13 results about "Bias field" patented technology

The application discloses an infrared image segmentation method based on a local region self-organizing mapping algorithm, which comprises the following steps: firstly, a local region self-organizing mapping algorithm is designed, a plurality of feature bias fields are extracted by moving a local sliding window before iterative evolution of a level set, and a plurality of feature local data driving terms are constructed under a multiplicative bias field model framework; secondly, a plurality of feature global data driving terms are calculated, and a plurality of feature hybrid data driving terms are constructed through an adaptive weight function; then, an adaptive regularization function is used to regularize an energy value range of the plurality of feature hybrid data driving terms, and an initial level set is driven to perform iterative evolution; then, in the iterative process of the level set, a hyperbolic tangent function is used to keep the level set in the iterative evolution, and a symbol rule feature of positive outside and negative inside and a distance rule feature with a modulus value of 1 are kept; meanwhile, a mean filter template is used to continuously smooth the level set and eliminate redundant non-boundary contour lines; finally, a gradient descent method is used to continuously iteratively solve a minimum value of an energy function until a convergence criterion of the level set is reached or a maximum iteration number is reached, and then the method is stopped, at this time, a position of a zero level set is output, and image segmentation is completed. The local region self-organizing mapping algorithm can accurately mine a foreground contour from an infrared image, and has good segmentation speed and precision.

A method for time-resolved pump-probe resonance spectroscopy, comprising the steps of:-exposing a sample (4) to a radio frequency pump pulse (20), where the radio frequency pump pulse (20) drives a resonance, where the resonance is electron spin resonance and / or nuclear magnetic resonance, where the sample (4) is located within a magnetic bias field (22); -detecting the sample (4) with an electron detection beam (7) from the electron source (6); detecting a detection beam characteristic of the electron detection beam (7) by means of a detector unit (8), the detection beam characteristic being dependent on a magnetic moment (25) of the sample (4), the magnetic moment (25) being dependent on the driven resonance, the detector unit (8) being configured to detect the detection beam characteristic of the electron detection beam (7) at a temporal resolution for time-resolved detection of the driven resonance; and determining a property of the resonance, in particular a state of the resonance, preferably a change in the state of the resonance, on the basis of the detected probe beam property.

A method to calculate performance of a magnetic element including a reference bilayer including a ferromagnetic reference layer having a reference magnetization and an antiferromagnetic layer pining the reference magnetization by exchange-bias, the antiferromagnetic layer including a metallic polycrystalline material having a grain volume distribution; the method including: measuring an exchange-bias field (Hex) of the antiferromagnetic layer as a function of temperature; fitting a grain volume distribution function to the measured exchange-bias fields (Hex) to determine parameters characterizing the volume distribution function; calculating a variation in the direction of the reference magnetization as a function of a direction of an exposure magnetic field (H); and calculating the exchange bias field (Hex) for any value of the in-plane exposure field (H).

The invention relates to the technical field of quantum computing and combinatorial optimization, and discloses an iterative bias field hierarchy QAOA solving method and device based on a quantum processor and a medium. In order to solve the problem that a parameterized quantum circuit is difficult to directly solve due to the fact that the graph scale is far larger than the number of available quantum bits, the method comprises the steps that S1, a core block set, a halo extension set and node affiliation mapping are obtained, and bias field parameters are initialized; s2, executing an inner layer quantum step, and calculating a first statistical magnitude and a second statistical magnitude by executing a quantum circuit on the QPU; s3, executing an outer layer interaction step, constructing an outer layer block diagram based on a cross-block edge, and obtaining outer layer soft variable estimation and outer layer soft orientation; s4, executing bias field recharge updating, calculating a next round of inner layer bias field candidate value, and updating the next round of inner layer bias field candidate value; and S5, judging a termination condition, and carrying out loop iteration or outputting a discrete solution. Through hierarchical iteration and bias field recharge, information loss caused by blocking is effectively reduced, and the solution stability on limited quantum hardware is improved.

