7405 results about "Nerve network" patented technology

Neural networks are constructed (programmed), trained on historical data, and used to predict any of (1) optimal patient dosage of a single drug, (2) optimal patient dosage of one drug in respect of the patient's concurrent usage of another drug, (3a) optimal patient drug dosage in respect of diverse patient characteristics, (3b) sensitivity of recommended patient drug dosage to the patient characteristics, (4a) expected outcome versus patient drug dosage, (4b) sensitivity of the expected outcome to variant drug dosage(s), (5) expected outcome(s) from drug dosage(s) other than the projected optimal dosage. Both human and economic costs of both optimal and sub-optimal drug therapies may be extrapolated from the exercise of various optimized and trained neural networks. Heretofore little recognized sensitivities-such as, for example, patient race in the administration of psychotropic drugs-are made manifest. Individual prescribing physicians employing deviant patterns of drug therapy may be recognized. Although not intended to prescribe drugs, nor even to set prescription drug dosage, the neural networks are very sophisticated and authoritative "helps" to physicians, and to physician reviewers, in answering "what if" questions.

This invention relates to intelligent video surveillance fuzzy logic neural networks, camera systems with local and network-shared communications for facial, physical condition and intoxication recognition. The device we reveal helps reduce underage drinking by detecting and refusing entrance or service to subjects under legal drinking age. The device we reveal can estimate attention of viewers of advertising, entertainment, displays and the like. The invention also relates to method, and Vision, Image and related-data, database-systems to reduce the volume of surveillance data through automatically recognizing and recording only occurrences of exceptions and elimination of non-events thereby achieving a reduction factor of up to 60,000. This invention permits members of the LastCall™ Network to share their databases of the facial recognition and identification of subjects recorded in the exception occurrences with participating members' databases: locally, citywide, nationally and internationally, depending upon level of sharing permission.

Systems and methods are disclosed for controlling and diagnosing the health of a motorized system. The systems may comprise a diagnostics system and a controller, wherein the diagnostics system employs a neural network, an expert system, and / or a data fusion component in order to assess the health of the motorized system according to one or more attributes associated therewith. The controller may operate the motorized system in accordance with a setpoint and / or a diagnostics signal from the diagnostics system. Also disclosed are methodologies for controlling and diagnosing the health of a motorized system, comprising operating a motor in the motorized system in a controlled fashion, and diagnosing the health of the motorized system according to a measured attribute associated with the motorized system, wherein the motor may be operated according to a setpoint and / or the diagnostics signal.

The invention discloses a vehicle type recognition method based on a rapid R-CNN deep neural network, which mainly comprises unsupervised deep learning, a multilayer CNN (Convolutional Neural Network), a regional advice network, network sharing and a softmax classifier. The vehicle type recognition method realizes a framework for implementing end-to-end vehicle detection and recognition by using one rapid R-CNN network in a real sense, and is capable of carrying out quick vehicle sub-category recognition with high accuracy and robustness under the environment of being applicable to the shape diversity, the illumination variation diversity, the background diversity and the like of vehicle targets.

The invention discloses a text named entity recognition method based on Bi-LSTM, CNN and CRF. The method includes the following steps: (1) using a convolutional nerve network to encode and convert information on text word character level to a character vector; (2) combining the character vector and word vector into a combination which, as an input, is transmitted to a bidirectional LSTM neural network to build a model for contextual information of every word; and (3) in the output end of the LSTM neural network, utilizing continuous conditional random fields to carry out label decoding to a whole sentence, and mark the entities in the sentence. The invention is an end-to-end model without the need of data pre-processing in the un-marked corpus with the exception of the pre-trained word vector, therefore the invention can be widely applied for statement marking of different languages and fields.

A system and method of detecting an aberrant message is provided. An ordered set of words within the message is detected. The set of words found within the message is linked to a corresponding set of expected words, the set of expected words having semantic attributes. A set of grammatical structures represented in the message is detected, based on the ordered set of words and the semantic attributes of the corresponding set of expected words. A cognitive noise vector comprising a quantitative measure of a deviation between grammatical structures represented in the message and an expected measure of grammatical structures for a message of the type is then determined. The cognitive noise vector may be processed by higher levels of the neural network and / or an external processor.

