297 results about "Prediction residual" patented technology

The invention discloses an energy storage power supply residual life prediction method and system based on dynamic weight distribution, and belongs to the technical field of energy storage power supply health management. The method comprises the following steps: acquiring voltage, current and temperature time sequence signals of energy storage power supply operation through a multi-source sensor, and constructing a degradation characteristic sequence by adopting a sliding window method; a degradation inflection point division stage is detected and identified by using a curvature extreme value, a historical degradation mode is matched based on a dynamic time warping algorithm, and an optimal model group is selected; respectively carrying out dynamic weight distribution on the time step length and the feature dimension by adopting a dual-channel attention network, and fusing a learnable coefficient with space-time attention output to generate a life prediction result; residual errors and feature drift are monitored and predicted in combination with an incremental learning mechanism, and model parameters are updated and a degradation knowledge base is expanded by adopting an elastic weight consolidation algorithm. According to the method, the key information capturing capability is enhanced through a space-time attention mechanism, the model adaptability is optimized in combination with incremental learning, and the energy storage power supply life prediction precision and the working condition generalization performance are remarkably improved.

The invention provides a dual-antenna attitude and orientation and robust self-adaption method based on integrated navigation, which comprises the following steps: combining an INS (inertial navigation system) and a dual-antenna GNSS (global navigation satellite system), and enabling the system to output continuous and stable high-precision carrier attitude information when a GNSS signal is interfered by using dual-antenna integrated navigation. According to the method, most of satellite end clock correction, ionosphere and troposphere errors and receiver end clock correction are eliminated by using double-antenna GNSS pseudo-range and carrier phase double difference, robust statistics are calculated through heading prediction residual vectors to obtain robust factors, an observation noise covariance matrix is expanded to reduce the influence of system noise and observation noise, and the system performance is improved. A Mahalanobis distance based on a prediction residual vector is introduced to detect whether a system is abnormal or not, a state prediction covariance matrix is adjusted through a self-adaptive factor, weight reduction of an abnormal INS dynamic model is achieved, and the stability and reliability of system attitude information are improved through a noise covariance self-adaptive control mechanism.

The invention discloses a refrigerated cabinet multi-sensor heterogeneous data fusion method based on artificial intelligence, and the method comprises the following steps: collecting multi-source heterogeneous data in the operation process of a refrigerated cabinet, and constructing a unified event time axis; preprocessing the multi-modal data, and generating a physical enhanced multi-modal tensor in combination with a heat-airflow-electricity model; constructing a disturbance activation vector and a modal mask pattern, and embedding the disturbance activation vector and the modal mask pattern into a multi-modal tensor; inputting the disturbance enhanced input data into an improved Tide model to perform long sequence modeling, and outputting a state prediction sequence; performing prediction residual analysis, constructing a dynamic anomaly scoring function, and fusing a disturbance activation vector and a modal mask graph to generate an anomaly type mark and confidence fusion state representation; sparse gradient uploading, parameter aggregation and structure synchronization are carried out among multiple devices by adopting a federal training framework. According to the method, the multi-modal data fusion precision and the anomaly recognition reliability of the refrigerated cabinet can be improved, and cross-equipment collaborative intelligent optimization is realized.

The invention relates to the technical field of disaster prevention and reduction of a power system, and discloses an icing risk early warning method based on multi-model fusion and residual time sequence characteristic analysis, which comprises the following steps: collecting meteorological data of a line area in real time, removing abnormal values through secondary judgment of a Pauta criterion and a trend, and standardizing; adopting a TEROL algorithm to screen high-weight key features; running SWD-BP, MUL-GRNN and ELM models in parallel, constructing a dynamic weight by combining DSI, an independence weight method and an entropy weight method, and calculating a final meteorological predicted value; generating a prediction residual signal, extracting time domain features such as a mean value and a peak value, and constructing a residual feature matrix through a sliding window; and inputting an LSTM model to process a time sequence dependency relationship, and judging an icing risk level. According to the method, meteorological prediction is optimized through multi-model dynamic fusion, and deviation is analyzed and corrected in combination with residual time sequence characteristics, so that the problem of weak generalization ability of a single model is effectively solved, and the accuracy of icing risk early warning is obviously improved.

