509 results about "Neural network system" patented technology

A maternal-fetal monitoring system for use during all stages of pregnancy, including antepartum and intrapartum stages. The maternal-fetal monitoring system of the subject invention comprises (1) a set of sensors; (2) an amplifying / filtering means; (3) a computing means; and (4) a graphical user interface. Accurate clinical data, which can be extracted and provided to the user in real-time using the system of the invention, include without limitation, maternal electrocardiogram (ECG) signals, maternal uterine activity signals (EHG), maternal heart rate, fetal ECG signals, and fetal heart rate. In a preferred embodiment, the maternal-fetal monitoring system of the invention includes an intelligence means, such as a neural network system, to analyze and interpret clinical data for use in clinical diagnosis antepartum, intrapartum and postpartum, as well as delivery strategy.

A maternal-fetal monitoring system for use during all stages of pregnancy, including antepartum and intrapartum stages. The maternal-fetal monitoring system of the subject invention comprises (1) a set of sensors; (2) an amplifying / filtering means; (3) a computing means; and (4) a graphical user interface. Accurate clinical data, which can be extracted and provided to the user in real-time using the system of the invention, include without limitation, maternal electrocardiogram (ECG) signals, maternal uterine activity signals (EHG), maternal heart rate, fetal ECG signals, and fetal heart rate. In a preferred embodiment, the maternal-fetal monitoring system of the invention includes an intelligence means, such as a neural network system, to analyze and interpret clinical data for use in clinical diagnosis antepartum, intrapartum and postpartum, as well as delivery strategy.

Dynamically updating neural network systems may be implemented to generate, train, evaluate and update artificial neural network data structures used by content distribution networks. Such systems and methods described herein may include generating and training neural networks, using neural networks to perform predictive analysis and other decision-making processes within content distribution networks, evaluating the performance of neural networks, and generating and training pluralities of replacement candidate neural networks within cloud computing architectures and / or other computing environments.

A maternal-fetal monitoring system for use during all stages of pregnancy, including antepartum and intrapartum stages. The maternal-fetal monitoring system of the subject invention comprises (1) a set of sensors; (2) an amplifying / filtering means; (3) a computing means; and (4) a graphical user interface. Accurate clinical data, which can be extracted and provided to the user in real-time using the system of the invention, include without limitation, maternal electrocardiogram (ECG) signals, maternal uterine activity signals (EHG), maternal heart rate, fetal ECG signals, and fetal heart rate. In a preferred embodiment, the maternal-fetal monitoring system of the invention includes an intelligence means, such as a neural network system, to analyze and interpret clinical data for use in clinical diagnosis antepartum, intrapartum and postpartum, as well as delivery strategy.

Techniques and systems are provided for tracking objects in one or more images. For example, a trained network can be applied to a first image of a sequence of images to detect one or more objects in the first image. The trained network can be applied to less than all images of the sequence of images. A second image of the sequence of images and a detection result from application of the trained network to the first image are obtained. The detection result includes the detected one or more objects from the first image. A first object tracker can be applied to the second image using the detection result from application of the trained network to the first image. Applying the first object tracker can include adjusting one or more locations of one or more bounding boxes associated with the detected one or more objects in the second image to track the detected one or more objects in the second image. A second object tracker can also be applied to the second image to track at least one object of the detected one or more objects in the second image. The second object tracker is applied to more images of the sequence of images than the trained network and the first object tracker. Object tracking can be performed for the second image based on application of the first object tracker and the second object tracker.

There is provided a neural network system for detection of malicious code, the neural network system comprising: an input receiver configured for receiving input text from one or more code input sources; a convolutional neural network unit including one or more convolutional layers, the convolutional unit configured for receiving the input text and processing the input text through the one or more convolutional layers; a recurrent neural network unit including one or more long short term memory layers, the recurrent neural network unit configured to process the output from the convolutional neural network unit to perform pattern recognition; and a classification unit including one or more classification layers, the classification unit configured to receive output data from the recurrent neural network unit to perform a determination of whether the input text or portions of the input text are malicious code or benign code.

