38 results about "Base class" patented technology

The invention discloses an MRI (Magnetic Resonance Imaging) sequence generation system and method, and the system comprises a basic sequence generator module which is used for defining an abstract base class and packaging general logic in a sequence generation process; the independent development tool chain module is used for providing a development environment independent of a system framework and compiling a sequence generator source code into a binary module; the sequence registering and loading module is used for automatically discovering and loading the binary module and registering a sequence generator class inheriting the self-abstract base class through a reflection mechanism; the RESTful API interface module is used for receiving an external request and calling a corresponding sequence generator object according to a sequence type; the asynchronous task management module is used for managing life cycles and state tracking of asynchronous tasks; and the parameter configuration management module is used for centrally managing sequence parameter configuration. According to the system and the method, the development threshold is reduced, and the expansibility, the maintainability and the concurrent processing capability of the system are improved.

The application relates to the technical field of code data, and discloses a method for solving nested object dictionary and automatically modifying existing codes, which comprises the following steps: S1, a command line module reads and stores parameters input by a user, and obtains existing codes and related information; S2, a file adding module adds a base class file in a base class file module to root directory information of the existing codes, wherein the base class file module comprises a to_json method and a to_dict method; S3, a file modifying module transmits init code and serialization style to an attribute acquisition module; and S4, the attribute acquisition module returns an attribute dictionary to the file modifying module. By implementing a base class, all objects inherit the class, and expression and serialization of the objects are easily realized.

The application relates to a function module operation method, device and equipment and a storage medium, the method comprising the following steps: a first function module class corresponding to a first function module is created; the first function module class inherits a base class provided by a preset function module development framework; a data service interface for data interaction between modules and an execution interface for executing business logic in a module are arranged in the base class; first output data corresponding to a second function module relied on by the first function module is acquired through the data service interface in the first function module class; the first output data is data provided externally to the second function module through the data service interface in the base class; the execution interface in the first function module class is called, the first function module is operated based on the first output data according to preset business logic. The application reduces the complexity of control logic, reduces resource consumption and improves the decoupling degree between modules through a data driving mode.

This invention provides a machine learning apparatus, method, and program that improve the classification performance of a classifier by utilizing local features of the input data. [Solution] A machine learning device that continuously learns a small number of new classes compared to the base classes, wherein the feature extraction unit 10 extracts features of the input data using weights learned based on the divided features of the input data. The base class classification unit 20 classifies the base classes based on the features of the input data. The new class classification unit 40 classifies the new classes based on the features of the input data. The attention attractor unit 30 regularizes the weights of the base classes and the weights of the new classes.

The invention discloses a numerical simulation program design method based on a microkernel architecture and related equipment, and relates to the technical field of numerical program development, and the key points of the technical scheme are as follows: packaging at least one basic component which is not related to a numerical simulation service in a nuclear reactor numerical simulation program to obtain at least one base class; constructing a microkernel based on the base class; setting at least one service expansion point for a corresponding base class in the microkernel according to business logic in different numerical simulation businesses, and abstracting each service expansion point into a virtual function interface; configuring a plug-in for the virtual function interface; calling a plug-in registration macro function to register each plug-in, and storing registration information into a plug-in registry; parameters input by a user are read from the file blocks, and entity plug-in class objects are constructed in combination with registration information in the plug-in registry; and according to the entity plug-in class object, loading the plug-in to a base class corresponding to the microkernel for assembly, and forming numerical simulation programs corresponding to different numerical simulation services.

The application discloses a generalized small sample semantic segmentation method based on word vector embedding-information self-completion, comprising: a base class learning stage: training a backbone network, a semantic word vector embedding module and a base class classifier using base class label samples to generate a base class prototype; a new class updating stage: training a new class classifier using one or five new class label samples to generate a new class prototype; an evaluation stage: inputting the base class prototype obtained in the base class learning stage and the new class prototype obtained in the new class updating stage into a self-completion module, using the base class prototype and the new class prototype to generate pseudo labels through initial prediction of the query image by the self-completion module, then extracting a query prototype in the query image through the pseudo labels, calculating the cosine similarity between the query prototype and the query image to generate an auxiliary prediction; finally, weighting the auxiliary prediction and the initial prediction to obtain a final prediction result. The application increases the inter-class distance of the model and excavates more information, and further improves the segmentation performance of the model.

