8 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.

This invention discloses a method for recognizing loading and unloading pallet status patterns. First, a pallet status dataset is constructed. Based on camera positions, the pallet status is divided into five categories and labeled. Next, a ResNet50 network model is constructed, adding ReLU, dropout, and logSoftmax regression layers to adapt to the dataset for sample training. After model convergence, a pallet status pattern recognition system is built. The video stream is input into the recognition system using the RTSP protocol to complete the initial recognition of the pallet status. Median filtering is used to denoise and improve the recognition accuracy, ultimately achieving an accuracy of 99%. This solves the problem of mismatch between work time and vehicle number caused by misjudgments in traditional algorithms. The license plate area is located using the YOLOv5 algorithm, and the target text is extracted using a text detection algorithm, combined with a convolutional recurrent neural network to recognize the license plate information. Finally, the recorded pallet vehicle docking and exit times and work duration data provide a basis for pallet management. This invention realizes the status recognition of each pallet, records the pallet operation status, and can effectively provide feedback on abnormal operations, facilitating management and review.

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.

The present application relates to cell injection instrument motion control technical field, especially based on the cell injection instrument motion intelligent control system of artificial intelligence, its technical scheme includes: through the construction multimodal perception fusion module and cell mechanics characteristic on-line identification module, fundamentally solved the problem of poor universality and high risk of injury caused by biological sample diversity; The introduction of adaptive motion planning and decision-making based on reinforcement learning center, the motion control problem is converted into a continuous learning optimization process; The designed feedforward and feedback composite execution module, especially its built-in iterative learning controller, effectively solves the bottleneck that the repeatability error is difficult to eliminate in the precision motion system; The integrated abnormal state autonomous diagnosis and recovery module gives the system high robustness and fault tolerance ability; Through the parallel real-time pattern recognition, the system can quickly and accurately diagnose a variety of common operation faults, and trigger intelligent recovery strategy, greatly reduce the dependence on artificial intervention.

The invention relates to the technical field of medical state monitoring, in particular to a cardiothoracic surgery drainage tube running state mode recognition system which comprises a drainage imaging positioning module, a leading edge rhythm judgment module, a liquid column direction judgment module, a state collaborative recognition module and a running state labeling module. According to the method, the visual range of the drainage liquid in the transparent section is positioned through the continuous images, the leading edge boundary and the starting and ending positions of the liquid column are extracted, the quantitative tracking capability of the dynamic characteristics of the liquid is enhanced, and the recognition accuracy of stagnation and flowing states is improved through leading edge displacement change and rhythmicity analysis; the judgment of the length change direction of the liquid column defines the flow direction of the liquid, reduces single-frame visual misjudgment, cooperatively judges the leading edge state and the direction of the liquid column, screens out a synchronous section, improves the stability and consistency of state recognition, completes state labeling in combination with an image timestamp, constructs a sequential and continuous operation state recognition system, and improves the accuracy of state recognition. And the intelligence and accuracy of drainage state monitoring are enhanced.

The invention relates to the technical field of motion control of cell injectors, in particular to an intelligent control system for motion of a cell injector based on artificial intelligence, and adopts the technical scheme that a multi-modal sensing fusion module and a cell mechanical property online identification module are constructed; the problems of poor universality and high injury risk caused by diversity of biological samples are fundamentally solved; a self-adaptive motion planning and decision center based on reinforcement learning is introduced, and a motion control problem is converted into a continuous learning optimization process; a designed feedforward and feedback composite execution module, especially a built-in iterative learning controller, effectively overcomes the bottleneck that repetitive errors in a precise motion system are difficult to eliminate; an integrated abnormal state autonomous diagnosis and recovery module endows the system with high robustness and fault-tolerant capability; through parallelized real-time mode recognition, the system can quickly and accurately diagnose various common operation faults and trigger an intelligent recovery strategy, so that the dependence on manual intervention is greatly reduced.