9results about How to "Strong real-time processing" patented technology

The invention provides a multi-degraded image restoration method based on self-adaptive prompt. The method comprises a two-stage hidden space diffusion model, and various degraded images can be flexibly processed. Specifically, the model generates a degradation cue by learning the difference between a degraded image and a clean image, and encodes the cue information in a hidden space. In the first stage, a degradation prompt is obtained through a degradation adaptive prompt learning mechanism as a key feature for guiding the diffusion model in the second stage to carry out image restoration. Finally, prompt information is integrated into a Fourier transform restoration network, and accurate restoration of different types of degraded images can be realized. The method not only improves the accuracy of image restoration, but also significantly reduces the calculation complexity, and is suitable for image restoration tasks in various real world scenes, including denoising, deblurring, defogging, rain removing, snow removing, raindrop removing and low illumination enhancement.

The invention belongs to the field of electroencephalogram emotion recognition, and provides an emotion recognition method and system for a multi-scale convolutional network based on bidirectional attention, and the method comprises the steps: segmenting an electroencephalogram signal according to a time window, extracting differential entropy (DE) and power spectral density (PSD) features, introducing an electroencephalogram map to embed spatial information, and capturing a spatial dependency relationship in the signal. Then, the preprocessed features are input into an MSA-TCN model, and the model combines the attention in the bidirectional features, the attention between the bidirectional features, a multi-scale convolution network and a time sequence convolution network to effectively extract dynamic time features and cross-channel spatial features; the invention aims to overcome the limitation of the prior art in emotion recognition through an innovative bidirectional attention mechanism and a multi-scale convolutional network structure (MSA-TCN).

The invention provides a track fastener state recognition method based on a three-section type parallel network architecture. The method comprises the steps that a track inspection image is acquired; constructing a convolutional neural network (CNN) backbone network, and performing multi-scale feature extraction on the image of the track fastener; performing convolution processing and up-sampling processing on the fastener features by using the local feature branches to obtain local fastener features; performing global average pooling and convolution processing on the fastener features by using the global feature branches; the method comprises the following steps of: processing the characteristics of the fastener by using a mask generation branch of a framework based on Vision Transform, and generating a prototype mask of the track fastener; local, global and mask fastener features are fused; performing non-maximum suppression and mask generation on the fastener feature map; and analyzing the health state of the track fastener. According to the method, the CNN backbone network and the Vision Transform are combined, a new three-section parallel network architecture is provided, accurate identification and state analysis of the track fastener are realized through instance segmentation, and the identification accuracy in a complex environment is improved.

The application provides a press operation information and operation and maintenance service edge computing system and method, which is independent of a press control PLC, comprises a dual-core edge computing controller, a data acquisition module, a communication module and sensors deployed on the press, has press information acquisition and data processing, fault diagnosis, motion pair temperature and friction block life analysis and prediction, remote data management and other functions, interacts with the main control PLC in real time to exchange press information data, and uploads the data information of the press to a cloud platform to provide support for remote service of the press. The application can obtain more press state information, effectively overcomes insufficient data analysis and processing capacity of the press main control PLC, and moves the detection sensors to the edge side, shortens the main control PLC scanning cycle, realizes real-time sensing and rapid response to the press operation information state, avoids overload, overheating, excessive wear and brake failure, and improves the safety, reliability and intelligent level of the press.

This invention provides a method and system for automatic vertex picking of DAS-based microseismic events, relating to the field of microseismic location technology. The method includes acquiring multi-channel data corresponding to microseismic events and performing demodulation analysis to form multi-channel waveform data; performing correlation analysis on the waveform data from different channels to establish an automatic picking correlation model; performing image transformation based on the automatic picking correlation model to form corresponding grayscale images; and performing location discrimination analysis based on the grayscale images to determine the event vertex channel. This method improves the interpretation accuracy and reliability of geological analysis of seismic images by removing noise while preserving geological structural features such as faults and strata.

This utility model provides a fully domestically produced task management main control board. The main control board consists of a 2K1000 carrier board and a YX5F200T baseboard, connected via a COME connector to achieve bus interface information exchange. The main control board has an 800MHz CPU, 1GB of memory, and external expansion via a J30J connector: two RS232 serial interfaces, one RS232 debug serial port, two 10 / 100 / 1000base-T adaptive Ethernet interfaces, and two CAN interfaces. An additional ISA bus interface for task management is provided via a PDS-90JW connector. The baseboard includes an mSATA interface for mounting an mSATA hard drive. The baseboard also has one HDMI interface and four USB interfaces. All components used in the task management main control board are 100% developed and delivered by domestic manufacturers, meeting the requirements of independent controllability.

The invention discloses an MVDR beam forming FPGA structure based on a multi-mode systolic array and an implementation method. The FPGA structure comprises a data acquisition module used for obtaining a sampling matrix of a floating point type; the FSM scheduling module is used for performing scheduling control on the multi-port storage RAM, the multi-mode calculation module and the data stream; the multi-mode calculation module comprises a division unit, a negation unit, a delay unit, a cache unit and a systolic array unit formed by N * N PE units, and is used for performing covariance matrix solving, LDL decomposition, triangular matrix and diagonal matrix inversion and calculating an optimal weight according to the solved inverse matrix; each PE unit is used for carrying out floating point complex number multiplication and floating point complex number accumulation operation on input data; and the multi-port memory RAM is provided with an X-dimensional read port and a Y-dimensional read / write port. The resource overhead is small, and the real-time performance and the hardware cost can be both considered.

The present invention belongs to the field of maritime radar and navigation and positioning technology, and specifically discloses a calibration method for multi-radar data fusion of a maritime radar system. The method combines the characteristics of pulse compression radar and electronic scanning radar, and adopts rigid transformation methods such as iterative nearest point algorithm to accurately calibrate the original data of different radars, ensuring the accurate alignment of data in the global positioning system GPS coordinate system, thereby significantly improving the conversion accuracy and consistency of radar data. The method of the present invention significantly improves the stability of the system in complex environments, reduces system deviations caused by inconsistent data, and ensures the reliable operation of ship navigation and collision avoidance systems. The method of the present invention has powerful real-time processing capabilities, can effectively cope with rapid changes in highly dynamic marine environments, and ensures that real-time conversion and fusion of radar data can be achieved when the ship is moving at high speed, ensuring the safe navigation of ships in complex and dynamic environments.

The invention provides a method and system for positioning the head of a driver in an aircraft cockpit, and relates to the technical field of image processing, and the method comprises the steps: obtaining the head images of the driver in the aircraft cockpit at a plurality of visual angles; extracting a plurality of feature points in the head image of the driver through an FAST algorithm; calculating a Harris response value of each feature point through a Harris corner response function; through a non-maximum suppression algorithm, a feature point with the maximum Harris response value is screened out from the driver head image to serve as a target feature point; calculating the main direction of the target feature point through a centroid method; generating a BRIEF descriptor of the target feature point according to the main direction of the target feature point; and according to the BRIEF descriptors of the target feature points, feature point matching is performed on the target feature points in the head image of the driver under each view angle through an FLANN algorithm, so that the head of the driver in an aircraft cockpit can be positioned.