16 results about "Orthogonal wavelet" patented technology

The invention discloses a meteorological prediction data compression storage optimization method, which belongs to the technical field of meteorological data processing, and comprises the following steps: obtaining preprocessed meteorological prediction data; performing three-dimensional wavelet decomposition on the preprocessed meteorological prediction data by using an orthogonal wavelet basis function to obtain a tensor; performing high-order tensor decomposition on the tensor to obtain a core tensor and a plurality of modal feature matrixes; regularization constraint dynamic adjustment and sparse processing are carried out on the rank of the core tensor, dimension reduction processing is carried out on the multiple modal feature matrixes, and the core tensor with the reduced rank and the multiple modal feature matrixes with the reduced storage scale are obtained and entropy coding and quantization processing are carried out on the core tensor and the multiple modal feature matrixes; obtaining the core tensor subjected to entropy coding and quantization processing and the compressed representation of a plurality of modal feature matrixes; and dynamically calibrating the storage precision and the information loss of the compression representation of the core tensor and the plurality of modal feature matrixes after entropy coding and quantization processing by using variational inference to complete the compression storage optimization of the meteorological prediction data.

The invention discloses an earphone space audio positioning method integrated with an MEMS sensor and a control system, and relates to the field of earphone space audio positioning. A temperature compensation MEMS sensor group and a microphone array are used for collecting motion data and sound wave signals, and signals are enhanced through orthogonal wavelet denoising and beam forming; the attitude is solved by adopting double-extended Kalman filtering and fusing data, and fuzzy logic is introduced to evaluate the credibility of the sensor; predicting a sound field by using a CNN model, and reconstructing the sound field in combination with an adaptive wave field synthesis technology; and based on auditory masking effect optimization balance, the influence of altitude on sound velocity is compensated. Through an ultra-precision sensor and an advanced algorithm, the positioning precision and reliability of earphone space audio are improved, and delay and power consumption are reduced; accurate sound source positioning, personalized sound field adaptation and cross-modal immersion experience in a complex scene are realized, and the audio experience of a user is improved.

The present application belongs to the field of seismic data processing and data optimization, in particular to a method and device for seismic prestack data optimization based on improved BEMD algorithm. The method of the present application uses improved BEMD algorithm and adaptive denoising algorithm to decompose prestack gathers into characteristic signals of different scales. Then, orthogonal wavelet transform denoising based on threshold is carried out on each component to remove most of the noise. Then, the correlation coefficient between each component and the original data is calculated, and the data is reconstructed based on the correlation coefficient. The effective signal is retained to the greatest extent, the interference of noise signal is removed, the signal-to-noise ratio of prestack gathers is improved, and a good data basis is provided for subsequent seismic prediction algorithms.

The present disclosure provides a spatiotemporal temperature modeling method for lithium battery packs, which belongs to the field of data processing technology and specifically includes: preprocessing output data; selecting a corresponding scaling function based on the data change, and constructing an orthogonal wavelet basis function based on the scaling function; collecting spatiotemporal data and projecting it on the orthogonal wavelet basis function to obtain a time coefficient that characterizes the temporal dynamics of the temperature field; establishing a relationship vector between the time coefficient and the system input to describe the time series dynamics of the system; solving a support vector regression online model, using the loss coefficient corresponding to the loss function to represent the loss of the support vector, and obtaining a support vector regression time coefficient model that characterizes the nonlinear temporal dynamics of the temperature in the battery pack; integrating the orthogonal wavelet basis function with the support vector regression time coefficient model to obtain a spatiotemporal dynamic model of the lithium battery to dynamically model the battery pack temperature in time and space. The solution disclosed in the present disclosure improves prediction efficiency and safety during use.

The invention belongs to the technical field of oil abrasive particle monitoring, and particularly relates to an abrasive particle feature extraction method based on differential signal band selection adaptive filtering, which comprises the following steps: carrying out common-mode rejection on an oil abrasive particle signal to obtain a differential signal to be detected; performing wavelet packet decomposition on the differential signal to be detected through an orthogonal wavelet basis function to obtain a multi-decomposition-scale feature vector; calculating a similarity index on each decomposition scale, and extracting a target scale feature vector; processing the target scale feature vector by adopting an improved minimum root mean square adaptive filter to obtain a filtering vector; performing wavelet reconstruction on the zero vector and the filtering vector to obtain a noise reduction signal; dividing the noise reduction signal into a plurality of segments, and calculating a composite identification index of each segment; calculating the weight of each segment according to the composite identification index, and weighting all segments to obtain an oil abrasive particle characteristic signal extraction result; according to the invention, the signal-to-noise ratio of the abrasive particle signal is improved, the self-adaptive capability is stronger, and the accurate detection of the oil abrasive particle characteristics is improved.