The invention discloses a method and a neural network system used for a man-machine conversation, and user equipment. The method used for the man-machine conversation comprises the following steps that: carrying out vectorization on a natural language problem and a knowledge base; through vector computation, obtaining an intermediate result vector which is based on the knowledge base and expresses similarity between the natural language problem and a knowledge base answer through vector computation; and according to a problem vector and the intermediate result vector, carrying out computation to obtain a correct natural language answer based on facts. By use of the method provided by the invention, the conversation and the questions and answers based on the knowledge base are combined, natural language interaction can be carried out with users, and on the basis of the knowledge base, the natural language answer based on the facts can be given.

A memory-centric neural network system and operating method thereof includes: a processing unit; semiconductor memory devices coupled to the processing unit, the semiconductor memory devices contain instructions executed by the processing unit; a weight matrix constructed with rows and columns of memory cells, inputs of the memory cells of a same row are connected to one of Axons, outputs of the memory cells of a same column are connected to one of Neurons; timestamp registers registering timestamps of the Axons and the Neurons; and a lookup table containing adjusting values indexed in accordance with the timestamps, the processing unit updates the weight matrix in accordance with the adjusting values.

A memory-centric neural network system and operating method thereof includes: a processing unit; semiconductor memory devices coupled to the processing unit, the semiconductor memory devices contain instructions executed by the processing unit; weight matrixes including a positive weight matrix and a negative weight matrix constructed with rows and columns of memory cells, inputs of the memory cells of a same row are connected to one of Axons, outputs of the memory cells of a same column are connected to one of Neurons; timestamp registers registering timestamps of the Axons and the Neurons; and a lookup table containing adjusting values indexed in accordance with the timestamps, the processing unit updates the weight matrixes in accordance with the adjusting values.

Systems and methods for monitoring uterus contraction activity and progress of labor. The system of the subject invention can comprises (1) a plurality of sensors; (2) an amplifying / filtering means; (3) a computing means; and (4) a graphical user interface. Accurate clinical data, which can be extracted and provided to the user in real-time using the system of the invention, include without limitation, progress of labor, prediction and monitoring of preterm labor, and intrauterine pressure prediction. In a preferred embodiment, the system of the invention includes an intelligence means, such as a neural network system, to analyze and interpret clinical data for use in clinical diagnosis as well as delivery strategy.

This application discloses a neural network that also functions as a connection oriented packet data network using an MPLS-type label switching technology. The neural network uses its intelligence to build and manage label switched paths (LSPs) to transport user packets and solve complex mathematical problems. However, the methods taught here can be applied to other data networks including ad-hoc, mobile, and traditional packet networks, cell or frame-switched networks, time-slot networks and the like.

A system and method for controlling information output based on user feedback about the information is provided that comprises a plurality of information sources providing information. The information sources may be electronic mail providers, chat participants, or page links. At least one neural network module selects one or more of a plurality of objects to receive information from the plurality of information sources based at least in part on a plurality of inputs and a plurality of weight values during that epoch. At least one server, associated with the neural network module, provides one or more of the objects to a plurality of recipients. The objects may comprise electronic mail messages, chat participants viewers, or slots within a link directory page. The recipients provide feedback about the information during an epoch. At the conclusion of an epoch, the neural network takes all of the feedback that has been provided from the recipients and generates a rating value for each of the plurality of objects. Based on the rating value and the selections made, the neural network redetermines the weight values within the network. The neural network then selects the objects to receive information during a subsequent epoch using the redetermined weight values and the inputs for that subsequent epoch.

A method includes determining object class probabilities of pixels in a first input image by examining the first input image in a forward propagation direction through layers of artificial neurons of an artificial neural network. The object class probabilities indicate likelihoods that the pixels represent different types of objects in the first input image. The method also includes selecting, for each of two or more of the pixels, an object class represented by the pixel by comparing the object class probabilities of the pixels with each other, determining an error associated with the object class that is selected for each pixel of the two or more pixels, determining one or more image perturbations by back-propagating the errors associated with the object classes selected for the pixels of the first input image through the layers of the neural network without modifying the neural network, and modifying a second input image by applying the one or more image perturbations to one or more of the first input image or the second input image prior to providing the second input image to the neural network for examination by the neurons in the neural network for automated object recognition in the second input image.

