27 results about "Orthogonal basis" patented technology

The invention belongs to the field of magnetic target body positioning, and relates to a measuring line vertical gradient-based magnetic multi-target positioning and magnetic moment inversion method, which comprises the following steps of: obtaining a unit orthogonal primary function and a primary function coefficient corresponding to each component of magnetic anomaly gradient data vertical to a measuring line, and calculating an energy curved surface of the magnetic anomaly gradient data vertical to the measuring line; detecting a local energy peak value of the energy curved surface, obtaining an energy peak value ratio sequence, obtaining a target number and horizontal position estimation corresponding to the target according to an energy sudden change value in the energy ratio sequence, and calculating a target depth corresponding to a measuring line vertical magnetic anomaly gradient energy extreme value by using a dichotomy method, and constructing a linear equation set about the target magnetic moment by using the measuring line vertical magnetic anomaly gradient data and the unit orthogonal basis function, and obtaining target magnetic moment parameter estimation through a least square method. According to the invention, the anisotropy difference of the estimation precision of the horizontal position of the target is effectively reduced, and the problem of target detection omission caused by large difference between the target burial depth and the magnetic moment module value in multi-target positioning is improved.

In order to overcome the defects of an existing low-rank model in non-stationary trend, pseudo-periodic mode and transient anomaly processing, the invention provides a time series prediction method based on self-adaptive low-rank representation, original time series data is decomposed into a low-rank component (L) and a sparse component (S) through robust principal component tracking (PCP) and principal component analysis (PCA) algorithms, and the low-rank component (L) and the sparse component (S) are subjected to low-rank prediction. And adaptive separation of a trend-periodic component and a residual (abnormal / transient) component is realized. According to the method, an orthogonal transformation matrix (A) is constructed, a data-driven orthogonal basis (B) and a Fourier orthogonal basis (UF, VF) are fused in the matrix, and time sequence data are mapped to a low-rank potential space with higher characterization capacity. The method adopts a convolution kernel norm minimization (CNNM) frame for prediction, a learnable dynamic search window is established under the frame, the size (wx, n) of the window is adaptively adjusted according to a sequence local feature (| un |), and through fusion of fast Fourier transform (FFT) decomposition and introduced motion vector information, the motion vector information of the motion vector is obtained. And accurate acquisition of multi-scale time sequence characteristics (such as periodic modes of different frequencies) is realized. Under the low-rank constraint, the method provided by the invention can effectively process the complex dynamic characteristics of high-dimensional sensing data, and meanwhile, the prediction precision of a non-stationary sequence is remarkably improved through a parameter adaptive mechanism.

The application discloses a material deformation displacement calculation method and a calculation system. The method comprises the following steps: in response to obtained design parameters, a corresponding structure and a geometric model are constructed, and a stiffness matrix K and a load matrix F are generated; the stiffness matrix K is stored in a sparse form by row compression, the load matrix F is input, and a Krylov subspace dimension m, an initial value U0 and an allowable error ε are selected; a set of standard orthogonal bases of the Krylov subspace is constructed; the best approximation U of the displacement in the Krylov subspace is calculated m ; the residual ||F-KU m || is calculated, and if the residual is less than the allowable error ε, the best approximation U m is output as the material deformation displacement; if the residual is greater than the allowable error ε, the best approximation U m is taken as a new initial value, the Krylov subspace is updated, and the material deformation displacement is recalculated. The application provides a stiffness calculation method which can be flexibly deployed and efficiently parallel, and realizes acceleration of finite element analysis.

The invention discloses a thermal-mechanical coupling field state inversion method and device for a compressor hot charging process, and relates to the technical field of intelligent manufacturing and digital twinning, and the method comprises the steps: carrying out the offline modeling based on historical temperature data and historical displacement data of a key node of a compressor impeller, obtaining a reduced-order model and a physical and data hybrid evolution model which meet preset conditions; performing order reduction processing on the observation state of the previous moment by adopting an order reduction model to obtain an order reduction state and an order reduction orthogonal basis matrix; calculating by adopting a physical and data hybrid evolution model based on the reduced-order state to obtain an estimated state at the current moment; based on the estimation state and current observation data obtained in real time, an extended Kalman filtering method is adopted for calculation, and a target estimation state is obtained; and performing inversion by using a preset projection function based on the target estimation state and the reduced-order orthogonal basis matrix to obtain an inversion result. According to the method, the accuracy of an inversion result is improved, and the assembly quality of high-end equipment can be improved.