A method for predicting risk and designing safety management systems of complex production and process systems which has been applied to an FPSO System operating in deep waters. The methods for the design were derived from the inclusion of a weight index in a fuzzy class belief variable in the risk model to assign the relative numerical value or importance a safety device or system has contain a risk hazards within the barrier. The weights index distributes the relative importance of risk events in series or parallel in several interactive risk and safety device systems. The fault tree, the FMECA and the Bow Tie now contains weights in fizzy belief class for implementing safety management programs critical to the process systems. The techniques uses the results of neural networks derived from fuzzy belief systems of weight index to implement the safety design systems thereby limiting use of experienced procedures and benchmarks. The weight index incorporate Safety Factors sets SFri {0, 0.1, 0.2 . . . 1}, and Markov Chain Network to allow the possibility of evaluating the impact of different risks or reliability of multifunctional systems in transient state process. The application of this technique and results of simulation to typical FPSO / Riser systems has been discussed in this invention.

The present invention extends to methods, systems, and computer program products for recognizing input gestures. A neural network is trained using example inputs and backpropagation to recognize specified input patterns. Input gesture data is representative of movements in contact on a multi-touch input display surface relative to one or more axes over time. Example inputs used for training the neural network to recognize a specified input pattern can be created from sampling input gesture data for example input gestures known to represent the specified input pattern. Trained neural networks can subsequently be used to recognize input gestures that are similar to known input gestures as the specified input pattern corresponding to the known input gestures.

A system for forecasting predicted thermal loads for a building comprises a thermal condition forecaster for forecasting weather conditions to be compensated by a building environmental control system and a thermal load predictor for modeling building environmental management system components to generate a predicted thermal load for a building for maintaining a set of environmental conditions. The thermal load predictor of the present invention is a neural network and, preferably, the neural network is a recurrent neural network that generates the predicted thermal load from short-term data. The recurrent neural network is trained by inputting building thermal mass data and building occupancy data for actual weather conditions and comparing the predicted thermal load generated by the recurrent neural network to the actual thermal load measured at the building. Training error is attributed to weights of the neurons processing the building thermal mass data and building occupancy data. Iteratively adjusting these weights to minimize the error optimizes the design of the recurrent neural network for these non-weather inputs.

The present invention, in one embodiment, is directed to a method and system for accelerating and improving the history matching of a well bore and / or reservoir simulation model using a neural network. The neural network provides a correlation between the calculated history match error and a selected set of parameters that characterize the well bore and / or the reservoir. The neural network iteratively varies a selection and / or the value of the parameters to provide at least one set of history match parameters having a value that provides a minimum for the calculated history matching error.

Provided are a method, system, and program for filtering communications received from over a network for a person-to-person communication program. A communication is received for the person-to person communication program. The communication is processed to determine predefined language statements. Information on the determined language statements is inputted into a neural network to produce an output value. A determination is made as to whether the output value indicates that the communication is unacceptable. The communication is forwarded to the person-to-person communication program unchanged if the output value indicates that the communication is acceptable. An action is performed with respect to the communication upon determining that the communication is unacceptable that differs from the forwarding of the communication that occurs if the output value indicates that the communication is acceptable.

An intrusion detection system (IDS) that uses application monitors for detecting application-based attacks against computer systems. The IDS implements application monitors in the form of a software program to learn and monitor the behavior of system programs in order to detect attacks against computer hosts. The application monitors implement machine learning algorithms to provide a mechanism for learning from previously observed behavior in order to recognize future attacks that it has not seen before. The application monitors include temporal locality algorithms to increased the accuracy of the IDS. The IDS of the present invention may comprise a string-matching program, a neural network, or a time series prediction algorithm for learning normal application behavior and for detecting anomalies.

A physical neural network is disclosed, which includes a connection network comprising a plurality of molecular conducting connections suspended within a connection gap formed between one or more input electrodes and one or more output electrodes. One or more molecular connections of the molecular conducting connections can be strengthened or weakened according to an application of an electric field across said connection gap. Thus, a plurality of physical neurons can be formed from said molecular conducting connections of said connection network. Additionally, a gate can be located adjacent said connection gap and which comes into contact with said connection network. The gate can be connected to logic circuitry which can activate or deactivate individual physical neurons among said plurality of physical neurons.

A method useful for facilitating choosing a treatment or treatment regime and for predicting the outcome of a treatment for a disorder which is diagnosed and monitored by a physician or other appropriately trained and licensed professional, such as for example, a psychologist, based upon the symptoms experienced by a patient. Unipolar depression is an example of such a disorder, however the model may find use with other disorders and conditions wherein the patient response to treatment is variable. In the preferred embodiment, the method for predicting patient response includes the steps of performing at least one measurement of a symptom on a patient and measuring that symptom so as to derive a baseline patient profile, such as for example, determining the symptom profile with time; defining a set of a plurality of predictor variables which define the data of the baseline patient profile, wherein the set of predictor variables includes predictive symptoms and a set of treatment options; deriving a model that represents the relationship between patient response and the set of predictor variables; and utilizing the model to predict the response of said patient to a treatment. A neural net architecture is utilized to define a non-linear, second order model which is utilized to analyze the patient data and generate the predictive database from entered patient data.

