14 results about "Spectral vector" patented technology

The application discloses a hyperspectral image real-time local anomaly detection method based on multi-line wave band processing, which comprises the following steps: reading hyperspectral image data, initializing the number of wave bands n determined by the number of filters, initializing the number of lines j based on wave band extraction, and initializing the interval number k between different wave bands; converting the data into data required for real-time detection processing according to the values of the number of wave bands n, the number of lines j and the interval number k; establishing a state equation of a spectral vector correlation matrix R(n) of hyperspectral data pixels; updating the inverse matrix R(n) of the correlation matrix R(n) by using a block matrix inversion formula ‑1 ; detecting the hyperspectral data required for real-time detection processing by using a multi-line wave band correlation matrix real-time local anomaly RX operator, and finally obtaining the result of the hyperspectral image real-time local anomaly detection. The application realizes local anomaly detection of the hyperspectral image, avoids high-dimensional data storage and repeated calculation, and has a good real-time local anomaly detection effect.

This invention provides a 3D printing metal material grinding device and grinding system, comprising: acquiring a sequence of hyperspectral images of metal powder particles to be ground, identifying and segmenting individual particles; extracting the pixel coordinates of the particle edge contour and the corresponding spectral response vector, and normalizing the spatial coordinates and spectral vectors respectively; constructing a first covariance matrix representing the spatial distribution of particles and a second covariance matrix representing the material distribution; performing singular value decomposition on the two matrices respectively, terminating the iteration when the relative change of the principal singular values ​​is less than a preset threshold, and obtaining singular values ​​and singular vectors; using the ratio of the two largest principal singular values ​​of the first matrix as the contour anisotropy parameter; using the sum of the singular values ​​of the second matrix plus the reciprocal of a preset constant as the material uniformity parameter; inputting the two types of metric parameters into a pre-trained control model, generating control commands and sending them to the grinding equipment actuator.

This invention provides a method and system for quality detection of edible fungi based on spectral features, belonging to the field of spectral detection technology. This invention acquires the light intensity sequence of a detection light source under a preset fluctuation state, extracts standard deviation, root mean square, and frequency features to construct a multidimensional fluctuation feature vector, achieving a comprehensive characterization of the light source's fluctuation characteristics. This provides rich input information for the offset prediction model, significantly improving the accuracy of spectral correction. Using the multidimensional fluctuation feature vector as the independent variable and the spectral offset as the dependent variable, an offset prediction model corresponding to each wavelength is established. Target wavelengths are then selected based on the goodness of fit, achieving high-precision correction of the original spectral data. The corrected spectral data is compared with the grade template spectral data in the quality template library using cosine similarity calculation, and the quality grade is selected by spectral vector matching, thereby significantly improving the stability and reliability of the edible fungi quality detection results.

The application provides an online water quality analysis method and system in a radioactive waste liquid treatment process, and relates to the technical field of online water quality monitoring.The method comprises the following steps: step 1, passing a clarified sample into an analysis pool, collecting Raman spectral characteristic peak data in real time through a plurality of groups of optical fiber Raman probe arrays arranged in the analysis pool along the flow direction of the waste liquid; performing principal component analysis on the Raman spectral characteristic peak data, extracting characteristic spectral vectors corresponding to main radioactive nuclides and interfering substances, and constructing a two-dimensional concentration distribution field according to the continuous change of the characteristic spectral vectors in the spatial position; and step 2, extracting an isochromatic line and performing discrete sampling on a specific interfering substance component in the two-dimensional concentration distribution field, calculating the minimum circumscribed circle of all sampling points, and obtaining the center coordinates and radius of the isochromatic line.The application realizes intelligent regulation and control of treatment process parameters and guarantees that the discharged water quality is stable and up to the standard.

This application relates to the field of intelligent detection technology, and in particular to a method and system for detecting mycotoxins in feed based on spectral analysis. The method includes: acquiring the near-infrared diffuse reflectance spectrum of the feed sample to be tested; constructing an original spectral vector based on the absorbance data of the near-infrared diffuse reflectance spectrum; determining the projection component of the original spectral vector on a pre-constructed qualified matrix space; subtracting the projection component from the original spectral vector to obtain a residual vector; calculating the first-order difference spectrum of the residual vector based on preset sensitivity weights; determining weighted eigenvalues ​​by local feature weighting of the first-order difference spectrum; determining the residual entropy value and the range value of the residual vector; and using the weighted eigenvalues, residual entropy value, and range value to determine the detection result of the feed sample to be tested. Through the above technical solution, more accurate detection results for mycotoxins in feed can be obtained.