The invention belongs to the technical field of electric power system time delay analysis, and relates to a Lagguerre-Taylor hybrid order reduction method for a multi-time-delay electric power system, which comprises the following steps of: S1, modeling the multi-time-delay electric power system and defining an order reduction problem; s2, a projection matrix is constructed, wherein a Laguerre function and Taylor are expanded and mixed; the method specifically comprises the steps that a projection matrix V1 is constructed based on scale Laguerre function expansion, a projection matrix V2 is constructed based on Taylor expansion, and a high-order block Krylov subspace orthogonal basis is generated based on a high-order block Arnoldi execution process; s3, constructing a Lagguerre-Taylor hybrid projection method of the multi-time-delay power system, and constructing an order reduction system; s4, analyzing the stability of the reduced-order system; according to the method, the reduced-order framework based on the projection matrix is constructed, the time-delay term algebraic structure of an original system is completely reserved, and the defect that the time-delay structure is damaged in an existing method is overcome.

The invention discloses an aircraft numerical simulation method based on an improved GMR-WENO algorithm and a medium, and relates to the field of aircraft performance tests.The method comprises the steps that a three-dimensional calculation area is determined, and tetrahedral mesh generation is carried out; setting fluid physical parameters of each grid at an initial moment; an improved GMR-WENO algorithm is adopted to carry out high-order reconstruction on fluid physical parameters; in the improved GMR-WENO algorithm, a real quadratic algebraic polynomial is constructed by applying an orthogonal basis function, and a virtual first-order polynomial and a virtual zero-order polynomial are constructed by applying an L2 projection technology; determining fluid physical parameters at the final moment based on the third-order reconstruction polynomial; and determining the aerodynamic performance of the aircraft according to the fluid physical parameters at the final moment. According to the method, the orthogonal basis function and the L2 projection technology are combined into the GMR-WENO method to simulate the distribution condition of the aerodynamic characteristics of the aircraft, and the efficiency and accuracy of performance testing of the aircraft are improved.

The invention discloses an AUV (Autonomous Underwater Vehicle) velocity field reduction method based on intrinsic orthogonal decomposition and neural network fusion, which comprises the following steps: constructing a velocity field snapshot matrix of an AUV, each velocity field snapshot being a multi-dimensional velocity field obtained by training sample simulation; preprocessing the speed field snapshot matrix, and then performing matrix decomposition by using a POD method to obtain a standard orthogonal basis matrix and a diagonal matrix; setting a truncation coefficient, determining the number of singular values needing to be reserved from the diagonal matrix by using the truncation coefficient, performing dimensionality reduction on the standard orthogonal basis matrix by taking the number as a dimensionality reduction order, and determining an order reduction coefficient matrix based on the standard orthogonal basis matrix after dimensionality reduction; constructing a working condition matrix, wherein the working condition matrix comprises working conditions of different dimensions; and fitting a mapping relation between the working condition matrix and the reduced-order coefficient transpose matrix by using a neural network so as to predict the velocity field under the to-be-predicted working condition.

The invention relates to a large-scale power system discrete eigenvalue parallel computing method based on surrounding channel integration. Comprising the following steps: performing contour integral spectrum transformation on a system state matrix, and determining characteristic value dominance; differentiated integral curves are designed based on characteristic value dominance, the number of characteristic values in each integral curve is estimated, and the initial dimension of the corresponding characteristic subspace is determined; determining the number of initial integral points of each integral curve by adopting a self-adaptive method, and performing self-adaptive integral point configuration on each integral curve; determining an initial subspace dimension of each block based on the number of feature values and the number of parallel integral blocks; performing singular value decomposition on a block basis matrix to construct a standard orthogonal basis; combining the orthogonal basis of each block to form a global orthogonal subspace basis; performing Rayleigh-Ritz projection in a global space to solve a dimension-reduced generalized feature value; and outputting the discrete eigenvalue and the eigenvector. And accurate and efficient calculation of discrete characteristic values of a large-scale power system is realized.

