141 results about "Discriminator" patented technology

The invention discloses an electric power system transient stability evaluation sample enhancement method, system and device based on a timing constraint generative adversarial network and a medium, and belongs to the technical field of electric power system safety analysis, and the method comprises the steps: constructing a sample enhancement model, generating transient time sequence data based on random noise and system feature tags by a generator, the discriminator outputs a sample authenticity score and a time sequence consistency score through a time sequence neural network, the physical constraint module calculates physical constraint loss of the generated data, the model is alternately trained in combination with multi-dimensional loss, the trained generator is utilized to synthesize samples, and qualified samples are screened through physical verification. According to the invention, by introducing the LSTM time sequence constraint, the WGAN-GP stable training mechanism and the multi-dimensional physical consistency constraint, three core problems of poor time sequence coherence, modal collapse and physical law violation in the existing sample enhancement method are cooperatively solved, and high-quality transient scarce samples can be generated.

The invention discloses a double-channel confrontation and progressive self-training driven cross-working-condition open set domain adaptive fault diagnosis method. In order to suppress an interactive negative migration effect, a three-stage two-way adversarial progressive self-training network DAPN is designed, separation of unknown target samples and feature aggregation of known samples are realized through cooperation of a gradient inversion layer and a two-way adversarial discriminator, and inter-class discrimination and intra-class aggregation are obviously enhanced. When the performance of the DAPN is evaluated in two open set domain adaptive scenes, for open set proportions of different intensity domain offsets and changes, the method always keeps high classification accuracy and unknown class detection rate, and the performance is obviously superior to that of a comparison baseline. According to the method, two-way confrontation and a progressive self-training mechanism are combined, so that accurate classification of known faults and effective identification of unknown faults are realized.

The invention relates to a mechanical arm control method and device, computer equipment and a readable storage medium. The method comprises the steps of generating a plurality of candidate trajectories for a target task through a target diffusion strategy model; the target diffusion strategy model is obtained by training an original diffusion strategy model based on collected example data; evaluating and screening the plurality of candidate trajectories according to a single-step value discriminator to obtain an optimal trajectory in the plurality of candidate trajectories; the single-step value discriminator is a multi-modal feature regression model constructed based on a target diffusion strategy model; and the target mechanical arm is controlled to execute the target task according to the optimal track. By the adoption of the control method and device, the control accuracy and safety of the mechanical arm can be improved.

Approaches presented herein provide for the use of reinforcement learning to fine-tune a generative model, such as a motion diffusion model, for a specific objective, such as to generate representations of human motion corresponding to provided text input. A discriminator can be used to guide the training of the generative model. In at least one embodiment, the discriminator can compare the input text and generated motion representation (or embeddings of each) to determine an alignment value or match score, for example, which can then be used to adjust the network parameters or weights of the generative model to improve the alignment between input text and generated motion.

The invention relates to a world model simulation system training method and system based on real record constraint and condition deduction, computer equipment and a medium, and the method comprises the steps: extracting a time-space anchor point of a long-tail event from real road test data, and marking behavior fingerprints for traffic participants in the time-space anchor point, so as to generate a time-space anchor point tensor; constructing a world model simulation system, wherein the model comprises a constraint module, a deduction engine and a discriminator; the constraint module is used for coding the traffic participant state and the space-time relationship in the space-time anchor tensor into a causal constraint embedding sequence; the deduction engine is used for deducing to obtain a predicted scene state according to the causal constraint embedding sequence and the vehicle control action sequence; the discriminator is used for generating a rationality score for the predicted scene state; and performing full constraint training, mixed constraint training and free deduction verification training on the world model simulation system in sequence. According to the method, closed-loop deduction training of the automatic driving strategy under real data distribution can be realized, and the problems of distribution offset and causal fracture in long-tail scene simulation are solved.