The invention relates to the field of biological medicine, and discloses a method and system for analyzing cough sound by using disease characteristics to diagnose respiratory diseases, and the method comprises the steps: deploying a six-microphone annular array to achieve the precise positioning and triggering of a sound source; self-adaptive spectral subtraction and Wiener filtering cascade are adopted to enhance the audio; segmenting a cough segment based on energy envelope; fusing the Mel-cepstrum, the linear prediction residual error, the harmonic energy ratio and the transient zero-crossing rate to construct a pathological feature matrix; extracting local, medium-range and global time sequence features through a three-branch parallel convolutional network; inputting a disease specific classifier to discriminate asthma, pneumonia and laryngitis respectively, and applying a feature decoupling regular term to improve interpretability. The system correspondingly realizes the modularized processing flow. According to the method, the cough sound collection quality and the disease subtype recognition accuracy in a complex environment are improved, meanwhile, the thermodynamic diagram is output to assist clinical decision making, and the diagnosis credibility and practicability are enhanced.

The invention provides a flight path prediction method and system based on predictive coding. The method comprises the following steps: collecting historical flight path data, and performing flight path prediction on the historical flight path data by using a reference prediction module to obtain a reference prediction point; calculating a high-order motion track residual error between the reference prediction point and the real track point by using a residual error encoder, and carrying out one-hot coding on the high-order motion track residual error to establish a residual error coding index; a high-order probability distribution mapping module is used for learning a deep mapping relation between historical flight path data and residual error coding indexes so as to carry out training; inputting a to-be-predicted historical flight track into the reference prediction module and the trained high-order probability distribution mapping module in parallel to respectively obtain a predicted track point and a predicted residual error coding index; decoding the prediction residual error coding index through a residual error decoder to obtain a residual error at a prediction moment; and adjusting the predicted track point based on the residual error at the prediction moment to obtain a final prediction result.

The invention provides an effective wave height real-time prediction method and device based on dynamic residual correction, and relates to the field of ocean engineering. The method comprises the following specific steps: acquiring wave height data and performing multi-dimensional feature screening; constructing an integrated filter fusing L1 trend filtering and variational mode decomposition, optimizing parameters by using a sea image optimization algorithm, introducing a causal sliding window to extract features so as to construct a time sequence input tensor, and inputting the time sequence input tensor into a stacked bidirectional long-short-term memory network based on an attention mechanism after noise addition standardization so as to obtain a basic predicted value; calculating a manifold coherent structure, PID dynamics and physical statistical characteristics, and cascading with the basic prediction characteristics to construct a comprehensive element characteristic vector; a LightGBM architecture is constructed, and a prediction residual error is fitted after optimization is carried out through a sea image optimization algorithm; and finally, executing linear reconstruction based on the dynamic safety threshold constraint, and outputting a real-time correction result. According to the method, the error evolution rule is deeply mined by using manifold geometric features, and the real-time precision and robustness of significant wave height prediction are remarkably improved.

To provide a method and apparatus for video processing.SOLUTION: A video processing method comprises: determining skipping of a transform process for a prediction residual based on one of width and height of the Cb component of a prediction block; signaling the maximum transform size for use in a transform skip in a sequence parameter set (SPS); and bypass-coding a parameter that specifies whether a transform-skip mode is selected.SELECTED DRAWING: Figure 21

The invention discloses a tobacco primary processing technology regulation and control method based on a large model, and the method comprises the steps: carrying out the feature extraction at an edge layer according to technological parameters, raw material detection data and environmental parameters collected in a historical time interval, and obtaining a plurality of feature vectors; according to the multiple feature vectors, a quality index is obtained through prediction in an edge layer through a preset prediction model, and according to the quality index, process parameters are adjusted for execution; and according to the quality index acquired after execution and the quality index obtained by prediction, obtaining a prediction residual moving average at a decision-making layer, and when the prediction residual moving average is continuously greater than an error threshold for multiple times, triggering model re-calibration and synchronizing to an edge layer. According to the method, the multi-modal features are fused in the edge layer, closed-loop control is realized through the real-time prediction model, and the prediction accuracy and the response speed are improved. In combination with a prediction residual moving average dynamic calibration mechanism, the problem of model failure caused by parameter mutation is effectively solved, and production continuity is guaranteed.

