212 results about "Neural network analysis" patented technology

A system and method for assessing the quality of spot weld joints between pieces of metal includes an ultrasound transducer probing a spot weld joint. The ultrasound transducer transmits ultrasonic radiation into the spot weld joint, receives corresponding echoes, and transforms the echoes into electrical signals. An image reconstructor connected to the ultrasound transducer transforms the electrical signals into numerical data representing an ultrasound image. A neural network connected to the image reconstructor analyzes the numerical data and an output system presents information representing the quality of the spot weld joint. The system is trained to assess the quality of spot weld joints by scanning a spot weld joint with an ultrasound transducer to produce the data set representing the joint; then physically deconstructing the joint to assess the joint quality.

A system and method for assessing the quality of spot weld joints between pieces of metal includes an ultrasound transducer probing a spot weld joint. The ultrasound transducer transmits ultrasonic radiation into the spot weld joint, receives corresponding echoes, and transforms the echoes into electrical signals. An image reconstructor connected to the ultrasound transducer transforms the electrical signals into numerical data representing an ultrasound image. A neural network connected to the image reconstructor analyzes the numerical data and an output system presents information representing the quality of the spot weld joint. The system is trained to assess the quality of spot weld joints by scanning a spot weld joint with an ultrasound transducer to produce the data set representing the joint; then physically deconstructing the joint to assess the joint quality.

Systems and computer-implemented methods are used for analyzing battery information. The battery information may be acquired from both passive data acquisition and active data acquisition. Active data may be used for feature extraction and parameter identification responsive to the input data relative to an electrical equivalent circuit model to develop geometric-based parameters and optimization-based parameters. These parameters can be combined with a decision fusion algorithm to develop internal battery parameters. Analysis processes including particle filter analysis, neural network analysis, and auto regressive moving average analysis can be used to analyze the internal battery parameters and develop battery health metrics. Additional decision fusion algorithms can be used to combine the internal battery parameters and the battery health metrics to develop state-of-health estimations, state-of-charge estimations, remaining-useful-life predictions, and end-of-life predictions for the battery.

An adaptive packet mechanism and method for optimizing data packet transmission through a network connection between a sending node and a receiving node. Current network conditions in the connection are periodically determined wherein the network conditions pertain to the latency and jitter of packet transmission between the sending node and receiving node. The measurements of latency and jitter are used to determine an optimum packet size and an optimum inter-packet interval for transmission of packet data between the sending node and the receiving node and are used in the transmission of data packets from the sending node to the receiving node. Network conditions may be determined by transmission of monitor or data packets and may be determined at either or both of the sending or receiving nodes and the optimum packet size and inter-packet interval are determined by a fuzzy logic analyzer, a neural network analyzer or a combined fuzzy logic / neural network analyzer.

A preemptive neural network database load balancer configured to observe, learn and predict the resource utilization that given incoming tasks utilize. Allows for efficient execution and use of system resources. Preemptively assigns incoming tasks to particular servers based on predicted CPU, memory, disk and network utilization for the incoming tasks. Direct write-based tasks to a master server and utilizes slave servers to handle read-based tasks. Read-base tasks are analyzed with a neural network to learn and predict the amount of resources that tasks will utilize. Tasks are assigned to a database server based on the predicted utilization of the incoming task and the predicted and observed resource utilization on each database server. The predicted resource utilization may be updated over time as the number of records, lookups, images, PDFs, fields, BLOBs and width of fields in the database change over time.

The invention provides a method for carrying out shale gas reservoir evaluation and finding a dessert area for shale gas exploration and development comprehensively through comprehensive application of rock physics, log data and omnibearing or wide-azimuth three-dimensional seismic data. The method is characterized by analyzing the characteristics of the dessert area in a shale reservoir by applying comprehensive geophysical exploration technologies of laboratory rock geophysical measurement, log data comprehensive analysis, rock physical modeling, vertical seismic section data, omnibearing or wide-azimuth three-dimensional seismic data high precision surface layer comprehensive modeling static correction processing, prestack de-noising processing, amplitude relative fidelity processing, resolution enhancement processing which is carried out by utilizing well constraint and well seismic data to drive ground seismic data, fine excision, iteration velocity analysis, interval velocity field analysis, prestack depth migration processing, high resolution processing, azimuth classification processing, high-precision anisotropy processing, prestack and post-stack data inversion processing, and neural network analysis and the like, and thus gas bearing characteristic prospect of the shale gas reservoir is evaluated accurately and the dessert area for shale gas exploration and development is delineated.