A computer-implemented method for generating synthetic data is provided. The method includes receiving user input specifying domain-specific requirements for synthetic data generation and selecting a scenario type. The scenario type is one of Seedless, Seeded, or combination of Seeded and Knowledge Base (KB). The method defines a structured schema based on the user input. The structured schema includes data fields, relationships between data fields, and distributional targets. Based on the structured schema, the method generates an initial set of synthetic data samples using a neural template-driven generation model trained on domain-specific data. The method applies adversarial contrastive sampling. This involves training a discriminator neural network to distinguish between the initial set of synthetic samples and real data samples. The discriminator neural network is used to identify generated samples similar to real data. A contrastive set of samples dissimilar to those identified as similar by the discriminator is generated. The method integrates the initial set of synthetic data samples with the contrastive set to create the synthetic data.

The invention relates to the technical field of data privacy protection and artificial intelligence crossing, in particular to a differential privacy and contrast learning fused data synthesis method, which comprises the following steps: S1, data acquisition and clustering: acquiring a real training data set, and clustering the data set by using a DBSCAN algorithm; s2, establishing double discriminators: designing a double discriminator framework comprising a privacy discriminator and a utility discriminator, injecting adaptive differential privacy noise based on a clustering structure into gradient updating by the privacy discriminator to realize privacy protection, and focusing on keeping the quality and authenticity of generated data by the utility discriminator. Precise balance between privacy protection and data utility is realized through a double-discriminator architecture. The privacy discriminator focuses on differential privacy constraints to ensure that the generated data meet strict privacy requirements; the utility discriminator restrains the data distribution consistency through the Wasserstein distance, effectively reduces the damage of the noise to the data utility, and solves the problem that the privacy and the utility are difficult to consider in the prior art.

The invention discloses an LGR-GAN-based power system instability scene generation method, system and device and a medium, and belongs to the technical field of artificial intelligence in power systems, and the method comprises the steps: obtaining time sequence data of a power system, and carrying out the data preprocessing; constructing a generative adversarial network, carrying out adversarial training on the generative adversarial network by utilizing the preprocessed time sequence data, and stabilizing a training process by adopting a loss function containing a gradient penalty term in training; and generating new power system time sequence data by using the trained generative adversarial network. According to the method, the defects of training stability, feature capture comprehensiveness and physical compliance in the prior art are overcome. According to the invention, a long-short-term memory network and a gating circulation unit are embedded by constructing a framework in which the generator and the discriminator cooperate with each other, so that full-scale accurate description of long-range evolution trend and short-term mutation details in the power transient process is realized.

The invention discloses a control method and device of a computing power demand prediction system and a storage medium, and relates to the technical field of system resource management, and the method comprises the steps: splicing input condition features and random Gaussian noise, and carrying out the fusion of the condition features and random Gaussian noise, and obtaining a high-dimensional hidden space representation; performing sequence decoding on the high-dimensional hidden space representation, arranging according to a decoding sequence, and determining a resource demand prediction sequence in a future time period; based on the resource demand prediction sequence and a real load sequence, calculating weighted loss according to a preset quantile, and outputting a composite prediction error value; extracting long-range features of the resource demand prediction sequence and the real load sequence, and obtaining a scalar score through feature scaling processing; and performing weighted fusion on the composite prediction error value and the scalar score to construct a joint optimization target so as to alternately update a generator and a discriminator and iteratively generate a prediction generator model. The problem of low resource utilization rate is solved, and the server utilization rate is improved.

The invention discloses a robot generative adversarial self-imitation learning method based on large language model feedback, and belongs to the technical field of computer systems based on specific calculation models, and the method comprises the steps: (1) initializing a strategy network, a discriminator network and a demonstration pool; (2) constructing and training a large language model feedback network: obtaining a training sample of the large language model feedback network, and training the large language model feedback network according to the training sample; and (3) an iterative learning step (33): updating the strategy network according to the reward calculated by the new cost function. According to the robot generative adversarial self-imitation learning method based on large language model feedback, the advantages of the large language model in the aspects of semantic comprehension, reasoning and knowledge organization are used as a potential award priori source, and a stable award signal can be provided when the demonstration data quality is poor, so that a strategy exceeding original demonstration is learned.

