3548 results about "Neutral network" patented technology

The invention discloses an auxiliary film reading system for a capsule endoscope image. A data acquisition module is used for acquiring capsule endoscopic image data of an examined patient; an image position classification module is used for classifying capsule endoscopic images according to different shot parts by using a first convolution neural network (CNN) to obtain image sequences of different shot parts; an image sequence description module is used for carrying out image feature extraction on the image sequences of different shot parts by using a second convolution neural network to obtain feature vector sequences of different alimentary canal part image sequences and for transforming image features in the feature vector sequences into descriptive texts by using a recursive neural network (RNN) to generating an auxiliary diagnosis report. Therefore, the work load of observing an alimentary canal image by a doctor can be reduced and thus the diagnosis efficiency of the doctor can be improved.

The invention discloses a text multi-label classification method based on semantic unit information, which comprises the following steps: establishing a semantic unit multi-label classification modelSU4MLC, taking a recurrent neural network sequence based on an attention mechanism to a sequence model as a baseline model for improvement, and improving the expression of the attention mechanism by improving a source end; Extracting semantic unit related information from the context representation of the source end of the baseline model by using hole convolution in deep learning to obtain semantic unit information; Combining the semantic unit information with the word level information by using a multi-layer mixed attention mechanism, and providing the combined information for a decoder; Anddecoding the tag sequence by using a decoder, thereby realizing text multi-tag classification based on semantic unit information. According to the method, the problems that an existing attention mechanism is easily influenced by noise and contributes to classification insufficiently can be solved, the contribution of the attention mechanism to text classification can be improved, and the text multi-label classification problem can be more efficiently solved.

Measurements of fluorescence spectra of fluid samples recovered downhole are processed to give the fluid composition. The processing may include a principal component analysis followed by a clustering method or a neutral network. Alternatively the processing may include a partial least squares regression. The latter can give the analysis of a mixture of three or more fluids.

The invention provides a face attribute recognition method based on a combined depth neural network. The method comprises the following steps: S1, performing area extraction on an inputted face image;S2, carrying out positioning on a facial important part and extracting a related area based on the area extraction result to obtain facial area features and facial key part features; S3, marking a real age and a gender of each face by using the area extraction result as a training sample, carrying out training based on a deep neural network, and outputting an age estimation value and a gender recognition result; and S4, on the basis of the facial area features outputted by the S2 and local area features of eyes and mouths, outputting expression and wearing prediction probabilities respectively according to a random forest algorithm, adding probabilities of corresponding attributes for different attributes, and outputting facial expression and wearing attribute results. In addition, the invention also provides a face attribute recognition apparatus based on a combined depth neural network.

The invention discloses a near infrared spectrum analyzing method based on the isolated component analysis and genetic neural network, which comprises the following steps for the acquired near infrared spectrum: firstly, effectively compressing spectrum data by using wavelet transform; secondly, extracting an independent component and a corresponding mixed coefficient matrix of a near infrared spectrum data matrix by using an isolated component analysis method; thirdly, building a three-layer BP neutral network, using the mixed coefficient matrix of a training sample as the input and correspondingly measured component concentration matrix as the output, and optimizing a neutral network structure by adopting a genetic algorithm, and obtaining a GA-BP neutral network by the training of the training sample; fourthly, predicting and analyzing the measured component concentration of the predicted set sample by using the GA-BA neutral network. The method enriches the chemical measurement method, widens the application range of the isolated component analysis and has favorable application prospect.

The invention discloses an automatic question generation method based on deep learning. The method comprises the following steps: constructing a training set (articles, answers and questions), a verification set (articles, answers and questions) and a prediction set (articles and answers); creating Encoder-based construction by using deep learning framework tensorflow A sequence of decoders to a sequence neural network model; Carrying out word segmentation, word list making and word embedding operations on sentences in the data set; Wherein the data set comprises a training set, a verificationset and a prediction set; The training set is used for training the model, the verification set is used for detecting whether the currently trained model is over-fitted or not, and if over-fitting isachieved, stopping training; Otherwise, continuing training; And decoding the prediction set by using the trained model to generate a problem. The method is good in generalization effect and low in labor cost, the generated questions are better matched with articles and answers, and the method can be widely applied to the fields of intelligent teaching, intelligent questions and answers, knowledge question and answer games and the like.

