78 results about "Class information" patented technology

The invention provides a drug temperature and humidity digital monitoring and quality tracing system based on a full life cycle, and relates to the technical field of digital monitoring, and the system comprises the steps: firstly collecting the temperature and humidity data of the whole link of drug production, storage, transportation to terminal sales, and obtaining a standardized data set through the verification and packaging of an industrial standard library; performing secondary screening through primary screening in combination with a dynamic temperature and humidity screening model to obtain a precise abnormal data set; associating drug full-cycle multi-class information, and constructing a temperature and humidity-drug state mapping relation based on a time sequence association algorithm to form a full-dimension association data set; based on the data set, combining with a quality risk assessment model for early warning, realizing full-period traceability through a reverse traceability algorithm, and generating an assessment result and a traceability link; and finally, relevant data are converted into a dynamic temperature and humidity monitoring curve, a quality risk early warning report and a full-life-cycle tracing atlas, and an interactive report is assembled, so that the temperature and humidity state of the medicine can be checked conveniently.

A computer acquires training data including first text, first class information indicating a class mapped to a single word contained in the first text, first position information indicating a position of the single word in the first text, and first range information indicating a range of a first named entity that includes the single word in the first text. The computer executes, based on the training data, machine learning of a machine learning model which is used to estimate, from text, class information, and position information, range information of a named entity included in the text.

The invention provides a camouflage object semantic segmentation method and device based on an adaptive candidate strategy, and a medium, and the technical scheme of the invention obtains the category code of a to-be-segmented image through a coarse feature extractor and a classifier, achieves the transmission of semantic category information, and enhances the semantic perception capability of a subsequent segmentation task. Initial prediction of a camouflage object is carried out through a target detector and an adaptive candidate strategy, an optimal target attention box is generated, and robust space guidance is provided for a core segmentation task; according to the method, category coding and a target attention box are combined, multi-source information is fused and reconstructed through a multi-guide feature fusion module and a multi-task perception decoder, and an accurate segmentation result is output.

The invention discloses a mixed-flow water turbine runner surface cavitation point detection method. The method comprises the following steps: acquiring a mixed-flow water turbine runner surface cavitation point image to be detected and a trained cavitation point detection model; the method comprises the following steps: scaling a to-be-detected turbine surface cavitation point image of a mixed-flow water turbine to a preset specification, then sending the image to a backbone network, performing shallow feature extraction through a convbn module in the backbone network, and then performing deep feature extraction through a uib module so as to gradually extract image features of each level through the backbone network; inputting the image features of each level into a Neck network in which a BiFPN module is introduced and a high-resolution detection layer is added, and performing cross-scale feature interaction fusion on the image features of each level through the Neck network to obtain a multi-scale to-be-detected cavitation point feature map; the multi-scale to-be-detected cavitation point feature map is sent into a detection head, the positions and confidence coefficients of cavitation points in the map are predicted, accurate positioning and classification of cavitation points of different sizes are achieved through a dynamic self-adaption mechanism of the detection head, and a detection result containing bounding box coordinates, confidence coefficient scores and category information is output. According to the invention, water turbine runner surface cavitation point detection under different scales can be realized.

The invention provides a target detection method and device based on a meta universe, electronic equipment and a storage medium, and the method comprises the steps: obtaining an initial image, carrying out the AI creation of the initial image in the meta universe to obtain an enhanced image with a label, training a preset teacher model according to the enhanced image and the initial image, and obtaining a target detection result; and optimizing the weight of the teacher model through a gradient descent algorithm to obtain a trained target teacher model and a teacher model weight, performing weight updating on a preset student model through an index moving average and the teacher model weight to obtain an initial student model, inputting the initial image to the initial student model, and obtaining the target teacher model. Obtaining a prediction bounding box and prediction category information, calculating distillation loss based on the prediction bounding box and the prediction category information, performing knowledge distillation on the initial student model according to the distillation loss to obtain a target detection model, and performing target detection through the target detection model; the target detection method based on the meta universe is more practical and more accurate.

