1089 results about "Random noise" patented technology

The invention discloses a user data intelligent protection method and system based on differential privacy, and belongs to the technical field of data protection, and the method specifically comprises the steps: collecting and preprocessing user health data; setting a dynamic privacy budget according to the data sensitivity, the analysis purpose and the privacy demand, and adding random noise to generate disturbance data by adopting a multi-strategy fused data disturbance strategy; statistical analysis is carried out, and an improved differential privacy algorithm is used to assess the privacy leakage risk, the protection degree and the data availability; based on an evaluation result, a privacy budget and a disturbance strategy are iteratively adjusted through a feedback loop mechanism; the health data analysis method provided by the invention not only can protect user privacy, but also can ensure data availability.

The invention relates to the technical field of vibration monitoring, in particular to a slurry pump motion monitoring management system based on data analysis, which comprises a multi-source acquisition module, a period judgment module, a trend focusing module and a dynamic management and control module. According to the method, vibration data are collected through a three-axis acceleration sensor, a fundamental frequency amplitude is extracted through FFT, time-frequency characteristics and periodic distribution are fused in combination with an extreme value interval sequence, the data representation dimension is enhanced, the rising slope is processed through moving average, the periodic variation is calculated, random noise is restrained, and trend continuity is enhanced. Monotonicity test is combined with dynamic threshold judgment to improve anomaly recognition sensitivity; a sliding window is used for segmenting multi-cycle data; a sudden change interval is positioned based on a pressure amplification rate second derivative; the limitation of a frequency domain on transient feature recognition is broken through, a pressure amplification rate three-dimensional vector is constructed, cosine similarity is calculated, and a working condition difference degree is quantified to dynamically bind a maintenance instruction; and closed-loop feedback is formed to improve the early warning and decision matching degree.

The invention relates to the technical field of cores, and discloses a method and a system for realizing USB flash disk equipment interaction based on a flash memory encryption technology, which comprises the following steps of: dividing an encryptable data block of a flash memory controller, and determining a dynamic entropy weight of the encryptable data block by utilizing an equipment interaction instruction and a random noise characteristic signal; generating a self-adaptive key generation sequence capable of encrypting data blocks through an address allocation mode and a dynamic entropy weight of a flash memory controller; calculating a side channel leakage risk index of the flash memory controller, and setting a security level label of an encryptable data block in combination with a self-adaptive key generation sequence; the method comprises the following steps: setting a differentiated security protection mechanism of an encipherable data block by constructing an access control matrix of the encipherable data block and an encryption risk area of a flash memory controller; and generating a secure interaction scheme of the USB flash disk equipment based on the self-adaptive key generation sequence in combination with the differential security protection mechanism and the access control matrix. According to the invention, the interaction safety and reliability of the USB flash disk equipment can be improved.

The disclosed embodiments disclose a video generation method, device, electronic device and storage medium, which obtain a starting frame image and an ending frame image of a target video to be generated, extract a first potential feature of the starting frame image and a second potential feature of the ending frame image, extract a first optical flow between the starting frame image and an intermediate frame image to be generated in the target video, and a second optical flow between the ending frame image and the intermediate frame image, distort the first potential feature based on the first optical flow, distort the second potential feature based on the second optical flow, fuse the distorted first potential feature and the distorted second potential feature to obtain a first distorted feature of the intermediate frame image, splice the first potential feature, the first distorted feature and the second potential feature with random noise respectively, and input them into a diffusion model for denoising to obtain a target video, thereby improving the quality of video generation.

The invention discloses a CGAN algorithm-based extreme weather scene generation method, medium and system, and the method comprises the steps: S1, obtaining historical data, so as to extract and obtain meteorological indexes and power grid operation state indexes under different types of extreme weather; s2, establishing a statistical characteristic index of a risk scene, screening out extreme weather influencing the safety and stability of the power grid according to the statistical characteristic index based on the meteorological index and the power grid operation state index, and forming a training set; s3, coupling the convolutional neural network and a Transform self-attention mechanism to establish a conditional generative adversarial network model capable of realizing extreme weather scene generation; and S4, respectively inputting the training set and the random noise into a generator and a discriminator of the conditional generative adversarial network model, and realizing extreme weather scene generation. According to the extreme weather scene generation method, the convolutional neural network and a Transform self-attention mechanism are fused, and the automatic feature extraction capability of the model is remarkably improved.

