179 results about "Nonlinear structure" patented technology

The invention discloses a CFD (computational fluid dynamics)/CSD (circuit switch data) coupled solving nonlinear aeroelasticity simulation method, comprising: firstly, carrying out calculation initialization; then solving the CFD of unsteady aerodynamic force; converting boundary load information; solving the CSD of nonlinear structure dynamic response; judging whether to exist computation or notby taking the response of structural finite-element mesh point displacement as a convergence criteria; if not exiting, carrying out the dynamic mesh deformation of a flow field after the surface displacement information of a computation model is converted; and then, continuing to compute until the convergence criteria is satisfied, thus finishing computation. By means of the simulation method disclosed by the invention, the problem of pneumatic nonlinearity and structural nonlinearity coupling and the problem of geometric large deformation dynamic mesh can be solved; and the simulation methoddisclosed by the invention can be applied on the nonlinear aeroelasticity analysis of aerospace aircrafts and the nonlinear aeroelasticity analysis of civil high-rise buildings and bridges.

The invention discloses a non-linear structure light illumination microscopic imaging method which comprises the following steps: 1) loading a computed hologram on a digital microscopic array; 2) generating a first spatial structure light field which meets sine distribution and is used for activating fluorescent protein, and radiating the first spatial structure light field to the surface of a sample, so as to convert a part of protein to be in an illuminated state from a dark state; 3) radiating the sample in a second spatial structure light field so as to enable fluorescent protein in the bright state to emit fluorescent light, collecting the fluorescent light, and imaging in a photoelectric detector; 4) repeating the step 2) and the step 3), acquiring a plurality of spatial frequencies, acquiring a plurality of initial phases in each direction to obtain a plurality of original images, and reestablishing a super-resolution image according to a GPU acceleration algorithm. The invention further discloses a non-linear structure light illumination microscopic imaging system. The non-linear structure light illumination microscopic imaging method has the advantages of relatively high system imaging resolution, high fluorescent drifting resistance, low phototoxicity and rapid imaging.

An improved test fixture to evaluate thin composite laminates commonly used in deployable space structures. The fixture is designed to impart a pure moment into the coupon, a necessary improvement to prior test methods where results are obtained by fitting material properties in a nonlinear structural analysis of the test. Fixture mechanics allow for direct calculation of the coupon flexural modulus and allowable flexural strain based on two key measurements, fixture displacement and applied load.

The invention discloses a super-resolution face recognition method based on deep belief networks, and relates to the technical field of face recognition. From a cognitive perspective, it is believed that an intrinsic relation exists between mutually corresponding face images differing in resolutions. But previous studies show that the method for expressing the intrinsic relation by linear approximation is restricted by linear approximation. Therefore, it is believed that the intrinsic relation is nonlinear. In view of outstanding performances of an artificial neural network on the nonlinear classification problem, a neural network algorithm is adopted to capture the nonlinear relation of the mutually corresponding face images differing in resolutions under the condition of posture change. Both theoretical research and neurophysiological research show that a deep structure, such as a system constructed by multiple layers of nonlinear processing units, should be constructed to build an intelligent processing system. According to the face recognition method, deep belief networks are adopted to extract a common nonlinear structure shared by mutually corresponding face images differing in resolutions.

The present invention relates to a classification and aggregation sparse representation face identification method based on a nuclear space. The method comprises the following steps: employing a convolutional neural network to extract facial features of a facial image, training a classification and aggregation dictionary, and identifying the image. The classification and aggregation sparse representation face identification method based on the nuclear space considers that the weighting of each training sample to the subspace construction is different and the train samples closed to a class center should have bigger weight to the subspace construction when the train samples are configured to perform sparse representation of test samples, a [Phi](Xc)Wc matrix is adopted to construct a new sparse representation dictionary, and classification concentration constraint terms are added in a sparse representation constraint. Compared with the prior, the classification and aggregation sparse representation face identification method based on the nuclear space is able to effectively reduce the fitting error of test samples in corresponding subspaces to allow samples with the same type to aggregate in the sparse representation so as to improve the face identification performance, enhance the capabilities of processing the non-linear structure and relation, effectively excavate hiding features of complex data and further improve the face identification performance.