The invention discloses a flexible job shop scheduling method based on a generative adversarial training framework. The method comprises the following steps: establishing a Markov decision process model for flexible job shop scheduling, and completing the design of a state space, an action space and a reward function; collecting expert scheduling tracks through a plurality of algorithms, and carrying out data cleaning and standardization processing; constructing a state encoder based on a graph attention network, mapping a scheduling environment state into low-dimensional vector representation, and designing a hierarchical strategy network to decompose a scheduling decision task; constructing a value network to provide stable value estimation so as to accelerate a reinforcement learning process, and constructing a discriminator network to guide a strategy search direction by generating an imitation reward; and finally, hybrid training based on near-end strategy optimization and generative adversarial imitation learning is executed, and through adversarial training and strategy gradient optimization, an intelligent agent learns to obtain a high-performance scheduling strategy with expert empirical performance and environment adaptability.

The application discloses an inertial data position conversion method and system based on a noise codebook and multiple physical discriminators, and the method comprises the following steps: acquiring a target position of a target device and first inertial data; performing multi-dimensional data enhancement processing on the first inertial data to obtain second inertial data; constructing a position-adaptive first noise codebook according to the second inertial data; performing extraction operation on the second inertial data through a decoupling generator to obtain motion semantic features and position noise features; obtaining a target noise code word according to the first noise codebook and the position noise features; and obtaining target inertial data of the target position through a gated fusion mechanism according to the target noise code word, the motion semantic features and the position noise features. The application can improve the robustness of inertial data and can be widely applied to the technical field of inertial data processing.

The present application provides a method for bearing fault diagnosis across working conditions by using a minimum entropy optimized prototype contrast network, and relates to the technical field of intelligent fault diagnosis. In the pre-training stage, an auxiliary domain discriminator is constructed to assist DA with the discriminant information of the classifier, the classification difficulty is evaluated by sample entropy, and the performance degradation in the DA process is inhibited. In the training stage, the learning vector quantization method is adopted to find the prototype. Through intra-domain prototype contrast learning, the sample features are closely gathered around the same prototype in the feature space, while being separated from the different prototypes. Then, the intra-class consistency of the features is enhanced, and the inter-class distinguishability is improved, so as to realize the precise alignment in the feature space. In addition, the cross-domain instance-prototype learning aligns the semantic structure in the shared embedding space, alleviates the negative transfer problem through the fine-grained alignment strategy, and improves the generalization ability of the model. Through the pseudo-label generation and the weighted loss function, the generalization performance of the model in the cross-working-condition small sample scene is improved.

The approaches presented here provide the use of reinforcement learning to fine-tune a generative model, such as a motion diffusion model, for a specific goal: to generate representations of human motion that correspond to the provided text input. A discriminator can be used to guide the training of the generative model. In at least one embodiment, the discriminator can compare the input text and the generated motion representation (or embeddings of both) to determine, for example, an alignment value or match score, which can then be used to adjust the network parameters or weights of the generative model to improve the alignment between the input text and the generated motion.

The invention discloses a catalytic cracking regeneration process parameter optimization method based on two-way adversarial training of a GAN framework, and the method comprises the steps: constructing a data-driven prediction model and a mechanism-driven prediction model, executing the two-way adversarial training under a generative adversarial network framework, enabling the two models to take the roles of a generator and a discriminator in turn, and achieving the mutual game and collaborative optimization, the data driving model learns to generate parameters conforming to physical laws through physical constraint verification of the mechanism model, the mechanism driving model is fused into actual production optimal practice through historical experience verification of the data model, and a fusion optimization model is generated and applied to process parameter control of the regenerator of the catalytic cracking device. Efficient recovery of catalyst activity and effective reduction of regeneration energy consumption are achieved, and the technical problems that a traditional method depends on artificial experience, optimization precision is not high, a data model lacks physical constraints, and a mechanism model is difficult to adapt to complex working conditions are solved.

