137 results about "Discriminative model" patented technology

The invention discloses a penetration discrimination method and system suitable for a double-layer dam foundation, and the method comprises the steps: S1, obtaining the geological parameters of a double-layer dam, and forming a geological parameter set; s2, constructing a seepage numerical model of the double-layer dam foundation, simulating the seepage field distribution condition of the double-layer dam foundation at each grid node, and obtaining seepage field data; s3, grid node seepage force is calculated, and a potential seepage failure area is determined; s4, monitoring pore water pressure and seepage flow changes of the preset monitoring points in the potential seepage failure area; s5, constructing a seepage failure judgment model, and outputting a seepage judgment result; and S6, performing grading processing according to the judgment result of seepage failure, and outputting grade seepage early warning information through a seepage grading model. According to the method, judgment, real-time monitoring and graded early warning of seepage damage of the double-layer dam foundation are achieved, and the safety of the heterogeneous double-layer dam is guaranteed.

The invention discloses a structure semantic prompt constraint-based text image generation method, which comprises the following steps of: taking a generative adversarial network as a generative model of a main body, and respectively learning and updating the generative model and a discrimination model by utilizing an existing sufficient training data set so as to finish model updating of the model and a training model; and on the basis of the generation model which completes model updating, a new image is generated by inputting the description text, so that the purpose of text image generation is achieved. According to the method, the consistency of the text and the image is remarkably enhanced while the image synthesis quality is improved. In order to reduce the dependence of the model on the training batch size and the training round, improve the training efficiency and optimize the resource demand, the difficult-to-load sample mining is introduced into the image generation task for the first time, and the difficult-to-load sample mining matching perception loss is proposed, so that the model can concentrate on the most challenging sample, and the image generation efficiency is improved. In practical application, good performance can be obtained even in an environment where computing resources are limited.

A training method and apparatus for a generative model. The training method comprises: acquiring a first training sample which comprises a first initial image and first information; inputting the first training sample into a generative model for model processing to obtain a first target image carrying steganographic information; inputting the first target image into a pre-trained discrimination model to obtain a first visibility score, wherein the first visibility score indicates the visibility of the steganographic information; and with the objective of reducing a first loss, adjusting parameters of the generative model, wherein the first loss is positively correlated to the first visibility score.

The invention relates to the technical field of voice processing, can be applied to business scenes such as financial science and technology, medical health and the like, and discloses a data tracing method, device, equipment and medium based on cooperative training, which comprises the following steps: obtaining a generative model and a discrimination model to output initial intermediate feature representation, generating voice data and inputting the voice data into the discrimination model to generate discrimination loss; updating the generation model based on the discrimination loss to obtain a traceable intermediate feature representation, generating an updated intermediate feature representation by using the updated generation model, generating training voice data, and inputting the training voice data and real data into the discrimination model for training to obtain an updated discrimination model; and when input voice data is received, the updated discrimination model is used for discriminating and outputting a detection result whether the input voice data is generated by the generation model or not. According to the invention, through cooperative training of the generative model and the discrimination model, the generative model outputs voice embedded with traceable features, the rejection capability of the discrimination model to unknown sources is improved, and waveform disturbance reduction and accurate source determination are realized.

The invention discloses a data security management method and system based on a cloud computing platform, and relates to the technical field of data security management, and the method comprises the steps: collecting original data of the cloud computing platform in real time, and carrying out the preprocessing; constructing a discrimination model based on a generative adversarial network (GAN) to generate a forged data sample, taking the forged data sample and a real data sample as input data, optimizing the discrimination model by using the input data based on an extreme learning machine, and discriminating attack data; and constructing a security situation model by using a hidden Markov model, optimizing parameters through a cluster particle algorithm, and outputting a predicted security situation based on real-time attack data. A discrimination model is optimized through a generative adversarial network and an extreme learning machine, the discrimination precision of attack data is effectively improved, a security situation model is established through a hidden Markov model, model parameters are optimized in combination with a cluster particle algorithm, a predicted attack path is combined with a deep Q learning network, and the security risk of attack data is improved. And attack path prediction is carried out through the dual deep Q network, and a defense strategy is adjusted in real time.

