606 results about "Sample sequence" patented technology

The invention discloses a multi-scene terminal signal-to-noise ratio estimation implementation method and device, and the method comprises the steps: obtaining a received signal sample sequence, platform attitude data, antenna pointing data and environment parameter data, and obtaining a feature vector through feature extraction and adaptive window processing; generating a compensation feature vector based on the feature vector and environment compensation data; performing feature fusion in combination with platform state data to obtain a scene feature vector; performing parameter optimization on the scene feature vector, establishing a dynamic memory mechanism, and generating memory state data; performing dynamic correction and robustness processing on a signal-to-noise ratio by using the memory state data to obtain a corrected signal-to-noise ratio; and performing quality evaluation based on the performance evaluation vector and the dynamic threshold vector, and outputting a final signal-to-noise ratio and a control parameter set. Through adaptive feature extraction, multi-dimensional compensation and a dynamic memory mechanism, the precision and reliability of signal-to-noise ratio estimation are improved.

The invention discloses a flow field measurement method based on an event camera, and the method comprises the steps: generating a PIV data set, each time sequence sample sequence comprising a plurality of frames of continuous particle images, a corresponding velocity vector field, and particle event data at all moments; establishing a flow field data acquisition device based on an event camera and a high-speed camera, acquiring real event data and real image data which are synchronous in time so as to adjust parameters of an event simulator, and verifying and updating particle event data in the PIV data set according to the adjusted event simulator so as to obtain a flow field data acquisition result; obtaining the updated PIV data set as a training data set; building an event camera optical flow method model, and training by adopting the training data set; and on the basis of the trained event camera optical flow method model, event sequences in two adjacent time periods are used as inputs to calculate a velocity vector field corresponding to a middle moment. According to the invention, the flow field velocity field at the required moment can be obtained based on the event data within a period of time.

The invention discloses a network security malicious traffic tracing method based on a generative adversarial network, which comprises the following steps: collecting network traffic data, selecting a target traffic sample, extracting a protocol behavior, a communication structure and a time evolution characteristic of the target traffic sample, and carrying out semantic coding; mapping the multiple types of features to a unified embedding space through a multi-view fusion mode; constructing a manifold set based on known malicious samples, and generating an intermediate state sample sequence; constructing an attack context and path disturbance information in combination with the target sample, and generating a guide vector; inputting the guide vector and the random variable into a generator, and outputting a potential malicious sample representation; identifying the authenticity of the sample and evaluating the path similarity by using a discrimination network; and finally, constructing an attack path graph according to the similarity and the spatial relationship, and calculating a shortest path sequence to a known malicious sample to realize traceability identification of potential malicious behaviors. According to the invention, tracking analysis of malicious traffic sources is realized by using the generative adversarial network.

The invention relates to a target driving movement grabbing control method used in a multi-scene environment, and the method comprises the steps: inputting an RGB-D sampling sequence of multiple sub-scenes, and initializing a 3D Gaussian model of the whole scene through employing a sparse 3D point cloud with semantics; acquiring an approximate pose of the target by querying coarse-grained Gaussian, navigating the mobile chassis to approach the target, and narrowing a pose estimation range of the target to a corresponding sub-scene; in the moving process, fine-grained gauss are optimized through diffusion-based depth completion, and the accurate pose of the target is obtained; gaussian editing is conducted on the fine-grained Gaussian model, and the robot operation end point pose is obtained through Gaussian repairing; and according to the operation starting point pose and the operation end point pose of the robot, a robot mechanical arm is controlled to complete corresponding actions. Compared with the prior art, the method has the advantages that rough scene prior and fine pose estimation are combined, efficient and accurate multi-scene moving grabbing control can be achieved, and it is ensured that the robot can accurately execute tasks in the complex multi-scene environment.