The invention discloses a concrete penetration depth prediction method, device, equipment and medium, and relates to the technical field of concrete anti-penetration performance analysis, and the technical scheme is as follows: constructing a state vector according to a physical variable set generated in a rigid elastic body penetration concrete process; constructing a dimension matrix according to the state vector, and performing singular value decomposition on the dimension matrix to obtain a null space orthogonal base; performing linear mapping on the null space orthogonal base through a pre-trained learnable parameter matrix to obtain a feature index matrix; inputting the logarithm mapping result of the state vector and the feature index matrix into a neural network for forward propagation calculation, and outputting dimensionless features; inputting the dimensionless features into a multi-objective optimization algorithm, searching in an operator space of the multi-objective optimization algorithm, and outputting an analytic formula of the dimensionless penetration depth when the Pareto front of formula complexity and fitting loss reaches an optimal solution; and predicting the penetration depth of the concrete based on the analytical formula.

The application discloses a fast calculation method and device for scattered electromagnetic field based on random matrix approximation, and belongs to the field of fast modeling of scattered field. The method comprises the following steps: precalculating an incident field vector and a reciprocal incident field vector; generating a Gaussian random matrix matched with the column number of a to-be-determined scattered response matrix; performing linear superposition on the incident field vector to obtain a synthetic incident field vector, and constructing a random projection matrix according to the scattered field vector of an equivalent current vector at a receiving point; performing linear superposition on the reciprocal incident field vector to obtain a reciprocal synthetic incident field vector, and constructing a projection coefficient matrix according to the response of a reciprocal equivalent current vector at a transmitting source; and calculating a complete scattered response matrix based on the product of an orthogonal basis matrix and the projection coefficient matrix. In the application, a large-scale geophysical electromagnetic complex scene can be adapted, higher calculation precision and stable convergence are achieved, and the reconstruction of the complete scattered response matrix can be completed at a lower relative error level.

The present invention belongs to the field of magnetic exploration and is a three-dimensional positioning and magnetic moment direction evaluation method based on magnetic anomaly vectors. The method comprises the following steps: obtaining uniformly distributed two-dimensional magnetic vector data; performing convolution on the two-dimensional magnetic vector data using a two-dimensional orthogonal basis function of magnetic vectors with a preset height and truncated by a window to obtain all orthogonal basis coefficients of the two-dimensional orthogonal basis of the magnetic vectors; calculating the sum of all orthogonal basis coefficients of the two-dimensional orthogonal basis of the magnetic vectors and the sum of the squares of all orthogonal basis coefficients to obtain energy data of pseudo-l1 norms and pseudo-l2 norms of each magnetic anomaly component; fusing the energy data to obtain inversion energy and total energy; performing two-dimensional constant false alarm rate detection on the total energy to obtain the number of targets and the horizontal position of each target; performing parameter inversion of the vertical position and magnetic moment direction; and after evaluating the vertical position and magnetic moment direction of all targets, avoiding the situation where targets with small energy peak values ​​are missed due to large differences between energy peaks.

This application discloses a method and apparatus for thermo-coupling field state inversion in compressor hot-assembly processes, relating to the fields of intelligent manufacturing and digital twin technology. The method includes: offline modeling based on historical temperature and displacement data of key nodes of the compressor impeller to obtain a reduced-order model and a physics-data hybrid evolution model that meet preset conditions; using the reduced-order model to reduce the order of the observed state at the previous moment, obtaining a reduced-order state and a reduced-order orthogonal basis matrix; calculating the estimated state at the current moment using the physics-data hybrid evolution model based on the reduced-order state; calculating the target estimated state using the extended Kalman filter method based on the estimated state and the real-time acquired current observation data; and performing inversion using a preset projection function based on the target estimated state and the reduced-order orthogonal basis matrix to obtain the inversion result. This method improves the accuracy of the inversion results and can improve the assembly quality of high-end equipment.

The present invention relates to the field of gradient coil manufacturing technology, and in particular to a design method for an insertable gradient coil based on an intrinsic basis of a curved surface. The method comprises: establishing an optimization model; constructing an objective function and performing sensitivity analysis and derivation; obtaining a set of linear equations related to the coefficients corresponding to the intrinsic basis through sensitivity derivation; obtaining the basis coefficients corresponding to the optimal stream function solution by solving the set of linear equations; defining the stream function through the intrinsic basis, obtaining the final stream function optimization result, and obtaining the gradient coil configuration. The advantages are: using a smooth intrinsic orthogonal basis to define the stream function; when applying this method to coil design, the resulting coil has good smoothness, avoiding the additional discussion of the coil smoothness problem in traditional methods; and having good application effects on both traditional cylindrical surfaces and complex general surfaces.