Systems and methods of limiting contact penetration in numerical simulation of non-linear structure response using implicit finite element analysis are described. According to one aspect, a finite element analysis (FEA) model of a structure is defined as a number of nodes and elements based on geometry and material properties of the structure. A time-marching analysis of the FEA model is then performed. The time-marching analysis results contain a number of solutions of non-linear structure response at respective time steps. Solution at each time step requires at least one iteration to compute. Non-linear structure response is determined in the following manner: 1) determining a search direction; 2) calculating a contact penetration parameter in the search direction; and 3) finding a minimum energy imbalance location along the search direction as a solution which is further restricted by the CPP such that contact penetration of the structure is substantially limited.

Adjustable ladders may include a pair of side rails having a plurality of rungs extending therebetween, and a pair of adjustable legs associated with the side rails. A pair of swing arms may include a first end pivotally coupled with a side rail, or a bracket associated with the side rail, as well as a second end pivotally coupled with an adjustable leg. The swing arm may be configured as a nonlinear structure. In one embodiment, the swing arm may include a first section that extends from a second section at a defined angle. The first and second sections may exhibit different lengths. Adjustment mechanisms may be pivotally coupled with an upper end of the adjustable legs. In one embodiment, the adjustment mechanisms may be configured such that, when actuated, they may be displaced in one direction while resisting displacement in a second, opposite direction.

Taught herein is a passive channel calibration method wherein a non-linear antenna array sets an antenna array to a non-linear formation that contains at least a combination of translation invariant dual array-element couples, detects single-azimuth ocean echoes via combinations of translation invariant dual array-element couples, estimates channel amplitude mismatch coefficients via the single-azimuth ocean echoes to implement amplitude calibration, and estimates channel phase mismatch coefficients via the single-azimuth ocean echoes after amplitude calibration and the known array position information to implement phase calibration.

The invention relates to a structural dynamics analysis method and system for an inflatable reentry vehicle considering nonlinear influences, and belongs to the field of areospacecraft reentry and return. According to the structural dynamics analysis method and system for the inflatable reentry vehicle considering nonlinear influences, characteristics of statics, modality and thermal modality of the inflatable reentry vehicle under influences of different inflation pressures, film thicknesses, hypersonic flow field pressures, temperatures and other nonlinear factors can be accurately described. Calculation methods of fluid-solid unidirectional coupling and thermo-solid unidirectional coupling are successfully introduced into a nonlinear structural dynamic simulation module, combined with an actual trajectory of the inflatable reentry vehicle, and an influence rule of changes of internal inflatable gas parameters on characteristics of statics, modality and thermal modality of the inflatable reentry vehicle is truly expressed. The structural dynamics analysis method and system for the inflatable reentry vehicle considering nonlinear influences have the advantages that the problem ofthe existing study on structural dynamics of inflatable reentry vehicle that the consideration of flow field non-linear and material none-linear is insufficient will be hopefully solved, and valuablereferences can be provided to shape-preserving design and structural safety design of the inflatable reentry vehicle.

A ladder apparatus and system relating to detachably securing a ladder to linear, or non-linear, structures such as, for example, a tree, a telephone pole, a building pillar, etc., thereby assisting preventing the ladder from lateral movement. The system provides for width, depth and angle adjustment to assist clasping to different structural support geometries being attached thereto.

The invention discloses a pseudorandom sequence generation method, which comprises: acquiring elements from a value range of {1,2,...,2<e>-1}, wherein e is a positive integer greater than 2; and generating a sequence in a linear recurrence mode based on the elements and a primitive polynomial over Z/(2e-1), wherein in the initial state of the sequence, at least one element is relatively prime to 2<e>-1. The method can generate the pseudorandom sequence with equivalent entropy preservability, rich nonlinear structures and quick periodical growth, balances sequence period and the number of sequence inequal circles, greatly reduces the complexity of sequence source processing in algorithm design and makes the sequential cipher system simple and safe. The invention also provides a method for building a stream cipher system, which comprises: generating the pseudorandom sequence with equivalent entropy preservability by the previous method and using the pseudorandom sequence as a core sequence source; and in a key word combination process, evenly distributing key output by using the elements or operation and providing a simple, safe and high-efficiency sequential cipher algorithm for guaranteeing data security.