The present application relates to the technical field of medical image recognition, in particular to a semi-supervised medical image classification method and system based on similarity threshold pseudo label, more unlabeled data is used by using similarity threshold pseudo label, the representative probability distribution of each class is calculated according to labeled data, and labeled data can provide more reliable class information;Unlabeled data is selected according to the confidence threshold to give pseudo label, then the probability distribution of the low confidence part of unlabeled data and the similarity between the representative probability distribution of the corresponding class is greater than the similarity threshold, and the unlabeled data is given pseudo label, so as to select more high-quality unlabeled data to participate in model training.The present application can utilize more high-quality unlabeled data on the confidence threshold method without reducing the accuracy of pseudo label, and further improve the robustness of model training and the accuracy of medical image classification.

The disclosure provides a camouflage object semantic segmentation method and device based on an adaptive candidate strategy, and a medium. The technical scheme of the disclosure obtains the class code of a to-be-segmented image through a coarse feature extractor and a classifier, realizes the transmission of semantic class information, and enhances the semantic perception ability of a subsequent segmentation task. An initial prediction of a camouflage object is performed through a target detector and an adaptive candidate strategy, and an optimal target attention box is generated to provide robust spatial guidance for a core segmentation task. The class code and the target attention box are combined, multi-source information is fused and reconstructed through a multi-guided feature fusion module and a multi-task perception decoder, and an accurate segmentation result is output.

To implement quick and smooth parking assistance close to a human sense without being affected by an environment of a parking space. A 3D semantic segmentation image having a plurality of pixel units, each pixel unit of the plurality of pixel units including the depth data and class information, are generated, an available parking space is searched for on the basis of the 3D semantic segmentation image, and a path to the searched parking space is planned and a vehicle is controlled. The present disclosure can be applied to a parking assistance system.

The application discloses a kind of edge cloud cooperation human activity recognition modeling methods based on heterogeneous multi-modal data, and is divided into two stages of centralized pre-training and multi-modal semi-supervised fine-tuning.In the first stage, by dynamic mask contrast learning, pre-train the basic model based on converter using the joint dataset with super-class information, so that it has the ability to extract robust features from any modal combination.In the second stage, the local unlabeled data is used by the end-side client to generate pseudo-labels through weak mask view and calculate losses based on strong mask view for local update;Cloud side aggregates heterogeneous model parameters from different clients, and fine-tunes the global model using a small amount of labeled data.The application effectively alleviates the data heterogeneity problem by separating single-modal feature encoding and cross-modal information fusion, and can efficiently train a high-performance multi-modal human activity recognition model using a small amount of labeled data and unlabeled data while protecting data privacy.

The present application relates to a kind of unmanned aerial vehicle micro target identification inspection method and system, belong to computer vision technical field.The present application utilizes the powerful computing power of cloud computing, based on DETR original model constructs micro target detection model, micro target detection model utilizes the query guide feature sequence generated by dynamic anchor frame module to initialize the position query of decoder, simultaneously, the present application will static learnable query vector as the input of decoder, so that model can utilize accurate position information, guide decoder to extract more pure, more comprehensive micro target content features, effectively improve the flexibility and accuracy of target detection.Finally, the class information and position information are output by prediction head.The present application fully releases the potential of cloud computing power while solving the problem of missed detection in dynamic scene, suitable for high-precision intelligent supervision task in complex scene, provides effective technical support for safety hazard management.

The application discloses a small sample face recognition method based on prototype calibration and adaptive fine-tuning, comprising the following steps: 1) pre-training the encoder on a large-scale face database, capturing rich semantic information and class distribution, constructing an evaluation set, and ensuring that the training identity information does not overlap with the constructed evaluation set; 2) initializing the classifier weight through the weight imprint method, ensuring smooth transition, and inducing the discriminative structure of the encoder; 3) calculating the cosine similarity between the fine-tuning class prototype and the pre-training prototype, fusing the pre-training class information through weighted average, and calibrating the biased fine-tuning class prototype; 4) introducing an adaptive margin loss function, adjusting the sample penalty strength, and selectively fine-tuning the BatchNorm layer and the classifier weight; and 5) using the field-aware similarity NAC to calibrate the unknown class rejection, enhancing the similarity relationship with the field sample, and obtaining the final matching score. The method can improve the recognition accuracy and rejection ability of the model in the small sample learning scene.

