2020 results about "Anisotropy" patented technology

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

The invention discloses a machine vision-based intelligent detection method for steel galvanized surface defects, which comprises the following steps: S1, acquiring and preprocessing a steel galvanized surface image to obtain a standardized image; s2, constructing a specular reflection probability graph according to the brightness distribution and the gradient magnitude, and calculating a reflection intensity value; s3, calculating a structure tensor matrix, determining a main direction angle and an anisotropic consistency coefficient, and generating a direction feature matrix; s4, establishing a multi-scale direction adaptive phase kernel function, and performing phase modulation in a frequency domain by adopting an improved phase stretching transformation algorithm; s5, inverse Fourier transform is executed, and a phase response matrix is extracted; s6, performing weighted fusion to obtain a comprehensive phase response diagram; and S7, setting a threshold value according to the noise variance and the statistical characteristics, executing binarization and morphological processing, and outputting a defect region and boundary coordinates. According to the invention, high-precision identification and boundary positioning of steel galvanized surface defects are realized.

The invention discloses a 3D printing path planning method based on electric arc additive anisotropy and a stress field, and the method comprises the steps: constructing a CAD three-dimensional model of a part, and obtaining the stress field of the part through finite element simulation; determining a slice plane; mapping the stress field to a slice plane to form a force flow line; according to the obtained force flow line, rotation transformation regeneration is carried out according to the anisotropy of the used electric arc additive, and a reference trajectory considering the anisotropy of the material is obtained; under the principle of alternate arc starting and extinguishing, the corresponding printing sequence in the layers and between the layers is further planned, and the reference trajectory lines are connected end to end to be planned into a continuous path; and generating a code file, and printing according to the printing sequence. The comprehensive mechanical property of a printed piece is improved.

The invention relates to the technical field of control or regulation systems, and discloses a cooperative control method for a photovoltaic intelligent manufacturing equipment production line, and the method comprises the steps: a control unit collects the stock and change rate of materials in a physical cache region in real time; establishing and mapping the physical cache region into a virtual viscoelastic dynamic model with non-Newtonian fluid characteristics; calculating a virtual elastic restoring force enabling the stock to return to a balance point and a virtual viscous damping force preventing the stock state from changing based on the model; wherein an asymmetric anisotropic damping generation strategy is executed, and a damping coefficient is dynamically split according to a material flowing trend; and finally, superposing the virtual adjustment correction after vector synthesis to the basic transmission speed to generate a dynamic speed instruction, and by constructing a virtual dynamic field with rheological characteristics and an asymmetric damping mechanism, the problem of nonlinear cascade oscillation in discrete logistics transmission is solved, and differential self-adaptive suppression of accumulation and evacuation risks is realized.

The invention relates to the technical field of artificial intelligence and remote sensing image processing, and particularly provides a hyperspectral image and laser radar data classification method based on a dynamic fusion network. The method comprises the following steps: preprocessing acquired multi-modal data, and constructing multi-scale input; a dual-scale local attention module is designed, and context information of different scales is fused in a self-adaptive weighted mode through gating soft pooling; a dynamic down-sampling feature enhancement module is designed, the down-sampling rate is dynamically adjusted according to the complexity of the feature map, and deep multi-scale interaction is carried out based on a Mama backbone; constructing a directional interactive attention module, extracting features in horizontal, vertical and diagonal directions through directional gating convolution, and capturing an anisotropic structure of a linear ground feature; through the design of a double-path classifier, fusing shallow space details and deep semantic information; and the model is trained, optimized and reasoned to obtain data classification, and the method improves the classification precision and the calculation efficiency.

The invention provides a coal mine high-precision three-dimensional geological modeling method and system based on multi-source heterogeneous data fusion, and relates to the technical field of coal mine geological modeling. The method comprises the following steps: establishing a unified semantic reference system and a time-space coordinate reference, and generating an alignment anchor point set; mapping the multi-source observation data to form an alignment feature set and calculating a source weight; according to a multi-evidence consistency rule and geomechanical prior inversion, a discontinuous geological structure is obtained; constructing an implicit stratigraphic function and applying a boundary jump condition to solve a stratigraphic boundary set; and taking the stratum boundary as a framework, and executing anisotropic joint estimation to obtain a property distribution model and an uncertainty result. According to the method, integrated modeling of the geological structure and the property of the complex structure area is achieved, and the precision, the stability and the credibility of the three-dimensional geological model are remarkably improved.

