69 results about "Space decomposition" patented technology

A present novel and non-trivial system, module, and method for constructing a flight path used by an avionics system are disclosed. A processor receives flight plan data and object data associated with terrain and obstacles. Free cells are extracted above the objects using a recursive space decomposition technique, and a reference path is formed through traversable free space determined from the availability of free cells. In an additional embodiment, threat data associated with hostile military weaponry and significant meteorological conditions could affect the availability of free cells. A genetic algorithm applying genetic operators which include mutators is employed with aircraft kinematic constraints to refine the reference path used to form a population of best path candidates. When a best path is reached after cycling through a re-generation process of path candidates, flight path data representative of the best path is generated and provided to at least one avionics system.

The invention discloses a sparse construction and reconstruction method of array space signals. Due to the fact that the angle of received incident signals changes constantly under the high dynamic condition, enough sampling signals can not be acquired to conduct traditional beam forming. According to the method, a gain test of the array space signals is conducted through the Capon beam forming method based on feature space decomposition, and establishment of a small-snapshot-number sparseness model and OPM reconstruction of the small-snapshot-number sparseness model are conducted based on the compressed sensing theory. The result shows that the method has the advantage of processing undersampled data and achieves estimation on the angle of arrival of the array signals. The method further shows that in the space signal processing field, sparsity makes extraction of related information faster and more effective, so that the signal acquisition cost and the signal processing cost are lowered. By means of the method, expected waveform gain can be obtained in the expected direction of arrival. Compared with a direct sampling method, the method can solve the small snapshot number problem occurring under the high dynamic condition and improve the robustness of a system.

The invention discloses a robot dress tactile sensing, information fusion and data processing method, including the following steps: 1) build up a robot dress tactile sense database in a computer; 2) acquire the data: a data acquisition card collects the data from all the sensors on the robot dress; 3) pre-treat the data in the buffers of the sensors; 4) compensate the sensor data; 5) amalgamate the sensor data; 6) display the amalgamates sensor data in a two or three dimensional image. In this way, the contour figure of the contactant and the pressure distribution at the contact face can be obtained. The invention can estimate and judge more precisely, more thoroughly and more reliably than a single sensor, can improve the spatial decomposition capability of the whole body of the robot, the definition, the measurement precision of the robot sensing image, classification precision and reliability, can improve interpretation and dynamic monitoring abilities, can reduce ambiguity and effectively increase the utility rate of the tactile data.

A method for automatically reconstructing topographical information for a given mesh, altering the mesh by introducing, deleting, or splitting existing polygons when needed. An OctTree space decomposition is used to achieve a log2-complexity search method to find the closest vertex in the polygonal soup to a given point in space. Linear complexities are used to find triangles connected to a given vertex and all triangles connected to a given triangle. The triangles are split to enforce conductivity.

Image signals are encoded following a spatial decomposition pattern that is based on a selected location within the image. The resulting sequence of sets of encoded image signals may be transmitted to a remote node for decoding and display. The display of the decoded signals is progressively updated as subsequent sets of encoded image signals are received and decoded. In a prefeired embodiment, the first set of encoded image signals comprises DC transformed signals corresponding to the original image, while the subsequent sets of encoded signals comprise AC transformed signals corresponding to the original image.

The invention discloses a method for rapidly and nondestructively detecting the colony count of chilled meat, which comprises the following steps of: S1. obtaining a chilled meat sample; S2. collecting a high-spectrum image of the sample; S3. calculating the reference value of the colony count per unit area of the sample; S4. subjecting the high-spectrum image to space decomposition so as to obtain spatial diffusion information of the sample at different wavelengths, wherein the Lorentz parameters of the component information of the representative sample is extracted by utilizing the spatial diffusion information according to a nonlinear regression method; and S5. obtaining the relationship between the Lorentz parameters and the reference value in a certain wavelength range so as to establish a mathematical model of the Lorentz parameters and the colony count of the sample according to a multivariate linear regression method, and predicting the colony count of the chilled meat sample by utilizing the model. By means of the method disclosed by the invention, rapid and nondestructive detection of the colony count of a meat product can be realized.