This invention uses linguistic principles, which together can be called Collocational Cohesion (CC), to evaluate and sort documents automatically into one or more user-defined categories, with a specified level of precision and recall. Human readers are not required to review all of the documents in a collection, so this invention can save time and money for any manner of large-scale document processing, including legal discovery, Sarbanes-Oxley compliance, creation and review of archives, and maintenance and monitoring of electronic and other communications. Categories for evaluation are user-defined, not pre-set, so that users can adopt either traditional categories (such as different business activities) or custom, highly specific categories (such as perceived risks or sensitive matters or topics). While the CC process is not itself a general tool for text searches, the application of the CC process to large collections of documents will result in classifications that allow for more efficient indexing and retrieval of information. This invention works by means of linguistic principles. Everyday communication (letters, reports, emails-all kinds of communication in language) does follow the grammatical patterns of a language, but forms of communication also follow other patterns that analysts can specify but that are not obvious to their authors. The CC process uses that additional information for the purposes of its users. Any communication exchange that can be recognized as a particular kind of discourse may be used as a category for classification and assessment. Specific linguistic characteristics that belong to the kind of discourse under study can be asserted and compared with a body of general language, both by inspection and by mathematical tests of significance. These characteristics can then be used to form the roster of words and collocations that specifies the discourse type and defines the category. When such a roster is applied to collections of documents, any document with a sufficient number of connections to the roster will be deemed to be a member of the category Larger documents can be evaluated for clusters of connections, either to identify portions of the larger document for further review, or to subcategorize portions with different linguistic characteristics. The CC process may be extended to create a roster of rosters belonging to many categories, thereby increasing the specificity of evaluation by multilevel application of this invention. The CC process works better than other processes used for document management that rely on non-linguistic means to characterize documents. Simple keyword searches either retrieve too many documents (for general keywords), or not the right documents (because a few keywords cannot adequately define a category), no matter how complex the logic of the search. Application of statistical analysis without attention to linguistic principles cannot be as effective as this invention, because the words of a language are not randomly distributed. The assumptions of statistics, whether simple inferential tests or advanced neural network analysis, are thus not a good fit for language. This invention puts basic principles of language first, and only then applies the speed of computer searches and the power of inferential statistics to the problem of evaluation and categorization of textual documents.

A method and system for data modeling that incorporates the advantages of both traditional response surface methodology (RSM) and neural networks is disclosed. The invention partitions the parameters into a first set of s simple parameters, where observable data are expressible as low order polynomials, and c complex parameters that reflect more complicated variation of the observed data. Variation of the data with the simple parameters is modeled using polynomials; and variation of the data with the complex parameters at each vertex is analyzed using a neural network. Variations with the simple parameters and with the complex parameters are expressed using a first sequence of shape functions and a second sequence of neural network functions. The first and second sequences are multiplicatively combined to form a composite response surface, dependent upon the parameter values, that can be used to identify an accurate model.

The invention discloses an intelligent blurry electrocardiogram on-line analyzer system, which consists of an on-line data interface, an electrocardial vector parallel computer, a data fusion nerve network, an electrocardial interval vector multi-domain analyzer, an expert system, an application software packet and an off-line data interface. By adopting the MIMD parallel computer, the multiplex nerve network analysis, multi-sensor time sequence data fusion and electrocardial vector multi-domain analysis for a 12 lead electrocardial vector, automatic analysis conclusion of a main blurry electrocardiogram and blurry electrocardiogram attached figure information comprising the information of blood pressure, pulse, blood oxygen, respiration, activity range and activity content and the like are integrated to form a diagnostic report of time domain analysis and frequency domain analysis of vital sign signals comprising a 12 lead electrocardiogram, time domain analysis of various electrocardial interval dispersions and wavelet analysis of various electrocardial interval variability.

PCT No. PCT / GB96 / 01821 Sec. 371 Date Feb. 3, 1998 Sec. 102(e) Date Feb. 3, 1998 PCT Filed Jul. 25, 1996 PCT Pub. No. WO97 / 05730 PCT Pub. Date Feb. 13, 1997A speech signal is subjected imperfect to vocal tract analysis model and the output therefrom is analyzed by a neural network. The output from the neural network is compared with the parameters stored in the network definition function, to derive measurement of the quality of the speech signal supplied to the source. The network definition function is determined by applying to the trainable processing apparatus a distortion perception measure indicative of the extent to which a distortion would be perceptible to a human listener.