This invention discloses a few-sample class-variable audio classification method based on a prototype adaptive network. The steps are as follows: 1) Extracting log-Mel spectrum from input audio samples; 2) In the basic stage, training an initial model consisting of a feature extractor, a prototype classifier, and a class-adjustable prototype adaptive network using base class samples; 3) Extracting representations from the base class training samples, calculating the mean of each base class representation as a prototype, and updating the prototype classifier; 4) In the incremental stage, if the number of categories increases, inputting the representations of the incremental class training samples into the class-adjustable prototype adaptive network to obtain the prototypes of each incremental class and updating the class-adjustable prototype adaptive network; if the number of categories decreases, deleting prototypes of unnecessary categories; 5) Then updating the prototype classifier using the class-adjustable prototype adaptive network; 6) Obtaining the classification category of the test sample. This invention can adaptively expand or shrink the prototype classifier, thereby continuously learning new categories while remembering or deleting old categories, achieving few-sample class-variable audio classification.

The application provides a method for adapting functions with various parameters in a virtual machine, comprising: creating a base class to store different virtual machine methods; creating a template class to provide a template function and containing various parameter function data, and returning an object array as a result to realize function calling of any virtual machine method; and creating an auxiliary class to encapsulate the parameter data, convert any parameter data into an interface of one data, realize adaptive calling of the function calling function with various parameters, store the function data with various parameters through the template function, and store any number of parameters into one data, so that any method in the virtual machine can be adapted, and any number and type of parameters can be adapted.

The application provides a visual-language model prompting method and system based on a coupling prompt field, and belongs to the fields of artificial intelligence and computer vision. The method comprises the following steps: mapping a base class task and a new class task to a shared feature space, defining a coupling prompt field, and enabling the base class and the new class task to form mutual constraints in the shared feature space; performing scale constraints on the coupling prompt field through projection layer norm alignment and coding layer layer-by-layer norm alignment; integrating the projection layer norm alignment loss and the coding layer layer-by-layer norm alignment loss, fusing the alignment loss and a task loss of a visual-language model, forming a model overall training target, and performing training; performing reasoning on the base class task and the new class task based on the trained model, and outputting a classification prediction result. The application solves the problems of existing end-to-end, decoupling prompt learning, base class-new class isolated optimization, easy falling into local optimum, norm drift and entanglement collapse, and improves the cross-task generalization and robustness of the model.

This invention discloses a few-sample incremental action recognition method and apparatus with test-time adaptive capability, relating to the field of video action recognition technology. The invention is based on a two-stage structure of "base class learning—new class adaptation": in the base class stage, the model learns general spatiotemporal features through sufficient samples and introduces a multi-head self-attention mechanism to improve generalization ability; in the new class stage, a hierarchical test-time adaptive mechanism is used to achieve self-updating of the model during inference. Using this invention, not only is the model's responsiveness to new class samples improved, but catastrophic forgetting and bias accumulation caused by noisy samples are also effectively suppressed, thereby achieving a stable and efficient incremental learning process, and thus improving the accuracy and efficiency of few-sample action recognition.

According to the software test engineering conversion and test method and device, the storage medium and the computer equipment provided by the invention, when the engineering structure is converted, the primitive class in the original test engineering test class is replaced with the new base class; the new base class is obtained through reconstruction by adding a data source setting capability and a data reading capability on the basis of the primordium class, so that test data of a specific scene can be accurately read; then, an original test method containing a plurality of scene cases and a test logic core method of the original test method are obtained through analysis from the test class, the workload of generating an independent test method for each scene case can be greatly reduced by integrating universal test logic in the core method, and the independent test method not only has an independent execution entry, but also has a test logic function; and the corresponding test data line in the data storage file can be accurately read and executed through the new base class. Therefore, a new test project is obtained through structure conversion based on each independent test method, and a large amount of workload and potential errors caused by manual modification can be avoided.

