41 results about "Non negativity" patented technology

A fast and rigorous multivariate curve resolution (MCR) algorithm is applied to remotely sensed spectral data. The algorithm is applicable in the solar-reflective spectral region, comprising the visible to the shortwave infrared (ranging from approximately 0.4 to 2.5 μm), midwave infrared, and thermal emission spectral region, comprising the thermal infrared (ranging from approximately 8 to 15 μm). For example, employing minimal a priori knowledge, notably non-negativity constraints on the extracted endmember profiles and a constant abundance constraint for the atmospheric upwelling component, MCR can be used to successfully compensate thermal infrared hyperspectral images for atmospheric upwelling and, thereby, transmittance effects. Further, MCR can accurately estimate the relative spectral absorption coefficients and thermal contrast distribution of a gas plume component near the minimum detectable quantity.

Methods and systems for estimating time-varying factor exposures of either an individual financial instrument or a portfolio of such instruments, through the solution of a constrained multi-criteria dynamic optimization problem, providing an estimation error function and one or more transition error functions to be minimized over a period of time. The factor exposures relay the influence of the factors on the return of the instrument or portfolio. The estimation error function provides the estimation error at each time interval between the return of the asset collection and a sum of products of each factor exposure and its respective factor. Each transition error function provides a transition error of each factor exposure between time intervals. In one embodiment, the constraints can include a budget constraint and non-negativity bounds applying to some or all of the factor exposures. In other embodiments, the method and system can be applied to estimating any time-varying weight that is used in a model, to relay the influence of one or more independent variables on a dependent financial or economic variable, through the solution of a constrained multi-criteria dynamic problem, minimizing estimation error and transition error terms. In other embodiments, the solution of a multi-criteria dynamic problem can be used as part of a method and system to determine structural breakpoints for each factor, and also as part of a method and system for determining optimal parameters to weight the transition error functions and selecting the factors included in the model.

The invention discloses a vehicle brand type identification method based on a fusion feature sparse coding model. The method comprises the following steps: 1) carrying out locating extraction on a front face region of a vehicle and pretreatment on a front face image of the vehicle; 2) extracting vehicle front face features and constructing a fusion feature; 3) constructing the sparse coding model based on the fusion feature; 4) constructing a non-negativity constraint sparse coding model; and 5) carrying out vehicle brand type identification through a reconstruction error minimum method. Classification of different vehicle brands is realized by effectively extracting the features of the front face of the vehicle; and the method is used for automatically extracting vehicle brand information in shot traffic gate videos and carrying out classification, thereby realizing intelligent management of gate video data.

The invention discloses a CT (Computed Tomography) image reconstruction method based on a variational inequality at a sparse sampling angle. The CT image reconstruction method comprises the following steps: (1) carrying out equal-angle interval projection scanning within an angle range of 0-180 degrees by aiming at a fan-beam CT to obtain spare projection data y, wherein an angle interval in a projection direction is between 2 and 6 degrees; (2) calculating a projection matrix A through an X-ray source, a detector and the position information of an object to be reconstructed; (3) according to the projection data y obtained in the step (1) and the projection matrix A obtained in the step (2), simultaneously introducing the sparsity and the non-negativity of an image gradient to serve as priori knowledge to obtain a reconstruction model of an image reconstruction problem at the sparse sampling angle; (4) converting the reconstruction model in the step (3) into a variational inequality form; and (5) solving the variational inequality in the step (4) to obtain a reconstructed image. On the premise that image reconstruction quality is guaranteed, reconstruction convergence speed can be quickened, and single-iteration time is shortened.

The invention discloses a method and a device for configuring the water resource. The method comprises the steps of A, establishing an optimized model by taking land utilization maximization and water consumption minimization as a goal and taking surface water capacity constraint, available water capacity constraint of underground water, surface water irrigation proportion constraint, irrigation water requirement constraint, land utilization constraint, water reservoir capacity constraint and irrigation water capacity non-negativity constraint as constraint conditions; B, disassembling the optimized model into an upper bound sub model and a lower bound sub model according to a region interactive algorithm, introducing penalty coefficients through a type-2 fuzzy type reduction method, converting the upper bound sub model and the lower bound sub model into corresponding linear programming models, and solving the linear programming models; C, selecting different underground water use proportions, bringing the different underground water use proportions into the optimized model to compute, and obtaining a relationship between the underground water use proportions and a crop planting structure. By utilizing the method and the device, disclosed by the invention, the plan on water resource utilization under multiple uncertain conditions can be carried out, and a reasonable agricultural irrigation method can be provided.