An edge routing system is described herein that uses a spatial decomposition to achieve faster routing, and more quickly generates a sparse visibility graph using a cone spanner. The system provides two approaches that can be used separately or in combination to achieve faster—and hence more scalable and more interactive—edge routing using approximate shortest paths. The first approach uses a spatial decomposition of the nodes in a graph, moving them slightly to obtain strictly disjoint convex hulls around groups of nodes, and then computing visibility graphs over these composite hulls rather than individual nodes. The second approach generates a sparse visibility-graph spanner to accelerate the process of producing the visibility graph. The system allows high quality obstacle avoiding edge routing for large diagrams in interactive diagramming applications where very fast refreshes of routing are used with many nodes moving at the same time.

The invention discloses a millimeter wave phased array test and calibration method, relates to a millimeter wave phased array calibrated error characteristic analysis and especially relates to the channel inconsistency of a phased-array antenna and a method for calculating element antenna phase center offset. The method comprises: step1, correlation functions including phased-array antenna channel inconsistency and element antenna phase center offset are established according to a phased-array antenna pattern obtained in the test; step2, range space decomposition is performed on an optimized target parameter; and step3, the range corresponding to the strong spatial spectrum component is calculated by adopting a deterministic method, and the range corresponding to the weak spatial spectrum component is optimized by adopting a genetic algorithm. According to the invention, the calibration of the phased array pointing accuracy can be realized under a condition of less test work, the test workload is greatly reduced, the labor, material resources and working time are saved, a complex and precise flat scanning frame is not needed to use, and the complexity and input cost of the system are reduced.

The invention discloses a blood-pressure measurement method based on video images. By adopting the method, the blood pressure is continuously measured by collecting a video image of a human face and performing space decomposition, time-domain filter and real-time processing on the face video image. The method particularly comprises the following steps: tracking a to-be-measured target region in real time to determine a ROI region; performing RGB three-channel separation on each frame of an image sequence of the obtained ROI region, summing and averaging to obtain a time sequence waveform in a period; removing the trend term from the time sequence waveform, and normalizing to obtain a time sequence signal; denoising the normalized signal by adopting an empirical mode decomposition method to obtain a three-channel time-domain signal with relatively high signal-to-noise ratio; and finally calculating the wave crest and wave trough of the time-domain signal, and establishing the relation between the time-domain signal and blood pressure to obtain a blood pressure value. The method is applicable to the fields of health big-data collection, remote medical treatment and the like.

The invention relates to a robust broadband self-adaptive beamforming method used for ultrasound imaging. The method comprises the steps that acquired ultrasonic echo data are converted from time domain to frequency domain, and band dividing is carried out; the covariance matrix of each subband divided from the band is calculated; according to the acquired covariance matrix, a feature space decomposition method is used to project an assumed steering vector into a signal subspace; a convex optimization method is used to estimate a true steering vector; the basic principle of a minimum variance method is used to acquire the beamforming processing result of each subband space; and the beamforming processing results of subband spaces are superimposed and are converted to time domain to acquire a final imaging output result. According to the invention, the broadband characteristic of an ultrasonic echo signal is fully used; the steering vector in real environment is reconstructed in frequency domain, so that the steering vector can have a robust performance in the case of phase distortion; and the ultrasound imaging quality can be improved under a non-homogeneous media.

The invention relates to image decomposition strategies and computer-based methods for implementing them. In one method of the invention, the ordering of tetrahedral shapes that define or approximate an image is performed in such a way that neighboring tetrahedral shapes can be identified, located and efficiently used. In one aspect, a binary location code array is used to represent an image and the method for identifying the neighbor shape employs a bit manipulation step in code or pseudo-code for operating a computer. In this aspect, the invention allows one to move between adjacent tetrahedra, and any data corresponding to the tetrahedra, in constant time.

The invention relates to a method and a device of excavation of a subject of text big data based on characteristic space decomposition. The method comprises two associated parts: one part is a space decomposition method based on the subject characteristic, the second part is an acceleration method based on model solution of multiple sub-spaces. The key of the space decomposition method is to utilize model characteristics to decouple the data samples and the subject assemblies, and therefore segmentation and decomposition of the data space and the subject space are achieved simultaneously, a plurality of sub-model spaces smaller than a full model space are obtained, and complexity of a storage space of a calculation solution algorithm is effectively reduced. At the same time relevant independence among the sub spaces can be utilized simultaneously to reflect the sub spaces to all kinds of parallel entities, and therefore time complexity of the calculation algorithm is effectively reduced. The method of the excavation of the subject of the text big data based on the characteristic space decomposition is capable of sufficiently utilizing parallel processing capability of a calculation device, and achieving parallel expansion processing of large-scaled subject modeling spaces and large-scaled data assemblies.