The invention discloses a titanium alloy microscopic structure prediction method and system based on a conditional generative adversarial network and a storage medium, and belongs to the following steps: firstly, constructing a process-structure mapping model, and taking the output of the model as a rule constraint condition; inputting the random noise vector and the rule constraint condition into a conditional generative adversarial network to generate a prediction image; according to the generative adversarial network, thermal dynamic constraints based on physical quantities of microscopic structures are introduced in the training process, so that the interpretability of a prediction result is improved. And carrying out quantitative comparison on the predicted image and the real image, verifying the consistency of the statistical characteristics, and if the verification is passed, outputting a prediction result. According to the method, end-to-end prediction from process parameters to microscopic structure images is realized, the limitation that only symbolization or parameterization prediction can be carried out in a traditional method is broken through, and the intuition, the interpretability and the engineering application value of the method are remarkably enhanced.

The invention provides a zero sample time sequence prediction method and system based on an adversarial neural network, and the method comprises the steps: constructing a time sequence generation module, training the generation module to stable convergence through obtained source domain data, and inputting collected random noise to generate a pseudo target sequence; constructing a time sequence prediction module, taking the pseudo sample sequence as a training set of the prediction module, and inputting the pseudo sample sequence into the prediction module for decomposition prediction; comparing the synthetic sequence with the output of the prediction module to obtain a prediction error, and feeding back the prediction error to the generation module to update generator parameters; through multiple rounds of generation-prediction-feedback closed-loop training, the obtained zero-sample linear architecture prediction model can effectively predict a corresponding future time sequence trend based on a synthetic pseudo sample under the condition that a target domain has no historical data completely. According to the method, data can be generated and self-training can be completed under the condition of no real data input, so that the quality of the generated data and the performance of the prediction model are synchronously improved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing differential privacy. One of the methods includes initializing a budget recycling-differential privacy framework; receiving overall privacy parameters for generating random noise values to provide differential privacy to a generated computation result; determining a portion of the overall privacy parameters to allocate to a differential privacy mechanism; determining a recycling probability to allocate to a recycling mechanism; and generating a noisy result for an input computation result according to the allocated portion of the privacy and the recycling probability.

Techniques for executing privacy-preserving aggregation queries on a database table include receiving a query, obtaining the true output, and generating deterministic pseudorandom noise based on the query and current database state. This noise is added to the true output to create a privacy-protected result. For subsequent queries, this process is repeated, potentially with updated state data, ensuring that privacy protection adapts to changes in the underlying data. The approach maintains consistency for repeated queries on unchanged data while providing fresh noise when data changes. It balances privacy protection with data utility, allowing for various query types while guarding against privacy attacks. The techniques include displaying the noisy output to a user interface. The techniques offer adaptive privacy protection, query flexibility, and efficient resource utilization, making them suitable for dynamic data environments requiring both privacy and analytical capabilities.

The invention discloses a spatial multiplexing acoustic hologram design method based on a physical model neural network. Parameters are determined according to a target spatial multiplexing design scene, a sound wave propagation model and a deep learning model are established, the input of the deep learning model is a random noise image, the output of the deep learning model is a transducer array selection area graph and a phase plane graph, and the sound wave propagation model and the deep learning model are combined to establish an overall model to be combined with a target sound field graph for training; and after training, obtaining a transducer array selection area graph and a phase plane graph. According to the method, the problem that artifacts exist in multichannel information due to lack of global optimization in a traditional angular spectrum iterative design method is solved; the method overcomes the defects that an existing deep learning method consumes time and labor and is poor in robustness, and specific parameters cannot be designed according to the design requirements of the target holographic sound field when a data set is constructed, optimization design can be carried out on multiple target holographic sound fields at the same time, and any holographic sound field can be designed in a self-adaptive mode.