The embodiment of the invention provides a component adaptation method and device, computer equipment, a computer readable storage medium and a computer program product, and belongs to the field of software. The component adaptation method comprises the following steps: acquiring an interface of a source framework component and a predefined component base class, wherein the component base class comprises a universal interface mapping rule; mapping an interface of the source frame component into an interface of a universal component based on the universal interface mapping rule, so as to convert the source frame component into the universal component; wherein the interface of the universal component is determined according to the interfaces of various framework components, and the universal component is suitable for various frameworks. According to the technical scheme of the embodiment of the invention, a method for efficiently converting the specific framework component into the universal component can be provided under the condition of not invading framework type selection, and the universal component is suitable for multi-framework access, so that the design cost of a component provider and the access cost of an access party are reduced, and the development efficiency is improved.

Continual learning techniques are described for extending the capabilities of a base model, which is trained to predict a set of existing or base classes, to generate a target model that is capable of making predictions for both the existing or base classes and additionally for making predictions for new or custom classes. The techniques described herein enable the target model to be trained such that the model can make predictions involving both base classes and custom classes with high levels of accuracy.

The application discloses a development framework and method for asynchronous remote calling based on Qt, the development framework for asynchronous remote calling based on Qt comprises a base class and a public calling function callFunc, the base class realizes a callSelfFunc function, a function is found and called through a reflection mechanism provided by Qt, and a called module inherits the base class, the public calling function "callFunc finds a called module object according to a module name, generates a QEventLoop object, and calls the callSelfFunc function of the called module through an invokeMethod provided by Qt, the development framework for asynchronous remote calling based on Qt is based on a development framework for message transmission based on Qt, a return value of the called module is called through the development framework for message transmission and is synchronously returned to a calling module, the logical splitting problem is solved, the implementation complexity is reduced, meanwhile, a non-thread-safe "called function can be called in a multi-thread environment, and some special application scenarios are adapted.

Providing adapting a base classifier to novel classes. A system (100) for adapting a base classifier to one or more novel classes is disclosed. The base classifier classifies instances into base classes by extracting feature representations from the instances using a feature extractor and matching them to class representations of the base classes. The base classifier is adapted using training data for the novel classes. Class representations of the novel classes are determined based on feature representations of instances of the novel classes. The class representations of the novel classes and the base classes are then adapted, wherein at least one class representation of the novel classes is adapted based on class representations of the base classes and at least one class representation of the base classes is adapted based on class representations of the novel classes. The adapted class representations of the base classes and the novel classes are associated with the base classifier.

The application provides an annotation and base class-based data screening method and system, comprising: establishing a programming language base class; establishing an aspect module and performing corresponding business logic assignment processing on fields according to the programming language base class and actual business requirements; assembling base class fields in the programming language base class into structured query language query conditions through the established Mybatis interception module, and then executing the structured query language to obtain corresponding results. The application puts all data screening related fields into a certain base class and extracts all assignment and business logic processing parts of the fields to an aspect class based on the industry mainstream open source framework SpringBoot, and the aspect class uniformly processes, so that the project group screening function code sharing is realized, the code is non-invasive, the coding speed of the developer is greatly improved, and the bug repair speed is also improved.

This invention relates to the field of computer technology, and more particularly to a method, apparatus, computer device, and storage medium for managing pop-up views. Based on a received trigger event of a pop-up view, the corresponding pop-up component is invoked to generate the corresponding pop-up view. An interface utility class file for managing the pop-up views is obtained based on a pre-established base class inherited from the pop-up view, and the pop-up views are managed based on this interface utility class file. In this invention, by inheriting the base class of the pop-up view, only one set of code needs to be written and maintained for the same pop-up generation and disappearance, providing methods for business use. Subsequent adjustments or modifications only require maintenance within this method, simplifying the maintenance of the pop-up view code and improving the management efficiency of pop-up views.

A computer-implemented method for semantic segmentation includes constructing a co-occurrence table that includes co-occurrences of predictions of a pre-trained model for base classes and labels for novel classes from the pre-trained model for base classes and from training data with novel classes. Classifiers are trained that associated with a base class and that classify an input into the base class and one of the novel classes that have co-occurrences with the base class according to the co-occurrence. A prediction is fused from the pre-trained model and the trained classifiers to obtain a final prediction result as a fully labeled image.

