31 results about "Optimal alignment" patented technology

The invention provides a cross-language text classification and processing method and system based on deep transfer learning, and relates to the technical field of text processing, and the method comprises the steps: extracting feature representations of a source language text and a target language text at different linguistic levels through a multi-level semantic transfer network; and determining an optimal alignment path, performing nonlinear mapping alignment to obtain fusion features, propagating category semantics by using a semantic bridging function, iteratively updating pseudo-tag confidence distribution of the target language text, and completing classification in combination with a multi-task learning model. According to the method, the problem of text classification in a cross-language scene is effectively solved, and the accuracy and efficiency of low-resource language text processing are improved.

Correction and / or other adjustments to optical systems of lithographic, metrology, and / or other apparatuses are generally based on information from previous manufacturing and / or metrology operations, user experience of the lithographic and / or metrology systems and / or processes, underlying optical physical principles, and / or other information. Advantageously, the present systems and methods use reinforcement learning to optimize alignment (and associated settings and / or other characteristics) of tens, hundreds, thousands, or millions of optical system components, and make corrections and / or other adjustments to optimize adjustments and / or processing time and / or cost.

The invention discloses a deep learning time sequence alignment method and system for a multi-view asynchronous sequence image, and the method comprises the following steps: carrying out the image screening of a multi-view asynchronous collected angiography sequence, and constructing a short sequence composed of three frames of continuous images; the method comprises the following steps of: extracting a backbone network by adopting an OfficientNet-B0 as a feature extraction backbone network, removing a classification head of the feature extraction backbone network, replacing the classification head with identical mapping, outputting convolutional features, and performing network optimization in combination with a soft dynamic regularization alignment loss function (soft DTW); extracting a feature vector describing the heart cycle progress through a network; calculating the cosine similarity of the feature vectors among different view angles, constructing a similarity matrix, and searching an optimal alignment path in the similarity matrix by using a soft dynamic time warping algorithm; and outputting a time sequence alignment frame and a corresponding similarity score according to the optimal alignment path. According to the method, the multi-view asynchronous sequence image can be effectively processed, time sequence alignment is realized, and a reliable alignment basis is provided for cardiovascular image analysis.

This invention discloses an automatic construction method for catenary support equipment archives based on multi-frame visual sequence alignment, belonging to the field of railway operation and maintenance technology. The method includes: acquiring trackside inspection videos; segmenting the frame data sequence based on the area change trajectory of the pole boundary frame to obtain several pole trajectories compressed into candidate pole nodes; extracting representative frames, kilometer marker area characters, license plate area characters, and pole type labels to obtain a candidate pole node sequence arranged by time; acquiring pre-stored real structured trackside data; constructing a key point dictionary based on the uniqueness of kilometer markers; generating a real pole number sequence arranged in line order; scoring all video frames covered by the candidate pole nodes frame by frame; using the Viterbi algorithm to solve the globally optimal alignment path from the candidate pole node sequence to the real pole number sequence; and generating catenary support equipment archives. This invention solves the problems of low accuracy in catenary support equipment archive construction, inability to effectively handle missed detections, false detections, and disordered sequences.

This invention discloses an embodied data generation method for category generalization operations, belonging to the field of embodied operation technology. The method includes: constructing a shape library containing warp mappings between templates and target shape spaces; acquiring the 3D model, pose trajectory, and function point annotations of source demonstration data; generating a new shape model through the shape library mapping; calculating the geometric deviation of key points; solving for the optimal alignment transformation sequence and correcting the robotic arm's motion trajectory; integrating the new model into the scene point cloud; and driving the virtual robotic arm to synthesize a new sequence of observation action pairs. This invention achieves function-aware nonlinear warp mapping, solves the function alignment problem under complex geometric deformations, and improves the generalization ability of embodied operations; it adopts a reality-to-reality paradigm, completing efficient data augmentation with a small number of annotations, significantly reducing data collection costs.