To provide a method and apparatus for video processing.SOLUTION: A video processing method comprises: determining skipping of a transform process for a prediction residual based on one of width and height of the Cr component of a prediction block; signaling the maximum block size for use in a transform skip in a sequence parameter set (SPS); and bypass-coding a parameter that specifies whether a transform-skip mode is selected.SELECTED DRAWING: Figure 21

The invention discloses a force control joint dynamic error compensation method based on Kalman filtering, and the method comprises the steps: collecting and preprocessing multi-source signal data, and forming a force control joint input data set; constructing an energy hierarchical Kalman filtering model, executing energy constraint correction and outputting state estimation; calculating a prediction residual error, carrying out time-frequency analysis, adjusting a compensation gain, and generating residual error information; torque and angular velocity signals are extracted, a semantic state is recognized, and semantic gating parameters are output; fusing the state estimation, the residual information and the semantic parameters to generate a dynamic compensation instruction signal; energy layer and residual information changes are monitored, self-calibration is triggered, and a feedback closed loop is formed. According to the invention, by introducing energy hierarchical Kalman filtering, time-frequency modulation compensation and a semantic gating feedback mechanism, dynamic error self-adaptive accurate compensation of the force control joint in a complex multi-disturbance environment is realized.

An apparatus configured to select a predetermined intra prediction mode out of a plurality of intra-prediction modes which includes a first set of intra-prediction modes and a second set of matrix-based intra-prediction modes. The apparatus is configured to select a subset of one or more secondary transforms dependent on the predetermined intra prediction mode so that the subset is nonempty in case of the predetermined intra prediction mode being contained in the first set of intra-prediction modes or in the second set of matrix-based intra-prediction modes. The apparatus is configured to derive a transformed version of a prediction residual for a predetermined block, which is related to a spatial domain version of the prediction residual of the predetermined block via a transform defined by a concatenation of a primary transform and a predetermined secondary transform out of the subset of secondary transforms.

The invention provides a voice reconstruction method and system based on entropy coding residual quantization and frequency spectrum restoration, and relates to the technical field of artificial intelligence voice signal processing, and the method comprises the steps: obtaining an original voice waveform, inputting the voice waveform into a neural voice coding and decoding model, firstly entering a coder to map the input voice waveform into acoustic potential representation, and then entering a frequency spectrum restoration model; performing residual quantization on the acoustic potential characterization layer by layer through a residual vector quantization module, introducing a gating-based dynamic layer number selection mechanism and entropy regularization constraint, enabling bits to be adaptively distributed among different voice segments, reconstructing reconstructed acoustic features of the potential characterization, inputting the reconstructed acoustic features into a decoder, restoring the reconstructed acoustic features into a time domain waveform, and outputting the time domain waveform. And mapping to a logarithmic magnitude spectrum domain through a spectrum repairing module, predicting a residual error in the logarithmic magnitude spectrum domain and performing confidence gating fusion to obtain a complex spectrum, and outputting after time domain synthesis to obtain reconstructed speech. According to the invention, high fidelity, intelligibility and transmission reliability of the voice can be considered at an extremely low bit rate.

