648 results about "Hybrid model" patented technology

The present solution is directed to a hybrid customer support model for providing remote online support service in conjunction with local onsite support service. A local technician provides on site support service for a device at a premise for a predetermined amount of time. The onsite support by the local technician may be limited to a subset of the tasks for resolving an issue with the device or otherwise be limited to applying a subset or scope of skills to resolving the issue. In one embodiment, the local technician has a first type of skill from a plurality of skills. The local technician identifies an issue with the device and sends the issue to a centralized service. The centralized service receives an identification of the issue—the resolution of which may include a plurality of tasks. In one embodiment, each of the plurality of tasks is performed by one or more remote technicians having a skill type corresponding to the task. A resource pool is managed via the centralized service to support information flow in the hybrid model. The centralized service tracks service information and results.

The embodiment of the invention provides a modeling method of an early warning model of mixed failures and a modeling system. The modeling method provided by the invention comprises the following steps of: generating a function analyzing module on the basis of HAZOP (Hazard and Operability Analysis) or FMEA (Failure Mode and Effects Analysis); generating a degeneration analyzing module on the basis of FMEA analyzing results and a theory of stochastic processes; generating an accident analyzing module according to state monitoring data and maintenance action information; generating an action analyzing module according to output results of the function analyzing module and the degeneration analyzing module through combining a DBN (Dynamic Bayesian Network) theory; taking the output of the accident analyzing module as an inference evidence and utilizing a DBN inference algorithm to process forward and backward inferences in the same time period to generate an evaluating module for outputting factors and consequences of system failures; taking the output results of the evaluating module and the accident analyzing module as the inference evidence and utilizing the DBN inference algorithm to process forward and backward inferences in the different time periods to generate a predicating module for outputting prospective degeneration tendencies of each member of the system. The model provided by the invention can be used for tracking the failure factors of the system and inferring possible failure consequences and probability.

A hybrid-model network application includes a native component that provides user-level access to features of a networked computing device and a web client component that provides access to web services of a server. The network application causes the networked computing device to receive content from the server. The content includes user interface content to be rendered by the web client component and code content associated with the user interface content that uses a native function of the networked computing device. The content is displayed in a user interface by way of the web client component. A request, associated with the code content, is received from a user interacting with the user interface. The device handles the request for use of the native function by the native component.

The present invention is directed to computer-based methods and system to perform root-cause analysis on an industrial process. The methods and system load process data for an industrial process from a historian database and build a hybrid first-principles and inferential model. The methods and system then executes the hybrid model to generate KPIs for the industrial process using the loaded process variables. The methods and system then selects a subset of the KPIs to represent an event occurring in the industrial process, and divides the data for the subset into multiple subset of time series. The system and methods select time intervals from the time series based on the data variability in the selected time intervals and perform a cross-correlation between the loaded process variables and the selected time interval, resulting in a cross-correlation score for each loaded process variable. The methods and system then select precursor candidates from the loaded process variables based on the cross-correlation scores and execute a parametric model for performing quantitative analysis of the selected precursor candidates, resulting in a strength of correlation score for each precursor candidate. The methods and system select root-cause variables from the selected precursor candidates based on the strength of correlation scores for analyzing the root-cause of the event.

The invention puts forward a Spark-based big data hybrid model mobile recommending method comprising four steps as follows: firstly, getting a user's commodity purchase data at a mobile end; secondly, extracting user historical data from a database and importing the user historical data to an HDFS, and extracting features, such as user behavior features, brand features, user's personal consumption features and cross features; thirdly, packaging a hybrid model on a Spark platform using an RDD operator, and embedding the model interface into a big data platform for calling; and fourthly, calling the hybrid model interface to extract feature data, setting training parameters of the model, and training the hybrid model. The model is estimated using a test data set and optimized, the trained hybrid model is saved, and relevant recommendation is made. The method can effectively improve the efficiency of recommendation under the condition of a larger amount of data and higher data sparseness.