The invention provides alignment marks with different duty ratios and an alignment method, and the alignment marks comprise a plurality of alignment marks which are located on a cutting channel and are distributed along the extending direction of the cutting channel, each alignment mark comprises a plurality of gratings which are arranged in parallel, the plurality of gratings are arranged at intervals along the extending direction of the cutting channel, and a gap is formed between every two adjacent gratings; the extension direction of the gratings is perpendicular to the extension direction of the cutting channel, and the plurality of gratings are rectangular; wherein in each alignment mark, the grating widths are the same, the duty ratios formed by the grating widths and the sum of the distances between the grating widths and the adjacent gratings are the same, and the duty ratios of the multiple alignment marks are gradually increased or decreased in the extending direction of the cutting channel. After the film layer is formed, a plurality of alignment marks are scanned in sequence, and the optimal alignment mark is selected from the alignment marks and fed back to serve as the alignment mark of the corresponding film layer during next photoetching, so that an ideal alignment signal is obtained.

The invention discloses a hydraulic ring geological disaster intelligent identification method based on deep learning, and the method comprises the following steps: 1, collecting original data of a plurality of sensors, and carrying out the preprocessing of the original data, and generating the original data of a unified structure; 2, projecting the original data of the unified structure to a low-dimensional space through local linear embedding, and generating a spatial feature vector; 3, calculating a Manhattan distance between the spatial feature vectors to construct a cumulative distance matrix, obtaining an optimal alignment path, and generating a time sequence feature vector; 4, analyzing the persistent features of the time sequence and spatial features, and constructing a persistent feature map; 5, generating a multi-dimensional feature vector set through an improved Markov random field; and 6, performing anomaly detection according to the Gaussian mixture model, generating disaster early warning information, and performing visual output. According to the method, multi-sensor data and deep learning are fused, and the accuracy and real-time performance of hydraulic ring geological disaster early warning are improved.

This invention relates to a method and system for constructing geographic entities and generating relationships, specifically including: identifying target vector layers from multiple sources, and extracting layer names, attribute field names, and their corresponding attribute values ​​from the target vector layers; performing multi-level filtering and comprehensive scoring based on layer names to determine the standard layer category for optimal alignment of layer names; mapping the attribute field names of the target vector layers to target standard fields using the standard layer category as a reference, and converting the attribute values ​​corresponding to the attribute field names into standard codes to achieve attribute standardization; acquiring the geometric data of the target vector layers, generating target vector entities based on the geometric data, and binding the corresponding standard attributes to the target vector entities; establishing topological relationships, hierarchical relationships, and cross-source entity referencing relationships between entities based on spatial topology analysis and semantic association analysis, thereby forming a structured geographic relationship network.

This invention relates to the technical field of geomagnetic positioning and discloses a geomagnetic sequence matching method, comprising the following steps: First, a sequence library is constructed using a geomagnetic reference map, and real-time geomagnetic data is received for matching. Second, sequence similarity is evaluated using a particle swarm optimization algorithm combined with a multidimensional dynamic time programming algorithm, incorporating the Maltrov criterion and roulette wheel optimization search. Then, the velocity and position of the particles are continuously adjusted using adaptive inertia weights. In each iteration, the fitness of each particle is calculated, and the individual optimal position and global optimal position of the particle are updated. When the maximum number of iterations or the particle's fitness threshold is reached, the iteration terminates, the optimal matching position is output, and finally, the user's final position is calculated using the geomagnetic sequence. The technical solution of this invention can find the optimal alignment path between two sequences in the time dimension using particle swarm optimization and simulated annealing algorithms without increasing computational load, improving matching accuracy and shortening computation time.

A feature dimension reduction method for a small sample classification task belongs to the technical field of data processing, and comprises the following steps: dividing image data into a training set and a test set; constructing a small sample image classification model based on class related feature orthogonal alignment; performing sample feature extraction, sample feature dimension reduction and sample feature orthogonal alignment on an input image by using the small sample image classification model, calculating sample similarity and classification loss, optimizing the model, and outputting an optimal category related feature reconstruction model; and classifying the test image by using the optimal model. According to the method, efficient dimensionality reduction can be completed on the premise that discrimination information is reserved to the maximum extent, the compactness and discrimination of feature representation are improved, and the generalization performance of a classification model is improved. According to the method, the optimal alignment of the query sample and the support sample in the local feature space is realized, the geometric structure integrity of the features is kept, the precision and stability of cross-task feature alignment are improved, and the accuracy and robustness of cross-domain classification are improved.