The invention provides a zero sample time sequence prediction method and system based on an adversarial neural network, and the method comprises the steps: constructing a time sequence generation module, training the generation module to stable convergence through obtained source domain data, and inputting collected random noise to generate a pseudo target sequence; constructing a time sequence prediction module, taking the pseudo sample sequence as a training set of the prediction module, and inputting the pseudo sample sequence into the prediction module for decomposition prediction; comparing the synthetic sequence with the output of the prediction module to obtain a prediction error, and feeding back the prediction error to the generation module to update generator parameters; through multiple rounds of generation-prediction-feedback closed-loop training, the obtained zero-sample linear architecture prediction model can effectively predict a corresponding future time sequence trend based on a synthetic pseudo sample under the condition that a target domain has no historical data completely. According to the method, data can be generated and self-training can be completed under the condition of no real data input, so that the quality of the generated data and the performance of the prediction model are synchronously improved.

The invention relates to the technical field of data processing, and discloses a secondary equipment hidden danger mining method and system based on a wave recording file and monitoring data. The method comprises the steps of calculating a conditional probability and intervention probability difference identification causal relationship by analyzing a recording file in combination with equipment topology and protection logic to construct a correlation graph, and performing Fourier transform and wavelet decomposition on a sampling sequence to extract multi-scale features; small signal test excitation is injected, a system identification estimation transfer function is fused with monitoring data to form a health feature vector to judge a health state, and a correlation map and the health state are input into a map neural network to calculate weighted attention coefficients and aggregate neighbor information to predict a fault propagation path; and establishing a degradation model, correcting the failure rate, predicting the residual life and generating a graded early warning report. According to the method and the device, the transformation from passive post analysis to active predictive maintenance is realized, and the timeliness, accuracy and systematicness of hidden danger identification of the secondary equipment are improved.

In the present disclosure a method and a system for generating an Optical Transport Network (OTN) DT network based on vertical federated learning are disclosed. The method includes: performing homomorphic encryption on a local fault root cause identifier to obtain an encrypted fault root cause identifier; receiving all encrypted alarm sample sequences corresponding to the encrypted fault root cause identifier; generating a single-domain training set according to the encrypted fault root cause identifier and the encrypted alarm sample sequences; training the local cross-domain fault root cause identification model according to the single-domain training set to obtain a model parameter update amount of the cross-domain fault root cause identification model; and reporting the model parameter update amount to the multi-domain orchestration system.

The invention discloses a coal moisture content and spatial distribution analysis method and system based on an image spectrum, and belongs to the technical field of coal moisture content analysis, and the method comprises the following steps: obtaining a visible light image and a hyperspectral image of to-be-detected coal; semantic segmentation is performed on the visible light image, and the visible light image is divided into a dry area and a water-containing area; the method comprises the following steps: extracting hyperspectral data of each pixel point in a hyperspectral image of a water-containing area, extracting spectral features of a plurality of wavebands by using a sliding window, and dynamically adjusting through an attention mechanism to extract spectral features related to moisture; constructing a sample sequence by taking the spectral feature and the spatial feature of each pixel point of the water-containing area as samples, inputting the sample sequence into a Transform model, and outputting a feature matrix; pooling the feature matrix, inputting the pooled feature matrix into a least square SVM model, and outputting the water content corresponding to each pixel point of the water-containing area; and performing graded developing treatment according to the water contents of different pixel points to obtain accurate coal water and spatial distribution conditions thereof.

The invention relates to the technical field of urban rail transit station-city collaborative planning, in particular to an ontology-based station-city collaborative data integration and planning prediction method, which comprises the following steps of: obtaining rail transit station passenger flow data, resident travel behavior data and station periphery built environment index data; forming a space-time sample sequence according to the unified space-time granularity of the site walking service area; constructing an urban rail transit station-city cooperation ontology, and carrying out semantic annotation and semantic fusion on the space-time sample sequence to generate a feature sequence; inputting the feature sequence into a multi-task space-time diagram convolutional neural network prediction model to output a passenger flow prediction result and establish an environment index prediction result; and calculating a feature contribution degree based on a Shapley additive interpretation value, optimizing a background sample set by using a genetic algorithm to determine a key action element set, outputting a planning index threshold and an intervention measure parameter, and realizing an interpretable station-city collaborative prediction and planning decision closed loop.

