12results about How to "Reduce redundant features" patented technology

The invention discloses an equipment state sensing system based on peak shifting and danger situation sensing, which relates to the technical field of electric digital data processing and comprises a data acquisition unit, a model training and optimizing unit, a situation sensing unit, a peak shifting regulation and control unit and a system interface and user interaction unit. According to the method, the device state data feature similarity is dynamically calculated by using the Gaussian kernel function, the similarity matrix is adjusted in combination with the gradient update item, dynamic weighting is performed in feature extraction, and redundant features are reduced by adopting the adaptive discarding probability, so that the feature extraction efficiency of the model in a high-dimensional feature space is improved; the problem that the dimension reduction effect is affected by too many redundant features of the equipment state data is solved. Equipment state features are extracted layer by layer through a deep forest algorithm of a multi-layer random forest structure, a decision tree is optimized in combination with a dynamic adjustment weight mechanism and a reinforcement learning strategy, and the classification capability of the model for equipment state changes and abnormal states is enhanced.

The invention belongs to the technical field of intelligent diagnosis of four-stroke diesel engines, and particularly relates to a PSO-RF intelligent diagnosis method and system for thermal faults of a marine diesel engine. The method comprises the following steps: designing a diesel engine simulation model by using simulation software; constructing a data set and dividing the data set; performing noise processing and normalization processing on the data in the data set; extracting characteristics which can best represent diesel engine faults in the data set; a random forest fault diagnosis model is constructed, parameter optimization is performed on the random forest model by using a particle swarm optimization algorithm, a PSO-RF intelligent diagnosis model is finally obtained, and fault diagnosis is performed by using the PSO-RF intelligent diagnosis model. The system comprises a diesel engine simulation unit, a data acquisition unit, a data processing unit, a feature extraction unit and a fault diagnosis unit. According to the method, automatic fault positioning and diagnosis can be realized, sailors are helped to quickly and accurately identify problems, the dependence on expert experience is reduced, and the diagnosis precision is remarkably improved.

The invention discloses an automatic driving vision multitask sensing method and system based on aerial view features, and belongs to the technical field of automatic driving. Comprising the following steps: acquiring images from a look-around camera of the automatic driving system and extracting features; a multi-task distillation module is adopted to cooperatively predict dense depth features, semantic features and content features, and a dense depth prediction result and a dense semantic prediction result are generated; splicing the content feature and the dense semantic prediction result, and generating a semantic enhanced aerial view feature by using the dense depth prediction result as a depth weighted value of the content feature; optimizing aerial view features; predicting the distribution probability of occupied grid categories, fusing the probability distribution of foreground categories and optimized aerial view features; and generating a target detection prediction result according to the fused features. According to the method, the relevance between the target detection task and the occupied grid prediction task is fully mined, the model performance is improved, deployment redundancy caused by multiple independent tasks is reduced, and therefore the training and operation and maintenance cost is reduced.

The application is suitable for the technical field of computer application, and provides a two-dimensional code image feature extraction method and device based on a CNN-MALT hybrid architecture, which comprises the following steps: inputting a target two-dimensional code image into a CNN preprocessing module of a preset lightweight image feature extraction network to generate first structure perception features; inputting the first structure perception features into a MALT encoder of the preset lightweight image feature extraction network, performing feature extraction on the first structure perception features based on a proxy attention mechanism to generate deep features; and inputting the deep features into a feature fusion module of the preset lightweight image feature extraction network to generate target features. Thus, the structure features of the two-dimensional code image are perceived by the CNN preprocessing module, the deep features are generated by the MALT encoder based on the proxy attention mechanism, and the deep features are fused by the feature fusion module, so as to ensure that the calculation complexity is reduced while the two-dimensional code image feature extraction accuracy is improved.

The embodiment of the invention provides an image segmentation model training method and device, an image segmentation method and device and electronic equipment, and relates to the technical field of image segmentation. The method comprises the following steps: inputting an annotated image and an unannotated image into an image segmentation model to respectively obtain an annotated prediction result and an unannotated prediction result, inputting an unannotated strong disturbance image into the image segmentation model, processing the unannotated strong disturbance image by a discarding module to obtain a strong disturbance prediction result, inputting an unannotated weak disturbance image into the image segmentation model, and obtaining a strong disturbance prediction result. The method comprises the following steps of: obtaining a strong disturbance prediction result, processing the strong disturbance prediction result through an attention enhancement module to obtain a weak disturbance prediction result, and carrying out iterative optimization according to a strong disturbance prediction result, the weak disturbance prediction result, an unlabeled prediction result and a labeled prediction result to obtain a mature image segmentation model. Through combination of the discarding module and the attention enhancement module, rich features and important features can be automatically learned under different data distributions, so that the segmentation accuracy of the model in a complex clinical scene can be improved.

The invention discloses a DRF-Net-based small target detection method under a view angle of an unmanned aerial vehicle. Constructing an unmanned aerial vehicle aerial photography small target detection data set; a frequency domain guide feature enhancement module FCSA is introduced in the shallow layer stage of the backbone network; a multi-frequency reconstruction module MFRB is introduced in the high-level stage; constructing DRF-Neck in a neck structure, generating a saliency graph S based on MFRB output features, and introducing saliency-driven deformable convolution SAR-DCN, content aware recombination S-CARAFE and a dynamic feedback controller DFC to form a closed-loop optimization mechanism of saliency generation-self-routing convolution-dynamic feedback; and finally, the features are input into a decoupling detection head, network parameters are optimized through joint optimization of cross entropy loss and AHIoU loss, and a small target detection result in an unmanned aerial vehicle aerial photographing scene is output by using non-maximum suppression in a reasoning stage. Compared with the prior art, the method has the advantages that the detection precision on the unmanned aerial vehicle aerial small target data set is improved, and the method can be applied to the fields of unmanned aerial vehicle inspection, urban security monitoring, traffic target detection and the like.