The application provides a speech text alignment model training method and device, electronic equipment and a storage medium, and relates to the field of large models. The method comprises the following steps: obtaining a pre-training model and text data, and performing time information prediction on each word in the text data based on the pre-training model to obtain a time prediction result corresponding to each word; obtaining speech data, and performing text reconstruction on the speech data based on the time prediction result to obtain reconstructed text data; performing optimal alignment path prediction on the reconstructed text data and the speech data based on a dynamic programming algorithm to obtain an optimal alignment path; and adjusting parameters of the pre-training model based on the optimal alignment path to obtain a speech text alignment model. The application solves the problem of low applicability of a speech text alignment model in related technologies.

The invention discloses an equipment fault diagnosis method and device, electronic equipment and a storage medium. The method comprises the following steps: according to different wavelet scales, respectively executing continuous wavelet transform on an acquired vibration signal, a current signal and a voltage signal of equipment to be diagnosed, and extracting an effective wavelet coefficient under each wavelet scale; calculating energy probability distribution of effective wavelet coefficients to obtain wavelet scale entropies of the vibration signal, the current signal and the voltage signal under each wavelet scale; determining dominant scale entropy corresponding to each time point in the three signals; based on the dominant scale entropy difference of the vibration signal and the voltage signal and the dominant scale entropy difference of the current signal and the voltage signal calculated for each time index tuple in the constructed alignment path set, solving a pre-constructed optimal alignment path function in combination with path smoothing constraints; performing time-frequency domain alignment on the multi-modal signal according to the solved optimal alignment path set; and carrying out equipment fault diagnosis based on the aligned multi-mode signals.

This invention discloses a method for measuring cavitation in plant xylem, comprising: calculating image parallax in an image sample set and recovering scene depth information; extracting the center line of the main stem based on the segmentation results of the main stem; determining the two-dimensional pixel coordinates of the target vibration measurement point above the plant roots along the direction of the center line of the main stem; and calculating the three-dimensional spatial position of the target vibration measurement point by combining the scene depth information corresponding to the target vibration measurement point; automatically coarsely aligning the target vibration measurement point above the plant roots; obtaining the optimal alignment posture of the laser Doppler vibrometer; collecting vibration signals from the target vibration measurement point above the plant roots; and measuring the cavitation in the plant xylem using the collected vibration signals. This invention addresses the problems of manual reliance, low efficiency, poor repeatability, and unstable signal quality caused by on-site interference in existing technologies for measurement point identification and LDV alignment, and achieves automatic positioning, automatic alignment, and cavitation event measurement of the position above the plant roots.

This invention discloses a multi-parameter monitoring method and system for seismic isolation bearings. The method includes: dividing multiple seismic isolation bearings into associated node groups and specifying a reference node and reference time; calculating in real time the time difference between the slave nodes and the reference node in the physical data within the physical associated node group, determining whether there is time drift in the slave nodes (the slave nodes are nodes other than the reference nodes); marking the slave nodes with time drift as nodes to be repaired, finding the optimal alignment path between the physical data sequence of the node to be repaired and the template sequence; establishing a nonlinear time mapping function; and being able to perform full waveform correction for complex time distortions such as delay, stretching, and compression, thereby suppressing the formation of data tearing at its source.

This invention provides a method and apparatus for Manchu speech synthesis based on character component modeling, belonging to the field of speech synthesis technology. The method includes: converting Manchu text into Latin characters according to transcription rules, generating a character embedding sequence, and generating feature templates containing dimensions such as fundamental frequency and pitch band based on the acoustic characteristics of Manchu pronunciation; concatenating the two templates into a high-dimensional input tensor, which is then fed into a pre-trained duration prediction network; constructing a mask, extracting prosodic features from the Manchu Latin-transcribed character embedding sequence to generate a prosodic vector, and adjusting the attention weights between characters and speech frames in the pre-trained feature network to obtain the prosodic space, Mel-spectral sequence, and optimal alignment path; finally, inputting the vector into a lightweight multi-band inverse short-time Fourier transform decoder for Manchu to obtain a complete synthesized speech waveform. This invention can significantly and effectively solve the core problem of text-speech frame matching deviation in low-resource Manchu scenarios, and its inference speed is significantly improved compared to general models.