The invention provides a calculation apparatus and method for an accelerator chip accelerating a deep neural network algorithm. The apparatus comprises a vector addition processor module, a vector function value calculator module and a vector multiplier-adder module, wherein the vector addition processor module performs vector addition or subtraction and / or vectorized operation of a pooling layer algorithm in the deep neural network algorithm; the vector function value calculator module performs vectorized operation of a nonlinear value in the deep neural network algorithm; the vector multiplier-adder module performs vector multiplication and addition operations; the three modules execute programmable instructions and interact to calculate a neuron value and a network output result of a neural network and a synaptic weight variation representing the effect intensity of input layer neurons to output layer neurons; and an intermediate value storage region is arranged in each of the three modules and a main memory is subjected to reading and writing operations. Therefore, the intermediate value reading and writing frequencies of the main memory can be reduced, the energy consumption of the accelerator chip can be reduced, and the problems of data missing and replacement in a data processing process can be avoided.

Automatic energy management is provided, in even the most complex multi-building system. The necessity of a human operator for managing energy in a complex, multi-building system is reduced and even eliminated. Computer-based monitoring and computer-based recognition of adverse energy events (such as the approach of a new energy peak) is highly advantageous in energy management. Immediate automatic querying of energy users within a system of buildings for energy curtailment possibilities is provided. Such immediate, automatic querying may be answered by the energy users through artificial intelligence and / or neural network technology provided to or programmed into the energy users, and the queried energy users may respond in real-time. Those real-time computerized responses with energy curtailment possibilities may be received automatically by a data processing facility, and processed in real-time. Advantageously, the responses from queried energy users with energy curtailment possibilities may be automatically processed into a round-robin curtailment rotation which may be implemented by a computer-based control system. Thus, impact on occupants is minimized, and energy use and energy cost may be beneficially reduced in an intelligent, real-time manner. The invention also provides for early-recognition of impending adverse energy events, optimal response to a particular energy situation, real-time analysis of energy-related data, etc.

A system and method of predicting future power plant operations is based upon an artificial neural network model including one or more hidden layers. The artificial neural network is developed (and trained) to build a model that is able to predict future time series values of a specific power plant operation parameter based on prior values. By accurately predicting the future values of the time series, power plant personnel are able to schedule future events in a cost-efficient, timely manner. The scheduled events may include providing an inventory of replacement parts, determining a proper number of turbines required to meet a predicted demand, determining the best time to perform maintenance on a turbine, etc. The inclusion of one or more hidden layers in the neural network model creates a prediction that is able to follow trends in the time series data, without overfitting.

Apparatus and methods for universal node design implementing a universal learning rule in a mixed signal spiking neural network. In one implementation, at one instance, the node apparatus, operable according to the parameterized universal learning model, receives a mixture of analog and spiking inputs, and generates a spiking output based on the model parameter for that node that is selected by the parameterized model for that specific mix of inputs. At another instance, the same node receives a different mix of inputs, that also may comprise only analog or only spiking inputs and generates an analog output based on a different value of the node parameter that is selected by the model for the second mix of inputs. In another implementation, the node apparatus may change its output from analog to spiking responsive to a training input for the same inputs.

The invention discloses a depth image human body joint positioning method based on a convolution nerve network. The method is characterized by comprising a training process and an identification process. The training process comprises the following steps: 1, inputting a training sample; 2, initializing a deep convolution nerve network and its parameters, wherein the parameters comprise a weight and a bias of each layer edge; and 3, by use of a forward algorithm and a backward algorithm, learning the parameters of the constructed convolution nerve network. The identification process comprises the following steps: 4, inputting a test sample; and 5, performing regression on the input test sample by use of the trained convolution nerve network to find positions of human body joints. According to the invention, by use of the deep convolution nerve network and large data, multiple challenges such as shielding, noise and the like can be resisted, and the accuracy is quite high; and at the same time, by means of parallel calculation, the effect of accurately positioning the human body joints in real time can be realized.