The invention discloses an empirical mode decomposition and deep learning hybrid model-based wind speed prediction method and system. The method comprises the following steps of S1, decomposing an original wind speed time sequence according to empirical mode decomposition so as to obtain a plurality of intrinsic mode functions; S2, establishing a training data set and a test data set for each intrinsic mode function; S3, inputting a training sample, in the training data set, of each intrinsic mode function into a stack type coding network to perform training so as to obtain a wind speed prediction sub-model; S4, inputting the test data set into corresponding wind speed prediction sub-models to perform prediction so as to obtain prediction output values of the wind speed prediction sub-models; and S5, performing combination superposition processing on the prediction output values of the wind speed prediction sub-models to obtain a final overall prediction output value. According to the method and the system, the prediction precision and robustness of the prediction models are effectively improved and higher short-term wind speed prediction precision can be achieved.

The present invention provides novel techniques for graphically modeling, displaying, and interacting with parametric hybrid models used to optimize and control components of industrial plants and enterprises. In particular, a graphical modeling tool of a control / optimization system for controlling a plant or enterprise is configured to transmit a graphical user interface to a user, wherein the graphical user interface enables a plurality of command inputs relating to a plurality of parametric hybrid models based on a security access level of the user. The parametric hybrid models may be displayed by the graphical user interface as nodes of a network with connections connecting the nodes. The user may graphically manipulate the nodes and connections associated with the parametric hybrids models to either modify optimization constraints of the model network, or actually modify the manner in which the parametric hybrid models function (e.g., inputs, outputs, parameters, and so forth, of the parametric hybrid models), depending on the access level of the user.

The invention provides a social emotion classification method based on multimodal fusion, which relates to information in the form of audio, visual and text. Most of affective computing research onlyextracts affective information by analyzing single-mode information, ignoring the relationship between information sources. The present invention proposes a 3D CNN ConvLSTM (3D CNN ConvLSTM) model forvideo information, and establishes spatio-temporal information for emotion recognition tasks through a cascade combination of a three-dimensional convolution neural network (C3D) and a convolution long-short-term memory recurrent neural network (ConvLSTM). For text messages, use CNN-RNN hybrid model is used to classify text emotion. Heterogeneous fusion of vision, audio and text is performed by decision-level fusion. The deep space-time feature learned by the invention effectively simulates the visual appearance and the motion information, and after fusing the text and the audio information,the accuracy of the emotion analysis is effectively improved.

The invention relates to an online state monitoring and fault diagnosis device and an online state monitoring and fault diagnosis method for a rotary machine. The device comprises a data acquisition device 101, a feature extraction device 102, a data management device 103, a display device 106, a device 107 such as a mouse, a keyboard or the like for setting parameters and managing equipment by a user, a multi-model detector training device 104, and a multi-model fault diagnosis device 105. The method comprises the following steps of: acquiring signals by using the data acquisition device; storing the signals, and extracting features of the signals by using the standard feature array extraction device; training a detector by using the training device for the detector for identification; performing identification by adopting the trained hybrid model detector; and outputting and recording the identification result. The device and the method can diagnose common rotary machine faults such as shaft eccentricity, bearing eccentricity, rolling body abrasion and the like, and have the advantages of high automation degree, capability of identifying multiple fault types, capability of realizing early diagnosis, good fault database expansibility and the like.

The present invention discloses an image description method based on a convolution cyclic hybrid model, and belongs to the deep learning field of machine learning. For text description, due to the fact that words in sentences have strong context relationships, text data can be encoded by using a language model. The image description method specifically comprises the steps of (1) extracting image characteristics; (2) encoding the image characteristics; (3) encoding image description texts; (4) training the model; and (5) generating image text description by utilization of the trained model. The image description method is widely used in machine vision and natural language processing, and new thought and solutions are provided in an image description method aspect. At present, in image description, text encoding is randomly generated, which has a certain blindness, and the effect is not good. The texts are encoded by utilization of word2Vec, the encoding problem of the description texts in image description is solved, and the defects of randomness, blindness and instability are remedied. Application ability of the image description is largely increased, and foundation is established for development of machine vision.