An implantable apparatus, such as a cochlear implant, for delivering electrical plasticity informative stimuli to a neural network of an implantee. The apparatus comprises a stimulator device (40) that generates stimulation signals, and an electrode array (20) that receives the stimulation signals and delivers the stimuli to the neural network of the implantee in response to the signals. The stimuli delivered to the implantee facilitates and/or controls the production and/or release of naturally occurring agents into the neural network to influence the functionality thereof.

The invention discloses a debris flow risk degree evaluation method. The method comprises a step of determining the characteristic parameters associated with debris flow three elements, a step of establishing the comprehensive information evaluation system of the three elements, obtaining a three-element initial information evaluation matrix, and calculating the information entropy of the three elements through matrix operation and an entropy method, a step of establishing a debris flow information entropy model, taking three sub information entropy as an input factor, through BP neural network effect, and outputting debris flow information entropy, a step of defining a debris flow risk degree level standard according to the relation between the information entropy theory and the fact whether debris flow occurs, and carrying out risk degree evaluation on a search object. The invention discloses a debris flow probability model, the mutual interaction mechanism of debris flow three elements in a debris flow inoculation process can be comprehensively reflected, the complex nonlinear and dynamic processes of debris flow can be represented, the risk degree of the research object (single channel/regional debris flow) can be forecasted.

The invention discloses a wearable device based on neural network adaptive health monitoring. The wearable device comprises an exercise state solving module, an exercise link judgment module, a dimension reduction solving module, a physiological parameter setting module and a physiological parameter collection module; the exercise state solving module collects acceleration data output by an acceleration sensor, high-frequency noise is filtered through wavelet transformation, and the exercise state of a user is divided; the exercise link judgment module subdivides the exercise state in combination with an angular velocity to obtain an exercise subdivision link; and according to the dimension reduction solving module, an optimized SVM is used to perform further fine division on partial pathological features hierarchically in combination with data at different levels. According to the wearable device, different feature dimension reduction samples are integrated in an analysis link, the amount of exercise is greatly reduced, correctness is guaranteed by emphatically calculating a verification link, the medical reference value of obtained user physiological parameters is increased, and power consumption standby capability is improved under the same condition.

The invention discloses a behavior intention fused surrounding dynamic vehicle trajectory prediction system and method. The system comprises a trajectory prediction module and a behavior intention prediction module, the trajectory prediction module takes information of historical trajectories of a target vehicle needing trajectory prediction and vehicles around the target vehicle as input of a long-short-term memory regression neural network, and prediction trajectories of a future time domain are obtained through network prediction; the behavior intention prediction module is used for obtaining probability distribution of behavior intentions obtained based on prediction tracks of a target vehicle and surrounding vehicles by considering behavior interaction between different vehicles and utilizing an LSTM classification neural network; the results of the two modules are fused and input into a multi-modal LSTM trajectory prediction neural network to obtain the position information of the final prediction trajectory. According to the method, the motion information of the vehicle and the information of the surrounding traffic environment are fully utilized, the dynamic change and uncertainty of the traffic environment are considered, the accuracy of trajectory prediction is improved, and the system and method are suitable for more complex driving scenes.

The invention discloses a prisoner psychological health state assessment method and a system based on multi-modal information. The method comprises the following steps: obtaining physiological signals, facial expression images and voice signals of reformed prisoners and prisoners to be tested after they experience virtual reality scenes, and extracting physiological signal features, facial expression image features and voice signal features from the obtained signals; inputting the physiological signal features, facial expression image features and voice signal features of the reformed prisonerinto a pre-trained neural network model, and outputting a psychological state assessment vector of the reformed prisoners; inputting the physiological signal features, the facial expression featuresand the voice signal features of the prisoners to be tested into a pre-trained neural network model, and outputting a psychological state assessment vector of the prisoners to be tested; calculating the psychological state assessment vector distance between the prisoners to be tested and the reformed prisoners; and assessing the mental health state of the prisoners according to the distance.