This invention discloses an audio information processing method and system, applied in the field of audio signal processing technology. The method includes extracting spectral features from audio data to be processed and performing spectral encoding on the spectral features to obtain a spectral vector representation; performing temporal encoding on the audio data to be processed to obtain a temporal vector representation; obtaining a first voiceprint result based on the spectral vector representation and the temporal vector representation; and obtaining a second voiceprint result based on the spectral vector representation and the temporal vector representation; integrating the first and second voiceprint results into a voiceprint embedding set; performing max pooling on the voiceprint embedding set to obtain a voiceprint feature embedding vector; and determining the voiceprint recognition result of the audio data to be processed based on the analysis results of the voiceprint feature embedding vector. The method of this invention improves the accuracy of voiceprint recognition results.

This invention discloses a method for predicting and correcting the uniformity of PE film thickness, belonging to the field of prediction and correction technology. It addresses the problem of unmet requirements for high stability and high consistency in film thickness control. By setting a monitoring time window, lateral thickness data of the PE film is collected within the time window. The thickness periodic characteristics are evaluated and thickness disturbance states are classified. Based on the classification results, a fluctuation analysis mechanism is triggered to acquire screw speed data and perform Fourier transform. Coupled with the thickness data, the coupling spectral density is calculated. Melt pressure and traction tension amplitude data are collected to construct spectral vector features. The dominant frequency peak is extracted to predict the time-domain disturbance, which is then corrected based on the coupling spectral density. The thickness deviation is judged based on the corrected time-domain disturbance, and the die head adjustment displacement is generated. During the correction process, the thickness change rate is collected to determine the correction status, improving the accuracy of thickness deviation identification and adjustment response precision, and enhancing the stability and consistency of film thickness control.

This invention discloses a DOA super-resolution estimation method and product based on Toeplitz covariance enhancement and neural networks. The method includes: acquiring received data from a uniform linear array, calculating the sample covariance matrix, and performing trace normalization; performing Toeplitz prior correction by averaging the elements of each diagonal and backfilling to construct the Toeplitz prior covariance, and performing Hermitization; based on the sample covariance matrix and the Toeplitz prior covariance, adopting a linear shrinkage model and determining the optimal shrinkage coefficient using the Frobenius norm mean square error as the criterion to obtain the shrinkage covariance matrix; performing vectorized projection onto an overcomplete array dictionary to obtain complex projection features; splitting the features into real and imaginary parts to form a dual channel, inputting it into a convolutional neural network to obtain a spatial spectral vector; and performing spectral peak search to extract the angles corresponding to the K peaks with the largest amplitudes to obtain the DOA estimation result.

This application relates to a method, apparatus, and electronic device for detecting near-infrared spectral quality parameters. The method includes: acquiring near-infrared spectral data of the substance to be tested to obtain a near-infrared spectral vector; inputting the near-infrared spectral vector into a pre-constructed, delinearized, lightweight RBF-KAN module structure for RBF feature mapping to generate an RBF feature vector; introducing a band-gating structure to perform feature fusion processing on the RBF feature vector to generate a fused feature vector after dimensionality reduction and batch normalization; introducing variational regularization constraints to construct an equivalent fusion weight matrix and an equivalent bias vector; and outputting the predicted value of the quality parameters of the substance to be tested. This solves the problems in related technologies, such as the difficulty in achieving rapid detection using physicochemical analysis methods, the susceptibility of portable near-infrared spectroscopy to environmental influences, the high cost and lack of band-level interpretability of deep learning models, leading to unstable prediction results and insufficient robustness and applicability.