The invention discloses a PCB component layout method, device and equipment based on a multi-modal large model, and relates to the technical field of electronic design automation, the method comprises the following steps: carrying out quality screening on a plurality of PCB design files which have been laid out to obtain a qualified design file set; constructing a serialized training sample set based on the qualified design file set; each training sample sequence comprises a plurality of training samples arranged according to a layout logic sequence; each training sample comprises multi-modal data representing a current layout state and a real position coordinate of a next component to be laid out; based on the serialized training sample set, training the open-source multi-modal large model to obtain a PCB component layout model; and iteratively generating a layout file of the target PCB based on the netlist file of the target PCB and the PCB component layout model. The method is high in generalization capability and low in cost based on the PCB component layout model trained by the layout data, and can realize rapid layout of components of a new PCB.

The embodiment of the invention provides a model training method, a carbon emission prediction method, a device and equipment. The method comprises the following steps: firstly, obtaining carbon emission sample sequence data; then, preprocessing the carbon emission sample data sequence to obtain preprocessed carbon emission sample sequence data; further, according to the preprocessed carbon emission analysis sample sequence data, determining a time sample feature and an interaction feature sample sequence; and then. Processing the sample time feature and the preprocessed power consumption sample sequence data to obtain a sample time feature component and a sample power consumption feature component; and finally, inputting the sample time characteristic component, the sample power consumption characteristic component, the interaction characteristic sample sequence and the preprocessed carbon emission sample sequence data into an initial Transform model for optimization training, and obtaining an improved target Transform model. In this way, the prediction precision of the prediction model and the generalization ability of the model are improved, and therefore accurate prediction of carbon emission data is achieved.

The invention requests to protect a multifunctional FFT (Fast Fourier Transform) hardware acceleration architecture capable of configuring software in real time, which comprises a first sample sequence rearrangement module, a first calculation unit, a second sample sequence rearrangement module, a second calculation unit, a third sample sequence rearrangement module, a butterfly operation circuit, a multiplexer and a dual-port RAM (Random Access Memory) which are sequentially connected end to end, in order to solve the technical problems that in an existing FFT hardware architecture, the operation length is fixed, the function is single, the fftshift function is lacked, and software dynamic configuration cannot be achieved, through software and hardware collaborative design, FFT hardware has operation flexibility, functional diversity and frequency spectrum processing capacity; the method can be widely applied to various digital signal processing scenes such as communication, images, radars and audios.

The invention discloses a distribution line early-stage fault time sequence hidden Markov modeling and identification method and system based on multistage evolution characteristics, and belongs to the field of distribution line early-stage fault identification. Comprising the steps of obtaining a current waveform sample sequence of an early fault in a distribution line; for each current waveform sample in the sequence, extracting a multi-dimensional time-frequency feature, and constructing a feature vector of each sample; performing fault stage identification on each sample by using the first-level hidden Markov model group, and outputting a fault stage tag sequence corresponding to each sample; combining the fault stage label sequences of the current sample and a plurality of previous historical samples to form a stage label sequence window; and respectively inputting the stage label sequence window into a second-level tree line fault hidden Markov model and a second-level non-tree line fault hidden Markov model, calculating a corresponding first average log-likelihood value and a corresponding second average log-likelihood value, comparing the two average log-likelihood values, and judging whether a current sample belongs to a tree line early fault or not.

The embodiment of the invention provides a decision model training method and device and an information processing method and device.The decision model training method comprises the steps that according to a preset decision data construction strategy, sample data associated with a target language model is processed into sample sequence data; extracting sample state data and sample action data from the sample sequence data, and inputting the sample state data and the sample action data into the target language model for processing to obtain prediction reward data; using sample reward data in the sample sequence data and the prediction reward data to update the target language model into an initial decision model; and processing service data corresponding to a service task into service sequence data according to the decision data construction strategy, and optimizing the initial decision model into a service decision model corresponding to the service task by using the service sequence data.

The invention provides a potential risk event intelligent analysis method based on comparative learning, and belongs to the technical field of risk event intelligent analysis. A memory unit is regarded as a prototype of a normal video mode, and on the basis of memorizing video data features, contrast learning is introduced into the memory unit for training. A data enhancement strategy is adopted: different video sections in the same video belong to positive samples; data enhancement is performed on a video, and spatio-temporal information of the video, such as disorder, random rotation of video frames, continuous repetition of single video frames and the like, is destroyed as a negative sample sequence. Reconstruction errors are used as a standard for judging the drowning event, the similarity between an input video stream and a memory unit is used as an auxiliary standard for judging the drowning event, and the specific mixed decision-making method is completed in a weighting mode. According to the method, the general problem of network reconstruction and the problem of a memory model in anomaly detection are solved.

