10results about How to "Guaranteed classification accuracy" patented technology

The invention discloses an RSVP normal form electroencephalogram electrode offset self-adaptive compensation method for an autonomous motion scene, is suitable for an electroencephalogram signal decoding task in a motion state, and belongs to the crossing field of computers and biomedical engineering. For the problem of artifact interference in a motion state, positive and negative sample pairs obtained by sampling in an anchoring feature space without artifacts and a feature space to be compensated with artifacts are introduced through supervised contrast learning based on a decoupling training strategy, and the compensation capability of a feature extractor is trained by applying InfoNCE loss. Meanwhile, on the basis of small-batch sample data characteristics, a lightweight feature extractor is designed, and the EEG signals are downsampled into spatiotemporal features based on spatiotemporal convolution for subsequent comparative learning. Compared with a complex feature extractor using a self-attention mechanism, a multi-scale feature extraction module and the like, the lightweight design has the advantages that classification indexes such as BA and RECALL are obviously improved, and the calculation cost is greatly reduced.

The invention discloses a scanning archive digital fine classification method based on image recognition, which comprises the following steps of: acquiring a scanning archive image, and performing layout correction and image enhancement to obtain a normalized image; performing character recognition and layout structure understanding on the standardized image to form a character result and a structure result; constructing a corresponding prototype generation space in combination with the category hierarchy system and the service attribute system; performing hierarchical constraint consistency scoring in the prototype space and generating an evidence packet; and determining category levels and service attributes according to the scores and the evidences, and outputting a fine classification result. According to the method, a method based on image recognition and hierarchical constraint reversible generation consistency evaluation is adopted, digital fine classification of the scanning archives is achieved, and the method has the advantages of being high in classification stability, high in result traceability and high in service adaptation capacity.

The utility model discloses a nut grading, extruding and shelling device which comprises a feeding hopper arranged at the top of the device and used for feeding nuts; the roller is located below the feeding hopper, a continuous grading slide way is arranged below the roller, the grading slide way extends in the axial direction of the roller, and the width of a gap in the grading slide way is continuously increased from the starting end to the tail end; the extrusion roller group is arranged below the grading slide way and comprises a plurality of groups of roller pairs, and gaps of each group of roller pairs correspond to different sections of the grading slide way; and the power system comprises a speed reducing motor and a belt assembly which are sequentially connected and drive the roller and the extrusion roller to synchronously rotate. And the nuts slide along the grading slide way under the action of gravity and are graded through the gradually increased gaps. The classified nuts respectively enter the corresponding extrusion roller pairs to be extruded and husked, and finally, the whole set of structure realizes continuous operation from feeding, classification to husking of the nuts under the support of the rack through the discharge port, so that the classification precision is ensured, and the husking efficiency is improved.

The present application belongs to the technical field of power transmission line engineering cableway design and construction, and particularly relates to a power transmission line cableway stockyard point mode recognition method and system based on slope compensation and road width dynamic correction, a storage medium and a computing device. The method comprises the following steps: S1, road feature extraction: including extracting road center line feature data, road final width feature data, and road unit direction feature data; S2, road feature enhancement processing; S3, calculating road curvature; S4, mode recognition classification: using a feature space dynamic weighting segmentation algorithm, through an iterative optimization process in a dynamic weighting road feature space, dynamically adjusting road feature weights, scaling the original road feature space, so that the left and right boundary points in the transformed space can be effectively separated by a hyperplane, realizing accurate two-classification of the stockyard points; S5, space topology consistency enhancement. The method can solve the problem of road width measurement distortion caused by steep slope terrain.

The application discloses a motor bearing fault detection system and method based on robust deep learning, belongs to the technical field of mechanical fault detection and intelligent sensing, and extracts features of original vibration signals with labels based on a supervised learning branch network, takes the original vibration signals as reference samples, takes the reference samples with the same fault category and different fault categories as positive samples and negative samples, introduces a triplet loss to optimize the inter-class separation and intra-class aggregation relationship in an embedding feature space, and generates embedding representation; based on an unsupervised learning branch network, the original vibration signals after artificial feature extraction based on the time domain and the frequency domain are encoded, a triplet loss is introduced for unsupervised embedding learning, and high-level feature embedding representation is generated; the embedding representation output by the two branch networks is fused, a double loss of the triplet loss and the center loss is introduced for training, and the bearing fault detection model after training is used for bearing fault detection.