The invention discloses a flame three-dimensional temperature distribution reconstruction method based on bi-spectral radiation information. The flame three-dimensional temperature distribution reconstruction method is characterized by comprising the following steps: 1) dividing a flame microelement according to acquired flame bi-spectral radiation information and establishing a flame radiation transfer equation; 2) setting a target function, iteratively updating a flame absorption coefficient according to a reconstruction algorithm and reconstructing a flame three-dimensional temperature field. The flame three-dimensional temperature distribution reconstruction method based on the bi-spectral radiation information, disclosed by the invention, has the benefits that in the iterative solution process of the algorithm, a non-negative least squares algorithm is used, so that the non-negativity of the spectral radiation intensity of each of parts of flame can be guaranteed; the absorption coefficient of each of the parts of the updated flame is also mapped to a set interval, so that the non-negativity of the absorption coefficient is guaranteed; the algorithm has stronger robustness, isindependent of an initial value and can still maintain higher accuracy and better convergence under the situations that inputted data is larger in error, and a random numerical value is set without providing a priori iterative initial value.

The invention relates to a low-field nuclear magnetic resonance (NMR) two-dimensional spectrum inversion algorithm which extends a PDCO (a primitive dual interior point method for convex objectives) to two-dimensional space for solving a two-dimensional inversion problem. Compared with other low-field NMR inversion algorithms reported in domestic and foreign literatures, the inversion algorithm of the invention uses L1 regularization and L2 regularization instead of just the L2 regularization, thereby getting closer to the sparseness of an original signal and acquiring spectral distribution that may more reflect a reality. The PDCO can ensure the non-negativity of a solution, and does not need additional nonnegative constraint calculation, which simplifies an inversion process. The inversion algorithm of the invention can also distinguish closely adjacent peaks and achieves high computational accuracy. The inversion algorithm of the invention has good robustness and may obtain stable inversion results under different SNR data.

The invention discloses a method for automatically analyzing a GC-MS (Gas Chromatography-Mass Spectrometry) overlapping peak to accurately recognize compounds. The method comprises the following steps: acquiring data information of compounds in a characterized sample by utilizing GC-MS, and extracting chromatographic peak information in the data information by adopting a multi-scale Gaussian smoothing function aiming at a chromatographic signal (EIC) under each m/z; and with the shape and retention time of the EIC chromatographic peak as the similarity criterion by utilizing a clustering method based on a density function, clustering the EIC peaks belonging to the same compound together, and realizing analysis of overlapping compounds in a single sample by adopting non-negativity constraint multivariate curve resolution-alternating least-squares. The method disclosed by the invention is capable of rapidly and accurately realizing automatic analysis of the compounds in the samples and screening differential compounds and has important value on analysis of complex plant samples, such as non-targeted metabonomic study.

The present invention discloses a hyperspectral image spatial resolution enhancement method. The method comprises the steps of: extracting reflectance spectrums of different scenes in an image, and obtaining a spectrum dictionary with high sparsity and low coherence; employing the sparsity, the non-negativity and the space structure self-similarity of signals in the hyperspectral scenes, performing analysis from the extracted scene reflectance spectrums to obtain a low spectral image with a high spatial resolution, performing solving through a synchronous orthogonal matching tracking algorithmto obtain a sparse encoding matrix; and finally, combining the spectrum dictionary and the sparse encoding matrix to obtain a target image. Through joint use of the space and the spectral informationof the image, the simulation data and real data experiment result shows that, the method can effectively reconstruct the hyperspectral image detailed information and the texture structure compared toa traditional method and a matrix decomposition method so as to effectively improve the waveband average peak signal to noise ratio, the waveband average structure similarity and the spectrum angle mapping and better maintain the spectrum information.

