9results about How to "Recognizable" patented technology

The application provides a WiFi cross-domain gesture recognition method based on domain-invariant feature extraction, and relates to the technical field of WiFi cross-domain gesture recognition.The method comprises the following steps: acquiring channel state information data collected by a WiFi card; performing data preprocessing on the channel state information data to obtain a preprocessed image; inputting the preprocessed image into a domain-invariant feature extraction network fused with a channel space mixed attention module and a ConvNeXt network to extract domain-invariant features; and outputting a gesture recognition result through a classifier from the domain-invariant features.The method can effectively suppress domain shift interference without relying on target domain samples and without complex signal processing, thereby improving the accuracy and robustness of WiFi cross-domain gesture recognition.

This invention proposes a symmetrically arranged cross-wing unmanned aerial vehicle (UAV) system, relating to the field of UAVs. It includes a fuselage structure, a power module, and a flight control module. The fuselage structure comprises a cylindrical fuselage and four wings symmetrically arranged in a cross pattern around the cylindrical surface of the fuselage; the four wings are arranged in an X-shape or a cross shape. The power module includes motors and propellers. The motors are located at the wingtips, with their output shafts aligned with the fuselage axis and facing the tail of the fuselage. The propellers are connected to the motor output shafts. The flight control module is located inside the fuselage and is used to control the output speed of the four motors. This system combines the advantages of a quadcopter FPV and a fixed-wing UAV, generating lift from the wings while also cruising and sprinting in fixed-wing form. Furthermore, by changing the motor speeds, it can control the pitch, yaw, and roll attitude of the UAV, eliminating the need for separate control surfaces, thus simplifying the structure and reducing costs.

The application discloses a kind of engineering cost auditing method and system based on multi-source data fusion.Belongs to the field of engineering cost auditing, the present application obtains the engineering quantity list of the engineering to be audited, construction site space-time image and construction log text data, input model trained by multi-modal data, generates cost declaration and physical live characteristic vector, then calculates correlation score and extracts empirical segment features, and with cost declaration features fusion generates semantic fusion feature matrix;Finally, identify sub-item engineering category, calculate its Mahalanobis distance with process feature prototype vector, if exceed threshold value, then determine abnormal, generate the audit result containing abnormal cost item and construction period lack of empirical information.The present application effectively solves the problem that existing technology cannot accurately and efficiently audit engineering cost.

The invention provides a multi-dimensional traffic grayscale routing method and system based on a traffic white list, and belongs to the technical field of data processing, and the method comprises the steps: receiving client request data containing a user identifier, channel information and at least one custom scene attribute; performing legality verification on the user identifier, the channel information and the custom scene attribute; matching the request data passing the verification with a preset multi-dimensional white list rule, the multi-dimensional white list rule at least comprising a user white list, a channel white list and at least one extension type white list, and the white lists of different dimensions have priorities; determining a target service version according to the matching result and the priority; and executing a routing decision based on the determined target service version, and forwarding the request data to the corresponding server, thereby realizing accurate identification and controllable distribution of the gray traffic.

This invention discloses an automatic modulation identification method for multimodal fusion wireless signals based on deep learning, belonging to the field of communication technology. First, a publicly available dataset is selected as the channel input data and merged according to modulation type to form a set of time-domain I / Q data. Then, a constellation diagram is created for each set of time-domain I / Q data, and the data and corresponding constellation diagrams are segmented according to the signal-to-noise ratio to form a training set. Next, a multimodal fusion neural network model based on the time-domain I / Q data and constellation diagram data is constructed and input into the training set for iteration, adjusting the learning rate until the network model reaches stability. Finally, a newly acquired radio signal to be identified is input into the trained multimodal fusion neural network model, which automatically outputs the modulation identification type of the wireless signal. This invention can more fully extract the fusion features of the signal in the time domain and the corresponding constellation diagram to optimize the information loss caused by downsampling, thereby obtaining more accurate signal phase and amplitude information.