The invention relates to the technical field of computers, and discloses a real-time rendering and interaction method and system for an immersive virtual reality scene. The method comprises the following steps: fusing tuner inertial data and eyeball tracking data, and constructing a prospective state prediction model; generating a predictive focus field in combination with scene visual saliency; synthesizing an anisotropic temporal-spatial resolution graph according to the predicted head angular velocity; gPU variable-rate coloring is driven to realize non-uniform rendering; and re-projection or dynamic fuzzy correction is executed in a self-adaptive manner according to the attitude prediction error before display. According to the technical scheme, the perception delay and the rendering load are remarkably reduced, and the frame rate stability and the visual immersion in a high-dynamic scene are improved.

The invention discloses a self-adaptive visual admittance control method fusing fluid characteristics and multi-modal perception, which comprises the following steps: designing a self-adaptive Bingham-shear thickening fluid virtual damping coefficient through nonlinear mapping based on sigmoid, and combining a threshold triggering behavior of a Bingham fluid and a sudden stiffening characteristic under the impact of the shear thickening fluid; the flexibility is enhanced under the action of small force, and the anti-interference capability is improved under impact. Besides, a force auxiliary function based on force amplitude is introduced, an anisotropic compliance strategy is combined, rigidity and damping are dynamically adjusted by identifying the main force direction, and the mechanism can reduce sensitivity to noise of a micro sensor and ensure stability and accuracy in the task execution process. Meanwhile, an environment attraction domain model is established in a feature space, Lyapunov analysis shows that the system has consistent final boundaries, stable convergence is ensured, and secondary correction is supported.

The invention discloses a volume cloud rendering method and system based on three-dimensional Gaussian splash, and the method comprises the steps: carrying out the feature matching and structure reconstruction of a volume cloud image through an SfM algorithm, obtaining a sparse point cloud in a scene, and representing the sparse point cloud as an anisotropic Gaussian ellipsoid; decomposing the illumination transmission process of the volume cloud into two parts of forward single scattering and internal multiple scattering based on a radiation transmission equation to obtain an illumination modeling formula embedded volume cloud rendering process; determining the contribution of each three-dimensional Gaussian ellipsoid to a rendering result according to the opacity index of the three-dimensional Gaussian ellipsoid, and dynamically cutting the low-contribution three-dimensional Gaussian ellipsoid by adopting a delay deletion strategy to reduce redundancy; a comprehensive loss function is constructed, so that the three-dimensional Gaussian model is focused on the volume cloud region and the structure detail reduction capability is enhanced; and outputting the optimized volume cloud rendering result and the three-dimensional Gaussian model. According to the method, the volume cloud rendering efficiency and quality are effectively improved on the premise of ensuring real-time rendering.

The invention discloses a remote sensing image space-time fusion method and device based on a selective state space model, and belongs to the technical field of remote sensing image processing and computer vision crossing. The method comprises the following steps: acquiring high-resolution and low-resolution image input, and extracting multi-scale features through a multi-layer encoder; capturing an anisotropic space structure in the remote sensing image by using a four-way two-dimensional selective scanning mechanism; designing a state space fusion module, decoupling and cooperatively processing space details and time dynamic information through a space and time sequence selective scanning fusion sub-module, and performing feature fusion by adopting adaptive gating parameters; and finally, reconstructing a high-resolution image through a symmetric decoder, and carrying out model optimization by adopting a composite loss function. On the premise of ensuring the linear calculation complexity, the spatial detail fidelity, the time continuity and the overall efficiency of the fused image are remarkably improved, and the method is suitable for large-scale remote sensing data processing.

The invention is suitable for the technical field of automatic driving, and provides an automatic driving vehicle blind area track prediction and planning method based on Transform, and the method comprises the steps: firstly constructing a key entrance point, calculating a shielding region, and then generating and maintaining a belief state of a ghost target in combination with a risk score; then, real and ghost targets are coded into Token in a unified mode, the Token is input into Transform for interactive modeling, and multi-modal track distribution is output; and finally, a variable anisotropic repulsive force potential field is constructed based on the predicted risk, and a safety control instruction is obtained through optimization solution and hard safety filtering. According to the method, continuous probabilistic estimation and interactive reasoning of the potential risk of the blind area are realized, a flexible defensive track can be generated according to the risk confidence and the dynamic state of the vehicle, and the traffic efficiency is guaranteed while the safety is improved.