The invention discloses an SAR image target recognition method based on a multilayer probability statistics model, so as to solve the problem that when input SAR image data has non-negativity in the existing method, only single-layer non-negative features of an SAR image can be extracted. The method comprises steps: a training set and a test set for the multilayer probability statistics model are built; parameters of the multilayer probability statistics model are initialized; a joint learning mode is used to train the multilayer probability statistics model, and a global variable is saved; the joint learning mode is used to test the multilayer probability statistics model, and all parameters of the model are obtained for subsequent recognition; and the parameters obtained by test are used for target recognition. Based on the multilayer probability statistics model, the joint learning mode is used to train and test the model, the SAR image feature extraction and target recognition are carried out, the model parameter complexity is reduced, the multilayer non-negative features of the SAR image are extracted, the SAR image target recognition performance and the stability are improved, and the method of the invention is used for carrying out target recognition on the SAR image.

The invention belongs to the computer network technology field and discloses a network distance prediction algorithm aiming for nodes in a mobile network. In order to solve the limitation in the prior art, the network distance prediction algorithm of the invention extends a basic network distance prediction algorithm through a matrix decomposition method, introduces input vectors and output vectors of a node coordinate, and performs network distance prediction through updating corresponding vectors. The algorithm comprises steps of performing a linear searching strategy, dynamically updating study speed parameters, reducing the influence of the study speed on the distance prediction, integrating a symmetric matrix decomposition method for processing measurement singular values and improving robustness and decomposing a nonnegative matrix, and the algorithm is used for guaranteeing the non-negativity and symmetry of the prediction distance and the practical meaning. The invention expands the universality of the distance prediction algorithm through introducing the linear searching strategy and matrix decomposition, improves the distance prediction accuracy and has obvious effect and strong practicability.

The disclosed apparatus, systems and methods relate to a framelet-based iterative algorithm for polychromatic CT which can reconstruct two components using a single scan. The algorithm can have various steps including a scaled-gradient descent step of constant or variant step sizes; a non-negativity step; a soft thresholding step; and a color reconstruction step.

An embodiment of the invention discloses a visible-light double-layer superposition transmission system and a method thereof. The system comprises a sending end and a receiving end. The sending end comprises a coding module and an LED array. The receiving end comprises a decoding module and a camera. The coding module divides information to be sent into two layers so as to carry out transmission mapping. Mapped data is modulated via OOK so as to acquire a transmission signal. Linear superposition is performed on the transmission signal so as to acquire a superposed signal. A direct current bias is added to the superposed signal and then the signal is used to drive the LED array. The decoding module processes an LED array image shot by the camera so as to acquire a non-negativity signal. The non-negativity signal is used to carry out direct current removing operation so as to acquire a bipolar signal, and detection and decoding are performed so as to acquire information to be sent through reduction. In the embodiment of the invention, two kinds of information with different attributes is divided into two layers so as to carry out transmission, a problem that uniform illumination is difficult to satisfy when a high transmission rate is realized is solved, and then technical effects of satisfying the uniform illumination and overcoming a channel correlation influence are reached.

A method for determining interior coordinates is disclosed. The method includes receiving information specifying an object having a plurality of sites and a boundary. Interior coordinates associated with each of the plurality of sites are determined based on the boundary. The interior coordinate associated with each of the plurality of sites represent a system of coordinates that satisfy several properties, including non-negativity and interior locality. At least one value associated with the plurality of sites is then interpolated using the interior coordinates.

The invention discloses a feature extraction method and a feature extraction device, which are applied in electronic equipment. The method comprises the steps: receiving user-project history behavior statistic data acquired by a server, through the electronic equipment; storing the statistic data into a storage module; and carrying out non-negative behavior feature extraction on the user-project history behavior statistic data, to obtain feature extraction data. According to the feature extraction method and device, the feature extraction data are stored in the storage module, the technical problems that user behavior features which can guarantee the excellent reductive property and non-negativity of the known data and well represent the user behavior rules cannot be extracted in the prior art can be solved, and the technical effects that the user behavior features which can guarantee the excellent reductive property and non-negativity of the known data and well represent the user behavior rules can be extracted can be realized.