The invention discloses a broadband uniaxial anisotropic complete matching layer absorption boundary method, and belongs to the field of computational electromagnetism. The method comprises the following steps: initializing electromagnetic field component coefficients (Ex, Ey, Hz), an excitation source and W-UPML parameters; constructing a matrix equation through an AH domain differential operator to solve a magnetic field component coefficient, and converting an AH domain field value into a time domain field value in combination with a domain inverse transformation formula; and high-efficiency calculation and boundary absorption of a broadband electromagnetic field are realized by utilizing a Hermite orthogonal basis function and an attenuation factor b. Compared with a traditional method, the time step size stability limitation is avoided through AH domain transformation, and the calculation efficiency is remarkably improved; the low-frequency wave absorption performance is enhanced by the introduced attenuation factor, and the reflection error is reduced by 20dB compared with that of the traditional UPML. The method is suitable for the fields of electromagnetic simulation, seismic wave simulation and the like, and effective technical support is provided for high-precision and broadband electromagnetic field analysis.

The present application relates to data analysis and processing technical field, and further relates to natural resource asset big data analysis and accounting method, the method comprises the following steps: step 1: obtaining the physical inventory of different resource types in each period, obtaining the loss amount of different resource loss types in each period;Step 2: the normalized matrix of each period is calculated according to the half-life weight resource side covariance matrix and loss side covariance matrix, and the resource side covariance matrix and loss side covariance matrix are respectively decomposed, and the orthogonal basis vector with the largest contribution to the overall variation is extracted;Step 3: according to the orthogonal basis vector and the smoothed singular spectrum, the resource contribution and liability cost are linearly combined to obtain the principal component difference after removing the stray noise.The present application can effectively suppress data jump, improve the continuity, accuracy and interpretability of the accounting result.

The application relates to the technical field of electric propulsion digital twin simulation, and discloses an electric propulsion digital twin simulation data reduction method, which comprises the following steps: acquiring simulation data of electric propulsion plasma; based on the simulation data, snapshot data is extracted in a grid unit of a calculation domain; singular value decomposition is performed on the snapshot data, a reduced orthogonal basis is established, and reduced data representation is obtained through projection; based on a plasma fluid control equation, a semi-discrete ordinary differential algebraic equation group is constructed, and a coefficient matrix of a reduced model is determined through least square fitting; real-time simulation of an electric propulsion digital twin system is performed by using the reduced model, and field data is output. According to the application, the calculation precision and the simulation result obtained by PIC / MCC simulation are in the same order of magnitude, the calculation time reaches the level of seconds, and the real-time simulation demand can be met.

The invention discloses a forest fire detection incremental learning method and system based on geometric path regularization, and the method comprises the steps: building a forest fire data set, and constructing a forest fire detection reference model; in the incremental training stage, aiming at newly-collected fire missing report or false report data, while standard task loss is calculated, a geometric path regularization loss item is introduced, and the regularization loss is specifically as follows: a subspace orthogonal basis of a reference model and a current model weight matrix is extracted by adopting QR decomposition, a transformation matrix connecting a new orthogonal basis and an old orthogonal basis is constructed, and a geometric path regularization loss item is introduced; the rotation amount of the subspace is approximately calculated through efficient Lie algebra projection operation, and the Floribenius norm of the rotation amount is used as regularization loss. According to the method, unnecessary geometric rotation of the model weight subspace in the updating process is directly constrained, new knowledge can be effectively learned, and meanwhile, the recognition capability on an old scene is stably kept, so that model performance degradation is prevented, and continuous and stable performance improvement of a detection model is realized.