An electronic comparison system includes input stages that successively provide bits of code words. One-shots connected to respective stages successively provide a first bit value until receiving a bit having a non-preferred value concurrently with an enable signal, and then provide a second, different bit value. An enable circuit provides the enable signal if at least one of the one-shots is providing the first bit value. A neural network system includes a crossbar with row and column electrodes and resistive memory elements at their intersections. A writing circuit stores weights in the elements. A signal source applies signals to the row electrodes. Comparators compare signals on the column electrodes to corresponding references using domain-wall neurons and store bit values in CMOS latches by comparison with a threshold.

Systems and methods for training a neural network to optimize network performance, including sampling an applied dropout rate for one or more nodes of the network to evaluate a current generalization performance of one or more training models. An optimized annealing schedule may be generated based on the sampling, wherein the optimized annealing schedule includes an altered dropout rate configured to improve a generalization performance of the network. A number of nodes of the network may be adjusted in accordance with a dropout rate specified in the optimized annealing schedule. The steps may then be iterated until the generalization performance of the network is maximized.

A system and method for controlling information output based on user feedback about the information is provided that comprises a plurality of information sources providing information. The information sources may be electronic mail providers, chat participants, or page links. At least one neural network module selects one or more of a plurality of objects to receive information from the plurality of information sources based at least in part on a plurality of inputs and a plurality of weight values during that epoch. At least one server, associated with the neural network module, provides one or more of the objects to a plurality of recipients. The objects may comprise electronic mail messages, chat participants viewers, or slots within a link directory page. The recipients provide feedback about the information during an epoch. At the conclusion of an epoch, the neural network takes all of the feedback that has been provided from the recipients and generates a rating value for each of the plurality of objects. Based on the rating value and the selections made, the neural network redetermines the weight values within the network. The neural network then selects the objects to receive information during a subsequent epoch using the redetermined weight values and the inputs for that subsequent epoch.

The invention relates to the field of automobile classification technologies and discloses a method and system for classifying automobile types based on a neural network. The method comprises the steps that a plurality of training samples are collected, and the training samples are classified based on the convolutional neural network, so a classifier containing label results is obtained; when the automobile types are classified, a video image to be detected is read in, a motion object in the image is detected, and sub-block processing is performed on the image according to the motion object; afterwards, classification processing is performed on each block of image through the classifier, so a detection result is obtained. Accordingly, a neural network system can be constructed easily and conveniently to serve as the classifier, the system is trained by using different automobile samples, the system is made to automatically study complex class conditional density of the samples, and therefore the problems caused by class conditional density functions of artificial hypothesis are avoided. Compared with an existing automobile type classification method, the method for classifying the automobile types based on the convolutional neural network has the advantages that the accuracy of classification is improved, and the classification speed is increased.

A system for intelligent market trading implemented with a neural network system (Machine Learning) comprising an input, one or more processors and an output. The input comprises a database of price of a symbol and a pair in a timeframe. The first processor is configured to receive the respective input to generate a technical analysis, a trading signal, and a market report with a probability value. The second processor generates a coefficient for regulating the probability value of the generated trading signals and technical analysis using fuzzy logic systems alongside of a neural network system. The third processor computes the correlation coefficient of symbols under Mesh topology and regulates the resultant probability value of each trading signals and technical analysis. The output comprises a processor to receive and evaluate the individual resultant probability values of the generated trading signals and technical analysis and delivers an output data. The output data is sent via notification to a user for detecting a suitable spot for placing their orders in the financial market.

Systems and methods for image modification to increase contrast between text and non-text pixels within the image. In one embodiment, an original document image is scaled to a predetermined size for processing by a convolutional neural network. The convolutional neural network identifies a probability that each pixel in the scaled is text and generates a heat map of these probabilities. The heat map is then scaled back to the size of the original document image, and the probabilities in the heat map are used to adjust the intensities of the text and non-text pixels. For positive text, intensities of text pixels are reduced and intensities of non-text pixels are increased in order to increase the contrast of the text against the background of the image. Optical character recognition may then be performed on the contrast-adjusted image.