The invention discloses a combined heat and power generation system control strategy based on an AME-TD3 algorithm, and belongs to the technical field of combined heat and power generation system optimization control. Comprising the following steps: S1, collecting system parameters and initializing a CHP running state; s2, when a target # imgabs0 # value is calculated, an entropy reward mechanism is introduced; s3, realizing exploration and utilization of an adaptive noise adjustment mechanism and a dynamic balance strategy based on Critic network evaluation; and S4, optimizing the control strategy in real time. Aiming at the problem that an existing TD3 algorithm is insufficient in exploratory performance, an entropy correction item is introduced into calculation of a target # imgabs1 # value, structural correction is conducted on a target value function, strategy updating is more efficient, and the effectiveness of overall strategy optimization is improved; aiming at the problem that random noise in a traditional TD3 algorithm cannot adapt to a complex environment, a dynamic self-adjusting noise generation function is added in a strategy updating process, so that the convergence speed is increased, the stability is enhanced, and the self-adaptive adjustment capability of a system to load fluctuation and environmental condition change is enhanced. The method has good expandability.

The invention relates to the technical field of electrical digital data processing, and discloses a lightweight-based multi-modal data enhancement method, which comprises the following steps: acquiring a multi-modal feature tensor; extracting a vector orthogonal projection scalar and determining the novelty; updating the global covariance matrix when the novelty is greater than a redundancy threshold, and maintaining the global covariance matrix in a register state when the novelty is not greater than the redundancy threshold; extracting a main diagonal variance component to determine a differential modulation coefficient; calculating a second-order moment manifold projection operator according to the global covariance matrix, and calibrating the operator by using a differential modulation coefficient; the calibrated operator is used for carrying out orthogonal projection on random noise to generate a structured disturbance vector, the structured disturbance vector is superposed to a multi-modal feature tensor to output enhanced features, a novelty judgment mechanism is used for restraining statistical deviation caused by steady-state redundant data, computing resource occupation is reduced, and it is ensured that semantic alignment between modals is maintained in enhanced feature distribution.

According to the flight simulation method and system based on the unmanned aerial vehicle-mounted backlight imaging technology, the key scientific problem of target perception and recognition of an unmanned aerial vehicle platform in the backlight environment is solved, and an innovative scheme is provided for cross research of high-dynamic-range imaging, target detection and intelligent flight control. A backlight imaging camera and a dynamic aperture control technology are introduced, and a multi-point calibration and multi-scale noise reduction method is combined, so that the traditional problems of insufficient imaging dynamic range, loss of image details, random noise interference and the like in a backlight environment are effectively solved. Meanwhile, based on the image enhancement method of the generative adversarial network, high-fidelity reconstruction of target texture and structural features under a complex illumination condition is realized, and the common problems of artifacts and color drift in a traditional enhancement algorithm are avoided. Furthermore, through the combination of a reinforced and optimized YOLO detection framework and a high-resolution feature pyramid network, a small-target high-precision detection model adapted to a backlight scene is successfully constructed, and the detection robustness and real-time performance are remarkably improved while the detection precision is kept.

The invention discloses a converter batching data generation and optimization method, which comprises the following steps: acquiring an original industrial data set in a converter batching process, preprocessing the original industrial data set, inputting preprocessed training data into an encoder, generating a mean vector and a variance vector of potential variable distribution, and optimizing the mean vector and the variance vector; constraining the similarity between the potential variable distribution and the standard Gaussian distribution through KL divergence loss; inputting the potential variable and the random noise vector into a generator to generate a simulation sample, and distinguishing a real sample from the generated sample through a discriminator; a forced discriminator module is introduced, and a discrimination result is converted into an additional loss item; and carrying out data generation on the missing batching parameters, outputting a complete batching data set conforming to process constraints, constructing a cost objective function, a quality objective function and a resource consumption objective function, constructing constraint conditions of chemical element content of a target finished product, optimizing the converter batching data, and generating optimized converter batching data.