The present application relates to the technical field of aviation safety and artificial intelligence, in particular to an ADS-B time series data adversarial sample generation method based on improved WGAN-GP, comprising the following steps: obtaining ADS-B historical flight data, extracting the longitude, latitude, height, speed and heading five-dimensional features to construct a time series data set; building an adversarial sample generation model based on an improved Wasserstein distance generative adversarial network, and configuring a multi-objective composite loss function for the generator; the present application builds a generator and a discriminator based on LSTM, designs a composite loss function that integrates adversarial loss, concealment loss and time series smoothness loss, adopts gradient penalty and asymmetric update strategy to train the model, and the generated adversarial sample has high concealment and physical consistency, can effectively avoid various unsupervised anomaly detection models, the model training is stable, and technical support is provided for building a safer air traffic control defense mechanism.

To control a device group for executing the same task with a simpler configuration.SOLUTION: The control apparatus 1 performs adversarial training of the generative model including the generator 121 that generates pseudo action data similar to true action data as the true action data for actions sequentially executed by each device in the device group to transition from one state to the next state at each time, and the discriminator 122 that discriminates between the pseudo action data generated by the generator 121 and the true action data, in the trained recurrent neural network constructed by the training of the first learning unit 11.SELECTED DRAWING: Figure 1

The invention discloses a multi-dimensional time sequence anomaly detection method and device based on an adaptive generative adversarial network, and a storage medium, and belongs to the technical field of artificial intelligence and data security. The core of the method is to construct an adaptive generative adversarial network comprising a signal reconstruction generator and a signal filtering discriminator. The generator adopts an LSTM (Long Short Term Memory) and Transform mixed encoder, effectively fuses short-term local features and long-term global dependence of a time sequence, and realizes high-precision reconstruction of a normal data mode. The discriminator innovatively integrates an adaptive threshold filtering (ATF) module, can automatically recognize and filter potential abnormal samples in training data before training, and reduces the influence of abnormal pollution. Meanwhile, a self-adaptive dynamic weighting loss function is designed, and the weight of a training sample is dynamically adjusted, so that a generator focuses on learning of a high-confidence normal sample. The method has the beneficial effects that the robustness of the model in a training set impure scene is remarkably improved, the problems that in the prior art, complex time sequence dependency relationship capture is insufficient, and the method is sensitive to abnormal pollution are solved, experiments on a plurality of public data sets show that the detection precision F1 and stability of the method are superior to those of an existing mainstream method, and the method is suitable for popularization and application. The method is especially suitable for industrial equipment monitoring, network security and other complex real scenes. The invention further correspondingly provides electronic equipment and a computer readable storage medium for implementing the method.

The invention discloses an electric power system control method and system based on adversarial training and terminal equipment thereof, and belongs to the field of electric power systems. Updating the discriminator based on the generated track set; obtaining a current state-action value function; updating the generator based on the approximate expert strategy probability and the current state-action value function; when the preset training target is not met, repeatedly executing an interaction action based on the current generator and the current discriminator, and obtaining a correction generator and a correction discriminator which meet the preset training target; obtaining a Markov agent based on the correction generator and the correction discriminator; actual operation data of the power system are obtained in real time, and a prevention and control strategy of the power system is obtained based on the Markov agent and the actual operation data. According to the adversarial training-based power system control method and system and the terminal equipment thereof disclosed by the invention, efficient and stable acquisition of a prevention and control strategy in dynamic operation of the power system is realized.

The invention discloses a training method and training device for a bearing lubrication fault data generation model, and relates to the technical field of rolling shaft intelligent fault diagnosis, and the method comprises the following steps: collecting a target domain real sample and a source domain real sample; constructing feature consistency loss based on the features of the source domain real sample, the source domain reconstruction sample, the target domain real sample and the target domain reconstruction sample extracted by the first discriminator and the second discriminator, and constructing style invariance classification loss based on the pseudo target domain sample and the corresponding real label; and taking the sum of the confrontation loss, the cyclic consistency loss, the identity loss, the feature consistency loss and the style invariance classification loss as the total loss, and training the generative model based on the total loss to obtain the trained generative model. According to the method, by introducing feature consistency loss and style invariance classification loss constraints, high fidelity of key fault features in the style migration process is ensured, and the quality of generated pseudo samples is higher.