The invention belongs to the technical field of compressor performance prediction, and particularly provides a method and system for determining a surge judgment critical value of a compressor based on multi-source data fusion. Comprising the steps that a compressor surge test system is built and operated, and a first surge critical value is calculated; obtaining multi-source historical data related to surge of the compressor, selecting characteristic variables to construct historical input vectors, and fitting the multiple regression model by using the historical input vectors to obtain a discrimination model; training the machine learning model to obtain a trained machine learning model; a real-time input vector is constructed based on data, collected in real time, of the to-be-monitored compressor, and then a second surge critical value and a third surge critical value are obtained; and fusing the first surge critical value, the second surge critical value and the third surge critical value to obtain a predicted surge critical value of the to-be-monitored compressor. According to the method, high-precision dynamic testing and historical data regression analysis are combined, and the actual value of the surge critical value is finally and quantitatively determined through multi-parameter coupling measurement and data mining.

A system connects a 5G telecommunications network to a smart device equipped with a camera. The smart device uses a discriminative model to create a textual description of the scene describing recognized objects and locally converts the textual description to textual data. The system receives the textual data from the smart device over the 5G network. After receiving the file, the system causes the smart device to delete the video. The system inputs the textual data into a remotely located large language model (LLM) and analyzes the textual data based on the textual description of the scene. The system generates a proposed workflow as an executable file based on the LLM's analysis, where the proposed workflow corresponds to commands for the smart device. The system transfers the executable file over the 5G network to the smart device and causes the smart device to execute the executable file.

The invention discloses a universal unstructured data index construction method and device and a medium, and is suitable for multi-modal data such as pictures, videos and texts. The method comprises the steps that adaptive deep learning models are adopted for feature extraction for different modal data; building a multi-level cluster structure based on a recursive clustering method of automatic anchor point selection and hyperplane division; automatically generating a semantic abstract for each cluster by using a large language model; an index structure is efficiently organized through information such as categories, clusters and abstracts, and multi-dimensional retrieval is supported; and during query processing, a discrimination model is trained at low cost by using a very small amount of existing labels in the clustering clusters, so that efficient clustering cluster screening and multi-class combined query are realized, and a user does not need to specify positive and negative samples. The method has the advantages of being high in automation degree, low in labeling cost, high in expandability, capable of supporting multi-modal data, unique in labeling inheritance and the like, and the index construction and retrieval efficiency of large-scale unstructured data is remarkably improved.

Provided are systems and methods that perform material classification of imagery using generative denoising diffusion models. Traditional material classification systems, which are predominantly based on discriminative models, face issues with generalization and diversity, particularly when encountering new or complex material compositions. Furthermore, these systems often function like black boxes, making it difficult to understand their decision-making processes or identify spurious correlations. The provided systems and methods address these challenges by leveraging the capabilities of diffusion models, which can provide better generalization and more transparent decision-making processes.

The invention relates to the technical field of debris flow disaster monitoring, and discloses a debris flow type discrimination model training method and device based on RFECV multiple models, computer equipment, a computer readable storage medium and a computer program product, and the method comprises the steps: S101, data acquisition and preprocessing; s102, carrying out feature processing; s103, data division is carried out; s104, carrying out sample unbalance processing; s105, preliminarily training the model; s106, optimizing a feature subset; s107, carrying out hyper-parameter range convergence; s108, performing hyper-parameter assignment; s109, training a candidate type discrimination model; s110, performing loop optimization judgment; and S111, outputting a discrimination result. According to the automatic feature optimization method based on RFECV, redundant features are effectively eliminated, and the interpretability and stability of the model are improved; a hyper-parameter adaptive adjustment mechanism is introduced, dynamic model optimization is achieved, training efficiency and generalization ability are enhanced, the problems that traditional classification depends on subjective experience, feature selection is difficult, and the model is unstable are solved, and efficient and accurate classification of debris flow types in the cold and cold mountainous area is achieved.

The invention relates to the technical field of computers, and discloses a chronic disease patient archive data analysis method and device, a storage medium and equipment, and the method comprises the steps: dividing training archive data into discrete variables and continuous variables, carrying out the processing of each class value in the discrete variables through one-hot coding, and carrying out the processing of each class value in the continuous variables through one-hot coding; a user-defined normalization method is applied; the discrete variables and the continuous variables after standardization processing are combined into condition vectors, two discrete variable combinations with equal probabilities are randomly selected, all combination probabilities are calculated, and sampling is completed; the generative adversarial network framework is used for training the generative model and the discrimination model for multiple times, the discrimination model is used for discriminating the generative model to obtain a trained generative model, synthetic data are generated, and a neural network used for patient feature analysis is trained to predict chronic diseases. According to the invention, the problem that complete analysis and privacy protection of archive data of chronic disease patients cannot be achieved at the same time in the prior art is solved.