The invention discloses a broadband full-Stokes polarization imaging device based on non-orthogonal four-channel polarization beam splitting, which comprises a dielectric substrate, a non-orthogonal polarization beam splitting metasurface and a focal plane detection array, and is characterized in that the non-orthogonal polarization beam splitting metasurface is composed of a plurality of sub-wavelength unit structures; the sub-wavelength unit structure is a single-layer anisotropic medium nano-column array and is configured to perform intrinsic-loss-free non-orthogonal four-channel beam splitting on incident light and form four spatially separated focuses on the same focal plane, and polarization states corresponding to the four channels form a regular tetrahedron measurement base on a Poincare sphere; therefore, full Stokes parameter measurement is completed by four pixels; the sub-wavelength unit structure forms a super-pixel unit, and the super-pixel unit is aligned and integrated with a focal plane detection array in a two-dimensional array form to form a compact polarization camera facing focal plane segmentation. The device is simple in structure, easy to integrate with the CMOS front end, small in device size, low in power consumption and high in mass production feasibility.

The invention provides a scanning electron microscope image edge detection method, which comprises the following steps: carrying out anisotropic diffusion filtering on a scanning electron microscope image to suppress noise and reserve edges to obtain a filtered image; an original scale gradient and a down-sampling scale gradient of the filtered image are calculated, a plurality of pixel regions in the original scale gradient and the down-sampling scale gradient are fused based on a plurality of adaptive weights to obtain a gradient map, the adaptive weights are determined based on the complexity of the texture of the filtered image, and each pixel region corresponds to one adaptive weight; multi-dimensional features are extracted from the gradient map, the multi-dimensional features are input into a machine learning model for threshold prediction, a continuous pixel-level threshold map is generated based on a predicted threshold, and a gradient threshold included in the continuous pixel-level threshold map is a critical value for distinguishing different types of pixels in the gradient map; and comparing the gradient map with the continuous pixel-level threshold map to obtain a target edge pixel, and generating an edge detection result map based on the target edge pixel.

The invention provides a method for measuring surface defects of automobile stamping parts by laser scanning, and belongs to the technical field of part surface defect detection.The method includes the steps that a high-curvature area and a low-curvature area are marked, a self-adaptive viewpoint planning path is constructed based on curvature distribution, dense sampling is adopted in the high-curvature area, and the low-curvature area is marked; a laser scanning device is driven to collect three-dimensional point cloud data, anisotropic thermal diffusion defect enhancement processing is performed on point cloud, a sliding window is adopted to calculate a multi-scale box counting fractal dimension to generate a fractal dimension field distribution diagram, a suspected defect area with the fractal dimension deviating from a normal reference value is identified, and a composite feature vector is extracted; a support vector machine classifier is input to judge a real defect, a defect depth value is corrected by adopting multispectral illumination to measure a bidirectional reflection distribution function parameter and combining with a phase measurement profilometry aiming at the condition of a large incident angle, a defect distribution diagram marked with defect information is generated, and the problem of low surface defect detection precision of a high-curvature area is solved.

The invention relates to the technical field of image segmentation, in particular to an anorectal focus automatic segmentation method based on deep learning, which comprises the following steps: acquiring an anorectal image pixel map, extracting contrast and direction offset to mark candidate focus points, screening overlapped marks to generate a focus activation mark map, and establishing a response map to generate a boundary response distribution map. And training the network to output a classification graph, and extracting a truncation path to complete image segmentation. According to the invention, through extracting the contrast value and the gradient amplitude of the local gray level co-occurrence matrix, accurate capturing of the spatial difference of the lesion area under a complex background is realized, through constructing a response map and direction consistency comparison mechanism and combining multi-dimensional features such as a direction gradient histogram and a structure tensor, the area discrimination capability and the edge classification precision are improved, and the accuracy of edge classification is improved. The texture stability is judged by means of anisotropic standard deviation, a fuzzy edge mask is set, truncation paths are screened in combination with a main direction vector included angle deviation trend, and continuity and stability of a boundary convergence position are ensured.