To provide a method and apparatus for video processing.SOLUTION: A video processing method comprises: determining skipping of a transform process for a prediction residual based on one of width and height of the dimension of a luma sample of a prediction block; signaling a parameter indicating the maximum transform block size for use in a transform skip in a sequence parameter set (SPS); and bypass-coding a parameter that specifies whether a transform-skip mode is selected.SELECTED DRAWING: Figure 21

The invention discloses a significant wave height two-stage space-time prediction method and system based on diffusion residual correction, and relates to the technical field of sea wave prediction, historical significant wave height data and corresponding historical wind field data are input into a significant wave height space-time prediction model for processing, and a preliminary significant wave height prediction result is obtained; and inputting the initial prediction error, historical significant wave height data and synchronous forecast wind field data into a residual error correction module based on a diffusion model, and compensating the initial prediction error to obtain a final significant wave height prediction result. A diffusion model based on an EDM framework is innovatively introduced to carry out refined reconstruction on a prediction residual error, and a basic prediction field, a synchronous wind field dynamic factor and a prediction aging code are used as multi-dimensional physical strong conditions to be injected into a diffusion generation process. According to the method, high-frequency texture details and non-linear fluctuation components missed by the first-stage model can be certainly recovered, long-term deviation accumulation is relieved to a certain extent, the high-wave region prediction quality is improved, and the prediction stability is improved.

The invention relates to the technical field of thermal signal early warning, and discloses a thermal signal analysis early warning method and system based on a knowledge graph, and the method comprises the following steps: S101, collecting thermal signal data; s102, acquiring an enthalpy value and a mass flow rate, and generating an energy conservation residual error; s103, evaluating a temperature change trend, generating a thermal inertia prediction residual error, evaluating a channel coupling degree to obtain a cross-channel coefficient, and determining a cross-channel coefficient residual error; s104, normalizing the energy conservation residual error, the thermal inertia prediction residual error and the cross-channel coefficient residual error respectively, and fusing to generate an abnormal score; and S105, mounting the energy conservation residual error, the thermal inertia prediction residual error, the cross-channel coefficient residual error and the abnormal score to a preset knowledge graph to generate a fault early warning triple. According to the invention, high-precision signal processing, multi-dimensional fault detection, intelligent reasoning and accurate early warning of the thermal system are realized.

A decoder and an encoder for coding a digital time-varying signal from or into a data stream are presented. The decoder is configured to decode the digital time-varying signal from the data stream in temporal blocks by decoding each temporally residual-predicted temporal block of the digital time-varying signal by predicting the respective temporally residual-predicted temporal block using a selected prediction mode out of a set of prediction modes to obtain a prediction signal, determining a prediction residual signal of the respective temporally residual-predicted temporal block, and correcting the prediction signal using the prediction residual signal, wherein, in the determining the prediction residual signal, sequentially decode second-stage-predicted residual samples of residual samples of the respective temporally residual-predicted temporal block along a sample order by deriving a second-stage residual sample prediction value for a currently decoded second-stage-predicted residual sample based on already decoded residual samples within a template of a predetermined number of sample positions preceding the currently decoded second-stage-predicted residual sample in sample order, decoding a second-stage-correction value for the currently decoded second-stage-predicted residual sample from the data stream, and correcting the second-stage residual sample prediction value using the second-stage-correction value.

The invention discloses a machine learning-based power distribution energy consumption diagnosis method. The method comprises the following steps of S1, collecting operation data and a connection relationship of each node in a power distribution system; s2, constructing a topological structure diagram model, and extracting node numbers and connection information; s3, calculating a connection strength factor, fusing the structure information with the time sequence features, and generating topological guide time sequence input; s4, dividing the time sequence into a multi-scale periodic window, and extracting trend change and time position features; s5, performing shielding operation according to periodicity and volatility, and generating an enhanced sample; s6, constructing a learning rate control strategy combining periodic synchronous scheduling and a linear warm-up mechanism; s7, calculating a prediction residual error, marking an abnormal node, and tracking a fault source node along a topological path; and S8, detecting performance drift, evaluating gradient fluctuation of the structure module, and executing local increment updating. According to the invention, the accuracy and adaptive capability of power distribution system energy consumption abnormity identification and fault location are improved.