The invention discloses a special equipment safety monitoring method based on the Internet of Things, and provides a multi-mode sensing data acquisition, noise removal, data standardization and label completion method. Rare abnormal samples are screened through unsupervised clustering and anomaly detection, and diverse pseudo-abnormal data are expanded through a conditional generative adversarial network. And deep representation learning, multi-modal feature mapping and migration fusion are further applied to construct a multi-modal collaborative abnormal feature set, and the multi-modal collaborative abnormal feature set is incorporated into an actively optimized security event discrimination model to realize dynamic rule adaptive evolution.

According to one embodiment of the present disclosure, the image restoration device comprises a memory that stores an original image captured by a camera and an artificial intelligence model and a processor that trains the artificial intelligence model, wherein the artificial intelligence model includes an image restoration model that crops the original image into preset patches and generates a restored image based on input data in which coordinate information of the patches is embedded into a cropped patch image and a discrimination model that Fourier-transforms the restored image generated by the image restoration model and distinguishes the Fourier-transformed restored image, and the processor is configured to perform adversarial learning of the image restoration model and the discrimination model and generate a restored image for a new original image captured by the camera from the image restoration model that has completed training.

Method, system and computer-accessible medium can be provided for generating one or more drug conjugates of one or more small molecules. For example, with such exemplary method, system and computer-accessible medium, a multimodal discriminative model can be trained to predict at least one peptide-ligand binding for one or more DNA ligands, a generative nucleotide model can be trained to generate a plurality of compounds. Further, a feedback can be provided from the multimodal discriminative model to fine-tune the generative nucleotide model so as to facilitate the generation of the drug conjugate(s).

The invention discloses an incremental sample screening method for power grid transient stability discrimination model training. The method comprises the steps of obtaining a basic sample set based on a plurality of initial operation modes of a power grid; obtaining a to-be-selected sample based on the generated power grid operation mode; calculating the distance between the to-be-selected sample and each basic sample in the basic sample set; constructing a neighbor sample subset of the to-be-selected sample based on the distance; calculating a neighbor distance between the to-be-selected sample and the neighbor sample subset; judging whether the to-be-selected sample is an incremental sample based on the neighbor distance; and training the power grid transient stability discrimination model by adopting the incremental sample. The screening of the incremental samples only depends on time domain simulation and distance calculation, the identification difficulty of the incremental samples can be greatly reduced, and the data requirement of the artificial intelligence model training for the transient stability discrimination of the power grid can be effectively reduced by identifying the key samples with important value for stability discrimination.

An encryption detection method includes performing a pre-analysis on a possibility of communication data being encrypted to obtain a pre-analysis result, calling, in response to the pre-analysis result indicating that the possibility is greater than a preset possibility threshold, a data encryption discriminative model to extract a data feature from the communication data, searching in a discriminative feature library for a matching prestored discriminative feature that matches the data feature based on a feature value of the data feature and discriminative feature values of a plurality of prestored discriminative features in the discriminative feature library, and determining, in response to the matching prestored discriminative feature being found, that the communication data is encrypted.

The invention discloses a geothermal fluid source discrimination method based on geochemical index and machine learning fusion, and relates to the technical field of geothermal resource intelligent exploration, and the method comprises the steps: firstly collecting and calculating multi-dimensional geochemical indexes, and constructing a standardized feature sequence; establishing an end member feature library by using unsupervised clustering; identifying the source type of the fluid through a discrimination model fused with an attention mechanism; aiming at the mixed source fluid, constructing an optimization model embedded with physical and chemical constraints, and quantitatively inverting the contribution proportion of each end member; and finally, on the basis of the time sequence proportion data obtained by inversion, predicting a future evolution trend by adopting a time convolutional network-long and short-term memory network model fused with an attention mechanism. According to the geothermal fluid source discrimination method based on geochemical index and machine learning fusion provided by the invention, the whole-process intelligent analysis of the geothermal fluid source from qualitative identification to quantitative prediction is realized.

The invention relates to the technical field of natural language processing, in particular to a method and system for optimizing large model questions and answers based on multi-scale fine-grained feedback, a candidate reply set is generated through multi-model collaboration, and then the large model questions and answers are obtained through a fine-grained judgment model of a multi-dimensional evaluation system including loyalty, integrity, conciseness and the like. The method comprises the following steps: performing structured scoring on each reply, generating a detailed scoring report, and finally, dynamically optimizing target model parameters by adopting a direct preference optimization algorithm and combining user-defined dimension weight distribution to enable an output result to adapt to different scene requirements. According to the method, through multi-model collaborative generation, fine-grained feedback learning and dynamic preference adaptation, the accuracy, reliability and scene adaptability of question and answer reply are remarkably improved, and the method can be widely applied to the fields such as intelligent customer service, medical consultation and education assistance which have strict requirements on answer quality.