The invention provides a cross-modal image-text retrieval method and system based on adaptive comparative learning. The method comprises the steps that two boundary parameters are introduced into probability distribution of each pair of positive samples, the probability of an image I corresponding to T is calculated, semantic consistency of each pair of positive samples is adjusted, and distribution characteristics of negative samples are distinguished and cloned; defining and calculating a saliency score of each batch of samples, and selecting negative sample sequences with the maximum saliency score and the minimum saliency score as an observation clone negative sample and a saliency negative sample; according to a Gaussian discriminant analysis method, predicting all potential clone negative samples of each batch of samples and generating anchor point samples; and iteratively updating the two boundary parameters according to the two boundary conditions of the similarity of the anchor point samples. According to the method, potential clone negative samples in each training batch are predicted by utilizing Gaussian discriminant analysis without additional training for clone negative samples easily appearing in an image-text data set. And a foundation is laid for subsequent further learning of potential high-dimensional key semantics of the cloned negative sample.

The invention belongs to the technical field of network security, and discloses an industrial control network unsupervised anomaly detection method and system based on a space-time codec, and the method comprises the following steps: 1, carrying out the data processing and sample construction of an industrial control network sensor; performing sampling and data processing on the historical sensor original data and the time sequence to obtain a sample sequence; step 2, constructing an unsupervised deep learning model based on a space-time codec and training the unsupervised deep learning model; step 3, sensor data spatio-temporal characteristic analysis, and outputting sensor data in a future predetermined time period as a predicted value; and 4, abnormal alarm: determining a confidence space by combining the predicted value output in the step 3 and the confidence coefficient, and triggering network abnormal alarm when the current actual sensor data exceeds a normal range. According to the invention, the identification rate and generalization of industrial control network anomaly detection are improved.

The invention relates to a rolling bearing residual life prediction method based on space-time degradation characteristic decoupling, and the method comprises the steps: obtaining a vibration signal in a full life cycle operation process of a rolling bearing, and obtaining a sample sequence after preprocessing; performing complete ensemble empirical mode decomposition on the sample sequence, and extracting a plurality of IMF signals; on the basis of statistical threshold criterion in combination with energy mutation and self-correlation structure mutation analysis, identifying a degradation starting moment, adding a label to a sample sequence, and dividing a training set and a test set; training the space-time degeneration decoupling network by using the training set to obtain an RUL prediction model; testing the RUL prediction model by using the test set to obtain a prediction result; the space-time degeneration decoupling network combines a degeneration guide feature deconstruction module and a collaborative modeling strategy of a time modeling branch and a space modeling branch, captures time dynamic characteristics and a space hierarchical structure, and improves the accuracy, stability and reliability of RUL prediction.

The invention provides a motor ripple counting method and device, equipment and a storage medium. The method comprises the following steps: reading sequence data of M ripple periods from a starting point of a pre-acquired motor ripple sampling sequence according to an initial single period value and a sampling period value of a ripple analog-to-digital converter, taking the sequence data as a processing sequence, carrying out correlation transformation according to a corresponding local sequence, and according to a prediction period of the pre-predicted processing sequence, carrying out correlation transformation on the processed sequence; and determining a target algorithm and a measuring point set, calculating to obtain an upper peak position corresponding to the related transformation sequence based on the measuring point set and the target algorithm, further determining the number of ripples of the processing sequence, performing accumulative counting, and starting from a new starting point position according to a time sequence, repeating the steps until the motor ripple sampling sequence is processed. Through the above method, ripple counting is performed on the motor ripple sampling sequence based on the measuring point set, the calculation amount is reduced, the accuracy is improved, and the influence of serious distortion and noise is avoided.

