13results about How to "Comprehensive feature extraction" patented technology

The invention relates to a plant non-histone lysine crotonylation site identification method and a plant non-histone lysine crotonylation site identification system. The method comprises the following steps: extracting non-histone lysine crotonylation Kcr sites from a protein sequence to form a data set; extracting a first feature vector by using a self-attention mechanism and a smoothing function through a protein language model ESM2; capturing a long-distance dependency relationship and context information in the protein sequence through a bidirectional long-short-term memory network, and extracting a second feature vector; and carrying out feature fusion processing and then carrying out classification prediction to obtain a Kcr site identification result. By constructing a protein sequence data set, ESM2 utilizes a self-attention mechanism and a smoothing function, and BiLSTM captures a long-distance dependency relationship and context information in the protein sequence, efficient feature extraction of the protein sequence is realized, and more comprehensive information can be provided through fusion processing, so that the detection precision of Kcr site detection and recognition is improved.

The invention relates to the technical field of attendance management, in particular to an attendance management method. According to the technical scheme, the method comprises the following steps: selecting and initializing an attendance system; daily attendance management, wherein the daily attendance management comprises a clock-in mode and clock-in exception processing; evaluating and examining and approving the automatic reissuing card; and attendance data analysis and report generation. According to the attendance management system, a multi-source data collection and comprehensive analysis mechanism is added, and based on a comprehensive judgment function and an equipment fault possibility evaluation function, an evaluation result is used for optimizing the judgment accuracy of automatic card supplement, so that misjudgment is avoided, and the work efficiency is improved. The automatic examination and approval decision function is used for automatically judging according to conditions such as comprehensive evaluation and late times of employees and manually examining and approving related information decisions, so that the workload and the management cost of the employees are reduced, the accuracy and the reliability of attendance data are improved, and the system can adapt to the requirements and changes of enterprise attendance management.

The application discloses a non-intrusive load sensing method fusing multi-time scale electrical appliance features. The method first carries out power consumption data collection and preprocessing, then detects whether an electrical appliance event occurs and whether a power sequence fluctuates. After the event ends and it is found that the power sequence is fluctuating, multi-time scale electrical appliance features are extracted, a sensing model is constructed and trained, and finally, the load sensing fusing the multi-time scale electrical appliance features is carried out. The application can comprehensively fuse the electrical appliance features on the multi-time scale, extract more comprehensive electrical appliance characteristics on different time scales, effectively balance the classification result errors on different time scales, and finally scientifically and reasonably perform the non-intrusive load sensing on the multi-time scale.

This invention discloses an imbalanced malware detection enhancement method based on the fusion of CWGAN-GP data augmentation and TEXTCNN–TRANSFORMER, comprising the following steps: S1, data acquisition and preprocessing: running an executable file in a controlled sandbox environment to dynamically capture its API call sequence, standardizing the API call sequence, and mapping it into a dense vector sequence; S2, data augmentation based on Conditional Wasserstein Generative Adversarial Network (CWGAN-GP) with gradient penalty mechanism: constructing a CWGAN-GP model conditioned on the category labels of minority malware classes, the model including a conditional generator G and a discriminator D with gradient penalty; inputting the minority malware samples obtained in step S1 and their corresponding category labels into the CWGAN-GP model for adversarial training until the model converges. The advantages of this invention are: high-quality data augmentation with semantic fidelity; comprehensive and complementary feature extraction; significant end-to-end performance improvement, especially in minority class identification; and strong model robustness and generalization ability.

The invention relates to a placenta image segmentation algorithm based on a learnable visual center mechanism and a multilayer perceptron. Automatic segmentation of a placenta can be realized by using fetal magnetic resonance imaging data. According to the algorithm, an encoder-decoder architecture is used, and a marked multilayer perceptron module is introduced to capture a long-distance remote dependency relationship and a local corner region of global features, so that complementation of global and local feature information is realized, and the defects of traditional convolution operation in the aspect are overcome; in addition, the model also uses a learnable visual center mechanism, all front superficial layer features are regulated and controlled by aggregating local key regions of an input image and utilizing context information of an interested region obtained by deep layer features, and feature fusion between levels from top to bottom is realized. Experimental results show that the provided algorithm achieves a good effect on a placenta image segmentation task.

The present invention discloses a laser point cloud three-dimensional target detection model and method for complex traffic scenes. The three-dimensional encoder in the model is conducive to the detection of long-distance targets and small targets. Sparse convolution and submanifold convolution can greatly improve the coding efficiency of voxel features. The residual structure of the two-dimensional encoder can maintain more complete information, which is conducive to the detection of long-distance targets and small targets. At the same time, the network is easier to optimize. Through self-calibration convolution, feature extraction can be performed at the original scale and self-calibration scale respectively, the receptive field is expanded, and more complete and rich features are extracted. Through spatial attention and channel attention, useful feature expression is enhanced in the spatial direction and channel direction, and useless information is suppressed. The final detection accuracy of the present invention is 81.88% for vehicles, 47.82% for pedestrians, and 69.81% for cyclists. The average accuracy is 66.25%, which is 9.9% higher than the existing VOXEL RCNN algorithm. 13.8FPS is achieved on the RTX 2080Ti display. The detection accuracy and speed can meet the perception needs of smart cars in complex traffic environments.