The invention discloses a bi-visual-angle face identification method based on multi-local correlation characteristic learning. Multiple local information in correlation characteristic learning is used to better know real non-linear structures among data, so that the face identification accuracy is improved. The realization process includes the steps of determining a plurality of local blocks of each training sample, constructing a uniform optimization framework of related characteristic learning and multi-local fusion, solving the correlation projection direction and multi-local fusion coefficient through alternative iteration, conducting feature extraction and characteristic fusion for training and testing samples, and using the nearest neighbor classifier for identification. Compared with the prior art, the bi-visual-angle face identification method is more effective and robust.

Flexible mounting brackets and support structures can be used to mount substantially linear panels to substantially non-linear support structures without undue stress. For example, at least one implementation comprises a flexible mounting bracket having an attachment interface located on a proximal portion of the bracket, and a mounting interface on a distal portion of the bracket. The proximal and distal portions of the bracket are pivotally connected with one or more torsion springs, so that the proximal and distal portions can move or otherwise adjust to accommodate any stressing. A system for creating a nonlinear support structure thus includes the flexible mounting bracket, as well as vertical support structure components that can be configured to maintain a non-linear shape. Panels mounted to a plurality of flexible brackets maintain the non-linear shape of the support structure components without undue stress or damage.

The invention discloses a multi-mode depth network embedding method for fusing structure and attribute information, and relates to the technical field of complex network analysis. The method comprisesthe steps of establishing a network adjacency matrix and an attribute matrix, performing preprocessing, serially inputting structural features and attribute features into an encoder and a decoder, outputting a reconstructed adjacency matrix and an attribute matrix, updating a parameter iterative calculation and the like, and finally taking the output of the encoder as a final node representation.Based on the depth learning method, the invention can overcome the shortcoming that the existing shallow linear method is difficult to depict the highly nonlinear structure of the network, can map nodes in the network to a low-dimensional embedded space, and effectively maintains the structural characteristics and attribute characteristics of the nodes.

The invention discloses a nonlinear structural part damage cyclic counting method and a nonlinear structural part fatigue life analysis method, and belongs to the field of part fatigue life analysis, for solving the problem of incapability of predicting fatigue life of a conventional nonlinear structural part under the condition of a multi-axis random load. The cyclic counting method comprises the following steps: 1, acquiring the multi-axis random load and establishing a load spectrum; 2, sampling the load spectrum point by point to form a load space vector including directions and sizes; 3, establishing a space coordinate system, incorporating the load space vector in the second step to a plurality of directions spaced at equal angles, and in each direction, according to a time sequence of vectors, forming a single-axis load spectrum in the direction; 4, calculating main strain or main stress under the single-axis load spectrum in the third step; 5, forming a strain spectrum or a stress spectrum; and 6, performing cyclic counting on the strain spectrum or the stress spectrum in the fifth step. According to the invention, the fatigue life of the nonlinear structural part under the multi-axis random load can be accurately predicted, and the analysis efficiency is high.

The invention relates to a multimode system feature dimensionality reduction method. The method comprises the steps that M sets of sample feature vectors under different operation modes Fn of a multimode system are acquired; the sample feature vectors are successively normalized to acquire normalized sample feature vectors; a multimode sample normalized feature matrix is constructed; a local linear embedding algorithm is used to carry out nonlinear dimensionality reduction on the normalized feature matrix; the most similar N-1 dimensional features of the same mode are selected; a multidimensional scaling algorithm is used to carry out linear dimensionality reduction on the matrix in the step (4); and two-dimensional feature matrixes with the largest difference in modes of different classes are selected. According to the invention, feature extraction technologies of non-linear and linear manifold learning algorithms are integrated; through effective feature dimensionality reduction of high-dimensional data, the difficulty of multimode system mode recognition is reduced; linearity and nonlinearity structures of high-dimensional data are maximally preserved; and domain characteristics and distance similarity of high-dimensional data are maintained.