The application discloses a specific behavior recognition method in a dense crowd environment, comprising the following steps: S1, data set acquisition, wherein the data set comprises a pedestrian detection data set and a behavior recognition data set; S2, data set preprocessing; S3, inputting images in the preprocessed pedestrian detection data set into a feature map pyramid network to extract corresponding features, generating a candidate region and class information of the candidate region through a region generation network; S4, removing overlapping targets by using a maximum value suppression algorithm; S5, performing specific behavior target recognition based on a classification recognition network of a residual network; S6, training grid parameters of the classification recognition network; S7, obtaining optimal grid parameters through step S6, and importing the optimal grid parameters into the classification recognition network, and testing through a behavior recognition data set. The method realizes the detection and recognition of specific behaviors in a dense crowd environment by fusing two stages of detection and recognition tasks for the special environment of the dense crowd.

To provide an equipment management system, a communication adapter, an equipment management method and a program for reducing the influence of the restriction of the storage capacity of equipment information.SOLUTION: A communication adapter 1 includes an apparatus information notification unit 114 which generates apparatus information notification information including apparatus information associated with the same time information about an illumination device 3 and a sensor 4 belonging to the same class, transmits the apparatus information notification information to a cloud server 2, and deletes apparatus information stored in an apparatus information buffer 121 each time the apparatus information notification information is transmitted. The cloud server 2 includes the equipment information acquisition unit 212 that, upon acquiring the equipment information notification information, stores the equipment information and the time information included in the equipment information notification information in association with each other in the equipment information buffer 221, and the equipment information aggregation unit 213 that, based on the class information, aggregates the equipment information using the equipment information associated with the same time information stored in the equipment information buffer 221.SELECTED DRAWING: Figure 3

This invention discloses a multimodal balanced learning method based on label reshaping. Starting from label-side design, this invention proposes for the first time to balance multimodal learning by reshaping the cross-modal label space. A reshaping matrix is ​​generated using the prediction output of complementary modalities to inject cross-modal inter-class information. Simultaneously, the reshaping intensity is adaptively calculated based on the confidence differences between modalities to control the degree of label space transformation, thereby balancing the mapping difficulty from the feature space to the label space for different modalities. Finally, a target parameter update strategy is employed, applying different optimization objectives to different parts of the model to ensure training stability. This method dynamically reshapes the label space corresponding to each sample and each modality, constructing a balanced soft supervision signal rich in inter-class relationships. This balances the learning difficulty of different modalities at the source and injects cross-modal inter-class information to enhance the model's discriminative ability.

The application provides a memory fault processing method and device, a storage medium and an electronic device, the method comprising: in response to a memory fault processing instruction, obtaining a heap memory dump file generated by a virtual machine corresponding to the memory fault processing instruction when a memory fault occurs; determining class information of each java class in the heap memory dump file; determining a target java class in each java class according to the class information of each java class; determining a target instance object occupying the most memory and an associated instance object referenced by the target instance object in all instance objects of the target java class; and analyzing the target instance object and the associated instance object to determine fault information having an association relationship with the memory fault. The method provided by the application can quickly and accurately determine factors causing the memory fault.

A method for training an image classification model includes obtaining first prediction class annotation information of a first image by using an image classification network based on a first model parameter of an offset network being fixed; determining a second model parameter corresponding to the image classification network by using a classification loss function based on the image content class information and the first prediction class annotation information; obtaining second prediction class annotation information of the first image by using the offset network based on the second model parameter of the image classification network being fixed; determining a third model parameter corresponding to the offset network by using the classification loss function based on the image content class information and the second prediction class annotation information; and training a semantic image segmentation network model based on the second model parameter and the third model parameter.

The invention discloses an ML-ELM-AE target motion pattern recognition method containing class information, and belongs to the technical field of situation cognition. An ELM-AE model is constructed, and an ML-ELM-AE model is constructed by stacking the ELM-AE model; carrying out the mapping of the motion features of the target through the ML-ELM-AE; a CELM classification model is constructed, and according to a difference vector set formed by inter-class samples, the weight of an input layer of the CELM classification model to a hidden layer and a hidden layer node bias item are optimized, so that the mapping of the samples from a feature space to a class space has regularity, and the precision and generalization ability of target motion pattern classification are improved.

