14results about How to "Reduce Model Complexity" patented technology

The invention discloses a virtual environment modeling method for an OHT system of a semiconductor factory, and belongs to the technical field of semiconductor production, and the method comprises the following steps: data collection and preprocessing: collecting field facility data of a semiconductor factory production line, operation related data of an OHT vehicle, and product information data, and carrying out the preprocessing of the data, so as to obtain basic data; virtual environment modeling: constructing a virtual environment model according to the basic data; simulation and optimization: carrying out simulation operation based on the constructed virtual environment model, and carrying out performance evaluation; then, optimization is carried out according to a simulation result and a performance evaluation condition; and verifying and applying a result. According to the method, accurate modeling and simulation are carried out on the OHT system, the configuration and scheduling strategy of the OHT system is optimized, resource utilization can be optimized, the cost can be reduced, and the stability, reliability and flexibility of the system can be enhanced.

The invention discloses a hydraulic runoff modeling method capable of reducing dimensions and simplifying, electronic equipment and a storage medium, and belongs to the technical field of water environment engineering. In order to improve the efficiency of urban hydraulic runoff modeling, the method comprises the following steps: acquiring rainfall time sequence data, raster data of an underlying surface digital elevation model (DEM) and underlying surface land utilization data; dividing and constructing a square grid, four vertex positions of each square grid, and a plane coordinate value and an elevation value at the geometric center of each square grid; model simplification topology construction is carried out, simulation inspection well nodes, simulation pipe duct connection and simulation sub catchment area polygons are constructed based on an SWMM model, and all simulation sub catchment area polygons are associated with the simulation inspection well nodes located in the centers of the simulation sub catchment area polygons; the method comprises the following steps: calculating runoff of each grid, calculating runoff of each grid, taking rainfall time sequence data as input of an SWMM model, constructing an SWMM simulation rain gauge, constructing an SWMM rainfall flood model, and calculating to obtain real-time dynamic simulation water quantity and water depth data in each grid. The simulation calculation efficiency is improved.

The invention provides a target object shielding and stacking identification and detection method based on a YOLOv8 network. The method comprises the following steps: S11, constructing a data set; s12, improving a YOLOv8 network, wherein the improvement comprises the steps of replacing a Conv unit of a Backbone module with an AKConv unit, adding an ECA attention mechanism in front of an SPFF layer, replacing a C2f unit of a Neck module with a VoV-GSCSP unit, replacing the Conv unit with a GSConv unit, replacing a detection head of a Head module with a DBB re-parameter unit, and using a GIoU loss function; s13, the data set is input into the improved YOLOv8 network for training, and the weight of the improved YOLOv8 network after training is completed is obtained; and S14, inputting the pictures of the stacked objects into the improved YOLOv8 network for detection and evaluation. The detection precision and the feature extraction capability of the model can be improved.

The invention relates to the technical field of neural network optical fiber measurement, and discloses a soft mechanical arm kinematics modeling method based on a convolutional neural network, which comprises the following steps: step 1, optical fiber curvature reconstruction: detecting the curvature and the curvature deflection angle of a soft mechanical arm through an optical fiber bragg grating sensor; when the soft mechanical arm is bent, the center wavelength of the fiber bragg grating sensor drifts, and curvature and angle information of an optical fiber is calculated based on the drifting distance. 2, three-dimensional position coordinates of the soft arm are obtained; step 3, training a convolutional neural network; and 4, the kinematics of the soft mechanical arm is predicted. Inverse kinematics modeling of the soft arm can be completed only on the basis of curvature information obtained from a fiber bragg grating (FBG) sensor and position information obtained from a visual sensor by adopting a convolutional neural network mode, and the soft arm inverse kinematics modeling method has very high generalization ability and is suitable for soft mechanical arms of different shapes, sizes and materials.

