37 results about "Denoising autoencoder" patented technology

The invention provides a hyperspectral image robustness classification method based on segmentation depth characteristics and low-rank representation. The method comprises the following steps that: firstly, in order to lower an influence of noise on feature extraction as far as possible, using a stack-based denoising autoencoder netework to carry out unsupervised feature extraction on a hyperspectral image; then, fully mining an intra-class similarity and an inter-class difference in the hyperspectral image to establish a robust classifier based on the low-rank representation; and finally, adopting an effective optimization method to carry out optimal solving on a target function. Under a situation that training data contains noise, a good classification effect can be obtained.

Embodiments of the invention are directed to systems, methods, and computer program products for cross-technology code analysis for redundancy identification and functionality recognition. In particular, the novel present invention provides a unique platform for analyzing software code across multiple coding language using a unique approach involving the use of denoising autoencoders. Embodiments of the inventions are configured to leverage a marginalized stacked denoising autoencoder approach to analyze software code, identify code redundancies, and improve efficiency for code storage and query ability by the use of a trained autoencoding module to autoencode software code attributes into vectorized data that can be compared to determine cross-platform functionality and redundancy within a software library.

The invention discloses a system and a method. The system includes a memory storing processor executable code for a denoised autoencoder, and one or more processors coupled to the memory to execute the processor executable code to receive raw signal data comprising signal noise, encode, by the denoised autoencoder, the raw signal data by performing a denoising autoencoder operation to produce a latent representation, and decode, by the denoised autoencoder, the latent representation to produce clean signal data reconstructed without the signal noise. A first filter is applied to a signal to emphasize activity within the signal and to produce a first modified signal, a rectifier and a second filter are applied to the first modified signal to smooth areas of the first modified signal with clinical importance and to produce a second modified signal, and high frequency energy zones of the second modified signal are automatically detected using an energy threshold to produce a weights vector.

The invention discloses a turbine engine performance degradation evaluation method based on a stacked-denoising auto-encoder, and belongs to the technical field of engine performance degradation evaluation. The turbine engine performance degradation evaluation method solves the problems that traditional multi-sensor data selection needs to rely on complex information evaluation criteria, extraction of degraded features in HI construction depends on a large number of signal processing techniques and expert experience, supervised training method label selection relies on manual participation, and a method is low in universality. Four denoising auto-encoders build stacked-denoising auto-encoders to extract the single node value of input data. Training set data carries out pre-training to thenetwork and uses a BP algorithm to fine-tune parameters. The extracted single node value is considered to be the health factor value at each cycle, and an HI curve of a training set is established. The test set is input to the trained stacked-denoising auto-encoder to obtain the health factor value at each cycle and construction an HI curve. The HI curves of training set and test set are subjectedto smoothing processing respectively, and the HI curves after smoothing processing are evaluated.

The invention discloses a fault diagnosis method based on incremental compensation and dynamic self-adaptive enhancement, which comprises the following steps: 1. collecting vibration signals in various states from a vibration sensor at the motor drive end; 2. predicting fault data of bearing equipment Processing; 3. Add random noise to the training samples as the input of the denoising autoencoder for unsupervised greedy layer-by-layer pre-training; 4. When there is new equipment status data, use the existing trained DAE model to add Extract the failure mode, and use the mode similarity algorithm to compare similar modes, then use the incremental active fusion algorithm to incrementally merge the newly added failure mode, and use the weight dynamic compensation algorithm to calculate the dynamic weight; And the unlabeled fault data weighted by dynamic deep learning training is used as the input vector to train the SVM classifier; 6. Use the BP algorithm to globally fine-tune the relevant parameters in the entire model; 7. Classify and diagnose fault types.

The invention relates to a soil heavy metal element content predicting method based on a terahertz spectrum and a deep automatic encoder. Compared with the prior art, the method overcomes the shortcoming that ingredients of soil cannot be comprehensively analyzed and generalized on a large scale. The method comprises the following steps: acquiring and pretreating a soil sample; construction of a predicting model based on the deep automatic encoder: stacking a denoising automatic encoder and a compression encoder on construction of a model to form the predicting model of the deep automatic encoder; training of the predicting model of the deep automatic encoder: inputting terahertz spectrum data for training a sample into the predicting model of the deep automatic encoder to finish training of the predicting model of the deep automatic encoder; and predicting the heavy metal element content of soil. By a structural model of the deep automatic encoder and the terahertz spectrum, ingredients of the heavy metal elements of the soil are analyzed and predicted.