Methods, systems, and devices for wireless communications are described. A transmitter may modulate a set of bits into an on-off keying (OOK) sample sequence for wireless transmission to a receiver in a set of frequency resources. The transmitter may apply a transform (e.g., a discrete Fourier transform (DFT)) to the OOK sample sequence to generate a frequency domain representation of the OOK sample sequence. In some cases. the transmitter may, using an orthogonal frequency division multiplexing (OFDM) waveform generator, generate an OFDM waveform based on mapping the frequency domain representation of the OOK sample sequence to a set of resource elements of the set of frequency resources. In some cases. the transmitter may transmit the OFDM waveform to the receiver via the set of frequency resources.

The invention relates to the technical field of time sequence data processing and deep learning, in particular to a time sequence filling method and system based on a coarse-to-fine filling normal form. In the invention, a preprocessing module is used for sampling an input sequence to obtain a subsequence rk with the length of Lk, and a regression prediction module is combined with a causal mask to generate a prediction value r'k with the same structure and the same length as the rk based on all subsequences {r1... rk} generated by the preprocessing module; the correction module performs up-sampling on the predicted value r'k to obtain an up-sampling sequence r ''k with the length of T; supplementing missing data of the original sequence based on the r ''k to obtain a corrected sequence X (k + 1); and traversing k = 1... K to obtain a correction sequence X (K + 1) as a final repair completion time sequence. The method overcomes the defects that the time sequence filling mode in the prior art does not consider the unfixed missing rate and missing value block distribution, and is beneficial to improving the filling accuracy.

The invention discloses a tooth health condition assessment method and device based on multi-source information. The method comprises the following steps: acquiring a tooth image data set and a tooth index data set; the tooth image data set comprises tooth image data; the tooth index data set comprises a time sampling sequence of tooth indexes; the time sampling sequence comprises a plurality of acquisition values; preprocessing the tooth image data set and the tooth index data set to obtain a preprocessed data set; the preprocessing data set comprises a preprocessed tooth image data set and a tooth index data set; and performing evaluation processing on the preprocessed data set to obtain a tooth health condition evaluation value. According to the invention, the tooth health condition of the user is rapidly and accurately evaluated by using the obtained various multi-source tooth monitoring data.

At least one embodiment of a method for encapsulating media data in a media file, the media data comprising a plurality of samples. After having generated a first track comprising a media data part storing a first sequence of samples of the plurality of samples, the first track further comprising a metadata part describing the first sequence of samples and having generated descriptive metadata describing a dependency between a given sample of the first track and another sample, the generated descriptive metadata being stored in the metadata part of the first track, the first track is encapsulated in the media file, the generated descriptive metadata comprising an offset reference for indicating a reference sample to be used for identifying the sample the given sample depends on.

Provided is a training method for a fault detection model, a device fault detection method and related apparatuses, relating to the field of computer technology. The method comprises: sampling a designated device based on a preset drone formation to obtain a sample sequence; performing position encoding on the sample sequence according to the drone formation to obtain a drone formation encoding result; inputting the sample sequence and the drone formation encoding result into a model to be trained to obtain a fault detection result output by the model to be trained; determining a loss value based on the fault detection result and a true value of a fault detection result of the sample sequence; and adjusting a model parameter of the model to be trained based on the loss value to obtain the fault detection model.

The invention relates to the technical field of tumor data processing, in particular to a methylation-based tumor data processing system. The system comprises the following modules: a methylated sample sequencing module, a sequencing difference site recognition module, a tumor gene sequence analysis module and a tumor subtype classification module, performing high-throughput sequencing on the to-be-detected clinical tumor DNA sample to obtain methylated tumor sequencing data; performing methylation level quantification on the methylated tumor sequencing data, and performing tumor difference site analysis to generate tumor difference site data; performing tumor generation key gene sequence identification according to the tumor difference site data to generate tumor methylation characteristic data; and performing tumor subtype tag identification according to the tumor methylation characteristic data to generate tumor type tag data. According to the method, accurate tumor typing is realized on the basis of tumor DNA methylation characteristic analysis, and the false negative rate of low-concentration sample detection is effectively reduced.