The invention discloses a hardware impulse neural network system, comprising: an input node layer and an unsupervised learning layer are connected through a synaptic connection unit in a neuron full connection mode; the unsupervised learning layer and the supervised learning layer are connected through another synaptic connection unit in a neuron full connection mode; the input node layer and theunsupervised learning layer are connected through a synaptic connection unit in a synaptic connection mode. The input node layer realizes the information input under different coding modes, the non-supervisory learning layer adopts the non-supervisory learning mode, and the supervisory learning layer adopts the supervisory learning mode. A synaptic connection unit is realized by an electronic synaptic device, so that that synaptic connection unit has a pulse time dependent plasticity STDP. The synaptic array unit receives as presynaptic pulses the stimulation signals from the neurons in the front layer and the postsynaptic pulses the action potential pulses excited by the neurons in the back layer. The time difference between the presynaptic pulses and the postsynaptic pulses determines the synaptic weight adjustment amount of the synaptic connection unit. The neural network system provided by the invention has a wide application value.

The invention is nerve net system which can realize heredity arithmetic. It is made up of computer nerve net model component and the interfaces. The character lies in: 1. after that the computer setsthe population size, coding type and length, heredity operation probability and the arithmetic ending condition of the arithmetic, the nerve net uses population size to realize the whole heredity operation including selection, crossing, mutation and personal adaptability value, and outputs the optimized calculation and result through computer; 2. designs the heredity operation nerve net model which can realize multi-father crossing operation and multi-gene mutation operation, realizes the two operation of two-value coding heredity arithmetic and real number coding heredity arithmetic.

The embodiment of the invention provides a neural network system and method executed by a computer and used for analyzing a relational network diagram, and the neural network system comprises a feature extraction layer which is used for extracting feature vectors of nodes in the relational network diagram; a deep neural network used for carrying out first processing on the feature vectors to obtain first output; a diagram neural network used for combining the adjacency information of the relational network diagram and carrying out second processing on the feature vectors to obtain second output, wherein the adjacency information being used for representing a connection relation between nodes contained in the relation network diagram; and a fusion layer used for fusing the first output andthe second output and outputting a prediction result for the node based on a fusion result.

The invention provides a depth neural network system and a method based on modulation mode identification of underwater acoustic communication. The system comprises: a data preprocessing part for preprocessing data of a plurality of modulation modes transmitted through underwater acoustic communication; A first layer neural network generates a feature extraction set of the first layer according toa plurality of modulation mode data transmitted from the pretreated underwater acoustic communication; The second layer neural network generates the second layer high-level feature set; The third layer neural network generates higher level feature set, the fourth layer neural network classifies and identifies the initial data by the feature set extracted in front; The fifth layer is the neural network layer, which generates the final modulation mode judgment and outputs the identified modulation mode. The depth neural network system and method based on the modulation mode recognition of underwater acoustic communication can simulate the actual use situation, improve the use effect in the actual underwater acoustic communication, more conveniently and efficiently complete the modulation recognition of underwater acoustic communication, and improve the accuracy of recognition and judgment.

The invention provides a roadway surrounding rock deformation predicting method based on a neural network and belongs to the method for predicting roadway surrounding rock deformation. Key influence factors of surrounding rock are obtained by means of layer analysis, site detection data under different geological conditions are collected and arranged, reliable data arrays obtained through monitoring serve as a training sample of the roadway surrounding rock deformation, the data of the training sample train the network system through trainlm functions, and a BP neural network model can be set up; the trained neural network is utilized for predicting the deformation amount at the initial excavation stage of a roadway, deflection of a surrounding rock top plate, moving-up amount of a bottom plate, displacement of roadway walls and generated maximum plastic zone damage depth are obtained according to input index parameters, the neural network predicts the roadway deformation of the initial excavation stage according to the prediction request, proper support parameters are selected for controlling support of the roadway, and safety accidents caused by instability of the rock are prevented. The reliable prediction data are provided for the roadway exploration and excavation processes to instruct the construction process of the roadway, and working procedures are arranged reasonably.

