217results about "Neural learning methods" patented technology

Updating an artificial neural network is disclosed. A node characteristic is represented using a fixed point node characteristic parameter. A network characteristic is represented using a fixed point network characteristic parameter. The fixed point node characteristic parameter and the fixed point network characteristic parameter are processed to determine a fixed point intermediate parameter having a larger size than either the fixed point node characteristic parameter or the fixed point network characteristic parameter. A value associated with the fixed point intermediate parameter is truncated according to a system truncation schema. The artificial neural network is updated according to the truncated value.

A computerized neural-network based mechanism for providing an intermediary configured for intervening in searches is described. Corresponding methods, computer-readable media, systems, devices, and apparatuses are also contemplated. The neural network can include a multi-headed attention layer. The intermediary may be, in some embodiments, a human “man in the middle” mechanism invoked where there is low confidence that pre-existing categories map to a user's search string. The mechanism provides a specially configured interface adapted to enable a search specialist to quickly select one or more categories that match or are otherwise associated with the search query from a set of acceptable categories. Received outputs and detected user behaviors are utilized to update a neural network model.

A user management method and apparatus are provided. The user management method includes verifying whether a user included in an input image is a registered user based on a first classification model classifying the registered user; storing a feature corresponding to the user and extracted from the input image in a database in response to the user not being the registered user; generating a second classification model classifying the registered user and a candidate user corresponding to one of features stored in the database; and determining whether the candidate user is to be registered based on the second classification model.

The invention discloses a convolutional neural network optimization and rapid target detection method and device. The optimization method comprises the steps of obtaining a first convolutional model trained by adopting floating point type sample image data; wherein a BN layer operation in the first convolution model is located behind convolution layer operation and is used for normalizing a numerical value obtained after the convolution layer operation to a preset data range; adjusting parameters of a convolutional layer according to the parameters of the BN layer; deleting the BN layer to obtain an adjusted second convolution model; adding a quantization layer before the convolution operation of the convolution layer in the second convolution model to obtain a third convolution model; wherein the quantization layer is used for quantizing input data to integer data of a predetermined bit, and enabling a convolution layer to perform convolution operation by using the integer data of thepredetermined bit. According to the method, the problems of large model body quantity and low operation speed are solved by optimizing the convolution operation.

A computer-implemented method, computer program product and computing system for defining a model that includes a plurality of variables. At least one variable of the plurality of variables is conditioned based, at least in part, upon a conditioning command received from a user, thus defining a conditioned variable. The model is inferenced based, at least in part, upon the conditioned variable.

The invention relates to an improved particle swarm-optimized neural network-based transformer fault diagnosis method. The method includes the following steps that: related data of dissolved gases in transformer oil and transformer fault information are obtained so as to be adopted as sample data, and a drop half-normal distribution scoring model is adopted to pre-estimate the data of the dissolved gases in the transformer oil; the network structure of a neural network is determined; and the parameters of the neural network are optimized by using an improved particle swarm algorithm; pre-estimated sample data are adopted to train the parameter-optimized neural network, so that a final neural network model can be obtained; and the neural network model is adopted to process transformer data to be evaluated, so that the fault type of a transformer can be obtained through diagnosis. With the method of the invention adopted, the interference of original data redundancy information can be reduced, and the validity of data evaluation can be improved; and convergence speed in the training of the neural network can be increased, and the search ability of parameter optimization can be improved, and the accuracy and reliability of transformer fault diagnosis can be improved finally.

A method for predicting corrosion in a pipeline wherein the pipeline is subject to corrosion by the steps of obtaining data of parameters associated with the pipeline, comparing the data against previously generated data of parameters associated with the pipeline, and calculating, based on differences between the data with the previously generated data the probability of corrosion in the pipeline. The calculation can be performed using a variety of algorithms and modeling methods.

The invention discloses a remote sensing image scene classification method based on an improved residual network, which comprises the following steps of (a) collecting and/or downloading scene imagesto obtain a remote sensing scene classification data set; (b) performing enhancement processing on the images in the remote sensing scene classification data set, and randomly dividing the images in the remote sensing scene classification data set into a training set and a test set; (c) improving the residual network structure, training the improved residual network by using the images in the training set, continuously optimizing the residual network structure by training, and testing the accuracy of the residual network by using the images in the test set after the training is completed; and(d) carrying out scene classification on the remote sensing images by using the trained residual network structure. According to the method, the scene classification is carried out on the remote sensing image by utilizing the improved residual network, the network is more suitable for obtaining the remote sensing image data set with large difficulty and complex background textures by adjusting thestructure of the residual network, and the classification accuracy is improved.