The invention provides a digital pathological image grading method and system, and the method comprises the steps: carrying out the cell recognition based on a Unet algorithm which carries out the coding, decoding and regression to the classification of pixel points with the same size as an original image, and extracting a cell part in a prostate pathological image; inputting the extracted cell part into a pre-training model and a classification algorithm based on a convolutional neural network (CNN), and detecting cancer cells contained in the cell part; and based on the detected relevant features of the cancer cells and the surrounding tissues thereof, predicting the pathological grading of the cancer cells through an algorithm of a network model CNN-RNN combining a pre-training model, a convolutional neural network and a recurrent neural network. The technical problems that the workload is large, the interpretation threshold is high, and errors and subjectivity exist in judgment of the proportion of cancer cells by naked eyes are solved.

The invention discloses a radial tire defect detection method, device and equipment, a medium and a product, and relates to the technical field of tire detection, and the method comprises the following steps: obtaining an X-ray image of a radial tire to be detected; inputting the X-ray image into a tire defect detection model to obtain a defect detection result; the defect detection result comprises a defect type and a defect position; the tire defect detection model is obtained by training a target neural network by adopting a training set; the target neural network is a neural network formed by combining a YOLOv8 network and a RepViT block; and displaying the defect detection result in real time through an image user interface. The tire defect detection efficiency can be improved.

The invention discloses an aerial image matching method, electronic equipment and a medium, and the method comprises the following steps: S1, collecting an aerial image data set, forming an image pair composed of a source image and a target image, and processing the image pair through an efficient Transform module; s2, inputting the processed data pair into a feature extraction network fused by a residual network and dynamic convolution, and extracting effective feature points in the image; s3, the feature matching network screens out matching point pairs which are highly consistent and have no repetition through bidirectional matching and Pearson's correlation evaluation, and a corresponding relation between the two images is established; and S4, calculating and outputting geometric transformation parameters between the two aerial images through a parameter regression network. Compared with the prior art, the aerial images in different time periods are used for comparison, feature extraction of local information is enriched while structure and context information in the aerial images is captured, a good effect is achieved in disaster monitoring, and disaster differences are visually reflected.

The invention discloses a device and method for monitoring the blocking state of an urban gutter inlet. The device comprises a data processing unit, a waterproof microphone, a humidity sensor, a liquid level sensor, an LED state indicator lamp, a protective shell and a data transmission interface, and the data processing unit is connected with the humidity sensor, the waterproof microphone and the liquid level sensor. The method comprises the steps of training a blockage judgment model of sound features, installing and setting a monitoring device, judging and processing blockage, early warning accumulated water, resetting dormancy and the like. According to the invention, the defects of low labor efficiency, high misjudgment of a single sensor and large interference of light environment on vision in the existing gutter inlet blockage monitoring are overcome.

The invention discloses a CP-VTON improvement-based virtual fitting system, and the system comprises a data input module which is used for inputting a human body image and a clothes image; a geometric matching module; the geometric matching module comprises two multi-dimensional feature extraction modules based on ELAN improvement, and the two multi-dimensional feature extraction modules are respectively used for extracting features of a human body image and a clothes image; the two ECA attention modules are respectively used for carrying out channel attention division on the features extracted by the ELAN improvement-based multi-dimensional feature extraction module; the similarity calculation module is used for performing similarity calculation on the outputs of the two ECA attention modules to form a series of corresponding relations; the regression network module is used for estimating parameters of the geometric model according to the corresponding relation; and the conversion module is used for performing distortion conversion processing on the clothes image through the obtained parameters. According to the CP-VTON-based improved virtual fitting system, feature extraction is enriched, the pattern texture memory ability is enhanced, and the degree of adaptation between the human body posture and the clothes is improved.

This invention discloses a robot grasping prediction method based on attention mechanism and low-rank tensor fusion. The method includes: acquiring a dataset of grasping images of a robot grasping multiple different objects; preprocessing the grasping image dataset and dividing it into a training set and a test set; training and testing a model based on attention mechanism and low-rank tensor fusion using the training set and the test set to obtain a trained model; acquiring tactile images of the target object grasped by the robot; inputting the tactile images into the trained model; and outputting a prediction result of whether the target object was successfully grasped. This invention enables the model to better capture key features and important information, achieves more comprehensive and efficient feature extraction by fusing multimodal information, generates richer fused features, and effectively predicts whether grasping is successful.

The application discloses a kind of image recognition method and system for liquid crystal panel defect detection, it is related to liquid crystal panel technical field.The method steps include: the original digital image of the liquid crystal panel to be measured is collected;The original digital image is preprocessed to obtain a standardized image;The standardized image is segmented by image segmentation algorithm based on the double coding of Transform-CNN, to obtain a defect mask image, the defect mask image is morphologically processed to obtain an optimized defect mask image;Pixel analysis is performed on the optimized defect mask image to obtain defect feature parameters, a comprehensive quality evaluation value is calculated based on the defect feature parameters, and the defect level is divided based on the comprehensive quality evaluation value, so as to complete the liquid crystal panel defect detection.