The invention discloses an X-ray image uneven display processing method based on distribution and reconstitution of measure spacing, which comprises the following steps: doing normalizing and logarithmic transformation to the inputted image, choosing linear or nonlinear wave filter method to do image measure spacing distribution, weighting the image information on different measures and reconstituting the weighted signals, doing normalizing and antilogarithm processing to the constructed signals to obtain the uneven image. It can adopt blank field smooth filter wave operator during the decomposable process.

The invention discloses a wavelet transform based extreme-low bit-rate video compressing and coding method. The wavelet transform based extreme-low bit-rate video compressing and coding method includes three steps: (1) subjecting a GOF (group of frame) image group to three-dimensional wavelet decomposition, selecting appropriate decomposition grades for time decomposition and space decomposition according to the size of the image group and formats including CIF (common intermediate format), QCIF (quarter common intermediate format), SIF (standard intermediate format) of videos, decomposing to obtain a coefficient matrix of wavelet decomposition; (2) subjecting the coefficient matrix to branching and blocking operation: firstly subjecting the coefficient matrix to first-grade virtual decomposition or second-grade virtual decomposition, and then blocking the coefficients after virtual decomposition; (3) realizing SPIHT (set partitioning in hierarchical tree) coding process. By the aid of the wavelet transform based extreme-low bit-rate video compressing and coding method, a conventional video wavelet 3D-SPIHT (three-dimensional set partitioning in hierarchical tree) algorithm is improved, a wavelet transformation coefficient is organized in a form of combining a virtual tree with a coefficient branching organization, a multi-threshold sub-band coding optimization method is utilized, important coefficient is coded preferentially, the purpose for displaying more effect information with few bit numbers is achieved, compression ratio can be improved, and compression efficiency is increased.

The invention provides an urban geography evolution simulation realizing method based on a parallel cellular automaton, which adopts the cellular automaton to simulate urban geographical phenomena, decomposes a cellular space into a plurality of subdomains, distributes every subdomain on every distributed processor for processing, and finally combines operation results of all the processors into a final simulation result. The urban geography evolution simulation realizing method based on the parallel cellular automaton overcomes the defects in the prior art, provides a parallel operational urban CA, solves the problem of speed bottleneck of the urban CA through network interconnected computer join operation, and achieves the aim of high-efficiency space calculation.

The object of the present invention is to provide a remote navigation path planning method based on energy consumption optimization and considering an ocean circulation influence for a UUV. The methodincludes following steps: determining a practical to-ground navigation speed of the UUV and ocean circulation information updated in real time; initializing various kinds of parameters required by anoptimization algorithm; randomly generating path control nodes through space decomposition modeling, and generating an initial path by employing B-Spline fitting; entering a QPSO algorithm iterationprocess, calculating a particle average optimal position corresponding to a current path, and calculating the energy consumption of the UUV corresponding to the current path; determining a final optimal solution through a corresponding optimization process; outputting a final path control node; and generating a path track with the optimal energy consumption through fitting according to the final control node. According to the method, an energy consumption value obtained through three optimization algorithms by regarding the energy consumption as the optimization target is far lower than the energy consumed through the same algorithms by regarding the navigation time as the optimization.

The invention discloses a non-contact radial artery waveform extraction method and mainly solves problems of robustness and accuracy in non-contact wrist radial artery pulse wave extraction in real scenes. An implementation process includes: (1) controlling a camera facing the human wrist to acquire a 30s video through a computer; (2) subjecting the acquired video to Laplacian pyramid to realize space decomposition, and subjecting sub-bands in pyramid decomposition to IIR filtering to complete time domain filtering, wherein amplification of radial artery pulse signals in the video is realizedin the process; (3) subjecting the amplified video to blocking processing, analyzing power spectra of all blocks, and selecting an area with highest signal-to-noise ratio as an interested area; (4) selecting the interested area as an input signal, dividing the area into three signal sources, and performing independent component analysis to finally obtain radial artery waveforms. By radial artery pulse characteristics, the wrist radial artery pulse signals are amplified and extracted through video motion, then accurate selection of the interested area is performed, and environmental noise interferences are reduced; by independent component analysis, pulse waveform accuracy is further improved.

