8 results about "Computer-aided technologies" patented technology

The invention discloses an exoskeleton motion signal peak valley intelligent detection method and system, and relates to the technical field of computer assistance. The method is used for solving the problems of control delay and high misjudgment rate caused by large motion signal noise interference and inaccurate processing in an exoskeleton system. The method comprises the steps of firstly, suppressing motion artifact noise and generating a high-signal-to-noise-ratio preprocessing signal through multi-modal signal collaborative noise reduction and dynamic energy entropy segmentation, secondly, constructing a parallel convolution attention network to extract multi-modal features, and extracting peak and valley candidate points in combination with dynamic weight fusion and multi-scale differential detection; a peak valley point set is optimized based on a variable structure density sensing clustering algorithm and density gradient analysis, artifact interference is eliminated, finally, a multi-rule confidence model is constructed by fusing time sequence prediction of a bidirectional gating circulation unit and biomechanical correlation, and a threshold value is dynamically adjusted to trigger an exoskeleton joint assistance instruction. Closed-loop processing from signal acquisition to real-time control is realized, and peak valley detection precision and response speed are remarkably improved.

A system may support adaptive user interfaces (UIs) for computer-aided technology (CAx) applications. The system may include a CAx tracking engine configured to track command usage of a computer-aided technology (CAx) application to obtain command usage data for the CAx application and a CAx training engine configured to train a machine learning model with the command usage data. The system may also include a CAx adaptive UI engine configured to obtain real-time command usage by a user of the CAx application; and apply the machine learning model to adaptively transform a UI of the CAx application, including by inputting the real-time command usage of the CAx application to the machine learning model to determine a predicted command based on the real-time command usage and dynamically updating a selected sub-section of the UI of the CAx application to present the predicted command to the user.

The invention discloses a bridge construction three-dimensional precision control system based on BIM and computer assistance, and relates to the technical field of bridge construction. The data acquisition module is used for acquiring data of a construction site through various sensors and measuring equipment; the data processing and analyzing module is used for processing and analyzing the collected data, comparing the data with design data in the BIM model and calculating a construction error; the precision control and deviation rectification module is used for obtaining a result according to the data processing and analysis module; a visualization and collaborative management module; according to the bridge construction three-dimensional precision control system based on BIM and computer aiding, real-time and accurate control of bridge construction three-dimensional precision is achieved through deep fusion of the BIM technology and the computer aiding technology and combination of advanced models and algorithms, and the system has good universality and expansibility, can adapt to bridge construction projects of different types and scales, and has good application prospects. The application prospect is wide.

The application discloses a digital heat manipulation method based on image programming and relates to the field of computer-aided (CAX) technology, characterized in that for a heat manipulation process involving multiple heat sources, in order to realize accurate control of a target surface temperature field, the process is first discretized according to a given time step to obtain the target temperature field in each time step; the temperature field of the target surface is characterized by a gray-scale image, the image is processed in a fuzzy manner in combination with a heat transfer equation to simulate the evolution process of the temperature field over time, and the required theoretical optimal heating mode is reversely solved; and the input power corresponding to each heat source is calculated by a characteristic decomposition algorithm to approximate the heating mode. Thus, the input power state corresponding to each heat source at each moment is determined, and digital heat manipulation is realized. The application provides a new heat manipulation idea, provides more efficient, accurate and unified operation, and lays a theoretical and technical foundation for automatic and accurate control of a microwave solidification manufacturing process.

A computer-implemented method of generating a thin volume mesh in a Computer-Aided technology (CAx) model of an object is described. In the method, a volume region lying between a first surface mesh having a first set of mesh parameters and a second surface mesh having a second set of mesh parameters is selected. This volume region has a thickness corresponding to the distance between the first and second surface mesh. prismatic cell volume mesh is generated to fill the entire volume region when the volume region is thin. Whether or not the volume region is thin is given by a crisp value calculated using a fuzzy logic inference scheme including the fuzzy sets thin and non-thin, and wherein the relationship between the first and second sets of mesh parameters and the thickness determine the membership of the fuzzy sets thin and non-thin.

The application discloses a bolt connection model construction method and device fusing physical information and deep learning, and the method comprises the following steps: obtaining a hysteresis response data set of a bolt connection interface according to an Ivan model through a bolt connection excitation; performing marking processing on the hysteresis response data set to obtain a target data set; constructing a target loss function according to the hysteresis response data set; constructing an initial training model based on a convolutional neural network and a long short-term memory network; and training the initial training model according to the target loss function and the target data set to obtain a bolt hysteresis system prediction model. The application can improve the efficiency and accuracy of identifying the bolt connection model and can be widely applied to the technical field of computer assistance.

The invention discloses an anorectal minimally invasive surgery type planning system based on AI, and relates to the technical field of computer assistance. The method is used for solving the problems that in a traditional fistula operation mode, starting point positioning depends on experience, fistula walking recognition is incomplete, and incision design is not individualized. The method comprises the following steps: firstly, segmenting a dentate line region through a U-Net network, enhancing tubular characteristics through Hessian filtering, extracting anal sinus morphological characteristics, inputting the anal sinus morphological characteristics into a classification network, and determining space coordinates of the anal sinus of a fistula origin; then, taking the coordinate as a starting point, tracking along a fistula main axis by adopting a direction constraint region growth algorithm, calculating the minimum distance between the fistula and sphincter, and constructing a three-dimensional relation map; evaluating stress changes of the sphincter in different separation paths by combining finite element analysis, and generating an optimized path with the minimum stress; and finally, establishing a projection coordinate system by taking the ischial tuberosity as a mark, fusing the tension distribution, the skin tension line and the nerve and blood vessel bundle projection, and generating an individualized arc-shaped minimally invasive incision trajectory.