1391 results about "Classification result" patented technology

This application relates to a method, apparatus, computer device, and storage medium for displaying medical images. In response to an instruction to acquire a first image sequence, a target type label for the first image sequence is determined. From a sequence of images to be identified containing various type labels, at least one second image sequence associated with the target type label is acquired. The first image sequence and at least one second image sequence are then displayed. Each type label is obtained based on a classification result determined according to each image sequence to be identified and a preset classification model. The embodiments of this application employ a preset classification model to automatically identify the image sequences to be identified, obtaining the classification results of each image sequence to be identified, thereby obtaining the type label for the image sequences to be identified. Compared to relying on manually entering tag information for medical images, the method proposed in this application can significantly reduce the error probability of acquiring the second image sequence based on the target type label, thereby greatly improving the accuracy of image sequence acquisition.

A quality-aware dynamic ECG Mamba state update method is proposed. The method inputs the signal quality index, motion state information, and preprocessing confidence level into the quality control branch of a state-space sequence modeling network. Based on the output of the quality control branch, at least one of the state writing intensity, state forgetting intensity, and state propagation intensity of the state-space sequence modeling network at each time point is adaptively modulated. The method outputs a long-term dynamic ECG representation based on the modulated state update result, along with event detection results or classification results. By implementing quality constraints on the state-space writing, retention, and forgetting of long-term dynamic ECG sequences, with control variables derived from SQI, IMU, and preprocessing confidence level, the method avoids low-quality segments contaminating the long-term memory state, effectively improving the problem of false accumulation in long Holter / patch / motion monitoring sequences.

The application discloses a government affair data processing method and device and electronic equipment. The method comprises the following steps: receiving government affair data input by a user; in the case that the government affair data comprises a question sequence corresponding to a government affair analysis scene and a government affair data table, determining a classification result corresponding to the question sequence, and extracting target feature data in the government affair data table, wherein the target feature data only comprises a file table header of the government affair data table and standardized data of a preset data volume; determining a query result corresponding to the target feature data, and determining a government affair analysis result corresponding to the government affair data according to the query result and the classification result of the question sequence. The application solves the technical problem that the data processing efficiency of the government affair scene is poor due to the fact that the intelligent government affair system in the related art has resource and privacy risks in data processing, and cannot effectively process table data exceeding the token length limit.

The application provides a three-dimensional stacked chip, a temperature control method and an electronic device. The chip comprises a logic die and a storage die stacked on the logic die; a first temperature sensor and a hierarchical temperature controller disposed in the logic die, the temperature controller being configured to control the first temperature sensor to collect first temperature data of the logic die; a second temperature sensor disposed in each layer of the storage die, and a temperature output pin shared by the storage dies, the temperature output pin being configured to output second temperature data of the storage die, the second temperature data being determined based on temperature collected by the second temperature sensor; and a temperature fusion controller connected with the temperature controller and the temperature output pin, and configured to perform scene classification based on the first temperature data and the second temperature data, and perform global temperature collaborative control or local area temperature control based on a scene classification result. Through the application, temperature collaborative control of the three-dimensional stacked chip can be realized.

This invention provides a medical ultrasound image recognition method based on generator domain alignment, relating to the field of image classification technology. The method includes: creating labeled generated medical ultrasound image samples for fine-tuning using a conditional diffusion model, where labels characterize the category corresponding to the generated medical ultrasound image sample; aligning each labeled generated medical ultrasound image sample to the generator domain using an unconditional diffusion model of the generator domain, obtaining a generator domain medical ultrasound image set; fine-tuning the source domain model using the generator domain medical ultrasound image set, obtaining a generator domain model; aligning a target medical ultrasound image to the generator domain using the unconditional diffusion model of the generator domain, obtaining a generator domain target medical ultrasound image; and inputting the generator domain target medical ultrasound image into the generator domain model to obtain a first predicted classification result for the target medical ultrasound image. This transforms a cross-domain task into an intra-domain prediction task, aligning both the source domain model and the target data to the same generator domain, thereby achieving accurate predicted classification results.

A method, apparatus, non-transitory computer readable medium, and system for data classification include obtaining input data and a policy graph. The policy graph includes a decision node indicating a machine learning classifier. Embodiments then generate, using the machine learning classifier, a classification result based on the input data and the decision node. Subsequently, embodiments generate a decision label for the input data based on the classification result.