The application discloses a source code processing method, computer equipment and program product, and relates to the technical field of computer program code analysis, and the method is characterized in that: an abstract syntax tree is converted into a hybrid code tree that integrates data semantics and control logic, unified standard node basic attributes and semantic information of data nodes and control nodes that inherit a unified base class are relied on, multiple independent intermediate representations are avoided to be built, and operation overhead in a code analysis process is reduced; the original parent-child hierarchical relationship of the abstract syntax tree is completely retained in a conversion process by using the hybrid code tree, sibling nodes in the same layer are arranged according to a source code lexical analysis sequence, and syntax, data and control semantic information are integrated into a hierarchical clear tree structure by combining the design of the data nodes and the control nodes in the hybrid code tree, without additionally adding jump edges, control flow edges and data flow edges, so that the query overhead of the analysis process and the system maintenance complexity are reduced, and the method is suitable for various source code analysis application scenarios such as vulnerability detection and compliance audit.

The present application relates to the technical field of artificial intelligence, and proposes a two-stage fine-tuning and decoupling reasoning method and device of a visual language model, which comprises: in the first stage, panoramic view and subject view are obtained through center cropping, and a learnable prompt vector is initialized for each view, the encoder is frozen, and the prompt vector is optimized to decouple view-specific semantics; in the second stage, the prompt vector is frozen, the text embedding stack is stacked as a trainable weight matrix, the linear classifier is optimized to enhance the discriminability of the base class, and the optimal fusion weight is searched on the validation set; the decoupling reasoning of the third stage comprises fusing the logits of the two views using the optimal weight fusion for the base class, adopting Dempster-Shafer evidence theory fusion for new classes, and introducing an uncertainty measure to realize robust prediction. The present application effectively balances the recognition performance of the base class and the generalization ability of the new class, and exhibits superior accuracy and robustness in a multi-modal classification task.

The invention relates to a function module operation method and device, equipment and a storage medium. The method comprises the steps that a first function module class corresponding to a first function module is created; the first function module class inherits a base class provided by a preset function module development framework; a data service interface for data interaction between the modules and an execution interface for executing business logic in the modules are arranged in the base class; obtaining first output data corresponding to a second function module on which the first function module depends through a data service interface in the first function module class; the first output data is data provided for the outside and set for the second function module through a data service interface in the base class; and calling an execution interface in the first function module class, and operating the first function module based on the first output data according to a preset service logic. According to the method, the control logic complexity is reduced through a data driving mode, the resource consumption is reduced, and the decoupling degree between the modules is improved.

The application discloses an incremental small sample instance segmentation method based on transfer learning, comprising the following steps: constructing an incremental small sample instance segmentation model, wherein the incremental small sample instance segmentation model comprises a feature extractor, a Transformer encoder, a Transformer decoder and two prediction heads connected in sequence; training the incremental small sample instance segmentation model; and using the trained incremental small sample segmentation model to segment foreground objects of base classes and new classes in an image to obtain the class and segmentation mask of each foreground object; the application adds a lightweight adapter and adopts a knowledge distillation strategy, aiming to overcome the problems that previous methods cannot fully utilize base class knowledge and the performance of old classes decreases when fine-tuning the model, and better integrate base class knowledge and new class knowledge, and better balance the stability-plasticity dilemma.

The invention discloses a test time adaptive small sample increment action recognition method and device, and relates to the technical field of video action recognition. According to the method, a double-stage structure of'base class learning-new class adaptation 'is taken as a core: in a base class stage, a model learns universal spatial-temporal characteristics through sufficient samples, and a multi-head self-attention mechanism is introduced to improve generalization ability; and in a new class stage, realizing self-updating of the model in a reasoning process by utilizing a hierarchical test time self-adaptive mechanism. By adopting the method, the response capability of the model to new samples is improved, and disastrous forgetting and offset accumulation caused by noise samples are effectively inhibited, so that a stable and efficient incremental learning process is realized, and the accuracy and efficiency of small sample action recognition are further improved.