This invention belongs to the field of gene sequence alignment technology, specifically relating to a haplotype-aware sequence-to-graph alignment method, system, device, and medium. This method directly embeds haplotype information into the edge association structure of a pan-genome graph, enabling the graph structure itself to express haplotype paths. Then, an improved haplotype-aware partial order alignment algorithm is used to map the sequence onto this graph, allowing it to identify and utilize the embedded haplotype information, ensuring global consistency of the alignment results. Finally, a path reuse backtracking based on global caching is used to obtain the optimal alignment path, significantly improving algorithm efficiency while maintaining accuracy.

The invention provides a full-language speech synthesis method and device based on character component modeling, and relates to the technical field of speech synthesis. The method comprises the following steps: converting Manchu into Latin characters according to a transcription rule, generating a character embedding sequence, generating a feature template containing dimensions such as a fundamental frequency and a pitch band based on full-language pronunciation acoustic characteristics, splicing the feature template and the feature template into a high-dimensional input tensor, and sending the high-dimensional input tensor into a pre-training time length prediction network; constructing a mask, extracting rhythm-related features from a character embedding sequence of Manchu Latin transcription to generate a rhythm vector, adjusting attention weights of characters and voice frames in a pre-training feature network to obtain a rhythm space, a Mel spectrum sequence and an optimal alignment path, and finally inputting the rhythm space, the Mel spectrum sequence and the optimal alignment path into a full-language lightweight multi-band inverse short-time Fourier transform decoder. And a complete synthetic voice waveform is obtained. According to the method, the core problem of text and voice frame matching deviation in a full-language low-resource scene can be obviously and effectively solved, and the reasoning speed is obviously improved compared with a general model.

This invention provides an adaptive depth-matching depth-domain seismic inversion method, comprising: Step 1, preprocessing well logging data; Step 2, obtaining the time vibration function to establish the relationship between depth-domain wave impedance and seismic records; Step 3, performing depth-domain well-seismic matching correction to achieve optimal alignment of two depth sequences; Step 4, constructing an initial depth-domain model; and Step 5, performing model-driven depth-domain inversion to obtain the depth-domain seismic inversion results. This adaptive depth-matching depth-domain seismic inversion method updates the velocity model in each iteration of the inversion process, and the depth-time relationship is corrected in real time during the iteration, making the inversion results more accurate and reliable. It avoids the accuracy loss caused by well logging data during depth-time conversion and overcomes the error accumulation that occurs during the conversion process.

The application relates to the technical field of computers and discloses a pilot training operation automatic evaluation method and system based on rule guidance and artificial intelligence fusion, which comprises the following steps: a probability semantic state machine model is constructed; a benchmark operation rule set constraint is converted into a semantic topological space with a state transition probability matrix; an artificial intelligence recognition algorithm is used to obtain multi-modal features of a target operation behavior of an operation subject, and an observation probability vector sequence representing operation probability distribution at each moment is output; the observation probability vector sequence is mapped to the semantic topological space, a state alignment relative entropy is calculated, an optimal alignment path is recursively searched, and an operation deviation cost is generated, and evaluation indexes are determined according to the operation deviation cost; and the semantic topological space with state transition weights is established, and the fault tolerance of evaluation logic to uncertainty of a perception layer is improved.

The application provides a method for high-precision parameterization adjustment of a completed curtain wall model based on point cloud, comprising the following steps: S4, for any curtain wall, searching for point cloud within a search radius R in the X-Y plane projection of three-dimensional point cloud information C2, taking a midpoint in the curtain wall as a radius, and obtaining three-dimensional point cloud information C3 for finding a curtain wall table top; S5, finding the curtain wall table top; S6, obtaining curtain wall point cloud feature points and forming a matrix A; S7, creating a BIM model of the curtain wall; S8, extracting design feature points on the BIM model in step S7 to form a matrix B, wherein the design feature points correspond to the matrix A; S9, first, bringing the matrix B into an ICP registration algorithm optimal alignment algorithm to obtain a rotation matrix; and then, adjusting the BIM model of the curtain wall by using the rotation matrix, so as to complete high-precision parameterization adjustment of the curtain wall BIM model by using the point cloud. The application can avoid the situation that feature point positioning deviation is caused by artificial subjective influence, and is beneficial to curtain wall maintenance and detection work in the later period.