The present application relates to a kind of plane five-pole closed-chain rehabilitation robot elimination modeling and force mapping control method, belong to rehabilitation robot control technical field.The method is aimed at the coupling of active joint and passive joint in plane five-pole closed-chain mechanism, control implementation difficulty under active joint coordinates and the problem that driving torque is difficult to accurately map as end force, establishes closed-chain position constraint equation, obtains velocity constraint relationship by derivation to constraint equation, under the nonsingular condition of passive joint constraint jacobian matrix, passive joint variable is explicitly eliminated, constraint consistent jacobian matrix is constructed, reduced dynamics equation under active joint coordinates is established, end force estimate is solved, and motor driving torque instruction is generated according to the end force estimate.The method is suitable for end impedance control, trajectory tracking control and rehabilitation training boost control of plane five-pole closed-chain rehabilitation robot.

The invention discloses a method and device for measuring the size of irregular objects, and relates to the field of optical measurement. The method is used to solve the problem that the method for measuring the size of irregular objects is complicated and has low precision when the lens with nonlinear distortion is too large. The method comprises: extracting the edge of the object from the image of the irregular object obtained by a large distortion camera to obtain the outermost edge coordinates of the irregular object; adding the four outermost edge coordinates corresponding to the longitudinal coordinates and the horizontal coordinates respectively included in the irregular object to the outermost edge point set; obtaining the four corner point coordinates of each outermost edge coordinate on the chessboard calibration plate according to the outermost edge coordinates and the chessboard corner points, and the four corner point coordinates belong to the image edge coordinate set; connecting the four corner point coordinates corresponding to each outermost edge coordinate diagonally to obtain the intersection point, traversing to determine the distance between each intersection point and the corresponding outermost edge coordinate, until the distance between each intersection point and the corresponding outermost edge coordinate is less than a third set value; obtaining the size of the irregular object according to the number of complete chessboards between the outermost edge points in the horizontal direction, the number of complete chessboards between the outermost edge points in the vertical direction, and the proportion of the area where the outermost edge point is located to the chessboard.

The invention provides a small sample machine learning-based off-axis three-reflector system generation method easy to install and adjust, relates to the technical field of optical system design, and provides a small sample machine learning-based off-axis three-reflector system generation scheme for solving the off-axis three-reflector system design problem under the requirement of rapid design and easy installation and adjustment. In order to reduce the installation and adjustment difficulty of an off-axis three-reflector system and reduce the training and data collection difficulty of machine learning, the method for training an SVR model by using a small sample data set and then automatically generating an off-axis multi-reflector system easy to install and adjust is constructed. When a data set is constructed, a small sample data collection method is designed, and the number of features in samples is reduced, so that the difficulty of construction of a training set and development of a machine learning model is reduced; then, constructing an SVR model, and training the SVR model based on the data set; and finally, generating a parameter combination of the off-axis three-mirror spherical system by using the trained SVR model, and generating the off-axis three-mirror free-form surface system based on the parameter combination.

This application provides an image contrast enhancement method, system, electronic device, and storage medium. The method includes: separating luminance and chrominance components from the YUV data of an input image; training the luminance component for contrast enhancement using an enhancement network model to obtain an enhancement coefficient grid for the luminance component and compensation coefficients for the chrominance component; enhancing the luminance component using the enhancement coefficient grid and enhancing the chrominance component using the compensation coefficients; and obtaining an enhanced image based on the enhanced luminance and chrominance components. This application can significantly reduce computational complexity while maintaining contrast enhancement effects, and can also suppress noise and eliminate video flicker.

A data-driven single-satellite timing estimation error correction method based on transfer learning relates to the field of satellite communication timing technology, including constructing a transfer learning network and a transfer perception network according to a data-driven paradigm; using a ground base station to receive a forwarding single-satellite signal, obtaining a code pseudorange state observation vector and a carrier pseudorange state observation vector, and generating a carrier integer fuzzy state vector; according to the carrier integer fuzzy state vector, using the decision of the transfer perception network, controlling the transfer learning network to output a timing deviation estimation value; the timing deviation estimation accuracy can be improved, while reducing the complexity of modeling.