The invention belongs to the technical field of computer application and provides a humming type music retrieval method and system based on note modeling. According to the humming type music retrieval method and system based on note modeling, modeling and parameter training are conducted on standard notes according to the Gaussian hybrid model technology, similarity calculation is conducted between a note resolved from a humming voice and a duration time sequence of the note and the standard notes of all key melodies in a base and the duration time sequences of the standard notes, and an optimal result is obtained. The humming type music retrieval method and system based on note modeling have the outstanding advantages that adding of new models and parameter training do not need to be conducted when new songs are added into a song library, and the popularization and application prospect is broad.

Machine training for determining sentiments in social network communications. A text document is extracted from a web site and tokenized into tokens. The tokens are input to a word to vector conversion model to generate word vectors. A term frequency inverse document frequency (TF-IDF) algorithm converts the word vectors to sentence vectors. A randomly selected subset the sentence vectors are tagged and used to train a classifier. The classifier takes a sentence vector and predicts a sentiment associated with the sentence vector. Predicted sentiment associated with each of the sentence vectors may be combined to generate a sentiment associated with the text document.

Motor drives, motor speed controllers, motor speed estimation systems, and methods are presented for controlling motor startup speed and for estimating motor speed during startup, in which a speed estimate controller provides a rotor speed estimate based on a first error term from a torque-based MRAS component for a first range of motor speeds and based on a second error term from a rotor flux-based MRAS component for a second range of speeds.

The invention discloses a solar photovoltaic power generation prediction method based on TCN-LSTM. The solar photovoltaic power generation prediction method comprises the following steps: data preprocessing: processing photovoltaic data of one year into photovoltaic data of four seasons of spring, summer, autumn and winter, removing useless photovoltaic data, arranging and concluding key featuresaffecting photovoltaic power generation in a photovoltaic data set, and carrying out the normalization processing; building and predicting a prediction model: building a hybrid model based on a time convolutional neural network and a long short-term memory network, and training the prediction model by adopting the training set data; inputting the training sample into a TCN-LSTM model; carrying outfeature extraction by a TCN; inputting the time sequence data into a two-layer extended causal convolution, adding the time sequence data with a one-dimensional convolution output result to obtain anoutput result, and inputting the output result into an LSTM model; and performing information abstraction of high-level features on the features of the output result of the TCN by the LSTM model, processing the features into a one-dimensional vector and inputs the one-dimensional vector into a full connection layer of the LSTM model, and directly outputting a photovoltaic power generation power prediction value at the next moment by the full connection layer.

The invention discloses an EMR (Electronic Medical Record) data drive based GDM (Gestational Diabetes Mellitus) forecasting method playing an increasing important role in smart medical service. The invention proposes a machine learning based GMD forecasting frame and constructs three forecasting frames including a full-domain data forecasting model, a staging data forecasting model and a weekly data forecasting model according different time window division methods for collected data. After a forecasting item is identified, high-dimensional EMR data digging is implemented through seven steps including input and ETL data cleaning, correlation of a medical record code and feature data, EMR data pre-treatment, secondary data treatment, feature engineering, machine learning and forecasting application. A mark data set related to definite diagnosis is constructed by using clinic data and is divided into two sub sets used for model training and testing. The method performs forecasting through supporting a support vector machine, a Bayesian network, a decision making tree and an integration based hybrid model and GDM mode classification is realized.

The invention relates to a speech emotion recognizing method based on a hidden Markov model (HMM) / self-organizing feature map neural network (SOFMNN) hybrid model. The speech emotion is recognized by combining an HMM model and an SOFMNN model through the method. The speech emotion recognizing method specifically comprises the following steps of: 1) establishing an emotion speech data base; 2) carrying out speech signal pretreatments including weighting treatment, denoising and framing windowing; 3) extracting the speech emotion characteristics including the extraction of time, energy, amplitude, fundamental frequency and formant of speech emotion signals; and 4) training and recognizing by using the HMM / SOFMNN hybrid model. Compared with the prior art, the invention overcomes the problem that the HMM is difficult to solve the problem of mutual overlapping among model categories by self and makes up the shortage in the aspect of obtaining timing information by SOFMNN, so that the speech emotion recognizing rate is improved.