The invention relates to a preceding vehicle following method based on a deep convolutional neutral network and solves the problems that a self-adaptive cruise system in the prior art has tedious visual ranging and controller design processes, cannot fine adjust control parameters according to driving habits of drivers and cannot meet different personal driving habits. Driving behavior data are trained, and under the condition of given visual sensing input, accurate control of vehicle accelerators and automatic pedals under multiple work conditions is realized by imitating human drivers according to feature description of the surroundings by the deep convolutional neutral network. All that is required is to fine adjust the deep neutral network parameters with different work condition training samples instead of designing the controller structure and adjusting the controller gain under different work conditions. The tedious visual ranging and controller design processes are avoided, and the vehicle following behavior of the drivers is simulated by adjusting the deep neutral network parameters.

The invention relates to a tide predicting method for the tide is influenced by various factors, including cyclical factors, such as tidal generation force, and non-cyclical factors, such as wind power, atmospheric pressure, coast characteristics, rainfall, dip angles of the lunar orbit and the like. The predicting accuracy of the traditional harmonic analysis method is influenced by partial tide number, and the traditional harmonic analysis method cannot analyze the influence of non-cyclical factors; the artificial neural network method developed recent years overcomes the defect that the non-cyclical factors cannot be predicted by the harmonic analysis method to a certain extent, but has great data volume required by study training samples and wide involve range, can cover various possible conditions, but has less station historical data of non-cyclical factors. The invention provides a predict model, wherein factors which influence tide non-cyclically, such as wind directions, rainfall, storm surge, coast characteristics and the like, can be fused into the model, and small sample data can receive more accurate results. In the method, a support vector machine (SVM)-based predict model is established, wherein, an SVM toolbox is imported into MATLAB 7.8; training sample data is trained by utilizing svmtrain function; the formed model is tested by using a test sample svmpredict function; and the trained and tested data can predict the tide in the same tide test station.

A triaxial magnetic electronic compass error compensation method based on depth learning. An implicit error model is trained for compensating non-linear error in magnetic compass measurement and improving the orientation accuracy of the magnetic compass. The error model training consists of two stages: a first stage is pre-training, and a second stage is reverse trimming by using a back-propagation algorithm for fine-tuning all layers of the network, and reducing the error of model training. The magnetic compass calibration and compensation procedure is to use depth learning algorithm training to obtain a non-linear error model, and the distorted measurement magnetic field is inversed back to a true magnetic field value, thereby reducing the calculation error of course angle. The invention aims at nonlinear error of magnetic compass and provides the error training method based on depth learning; compared to random initialization of a traditional neural network, the weight of each layer locates in a better position of parameter space, so as help to improve the convergence of the algorithm and model training accuracy and achieve high-precision orientation of magnetic compass.

A tunnel fire early-warning controlling method based on multi-sensor data fusion technology and a system using the tunnel fire early-warning controlling method based on the multi-sensor data fusion technology comprise steps as below: recording of history data of a main sensor group, reading of data of an auxiliary sensor group at a regular time, calculation of a neural network, passing back of a calculation result, execution controlling on site and the like, and the system specially using the tunnel fire early-warning controlling method based on the multi-sensor data fusion technology comprises a site detection device, a data processing device and an alarming displaying device. The tunnel fire early-warning controlling method based on the multi-sensor data fusion technology comprises a dynamic and flexible fire disaster threshold value adjusting method, namely, a method calculating with a neural network to analyze current data of the history data of the main sensor group and the auxiliary sensor group to obtain a fire disaster threshold value of current environment factors, and the original fire disaster threshold value is replaced by the new fire disaster threshold value. The tunnel fire early-warning controlling method based on the multi-sensor data fusion technology can effectively reduce judgment differences of a fire disaster caused by changing of environment of the tunnel, and reduce misstatement rate and missing reporting rate of the funnel fire disaster early-warning system.