The present invention relates to a terminal for providing vibration feedback on the basis of user behavior recognition. The terminal possessed by a user comprises: a memory for storing at least one instruction; and a processor, wherein the at least one instruction is executed by the processor so that the terminal: acquires a classification result about a counterpart user behavior type obtained by providing, to a pre-trained behavior type classification model, data generated by detecting the movement of the counterpart user in one or more wearable patches attached to the body of the counterpart user; acquires a vibration pattern corresponding to the acquired classification result on the basis of a mapping table in which a predetermined vibration pattern is defined for respective behavior types; and controls that vibration is generated according to the acquired vibration pattern in the wearable patch attached to the body of the user.

Provided are a resistive random-access memory (RRAM)-based blood test system and method. The system comprises: a computing chip, an RRAM chip, a power supply module, and a sensor module; the computing chip is connected to the RRAM chip; the RRAM chip is connected to the sensor module; and the power supply module is connected to the computing chip and the RRAM chip; the sensor module is configured to test a substance in blood and convert test result data into a plurality of pieces of first voltage data; the RRAM chip is configured to preprocess the received plurality of pieces of first voltage data and obtain second current data; and the computing chip is configured to, based on the second current data, perform a function computation on the second current data according to a preset function computation model, and obtain a blood-based classification result.

Embodiments of the present application provide an image recognition method, the method comprising: constructing a neural network model, and training the neural network model through pre-acquired sample image data to obtain an image recognition model, the neural network model being composed of an input layer, a plurality of image feature capturing modules, a full connection layer, and an output layer in series; wherein the image feature capturing module is composed of a first channel integration module, a first convolution module, a feature map splicing module, a second channel integration module, a spatial attention module, a first activation layer, a second convolution module, and a second activation layer in series; acquiring an image to be recognized, inputting the image to be recognized into the image recognition model; and outputting a classification result of the image to be recognized recognized by the image recognition model. The technical solution of the embodiments of the present application can improve the applicability of image recognition based on a neural network model.

The application relates to the technical field of image recognition, and discloses a mouse species recognition method based on image recognition. An original mouse image is input into a posture decoupling recognition network, and a heat map is output based on an anatomical prior anchor point; a rotation bias angle is calculated according to the heat map coordinates, an affine transformation is performed on the image, and a distorted body is cut and reorganized into independent local image blocks of a head, a trunk and a tail at a standard visual angle; spatial features are extracted by using parallel convolution channels, local features are mapped to a global feature space by a cross-region cross-attention mechanism, a species discrimination vector is fused and generated, and a classification result is output. The application converts non-standard posture restoration into extraction and reorganization of standard local features, eliminates the interference of body distortion and local occlusion in a limited space on global feature extraction, and overcomes the misclassification defects caused by the sensitivity of a conventional network to non-standard postures.

The present application relates to the technical field of wireless communication security, in particular to a radio frequency fingerprint identification method for wireless node identity authentication. The method solves the problems of weak radio frequency fingerprint characteristics in short frame wireless signals, insufficient discrimination ability of lightweight models, and difficulty in rigid alignment and distillation of heterogeneous teacher-student models. The method obtains training wireless signal samples with identity labels and converts them into time-frequency spectrum samples; inputs the time-frequency spectrum samples into a teacher recognition model and a student recognition model to obtain classification results and global features; projects the teacher global features and the student global features, generates a cross-dimensional correlation matrix according to the projection feature component correlation, and normalizes to obtain alignment-free attention weights; constructs an alignment-free feature distillation loss based on the weights, and jointly trains to obtain a lightweight radio frequency fingerprint identification model; the model is used to output the identity category of the wireless node to be verified during inference, which is used for wireless node access authentication and physical layer identity authentication.

The application discloses a boride identification method, system and related device, and relates to the field of alloys.The method comprises the following steps: obtaining a metallographic image of a titanium-aluminum alloy; converting the metallographic image into a first image represented by color in terms of hue, saturation and brightness; determining a saturation threshold and a brightness threshold; dividing each pixel point in the first image into a boride category and a background category according to the saturation threshold and the brightness threshold; obtaining a binary image of the first image according to the classification results of the pixel points; identifying at least one target region composed of multiple target pixel points in the binary image by using a continuous region identification algorithm; and determining the target region as a boride region.The application screens the pixel points by using saturation and brightness, so that the possibility of selecting a shallow corrosion structure is reduced; after a suspected boride region is selected, the continuous region identification algorithm is used to identify and screen according to the shape characteristics of the boride. Therefore, the application can effectively improve the identification accuracy of the boride.