The present invention provides novel techniques for graphically modeling, displaying, and interacting with parametric hybrid models used to optimize and control components of industrial plants and enterprises. In particular, a graphical modeling tool of a control / optimization system for controlling a plant or enterprise is configured to transmit a graphical user interface to a user, wherein the graphical user interface enables a plurality of command inputs relating to a plurality of parametric hybrid models based on a security access level of the user. The parametric hybrid models may be displayed by the graphical user interface as nodes of a network with connections connecting the nodes. The user may graphically manipulate the nodes and connections associated with the parametric hybrids models to either modify optimization constraints of the model network, or actually modify the manner in which the parametric hybrid models function (e.g., inputs, outputs, parameters, and so forth, of the parametric hybrid models), depending on the access level of the user.

The invention discloses an air compressor monitoring diagnosis system and method adopting an adaptive kernel Gaussian hybrid model, and relates to the field of air compressor control technologies. The system comprises a site equipment layer, an equipment control layer and a management and monitoring layer. The site equipment layer is composed of PLCs200, sensors, air compressors, actuators and a water pump, and with the PLCs200 as slave stations, control over the site equipment layer is completed. The equipment control layer comprises an upper computer and a PLC300, with the PLC300 as a master station, the whole air compressor system is controlled through a variable-structure adaptive PID controller based on a support vector machine, and the upper computer monitors the air compressor system. The equipment control layer is in communication with the management and monitoring layer through the industrial Ethernet, and then remote monitoring and data transmission of the upper computer are achieved. The Gaussian hybrid model and the kernel principal component analysis method are integrated in the fault diagnosis method adopted in the upper computer, optimal kernel function parameters are solved through the iterative optimization method, and the purpose of distinguishing different mode data is achieved. The air compressor monitoring diagnosis system and method have higher diagnosis precision and higher practical value.

The invention discloses a short-term power load prediction method based on a CNN-IPSO-GRU hybrid model. The method comprises the following steps: firstly, collecting historical load, meteorological factors, date information and other data of a power grid, performing data normalization processing, dividing a training set and a test set, extracting a multi-dimensional feature vector representing load change by using a convolutional neural network technology, and constructing a time sequence as input of a model; constructing a gating circulation unit network prediction model, optimizing the gating circulation unit network prediction model by utilizing the training set data through an improved particle swarm algorithm to obtain two optimal prediction model parameters, and reestablishing a gating circulation unit network model according to the obtained optimal prediction model parameters; and finally, realizing the short-term load prediction of the power grid by test set data. The method provided by the invention can accurately predict the short-term load change trend of the power grid, and further plays an important role in reducing the loss of the generator set and ensuring economic and reliable operation of the power grid.

The invention discloses a chemical production on-line fault detection and diagnosis technique based on a physical-large data hybrid model. The chemical production on-line fault detection and diagnosis technique is characterized by comprising the following steps: after selecting a target operation unit, scanning all historical data of the unit, and after parameters are verified, establishing an accident knowledge base and a parameter model; in later on-line detection process, directly loading on-line data into the parameter model, scanning so as to obtain fault data in the on-line data, making an alarm, and comparing the fault data with data in the accident knowledge base, thereby obtaining fault reasons. The method disclosed by the invention is applied to confirmed single chemical unit operation, a reliable physical model can be established, a chemometrical method is used in the whole production process, a big-data processing technique is introduced to process real-time data of years, operators focus on faults of performance indexes within a controllable variable range, and the influence of uncontrollable production process variable to fault detection is eliminated.