A mountain geological disaster monitoring and prewarning system based on digital twin driving comprises data acquisition sensors arranged on a physical layer, a DT monitoring module, a DT prediction module and a DT prewarning module arranged on an information layer, and clients on a visualization layer. A monitoring and prewarning method comprises the following steps: Step 1, investigation is carried out on a geological environment, and a monitoring plan is obtained for focused monitoring on a current area; Step 2, real-time data is obtained according to the monitoring plan obtained in Step 1,and historical geological disaster data are all stored in a geological disaster digital twin database and analyzed in the DT prediction module; Step 3, BP neural network analysis is carried out in the DT prediction module based on the historical geological disaster data to obtain a geological disaster prediction model; and Step 4, the monitoring data obtained in Step 2 is stored in the geologicaldisaster twin database to obtain prediction results, and the prediction results are sent to the clients for visualization. The system and method provided by the invention has the characteristics thatthe personal safety of residents, tourists and the like can be protected, and economic losses can be reduced.

A method is disclosed for interrogating enclosed spaces such as sewers and the like by commanding a camera to travel through the enclosed space while transmitting the video feed from the camera to a remote location for viewing and processing. The processing involves image manipulation before analyzing frames of the video using a neural network developed for this task to identify defects from a library of known defects. Once a new defect is identified, it is inserted into the model to augment the library and improve the accuracy of the program. The operator can pause the process to annotate the images or override the model's determination of the defect for further enhancement of the methodology.

A system identifies areas of interest (e.g., locations of text or objects) in an image in a way that reduces memory requirements, computer processing requirements, and computation time. The system analyzes a downscaled version of an input image using a convolutional neural network that has been trained to recognize areas of interest in coarse, low resolution, images. Based on the output of the coarse neural network, the system predicts particular segments of the image that are most likely to include areas of interest. A second convolutional neural network that has been trained to identify areas of interest in fine, high resolution images analyzes only those segments of the image that the coarse neural network selected for further examination. A reconstruction of the analysis locates likely areas of interest for the whole image.

The invention discloses a college entrance examination voluntary artificial intelligence filling system, which is composed of the following parts: automatic data collector EADH, college entrance examination big data knowledge base CDKB, deep convolutional neural network perception component DCNA, deep convolutional neural network weight component, deep volume Productive neural network analysis component DCNS, fast Monte Carlo search tree optimization MROP, college entrance examination strategy optimization model CSOM, college entrance examination volunteer form automatic generator AFAG, user operation interface, background management module, university and professional information query module, score line and enrollment plan query modules, simulated reporting and accurate reporting modules, user voluntary evaluation modules, college and professional intelligent recommendation modules, occupational employment recommendation voluntary modules, user characteristic evaluation modules, and college entrance examination policy news and enrollment dynamic modules. Intelligent technology is used to assist the decision-making function of college entrance examination volunteers.

The invention relates to a non-contact atrial fibrillation intelligent detection system based on a deep convolution residual network. Heart impact signals generated during cardiac ejection of the human body can be collected, and whether or not the heart has atrial fibrillation is analyzed by using the deep convolution residual network. The system includes two parts including a heart impact signalcollection part and a deep convolution residual neural network analysis part. The heart impact signal collection part includes a piezoelectric film sensor, a piezoelectric signal processing module, anAD conversion circuit and a processor. Collected data is uploaded to a server running a deep convolution residual network algorithm, and a judgment result is turned back. A deep convolution residualneural network model includes a one-dimensional convolution layer, a downsampling layer, a batch normalization layer, an activation layer and a fully connected layer. A residual structure contained inthe model is formed by adding original signals and deep features extracted after several convolutional sample normalization activation operations. The system can be used for uninterrupted monitoringof the human body at night and give a judgement prompt in time to facilitate timely treatment of disease conditions.