The invention relates to a Parkinson's disease treatment effect prediction method based on a multi-modal image model and a related device. The method comprises the following steps: acquiring a multi-modal vector of a Parkinson's disease patient; aligning the multi-modal vectors on a time axis, and constructing a time point data element sample sequence; deploying a double-flow cross attention encoder for a sample sequence in each time point data element, and outputting a fused multi-modal feature through the double-flow cross attention encoder; and connecting the fused multi-modal features with digital clinical treatment scheme vectors corresponding to corresponding time points to form time point comprehensive feature vectors, inputting the time point comprehensive feature vectors to a time sequence information aggregation gating circulation unit, outputting final time point aggregation features, and inputting the final time point aggregation features to a multi-task adaptive prediction head. A UPDRS total score or a specific sub-scale score for the patient at a future preset point in time is predicted. According to the method, time sequence modeling is carried out on multi-mode and multi-time-point data, so that the accuracy and interpretability of Parkinson's disease treatment effect prediction are improved.

The invention discloses an underwater acoustic time-varying channel prediction method based on kernel recursive least squares, and relates to the technical field of ocean communication, and the method comprises the steps: obtaining channel sample data, and constructing a gain training sample and a time delay training sample; and constructing a multipath gain prediction model and a multipath time delay prediction model of any path p by using a kernel recursion least square method, and training corresponding prediction models. And utilizing the trained corresponding prediction model to predict and obtain a multipath gain prediction value and a multipath time delay prediction value of the prediction moment T according to the historical sample sequences of a plurality of continuous moments before the prediction moment T. And integrating the multipath gain prediction values and the multipath time delay prediction values of all paths at the prediction time T to obtain a channel prediction value of the underwater sound time-varying channel at the prediction time T. According to the method, real-time tracking prediction can be carried out on the underwater sound time-varying channel in a complex and changeable underwater environment, and performance improvement of an underwater sound communication system is facilitated.

A time-interleaved analog-to-digital converter includes a plurality of channel circuitries, an output circuit, and a calibration circuitry. The plurality of channel circuitries are configured to sample an input signal to generate a plurality of first digital codes according to the input signal. The output circuit is configured to output a second digital code according to the plurality of first digital codes. The calibration circuitry is configured to adjust a sampling sequence of the plurality of channel circuitries for the input signal during an initial period, and control the plurality of channel circuitries to sample the input signal in the adjusted sampling sequence during an analog-to-digital conversion period.

The invention relates to the technical field of artificial intelligence application, and discloses an enzyme EC number prediction method, and the method comprises the steps: obtaining the sample sequence characteristics of a to-be-predicted sample containing a substrate SMILES sequence and a product SMILES sequence through a target BERT model; constructing a molecular object and feature coding based on atom mapping, atom truncation and sequence analysis, constructing a reaction graph of a to-be-predicted sample, inputting the reaction graph into a target graph isomorphic neural network, and constructing molecular graph features of the to-be-predicted sample based on a recursive neighborhood aggregation mechanism; and fusing the sample sequence features of the to-be-predicted sample with the molecular map features by using a bidirectional cross attention mechanism to obtain multi-modal features, inputting the multi-modal features into the multi-layer perceptron, and obtaining the prediction probability of the enzyme EC number of the to-be-predicted sample. According to the method, efficient and accurate end-to-end prediction of enzyme EC numbering is realized through the multi-dimensional chemical spatial characteristics of the collaborative modeling reaction.

The invention relates to the technical field of risk data processing, and provides a big data driven compliance risk prediction and management method. The method comprises the steps that big data retrieval is carried out, and a risk feature library is mined; traversing the feature library, marking positive and negative sample sequences based on feature correlation, performing polarization processing, and determining risk sample data; supervising and training the risk prediction model by taking the prediction time phase as a constraint; reading target data, performing fuzzy screening and vectorization, and determining a standardized data system; transmitting the data to a prediction model, matching risk features and generating a risk single column; and performing compliance risk control management based on the single-row determination plan. The technical problems of inaccurate risk prediction and low management efficiency caused by huge business data and complex and changeable compliance risks are solved, the risk plan is automatically generated based on the prediction result, the accuracy and timeliness of compliance risk prediction are improved, the pertinence and effectiveness of risk management are enhanced, and the risk management efficiency is improved. And the risk influence degree is reduced.