Room-temperature metal-insulator-semiconductor (MIS) capacitive structures are used to provide unusually large charge transport. Conductivities can be more than 10 times (often orders of magnitude larger) than expected from the classical properties of these structures. This unusual behavior is due to topological states that form within the MIS structures under bias and in the presence of both in-plane and out-of-plane bias field components. One feature of this new physical effect is the transverse supercurrent flow across the MIS structure. Another feature of this new physical phenomenon is the observation of the quantum Hall effect at room temperature and in the absence of a large magnetic field.

The application provides a power plant electric power equipment overheating fault detection method based on visual recognition technology, which can accurately detect the overheating fault of the power plant electric power equipment and improves the efficiency and accuracy of fault detection. The method comprises the following steps: S100, collecting image data of the power plant electric power equipment by using an infrared thermal imager; S200, pre-processing the collected image data, including denoising, enhancement and other operations; S300, segmenting the target region of the pre-processed overheating image by using an improved VLSBC level set contour model with a fusion bias field; S400, identifying the fault image region by comparing the support vector machine (SVM) algorithm; and S500, calculating the similarity value between the two by using the cosine similarity method, comparing the characteristic of the known fault mode, judging whether the equipment has a fault and the type and degree of the fault, and triggering the corresponding alarm mechanism. The application automatically learns and extracts the fault characteristics in the image by using the deep learning technology, thereby improving the efficiency and accuracy of feature extraction.

The invention provides an orbital moment magnetic memory and a preparation method thereof, the orbital moment magnetic memory comprises a substrate, an orbital Hall layer, a free layer, a barrier layer and a binding layer which are sequentially arranged from bottom to top, the orbital Hall layer comprises a metal layer and a FeMn layer which are arranged up and down, the free layer comprises a first Co2Fe6B2 layer and a Fe3GaTe2 layer which are arranged up and down, and the binding layer comprises a second Co2Fe6B2 layer and a second Fe3GaTe2 layer which are arranged up and down. And the bundling layer comprises a [Co / Pt] n layer, a Ta layer and a second Co2Fe6B2 layer which are arranged up and down. The structure utilizes the track Hall layer to efficiently generate a track flow, exchange a bias field and a stray field, and realizes spin polarization and magnetization flipping at the same time under low current through the strong spin-track coupling characteristic of the Fe3GaTe2 layer. According to the orbital moment magnetic memory, the power consumption is reduced while the relatively high writing speed is kept, the excellent performance is shown in high-speed data access, and basic support is provided for application in the fields of future orbital electronics, magnetic storage and the like.

According to one aspect of the disclosure, a sensor includes a substrate; a back bias magnet arranged to generate a bias field at least having components in a plane parallel to a surface of the substrate, the bias field having a horizontal symmetry axis within the plane; and a plurality of sensing element groups disposed at different locations on a surface of the substrate and laid out along a common line aligned with the horizontal symmetry axis of the back bias magnet, each of the plurality of sensing element groups having one or more tunneling magnetoresistance (TMR) vortices having an axis of maximum sensitivity aligned with the common line.

The application belongs to the technical field of sub-seasonal prediction, and discloses a sub-seasonal prediction bias correction method, system, device and medium based on Swin Transformer, to solve the problem of large prediction bias. The method comprises: obtaining sub-seasonal historical prediction data as to-be-corrected training data, and ERA5 reanalysis data and ground observation data as true value reference data, and performing pretreatment; constructing a correction model by combining an autoregressive Swin Transformer network with a sliding time window, inputting the to-be-corrected training data of a preset time length in the past, and predicting the bias field of a preset time length in the future; adopting a point-to-point residual correction form to output the corrected meteorological element field; comparing the corrected meteorological element field with the true value reference data, calculating the error by a composite loss function, and optimizing the network weight by back propagation, to obtain an optimized correction model; and inputting the sub-seasonal future prediction data into the model, and outputting the corrected meteorological element field.