Genetically adaptive neural network systems and methods provide environmentally adaptable classification algorithms for use, among other things, in multi-static active sonar classification. Classification training occurs in-situ with data acquired at the onset of data collection to improve the classification of sonar energy detections in difficult littoral environments. Accordingly, in-situ training sets are developed while the training process is supervised and refined. Candidate weights vectors evolve through genetic-based search procedures, and the fitness of candidate weight vectors is evaluated. Feature vectors of interest may be classified using multiple neural networks and statistical averaging techniques to provide accurate and reliable signal classification.

The invented method for accurate tracking the maneuvering target based on neural net adopts double parallel structure being composed of two filters. The difference of states between the parallel double filters is picked-up as the character vector of the state of target to be estimate, which is input to the neutral net system. Based on the character vector input, the neural net system, which has been trained off-line before tracking estimation, outputs the predicted value of the acceleration variance of the target to be estimated at present time. Adaptive filtering the said predicted value based on the current statistical model obtains the fast and accurate tracking the target to be estimate. The invented method possesses favorable adaptive tracking ability.

The invention relates to a controller structure in a neural network system and a structure thereof used for automatically distinguishing and automatically improving the control precision of the system; wherein, the controller structure of the neural network system comprises a neural network control structure, a basis function structure linearly independent in the system control, and an analysis and iterative training structure for corresponding weight and weight value; according to samples within a coverable control range, the weight value of the neural network basis function is adjusted, and the neural network correction treatment on the expected output value is carried out in practical control so as to improve the output control precision. The neural network system distinguishing structure is characterized in that the structure comprises a neural network distinguishing structure, a basis function structure linearly independent in the system distinguishing and an analysis and iterative training structure for corresponding weight and weight value; according to samples within a coverable control range, the weight value of the neural network basis function is adjusted, and the description function of the control system is re-built by the basis function and the weight value of the neural network.

A learning method of a semiconductor device of the present invention comprises a neuro device having a multiplier as a synapse in which a weight varies according to an input weight voltage, and functioning as a neural network system that processes analog data, comprising a step A of inputting predetermined input data to the neuro device and calculating an error between a target value of an output of the neuro device with respect to the input data and an actual output, a step B of calculating variation amount in the error by varying a weight of the multiplier thereafter, and a step C of varying the weight of the multiplier based on the variation amount in the error, wherein in the steps B and C, after inputting a reset voltage for setting the weight to a substantially constant value to the multiplier as the weight voltage, the weight is varied by inputting the weight voltage corresponding to the weight to be varied.

The invention discloses an image defogging method and a system based on a deep learning neural network. The method comprises the following steps of inputting an image with fog into a deep learning neural network system; using the deep learning neural network system to carry out characteristic extraction on the image with fog, and carrying out autonomous learning and extracting a fog correlation characteristic; carrying out multiscale mapping on the image with fog, extracting the characteristic of the image with fog in a concentrative mode under different scales and forming a characteristic graph; carrying out local extremum on each pixel in the characteristic graph, maintaining a resolution to be unchanged and acquiring the processed image; carrying out nonlinear regression operation on the processed image and acquiring initial transmissivity t(x); using a guided filter to optimize the transmissivity and carrying out image smoothing processing on the processed image; calculating an atmospheric light parameter; and according to the initial transmissivity t(x) and the atmospheric light parameter, recovering a fogless image. In the invention, connection is established between the system and an existing defogging method, and under the condition that efficiency and easy implementation are guaranteed, compared with the existing method, the method has better defogging performance.

The systems and methods herein relate to artificial neural networks. The systems and methods examine an input image having a plurality of instances using an artificial neural network, and generate an affinity graph based on the input image. The affinity graph is configured to indicate positions of the instances within the input image. The systems and methods further identify a number of instances of the input image by clustering the instances based on the affinity graph.