The invention discloses a tumor MRI weak supervised learning analysis modeling method, which comprises the following steps: S1, constructing a tumor MRI segmentation network based on a full convolutional neural network to realize tumor MRI preliminary segmentation; S2, taking the tumor MRI segmentation network as a generator, taking the classification model as a discriminator, and proposing a generative adversarial training algorithm of the tumor MRI segmentation network; and S3, taking unlabeled image data in the medical image as additional input condition information of the generator model and the discriminator model, guiding a data generation process, and enhancing the stability of the segmentation-generative adversarial network. The invention also discloses a model established by usingthe tumor MRI weak supervised learning analysis modeling method. According to the method, the tumor target region and the endangered organ can be more accurately and automatically segmented under thecondition of fewer training samples, and the method is applied to target region and endangered organ sketching of tumor radiotherapy.

An object of the invention is an automated analysis system for analyzing at least one of scientific, technological and business information. The automated analysis system comprises means 100 for processing a data amount to accomplish a collection data form, means for structuring the collection data form describing the content of a source document, means for automatically identifying input documents in data warehouses comprising similar structured data forms as said structured collection data form, means for preprocessing and tokenizing said input documents, and the automated analysis system comprises a classification engine using the tokenized and preprocessed documents as input classifying each item of the input documents, and said means for processing, means for structuring, means for automatically identifying, means for preprocessing and tokenizing and said classification engine being configured to perform their tasks in one or more modelling round(s).

A system and method for implementing a neural network based vehicle dynamics model are disclosed. A particular embodiment includes: training a machine learning system with a training dataset corresponding to a desired autonomous vehicle simulation environment; receiving vehicle control command data and vehicle status data, the vehicle control command data not including vehicle component types or characteristics of a specific vehicle; by use of the trained machine learning system, the vehicle control command data, and vehicle status data, generating simulated vehicle dynamics data including predicted vehicle acceleration data; providing the simulated vehicle dynamics data to an autonomous vehicle simulation system implementing the autonomous vehicle simulation environment; and using data produced by the autonomous vehicle simulation system to modify the vehicle status data for a subsequent iteration.

The invention provides a malicious software API (Application Program Interface) call sequence detection method based on graph convolution. The method comprises the following steps: acquiring and recording API call sequence information of processes and sub-processes when a large number of software samples run; performing vectorization processing on the API calling sequence information; extracting aparameter relationship, a dependency relationship and a sequence relationship of the API function; establishing an API call graph; inputting the API call graph into a graph convolutional neural network for training to obtain a malicious software detection network model; collecting API calling sequence information of processes and sub-processes when the executable file to be detected runs; constructing an API call graph of the executable file to be detected, then inputting the API call graph of the executable file to be detected into the malicious software detection network model, If the output result of the malicious software detection network model is 1, indicating that the judgment result is malicious software; If the output result of the malicious software detection network model is 0,indicating that the judgment result is normal software.

The invention is suitable for the technical field of ice shape prediction, and provides a deep belief network-based airfoil profile icing ice shape prediction method and device. The method comprises the following steps: constructing and training a Fourier coefficient deep belief network model and an upper and lower limit deep belief network model in advance; performing data normalization on the icing condition to be predicted to obtain a normalized icing condition; inputting the normalized icing condition into the Fourier coefficient depth confidence network model and the upper and lower limitdepth confidence network model; substituting ai, bi, xiu and xil into an ice-shaped curve Fourier series expansion formula to obtain a wing-shaped icing ice-shaped curve; the Fourier coefficient deepbelief network model and the upper and lower limit deep belief network model are composed of a plurality of restricted Boltzmann machines and a BP neural network layer. According to the method, the network training time is greatly reduced, the network prediction precision is improved, and the technical problems that gradient disappearance and local minimization are likely to happen to a pure BP neural network are solved.

The invention discloses a parking space detection method based on depth learning, comprising the following steps: obtaining a target image of the parking space; setting a training set and a test set of the parking space target image; a tag being arranged for that target region of interest, and the coordinate and categories of the target region being marked; initializing neural network parameters;putting the training set and tag information into the neural network to participate in training; quantitative evaluation of training results. A parking space detection system based on depth learning comprises an image acquisition module, an initial setting module and a training control module. The invention effectively solves the shortcomings of traditional image method that the parking space environment is highly demanded, and has strong adaptability, and the characteristic points of the parking space can be perfectly detected and positioned even in extreme environment, which is convenient for automatic and accurate parking alignment of vehicles, has the advantages of good robustness, and improves the user experience at the same time. The method and system can be widely used in the fieldof parking space recognition.