The invention discloses a BP neural network construction and identification method for identifying different brown rice grains, adopting the following technical scheme: including the following steps: 1) acquiring images: 2) image preprocessing: 3) extracting image feature information of different brown rice grains: 4) main Component analysis reduces dimensionality of image feature information; 5) Design BP neural network structure: 6) Train neural network, and use the BP network neural network constructed by any of the above-mentioned construction methods to identify different brown rice grains. The method proposed by the present invention obtains images of different types of brown rice grains through black as the background, uses image processing technology to obtain its characteristic information, and uses principal component analysis to reduce the dimensionality of the characteristic information, and finally uses BP neural network to perform different types of brown rice grains. identify. The method can identify different types of grains objectively, accurately and quickly, and overcomes the disadvantages of traditional manual detection.

The invention discloses a flood forecasting method based on cluster analysis and real time correction, which comprises the following steps: 1) using PCA(Principal Component Analysis) to perform dimensionality reduction to the input of a model; 2) using the K-means clustering method to conduct clustering analysis on original data; dividing the flood data into different classifications; and then training different SVM models; when a testing sample is inputted, using the clustering center to determine the classification of the test sample and predicting the corresponding model to obtain a predicted value q; and 3) using a BP neural network for real time correction; calculating the error sequence between the predicated value and the actual value; using the error sequence data to train the BP neural network error correction model to obtain the error correction value qe. The final forecasting result is the model predicted value q plus the error correction value qe. According to the invention, the original hydrological data are divided into several classifications by cluster analysis, and through the training of the models, forecasting can be available by the multiple models. Then, real-time correction is achieved by the BP neural network to improve the forecasting accuracy for the time of flood peak.

The invention discloses an event type recognition method and device. The method comprises the following steps that word segmentation is performed on all texts in a training set, a word vector space model is trained after word class treatment is extracted, text characteristics are extracted, and texts are shown as characteristic vectors; even type clustering is performed on the training set, and a neutral network model with type cluster regularization items is trained; test samples are also analyzed, word class treatment is extracted, and the trained word vector model is trained for obtaining character representations; by means of the neutral network model with the type cluster normalization items, event type recognition is performed. By means of the technical scheme, problems brought by annotation data imbalance are relieved by means of type sharing information in the same group.

The invention relates to the field of coal mine safety, in particular to a distributed optical fiber temperature measurement-based fire early warning method for a belt conveyor. The method comprises the following steps of: dividing a temperature measuring optical fiber into a temperature measuring point, a channel and an area, and processing temperature data measured by the temperature measuring optical fiber by an absolute temperature early warning method, an area relative temperature difference early warning method, an area normal distribution early warning method, a measuring point temperature rise slope early warning method and a measuring point temperature rise variation trend early warning method to acquire a channel threshold early warning characteristic value, an area threshold early warning characteristic value, an area relative temperature early warning characteristic value, an area temperature normal distribution statistical characteristic value, a measuring point threshold early warning characteristic value, a measuring point temperature rise slope early warning characteristic value and a measuring point temperature rise accumulated trend early warning characteristic value; inputting the characteristic values and the measuring point temperature rise accumulated trend early warning characteristic value into a back propagation (BP) neural network model; and outputting a warning coefficient, an early warning coefficient and a safety coefficient by using the BP neural network model.

The invention discloses an unmanned vehicle speed control method based on PSO (Particle Swarm Optimization) and an RBF neutral network. The method comprises the following specific steps: 1, establishing an unmanned vehicle speed control system configuration; 2, building a vehicle speed tracking closed loop control mathematic dynamic model; 3, building an unmanned vehicle speed control driver model based on a fuzzy RBF neutral network structure; 4, fuzzifying an input variable of the driver model to obtain a fuzzy value, and utilizing the fuzzy value to establish an input and output variable membership function; 5, establishing a driver model fuzzy control rule list on the basis of the step 3, the step 4, the experience of a driver and measurement data; 6, calculating the fitness of each rule in the driver model, and completing fuzzification and normalization calculation; 7, establishing an improved PSO control flow on the basis of PSO; and 8, establishing a vehicle speed control flow on the basis of the improved PSO and a fuzzy RBF neutral network algorithm. According to the vehicle speed control method, the vehicle speed tracking error is low, and the anti-interference capability is high.