A detection method and system for a leaked gas based on multi-data fusion, and an electronic device, relating to the technical field of gas detection. The method comprises: acquiring a plurality of compressed images obtained by respectively compressing a plurality of first infrared images; on the basis of the plurality of compressed images, predicting a position of a potential leaked gas as a candidate position; extracting, respectively from a plurality of second infrared images, sub-images at the candidate position as infrared sub-images; and on the basis of all of the infrared sub-images, predicting a first classification result for the candidate position, wherein the first classification result is used for indicating the existence of a leaked gas at the candidate position or the absence of a leaked gas at the candidate position. By means of embodiments of the present application, rapid and accurate detection of a leaked gas can be achieved.

The application discloses a node classification method and device based on a decoupling graph, equipment, medium and product, and relates to the technical field of neural networks. The method comprises the following steps: obtaining a text attribute graph and an input prompt; inputting text information corresponding to each node into a large language model to obtain overall text representation corresponding to each node; inputting overall text representation corresponding to a target node into multiple decoupling channels, each decoupling channel aggregates neighbor information of the target node according to different graph structures to obtain multiple hidden layer representations; optimizing the input prompt based on the multiple hidden layer representations of the target node to obtain an optimized prompt, and inputting the optimized prompt into the large language model to obtain a node classification result. The application obtains hidden layer features containing different aspect graph structure information through decoupling channels, and optimizes the input prompt based on the hidden layer features, so that the large language model can better handle the node classification task of the text attribute graph, and effectively improves the processing performance of the large language model on the text attribute graph.

The application provides a data classification method based on a fitting classification model and a user portrait, comprising the following steps: step 1, obtaining target data of a user, and dividing the target data into digital data and analog data according to the data type; step 2, classifying the digital data based on the fitting classification model to obtain a first classification result; step 3, classifying the analog data based on the user portrait to obtain a second classification result; and step 4, taking the first classification result and the second classification result as a final classification result of the target data. The method can realize more accurate classification of digital data and accurate classification of analog data of a non-digital data type.

The application provides a spatial perception enhancement and pathological image classification method for processing class imbalance, aiming to enhance the model's perception of spatial information and process class imbalance. The specific steps are as follows: in the preprocessing stage, the image is segmented, the background is filtered out, and the image block is cut, and the coordinates are recorded; in the feature extraction and fusion stage, the pathological features are extracted through a pre-trained convolutional model, then the coordinates are normalized to form position features, and the pathological features and position features are fused to form comprehensive features with spatial information. In the reasoning stage, a multi-scale feature enhancement network is introduced, which can capture local features through convolution operations and model spatial relationships through attention mechanisms, thus achieving more comprehensive processing of local and spatial information. Finally, the parallel classifier is used to solve the problems of class imbalance and sample shortage, and the final pathological classification result is obtained. This method can better assist pathologists in improving the diagnosis accuracy and reducing subjective differences in the diagnosis of chondroma.

This invention provides a method, apparatus, processor, and electronic device for determining vehicle type. The method further includes: acquiring three-dimensional point cloud data of a target vehicle, wherein the three-dimensional point cloud data is used to characterize the outline of the target vehicle; converting the three-dimensional point cloud data to obtain a one-dimensional feature array corresponding to the target vehicle; determining a target feature array whose similarity to the one-dimensional feature array is higher than a similarity threshold from at least one pre-stored standard feature array corresponding to at least one standard vehicle model, wherein the standard feature array is used to characterize the classification result of the standard vehicle model; and determining the classification result corresponding to the target feature array as the target classification result for the target vehicle, wherein the target classification result is used to characterize the vehicle type of the target vehicle. This invention solves the technical problem of high resource consumption in the vehicle recognition process.