The application relates to a magnetic shielding dynamic degaussing simulation analysis method, an optimization method and a medium, wherein the simulation analysis method comprises the following steps: constructing a three-dimensional finite element model of a magnetic shielding structure, an internal air domain and an external infinite air domain; applying a magnetic insulation boundary condition to the outer surface of the external infinite air domain, and applying a constant geomagnetic bias field as a global static background field; applying a periodic alternating and exponentially decaying degaussing excitation to the magnetic shielding structure; under the degaussing excitation, based on a dynamic magnetization model, a multi-cycle step iteration and transient separation solving strategy is adopted to solve the dynamic evolution process of the magnetization intensity of the magnetic shielding material; after the degaussing excitation ends, the residual magnetization intensity distribution of the magnetic shielding material is determined, and the residual magnetic field distribution of the internal air domain is obtained based on the residual magnetization intensity distribution, so that the prediction accuracy and efficiency of the residual magnetic field are improved.

The invention discloses a panoramic CBCT (cone beam computed tomography) brightness equalization method based on Gaussian low-frequency modeling and Poisson reconstruction combined bias estimation, which relates to the technical field of medical image equalization and comprises the following steps: converting a Raw format of an image data file to be processed into a 32-bit floating point data format according to an ini configuration file; creating a Gaussian kernel according to the mathematical expression; a Laplace residual error is constructed; and superposing the images corrected by the bias field according to the proportion. According to the method, the brightness low-frequency trend and the structure gradient change are captured at the same time through a double-branch bias field learning mechanism, rapid and accurate estimation of the bias field is achieved, and the high-robustness bias correction model is obtained through adaptive fusion. And a gray scale stabilization strategy is further introduced, the brightness consistency and contrast optimization of the CBCT panoramic image are realized under the condition that the window width and the window level do not need to be manually adjusted, and the organization detail recognizable degree and the cross-device imaging consistency are effectively improved, so that the clinical diagnosis quality and stability are remarkably improved.

The invention relates to the technical field of weather forecasting, in particular to a data-driven ocean near-real-time observation rolling integrated forecasting method and device, and the method comprises the following steps: obtaining observation and forecasting data, calculating a difference, updating a deviation library, extracting an expected deviation, debiasing an original forecasting field, and generating a candidate integrated temperature and salt field; and calculating the density value by hardware to generate an inconsistent mask, screening unstable points based on the mask, adjusting the weight matrix, iteratively updating until the unstable points are smaller than a threshold value, and outputting a forecast result. According to the method, the difference value of near-real-time observation and forecasting is calculated, a rolling model deviation database is dynamically updated, an expected deviation field is subtracted when new forecasting is generated, a depolarization candidate state field is formed, device deviation is restrained, hardware is used for calculating density in an accelerated mode, vertical adjacent density is compared to generate inconsistent masks, and unstable grid points are screened. And adjusting the integrated weight matrix, and iteratively updating the candidate integrated temperature and salt field until the unstable point reaches the standard, so as to ensure that the result meets the physical rule constraint.

The application relates to a training method of a coherent Ising machine and related equipment. The training method comprises the following steps: injecting noise into the coherent Ising machine, so that, under the joint action of physical parameters and the noise, the continuous amplitude vector of the light pulse generated by the coherent Ising machine evolves to a steady-state distribution state subject to the Gibbs distribution according to the Langevin dynamics evolution rule, the physical parameters include pumping parameters, saturation parameters, coupling coefficients, bias field parameters and gains; after the continuous amplitude vector collected under the current steady-state distribution is taken as negative-phase amplitude data, one or more of the physical parameters are updated based on the update amount obtained from the deviation between the negative-phase amplitude data and positive-phase amplitude data, so that the continuous amplitude vector reaches a target steady-state distribution. The application solves the problem that in the traditional CIM machine, the parameters are mostly fixed or empirically adjusted, and the steady-state sample distribution of the light pulse changes greatly due to environmental drift and device errors.