4 results about "Orthogonal wavelet" patented technology

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.

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.

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 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.