The embodiment of the application provides a motion trajectory generation method and device based on imitation learning, equipment and medium, belonging to the field of artificial intelligence. The method comprises the following steps: mixing the synthetic samples and the preset real samples to obtain mixed samples; performing trajectory discrimination on the target samples in the mixed samples by a target trajectory discriminator to obtain adversarial reward data, performing value discrimination on the target samples by a target value discriminator to obtain state value data, and calculating target reward data according to the adversarial reward data and the state value data; updating the network parameters of the original action generation network according to the target reward data to obtain a target action generation network; and performing action generation on the preset target environment state through the target action generation network to obtain a target motion trajectory generation action, and performing motion trajectory generation through the target motion trajectory generation action, so that the accuracy of the motion trajectory generation can be improved.

The invention provides a machining path planning method and system based on artificial intelligence, and the method comprises the steps: firstly, generating a plurality of groups of spiral tool path trajectories through employing a finite element method in combination with an interpolation algorithm, and constructing a training data set; secondly, building a generative adversarial network composed of a generator and a discriminator, and training to obtain a spiral machining path generation network capable of generating a spiral machining path meeting the requirements of a machining area in a self-adaptive manner; then, an open source CNC speed planning module is used as a reinforcement learning environment, a machining path generation network and a value network are used as intelligent agents, and under the constraint of the maximum speed, acceleration and step, the generation network is optimized through iterative training, so that the aim of the shortest machining time is achieved; and finally, outputting the optimal path to CNC equipment to execute machining. According to the method, the path generation adaptability and the constraint adaptability are improved, the machining time is shortened compared with a traditional method, and the cavity machining efficiency and the intelligent level are remarkably improved.

The application discloses a kind of based on semi-supervised learning's inquiry exception detection method, 1) preparation is used for foreign trade inquiry detection training and test data, the data includes normal inquiry data, junk inquiry data;2) data preprocessing, step includes noise word morph transformation, sample expansion;3) loading Bert-Large model, the Embedding of text is extracted;4) setting generator and discriminator model architecture: define generator Generator, Discriminator model structure and loss function;5) loading Generator, Discriminator model, using normal inquiry vector data, train model;Just can test the accuracy of model.

The embodiment of the invention provides an intelligent driving data compression method and device, a vehicle, a medium and a program product, and the method comprises the following steps: obtaining a real data set and a coding vector set, the real data set comprising a plurality of real samples of intelligent driving data, and the coding vector set comprising a plurality of coding samples of coding vectors; the generative adversarial network is iteratively trained by utilizing the real data set and the coding vector set, the generative adversarial network comprises a generator and a discriminator, the discriminator is trained by utilizing a real sample in the real data set, the generator is trained by utilizing a coding sample in the coding vector set, and parameters of the generator and the discriminator are updated by utilizing loss in the training process. The total training loss of the generative adversarial network is less than the target loss; and compressing the intelligent driving data by using the trained generator. Therefore, the problems that the compression rate is low, key information is easy to lose, the integrity and availability of data cannot be ensured and the like when the intelligent driving data is compressed in the prior art are solved.

The invention relates to a training method and device of a network attack detection model, computer equipment, a medium and a product. The method comprises the following steps: constructing an original traffic feature according to current network data, constructing a detection model, generating an action feature vector according to the original traffic feature through a reinforcement learning agent, screening from the current network data through the action feature vector to obtain first training data, on the basis of the first training data and second training data output by the generator, performing multi-round alternate training on the detection model; wherein each round of alternate training comprises training the discriminator based on the first training data and the second training data and training the generator based on a judgment result of the trained discriminator on the second training data. By adopting the method, the detection accuracy can be improved.