This invention discloses an apparatus and method for building web front-end applications based on object technology, belonging to the field of Internet technology. It solves the technical problems of existing technologies for building web front-end applications, such as the high learning cost caused by the separation of HTML, CSS, and JavaScript in traditional native methods; difficulties in data access between components during runtime in component-based methods; instability of common basic code and redundant code during component reuse; and difficulties in code understanding, porting, and debugging due to significant differences between component-based implementations and traditional native methods. It includes a main application base class, page view base class, storage base class, event base class, and loading and packaging tool base class, encapsulating common and basic functions in these base classes, while the specific page and function-related code required by the web application is implemented by their respective derived classes. This invention is convenient and quick to build, has a low learning cost, overcomes the problems of unstable common basic code and redundant code in existing component-based methods, and reduces the complexity of inter-component access and operation.

This invention provides a data processing method, apparatus, electronic device, and storage medium. The method involves obtaining the overridden function table of subclasses in the executable file of an application. The subclasses inherit from a base class in the executable file. The overridden function table includes the addresses of the original functions in the base class that need to be replaced, the class name of the base class, and the address of the target function used to replace the original functions. The method then determines the virtual function table of the base class, which includes the addresses of functions in the base class. The method searches for the address of the original function in the base class's virtual function table. Finally, the address of the target function replaces the address of the original function in the base class's virtual function table. This avoids replacing the address of a function in the virtual function table that should not have been replaced with the address of the target function, thus preventing replacement errors and preventing the application from calling the wrong function, ultimately preventing application runtime errors.

This invention discloses a unified intermediate representation data model and construction method for hardware design, belonging to the field of electronic design automation. The model includes: a unified representation base class; structural relationship graph representation and temporal behavior representation, describing the structural composition and runtime behavior of the design, respectively; formal semantic consistency constraints to ensure semantic consistency between structure and behavior; and a semantic association architecture to establish bidirectional semantic mapping between various scenario representations such as HDL code, schematics, and waveforms and the unified representation base class. The construction method based on this model includes an incremental synchronization process, achieving automatic synchronization after any representation modification through change capture, cross-representation mapping indexing, and change propagation rules. This invention also provides an AI-assisted design method based on this model, improving AI interaction efficiency and the automation level of design verification through multi-dimensional context construction, syntax stripping, incremental interaction protocols, and automatic closed-loop verification.

The invention discloses a front-end component development method, front-end component development equipment and a medium. The method comprises the following steps: defining JSON (JavaScript Object Notation) metadata in a unified format; the method comprises the following steps: initializing a base class framework of metadata processing, and generating a BaseFramework base class instance; according to the BaseFramework base class instance, life cycle processing is carried out on JSON metadata, and application layer metadata is generated; according to a preset framework adaptation rule, creating a target framework adapter to convert the application layer metadata into a response type model adaptive to a target framework; and according to the BaseFramework base class instance, carrying out analysis rendering processing on the response type model, and generating a complete front-end page. Through the metadata base class and the adapter mode, the code quantity is greatly reduced, the code reusability is remarkably improved, and the front-end component development efficiency is greatly improved. Through a unified life cycle management and authority verification mechanism, the behaviors of the components under different frameworks are consistent, and the stability is enhanced.

The application relates to the field of PLC automatic control technology, in particular to a control program design method based on PLC, which comprises the following steps: S1, constructing a PLC control program module, wherein the PLC program control module comprises an execution layer; S2, obtaining the execution layer in the step S1, constructing a first base class FB_BaseAll and a second base class FB_Axis in the execution layer, and extending device classes of the first base class FB_BaseAll and the second base class FB_Axis. According to the application, the first base class FB_BaseAll and the second base class FB_Axis are constructed in the execution layer, and the device classes are extended based on the two base classes, so that the code reusability and the modularization can be improved, and the redundancy can be reduced and the maintainability can be improved.

This application specifically relates to a zero-shot target detection method based on a visual-language pre-trained model and category prototypes. The method includes: after extracting the region of interest (ROI) of the target to be detected, extracting region features from the ROI and inputting them into a pre-set visual pre-trained model to obtain a visual embedding vector; inputting the text information of the base class and novel class into a pre-set text pre-trained model to obtain a text embedding vector; constructing a category based on the classification scores of the base class and novel class; and generating a classification result for the target to be detected based on the alignment results of the visual embedding vector and the text embedding vector, and the similarity results between the ROI and the feature-constructed category. This solves the problems of high dataset annotation costs, high resource consumption, and a lack of optimization methods for the pre-trained model during pre-training, which reduce the accuracy of zero-shot target detection.