The invention discloses a method for judging and positioning bridge cable corrosion. The method comprises the following steps of: arranging a sensor on a cable to be detected, acquiring an actual measured signal by using the sensor and reading the signal into a laptop by using a data acquisition card; establishing a fine numerical model for the cable corrosion judgment by adopting the finite element technology according to geometric dimension, material characteristics and excitation signal conditions of the cable, initializing the numerical model, analyzing and calculating to obtain a simulation response signal, and providing a standard comparison signal for the actual measured signal; using the actual measured signal as a judgment signal of the cable corrosion, and comparing the judgment signal and the standard comparison signal and analyzing the judgment signal by adopting a neural network analysis method, if the damage does not exceed a threshold value, judging that the cable is not corroded, otherwise alarming and positioning the corroded part; generating a recording report and recording a local database. The method for judging and positioning the bridge cable corrosion has the advantages of high resolution, high judging precision and no influence on normal use of the cable.

Methods and systems for monitoring operation integrity during hydrocarbon production or fluid injection operations by receiving microseismic data; processing the data to obtain data panels corresponding to microseismic data measured over a time interval; determining, with a neural network analysis, whether any of the data panels includes a noise event or a non-noise event; calculating, for each data panel including a non-noise event, trigger values for data traces corresponding to sensor receivers of the microseismic monitoring system; selecting, as a triggered data panel, at least one data panel that satisfies triggering criteria; selecting, as a non-trivial data panel, at least one triggered data panel that satisfies spectral density criteria; calculating a value for each of at least two event attributes of the event; determining an event score based on the event attribute values; and classifying the event into at least one event category based on the event score.

A computer based method for analyzing microarray chip information. A computer implemented artificial neural network (ANN) is trained by back propagation of error using a set of training microarray chip input vectors to create a trained ANN. At least one set of test data is applied to the trained ANN to generate a prediction. The trained ANN numerically analyzing with respect to a subset of the input vectors to identify those elements of the input vector which are most effective in obtaining the prediction. the set of input vectors is reduced in dimension to contain data only from those genes found most effective in obtaining the prediction to form a dimensionally reduced set of input vectors. The neural network is retrained using the dimensionally reduced set of input vectors by back propagation of error to generate a retrained network. The at least one set of test data is reapplied to the retrained neural network to generate a second prediction.

Systems and computer-implemented methods are used for analyzing battery information. The battery information may be acquired from both passive data acquisition and active data acquisition. Active data may be used for feature extraction and parameter identification responsive to the input data relative to an electrical equivalent circuit model to develop geometric-based parameters and optimization-based parameters. These parameters can be combined with a decision fusion algorithm to develop internal battery parameters. Analysis processes including particle filter analysis, neural network analysis, and auto regressive moving average analysis can be used to analyze the internal battery parameters and develop battery health metrics. Additional decision fusion algorithms can be used to combine the internal battery parameters and the battery health metrics to develop state-of-health estimations, state-of-charge estimations, remaining-useful-life predictions, and end-of-life predictions for the battery.

The invention discloses a self-defined model of artificial neural network analysis system and method. The system and method use of a powerful database system as a background, the computer automatically through artificial neural network model of arbitrary custom, and in accordance with its definition of the artificial neural network model for automatic data analysis and forecasting training. This makes the original artificial neural network professionals need to take several months of artificial neural network data analysis, only just a few minutes to complete, at the same time, as a result of a custom graphical modeling and engineering management makes artificial steps Neural network analysis more simple and fast. This invention changed the artificial neural network analysis by completely relying on the traditional method of programming, Neural network analysis makes further leap to the intelligent, personalized self-definition stage. Through the system and method for the analysis of the provision of professional staff, and rapid analysis of neural network services, thereby greatly improving the artificial neural network analysis of quality and efficiency.

A first Internet of Things (IoT) sensor network remote sensor station and network comprising a sensor network parameter processing, warning and control system with electronic, specifically programmed, specialized sensor network communication computer machine including electronic artificial intelligence expert system processing with transceivers and / or electrical or optical connections for communicating with IoT sensors and other different followed or following IoT remote sensor stations. Monitor units collect information from and communicate with remote sensor stations and further analyze collected information with artificial intelligence expert systems processing comprising expert input with multiple propositional expert system instructions defining multiple ranges of sensor variables. Further artificial intelligence expert system decision making provides Multiple-Input / Multiple-Output (MIMO) expert system operation with dispatch of electronic and / or optical communication warnings and / or corrective action control signals addressing MIMO urgent and composite degrees of concerns. Further artificial intelligence expert system comprises one or more of expert system processing, speech recognition, natural language processing, image analysis, fuzzy logic and / or neural network analysis. In some embodiments, hierarchical expert or fuzzy systems, neural network and / or adaptive feedback control implementation are disclosed resulting in substantial simplification of computational requirements.