Systems and methods for managing components of physical systems, including decomposing raw time series by extracting an aging trend and a fluctuation term from the time series using an objective function of an optimization problem, the objective function minimizing reconstruction error and ensuring flatness of the fluctuation term over time. The optimization problem is transformed into a Quadratic Programming (QP) formulation including a monotonicity constraint and a non-negativity constraint, the constraints being merged together to reduce computational costs. An aging score and a confidence score are generated for the extracted aging trend to determine a severeness of aging for one or more components of the physical system, and the aging score and confidence score are fused to provide a fused ranking for the extracted aging trend for predicting future failures of the components.

The invention discloses a multi-fractal network flow reconstruction method. According to the method, a roughest scale coefficient and a random multiplication factor are generated and determined by adopting pareto distribution of a heavy tail distribution characteristic, so that the non-negativity of scale coefficients and wavelet coefficients is ensured, the reconstructed flow is accordant with the heavy tail distribution characteristic of practical flow, and the network flow can be reconstructed more finely and accurately. Network flow signals reconstructed with the method disclosed by the invention is applied to network analysis and management activities such as network planning, load balance, blockage control, buffer design and the like.

The invention belongs to the technical field of radar imaging and discloses a total variation regularization correlated imaging method based on non-negative constraint on a target scattering coefficient. Firstly, a radar correlated imaging model is established according to a geometric relation of correlated imaging. The amplitude and phase position of the target scattering coefficient are split and a total variation regularization function is established under the condition of nonnegative constrain. The regularization function is optimally solved by using an alternating direction multiplier method. The total variation regularization correlated imaging method considers information of the amplitude and phase position of the scattering coefficient separately, makes full use of the non-negativity of the amplitude of the scattering coefficient to reestablish a radar target scene, thus the establishment of the model is more close to an actual situation, which is beneficial to get a better imaging result. Compared with a commonly used correlated imaging algorithm, the method of the invention can better reserve information of edges between areas of reconstructed image and strengthen the smoothness of the areas.

The invention discloses an estimation method for missing information of high-dimensional symmetric sparse network based on matrix decomposition. The method comprises the following steps of: firstly, initializing a low-dimensional potential characteristic matrix, and determining the number of the low-dimensional potential characteristic matrixes and initialization numerical values of elements in the matrix; Designing an objective function based on known elements in the high-dimensional symmetric sparse network; Designing a solving algorithm of the objective function by utilizing a gradient learning method; Minimizing the objective function through a solving algorithm to obtain a potential characteristic matrix; Multiplying the potential feature matrix to obtain an estimation matrix of the high-dimensional symmetric sparse network, and obtaining missing information in the high-dimensional symmetric sparse network through the estimation matrix. According to the method, the defect that useful information is extracted from a high-dimensional sparse matrix in the prior art is overcome, missing information estimation accuracy and calculation efficiency are improved, and prediction symmetry and non-negativity are guaranteed to be met.

The invention relates to a method for face recognition by adopting two-dimensional non-negative sparse partial least squares. The method comprises the following steps: firstly constructing a category matrix of a face training sample set, then constructing an objective function for realizing minimum loss of the amount of information in the projection of the face training sample set and the category matrix, afterwards adding non-negativity constraint and sparsity constraint into the objective function to obtain a convergent non-negative basis matrix, projecting the face training sample set on the basis matrix to obtain a test sample coefficient matrix, also carrying out the operation on a test sample to obtain a coefficient matrix of the test sample, determining that certain element matrices in the coefficient matrix of the test sample and the test sample coefficient matrix fall into the same category by using a nearest neighbor strategy, and then considering that a person on the test sample corresponding to the coefficient matrix of the test sample is the same person on the training sample corresponding to the element matrix. The method is high in recognition rate and robustness, and as only the basis matrix needs iterative solution, the operation is simplified, the time complexity is reduced, and the recognition speed is high.