The present invention provides a method and device for estimating abundance of hyperspectral remote sensing images based on condition number weighting, the method comprising: obtaining a hyperspectral data matrix and an end metadata matrix; using biorthogonal wavelets to perform wavelet packet decomposition on the hyperspectral data matrix and the end metadata matrix respectively to obtain a corresponding wavelet packet tree; obtaining a coefficient matrix at each node of the wavelet packet tree corresponding to the end metadata matrix; calculating the condition number of each coefficient matrix; calculating the weight of each node according to each condition number; using the hyperspectral data matrix and the end metadata matrix corresponding to each node as inputs of the least squares method, multiplying the result output by each least squares method by the weight of the corresponding node, and finally summing each node to obtain an abundance estimation result. The present invention uses the condition number weighting of the coefficient matrix after wavelet packet decomposition to maximize the use of effective information in the data, thereby improving the least squares estimation and improving the unmixing accuracy of the hyperspectral remote sensing image.

The invention discloses an analog circuit fault diagnosis method based on improved tunable orthogonal wavelet transform (TQWT) and a deep belief network (DBN), and aims to improve the efficiency and accuracy of fault diagnosis. The method comprises the following steps of: 1, refining circuit signal data by adopting a TQWT algorithm, and more effectively capturing the local characteristics of the signal by utilizing the characteristics of the TQWT, especially when the complex circuit signal with larger noise interference is processed; in order to optimize the performance of the TQWT algorithm, a cuckoo search algorithm is adopted to dynamically adjust the Q factor of the TQWT, and it is ensured that the optimal Q factor is selected in a specific application scene, so that the fault feature separation capability is improved. And after the data are refined, inputting the refined data into an Inception module to carry out feature extraction. The Inception module can effectively capture key features in circuit signals due to unique parallel processing capability and multi-scale feature extraction advantages, so that the recognition capability of the model on various fault modes is remarkably enhanced. After feature extraction is completed, a multi-core support vector machine (MKL-SVM) is finally constructed for fault classification. The MKL-SVM can improve the accuracy and robustness of the classification model by integrating a plurality of kernel functions, and can adapt to different types of fault classification requirements.

The invention discloses a driving steady-state control method and system based on a deep belief network, and belongs to the technical field of steady-state control of an electric forklift driving system.The method comprises the steps that all data of an electric forklift are collected, and noise reduction processing is conducted on original data through a wavelet soft threshold method; carrying out orthogonal wavelet decomposition on the denoised data, carrying out convolution interlaced point sampling, and finally extracting time domain features and frequency domain features of the data; eliminating the influence of the value range and dimension difference of the time domain and frequency domain features on the deep belief network model through a minimum-maximum normalization method; and obtaining a model layer structure and parameters of steady state control based on the deep belief network according to the normalized time domain and frequency domain feature vectors. The method has the advantage that the dynamic performance and the steady-state precision of the starting system are improved.

The present application belongs to the technical field of oil liquid abrasive particle monitoring, and particularly relates to a kind of abrasive particle feature extraction methods based on differential signal band selection adaptive filtering, including obtaining the differential signal to be detected by carrying out common mode rejection to oil liquid abrasive particle signal;Obtain multi-decomposition scale feature vector by wavelet packet decomposition to the differential signal to be detected through orthogonal wavelet base function;Calculate similarity index on each decomposition scale, extract target scale feature vector;Obtain filter vector by processing target scale feature vector using improved least mean square root adaptive filter;Carry out wavelet reconstruction to zero vector and filter vector to obtain noise reduction signal;Divide noise reduction signal into multiple segments, calculate composite recognition index of each segment;According to composite recognition index, calculate the weight of each segment, and obtain oil liquid abrasive particle feature signal extraction result by weighting all segments;The present application improves the signal-to-noise ratio of abrasive particle signal, has stronger adaptive ability, and helps to improve the accurate detection of oil liquid abrasive particle feature.

The invention provides a pet myocardial thickness data intelligent analysis and prediction early warning method and system, and relates to the technical field of pet medical image processing, and the method comprises the steps: employing Canny edge detection and a multi-feature constraint region growth algorithm to extract a myocardial boundary, employing orthogonal wavelet decomposition and reconstruction to optimize a myocardial contour, and achieving the sub-pixel precision myocardial thickness measurement; hilbert transform and a Bayesian network are utilized to predict and analyze the heart rate variability index, the cardiac output and the ventricular ejection fraction, and when the lesion risk probability exceeds a threshold value, early warning is triggered. According to the invention, the myocardial thickness measurement precision is improved, and early warning of heart diseases is realized.