The invention discloses a video generation method, belongs to the technical field of artificial intelligence, particularly relates to the technical fields of computer vision, deep learning, large models and the like, and can be applied to scenes such as digital humans and the like. The method comprises the following steps: extracting a target audio feature of a target audio and a virtual image feature of a virtual image in a reference image; based on an attention mechanism, processing the target audio feature and the initial video feature to obtain a target video feature, the initial video feature being formed by splicing a virtual image feature and random noise; and decoding the target video feature to obtain a target video, the target video comprising a video frame of the virtual image expressed based on the target audio speech. The invention further provides a video generation device, an intelligent agent, electronic equipment and a storage medium.

The invention belongs to the field of data processing, and provides an equipment comfort measurement fault identification method, system and equipment, and the method comprises the steps: obtaining a plurality of modal sequences which are consistent with a sampling signal segment in length and are aligned in bit sequence through variational modal decomposition; traversing each sampling signal segment according to a bit sequence to form a signal segment extended wave; extracting the peak value of the signal segment extended wave and the characteristic of the bit sequence of the peak value to generate an extended wave ratio sequence; the difference between the wave ratio sequence of each sampling signal segment and the original sequence is used as the wave ratio fall; and screening each sampling signal segment according to the wave ratio fall by using the median of the wave ratio fall of all the sampling signal segments, and identifying a signal comfort abnormal segment. The method is not sensitive to amplitude fluctuation of random noise, but is more sensitive to envelope impact and bit sequence displacement of a specific frequency band, can prompt slight problems such as guide shoe clearance, guide rail joint impact and abnormal transmission of a traction machine in the early stage, and is beneficial to preventive maintenance.

The invention discloses a method and a system for capturing an ultra-wideband navigation signal of a low-orbit satellite, which are used for solving the technical problem that the existing method for capturing the ultra-wideband navigation signal of the low-orbit satellite is easy to cause that the signal cannot be locked, and finally the capturing success rate is low. The method comprises the following steps: acquiring ka-band low-orbit satellite signals and low-orbit satellite pseudo-random noise codes corresponding to a plurality of low-orbit satellite ultra-wideband navigation signals; generating a complex signal and an original intermediate-frequency signal according to a preset sampling rate, preset skipping time and the ka-band low-orbit satellite signal; constructing a pseudo-code spectrum library based on the pseudo-random noise codes of the low-orbit satellites, and outputting the pseudo-code spectrum library; determining a discrete frequency search sequence based on known intermediate frequency parameters of the original intermediate frequency signal, performing matrix construction in combination with the complex signal and the pseudo code spectrum library, and outputting a three-dimensional result matrix; and capturing and judging the ultra-wideband navigation signals of the low-orbit satellites by adopting the three-dimensional result matrix, and determining a plurality of successfully captured ultra-wideband navigation signals of the low-orbit satellites.

The invention discloses a first view angle video generation method and device based on a mask diffusion model and fixation point constraint, and the method comprises the steps: constructing an end-to-end deep learning frame for the demands of video filling, prediction and visual attention region control in the generation of a first view angle video; the diversified first-view-angle video generation conforming to the visual logic is realized. The method comprises the following steps: firstly, dividing an input video into a condition frame set and an unknown frame set, and adding random noise to an unknown frame by using a dynamic mask module I to generate a noisized frame; performing reverse de-noising generation on the video through a full 3D convolutional neural network module II, and guiding the network to generate video content conforming to a space-time law by taking a diffusion step and a fixation point track as conditional constraints; and finally, saliency prediction is carried out by using a fixation point positioning module III. By jointly optimizing the loss of the reverse denoising generation process and the loss of the fixation point probability graph, the reasonability and diversity of the generated video are further improved. According to the method, through joint optimization of the mask diffusion strategy and fixation point constraint, the space-time continuity, the anti-noise capability and the adaptability to the fixation point trajectory of the generated video can be effectively improved, and the method is particularly suitable for a complex multi-face expression interaction scene.

