4 results about "Pattern recognition system" patented technology

This invention relates to a multi-task motion pattern recognition system and method for underwater exoskeleton robots. Through innovative data preprocessing and network architecture design, it utilizes a small amount of IMU data to synchronously, in real-time, and with high precision complete motion pattern classification and continuous motion phase prediction, thereby driving the exoskeleton to achieve precise and synchronous assisted control. The multi-task motion pattern recognition system for underwater exoskeleton robots includes a sensing module for acquiring raw posture data of a diver; a data processing module for receiving and calculating relative IMU posture data within the human coordinate system to construct an input tensor; an intent recognition module, including a multi-task deep neural network based on an inverted Transformer architecture, for receiving the input tensor and synchronously outputting motion pattern classification results and continuous motion phase prediction results; and a control module for generating control signals for the underwater exoskeleton based on the motion pattern classification results and continuous motion phase prediction results.

The application relates to the technical field of information technology of construction engineering, and particularly discloses a bidding document review auxiliary method and system based on artificial intelligence, which comprises bidding document preprocessing, technical specification similarity analysis, commercial specification consistency analysis and analysis result collection. The scheme establishes an objective quantitative evaluation system based on the combination of TF-IDF statistical characteristics and pre-training word vector semantic characteristics, provides accurate numerical similarity scores, enhances the robustness of technical specification text content detection through deep semantic understanding, realizes integrated data analysis through a unified numerical extraction framework, can find cross-table type correlation abnormalities, adopts adaptive extraction technology based on numerical pattern recognition, constructs a multi-dimensional abnormal pattern recognition system, effectively detects bid-rigging behavior among bidders, introduces feature contribution degree analysis and abnormal pattern recognition mechanism, and greatly improves the reliability of analysis results.

The invention relates to a method for pattern recognition to assemble printed circuit boards by digital printing and a system for implementing the method. In the method, pattern recognition techniques are applied to extract information from imaging of the printed circuit board, thereby enabling automated printing of a targeted object on the board. The method comprises digital printing of a material that is uniformly coated on a transparent donor substrate and positioned in parallel to a receiver substrate. A laser pulse irradiates the donor substrate, resulting in detachment and transfer of a portion of the material onto the receiver substrate. The method enables reduction of overall processing time while achieving high alignment accuracy and high printing resolution.

The application discloses a big data highway accident mode recognition system, comprising: a multi-source data acquisition module for collecting environmental data, facility operation data and vehicle behavior data of a highway section; a data processing module connected with the multi-source data acquisition module, used for fusing and processing various collected data to generate a spatio-temporal aligned joint data set; a mode recognition module connected with the data processing module, used for extracting and fusing multi-dimensional features from the joint data set, and identifying the cause mode and precursor mode of the accident based on the features; and an early warning output module connected with the mode recognition module, used for issuing a road section risk early warning information; the application integrates three heterogeneous data sources of environment, facility and vehicle behavior, constructs a multi-dimensional three-dimensional risk perception system, significantly improves the discovery ability and recognition accuracy of implicit or early risks such as thin ice and fog, and realizes the leap from single appearance monitoring to multi-dimensional essential perception of highway risks.