The present invention provides a wavelet domain NMF image unmixing method based on adaptive local neighborhood constraints, including: performing biorthogonal wavelet packet transform on a hyperspectral data matrix and an end-member data matrix respectively; obtaining the coefficient matrix at each node of a wavelet packet tree corresponding to the end-member data matrix, and finding the node with the lowest condition number; using the correlation matrix data corresponding to the node with the lowest condition number as an input of an NMF model based on adaptive local neighborhood weighted constraints, iteratively updating the abundance matrix and the end-member matrix until the model converges, and outputting the iteratively updated abundance matrix and the end-member matrix when converged; and reconstructing the end-members using the wavelet packet tree nodes at the lowest level of the condition number. The ill-conditioned matrix problem that occurs during the unmixing process is overcome, and the unmixing precision effect is further improved.

This invention discloses an electrocardiogram (ECG) synthesis method and system based on adaptive dual encoders and wavelet fusion. The method first calibrates the time offset between the cardiac impulse signal and the reference ECG through cross-correlation alignment; it then constructs an adaptive orthogonal wavelet decomposition module using learnable perturbation terms to separate multi-scale components; subsequently, it employs parallel feature extraction using dual encoders in the time and wavelet domains, and achieves dynamic fusion of heterogeneous features through a gated cross-attention mechanism; a bottleneck layer introduces bidirectional temporal convolution to enhance long-range dependency modeling and correct phase offset; in the decoding stage, multi-scale wavelet heuristic decomposition guides upsampling to reconstruct fine-grained morphology. This invention effectively solves the problems of weak individualized modeling capability and loss of high-frequency details in existing technologies, possessing advantages of high fidelity and low computational complexity, and is suitable for real-time health monitoring of edge devices.

The present disclosure provides an ISAL image quality assessment method based on the Bior and RDSIFT algorithms, which can be applied to the field of imaging detection. The method includes: using a biorthogonal wavelet basis algorithm (Bior) to process the ISAL laser inverse synthetic aperture image to obtain time-frequency domain features; using a feature transformation algorithm (RDSIFT) to process the position information of pixel points in the ISAL laser inverse synthetic aperture image to obtain directional features; convolving the grayscale features and directional features of the ISAL laser inverse synthetic aperture image to obtain scale-invariant features; performing a gradient transformation on the scale-invariant features to obtain texture features; and fusing the time-frequency domain features and texture features into a support vector machine model to output an image quality assessment result.

This invention discloses a drive steady-state control method and control system based on deep belief networks, belonging to the field of steady-state control technology for electric forklift drive systems. The method involves collecting various data from the electric forklift and denoising the original data using wavelet soft thresholding. The denoised data is then subjected to orthogonal wavelet decomposition and convolutional sampling with intervals to extract time-domain and frequency-domain features. A minimum-maximum normalization method is used to eliminate the influence of differences in the value range and dimensions of the time-domain and frequency-domain features on the deep belief network model. Based on the normalized time-domain and frequency-domain feature vectors, the model layer structure and parameters for steady-state control based on the deep belief network are obtained. The advantages of this invention are improved dynamic performance and steady-state accuracy of the drive system.

The application discloses a kind of iron polymetallic ore ore-induced anomaly extraction methods under the double-orthogonal wavelet base Brige-Massart strategy, it is related to geological exploration technical field.The main content includes: using the orthogonal wavelet base of multi-resolution analysis structure, complete orthogonal wavelet transform, complete decomposition, prediction and update;For the high-frequency strong interference noise caused by volcanic rock, intrusive rock and near-surface uneven body in gravity and magnetic data, using double-orthogonal wavelet base Brige-Massart strategy eliminates interference noise and keeps the original information of the anomaly caused by iron ore body;Test the actual effect of different wavelet base and denoising method on the anomaly caused by iron polymetallic ore body of different depth.The application further improves the signal extraction efficiency and precision of the anomaly caused by iron mine, linearly shifts the original signal by linear phase filtering, improves the symmetry of orthogonal wavelet, so as to reduce the signal distortion caused by volcanic rock and other high-frequency geological bodies.

The application discloses a multi-event data parallel transmission method based on a nerve conduction mechanism, which comprises the following steps: inputting data into a neuron LIF model with feedback to obtain a pulse sequence; using different mutually orthogonal wavelet functions to represent the pulse for different events to obtain a wavelet represented pulse sequence; adding the wavelet pulse sequences of all events to form a sending signal and sending it outward; using different wavelets to perform correlation operation on the receiving signal to separate the pulse sequences of each event; and using an integrator to calculate the number of pulses in a time window to restore the numerical data of each event. The application can realize real-time parallel transmission of multi-events, can use a neuron model to represent event information with a pulse sequence, can use mutually orthogonal wavelets as carriers to realize multiplexing, and can realize real-time transmission without waiting in the transmission process, and can be widely applied to real-time transmission of multi-events.