A method for first arrival picking of seismic data includes receiving a seismic shot record data set from a seismic event, extracting texture features from the seismic shot record data set, calculating an energy ratio matrix from the seismic shot record data set, using fuzzy c-means to cluster the texture features into a strong linear cluster, a moderate linear cluster, and a random noise cluster, calculating a first arrival class matrix from the moderate linear cluster, multiplying the first arrival class matrix by the energy ratio matrix to obtain a first arrival pick matrix, and identifying first arrival picks from the first arrival pick matrix.

The invention discloses a digital human generation method and device based on audio driving and a computer storage medium, and relates to the technical field of digital human speech generation, and the method comprises the steps: extracting the audio features of an input audio, and carrying out the partitioning according to the amplitude, and obtaining audio blocks; inputting the audio blocks into the DIT model, and performing sliding block processing on the audio blocks in combination with random noise and time vectors to obtain audio drive to generate feature key points; identifying face key points of an input face image, performing feature extraction on the face image, and determining original feature key points of a to-be-driven face; according to the time sequence information of the input audio, performing feature fusion on the audio drive generation feature key points and the to-be-driven face original feature key points to obtain fusion key point features; and based on the fused key point features, driving the facial expressions and actions of the digital human, and generating the facial animation matched with the input audio, so that high synchronization of the facial animation and the audio is realized, and the naturalness of the digital human speaking is improved.

The invention discloses an airborne laser radar sounding data processing method and system, and relates to the technical field of marine surveying and mapping and earth observation, and the method comprises the following steps: collecting original full-waveform data of an airborne laser radar sounding system, the original full-waveform data comprising infrared laser channel data and blue-green laser multichannel data; joint denoising processing is carried out on the original full-waveform data to obtain denoised waveform data, and the joint denoising processing comprises background noise modeling and removing based on machine learning and random noise filtering based on frequency domain low-pass filtering; a collaborative joint denoising strategy is formed by fusing background noise modeling based on machine learning and frequency domain low-pass filtering based on signal-to-noise ratio optimization. According to the method, complex background noise can be accurately estimated and removed, random noise can be adaptively filtered out, weak underwater echo signals are reserved to the maximum extent, and the problem of water depth information loss caused by excessive smoothness is effectively avoided.

A method is provided for enhancing feature integration in capsule networks using GAN-augmented latent space. The method comprises training an autoencoder to encode input data into a latent space representation that captures essential features; training a generative adversarial network (GAN) to generate synthetic features, wherein the GAN includes (a) a generator configured to produce synthetic features from random noise, and (b) a discriminator configured to evaluate the quality of the synthetic features by comparing them with real features from the latent space representation; combining the latent space representation with the synthetic features to form an augmented latent space; generating routing coefficients for the capsule network based on the augmented latent space; and applying the routing coefficients to modulate dynamic routing between capsule layers in the capsule network.

An industrial product cumulative damage image generation method based on an improved DCGAN is characterized by comprising the following steps: step 1, based on a DCGAN model framework, an industrial product cumulative damage image generation network IPD-GAN based on the improved DCGAN is constructed, and the IPD-GAN is provided with a generator and a discriminator; 2, the generator obtains random noise, processes the random noise, generates a damage image and transmits the damage image to a discriminator; step 3, the discriminator obtains a real image, carries out true and false discrimination on the real image and the damaged image, adopts a Wasserstein distance loss function with a gradient penalty term as a model to resist loss according to a discrimination result, guides training of the generator in combination with L1 loss and SSIM loss, and comprehensively optimizes system performance; and step 4, generating a damaged image by using the trained IPD-GAN. The method has the advantages that the problems of unstable training and gradient disappearance existing in the DCGAN are solved.