The invention discloses a lightweight WSN intrusion detection method based on incremental principal component analysis and a decision tree, and the method comprises the following steps: 1, data preprocessing: carrying out the data cleaning and feature coding of an original CICIDS2017 data set; 2, dividing a CICIDS2017 data set into a training set and a test set according to the proportions of 70% and 30%, and respectively carrying out standardization operation on the training set and the test set; 3, further simplifying a data structure based on standardized operation by adopting a feature extraction technology; 4, after feature extraction and dimension reduction of the data are completed, a decision tree classifier with information gain as a division standard is adopted to classify the network flow data expressed in a low-dimension form, and grid search cross validation is used to optimize hyper-parameters; the security requirement can be effectively met, and meanwhile, the resource limitation of the WSN can be better adapted.

A data-driven single-star time service estimation error correction method based on transfer learning relates to the technical field of satellite communication time service, and comprises the following steps: constructing a transfer learning network and a transfer sensing network according to a data-driven normal form; the method comprises the following steps: receiving a forwarded single-satellite signal by adopting a ground base station, obtaining a code pseudo-range state observation vector and a carrier pseudo-range state observation vector, and generating a carrier integer fuzzy state vector; controlling the migration learning network to output a time service deviation estimated value by using the decision of the migration sensing network according to the carrier whole cycle fuzzy state vector; the time service deviation estimation precision can be improved, and meanwhile, the modeling complexity is reduced.

The present invention belongs to the technical field of vehicle automatic driving prediction control, and discloses a vehicle automatic driving prediction control method based on a dynamic neural network Hammerstein model, including: S1. Constructing a Hammerstein model based on a dynamic neural network, the Hammerstein model includes a static nonlinear module and a dynamic linear module connected in series, and the static nonlinear module includes a multi-layer feedforward neural network and a map table for reflecting the relationship curve between the tire side slip angle and the tire lateral force; S2. Designing a prediction controller based on the Hammerstein model, and the prediction controller calculates the control quantity of the vehicle according to the expected state quantity and the actual state quantity output by the vehicle system in real-time feedback, and inputs the control quantity into the vehicle control system. In summary, the Hammerstein model of the present invention has the advantages of high modeling accuracy and rapidity, thereby effectively ensuring the accuracy of vehicle nonlinear modeling and reducing modeling complexity.

The invention provides a target object reconstruction difficulty level prediction method, system and device and a storage medium, and relates to the technical field of information processing, the method comprises the following steps: constructing an initial prediction factor database based on psychological assessment data and target object static information; constructing a machine learning algorithm model, and training the machine learning algorithm model according to the initial prediction factor database to obtain an initial prediction model; performing importance sorting on the predictive factors in the initial predictive factor database, and obtaining a middle-term predictive factor database according to a sorting result in combination with the dynamic information of the target object; training the initial prediction model according to the mid-term prediction factor database to obtain a target prediction model; and obtaining feature data of the target object, and inputting the feature data into the target prediction model to obtain a transformation difficulty prediction result. According to the method, the transformation difficulty degree of the target object can be objectively and comprehensively judged, and theoretical support, basis and reference are provided for transformation management and prevention and control work of the target object.

The present invention discloses an FPGA task scheduling optimization method and system using DQN, defines a non-uniformly distributed FPGA on-chip reconfigurable resource model, models CLB, BRAM and DSP resources, uses the modeling results as the placement chip carrier of the online task to characterize the online task, uses the optional modeling results of the online task as the target chip, uses the fixed-length data defined by the online task and the target chip as the input data of the DQN network, uses the reward function of DQN as the feedback of the neural network, and updates it with the DQN update value function; uses DQN and the optimal longest fit algorithm to jointly train the DQN network; finds a suitable target chip and the optimal placement result in each scheduling event, inputs the online task and the target chip into the trained DQN network, selects the target chip with the highest reward value as the running chip of the corresponding task, and realizes task scheduling optimization. The present invention improves the scalability of the model by fixed-length input.