36 results about "Matrix norm" patented technology

The invention discloses a beam structure damage identification method based on modal flexibility curvature matrix norms. The beam structure damage identification method includes the steps of conducting modal testing to obtain modal flexibility matrixes before and after beam structure damage respectively; acquiring curvatures of the flexibility matrixes before and after beam structure damage, sequentially acquiring the norms of column vectors of flexibility curvature matrixes and utilizing norm differences to position structure damage; computing a beam structure joint damage degree according to relative change of the flexibility curvature matrix norms so as to obtain a unit damage degree through computation. The beam structure damage identification method is capable of positioning single-damage and multi-damage working conditions of a beam structure effectively and capable of identifying the damage degree precisely, well overcomes the defect that existing modal flexibility curvature indexes cannot be used for damage degree identification, and can be applied to nondestructive testing and damage degree evaluation of the beam structure.

The invention relates to a system for calculating power supply abundance of a urban power network, which is characterized by comprising an equivalent model fitting module, a trust domain subproblem solution module, a target function real descend value solution module, a target function searching step-length searching direction adjusting module and an alternating current power flow calculating module; the network basic data and the calculated initial value of the city electric network are input into a calculating system; in the equivalent model fitting module, the equivalent second-order model of the target function is output according to the basic data and calculated initial value; when the HESSEN matrix norm value of the equivalent second-order model is smaller than permissible error, namely the abundance value of power supply capability of the power network is output finally by the target function real descend value solution module. The system for calculating power supply abundance of the urban power network fully exerts the advantages of effectiveness and local superlinear convergence when the trust domain method is used for solving morbidity optimization problem, simultaneously the alternating current power flow algorithm reflects the influence of voltage level of the system, transmission capability of branches and reactive power flow to the power supply capability of the network precisely, and the voltage constraint of all nodes in the network can be calculated.

The invention discloses a time delay power system electromechanical oscillation mode computing method based on the SOD-PS-R (Solution Operator Discretization-Pesudo Spectrum and Rotation) algorithm. According to the relation between an eigenvalue of a time delay power system model and an operator eigenvalue of the time delay power system model, computing the eigenvalue of the time delay power system model is converted into computing the operator eigenvalue, so that a problem about computing an electromechanical oscillation mode of a time delay power system is converted into a problem about computing the operator eigenvalue; the method is used for the electromechanical oscillation mode of a large-scale time delay power system. The SOD-PS-R requires computing the maximal setting number eigenvalue of an operator discretization approximate matrix norm only, therefore one computing is enough for obtaining the electromechanical oscillation mode of the large-scale time delay power system.

The invention discloses a dam break grading early-warning index extraction method for a homogeneous earth dam. The method comprises the steps that a sliding window method is used for dividing a value measuring sequence, a earth dam monitoring physical quantity sliding window matrix is extracted based on principal component analysis (PCA) or kernel principal component analysis algorithm (KPCA), and the evolution of the principal component number/principal component matrix norm with earth dam conditions on the basis of 85% contribution rate is determined. Actual dam break tests indicate that the enlargement and the sudden changes of the two indexes correspond to the condition deterioration and the ultimate break of an earth dam respectively, and it is full demonstrated that the principal component number or the principal component matrix norm can be used as the first-grade early-warning index and the second-grade early-warning index for the safety condition of the earth dam; mutual checking can be conducted between the principal component number and the principal component matrix norm, wherein the first-grade early-warning index corresponds to the earth dam condition deterioration, and the second-grade early-warning index corresponds to the earth dam break.

A convex regularized loss function is minimized respective to a prediction tensor of order K to generate an optimized prediction tensor of order K where K>2. The convex regularized loss function comprises a linear combination of (i) a loss function comparing the prediction tensor and an observation tensor of order K representing a set of observations and (ii) a regularization parameter including a K-th order matrix norm decomposition of the tensor trace norm of the prediction tensor. In some such embodiments, the observation tensor of order K represents a set of social network observations and includes at least dimensions corresponding to (1) users, (2) items, and (3) tags. The optimized prediction tensor of order K is suitably used to perform inference operations.