The application provides a three-dimensional implant crown intelligent selection and generation method based on U-Net, comprising the following steps: converting a three-dimensional tooth model in STL format into a high-resolution two-dimensional projection image; taking a U-Net network structure with VGG16 as a backbone network, combining a bar pooling module, and designing a neural network model for accurately capturing tooth sequential arrangement features; training the neural network model by using an adaptive learning rate adjustment strategy and a cosine annealing learning rate scheduling strategy, and performing semantic segmentation on the two-dimensional projection image to obtain a 2D tooth classification result image; identifying missing tooth labels based on the 2D tooth classification result image, calculating the center points of left and right adjacent teeth of the missing tooth, calculating the center position coordinates of the missing tooth in the 2D image through the center points of the left and right adjacent teeth, and mapping the center position coordinates to a three-dimensional space to obtain three-dimensional center coordinates of the missing tooth; and moving the template tooth center point to the three-dimensional center coordinates of the missing tooth to generate a three-dimensional crown model that matches the missing tooth position.

The application discloses a geophysical model construction system based on VI theory and relates to the technical field of geophysical data processing.The application establishes multiple geological identification layers according to the classification results of multiple source geological data, splits each geological identification layer into a rendering identification area, sets a unique identification tag according to the geological data and the space-time position of each rendering identification area, compares the rendering identification areas at the same space-time position, records the rendering identification areas as temporary anchor areas according to the comparison results, sequentially splices and fuses the rendering identification areas of the same geological data type and different geological data type according to the unique identification tag to obtain a geological physical area, sets a geological data change direction with the temporary anchor area as a data change reference direction, preferentially update the rendering identification area in the corresponding direction according to the geological data change direction, and splices each geological physical area to obtain a geophysical model.

The present application relates to the technical field of fault type identification acceleration, in particular to a kind of multi-modal sound feature fusion's fluid transmission equipment fault classification acceleration method and system;After sound sensor collects fluid transmission component operation sound signal, pre-processing is removed interference noise;Time domain feature extraction module extracts multi-scale space-time feature by multi-scale time convolution fusion unit;Frequency domain feature extraction module is converted by FFT, amplitude and phase separation processing and IFFT restoration, and time domain feature after frequency domain processing is obtained;Dual-domain feature fusion module fuses dual-domain feature by real Gabor wavelet function, and then excavates deep feature by Transform module;Fault diagnosis module outputs fault classification result by convolution, fully connected and Softmax function, and hardware platform outputs recognition accuracy by UART serial communication.This application detects comprehensively, responds quickly, has strong stability, low power consumption, good portability, can be adapted to different environments, identifies multiple fault types, and can also be extended to other related fields.

This invention provides an EEG emotion recognition method based on gating spatiotemporal mechanisms and a bidirectional state-space model, relating to the fields of biomedical engineering and artificial intelligence. The EEG emotion recognition method includes: acquiring shallow features of an EEG signal; projecting the shallow features to obtain corresponding high-dimensional features; performing hierarchical gating processing on the high-dimensional features in both temporal and spatial dimensions to obtain spatiotemporal aggregated features; performing bidirectional state-space encoding on the spatiotemporal aggregated features to obtain fusion features; and performing weighted processing on the fusion features to obtain the emotion classification result of the EEG signal. The EEG emotion recognition method provided by this invention can effectively filter out local noise and efficiently model long-term and long-distance dependencies, constructing a computationally lightweight hybrid neural network architecture to meet the requirements of real-time and robust EEG emotion recognition.

The present application relates to the technical field of network monitoring, and more particularly to a network data intelligent monitoring method and device and electronic equipment, the method comprising: constructing a single node evaluation index based on the communication data of each node in the target network, and then classifying each node according to the single node evaluation index of each node, obtaining the consensus participation record of each node in the monitoring window, and constructing a node consensus activity model to correct the classification process of the node; constructing a transmission pressure set of each node, and identifying a pressure convergence node based on the classification results and the transmission pressure set of each node; determining each network consistency index based on the broadcast receiving time of each node in the monitoring window, and determining the health status of the target network in the monitoring window in combination with the pressure convergence node. The present application effectively improves the monitoring efficiency of the blockchain network.