The invention provides a discrimination method, a model training method and device, a discrimination system and a related product, and belongs to the technical field of artificial intelligence. The judgment method comprises the steps of calling a first judgment model to perform first judgment on a target agent based on target execution data under the condition that a judgment request for the target agent is received, and obtaining a first judgment result; under the condition that the first judgment result meets a preset screening condition, calling a second judgment model to perform second judgment on the target agent based on the target execution data to obtain a second judgment result; wherein the target execution data is execution data which is carried in the discrimination request and is used for representing that the target agent executes the target instruction, and the preset screening condition is used for determining whether to call a second discrimination model to discriminate the target agent again according to the confirmation degree of the first discrimination result. According to the embodiment of the invention, resource overhead and time delay can be reduced.

The present invention relates to the field of artificial intelligence technology, and more particularly to an artificial intelligence-based text classification method, apparatus, computer equipment, and medium. The method semantically extracts a content embedding vector of a content term, determines a distance evaluation value for each attribute term based on the term's position, concatenates the content term with each attribute term, and then inputs each into an association discrimination model. Based on the output, a semantic evaluation value for each attribute term is determined. Based on the distance evaluation value and the semantic evaluation value, associated terms of the content term are determined from all attribute terms. Associated terms encoded with the associated terms are added to the content embedding vector to obtain a target embedding vector. All target embedding vectors are concatenated and classified using a classification model. Associated terms of the content term are determined based on their position and semantics. Feature fusion is performed on the content terms and associated terms, so that the fusion result fully represents the semantic information of the tabular text, effectively improving the classification accuracy of the tabular text.

Exemplary embodiments of the present application provide a service analysis method and apparatus for a network device, an electronic device, and a storage medium. The method comprises: acquiring an application data sequence and a network data sequence from a service data sequence of a network device; on the basis of the application data sequence and a pretrained discriminative model, determining one or more first application events of an application on a user terminal and corresponding first time information; on the basis of the network data sequence and a pre-established first event determination rule, determining a first network event occurring in the user terminal and corresponding second time information; associating the one or more first application events with the first network event on the basis of the first time information and the second time information; and generating a service analysis result of the network device on the basis of the result of the association.

The invention discloses a method for constructing a stable state judgment model of a medium-voltage direct-current integrated power system. The method comprises the following steps of: making a data set from original simulation data; the invention discloses a construction method of a convolutional neural network model containing a time attention mechanism (TA) and spectral normalization (SN). In the training process, the neural networks with the same initial parameters and the structure are constructed and are respectively a first sub-network and a second sub-network; in the training process, a labeled training set is injected into the first sub-network to calculate cross entropy loss, a non-labeled training set is respectively input into the first sub-network and the second sub-network, consistency loss is calculated, the weight of the first sub-network is updated through back propagation of combined loss obtained after weighting of the first sub-network and the second sub-network, and the cross entropy loss is calculated. And then updating the weight of the second sub-network through exponential moving average. According to the method, dependence on labeled samples can be reduced, and after training is completed, the stable state of the system can be rapidly and accurately judged according to the bus voltage after large disturbance.

An embodiment of the present invention provides a training method, a video generation method, a device, an electronic device, and a readable storage medium for a video generation model. The method includes: obtaining a plurality of sample videos; constructing a generative adversarial network, which includes a generative model and a discriminative model; inputting the sample videos into the generative model to obtain predicted video frames; inputting the predicted video frames and the sample videos into the discriminative model to obtain a discrimination result; the discriminative model is used to determine whether the predicted video frames match the sample videos; training the generative adversarial network based on the discrimination results of each sample video until a training stop condition is met, to obtain a video generation model. According to the embodiment of the present invention, the accuracy of the trained video generation model can be improved.

A computer system is configured to remove motion artifacts in medical images using a generative adversarial network (GAN). The computer system instantiates the GAN having one or more generative network(s) and one or more discriminative network(s) that are pitted against each other to train a generative model and a discriminative model. The training uses a training dataset including a plurality of medical images that are previously classified as without significant motion artifacts for diagnostic purposes. The discriminative model is trained to classify medical images as real or artificial. The generative model is trained to enhance the quality of a medical image and remove motion artifacts by producing a medical image directly from a post-contrast image, without using a pre-contrast mask.