The invention discloses a fault diagnosis method for a high-speed heavy-load input stage under an unbalanced sample. The specific steps are: use different simulated fault modes of a low-speed gearbox experimental platform to monitor vibration signals, obtain low-speed fault spectrum data, and obtain different fault modes through spectrum shifting. The fault spectrum data of the high-speed gearbox experimental platform; the fault spectrum data of the high-speed gearbox experimental platform is migrated to the fault mode of the helicopter input stage gearbox, and the fault spectrum data of the input stage of different fault modes are obtained; the fault spectrum data of the input stage obtained by two migrations and the normal In the mode, the actual running state monitors the vibration signal spectrum data, pre-trains the deep stack denoising autoencoder SDAE, and fine-tunes the SDAE with a small number of fault data samples at the input level during actual operation. The trained SDAE is used for automatic feature extraction and fault classification of high-speed heavy-duty input stage vibration signals. The invention solves the fault diagnosis problem of the helicopter's high-speed and heavy-load input stage under the condition of unbalanced samples.

The invention discloses an insulation fault diagnosis method for high-voltage equipment. The method comprises the steps of (1) acquiring a polluted training sample for representing partial discharge signals of a plurality of insulation fault types of the high-voltage equipment, extracting phase resolution pulse sequence data based on the polluted training sample, and normalizing the phase resolution pulse sequence data; (2) training a constructed deep sparse denoising autoencoder through the normalized phase resolution pulse sequence data; and (3) inputting the polluted partial discharge signals of the to-be-identified high-voltage equipment into the trained deep sparse denoising autoencoder, and obtaining the defect type of the high-voltage equipment based on output of an output classification layer of the deep sparse denoising autoencoder. In addition, the invention further discloses an insulation fault diagnosis system for the high-voltage equipment. The system comprises a data preprocessing module and a fault identification module.

A denoising autoencoder training method, an electrocardiography signal denoising method, and related apparatuses and devices, which use artificial intelligence to perform electrocardiography signal denoising, and may be applied to fields such as smart electrocardiogram detection. A reference electrocardiography signal to be denoised comprising significant features such as R peak positions and R-R distances of an electrocardiography signal to be denoised is extracted from said electrocardiography signal, and denoising is performed on a remaining electrocardiography signal to be denoised by means of a target denoising autoencoder after said reference electrocardiography signal has been removed from said electrocardiography signal, to prevent the target denoising autoencoder from performing denoising processing on the significant features in said electrocardiography signal, so that said reference electrocardiography signal and the denoised remaining electrocardiography signal are superimposed to obtain a denoised electrocardiography signal, wherein the R peak positions in said electrocardiography signal are better maintained, and distortion of the denoised electrocardiography signal is reduced.

A rolling early warning method and system of dynamic security risk situation for large scale hybrid alternating current / direct current grids, which includes: constructing the original feature set of the fast total transfer capability (TTC) estimation model; generating the training sample set of the fast TTC estimation model based on the network topology and the forecast information of a period of time in the future; constructing the fast TTC estimation model based on stacking denoising autoencoders and extreme learning machine; generating the future operating conditions (OCs), and determine the type of preventive control actions needed to ensure the system security based on the fast TTC estimation model and the heuristic search algorithm; and conducting the layered and hierarchical early warning for OCs according to the type of operating state and the type of preventive control actions that is needed.

The invention discloses a fault isolation and identification method in a nonlinear process based on a denoising self-encoder. Use the sensor to collect training data and test data, construct statistics in the denoising autoencoder residual space and hidden feature space; calculate the process monitoring statistics of the test data, when the test data process monitoring statistics exceed the control limit of the process monitoring statistics, Then there is a fault; the objective function is established by introducing a regularization term, and the fault amplitude is obtained based on the adaptive moment estimation solution, and the fault amplitude of each sensor is used to judge the fault of each sensor, so as to realize the isolation and identification of the fault. The invention can meet the speed and accuracy requirements of large-scale process fault diagnosis in industrial process, and provides reliable and effective technical support for industrial production process control.