The invention discloses a low-overlapping-rate point cloud registration method based on topology-metric decoupling, and belongs to the technical field of computer vision and three-dimensional reconstruction, and the method comprises the steps: scanning an object through a three-dimensional laser scanning collection terminal, collecting point clouds at two different angles as a source point cloud and a target point cloud, and achieving high-robustness registration in a difficult scene with an extremely low overlapping rate; according to the method, through the asymmetric association strategy, the zero solution problem under the low overlapping rate is solved through the one-way matching union set, and the recall rate is remarkably increased; meanwhile, through a DGTG geometric pruning and probability manifold optimization mechanism, a voxel quantization error is eliminated by using anisotropic covariance and Lie algebra iteration, a translation error and a rotation error are greatly reduced, and a precision bottleneck caused by voxelization is effectively broken through. According to the method, the existence of a solution can be ensured under the condition that the overlapping rate is extremely low, voxel quantization errors can be eliminated through manifold optimization based on covariance, and high-precision and high-efficiency registration of low-overlapping-rate point clouds is achieved.

The invention discloses a heavy-duty gas turbine blade creep fatigue life design method considering an anisotropic effect, and relates to the technical field of gas turbine key component creep fatigue life evaluation.The method comprises the steps that orientation factor parameters on characteristic orientation are determined based on tensile data on [001] crystal orientation; constructing a modified stress relaxation formula; constructing an anisotropic failure strain energy density equation based on the pure creep performance data and the orientation factor parameters, and determining an upper platform of failure inelastic strain energy density on [001] crystal orientation; further determining an upper platform of the failure inelastic strain energy density on the feature orientation; constructing a creep damage prediction model based on a modified stress relaxation formula and an anisotropic failure strain energy density equation; and adopting the creep damage prediction model to obtain the creep fatigue life of the anisotropic turbine blade material sample. The method meets the actual requirements of service life evaluation of the high-temperature part of the heavy-duty gas turbine.

The invention relates to the field of digital image processing, in particular to a super-resolution imaging method based on focal plane splicing and adaptive fusion. According to the method, sub-pixel offset among nine CCDs is preset through hardware, and nine frames of low-resolution image sequences with accurate displacement are obtained in push-broom. A central image is taken as a reference frame, high-precision mapping is realized based on hardware offset, motion estimation errors are avoided, effective pixels are screened by calculating robustness weight, an anisotropic Gaussian kernel function with a self-adaptive local structure is constructed so as to maintain image edge and detail features, and each frame is accumulated to a high-resolution grid in a weighting mode, so that a high-resolution image is obtained. And a sample compensation mechanism based on cumulative robustness is introduced, a fusion strategy is adaptively adjusted in an information insufficient area, and finally a high-resolution image is generated through normalization. The method significantly improves the imaging quality, suppresses artifacts and noise, and is suitable for the field of satellite remote sensing.

The invention belongs to the technical field of computer vision and three-dimensional point cloud processing. The invention provides an anisotropic point cloud convolution method based on local geometric self-adaption. According to the embodiment of the invention, the principal component analysis is carried out on the neighborhood point cloud to estimate the principal direction of the local geometric structure, and the affine transformation matrix is constructed; and performing dynamic deformation on the basic kernel point of the convolution operator by using the matrix, so that the shape and the direction of the convolution kernel are dynamically aligned with the local structure of the point cloud. The convolution operator is integrated into a point cloud registration network, and can be used for feature extraction of three-dimensional point clouds, especially underwater multi-beam depth finder point clouds. The convolution operator constructed by the method can effectively capture the structure priori of the strong anisotropic point cloud, and solves the problems of structure information loss and limited feature extraction capability caused by the isotropic kernel processing the data.

The invention relates to the technical field of elevators, in particular to an elevator traction machine vibration reconstruction method and system based on a millimeter wave radar. The method comprises the steps that in the elevator running process, echo signals of a millimeter wave radar are collected; firstly, a continuous phase sequence is obtained through echo signals; generating a rough vibration signal in a radar sight line direction according to a phase-displacement mapping relation, calculating a physical confidence coefficient, constructing a dual-channel physical prior tensor, synthesizing a three-axis reconstruction vibration signal tensor by using a PGGN physical gating generation network, and finally performing physical rationality evaluation on the three-axis reconstruction vibration signal tensor. And adjusting the gating parameters of the anisotropic physical gating unit according to the evaluation result. On the premise that a contact sensor does not need to be installed on the surface of the traction machine for a long time, physical consistency reconstruction of the three-axis vibration signals of the elevator traction machine is achieved.