The invention relates to the technical field of data processing, in particular to a health management information prompting method and device based on wearable equipment, and the method comprises the steps: collecting first-class information and second-class information of a target object through the wearable equipment, and obtaining basic health information of the target object; the first type of information is used for representing physiological signals of the target object; the second type of information is used for representing the pulse waveform and the body surface state of the target object; the basic health information is used for representing historical body health examination information of the target object; analyzing the first type of information and the basic health information to obtain a first analysis result; analyzing the second type of information and the basic health information to obtain a second analysis result; based on a pre-trained multi-modal feature fusion model, performing fusion analysis on the first analysis result and the second analysis result to generate a comprehensive analysis result representing the physiological state of the target object; and according to the comprehensive analysis result, the wearable device carries out health management information prompting on the target object.

Enhancement of discovery and PC5 connection for PC5 based AI / ML is described herein. As will be described in greater detail below, a WTRU performs ProSe discover the other WTRU supporting PC5 based AI / ML splitting with consideration of supported AI / ML operation, the WTRU's capability. ProSe Discovery Code may be enhanced to include supported AI / ML model for the application using PC5 based AI / ML. The WTRU's capability information or class information of the WTRU's capability may be included for Discovery. Some of those information can be negotiated during or after PC5 Connection setup as alternative.

This technology provides acoustic event localization that can detect unknown classes by updating a pre-trained acoustic event localization model using a small number of unknown acoustic signals. [Solution] The learning device includes an embedding extraction unit that uses an extraction unit included in a learned acoustic event localization model to extract embeddings for information based on acoustic signals included in prototype creation data; a prototype creation unit that uses the embeddings extracted by the embedding extraction unit and the class information and angle information included in the prototype creation data to create a prototype for each combination of class and angle range; and an update unit that updates the learned acoustic event localization model by adding the prototypes created by the prototype creation unit to the classification unit included in the learned acoustic event localization model.

The present disclosure provides a method (1000) and a system (102) for generating a real-time HD street map (110) using camera sensors (104), LiDAR sensors (106), and a standard definition map (SD card) (108). The method discloses the extraction of feature vectors corresponding to a plurality of street-related features from the camera sensors (104), LiDAR sensors (106), and SD card (108). The feature vectors are transformed into a common space to generate learned feature vectors.The method further discloses the prediction, based on learned feature vectors, of: a) position and class information associated with a variety of polylines corresponding to one or more of the variety of road-related features, b) position information of a variety of centerlines and centerline connectivity information, and c) position and class information corresponding to a variety of polylines associated with a variety of traffic elements and relationships between a variety of centerlines and a variety of traffic elements.

This invention provides a method for generating vehicle software programs, related equipment, and a storage medium, relating to the field of software configuration technology. The method includes: obtaining a preset target node configuration file; parsing the target node configuration file to generate target node configuration class information for each configurable functional node based on its node attribute information and node hierarchy; obtaining preset basic function source code and node configuration resource files to determine the target node entity class information for each configurable functional node; and combining the target node configuration class information with the target node entity class information to obtain the configured functional node. Extracting node configuration information improves development efficiency, reduces repetitive code in node creation and association during tool development, effectively improves program running efficiency, and accelerates program execution speed.

A kind of refrigerating unit fault diagnosis method based on semi-supervised contrast learning, method contains three parts, first, design refrigerating unit data enhancement technique carries out data enhancement to original data, data is combined with pair constraint, create labeled data set with pair constraint relationship.Then, construct sample pair prediction network based on contrast learning, train model using labeled data set with pair constraint relationship, learn the association between a pair of data for creating full data set with pair constraint relationship.Finally, construct semi-supervised equipment fault diagnosis network based on memory enhancement, from full data set with pair constraint relationship, the class information implied in data is grabbed, and a memory module is introduced to make the features extracted by semi-supervised equipment fault diagnosis network more distinctive.The method proposed in the application can effectively mine the internal correlation of unlabeled data of the same fault type and improve the performance of refrigerating unit equipment diagnosis.

This electronic device may comprise: a memory, which stores instructions and includes one or more storage media; and at least one processor including processing circuitry. The electronic device can: convert first input data into a plurality of pieces of first feature information by using a feature extraction model; generate a first classification model on the basis of the first input data and class information corresponding to each piece of input data; generate class-specific representative information on the basis of the first feature information belonging to the same class; transmit, to a plurality of external devices, the feature extraction model, the first classification model and the class-specific representative information; and receive a retrained feature extraction model by using first data and second data classified on the basis of the class-specific representative information.