An edge routing system is described herein that uses a spatial decomposition to achieve faster routing, and more quickly generates a sparse visibility graph using a cone spanner. The system provides two approaches that can be used separately or in combination to achieve faster-and hence more scalable and more interactive-edge routing using approximate shortest paths. The first approach uses a spatial decomposition of the nodes in a graph, moving them slightly to obtain strictly disjoint convex hulls around groups of nodes, and then computing visibility graphs over these composite hulls rather than individual nodes. The second approach generates a sparse visibility-graph spanner to accelerate the process of producing the visibility graph. The system allows high quality obstacle avoiding edge routing for large diagrams in interactive diagramming applications where very fast refreshes of routing are used with many nodes moving at the same time.

A method for converting a software environment defined using flat name space into an equipment model is provided. The method includes decomposing the flat name space into a plurality of tokens, and assigning each token to a corresponding level of a plurality of levels included in an equipment hierarchy. The method also includes translating each token into a human-readable name, and creating an equipment model based on the human-readable name and the corresponding level assigned to each token.

The invention provides an eigen-space decomposition-based multi-apodization fast adaptive beam-forming method. The method comprises the steps of obtaining weight vectors of various channels by adopting EIBMV beam-forming in advance in a preset imaging environment; and carrying out beam-forming by adopting a principal component obtained through principal component analysis of the weight vectors as a real EIBMV weight, wherein the weight vectors which are determined in advance can be adopted as the beam-forming weights, so that the calculation process is simplified. EIBMV beam-forming for improving the contrast, PCA for reducing the calculated quantity and DAX for improving the image resolution and NSI are combined together, so that one-dimensional and one-dimensional eigen-space decomposition-based multi-apodization fast adaptive beam-forming method is achieved, and the problems of poor image contrast and poor resolution of an image obtained through MV beam-forming are solved.

The invention discloses a global energy interconnection power balance optimization method based on space-time decomposition, which comprises the following steps: constructing an intercontinental powergrid partition power balance mixed integer optimization model and a global energy Internet power balance linear optimization model, and carrying out optimization calculation to obtain the operation state of a partition generator set; optimizing and calculating the intercontinental tie line transmission power and the renewable energy generation power based on a partition unit operation state optimization result; and finally, performing iterative optimization calculation between the intercontinental power grid partition power balance mixed integer optimization model and the global energy Internet power balance linear optimization model to obtain an optimal power balance optimization result. A global energy interconnection power balance optimization basic principle based on time-space decomposition iteration is determined, and modeling and optimization calculation steps realized by a computer are provided. A global energy Internet power balance optimization method suitable for distributed calculation can be provided, so that the calculation efficiency is higher, the calculation speed is higher, and the method is more suitable for large-scale data calculation.

The invention relates to a leukocyte extraction and classification method based on an improved K-means and a convolutional neural network. The method comprises the steps of firstly, selecting an initial clustering center according to cell image gray level distribution, and clustering all pixels of an image initially according to the principle of proximity; then, improving the Euclidean distance ofthe FWSA-KM algorithm; before the extraction of leukocytes, carrying out the color space decomposition firstly, and carrying out the cell nucleus and cytoplasm extraction by adopting a color component beneficial to leukocyte segmentation and an improved K-means algorithm; separating a complex adhesion part by adopting a watershed algorithm; and finally, performing classification based on the convolutional neural network. According to the method, the leukocyte nucleus segmentation precision and the cytoplasm segmentation precision are 95.81% and 91.28% respectively, and compared with a traditional segmentation method, the precisions are greatly improved, the classification accuracy can reach 98.96% at most, the classification average time is 0.39 s, and compared with an existing leukocyteclassification algorithm, the CNN classification method not only has obvious advantages, but also has the great improvement space.

The invention provides an intelligent monitoring method for leakage of a natural gas pipeline. The method comprises the following steps: collecting an audio signal during operation of the natural gas pipeline by using a sound array; performing imaging positioning on the audio signal by using a beam forming algorithm based on symbol coherence and feature space decomposition; obtaining an audio frequency spectrum in the direction of direction of arrival through a linear constraint minimum variance algorithm; and carrying out standardization processing by taking the frequency spectrum formed by the beam as a characteristic, and inputting the characteristic into a pre-trained natural gas leakage diagnosis model to obtain a pipeline condition. According to the intelligent monitoring method for leakage of the natural gas pipeline, a beam forming algorithm based on symbol coherence and feature space decomposition is used, side lobes are better restrained, the acoustic imaging resolution is better improved, high-contrast and high-resolution acoustic imaging and accurate positioning can be carried out on a leakage source on an on-site image, the accuracy of natural gas leakage diagnosis is effectively improved, and meanwhile, the workload and troubleshooting difficulty of operators on duty are greatly reduced.