Provided is a mild cognitive impairment (MCI) screening method with which it is possible to easily screen for MCI with high accuracy in a short time, without using a large, complicated, expensive device, and without requiring specialized knowledge. Also provided are a computer program and screening device that are associated with said method.　A mild cognitive impairment screening method according to the present invention includes: a measurement step S1 for measuring a finger tapping motion that is an opening / closing motion of two fingers; a calculation step S3 for calculating a prescribed feature quantity on the basis of measurement data obtained in the measurement step within a prescribed time period during finger tapping of only the right hand and / or during finger tapping of only the left hand and / or during finger tapping of both hands simultaneously and / or during finger tapping of both hands alternately; and a determination step S5 for performing a determination regarding mild cognitive impairment on the basis of the feature quantity calculated in the calculation step S3. For the determination step S5, a discriminative model is generated (step S4) by machine learning including artificial intelligence, and the generated discriminative model is used to perform the determination regarding mild cognitive impairment.

The invention relates to a loyalty-driven large model preference optimization method, which comprises the following steps of: S1, acquiring question and answer data pairs containing question cues and reference text blocks; s2, inputting the question cue word into the to-be-optimized large model, and obtaining a model answer; s3, performing fine-grained splitting on the model answer to obtain a plurality of statement unit sets; s4, performing fact reasoning verification on the statement unit set by using a preset discrimination model and taking the reference text block as a basis; s5, calculating an answer loyalty score of the to-be-optimized large model according to a verification result of the statement unit; s6, judging whether the score is lower than a preset threshold value or not; if yes, a preference optimization data item is constructed; and S7, forming a training data set according to the preference optimization data items, and performing training update on the to-be-optimized large model. Through the automatic loyalty evaluation and preference data construction process, the illusion problem of the large model in the professional field is solved, and the reasoning credibility of the model can be improved without manual large-scale labeling.

A method, device, storage medium, and electronic device for generating a three-dimensional model view provided by the present invention are applied to the field of computer vision technology. The present invention obtains a target three-dimensional model; uses a pre-trained orientation discrimination model to adjust the orientation of the target three-dimensional model to obtain a target three-dimensional model with the adjusted orientation; based on the target three-dimensional model with the adjusted orientation, performs multi-view rendering on the target three-dimensional model according to preset parameters to obtain a view rendering result, where the view rendering result includes multiple views of the target three-dimensional model. The present invention adjusts the orientation of the target three-dimensional model through an automated technique and performs rendering with preset parameters based on this, thereby quickly and efficiently generating multiple views, thus supporting the processing of large-scale three-dimensional models, significantly improving the generation speed and shortening the rendering time to meet the needs of a large number of cultural relic digitizations.

The invention provides a text generation method, a training method and device of a generation model, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the technical field of deep learning, natural language processing and large models. According to the specific implementation scheme, the theme and the field of a to-be-generated text are determined according to input information; and in response to triggering of the first writing mode, generating a target text conforming to the first writing mode by using a generative model according to the theme and the field, the generative model being obtained by adversarial training with a discrimination model, and the discrimination model being used for discriminating the writing mode of the text generated by the generative model, the writing mode is one of a first writing mode and a second writing mode.

The embodiment of the invention provides a data processing method and device, equipment, a medium and a program product, and the method comprises the steps: obtaining a first sample data set which comprises M pieces of first sample data and N pieces of unlabeled data; performing active learning processing on the N pieces of unlabeled data according to an active learning mode to obtain labeled K pieces of second sample data; performing sample enhancement processing on the M pieces of first sample data and the K pieces of second sample data to obtain a second sample data set, and constructing a model training data set based on the second sample data set, the M pieces of first sample data and the K pieces of second sample data; and training the text discrimination model by adopting the model training data set. According to the method, active learning and sample enhancement can be performed on a large amount of unlabeled data through a small amount of labeled risk control data, so that richer business sample data is provided for model training, and the accuracy of a text discrimination model is improved.

The invention discloses an identification method, device and equipment for predicting a drilling operation state by jointly using deep learning and a tree model, and a medium, and the method comprises the steps: carrying out the mathematical transformation and analysis of obtained time parameters related to drilling operation, and screening out a drilling parameter feature set closely related to the drilling operation state; obtaining an autoregression model and a multivariate classification model which are connected in series as an operation state discrimination model; the screened drilling parameter feature set is input into an operation state judgment model, and drilling operation parameters of the next time period are predicted through an autoregression model; the multivariate classification model is used for judging drilling operation states at current and future moments. The method can be widely applied to the field of petroleum drilling engineering and drilling operation sites.