The invention provides a stratospheric large-scale MIMO user grouping and precoding method and system, and the method comprises the steps: an optimization problem design step: designing an optimization problem according to an outer-layer precoding matrix and an inner-layer precoding matrix on the basis that it is assumed that multiple users of a stratospheric large-scale MIMO communication systemare grouped; Optimization problem simplification step: carrying out optimization problem simplification; According to the method, a matrix norm inequality and a zero space criterion are adopted, an obtained optimization problem is simplified, the power of a signal is explored to be mainly concentrated on a statistical intrinsic mode StacticalEgigades of a channel, and the statistical intrinsic mode StacalEgigades are obtained. In the invention, the CSI of the effective channel for designing the inner-layer precoding matrix is also effectively reduced, so that the grouping scheme and the precoding scheme can effectively explore the key technology of the stratospheric large-scale MIMO communication system.

The invention relates to a road surface grade recognition system and method based on the suspension dynamic stroke, and belongs to the technical field of road surface recognition in vehicle engineering. The road surface grade recognition system is composed of a signal acquisition module, a signal pre-processing module, a computational processing module, a characteristic matrix establishment module, a judgment matrix establishment module and a road surface grade recognition module; vehicle speed signals and all suspension dynamic stroke signals obtained through sampling at equal distances are subjected to wavelet denoising and signal intercepting, estimation values of all suspension road surface unevenness coefficients under the given distance are calculated, and thus a characteristic matrix is established; and the road surface grade under the given distance is recognized by comparing the similarity between the characteristic matrix and road surface judgement matrixes of different grades. Calculation of the matrixes and calculation of matrix norms are introduced, and the grade of the ISO road surface within the given distance can be recognized effectively, accurately and easily while a novel technology is provided for road surface grade recognition.

As presswork is applied in real life most widely, the anti-counterfeiting protection is increasingly demanded, but the printing-scanning process is a main obstacle in anti-counterfeiting protection of the presswork. The existing anti-counterfeiting digital watermarking algorithm is used to insert digital watermarks in original images, if the insertion strength of the watermark can not be well controlled, the further use of digital images can be seriously affected, and the anti-counterfeiting digital watermarking algorithm further lacks of research on the capability of resisting the secondary printing-scanning process. The invention provides a printing anti-counterfeiting watermarking algorithm based on norm and norm mean comparison, which comprises the steps of: carrying out the discrete wavelet transform to original images to divide a wavelet low-frequency appropriator subband into subblocks which are not overlapped with each other, carrying out singular value decomposition to reach subblock, and judging the relation between the singular value matrix norm of each subblock and the mean value of the singular value matrix norms of all subblocks to generate a zero watermark sequence. Experimental results show that the invention can resist the attacks of primary or secondary printing-scanning.

The invention discloses an underwater sound source matching field positioning method based on diffusion mapping, and the method comprises the steps: (1) selecting distance and depth grids, and calculating a copy field vector and a normalized copy field vector matrix at each grid through a sound field model; (2) calculating a covariance matrix and carrying out matrix norm normalization; (3) constructing the copy field vector matrix and the covariance matrix into a matrix set, and calculating an index Riemannian distance between every two matrixes in the set; (4) constructing a diffusion kernel matrix according to the distance matrix, performing eigendecomposition, and constructing a diffusion mapping graph according to eigenvalues and eigenvectors; and (5) calculating the distance between the covariance matrix on the diffusion map and different copy field vector matrixes, wherein the position corresponding to the shortest distance is the sound source position. Compared with a conventional matching field algorithm, passive positioning can be effectively performed on a target sound source in a low signal-to-noise ratio environment; the side lobe level can be effectively reduced, and the main lobe resolution is improved.

The invention discloses a method for analyzing a skiing motion sequence based on hidden Markov, and belongs to the field of motion attitude data processing and analysis. The implementation method comprises the following steps: windowing original attitude data of a skier acquired by a sensor, dividing the original attitude data into data frames on a time sequence, and extracting a'symbolic value 'representing an attitude matrix by utilizing SVD and a matrix norm; building a probability transfer model of the skiing motion sequence based on hidden Markov, optimizing the probability transfer modelparameters of the skiing motion sequence by using the'symbolic value 'sequence on the time sequence, and calculating the optimal path of the hidden state of the skiing motion posture sequence. The probability of each skiing state at the next moment is calculated through the optimal hidden state path and the probability transfer model, then the skiing state at the next moment is predicted, the motion state of a skier can be evaluated according to the motion posture prediction data of skiing of the skier, and then the training effect on a skiing trainer is improved.