The application discloses a medical image segmentation method and system based on a scale guide convolutional neural network, wherein a scale map learning module is added in the network structure to learn a scale map from a feature map; the scale map label is obtained by calculating and converting a boundary map of the original label of the medical image, and the learned scale map in the network is supervised correspondingly; and the network learns the scale map, calculates the offset to guide the receptive field of the convolution. The method can expand the receptive field of the convolution in the neural network, enables the network to better learn the in-class information in the global rather than being limited to local features, and can guide the convolution to better learn the boundary information and improve the segmentation effect of the boundary. The application can significantly improve the medical image segmentation effect.

In a medical image processing apparatus including a processor, the processor sequentially acquires time-series medical images and causes a display unit to sequentially display the acquired medical images. Further, the processor performs a process of acquiring, based on the acquired medical images, information related to a position of a region of interest in the medical images and classifying the region of interest into a class among a plurality of classes, and displays class information indicating the class of the classified region of interest such that the class information is superimposed at a position of the region of interest in a medical image displayed on the display unit. Further, the processor changes a relative position of the superimposed class information with respect to the region of interest, in accordance with an elapsed time from recognition of the region of interest.

The present invention provides a vehicle class data generation device, etc., that generates vehicle class data indicating the vehicle class that a driver can drive, taking into account the restrictions attached to the license type and their removal. [Solution] The vehicle class data generation device 3 includes: an acquisition means 310 that acquires license type information stored in the IC chip of a driver's license, restriction information indicating the restrictions attached to the license type by restriction text, and release information indicating the removal of restrictions by release text; a restriction release means 320 that extracts vehicle class information corresponding to the restriction text acquired by the acquisition means 310 based on a vehicle class table 300 which associates the restriction text with vehicle class information indicating the vehicle class that can be driven by the restriction text, and deletes the vehicle class information according to the release text acquired by the acquisition means 310; and a vehicle class data generation means 330 that uses the license type information and the vehicle class information that remains without being deleted by the restriction release means 320 to generate vehicle class data indicating the vehicle class that the holder of the driver's license can drive.

The present application relates to the technical field of image generation, and in particular to a multi-class and multi-azimuth angle SAR image generation method. The method first acquires an MSTAR data set; encodes the class label into a one-hot code, connects the one-hot vector and a latent code sampled from a uniform distribution in the channel dimension as conditional information, and inputs the conditional information and random noise into a generator to obtain a generated image; obtains a probability that the input image is consistent with the class label and is a real image, and a predicted latent code; alternately adjusts the parameters of the generator and the discriminator, so that the generator generates a fake image that conforms to the class information, and the latent code decouples the azimuth angle information; and controls the class label and the latent code to generate a multi-class and multi-azimuth angle SAR image. For different target image generation tasks, the present application does not need to use different target data sets to train the network multiple times, but generates a class and azimuth angle controllable SAR image through the class label and the latent code.

The invention provides a code utilization rate calculation method and device, electronic equipment and a computer medium, and relates to the technical field of data processing, in particular to the technical fields of application program development, data compression and the like. According to the specific implementation scheme, a link mapping file generated by compiling the application program is obtained, symbol information of a class defined in the application program is analyzed from the link mapping file, and a first class set is constructed; by intercepting an initialization method of each class to be monitored in a runtime environment of the application program, recording class information corresponding to a triggered initialization event, and constructing a second class set; and determining code utilization rate information of the application program according to a comparison result of the first class set and the second class set.

An item identification system includes a resolver configured to resolve item classes from item identifiers provided by radio frequency identification (RFID) tags and a machine learning system that is trained to learn associations between data about items, such as images of items, and resolved item classes. When an item is to be identified, data about the item can be captured and routed to the learning system. The learning system may be sufficiently trained such that it is able to identify the item, even if the item does not have an associated RFID tag to provide item class information. If the item does have an associated RFID tag, an item class of the item may be resolved from information provided by the RFID tag and is used along with the captured data about the item to further train the learning system.