The invention discloses a wireless transmission method for high-dimensional damaged data based on a noise-reduction self-encoder. The method of the invention includes model training and end-to-end transmission. Model training firstly performs data preprocessing on the historical perception data set, and divides it based on the K-fold cross-validation method; then builds a noise reduction autoencoder model, and trains based on the proposed new noise adding method of introducing random Gaussian noise in batches Denoising autoencoder models. In end-to-end transmission, firstly, the trained noise reduction autoencoder is split into two parts and deployed at the sending end and the receiving end, and then preprocessing and dimensionality reduction operations are performed on the perception data of unknown types of noise interference at the sending end, and the dimensionality reduction The data is transmitted to the receiving end, and finally the reconstruction operation is performed on the receiving end to obtain the reconstructed data of the undamaged sensing data. The method of the invention can effectively perform dimension reduction transmission, noise reduction processing and reconstruction of high-dimensional damaged perception data, and can filter out noise interference and dimension reduction transmission when collecting data in harsh environments.

The invention discloses an image recognition method based on human vision. This method constructs an image recognition structure capable of cross-problem recognition based on deep learning and human vision. Using this model structure, one model can recognize images in multiple problem domains, which is a further simulation of the human visual system. This method uses the nature of human visual perception, that is, the HMAX method, to directly extract the features of the original image, which reduces the complicated preprocessing steps and improves the calculation efficiency and feasibility of the method. The SDA method reduces the number of parameters in deep learning and improves the generality of the algorithm. To improve the training performance of general forward BP. From the actual experimental results, the classification accuracy of this method is also higher than other classification methods. Therefore, this method is an efficient and feasible image recognition method, and has universal applicability in the field of image recognition.

The invention discloses a SAR image change detection method based on a stacked semi-supervised self-adaptive denoising self-encoder, which solves the problem of low detection accuracy of coherent speckle noise points and many edge change regions in the existing method. The implementation steps are: first generate a multi-scale difference guidance map; use the phase 1 image as input to train SDAE; use the multi-scale difference guidance map, phase 1 and phase 2 images as input to train SSADAE, and SSADAE adaptive error function The weights obtained by SDAE training are used in the method; then SSADAE is used to calculate the feature vectors of phase 1 and phase 2 images; the two are subtracted to obtain the difference vector, and then FCM classification is performed on it to obtain the change detection result map. The present invention firstly proposes a multi-scale difference guidance map, which can highlight the change area in the difference map; the SSADAE proposed later can use a small number of marked samples in the image to further improve the accuracy of change detection.

The invention relates to a method for establishing a SCMA codec model based on a noise reduction autoencoder, comprising: establishing an SCMA encoder based on a noise reduction autoencoder and a fully connected neural network, and mapping the user's original input data into a codeword ; Superimpose and transmit the codewords of all users on each resource block, and then superimpose the channel noise on the signal on each resource block; establish a SCMA decoder based on a fully connected neural network at the receiving end to decode the original input of all users Data; train the denoising autoencoder-based SCMA codec model; test the BER performance of the above denoising autoencoder-based SCMA codec model. Compared with the traditional SCMA system, the present invention reduces the complexity of encoding and decoding; compared with the existing SCMA system model based on deep learning, the present invention further reduces the bit error rate; compared with the existing deep learning-based SCMA system model, the invention has faster training convergence speed.

The invention discloses a chip surface defect detection method based on a convolutional de-noising auto-encoder. The method comprises the following steps: 1, constructing defect-free image reconstruction based on the convolutional de-noising auto-encoder; 2, constructing a residual image based on the overlapped area; and 3, performing defect detection based on the residual image. According to themethod, weak defects with the characteristics of low defect and background contrast ratio, small defect and the like can be effectively detected, the robustness is very high, and the chip surface detection precision is improved.

The invention discloses an LSTM fiber optic gyroscope temperature compensation modeling method based on deep embedded clustering, which includes: collecting temperature and fiber optic gyroscope zero bias data to construct a training data set, and training a denoising autoencoder layer by layer; Noisy autoencoder, constructing a deep autoencoder; based on a deep autoencoder, mapping the input x to obtain an embedded point z; calculating the soft allocation between the embedded point z and the cluster center, and constructing an auxiliary target allocation; using soft allocation and auxiliary target allocation The kl divergence is the objective function, iterates between calculating the auxiliary objective function and minimizing the kl divergence, updating the depth autoencoder parameters and cluster centers; segmenting according to the clustering results, using LSTM neural network on each segment The temperature compensation model of the fiber optic gyroscope is obtained through training. The invention can realize the temperature compensation of the gyroscope output zero bias error, obtain good fitting and prediction effects and high temperature environment adaptability, and improve the product precision of the optical fiber gyroscope.