The invention discloses a method, device and system for constructing a credit risk assessment model and a storage medium. In the scheme, a first training sample set is obtained, and the first training sample set comprises a plurality of first features and credit risk scores corresponding to corresponding samples. A Pearson correlation coefficient between each first feature and the credit risk score is calculated. And screening out a plurality of second features from the plurality of first features based on a Pearson correlation coefficient between each first feature and the credit risk score. And constructing an initial credit risk assessment model based on the plurality of second features. And iteratively optimizing the initial credit risk assessment model based on a simulated annealing algorithm to obtain a final credit risk assessment model. According to the method, feature selection is performed through the Pearson correlation coefficient, and parameter optimization is performed by using the simulated annealing algorithm, so that the selected features are more comprehensive and are prevented from falling into a local optimal solution at the same time, and the accuracy of the high credit risk assessment model is improved.

This invention discloses a method, apparatus, and medium for predicting lymph node metastasis. The method involves acquiring a three-dimensional image of a lymph node to be detected and determining the region of interest (ROI) within the image. Feature extraction is performed on the ROI to obtain local features corresponding to the three-dimensional lymph node image. The ROI data is then expanded to obtain pixel blocks to be processed, and feature extraction is performed on these pixel blocks to obtain global features corresponding to the three-dimensional lymph node image. Based on the local and global features, the lymph node metastasis prediction result for the three-dimensional lymph node image is determined. This invention achieves a comprehensive description of the features of the three-dimensional lymph node image through the complementarity of local and global features, solving the problem of low accuracy in lymph node metastasis prediction and improving its reliability. Furthermore, the local features only include the features of the lymph node itself, reducing redundant features.

This invention discloses a lightweight asphalt road defect detection method and system with partial multi-scale attention, belonging to the field of computer vision and intelligent transportation technology. This invention is applicable to the real-time detection and precise localization of defects such as cracks and potholes. It employs PMA modules, SCConv modules, and the inner-AIoU loss function to construct the required asphalt road detection model, achieving accurate detection of the road image to be detected. The model constructed in this invention, while maintaining its lightweight advantage, achieves significant improvements in speed, substantial increases in defect detection accuracy, and diversification of detected defect categories, fully meeting the needs of real-time detection. Furthermore, the detection model constructed in this invention also possesses good portability and scalability, making its integration with hardware devices more convenient.

The application discloses a bearing steel grinding burn identification method based on multiple magnetic parameters, and relates to the technical field of nondestructive testing.The method first constructs a training set and a test set containing multiple samples, and uses an X-ray stress meter to calibrate the burn state of the training set samples to obtain training set burn samples; the electromagnetic parameters of the training set burn samples are acquired through a micro-magnetic multi-parameter detector, and a judgment index feature based on a physical model is extracted; subsequently, the features are reduced in dimension by using a stacked auto-encoder, and the low-dimensional features are clustered by using a K-means clustering algorithm to automatically calibrate the state of the training set samples and determine a burn judgment threshold; finally, for the test set samples, whether burn occurs is judged by comparing the low-dimensional feature vectors thereof with the threshold value.The application can nondestructively and quickly acquire magnetic characteristic signals, effectively detect grinding burn and reduce misjudgment, and has the advantages of fast detection speed and high precision.

The invention belongs to the technical field of image processing, and particularly relates to a low-light image detection algorithm based on multi-scale feature extraction, which comprises a multi-scale feature extraction module, a loss function and a space-to-depth sampling module, and is characterized in that the multi-scale feature extraction module is added into a feature extraction network of an enhancement model; the loss function comprises an exposure control loss function, a color constancy loss function, an illumination smooth loss function and a mixed loss function, and the space-to-depth sampling module is composed of a space-to-depth layer and a non-convolution step length layer; multi-branch step-by-step pixel feature extraction is carried out on a multi-level feature extraction network, space and channel reconstruction convolution is used for replacing standard convolution operation in the last-level feature extraction stage, redundant features are reduced, calculation complexity is reduced, and meanwhile the correlation degree between pixels can be well improved.

The invention discloses an unsupervised ventricle image segmentation method based on local contrast learning, and relates to the field of image processing. Comprising the following steps: extracting each pixel in an image and a local neighborhood around the pixel as a patch taking the pixel as a center; encoding the patch taking the pixel as the center into a high-dimensional potential space; and image segmentation: carrying out segmentation based on spectrum k-MEANS clustering. According to the invention, image input of various sizes can be accepted through a pooling-free symmetric full-convolution encoder and decoder network, and image information can be efficiently extracted and recovered; meanwhile, designing a comprehensive loss function, pixel patch contrast loss, local contrast learning loss and global reconstruction loss so as to more effectively capture microcosmic and macroscopic characteristics of the ventricle; besides, a residual structure based on SCCON is added in the encoder and the decoder, redundant features are removed, learning of key features is enhanced, and the segmentation precision of the MRI ventricular image is greatly improved.