5results about How to "Valid encoding" patented technology

This invention proposes a brain-inspired navigation method based on DSI decoupling representation and composite potential field path optimization. The method includes: acquiring a state sequence and calculating a successor representation matrix; obtaining a DSI representation from the successor representation matrix; generating a navigation path sequence based on the DSI representation; constructing a total potential function from the navigation path sequence; generating an optimal path sequence based on the total potential function and simultaneously introducing obstacle information; using the optimal state sequence extracted from the optimal path sequence as a supervision signal to update the DSI representation using gradient descent, and storing the obtained corrected DSI representation in a calibration experience buffer; repeatedly executing online navigation, path optimization, and calibration steps using the corrected DSI representation in the calibration experience buffer, and obtaining the optimal brain-inspired navigation sequence after a preset number of iterations. This invention applies composite potential energy optimization and obstacle constraints to the initial path, resulting in a geometrically more compact and safer path.

The invention relates to the technical field of laser communication microwave coding, in particular to an interstellar laser communication microwave coding method, which comprises the following steps: acquiring a microwave signal to be transmitted, converting the microwave signal into a digital signal, arranging the acquired digital signal according to a magic square construction rule, generating a magic square code, loading the magic square code onto a laser beam, and transmitting the magic square code to the laser beam. And transmitting the laser beam loaded with the microwave signal to a destination. A mathematical principle of a magic square is utilized, microwave signals to be transmitted are converted into digits, then the digits are arranged in the magic square according to a certain rule, the digits are loaded to light waves through laser beams, transmission of the microwave signals is achieved, the signals are effectively coded and transmitted, the anti-interference capacity of the signals is improved, the microwave signals are loaded to the laser beams, and the transmission efficiency is improved. According to the invention, high-speed and high-bandwidth transmission can be realized, different coding modes can be realized through different magic square construction methods, and the flexibility is very high.

The present application relates to the field of computer vision, in particular to the subject matter of video processing and video encoding, and more particularly to a method, decoder, encoder and computer readable medium for video encoding using a machine learning model. According to a first aspect, a method of processing image and / or video data by a decoder is provided. The method includes continuously decoding a plurality of segments of an image or video. For one or more of the plurality of segments, the decoding comprises: decoding a corresponding encoded residual segment to obtain a decoded residual segment; generating predicted segments from respective reference segments using a machine learning model, wherein the respective reference segments are selected from one or more reference segments stored in a set of reference segments; generating a reconstructed fragment based on the prediction fragment and the decoded residual fragment; storing the reconstructed fragment in the reference fragment set; and updating parameters of the machine learning model based on the corresponding reference segments and the reconstructed segments.

The application discloses a printed circuit board defect detection method and system based on deep learning. The application uses an industrial camera to collect a printed circuit board image and pre-processes the same; the pre-processed printed circuit board image is input into a pre-trained improved YOLOv12 printed circuit board defect detection model to obtain a detection result. The NAMHSA module designed by the application can effectively capture global context and local dependency, thereby effectively capturing fine-grained features of printed circuit board defects. The C3K2-SSMSCA module designed by the application can not only effectively reduce the loss of local details and spatial position information, effectively enhance useful channels and suppress irrelevant channels, but also emphasize meaningful features and suppress meaningless features in the spatial dimension. The application can effectively reduce the interference of non-defect targets and background noise, effectively and adaptively extract printed circuit board defect features, and thereby improve the detection precision of printed circuit board defects.

This invention provides a method and system for enhancing the cross-linguistic capabilities of a large language model, applicable to the fields of natural language processing and large language model technology. The method includes: constructing a probe corpus; based on the probe corpus, obtaining neuron sets corresponding to the source language and neuron sets corresponding to the target language through a large language model; obtaining overlapping neuron sets and activation matrices of the overlapping neuron sets for the source and target language sets; calculating the dependency score between each candidate bridging language and the activation matrix of the overlapping neuron sets based on the activation matrices of the overlapping neuron sets, and determining the optimal bridging language from the candidate bridging languages; and providing a Cross-lingual In Context Learning (X-ICL) construction strategy for cross-linguistic tasks based on the optimal bridging language. Thus, a significant performance improvement in X-ICL can be achieved with zero samples and without fine-tuning on cross-linguistic tasks and low-resource languages ​​covering multiple language families.