A three-dimensional data encoding method of encoding three-dimensional points includes: calculating a predicted value of attribute information of a first three-dimensional point in a prediction mode, using one or more items of attribute information of one or more second three-dimensional points in the vicinity of the first three-dimensional point; calculating a prediction residual that is a difference between the attribute information of the first three-dimensional point and the predicted value; and generating a bitstream including the prediction residual and prediction mode information indicating the prediction mode. The prediction mode is: one prediction mode among two or more prediction modes when a type of the attribute information of the first three-dimensional point is first attribute information including elements more than a predetermined threshold value; and one fixed prediction mode when the type is second attribute information including elements equal to or less than the predetermined threshold value.

The invention belongs to the field of real-time monitoring, particularly relates to a chemical process real-time monitoring method and system based on digital twinning, and aims to solve the technical problem that an existing method lacks an online model correction and updating mechanism. The monitoring method comprises the following steps: S1, constructing a state snapshot data set, and synchronously collecting real-time measurement data; s2, identifying a process fluctuation interval, and selecting to-be-selected state snapshots to form an optimal snapshot matrix; s3, calculating a reduced-order truncation error energy ratio, and constructing a reduced-order model based on a reduced-order basis function; s4, in the real-time monitoring stage, if the norm of the prediction residual error is smaller than a monitoring threshold value, a Kalman filtering algorithm is adopted to correct the state coefficient of the reduced-order model; otherwise, calculating the projection error of the prediction residual on each primary function in the standby primary function library; and outputting a full-order state vector reconstructed based on the corrected or updated reduced-order model. The monitoring method provided by the invention ensures the continuous effectiveness and accuracy of monitoring when the chemical process changes.

A 3D data decoding apparatus for decoding encoded data includes a mesh prediction unit that is configured to derive a prediction value of a base mesh vertex position and / or a base mesh attribute from the encoded data and an arithmetic decoder that is configured to arithmetically decode a prediction residual. The arithmetic decoder decodes M first bins of a prefix of a coefficient of the prediction residual by using a context, decodes N first bins of a suffix of a coefficient of the prediction residual by using another context, and adds the prediction value and the prediction residual to derive the base mesh vertex position and / or the base mesh attribute.

The invention provides a time series data prediction deviation correction method and system of a power system. A systematic deviation model is established by extracting a time dimension feature in a prediction residual between reference prediction data of an electric power time sequence and actual electric power time sequence data and combining an exogenous variable feature (time period regularity features such as temperature, irradiation, load, power generation and holiday and festival prompt); the deviation estimate is regarded as a combined effect interpretable by time dimension deviation components (hours and weeks) and exogenous deviation components. And then, adjusting the reference prediction data according to the deviation estimation so as to obtain corrected power time sequence data. According to the technical scheme, through a staged prediction and correction mechanism, while the calculation efficiency is ensured, the problems that systematic deviation always exists in the power time sequence data prediction process, so that the scheduling strategy of a power system is frequently adjusted, and the scheduling efficiency is low are effectively solved. The service life of equipment is shortened; and the power fluctuation of a computing power system is large.

Systems and methods are provided. A method includes providing, by a computing system comprising one or more computing devices, a plurality of input values to a first machine-learned model. The method includes generating, by the computing system using the first machine-learned model based on the plurality of input values, a saliency map. In the method, the first machine-learned model is a model that was trained to predict a prediction residual associated with a second machine-learned model.

A decoder for decoding a digital time-varying signal from a data stream is presented. The decoder is configured to decode the digital time-varying signal from the data stream in non- overlapping temporal blocks by decoding each of transform-coded temporal blocks of the non-overlapping temporal blocks of the digital time-varying signal by predicting the respective transform-coded temporal block using a selected prediction mode out of a set of prediction modes to obtain a prediction signal, decoding coefficients from the data stream, the coefficients representing a prediction residual signal of the respective transform-coded temporal block in a transform domain, subjecting the coefficients to a predetermined re-transformation from the transform domain to time domain to obtain a time-domain prediction residual signal representing a prediction residual signal of the respective transform-coded temporal block in a time domain, and correcting the prediction signal using the time-domain prediction residual signal, wherein the predetermined re-transformation is a non-overlapping transform.