This application discloses a method for constructing a three-dimensional structural model based on cross-section analysis, belonging to the field of geological model construction technology. The method includes: acquiring borehole trajectory data and observation data for a target geological area, and determining virtual intersection points between each borehole trajectory; calculating the local gradient deviation and spectral vector of each borehole trajectory at the virtual intersection point based on the observation data; constructing a confidence weighting matrix based on the intersection angle and local gradient deviation between each borehole trajectory; determining the optimal observation value at the virtual intersection point with the smallest spectral vector error based on the confidence weighting matrix and the spectral vector deviation between each borehole trajectory; updating the observation data with the optimal observation value to generate a corrected observation sequence, and constructing a three-dimensional structural model based on the corrected observation sequence. This application achieves the technical effect of improving the reconstruction accuracy of the model structure under complex borehole layout conditions and enhancing the continuity of topological fractures or attribute fields in the model.

This invention discloses a method and system for complex terrain path planning of a hybrid mineral exploration robot, relating to the field of robotics technology. The method reads digital elevation model data and multispectral remote sensing image data of the task area, and generates regular terrain raster units and a lithology classification map. A lithology raster distribution matrix is ​​generated through spatial registration. Based on the lithology raster distribution matrix, lithology boundaries are detected, a set of boundary lines is generated, and the curvature value Ki and normal vector Ni of sampling points are calculated. Path expansion is performed to generate a set of candidate paths, and the number of path crossings Nb and the boundary neighborhood movement distance Db are calculated. The coordinates of the intersection positions of path segments and lithology boundary lines are read, and the entry and exit area raster areas are determined. The lithology spectral vector difference ΔS and the cumulative boundary structure Cb are calculated. A lithology cross-boundary structure quantity Q is constructed, and path complexity is evaluated. When the path complexity evaluation indicates that the path structure is stable, a path evaluation quantity F is constructed, and a comprehensive path evaluation is performed.

This application belongs to the field of skin injury image assessment technology, specifically providing a method and system for assessing skin injury caused by chemotherapy drug extravasation. The method mainly includes: acquiring a multispectral image of the affected area; acquiring a damage reference spectral vector representing typical drug-induced damage characteristics; based on the damage reference spectral vector, performing orthogonal projection decomposition on the spectrum of each pixel in the multispectral image to generate a projection component image and a spectral residual image; extracting the local texture pattern of the spectral residual image to generate a residual texture map for quantifying the heterogeneity of tissue microstructure. This application effectively solves the problem in existing technologies where early drug extravasation injury and subcutaneous bruising are difficult to accurately distinguish under conventional visual conditions, easily leading to clinical misjudgment and delayed intervention. It enables early warning of the risk of early, occult tissue necrosis caused by chemotherapy drug extravasation, thereby potentially reducing the incidence of severe tissue damage.

This invention discloses a molecular structure prediction method based on dual-core one-dimensional nuclear magnetic resonance (NMR) raw spectra. The method directly acquires the raw spectral sequences of hydrogen and carbon nuclei, performs preprocessing, raw spectrum encoding, and fixed-query resampling respectively, to obtain dual-core grounded latent variables. Within the latent variable space, it further performs intranuclear self-attention updates and cross-nuclear bidirectional cross-attention updates to obtain dual-core inference latent variables. Based on real chemical shift information, it constructs chemical shift-assisted monitoring targets. Then, it constructs a unified spectral memory sequence and a global spectral vector from the dual-core inference latent variables, and inputs them into a cross-modal molecular structure prediction network. This network jointly performs spectrum-molecule comparison, spectrum-molecule matching, and molecular structure generation, outputting candidate molecular structures. This invention improves the utilization of continuous information from raw spectra, the modeling ability of complementary dual-core information, and the unified prediction capability of molecular structures.

This invention relates to the field of plant growth status monitoring technology, specifically to a method for monitoring the growth status of ancient trees based on spectral sensors. The method involves: first, collecting and recording hyperspectral data of the canopy of ancient trees in the monitoring area; processing this data to obtain the original reflectance spectral vector, forming samples, and labeling them with growth status; then, extracting multi-scale differential ripple features corrected for local scattering, embedding red edge and moisture parameters, and constructing a decay trajectory index to form a decay fingerprint; extracting key absorption window features; and fusing these three features to obtain a spectral physiological stress fingerprint; constructing a multi-receptor field convolutional network, and training the model through spectral segmentation, multi-receptor field convolution, decay trajectory-guided recalibration, classification output, and ordered weighted cross-entropy; finally, deploying the model in a monitoring system to automatically identify new spectra and output identification results, confidence levels, and risk warnings. This invention improves identification accuracy and automatically outputs precise identification results by extracting features sensitive to physiological stress and resistant to interference.