Neural networks for optimal estimation (including prediction) and / or control involve an execution step and a learning step, and are characterized by the learning step being performed by neural computations. The set of learning rules cause the circuit's connection strengths to learn to approximate the optimal estimation and / or control function that minimizes estimation error and / or a measure of control cost. The classical Kalman filter and the classical Kalman optimal controller are important examples of such an optimal estimation and / or control function. The circuit uses only a stream of noisy measurements to infer relevant properties of the external dynamical system, learn the optimal estimation and / or control function, and apply its learning of this optimal function to input data streams in an online manner. In this way, the circuit simultaneously learns and generates estimates and / or control output signals that are optimal, given the network's current state of learning.

A method of building predictive statistical models provides a dedicated aggregation module for each transactional record source. Each aggregation module aggregates the transactional records using a neural network function to produce a scalar output which can then be input to a traditional modeling function, which may employ either logistic regression, neural network, or radial basis function techniques. The output of the aggregation modules can be saved, and updated aggregation values can be updated by processing new transaction records and combining the new transaction values with the previous output values using a blending function. Parameters of the neural network in the aggregation module may be calculated simultaneously with the parameters of the traditional modeling module.

Methods of detecting an object in an image using a convolutional neural-network-based architecture that processes multiple feature maps of differing scales from differing convolution layers within a convolutional network to create a regional-proposal bounding box. The bounding box is projected back to the feature maps of the individual convolution layers to obtain a set of regions of interest (ROIs) and a corresponding set of context regions that provide additional context for the ROIs. These ROIs and context regions are processed to create a confidence score representing a confidence that the object detected in the bounding box is the desired object. These processes allow the method to utilize deep features encoded in both the global and the local representation for object regions, allowing the method to robustly deal with challenges in the problem of object detection. Software for executing the disclosed methods within an object-detection system is also disclosed.

The invention provides a method for assisting handover of a user terminal's, UT's, communication session in an integrated multi RAT network, said method to be carried out by an RNCI being installed in said network, said method comprising the following steps:— receiving a “Handover Candidate” message comprising a message type identifier information element identifying said message as being a “Handover Candidate” message and which message identifies said session and which message further identifies a candidate RNC of said network, wherein said candidate RNC constitutes an RNC handover candidate for said session,— establishing the identity of said RNC by investigating said message,— associating said communication session with said candidate RNC identified in the previous step. The invention provides an RNC1, RNC2, and software, realising the method according to the invention.

A method and system for implementing a neuro-controller. One example of a neuro-controller is a brain-like stochastic search. Another example is a neuro-controller for controlling a hypersonic aircraft. Using a variety of learning techniques, the method and system provide adaptable control of external devices (e.g., airplanes, plants, factories, and financial systems).

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.

The invention discloses a multi-scale target detection method based on a deep convolutional neural network. The method comprises the steps of (1) data acquisition; (2) data processing; (3) model construction; (4) loss function definition; (5) model training; and (6) model verification. The method combines the ability of extracting image high-level semantic information of the deep convolutional neural network, the ability of generating candidate regions of region generation networks, the repair and mapping abilities of a content-aware region-of-interest pooling layer and the precise classification ability of multi-task classification networks, and therefore multi-scale target detection is completed more accurately and efficiently.

A mechanism for compiling a generative description of an inference task into a neural network. First, an arbitrary generative probabilistic model from the exponential family is specified (or received). The model characterizes a conditional probability distribution for measurement data given a set of latent variables. A factor graph is generated for the generative probabilistic model. Each factor node of the factor graph is expanded into a corresponding sequence of arithmetic operations, based on a specified inference task and a kind of message passing algorithm. The factor graph and the sequences of arithmetic operations specify the structure of a neural network for performance of the inference task. A learning algorithm is executed, to determine values of parameters of the neural network. The neural network is then ready for performing inference on operational measurements.

The invention discloses an attention mechanism-based in-depth learning diabetic retinopathy classification method comprising the following steps: a series of eye ground images are chosen as original data samples which are then subjected to normalization preprocessing operation, the preprocessed original data samples are divided into a training set and a testing set after being cut, a main neutralnetwork is subjected to parameter initializing and fine tuning operation, images of the training set are input into the main neutral network and then are trained, and a characteristic graph is generated; parameters of the main neutral network are fixed, the images of the training set are adopted for training an attention network, pathology candidate zone degree graphs are output and normalized, anattention graph is obtained, an attention mechanism is obtained after the attention graph is multiplied by the characteristic graph, an obtained result of the attention mechanism is input into the main neutral network, the images of the training set are adopted for training operation, and finally a diabetic retinopathy grade classification model is obtained. Via the method disclosed in the invention, the attention mechanism is introduced, a diabetic retinopathy zone data set is used for training the same, and information characteristics of a retinopathy zone is enhanced while original networkcharacteristics are reserved.