The invention relates to a longitudinal profile alignment method and device before and after steel rail polishing and a storage medium, which are applied to the technical field of steel rail maintenance, and the method comprises the following steps: establishing a self-adaptive processing framework of first detection and then decision making, and intelligently selecting an optimal alignment algorithm path according to the characteristics of steel rail profile data; alignment can be effectively completed no matter in a section with remarkable geometrical characteristics such as a steel rail joint or in a smooth steel rail section without characteristics, the problems that in the prior art, an alignment method adopting a single algorithm or a fixed process is poor in robustness and insufficient in adaptability under variable working conditions are successfully solved, and the method has high robustness and high adaptability. Moreover, according to the method provided by the invention, the whole process from data input, feature detection, strategy selection to final alignment output does not need any manual point selection or intervention, the automation of the whole process is realized, the labor cost is greatly reduced, and the working efficiency of steel rail maintenance data processing is improved.

The application discloses a kind of time series data alignment method and system based on cross-modal semantic consistency related to cross-modal data processing, sequence modeling and artificial intelligence technical field.The method comprises obtaining target video data and corresponding target text label data;Based on pre-trained alignment model, the target video data is executed multi-scale time series segmentation to extract visual features, and the target text label data is executed standardization representation and global semantic coding to extract semantic features;By dynamic programming algorithm, the visual features and semantic features are adaptively aligned, and the optimal alignment path is obtained;According to the optimal alignment path, the semantic matching degree between video and text is output.The application solves the problems of long sequence modeling, cross-scale alignment and dynamic fusion through three-level technical architecture of visual feature pyramid modeling, text semantic unit extraction and cross-modal adaptive alignment and global semantic consistency constraint.

This invention discloses a TOSA (Optical Oriented Array Alignment) optical path active alignment method and system, relating to the field of optoelectronic device alignment technology. The invention drives the TOSA device to output a test optical signal and collects the acquisition dimension parameters and optical path communication transmission status parameters of the TOSA device to be aligned. Based on the acquisition dimension parameters and optical path communication transmission status parameters, an adaptive search strategy is generated. The effectiveness of the generated adaptive search strategy is monitored, and a decision is made on whether to adopt an optimization strategy. Based on sample alignment data, a joint evaluation is performed and the search path is dynamically corrected to determine the target alignment position of the device to be aligned. This helps to solve the problem that existing methods struggle to simultaneously consider historical sample utilization efficiency, search path optimization, and the rapid and accurate determination of the optimal alignment position of the current device in scenarios where there are many acquisition dimensions and dynamically changing communication transmission statuses in the TOSA optical path active alignment process.

This invention provides a method and design for the alignment of high-speed maglev turnouts, including determining the angle constraint between adjacent turnout beams based on the geometric relationship constraints of the track; constructing a straight-line approximation curve design using the standard stator unit length as a constraint; constructing a curve-to-straight-broken-line alignment design using circular curve segments; verifying the parameters of the designed transition curve and circular curve using at least the angle constraint between adjacent turnout beams; solving all parameters of the transition-circular-broken-line turnout alignment after verification; verifying the mechanical performance of the turnout alignment using static stress-deformation, dynamic analysis models, and modal analysis models; and selecting the optimal alignment parameters for the optimal turnout alignment while meeting the mechanical performance requirements. This invention solves the problem that existing turnout alignments are insufficient to meet the needs of practical engineering for turnouts with different levels of lateral passing speed.

The application provides a point-to-feature-based digital-to-analog alignment method, acquires point-to-feature, and the point-to-feature includes a nominal point and an actual point; the nominal point is a key feature in grid model data of a workpiece to be measured; the actual point is a key feature in point cloud data of the workpiece to be measured and corresponds to the nominal point; a target point set is determined, and a target point in the target point set is an actual point corrected according to the nominal point in an effective direction; an initial alignment result is determined according to the target point set and a nominal point set under a no-coordinate-system constraint condition or a preset coordinate-system constraint condition; and the initial alignment result is iteratively optimized according to a deviation distance constraint to obtain an optimal alignment result. The application is beneficial to selecting stable and reliable key features through the diversity of point-to-feature, so that the application is widely used in fast matching scenes; the alignment result freedom is limited through effective direction constraint and coordinate system constraint, and then interference point pairs are removed through iterative optimization of the deviation distance constraint, so that the accuracy of the alignment result is improved.