The invention discloses a nasopharyngeal carcinoma auxiliary diagnosis model construction and auxiliary diagnosis method and system, and relates to the technical field of medical data processing. Thenasopharyngeal carcinoma auxiliary diagnosis model construction method comprises the steps: sample acquiring: acquiring a nasal endoscope image, wherein the nasal endoscope image comprises a nasopharyngeal carcinoma group and a non-nasopharyngeal carcinoma group; preprocessing: preprocessing the nasal endoscope image; and model training: inputting the preprocessed nasal endoscope image into a convolutional neural network, and training the convolutional neural network to obtain a nasopharyngeal carcinoma auxiliary diagnosis model. According to the invention, the nasopharyngeal endoscope image can be analyzed, and the predicted illness probability is output in real time to assist a doctor in nasopharyngeal carcinoma diagnosis, so that the accuracy of nasopharyngeal carcinoma diagnosis can beeffectively improved, and the biopsy detection rate is increased so as to achieve the purposes of early screening, early diagnosis and early treatment of nasopharyngeal carcinoma and improvement of treatment effect and prognosis of patients.

The invention discloses a sparse code division multiple access signal detection method based on a depth neural network. First, the relationship between users and resources is expressed as factor graph, Then the iterative transfer of information on the factor graph is transformed into the forward transfer of neural network, and then the signal detection error is estimated and the estimated value isused as the input data of the neural network, and then the gradient descent method is used to train the neural network to obtain better coefficients, and finally the trained network is used for SCMAsignal detection. Because of the use of neural network architecture, this method can be implemented on the corresponding AI chip. Compared with the traditional methods, this method can improve the performance and transfer the related computations to the high-speed parallel processing AI chip, which can effectively reduce the time delay caused by SCMA signal detection.

The embodiment of the invention provides a pedestrian detection method and device. The pedestrian detection method comprises the steps that a to-be-processed image is acquired; scene information of each scene to which pixels belong in the to-be-processed image is analyzed; and pedestrians in the to-be-processed image are detected in combination with the scene information of each scene to which the pixels belong so as to determine locations of the pedestrians in the to-be-processed image. According to the pedestrian detection method and device, pedestrian detection is performed in combination with the scene information in the image, false positive results generated by a pedestrian detection algorithm can be effectively reduced by use of the scene information, and meanwhile the detection precision of the pedestrian detection algorithm can be improved by use of the scene information.

The invention discloses a network user behavior prediction system, and the system comprises a data collection and storage module, a data preprocessing module, a user network behavior analysis module and a data presentation module, wherein the data collection and storage module, the data preprocessing module, the user network behavior analysis module and the data presentation module are connected sequentially. The data collection and storage module is used for collecting and storing the useful mobile Internet data of a user through collection equipment. The data preprocessing module is used for carrying out the data clearing and cleaning of the useful data, filtering the data comprising noise and abnormality, forming an effective data set of user's behavior analysis, and enabling the effective data set to be transmitted to the user network behavior analysis module. The user network behavior analysis module is used for carrying out the arrangement and analysis of the effective data set, carrying out the analysis of the behaviors of the user, and outputting a user's behavior analysis result. The data presentation module is used for presenting the user's behavior analysis result to the user. The system supports the analysis and mining of a large amount of mobile Internet data of the user, and is good in prediction effect.

The invention relates to a short-term power load forecasting method which is characterized by comprising the steps of: selecting a similar day set; constructing a RBF (Radial Basis Function) neural network forecasting model optimized by a bat algorithm; forecasting a power load on a forecasting day by utilizing the RBF neural network forecasting model optimized by the bat algorithm; and the like.According to the invention, on the basis of conventional gray correlation analysis, a similar day with a higher similarity is selected by adopting a distance similarity and shape proximity correlatedcomprehensive gray correlation degree, the defect that a conventional gray correlation analysis method only considers a geometric similarity degree among data sequences, but ignores a number proximitydegree when selecting the similar day is made up, and forecasting accuracy is improved; a weight of a RBF neural network is optimized by utilizing the bat algorithm, so that the defect that the RBF neural network is easy to fall into local optimization can be overcome, a convergence rate of the integral network is improved and computation efficiency of the integral network is improved; and the short-term power load forecasting method has the advantages of scientificity, reasonability, high applicability, good effect and the like.