The invention discloses a multivariable time sequence anomaly detection method based on spatial-temporal feature fusion, and the method comprises the steps: outputting time sequence features through employing a series structure of a multi-scale TCN and a lightweight Transform network; constructing an adjacency matrix through a static graph learner and a dynamic graph learner, processing by using a multi-order GCN based on the adjacency matrix, and outputting spatial features; performing weighted fusion on the time sequence features and the spatial features by using a gating mechanism, and outputting a predicted value in combination with multiple residual connection; in a training process, dynamically calculating a sample weight according to a predicted residual error and calculating a weighting loss; and according to a residual error between a predicted value and a real value, generating an abnormal score through a reconstruction error calculation mechanism based on principal component analysis, and comparing a threshold value to complete abnormal detection.

The invention discloses a video coding method and device and a video decoding method and device based on residual prediction, and is applied to the technical field of video processing. The coding method comprises the following steps: determining a motion vector and a disparity vector of a current coding block; determining a first parallax compensation block corresponding to the current coding block in the reference view according to the parallax vector; determining a prediction residual according to the current coding block, the first parallax compensation block, and the motion vector and the parallax vector of the first parallax compensation block; or determining a first time domain motion compensation block corresponding to the current coding block in the previous frame and / or the next frame of image of the current coding block according to the motion vector; determining a prediction residual according to the current coding block, the first time domain motion compensation block, and the motion vector and the disparity vector of the current coding block; and coding the current coding block according to the prediction residual error. According to the method, the accuracy of the obtained prediction residual error can be improved, so that the coding compression efficiency and the coding effect of the multi-view video are improved.

There is provided an information processing apparatus and method to make it possible to reduce a decrease in encoding efficiency. A predictive residual that is a difference between a reflected light intensity that is attribute data of a point cloud representing an object having a three-dimensional shape as a set of points and a predicted value of the reflected light intensity generated by using a reflection model of light on a surface of the object is generated by decoding encoded data of the predictive residual, a coefficient of the reflection model is derived, a predicted value is derived by performing prediction processing by using the reflection model and the coefficient, and the reflected light intensity is generated by adding the predictive residual obtained by decoding the encoded data and the derived predicted value. The present disclosure can be applied to, for example, an information processing apparatus, an electronic device, an image processing method, a program or the like.

A method and apparatus for processing video data are provided. An exemplary method includes: decompressing compressed frames to generate key frames representing a face; generating a first set of parameters for the key frames, the first set of parameters being associated with a three-dimensional (3D) facial representation of the face; reconstructing a second set of parameters for each of one or more inter frames according to a compressed inter prediction residual of the second set of parameters; and generating a video including the face based on the key frames, the first set of parameters, and the second set of parameters.

Relates to improving accuracy of a primary predictive model based on a residual model. A method includes determining, using a primary predictive model, a first set of training prediction residuals based on a first labeled training set and a first set of testing prediction residuals based on a first labeled testing set. A second dataset includes a second labeled training set, labeled based on the first set of training prediction residuals, and a second labeled testing set, labeled based on the first set of testing prediction residuals. The method further includes training the residual model using the second labeled training set. The primary predictive model is adjusted based on a set of training predictions and a set of testing predictions of the residual model to produce more accurate predictions.

A three-dimensional data encoding method encodes a plurality of three-dimensional points, and includes: selecting one of two or more prediction modes for calculating a predicted value of an attribute information item of the first three-dimensional point, in accordance with attribute information items of one or more second three-dimensional points in the vicinity of a first three-dimensional point; calculating the predicted value by the selected prediction mode; calculating, as a prediction residual, a difference between a value of the attribute information item of the first three-dimensional point and the calculated predicted value; and generating a bit stream that includes the one prediction mode and the prediction residual.