The invention particularly relates to a B spline curve offset-selfing elimination method based on Legendre polynomial expansion. The method comprises the following steps of: obtaining a Legendre coefficient auxiliary matrix on the basis of a Legendre polynomial value and a Gauss-Legendre quadrature node weight; the method comprises the following steps: mapping a Gauss-Legendre quadrature node to a non-zero length node interval to obtain a corresponding normal direction; acquiring a corresponding offset point according to the normal direction and the specified distance; based on the Legendre coefficient auxiliary matrix and the offset point, obtaining a Legendre coefficient; compared with a Taylor expansion method, the Legendre orthogonal basis expansion method has the advantages that the approximation convergence rate is better; and the error can be further controlled through calculation of the truncation error of the Legendre coefficient and subsequent judgment and processing. The Legendre coefficient auxiliary matrix and the Legendre-Bezier transformation matrix are pre-calculated static data, so that global repeated calling for multiple times can be realized, and the approximation efficiency is improved. And when the tangent intersection detection triggered by the adjoint matrix eigenvalue method fails, returning is executed, and real intersection and tangent intersection are distinguished based on a GCD decomposition method, so that the selfing detection efficiency is further improved.

The invention discloses a device thermal management-oriented simulation model order reduction method and system, and belongs to the technical field of numerical calculation and simulation analysis. The method aims at solving the problems that an existing finite element thermal simulation full-order system is large in calculation amount and difficult to process multiple heat sources efficiently, and a projection method is high in memory consumption. The method comprises the following steps: constructing a full-order heat conduction first state equation containing an input matrix representing multiple heat sources or multiple boundary conditions; performing iteration processing on the input matrix by applying a block second-order Arnoldi algorithm to generate an orthogonal basis matrix; reducing the order of the first state equation by using the orthogonal basis matrix and adopting a Galerkin projection method, and constructing a second state equation; and finally, carrying out time domain iteration solution and reduction on the reduced-order equation to obtain a full-order temperature field response result. According to the method, the dynamic characteristics under multiple heat sources are effectively captured by adopting the block second-order Arnoldi algorithm, the memory occupation is remarkably reduced by adopting Galerkin projection, and the simulation time is greatly shortened while high precision is ensured.

The invention discloses a self-adaptive model order reduction method and device, computer equipment and a storage medium. The method comprises the steps that initialization parameters are acquired; performing loop iteration until the actual moment matching order reaches the maximum total moment matching order; calculating residual errors of all moment matching points, identifying the moment matching point with the maximum residual error and recording the maximum residual error; residual errors of all the test points are calculated, the test point with the maximum residual error is recognized, and the maximum residual error is recorded; carrying out convergence check on the maximum value between the maximum residual error of the moment matching point and the maximum residual error of the test point, and if the maximum value is smaller than the tolerance, jumping out of circulation; and dynamically selecting to execute an order-increasing or point-increasing strategy based on the balance parameter: constructing an order-reducing model based on the finally obtained standard orthogonal basis matrix. The method aims at automatically solving the problem of balance between precision and speed in broadband simulation, the high-precision electromagnetic system reduced-order model can be obtained at the calculation cost as small as possible, the calculation efficiency is remarkably improved, and resource consumption is reduced.

The embodiment of the invention provides a matrix decomposition method and device based on a mercuric chloride chip, equipment and a storage medium. The method comprises the following steps: acquiring a to-be-decomposed original matrix; aiming at each column vector of the original matrix, performing projection processing on each column vector to obtain a middle orthogonal basis vector; performing iterative projection processing on the intermediate orthogonal basis vector to obtain a next intermediate orthogonal basis vector until the number of iterative projection times reaches a preset number of projection iterations, taking the next intermediate orthogonal basis vector obtained by the last iteration as an orthogonal basis vector of the corresponding column vector, and generating a corresponding coefficient vector according to the orthogonal basis vector; sequentially constructing an orthogonal matrix based on a plurality of orthogonal basis vectors corresponding to a plurality of column vectors contained in the original matrix, and constructing a coefficient matrix based on a plurality of corresponding coefficient vectors; and decomposing the original matrix based on the orthogonal matrix and the coefficient matrix to obtain a decomposition result corresponding to the original matrix. Therefore, the accuracy of matrix decomposition based on the mercuric chloride chip can be improved.

The invention provides an irregular time sequence prediction method and device, a medium and a program product, and the method comprises the steps: obtaining a to-be-predicted irregular time sequence, the irregular time sequence comprising time stamps with non-uniform time intervals and corresponding observation values; expanding the data on an orthogonal basis function set to respectively obtain a corresponding trend coefficient vector and a periodic coefficient vector, the orthogonal basis function set comprising a trend basis function set and a periodic basis function set; performing fusion modeling processing on the trend coefficient vector and the period coefficient vector to extract a depth feature vector reflecting a trend and period coupling relationship; and based on the depth feature vector, predicting a trend coefficient and a period coefficient corresponding to the target time period, and according to the trend coefficient and the period coefficient, combining a corresponding trend basis function set and a period basis function set, performing reconstruction to obtain a prediction sequence of the target time period. According to the method, through bistatic decomposition and fusion modeling, the precision and stability of irregular time sequence prediction are effectively improved.