The present application relates to the technical field of bridge nondestructive testing, and particularly relates to a multi-modal guided wave steel bridge deck fatigue crack intelligent fusion detection method and system, the intelligent fusion detection method comprising: scanning a to-be-detected area of a steel bridge deck by using ACFM detection technology, positioning a suspicious area, and merging the suspicious area and a high-risk area to form a fine detection target set; based on the fine detection target set, outputting an EMAT detection instruction for guiding EMAT detection of each fine detection target by using a hybrid intelligent decision model, and performing targeted fine detection on the fine detection target to obtain EMAT characteristic parameters; combining the ACFM characteristic parameters and the EMAT characteristic parameters of the fine detection target into a fusion feature vector, and inputting the fusion feature vector into a PINN evaluation model comprising a second classifier, and outputting a final defect classification result of the fine detection target and a quantitative physical parameter of the defect by the PINN evaluation model. Through efficient cooperation of ACFM and EMAT, the present application realizes integrated intelligent detection of "screening-decision-fine detection-evaluation" from surface to internal fatigue cracks.

This application relates to a method for karst detection in submarine tunnels, comprising: obtaining a first-level classification result of the karst development section of the area to be tested based on the obtained classification parameters of the area to be tested; obtaining classification parameters by employing several detection methods corresponding to the first-level classification result of the karst development section of the area to be tested, and obtaining a second-level classification result of the karst development section of the area to be tested based on the classification parameters; using the second-level classification result as the first-level classification result, and re-obtaining the second-level classification result, until the first-level classification result and the second-level classification result are the same. This method, through a closed-loop process of "preliminary classification—precise detection—dynamic adjustment—iterative verification—3D modeling," effectively controls engineering costs while ensuring detection accuracy, and is suitable for karst detection projects in marine environments with high water pressure and complex overburden layers.

This invention relates to a remote sensing interpretation method and system that integrates multi-source spatiotemporal spectral features and a visual model. First, optical imagery, harmonic data, and synthetic aperture radar (SAR) data of the target area are acquired and preprocessed. Next, spatiotemporal features of the optical imagery and harmonic data are extracted separately and fused using a cross-attention mechanism to generate fused features. Then, the fused features and SAR data are subjected to image serialization, temporal encoding, and spatial encoding processing, and spatiotemporal features are extracted using a self-attention mechanism. Finally, the spatiotemporal features are decoded and the multi-source feature convolution results are fused to generate pixel-level land cover classification results. This invention introduces harmonic data to capture the temporal variation trend of ground features, utilizes the visual Transformer self-attention mechanism to capture spatiotemporal interdependencies, and combines multi-source data to capture multi-dimensional features of ground features, avoiding the limitations of a single data source. Furthermore, the fusion of multi-source temporal features allows the model to adapt to different geographical environments, improving generalization performance.

This invention relates to the field of intrinsic ability prediction technology and discloses a method for predicting the intrinsic ability characteristics of the elderly based on machine learning. First, intrinsic ability score data of the elderly in five dimensions, including cognition and psychology, are acquired, and heterogeneity classification is performed using latent profile analysis. Using the classification results as labels, random forest and LASSO regression are used in parallel to screen key predictive factors to form a feature subset. A classification prediction model is built based on XGBoost, and hyperparameters are optimized through cross-validation and grid search. The SHAP and LIME algorithms are integrated to achieve both global and local interpretability. Finally, the model is deployed on an online interactive platform, where inputting individual characteristics outputs the IC propensity classification, probability, and feature contribution explanation. This invention achieves accurate classification and interpretable prediction of the intrinsic abilities of the elderly, reduces the dependence of assessment on professionals and the environment, and is suitable for large-scale application in grassroots elderly care scenarios.

This invention discloses a fractal analysis method for top coal seam fractures based on CT imaging, belonging to the field of fracture fractal detection. The method includes the following steps: based on CT scan data of top coal samples, a three-dimensional digital model containing fracture information is obtained through three-dimensional reconstruction; the three-dimensional digital model is processed using image processing methods to extract the fracture network and calculate topological parameters; the fracture network is processed using fractal analysis methods to calculate the global fractal dimension and local dimensional statistical features; the topological parameters, global fractal dimension, and local dimensional statistical features are fused to form a multi-scale feature vector; based on the multi-scale feature vector, a machine learning model is used to obtain the top coal fracture classification result. This invention overcomes the limitations of traditional methods that rely on a single indicator and are highly subjective, achieving objective and accurate identification of the development level of top coal fractures.