This invention discloses a method and system for generating adversarial examples based on a self-attention mechanism, comprising: constructing a generator model using a convolutional neural network; constructing a multi-convolutional discriminator model; designing a loss function, wherein the loss function consists of a misjudgment loss function, an adversarial loss function, and a hinge loss function; the misjudgment loss function is used to guide the generator model to train in the direction of adversarial examples, the adversarial loss function is used to maintain the dynamic balance between the performance of the generator and the discriminator, and the hinge loss function is used to limit the size of the adversarial perturbation output by the generator model; under the guidance of the loss function, the generator model and the multi-convolutional discriminator model are alternately optimized and adversarially trained using a training dataset, and after the performance of the generator model and the multi-convolutional discriminator model reaches a dynamic balance, the trained generator model is saved; the original sample is input into the trained generator model to generate adversarial perturbations, and the generated adversarial perturbations are superimposed with the original sample to obtain adversarial examples.

The application provides a transmission high-low temperature cycle endurance test method and system, which comprises the following steps: collecting basic parameters, key parameters and implicit parameters of the transmission at a physical layer through a dual-mode sensing unit, mapping a physical test state through a digital twin model at a virtual layer to generate a virtual sensing feature set, and corresponding integration into a dual-mode sensing feature tensor; calculating a dynamic adaptation degree between a test requirement feature matrix and the dual-mode sensing feature tensor in real time through a preset multi-field coupling feature discriminator, generating a three-dimensional composite feature map based on the dynamic adaptation degree; constructing a virtual-real linkage adaptive test framework according to the three-dimensional composite feature map; extracting target data from the dual-mode sensing data and the test requirement document and filling it into the corresponding analysis sub-column, synchronously passing through the coupling correlation path in the three-dimensional composite feature map to complete the corresponding endurance test. The application can comprehensively and accurately complete the endurance test, and the test efficiency is improved.

An example computer system for providing countermeasures for a ransomware attack can include: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to generate a key by to: create a salt using artificial intelligence; form a data section by the salt and an original key; and form a dummy section to fill out a length of the key.

The invention belongs to the field of power supply distribution optimization, and particularly relates to an adversarial learning-based power supply distribution unit multi-terminal cooperative correction system, which comprises an acquisition module for realizing differential data acquisition and anomaly recognition through an enhanced acquisition adjustment model, and a preprocessing unit for performing noise reduction and sparse processing by adopting a distributed filtering model under a federated framework; the self-calibration module predicts temperature change through a distributed LS-SVR generator, predicts voltage and current drift amount by combining a BP neural network discriminator of particle swarm optimization, performs adversarial training by using a comprehensive adversarial loss function, and dynamically adjusts a real-time demand distribution coefficient to enable the deviation between the real-time demand distribution coefficient and a true value to meet a threshold value; according to the invention, the collection efficiency, the correction precision and the adaptability to the dynamic change of the complex load are remarkably improved.

The invention discloses an atomic problem generation and selection method and system based on mutual information distillation, and a computer medium, and belongs to the technical field of information retrieval and natural language processing.The scheme comprises the steps that a mutual information discriminator is trained based on comparative learning to quantify the information association strength of problems and content blocks; generating diversified candidate questions by using a large language model; selecting an overall optimal subset from the candidate problems through a sub-modulus optimization selection algorithm on the basis of a mutual information estimation value and considering the problem coverage degree; and finally, by adopting a negative alignment filtering mechanism, through vector database retrieval and large language model discrimination, actively identifying a generalization problem lacking a determiner, and performing specificity enhancement, so that cross-content block retrieval confusion caused by fuzzy anaphora is avoided. According to the method, the high-quality and high-specificity atomic problem index is constructed for a retrieval enhancement generation (RAG) system, the accuracy of the retrieval system is remarkably improved, false detection is reduced, and the user experience is improved.