The invention discloses a complex industrial process data modeling method based on a dynamic convolutional neural network. The method comprises the following steps: process variables with strong correlation with industrial process objective variables are selected, and through sampling, a time sequence for each process variable is obtained; an equal depth box plot is used to carry out abnormal point detection and elimination on the time sequences, and a linear interpolation method is then used for filling; the time sequence for each process variable in a former process time delay range at the sampling moment of the objective variable is extracted, a two-dimensional matrix containing dynamic characteristics of the process is formed, and picture samples are formed; and the dynamic convolutional neural network is built to analyze the dynamic characteristics of the industrial process data, the time and space relation of each sensitive variable is recognized automatically, and a prediction model for the objective variable is built. A large amount of historical data accumulated in the actual production process field is used, a data model of predicting an unpredictable objective variable by using a predictable process variable is built accurately, and an important role is played in online production process evaluation, dynamic adjustment and even energy conservation and emission reduction.

The invention discloses a judgment method of ultra-high voltage equipment local discharge detection data. The judgment method comprises the following steps: sampling a continuous ultrasonic frequencysignal to reduce to the continuous sound wave frequency signal capable of being heard by the human ear; continuously intercepting a frame sound wave frequency signal with a set time length; extractinga Mayer frequency cepstrum coefficient of the frame sound wave frequency signal as a to-be-identified fault discharge feature; sending the extracted to-be-identified fault discharge feature into a CNN convolution neural network, enabling the to-be-identified fault discharge feature to enter a fault classifier of a CNN convolution neural network output classification layer through CNN convolutionneural network analysis, wherein the CNN convolution neural network identifies the to-be-identified fault discharge feature and outputs the to-be-identified fault discharge feature according to the fault classifier formed by learning the known fault discharge feature in advance. The mode learning and identification are performed on the fault type by directly using the convolution neural network CNN, the identification accuracy rate is improved, and the manual intervention is reduced or avoided.

The invention discloses a distributed cluster hardware fault early warning method and system. The method comprises: a performance monitoring module acquires hardware index data in real time and transmits the hardware index data to a data analysis module; the data analysis module performs neural network learning and analysis on all the acquired hardware index data, and predicts whether the hardware will have a fault; the data analysis module is constructed based on a neural network model; and if the hardware is predicted to have a fault, a hardware early warning management module controls an alarm module via a network server to start alarming. The data analysis module performs neural network analysis, so that the hardware fault can be detected out before the fault happens, then the fault solution efficiency and the availability of the cluster system are improved, and the accuracy of fault detection is improved.

Provided is a method for detecting an abnormal session including a request message received by a server from a client and a response message generated by the server, the method including transforming at least a part of messages included in the session into data in the form of a matrix, transforming the data in the form of the matrix into a representation vector a dimension of which is lower than a dimension of the matrix of the data using a convolutional neural network, and determining whether the session is abnormal by arranging the representation vectors obtained from the messages in an order in which the messages are generated to compose a first representation vector sequence, and analyzing the first to representation vector sequence using an long short term memory (LSTM) neural network.

The invention relates to a system and method for diagnosis and treatment of a breathing pattern of a patient. Described is a system including a sensor and a processing arrangement. The sensor measures data corresponding to a patient's breathing patterns. The processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of a REM sleep state. In another embodiment, the processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of one of the following states: (i) a wake state and (ii) a sleep state. In another embodiment, a neural network analyzes the data to determine whether the breathing patterns are indicative of one of the following states: (i) a REM sleep state, (ii) a wake state and (iii) a sleep state. In another embodiment, the processing arrangement analyzes the data to determine whether the breathing pattern is indicative of an arousal.

The invention relates to a picture stylization processing method based on deep learning. The method includes steps: S1: establishing and training a neural network; S2: segmenting a to-be-processed picture into a plurality of ultra-pixels; S3: analyzing each ultra-pixel by employing the trained neural network, and marking the environment category with the highest adaption degree therefor; S4: defining the environment category with the maximum marking frequency of the ultra-pixel of all the ultra-pixels as the environment category of the picture; S5: extracting an object in the picture, and determining a target major object according to the position of the object in the picture; and S6: sharpening or blurring a background according to a stylization processing requirement, wherein the stylization processing requirement comprises background strengthening and background weakening. Compared with the prior art, the method is advantaged by fast processing speed and good effect.