The invention discloses a space electromagnetic wave feature extraction and identification method and device for a lightning positioning system. The method comprises the following steps: acquiring a measured sampling sequence of measured lightning electromagnetic waves, and performing windowing, resampling to a target length L and normalization processing on the measured sampling sequence; constructing a reference prototype library; obtaining a sample sequence of each type of waveform from the reference prototype library; respectively determining similarity measurement between the tested sampling sequence and each sample sequence; outputting an identification result according to a comparison result of all the similarity measures and a preset threshold value; the identification result comprises the category label, the corresponding minimum similarity metric value and the optimal alignment translation amount.

The invention provides an alignment method of a mask alignment system and the mask alignment system.The method comprises the steps that light intensity data projected to a workpiece table where a target wafer is located and position data of the workpiece table are obtained; performing filtering processing and denoising processing on the light intensity data in sequence to obtain target light intensity data; performing position compensation processing on the position data to obtain target position data; and establishing a function fitting model according to the normalized target light intensity data and target position data, and determining an optimal alignment position by solving the function fitting model. The problem of interference of noise and dynamic disturbance on mask alignment precision is solved to a certain extent, and sub-nano-scale alignment precision is achieved.

A method and apparatus for assembling electronic devices improve the alignment efficiency of assembled parts in the electronic devices. The method includes: acquiring an initial alignment coordinate set and a final alignment coordinate set of a robotic arm in an alignment system during n alignment operations; obtaining Kalman coefficients based on the initial and final alignment coordinate sets; calculating the (n+1)th initial alignment coordinate of the robotic arm in the (n+1)th alignment operation based on the Kalman coefficients; and controlling the robotic arm to pick up the (n+1)th first component and move it to the (n+1)th initial alignment coordinate for the (n+1)th alignment operation. This method utilizes a Kalman filtering algorithm to calculate the relationship between the initial and final alignment positions of the robotic arm to estimate the initial alignment position for the next alignment operation, enabling automatic and rapid identification of optimal alignment positions, thereby reducing alignment time and improving alignment efficiency.

The invention relates to the technical field of optical installation and adjustment, in particular to an optimized installation and adjustment method for a multi-working-condition beam expanding system and related products, and the method comprises the steps: building a multi-physical-field model of the beam expanding system, and determining a quantitative mapping relation between the installation and adjustment amount of the beam expanding system and a characteristic wavefront aberration coefficient under different working conditions based on the model; measuring the actual characteristic wavefront aberration distribution of the beam expanding system; based on the actual characteristic wavefront aberration and the quantitative mapping relation, the optimal installation and adjustment amount under different working conditions is obtained through solving; adjusting the beam expanding system according to the optimal adjustment amount; according to the method, the quantitative mapping relation including different temperatures, postures and other working condition influences is constructed in advance, the optimal solution capable of balancing and considering the multiple working condition influences can be calculated at a time when the installation and adjustment amount is solved, and therefore the low-efficiency mode that repeated iteration and trial and error adjustment need to be carried out under different working conditions in the past is changed through the method, and the adjustment accuracy is improved. Therefore, the installation and adjustment efficiency of the multi-optical system is improved, and the development period is remarkably shortened.

The invention relates to the technical field of voice keyword detection, in particular to a frame asynchronous dual-channel keyword detection method and device, equipment and a storage medium. Comprises: extracting an acoustic feature sequence of an original audio waveform; performing attention guidance on the acoustic feature sequence to generate a bias acoustic feature sequence; determining a verification activation threshold and a current frame skipping upper limit; performing frame asynchronous path search on the bias acoustic feature sequence based on the current frame skipping upper limit to obtain an optimal alignment path and a first path score; if the first path score exceeds a verification activation threshold value, obtaining an initial timestamp, determining a target timestamp, and obtaining a cutting feature sequence; performing cross attention interaction based on the keyword representation and the clipping feature sequence, and generating a sequence prediction probability to generate a second path score; and determining a comprehensive confidence threshold, and performing weighted fusion on the first path score and the second path score to obtain a comprehensive confidence so as to judge whether the keyword is triggered or not. The resource utilization efficiency is improved.