The invention relates to a greenhouse wireless sensor network control node device for intensive aquiculture by using a wireless measurement and control method, which is installed in a wireless measurement and control network of intensive aquiculture. The device is arranged in a culturing farm of an intensive aquiculture region, a collection node is used for collecting the parameters of water quality environment sensors, and the data collected by the sensor group is preprocessed by combining a rule base and an information fusion algorithm. A radial basis function neutral network algorithm (RBF algorithm) and the fuzzy computing technology are adopted as an algorithm model of information fusion, the collected data is subject to field level data fusion, and a ZigBee wireless module is used for sending fused abnormal water quality environment state and environment parameter data to a convergent node. The convergent node uploads the data to a monitoring center, and the monitoring center can monitor the growth conditions of cultured organisms, environmental conditions, control device operating conditions and the like of a plurality of culturing farms within a monitoring range in real time. The collection node can also receive the feedback information of the convergent node and the monitoring center and adjusts the parameters of the information fusion algorithm. The device is an information collection device for a wireless sensor network, has high efficiency, reliability and convenient operation, and is used for solving the difficulties of data real-time collection for culturing environment and filed level data fusion in the intensive aquiculture process by using the wireless sensors.

A method of automatically assessing skeletal age of hand radiographs, comprising: providing a radiographic image of both two hands; cropping a first image of the left hand or the right hand; rotating the first image to make the fingertip of the medius point upwards the vertical; cropping a second image of the medius; segmenting the phalanges of the medius to acquire a third image; extracting a plurality of physiological features of the third image to acquire a first data; extracting a plurality of morphological features of the third image to obtain a second data; delivering the first data and the second data to a neural network for training; and outputting an assessment of the skeletal age.

The invention provides a bill text recognition method and system. The method comprises the steps that a bill text image is acquired; performing text region detection on the bill text image by adoptinga target text detection model to generate a target text image; and performing text recognition on the target text image by adopting a target text recognition algorithm to generate a text recognitionresult. Detecting and identifying the bill text image through a deep learning neural network model; the recognition method is simple, compared with the prior art, the method has the advantages that features do not need to be extracted manually, an effective text detection area can be obtained more quickly, the overall detection and recognition efficiency and accuracy are improved finally, structured extraction and storage of image text data are achieved, effective support is provided for subsequent detection and classification, and the electronic and intelligent levels of bill archiving and classification are further improved.

The invention provides a CNN-DNN hybrid neural network based noise reduction method. The method is implemented by the following steps that 1, a CNN-DNN hybrid neural network noise reduction model is established; 2, a training set is established for training the CNN-DNN hybrid neural network noise reduction model established in the step 1; and 3, a speech signal needing to be subjected to noise reduction is input to the trained CNN-DNN hybrid neural network noise reduction model in the step 3, and a clean speech signal spectrum is output. The CNN-DNN hybrid neural network based noise reductionmethod has better automatic identification separation and removal capabilities on transient noise and non-transient noise.

The invention relates to a training test method of a BP neural network regression model and is applied to an oak laser cutting system to predict notch widths. The method mainly comprises steps that a,data acquisition, data sets of N experiment samples are acquired, and M sets of experiment data are totally included; b, data pre-processing; c, data grouping; d, optimization searching of super parameters of a BP neural network and initialization; e, first-time training of the BP neural network; f, second-time training of the BP neural network; and g, training accomplishment of the BP neural network, the notch widths of the oak laser cutting system under different parameters are predicted. The method is advantaged in that over-concentrated and over-sparse data conditions are trained, and thetraining effect of the BP neural network is improved.