The invention discloses a temperature control system and method of a micro-fluidic chip, and relates to the technical field of micro-fluidic temperature control, and the method comprises the following steps: in the manufacturing process of the micro-fluidic chip, embedding topological insulator nanowires into a substrate in a directional arrangement mode to form a pre-locking array, and meanwhile, generating an anisotropic heat conduction network under the continuous action of a sound-magnetic field, a topological vortex invariant is calculated, and a heat conduction weight coefficient is generated; arranging a fluorescent layer on the surface of a micro-reaction cavity of the anisotropic heat conduction network, collecting a fluorescence lifetime attenuation curve in real time, and calculating a transient temperature value and a temperature change rate according to a heat conduction constraint variation model and a heat conduction weight system; nonlinear feed-forward compensation power is calculated according to the transient temperature value and the temperature change rate, feedback compensation power is calculated in combination with a deviation field of the target temperature and the real-time temperature, and a compensation power instruction is generated; according to the method, the thermal pilot frequency domain filtering function is constructed through the compensation power instruction, so that the uniformity and response speed of thermal response are improved.

The application belongs to the technical field of magnesium alloy and provides a low anisotropy magnesium alloy, which comprises the following raw materials in percentage by mass: Gd 5.80-7.20 wt%, Y 0.02-2.00 wt%, Zr 0.30-0.60 wt%, Zn 0-0.60 wt%, Mn 0-0.10 wt%, Sb 0.02-0.3 wt%, Sr 0.02-0.05 wt%, Sn 0.04-0.1 wt%, and the balance being magnesium. The application also provides a method for preparing large structural parts in a short process. The application utilizes the atomic misplacement and the difference in elastic modulus between the matrix and the Gd and Y elements to improve the mechanical properties, thereby ensuring the basic performance indicators of the alloy system, and utilizes the combined action of rare earth elements and precise control of the forming process to realize the short process preparation of large structural parts.

The invention discloses a water conservancy spraying area identification system based on a laser radar, and the system comprises a data acquisition and modeling module which completes the distortion removal of a point cloud, generates a signed distance field and a reflection intensity body, and builds tangential, normal and auxiliary normal coordinates; the section slicing module obtains a section point set; the feasibility feature and anisotropy Morse function module constructs a scalar field and generates a gradient field; the complex construction module is used for establishing a two-dimensional Morse-Smal complex to obtain an initial spraying area mask and a section boundary candidate; the boundary completion module is used for realizing a homotopy-preserving closed boundary according to the distance field gradient; the persistent coherence screening module is used for obtaining a stable section boundary; and the section calculation and coverage initialization module is used for completing section type fitting and information criterion selection and outputting an operation section size vector and a strip coverage initialization parameter. According to the method, the sprayable area and the section size are integrally identified, the boundary is continuous and consistent, and the method adapts to complex working conditions.

The invention relates to the technical field of optical fiber device manufacturing, and discloses a femtosecond laser inscribing low-polarization-dependent-loss optical fiber grating method and system, and the method comprises the steps: firstly, synthesizing an initial phase comprising a flat-topped beam phase, an astigmatism correction phase and a defocus phase, and loading the initial phase to a spatial light phase modulator; therefore, a preset circular flat-top focusing light spot is generated. Secondly, collecting a three-dimensional light field image sequence of a focusing light spot in a low-power detection mode, reconstructing light intensity distribution, extracting effective focal depth and ovality characteristic parameters, dynamically optimizing a phase through a closed-loop feedback mechanism until light field characteristics reach a preset target, and obtaining an optimized phase; and finally, loading an optimized phase in a high-power inscribing mode, and controlling the axial scanning of the optical fiber to inscribe the grating. Accurate active regulation and control of the three-dimensional shape of the focused light field and closed-loop self-optimization of process parameters are realized, birefringence caused by anisotropy of a refractive index modulation area is inhibited from a physical mechanism, and polarization dependent loss of the fiber bragg grating is fundamentally reduced.

The invention discloses a tunnel cross-sectional image analysis method based on image processing, and aims to solve the problems that a surface image is not clear in correspondence with a transient seismic wave method, a geological radar and a resistivity imaging section space, and anomalies at a certain distance in front are difficult to map to a tunnel face. According to the method, anisotropic reforming is carried out by adopting a Fourier neural operator, cross-modal Transform registration of a micro attention mask based on sector geometry, curve mileage and section polar coordinate position coding is combined, analytic geometry mapping and uncertainty propagation are matched, tunnel face structure traces and water seepage texture evidences are fused, and the tunnel face structure traces and the water seepage texture evidences are combined. The technical effects of accurate positioning of abnormity on the tunnel face, position confidence range estimation, risk grading early warning, stripe artifact suppression, abnormal boundary reservation and output of structured results of mileage stake numbers, azimuth angles, distance intervals and the like are achieved.