The invention belongs to the technical field of a radar tracking system and a similar system, and discloses a plot clustering method based on a fuzzy-C means. The method includes steps: grouping measuring values; removing clutters; estimating the numbers of targets in groups; selecting an initial center; calculating a grade of membership matrix Ut; performing defuzzification; estimating the integrity of a cluster; updating a clustering matrix Ut+1 and a center vi of the cluster; comparing Ut and Ut+1 by employing a matrix norm; if a relation of Ut+1 and Ut shown in the description is less thanor equal to epsilon, stopping the process; otherwise, enabling t=t+1 for a new round of updating; and finally defuzzifying the measuring values according to the clustering matrix. According to the method, the prediction positioning and the measuring rate are considered to find the initial target center; the clustering integrity is considered in an iteration process of an FCM algorithm; and compared with the conventional method, the robustness and the validity are better, the method can be used for correct clustering of multiple maneuvering targets detected by radar, and the targets can be better tracked.

The invention discloses a rapid MUSIC spectrum decomposition method, device and equipment for a large-scale antenna, and the method comprises the steps: receiving a signal X, and estimating a high-dimensional autocorrelation matrix R according to the signal X; performing skeleton extraction on the high-dimensional autocorrelation matrix R to obtain a low-dimensional representation matrix C; calculating to obtain a low-rank matrix Y and obtaining a low-dimensional approximate decomposition CY of the high-dimensional autocorrelation matrix R; obtaining an SVD approximate decomposition (shown inthe description) of the high-dimensional autocorrelation matrix R is obtained by conducting SVD decomposition on the low-dimensional approximate decomposition CY; constructing a signal space K by means of the SVD approximate decomposition (shown in the description) of the high-dimensional autocorrelation matrix R, estimating a spatial spectrum P (theta) by means of the signal space K, and conducting target signal detection and estimation according to the spatial spectrum P (theta). According to the invention, while estimating the MUSIC spatial spectrum accurately, the calculation complexity ofSVD is reduced from cubic growth to square and even linear growth rate, high-precision and low-complexity MUSIC spatial spectrum estimation is realized, matrix norm minimization is used as an optimization criterion, and the approximate error precision of a high-dimensional autocorrelation matrix is ensured.

The invention discloses a transmitting and receiving robust design method used for extended target detection. An interference form of an unrelated target echo is constructed; interference informationis assumed to be uncertain; and a radar transmitting and receiving robust design method for resisting uncertain interference is proposed, so that the adaptability of a system to a complicated environment is enhanced and the detection performance of a radar under strong unrelated target echo interference is improved; an efficient sequence iterative optimization algorithm is proposed; two max-min robust problems for solving s and w respectively are involved in each iterative step; based on a calculation property of a consistent matrix norm, the uncertainty of the robust problems can be analyzed;the robust problem is converted into a conventional maximization problem; efficient solving is performed by virtue of MVDR and Dinkelbach methods; a quick and remarkable optimization effect can be achieved; and real-time adaptive robust design becomes possible.

The invention discloses a vehicle optimal torque allocation algorithm objective function building method and belongs to the field of new energy automobile control. To achieve the purpose that the vehicle torque is allocated more comprehensively and more systematically, a longitudinal motion equation along the x axis and a yawing motion equation around the mass center of an automobile are built, the two equations are written in a matrix form, the matrix norm form of the objective function is built, and the effects that the FEID-EV dynamic property is guaranteed, meanwhile, the stability under steering conditions is improved, and the probability of traffic accidents is lowered are achieved.

The invention relates to a power distribution network fault analyzing method and device based on tidal current distribution characteristics, and belongs to the field of power distribution network fault analysis. According to the method, historical fault section data of a power distribution network is intercepted, tidal current distribution in case of a fault of the power distribution network is described through computing a sensitivity matrix norm by adopting a generalized sensitivity analytical method, faults are classified by utilizing an automatic dynamic self-adapting clustering method, and a fault pattern base is established; network topology of a current fault is dynamically analyzed, and a to-be-measured branch circuit in the current fault power distribution network is confirmed; and the fault is diagnosed in an online manner. The method disclosed by the invention mainly aims to extract the network topology and the physical characteristics at the moment of the fault and extract corresponding numerical characteristics so as to perform accurate diagnosis on the fault. As the fault pattern base is directly established, alteration and deletion of intermediate regulations are avoided, simulation of functional relation between fault information and fault elements is not needed, the problem brought by a nonlinear system is solved, and the power distribution network fault analyzing method and the power distribution network fault analyzing device are suitable for online/offline fault diagnosis of any linear/nonlinear system.