This invention proposes a hierarchical label text classification method based on the MOE model, comprising the following steps: encoding the input text using a shared encoder to obtain a unified vector representation of the text; inputting the text vector representation into a router module to calculate the probability distribution of the first-level labels, and activating the top-k expert modules corresponding to the first-level labels; the activated expert modules performing classification predictions within their respective second-level label subspaces; and weighted fusion of the prediction results from the expert modules to obtain the final hierarchical label classification result. This invention extracts text semantic features uniformly through a shared encoder, avoiding redundant learning across branches; it allows samples to simultaneously activate multiple candidate experts through a Top-k soft routing mechanism, breaking the rigid single-path limitation of traditional top-down methods; and it combines routing weights with expert outputs, using the maximization of joint probability as the decision criterion, significantly reducing the number of model parameters and the risk of error cascading while ensuring classification accuracy.

The invention relates to an intelligent algorithm and device for motion classification and recognition, electronic equipment and a storage medium. The method comprises the steps of synchronously collecting windowed original motion data of multiple parts of a motion object, calculating feature values in a feature engineering framework according to the original motion data, and screening excellent feature values through a mode of calculating information gain. Two original classifiers XGBoost and LightGBM are mixed, the weights of the two classifiers are adjusted in real time based on probability distribution entropy, and a hybrid classifier with adaptive weight adjustment is constructed. A large amount of original motion data is adopted to train a hybrid classifier, a cross validation result is used as a basis to carry out parameter adjustment and optimization on a new classifier, and a classifier most suitable for a current classification scene is constructed. On the basis of the classifier, an outlier confidence screening module is added, a transition stage in the motion conversion process can be accurately captured, motion classification is made to better fit the actual motion condition, and the classification efficiency is improved while the classification precision is guaranteed.

The invention discloses a multi-spectrogram lightweight acoustic scene classification method and device for auditory auxiliary equipment, equipment and a storage medium. The method comprises the following steps: firstly, acquiring a hearing-aid front-end environment audio signal to be processed, converting the hearing-aid front-end environment audio signal into a Mel spectrum feature map, a constant Q transformation spectrum feature map and a first-order difference spectrum feature map, and then stacking the Mel spectrum feature map, the constant Q transformation spectrum feature map and the first-order difference spectrum feature map to form a multi-channel spectrum feature map containing complementary time-frequency information as model input; then differential shallow layer feature extraction is carried out through an asymmetric multi-stream module, and depth features are extracted on a low-power-consumption computing platform by using a lightweight backbone network with a ghost bottleneck module and coordinate attention as cores; a spectral morphology module, a frequency band attention module and a channel weight balance fusion module are introduced, and multi-dimensional enhancement and dynamic weighting are carried out on the depth features; and finally, outputting an audio scene classification result by the classifier. According to the method, the classification performance of complex acoustic scenes can be improved while the model parameter quantity and the operation power consumption can be remarkably reduced.

The application discloses a kind of based on spectral gradient field guide's bayesian adaptive graph convolution hyperspectral remote sensing image classification method, utilize the spectral information of pixel in hyperspectral remote sensing image and spatial neighborhood relationship to construct initial feature representation;Introduce spectral gradient field to describe local spectral variation amplitude and direction, adaptively adjust the connection relationship between pixel nodes;According to local spectral difference, dynamically expand or shrink neighborhood range, optimize the expression of graph structure in complex heterogeneous region;With Beta distribution, the connection probability between nodes is modeled, the robustness and stability of graph topology structure are improved;For large-scale hyperspectral scene, construct block reasoning mechanism, divide the image into several spatial sub-blocks and complete graph construction and classification reasoning independently, reduce the computational complexity and storage overhead;Pixel-level classification prediction is carried out on the whole image, and high-precision classification result map is generated.The application enhances the expression ability of strong spatial heterogeneity region, and improves the hyperspectral image classification precision and robustness.

The application provides a skeleton behavior recognition video frame extraction method and system under multiple time scales, and the technical points are as follows: first, a target detection algorithm is used to frame the person in the video to obtain the position information of the person in the video; then, a human key point estimation algorithm is used to obtain the key point position of the person information in the video; subsequently, the stacked human key point heat map is uniformly sampled, and the uniformly sampled frame is low, medium and high frequency sampled, so that the model can learn the features under different scales of the video from coarse granularity to fine granularity in a hierarchical manner, thereby enhancing the understanding ability of 3D-CNN for long videos; finally, the convolution channels of the frames under different frequencies in multiple scales are obtained in a parallel manner, the corresponding feature information is obtained, the feature information is subjected to judgment and normalization processing, according to the probability result after recognition, and finally the behavior recognition category is output. The application can improve the performance of the model on long videos while ensuring the accuracy of classification.