The invention discloses a coal mine disaster early warning method based on multi-source data fusion and a time-space diagram network. The method comprises the following steps: (a) collecting multi-source heterogeneous monitoring data; (b) constructing a geologic-physical dual constraint anisotropic space-time diagram; (c) training the graph by using a geologic-physical anisotropic space-time graph neural network, wherein a graph convolutional layer of the network is designed into a propagation mode which is learned and defined at the same time; and (d) predicting a disaster risk by using the trained model and generating an early warning. According to the method, geological and physical priori knowledge is taken as constraints to be incorporated into the topological structure of the GNN model, so that the model can follow physical laws, disaster propagation under anisotropic geological conditions is predicted, and the accuracy and timeliness of early warning can be improved.

The invention relates to the technical field of high-temperature alloy additive manufacturing, and discloses a method for preparing a low-crack-sensitivity nickel-based high-temperature alloy through selective laser melting and application of the low-crack-sensitivity nickel-based high-temperature alloy. And selective laser melting forming treatment is conducted on the core-shell structure powder, specifically, regional laser scanning, interlayer pulse pressurization and in-situ heat treatment are conducted, and the low-crack-sensitivity nickel-based high-temperature alloy is obtained. According to the invention, synchronous regulation and control of microcrack closing and nanophase precipitation are realized through design of core-shell structure powder, laser regional dynamic energy control scanning of hot lines and interlayer in-situ densification. By means of the method, the SLM forming crack density can be reduced to be smaller than or equal to 0.1 mm / mm, the anisotropy index is smaller than or equal to 1.05, the post-treatment hot isostatic pressing link is omitted, and the method can be applied to thin-wall high-cycle fatigue parts such as aero-engine flame tubes and aerospace turbine pump rotors.

The invention belongs to the technical field of data processing, and particularly relates to a multi-view cloud high-precision fusion splicing method based on semantic geometry collaborative optimization, which comprises the following steps: extracting and quantifying features of multi-view cloud, endowing each point cloud with a semantic label, calculating the confidence coefficient of classification of each point cloud, and simultaneously calculating the normal vector and curvature geometric features of each point cloud. And performing robust initial registration based on semantic constraint, and outputting a robust initial transformation matrix. And constructing a collaborative energy function, carrying out dynamic quantitative coupling on semantic confidence and geometric stability, and carrying out iterative registration. And realizing anisotropic point cloud fusion based on semantic guidance, and outputting a fused and spliced three-dimensional point cloud model. And finally, verifying the performance of the method, and measuring fusion splicing errors. According to the method, the semantic features of the point cloud data are effectively extracted and utilized, the precision and robustness of point cloud fusion splicing are improved, and geometric details of a final model are reserved to the maximum extent.

The invention relates to a perpendicular anisotropic magnetic memory based on rare earth doped antiferromagnetic, which comprises a thin film structure, and the thin film structure sequentially comprises a buffer layer, a heavy metal layer, a perpendicular magnetic anisotropic layer, a rare earth doped antiferromagnetic layer and a protective layer from bottom to top, according to the invention, the magnetic texture is changed by doping rare earth elements into the collinear antiferromagnetic to break the symmetry of the system, and the spin-orbit torque is utilized to drive the magnetic state of the memory device with perpendicular magnetic anisotropy to overturn only by means of current under the condition of no external field assistance. The method provides a new implementation path for a low-power-consumption and high-density spin storage technology, has a good application prospect, and is expected to promote actual development and application of novel magnetic storage devices in the future.

The invention provides a composite material damage type identification method based on multiple characteristic parameters of an acoustic emission signal, which comprises the following steps: designing and carrying out an acoustic emission signal acquisition test aiming at a composite material damage evolution behavior; key acoustic emission signal features are screened out based on a principal component analysis method, and the corresponding relation between a clustering result and a damage form is verified; a transient damage contribution index is proposed based on a clustering result to represent the contribution degree of each damage to the overall damage at a certain moment, and a damage function is proposed in combination with strain energy. The method effectively solves the bottleneck problems of high feature redundancy and serious mode confusion of the traditional acoustic emission signal, perfects the spatial correlation model of the energy release rate and the damage mode in the composite material progressive damage theory, provides an experimental basis for establishing an anisotropic damage constitutive equation considering the laying layer direction, and has a wide application prospect. And a new method is provided for online monitoring of a composite material structure under a complex working condition.