6results about How to "Address imbalances" patented technology

The application relates to the technical field of deep learning, in particular to a traffic scene image small target detection method and device, equipment and a storage medium. The method comprises the following steps: acquiring a traffic scene image and a detection result of a small target in the traffic scene image, generating a training data set according to the traffic scene image and the detection result of the small target in the traffic scene image; constructing a convolutional neural network based on a feature extraction module, a feature fusion module, a sample distribution module and a positioning classification module, and optimizing the sample distribution module and the positioning classification module of the convolutional neural network to obtain an optimized convolutional neural network; training the optimized convolutional neural network by using the training data set, updating network parameters of the optimized convolutional neural network based on gradient back propagation, and stopping the training until a preset training stop condition is met; and detecting small targets in the traffic scene image by using the trained convolutional neural network. Thus, the problems of small target sample imbalance and poor positioning capability in the related art are solved.

This invention discloses an abnormal driving recognition method based on pedal data and facial expression multimodal data. The process is as follows: Step 1: Collect vehicle driving data and facial video data, and label real facial abnormal expressions to obtain original multimodal data composed of multiple original multimodal samples; Step 2: Train an original abnormal driving model based on the original multimodal dataset; Step 3: Perform noise diffusion on each original multimodal sample to obtain diffused multimodal samples, and select effective multimodal samples from the diffused multimodal samples to construct a multimodal dataset; Step 4: Process the multimodal dataset using the original abnormal driving model, and select prediction-consistent multimodal samples to construct a prediction-consistent multimodal dataset; Step 5: Train the original abnormal driving model again using the prediction-consistent multimodal data to obtain an abnormal driving recognition model; Step 6: Use the abnormal driving recognition model to predict the abnormal driving prediction result.

The application discloses a reinforcing cage hoisting device for pile testing of a cast-in-place pile and relates to the field of hoisting devices.The reinforcing cage hoisting device comprises a hoisting vehicle, a movable hoisting frame is installed on the hoisting vehicle, and a counterweight is movably installed on the movable hoisting frame; further comprising a counterweight moving device, the counterweight moving device is installed on the hoisting vehicle, the movable hoisting frame and the counterweight are both installed on the counterweight moving device, and the counterweight moving device is used for moving the counterweight.It should be noted that in the embodiment of the application, when the hoisting vehicle is pulled up, the counterweight is automatically extended to the lifting side to form an active balance moment without external power, and meanwhile, the counterweight frame is rotated and unfolded to further shift the center of gravity of the hoisting vehicle; in addition, the earth boring motor drives the drill rod to rotate and drill into the ground to provide additional pulling force, effectively solving the problems of unbalance and overturning of the hoisting vehicle and bringing about remarkable effects of safety, economy and convenient operation.

The application discloses a matchstick person posture intelligent monitoring method based on orthogonal amplification, collects a monitoring scene video, extracts human key points, generates a non-private matchstick person skeleton sample, extracts four types of features of a skeleton structure, motion, support contact and data distribution, adaptively matches a plurality of Hadamard, DCT and Wavelet orthogonal sampling matrices for different skeleton states, realizes sample amplification through point-by-point multiplication of the matrices, and adds a skeleton semantic check to remove invalid samples; and a BiGRU-Attention, TCN or Transformer time sequence model is used to complete fall and long-time lying risk identification. The application solves the problems of traditional monitoring privacy leakage, high-risk posture sample scarcity and random amplification to generate invalid skeleton samples, and is applicable to home, nursing ward and old-age care institution scenes.

This invention proposes a method for detecting fake reviews based on big data, comprising: acquiring a review dataset, wherein the data in the review dataset includes review texts from multiple users and corresponding user behavior data, and performing data preprocessing; extracting review text features from the review dataset, and extracting user behavior features and review behavior features from the user behavior data; defining relationship categories based on the review text and user behavior features, and constructing a multidimensional relationship enhancement graph (MREGC) based on multiple relationships; using a dynamic adaptive feature enhancement graph neural network to learn features on the MREGC relationship graph, obtaining the embedding representation of each node in the multidimensional relationship enhancement graph, and aggregating the embedding representations; inputting the aggregated features into a classifier to determine whether a review is a fake review, and outputting a fake review label if the review is classified as a fake review; this invention, by fusing residual networks and multi-relationship review graphs, fully considers the multidimensional relationships and deep features between reviews, enabling the model to more accurately identify fake reviews, and significantly improving the accuracy and robustness of fake review detection.

This invention belongs to the field of data processing technology and provides a method and system for processing job application data for overseas recruitment. The method includes: analyzing job request texts uploaded by recruiting users to derive multiple key dimensions for judging job-person suitability, generating a job requirement feature vector; analyzing the relationships between these key dimensions and constructing a dynamic weight allocation network using a graph neural network; parsing each job request text in the set of uploaded job request texts to generate a job text feature vector; based on the dynamic weight allocation network, using semantic bridging to perform multi-dimensional joint similarity calculation between the job text feature vector and the job requirement feature vector to obtain the comprehensive suitability of each job request text; and removing job request texts with a comprehensive suitability below a preset threshold as content that does not belong to the requested business type. This invention enables intelligent and precise filtering of massive amounts of business texts.