A new textile material produced by a new weaving method is described, said textile material comprising warp and weft yarns in the form of single- or double-ply tapes and preferably partially stabilized fiber tapes. These fibers are formed into nonlinear structures during the weaving process. Non-linear fibers can then be straightened by pulling the tape longitudinally. Double warp yarns and double weft yarns include unconnected tapes. This separation of the constituent tapes of the double-ply warp tape and the double-ply weft tape makes it possible for them to slide relative to each other by pulling in the longitudinal and transverse directions, yet without causing changes in the textile structure. These new fabrics solve the problem of non-uniform fiber distribution and orientation due to stretched compression and stretched crumpling/creasing, respectively, on the inside and outside when bending a tape-like woven fabric into shape. In addition, by utilizing double warp yarns and double weft yarns, it is also possible to form a fabric with relatively flat sections and thicker/raised wide ribbed sections that somewhat resembles a contoured material in cross-section. Other fabrics like those comprising inclined weft tapes, warp and weft tapes with shapes, forming shapes within their bodies can also be manufactured.

This invention has provided an automatic identification method for human face, basing on the shape of data stream; it identifies the unknown human face sample by non-supervision or hemi-supervision method, and using the non-linear structure information of the data of human face. It will carry out the non-supervision identification by extracting the spectrum characters of human face, when any label information can't be gained. And it will carry out hemi-supervision identification for human face by the method to reset the labels among the linear neighbor, when only a part of information of human face label information can be gained.

A hybrid test online model updating method based on the LSSVM comprises the steps of collecting the offline samples of a nonlinear structure constitutive model, and constructing a training sample set;optimizing constitutive model parameters according to the training sample set, training a model by using the current model parameters and the selected sample set, and taking the trained model as a structure prediction model; after a motion equation of the whole structure of the hybrid test is established, adopting a numerical integration algorithm to solve the target displacement of the test substructure of the i-th step of the hybrid test and the target displacement of the numerical substructure; and deleting the first sample in the current training sample set, and adding the sample of the test substructure in the step, thereby updating the training sample set, and then obtaining an updated structure prediction model. According to the method, the initial model of the nonlinear structureis established based on the big data, then the model training sample set is continuously updated online, the model parameters are optimized, the constitutive model is updated online in real time, andtherefore the purpose of accurately predicting the numerical value substructure restoring force is achieved.

The invention provides a video small target detection method based on a depth convolution neural network,. Based on the characteristics of multi-layer nonlinear structure of deep convolution neural network, the video object features are modeled as a whole. Combined with the fast and accurate detection of common video objects by Faster RCNN and the effective path added by ResNet in the feature extraction layer of depth network, With the simplification of the network structure, a depth network structure ERF-Net (Efficient Residual Faster rcnn) based video target detection method is proposed, which can detect video objects quickly and small objects at the same time.. The invention has the advantages that the targets with different distances and different scales in the video are accurately andquickly detected, the effect and efficiency of small target detection are improved, and a good foundation is provided for subsequent target tracking, target recognition, and the like.

A manifold-based linear regression learning method comprises the steps of constructing a predictor Kn of an nth-type training sample; utilizing the nth-type training sample Kn for calculating an nth-type mapping matrix Hn, wherein Hn is obtained through the equation that Hn=Kn(Kn<T>Kn)<-1>Kn<T>; utilizing the nth-type mapping matrix Hn for calculating a linear regression image corresponding to each image y in the type n; constructing a similarity matrix Sij, wherein Sij is obtained in two modes, according to one mode, if 1(xi)=1(xj), Sij=1, if not, Sij=0, and i and j range from 1 to M, according to the other mode, if 1(xi)=1(xj), xi belongs to the k-nearest neighbor of xj or xj belongs to the k-nearest neighbor of xi, Sij meets the equation that Sij=exp(-||xi-xj||<2>/t), t meets the equation specified in the specification, xik represents the k-nearest neighbor of the xi sample, and if not, Sij=0; calculating a feature conversion matrix W. According to the manifold-based linear regression learning method, manifold learning and a linear regression classification model are combined, the nonlinear structure in the high-dimensional space can be kept, the sample can be mapped to the linear subspace easier to classify, the manifold-based linear regression learning method is high in practicability, easy to implement and feasible, and the classification purposes of human face recongnition, biometric feature recognition and the like can be achieved.