is added, but also more factors of the hidden layer between the input layer and the output layer is added, so that the machine learning method and the machine learning device have the advantages of satisfying the computational efficiency and guaranteeing the learning accuracy of a wide learning method.

Banding artifacts in steady state free precession (SSFP) magnetic resonance imaging (MRI) are reduced by acquiring and combining multiple SSFP images in an augmented matrix where an acquisition vector in k-space is equal to a Fourier matrix of the trajectory on a known distortion operator for each trajectory in k-space.

The invention discloses an MIMO detecting preprocessing device and method. The device comprises a channel transfer matrix input cache module, an MMSE-SQRD preprocessing core module, an upper triangular matrix R cache module, a sorting information cache module, a unitary matrix Q cache module, a product computing module and a product computing result cache module. According to the MIMO detecting preprocessing device and method, QR decomposition of an augmented matrix of a channel transfer matrix received by an MIMO receiving end is achieved, sorting operation is added in the matrix QR decomposition process, a unitary matrix and an upper triangular matrix after QR decomposition are obtained, the sorting information vectors of the matrixes are obtained, the device and method are suitable for fields such as MIMO detecting, and the obtained unitary matrix and the obtained upper triangular matrix after QR decomposition can be effectively reused in next-level MMSE and K-Best detecting.

The invention provides a minimum miscible phase pressure computing method of low-permeability reservoir CO2 flooding. The minimum miscible phase pressure computing method of low-permeability reservoirCO2 flooding comprises the following steps that minimum miscible phase pressure and underlying parameters of four or more samples are obtained by adopting a slim-tube displacement experimental method; the underlying parameters of the four or more samples are substituted through a theoretical formula method to calculate and obtain the minimum miscible phase pressure ; the minimum miscible phase pressure error of a same sample is obtained by comparing the slim-tube displacement experimental method and the theoretical formula method, and empirical constants are obtained by substituting the underlying parameters of four selected samples into a logarithm formula and establishing an augmented matrix; and a computational formula model is obtained by fitting the minimum miscible phase pressure ina low permeability reservoir and the underlying parameters. According to the minimum miscible phase pressure computing method of the low-permeability reservoir CO2 flooding, the minimum miscible pressure determined by the slim-tube displacement experimental method is more accurate, the theoretical formula method is more convenient and fast, and the cost is lower; at the same time, calculation requirements of the low-permeability reservoir in the Northern Jiangsu Basin can be met, a new derivation computational formula is more accurate in computational accuracy, the theoretical basis is higher, the labor intensity is low, and the minimum miscible pressure computing method of the low-permeability reservoir CO2 flooding is suitable for the use of low-permeability reservoir in the Northern Jiangsu Basin.

The invention discloses a batch process weak fault monitoring model establishment method based on tendency turning points, comprising the following steps: 1, acquiring I batches of normal operating data; 2, sectioning the batch process; 3, extracting turning points by adopting a method based on the tendency turning point, and ranking the turning points according to the orthogonal distance; 4, checking the turning points extracted with variables among the batches; 5, aggregating the tendency turning points of all variables in the batches and verifying the truth or falsity; 6, constructing a time sequence augmented matrix by using a time lag expanded analysis data array, and performing PCA (Principal Component Analysis) on the time sequence augmented matrix; 7, clustering all established models according to the similarity of the load direction at each phase; and 8, calculating T2 and SPE (Synergistic Processing Element) statistics and a control limit of each type. The invention further provides a batch process weak fault monitoring method based on tendency turning points.

The invention discloses a uniform channel decomposition method for a vertical layered space-time coding communication system which mainly solves the problem that the traditional uniform channel decomposition method is greatly influenced by channel errors. The decomposition method is as follows: (1) a transmitting terminal carries out singular value decomposition on an obtained channel matrix*with errors; (2) the transmitting terminal and a receiving terminal respectively build an augmented matrix J by utilizing a unitary matrix*decomposed by the singular value; (3) the transmitting terminal and the receiving terminal respectively carry out geometric mean decomposition on the augmented matrix J; (4) the transmitting terminal designs a pretreatment matrix F according to a diagonal matrix*obtained according to the singular value decomposition and a unitary matrix P<J>, and the pretreatment matrix pretreats transmitting signals; (5) the receiving end obtains column vectors of an after-treatment matrix W according to results decomposed the geometric mean; (6) the receiving end decodes vectors y of the receiving signals by adopting a successive interference cancellation method. The method has the advantages of low bit error rate and high system volume and is used for the design of a transceiver of a multi-input multi-output communication system.

The invention discloses a method for diagnosing a metering fault of an electric energy meter in a limited range based on high-frequency current sampling, relates to the technical field of electric energy metering, and solves the problem that the electric energy meter error estimation precision or hit rate is seriously limited by loss estimation precision in the prior art. According to the technical scheme, the method comprises the following steps of: collecting current instantaneous values of a general meter and branch meters; obtaining a total sample space after multiple times of acquisition; establishing a coefficient matrix and an augmented matrix, and judging whether the ranks of the coefficient matrix and the augmented matrix are equal or not; if the collected data is invalid, collecting at least one group of data again, and discharging the same group number of data to obtain a new overall sample space until the ranks of the coefficient matrix and the augmented matrix are equal; and establishing an error calculation model, solving the error calculation model, and comparing the result with a precision grade threshold of the electric energy meter to obtain a fault diagnosis result. Accurate calculation of the error of an electric energy meter in operation can be realized, the electric energy meter error estimation precision and hit rate can be effectively improved, and human factor interference can be effectively eliminated.