The invention provides a Contourlet transform-based drawing method. A drawing space is decomposed into coarseness information and detail information with different scales by using Laplacian pyramid transformation in Contourlet, and an adaptive sampling method is provided according to an analysis result. Multi-scale direction sub-bands of the drawing space are obtained through a direction filter group in the Contourlet by using a multi-scale analysis result, and a final image is re-constructed by using a multi-scale anisotropic filter. The Contourlet transform-based drawing method is strong in transportability, is suitable for multiple light ray tracing algorithms, and solves the problem that the conventional drawing method causes deformation during boundary filtration.

The invention discloses a Mars entering flight state uncertainty quantification method. Random Mars entering dynamics is converted into an equivalent deterministic dynamics differential equation set through a polynomial chaos theory. The updating of the orthogonal basis in the polynomial chaos is realized by utilizing the spectral decomposition theory when the statistical characteristics of the Mars entering the flight state uncertainty change drastically, so that the uncertainty quantization accuracy is ensured under the condition that the polynomial order is not increased. And violent increase of the equation dimension under the condition of uncertainty of the initial state is inhibited by adopting a random space decomposition theory, so that the calculation efficiency is improved. The method is suitable for instantaneous state uncertainty quantification and state evolution uncertainty quantification of Mars entering flight.

The invention belongs to a palm print identification method based on subspace characteristic extraction and especially relates to a palm print identification method based on a projection operator and a wavelet transform. The method comprises the following steps of constructing a palm print database; carrying out the wavelet transform on an image in the palm print database; using palm print data after the wavelet transform to construct a null space of the projection operator; using a singular value decomposition method to calculate a discriminating projection of the space; using the discriminating projection to carry out characteristic extraction on the palm print data so as to obtain the palm print database after dimension reduction; after the wavelet transform is performed on a palm print image to be tested, using the discriminating projection to carry out dimension reduction, and then using the palm print database after the dimension reduction and using a nearest neighbor classifier to classify a sample to be tested after the dimension reduction. The method has advantages that certain robustness is possessed; the method is insensitive to changes of palm print image illumination; the projection operator is used and a space decomposition property is possessed; a dimension of a decomposed subspace is fixed so that a condition that a characteristic dimension is selected by a person in other methods is avoided.

The invention discloses an early fault detection method for a gearbox. The method comprises the following steps: S1, carrying out signal collection: collecting vibration signals of a gearbox in normaland fault working states, and carrying out the decomposition of the vibration signals through an empirical mode decomposition method; S2, carrying out space decomposition: decomposing the original space into a plurality of subspaces in combination with a depth decomposition theorem, and establishing a dynamic principal component model of each subspace; S3, updating the model: through a sliding window algorithm, updating the model in real time by considering the time sequence correlation of the data; and S4, carrying out control limit calculation: judging whether the gearbox has a fault or not according to the statistics and a control limit, and updating the model to recalculate the control limit. According to the invention, the dynamic principal component model is established based on deep recursion dynamic principal component analysis, so that early fault intelligent detection of the gearbox is realized. Compared with a traditional early fault detection method, the disclosed methodhas the advantages of self-adaptability, high detection rate, high detection speed and the like.

The invention discloses an image edge detection method based on a multi-scale Gabor first-order derivative, and the method specifically comprises the steps of: 1, converting a to-be-detected color image into a CIE LAB color space, and decomposing the to-be-detected color image into an L channel image, an A channel image and a B channel image; 2, constructing a multi-scale and multi-direction Gaborfirst-order derivative filter bank, and respectively filtering the L channel image, the A channel image and the B channel image to obtain a filtering response; 3, calculating an edge intensity graph,an edge directional graph and an edge scale graph according to the filtering response, performing non-maximum suppression according to the edge intensity graph and the edge directional graph, and compensating edge intensity in combination with the edge scale graph to obtain a compensated edge intensity graph; and 4, acquiring an image edge through a double-threshold method according to the compensated edge intensity graph obtained in the step 3. According to the image edge detection method based on the multi-scale Gabor first-order derivative, the problem of edge discontinuity in the prior art is solved.