This invention discloses a rapid bridge damping ratio identification method based on a stationary inspection vehicle, belonging to the field of bridge structure vibration testing technology. The method includes: parking the inspection vehicle at the mid-span of the bridge under inspection; exciting the bridge vibration using a passing vehicle and then driving away; collecting the acceleration response of the inspection vehicle in the attenuation segment; establishing a dynamic model of the attenuation segment of the vehicle-bridge coupled system; constructing a two-degree-of-freedom joint state model, internalizing the vehicle-bridge interaction force as a linear combination of system state variables; extracting the effective attenuation segment through signal preprocessing and introducing a dual anti-noise mechanism of adaptive signal-to-noise ratio weights and Huber weights; and employing a profiled two-stage parameter identification strategy, first locking the frequency and modal mass, and then finely scanning and identifying the bridge's first-order damping ratio. This invention requires only a single inspection vehicle and an acceleration sensor, eliminating the need to deploy sensors on the bridge structure. It has strong anti-interference capabilities, high identification accuracy, and is suitable for rapid inspection of small-to-medium span bridges.

The application belongs to the technical field of biological immune detection, and discloses a pig delta coronavirus double-antibody sandwich ELISA detection kit based on a nano antibody-HRP fusion protein as well as detection and application thereof. A PDCoV nucleocapsid protein (N protein) is taken as a detection target, and two strains of nano antibodies capable of recognizing different spatial epitopes of the PDCoV N protein are taken as core recognition elements to construct a sandwich type immune detection system. One strain of the nano antibodies is taken as a solid-phase capture antibody, the capture antibody is an unmodified nano antibody, is fixed on the surface of a solid-phase carrier, and is used for specifically capturing the PDCoV antigen in a sample. The other strain of the nano antibodies is gene-fused with a horseradish peroxidase to form a detection antibody with antigen recognition and signal catalysis functions. The minimum detection limit reaches 2.75 x 10 2 TCID 50 / 100 muL. The virus shedding can be detected 24 hours after the piglet is infected.

This invention discloses an endoscopic polyp detection method based on an improved YOLO v8. Addressing the difficulty in detecting polyps in endoscopic images and the tendency to miss them during endoscope movement, the YOLO v8 detection model is improved in three ways: Channel attention (CA) is added after several C2f modules in the Backbone and Head layers of the original model to strengthen the focus on key features and improve the model's representation ability; a small target detection layer is added to the feature pyramid output layer to improve small target detection capability through position regression of small-sized feature maps; and WloU is used instead of CloU in the original model, which can adaptively adjust the loss term according to the target size and shape, improving the accuracy of small target localization. Based on this, an endoscopic polyp detection method is proposed. The key steps of the method are as follows: First, the relevant public dataset is obtained, preprocessed, and appropriately divided into training, testing, and validation sets; second, a detection model is constructed using the improved YOLO v8; finally, the model parameters are initialized for model training, and the detection accuracy and generalization ability of the model are evaluated using the test set. This method can accurately identify polyp areas in endoscopic images, reduce the false negative rate, and improve the accuracy and reliability of endoscopic examinations, thereby assisting medical staff in improving the accuracy of pathological analysis and disease diagnosis, while also increasing the work efficiency of medical staff.

The application discloses a kind of image mark gray compensation method, system, electronic equipment and medium, the method relates to computer vision detection field, the method specifically includes: obtaining the image mark containing image to be compensated image;Based on the grid side length of pre-set, the preset region containing image mark in to-be-compensated image is carried out grid down sampling processing, and down sampling image is obtained;Wherein, grid side length is less than the smaller value in width, height of image mark in image plane;The gray difference value of the gray value of each down sampling pixel and pre-set target gray value is obtained, and all gray difference values are mapped and arranged according to the spatial coordinates of corresponding down sampling pixel, and difference value image is generated;Difference value image is carried out spatial interpolation up sampling processing, and compensation amount image is obtained;Based on compensation amount image, pre-set region is compensated in gray scale.The method provided by the application image mark still has discernibility after being compensated in gray scale.