The invention discloses a mechanical arm operation track optimization method based on a generative adversarial network. The method comprises the steps that mechanical arm operation track data are collected, a generative adversarial network model is constructed, and a generator generates a mechanical arm path track according to random noise and task characteristic parameters; distinguishing the generated path track and the real path track through a discriminator, and obtaining an optimization generation result; according to the optimization generation result, an optimal track meeting task requirements is screened out through track feature scoring and a screening mechanism; by means of the method, the efficiency and adaptability of mechanical arm trajectory planning are remarkably improved, task execution energy consumption is effectively reduced, task completion time is shortened, trajectory smoothness and accuracy are improved, and the method is suitable for mechanical arm operation optimization in the fields of industrial manufacturing, logistics carrying and the like.

The present invention relates to a method for generating images from text. Based on the Transformer module and the AttnGAN network, after encoding the text through a text encoder, sentence features and word features are obtained. The sentence features are passed through a conditional enhancement module to obtain feature vectors, which are fused with random noise vectors and input into the Transformer module for learning. An improved feature vector is output and input into a generator to generate an initial image of 64*64 pixels roughly. The initial synthesized image and the improved feature vector are input into a discriminator for discrimination, and the generator is trained according to a loss function. Sequentially, the improved feature vector from the previous step and the word features are input into a neural network for upsampling to obtain a fused vector, which is then input into the generator to obtain images of 128*128 pixels and 256*256 pixels. The images generated by the present invention have clearer detail contours compared to the images generated by the previous traditional AttnGAN method.

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 self-attention guided anomaly generation method based on a latent variable diffusion model, and belongs to the technical field of computer vision. According to the method, reverse denoising diffusion processing is performed on random noise through a text-guided latent variable diffusion model so as to generate an image with specified content. In order to realize synchronous generation of normal-abnormal sample pairs, a de-noising network self-attention layer is utilized to accurately control generated normal sample images and abnormal sample images, so that the consistency of normal areas and the authenticity of the abnormal images are ensured. In addition, a corresponding abnormal mask is generated by comparing the feature difference between the generated normal sample and the abnormal sample. The method can be used for generating the abnormal sample and has a wide application prospect.

The invention provides a friction force identification method for a trailing edge flap driving mechanism with a bearing, and the method comprises the steps: applying a series of improved triangular waves with different speeds to a driver, carrying out the closed-loop control, carrying out the zero-phase digital filtering of a collected flap angle signal, and suppressing the interference of random noise to the signal, adjusting a buffeting phenomenon of a derivation step length inhibition speed to obtain a flap angular speed, then intercepting a constant-speed ascending section and a constant-speed descending section, then assigning a series of angular speeds, carrying out friction model identification on a relation between friction force and the speed, and then identifying a relation between parameters obtained by identification and a flap angle; finally, the friction force about the flap angle and the flap angular velocity in the flap driving mechanism is obtained.

Examples of the disclosure provide for a scatter network device. In some examples, the scatter network device includes a non-transitory memory, at least one processor, and a key exchange application stored in the non-transitory memory. When executed by the at least one processor, the key exchange application generates a key exchange request encrypted according to a public encryption key of a first network endpoint, wherein the encrypted key exchange request is indistinguishable from uniform random noise, and transmits the key exchange request to the first network endpoint via a first communication band, wherein the scatter network device transmits authenticated messages via a second communication band.

The invention discloses a federated learning model compression method based on data-free knowledge distillation and multi-teacher knowledge distillation, and belongs to the technical field of federated learning. According to the technical scheme, a central server is used for pre-training a model to constrain random noise and labels, a loss function is optimized after the model is input into an image generator, and intermediate features are extracted and stored in a feature pool; and after the client uploads the model, sampling from the feature pool to generate a diversified composite image as training data, and training a local aggregation model by combining a pre-training model and a client integrated model as teachers. The beneficial effects are that the FedPET method provided by the invention combines data-free knowledge distillation and integrated knowledge distillation, and innovatively introduces channel feature exchange and multi-teacher model guidance, so that the model compression efficiency, generation diversity, data privacy protection, communication efficiency and accuracy of federal learning are significantly improved; the method has obvious advantages especially in heterogeneous data distribution and resource limited scenes, and an efficient, safe and accurate solution is provided for practical application of federated learning.