The invention provides a turbomachine design optimization method and system based on a dimension similarity principle. The method comprises the steps that a dimension matrix is established based on turbomachine flow field physical quantity parameters, a constraint equation is solved, a standard orthogonal basis of scaling parameters is obtained, and the scaling parameters are generated; generating enhanced data samples based on the scaling parameters and training a neural network; and training geometric parameters, working condition parameters and space coordinates of the target turbomachinery based on the trained neural network to complete optimization. According to the method, through combination of the deep learning model and the dimension similar data enhancement method, high-accuracy physical field prediction can be obtained under the condition of few samples, so that the iterative design period of engineering fields such as turbomachinery can be remarkably shortened. The method has obvious advantages in generalization ability of cross-working-condition and cross-design-variable combination, the dependence on massive numerical simulation data is effectively reduced, and the overall efficiency of optimization design is greatly improved.

The invention relates to the technical field of electric propulsion digital twinning simulation, and discloses an electric propulsion digital twinning simulation data order reduction method, which comprises the following steps: acquiring simulation data of electric propulsion plasma; based on the simulation data, extracting snapshot data in a grid unit of a computational domain; performing singular value decomposition on the snapshot data, establishing a reduced-order orthogonal basis, and obtaining a reduced-order data representation through projection; based on a plasma fluid control equation, constructing a semi-discrete ordinary differential algebraic equation set, and determining a coefficient matrix of the reduced-order model through least square fitting; and performing real-time simulation of the electric propulsion digital twin system by using the reduced-order model, and outputting field data. According to the method, the calculation precision and the simulation result obtained through PIC / MCC simulation are in the same magnitude, the calculation time reaches the second level, and the real-time simulation requirement can be met.

The application discloses a hybrid algorithm based on a classical discontinuous finite element and a variational quantum linear solver, relates to the technical field of quantum-classical hybrid computing crossover, and comprises the following steps: adopting a discontinuous finite element method to perform spatial dispersion on fluid control equations, using a high-order basis function to describe internal solution of a unit and exchanging unit interface information through numerical flux to form a semi-discrete form of a common differential equation group; adopting an implicit time advancing format on the semi-discrete common differential equation group to convert the physical quantity updating problem of the current time step into a solving task of a high-dimensional sparse linear equation group; applying a generalized minimum residual method on the high-dimensional sparse linear equation group on a classical computing platform, constructing a set of orthogonal basis vectors of a Krylov subspace through an Arnoldi process, and projecting the original equation group to the low-dimensional subspace to obtain a dense linear system with reduced dimension; and constructing a variational quantum solving process of an adaptive noise-containing medium-scale quantum processor based on the low-dimensional dense linear system.

The invention provides a Hermitian matrix eigenvalue solving method and an FPGA (Field Programmable Gate Array) solving system thereof, relates to the technical field of signal processing, and solves the problem of how to make up defects of a current solving method from multiple aspects such as precision, convergence speed, eigenvalue generation and resource consumption. The method comprises the following steps: constructing m orthogonal bases which are mutually orthogonal from m columns of a Hermitian matrix, combining the m orthogonal bases into an eigenvalue matrix Q and an eigenvector matrix R, and multiplying the eigenvalue matrix Q and the eigenvector matrix R to obtain a new Hermitian matrix; through multiple rounds of iteration, the matrix Q and the matrix R obtained through the last iteration are solving results; the input data in the solving process adopts a double-precision floating-point number form and is kept; during solving, an accumulator is adopted to calculate a complex matrix multiplication result; when the matrixes are multiplied, each matrix element outputs data in a real part and imaginary part alternation mode, and a result is obtained after alternation multiplication is carried out by a plurality of floating-point complex multipliers. According to the method, a high-precision and rapid Hermitian matrix eigenvalue decomposition effect can be realized under the condition of low FPGA (Field Programmable Gate Array) resource consumption.