Embodiments of the invention provide a method and device for multilingual hybrid model establishment and data acquisition, and electronic equipment. The method includes determining a modeling unit ofan acoustic model according to a speech unit contained by multilanguage, and establishing the acoustic model based on a deep neural network, wherein the modeling unit is the context free speech unit;obtaining multilingual hybrid speech training data, converting a hybrid speech signal in the multilingual hybrid speech training data into an eigenvector sequence, and converting a hybrid labelling text corresponding to the hybrid speech signal into a hybrid label sequence of the modeling unit based on the acoustic model; training the acoustic model by using the eigenvector sequence and the hybridlabel sequence; obtaining multilingual hybrid corpus data to train a language model; and establishing a multilingual hybrid speech recognition system according to the acoustic model and the languagemodel. According to the embodiments, recognition accuracy of speech data mixing multiple languages can be enhanced.

A two-tiered classification system that can be integrated with the current algorithm used by pathologists for identification of the site of origin for ‘malignancy with unknown primary ’ is presented. In use, morphology, immunohistochemical (IHC) studies, and microarry-based top tier gene expression classifiers first subclassify cytokeratin positive carcinomas into adenocarcinoma, squamous cell carcinoma, neuroendocrine carcinoma and urothelial carcinoma. Subsequently, organ-specific IHC-markers, if available, are used in conjunction with microarray-based second tier gene expression classifiers to assign the primary site of origin to the sample. This new hybrid approach combines IHC with a hierarchy of quantitative gene expression based classifiers into an algorithmic method that can assist pathologists to further refine and support their decision making process.

An earth-boring tool system that includes a drilling assembly for drilling a wellbore and a surface control unit. The surface control unit includes a prediction system that is configured to train a hybrid physics and machine-learning model based on input data, provide, via the hybrid model, a predictive model representing a rate of penetration of an earth-boring tool and wear of the earth-boring tool during a planned drilling operation, provide one or more recommendations of drilling parameters based on the predictive model, utilize the one or more recommendations in a drilling operation, receive real-time data from the drilling operation, retrain the hybrid model based on a combination of the input data and the real-time data, and provide, via the retrained model, an updated predictive model of a rate of penetration of an earth-boring tool and wear of the earth-boring tool during a remainder of the planned drilling operation.

The invention discloses a road ponding image detection early warning method based on a hybrid model, belongs to the technical field of big data, realizes the real-time ponding detection of different monitoring cameras on a road based on deep learning image characteristics, generates the ponding severity level data, and solves the defects of complex installation, easy damage and the like of a traditional detection instrument. An interactive area configuration mode is adopted to adapt to monitoring cameras in different scenes. According to the method, the deep neural network characteristics andthe improved ResNet residual network are adopted, the spatio-temporal characteristics of the monitoring video are combined, the ponding confidence is calculated in a mixed mode, so that the detectionaccuracy is improved, and the requirement for the actual application accuracy is met based on the monitoring video condition.

The invention discloses a text classification algorithm based on a circulating neural network variant and a convolution neural network, The algorithm includes step 1, preprocessing that data set SogouC and the data set THUCNews, dividing the two data set into a training set and a test set, and training the text data in the respective training set and the test set into sentence vectors; step 2, using the training set text of the two datasets in Step 1 to establish the BGRU-CNN hybrid model; step 3, establishing an objective function, and using a random gradient descent method to train that BGRUestablish in the step 2; step 4, inputting the text sentence vector of the test set into the BGRU trained in step 3 in the two data sets. The classification results are obtained. The invention solvesthe problems that the difficulty of extracting the semantic key features is great and the classification effect is poor when the long text is classified in the prior art.

The invention discloses a characteristic value Gaussian statistical property-based polarized synthetic aperture radar (SAR) image classification method, which mainly solves the problems that the prior art is insufficient on cognition of characteristic distribution properties and the category judgment limit needs man-made determination. The method comprises the following steps of: 1) performing characteristic value decomposition on all pixel points of polarized SAR images to be classified; 2) selecting different homogeneous regions as the most basic category representative regions, and extracting characteristic values for representing the homogeneous regions; 3) estimating Gaussian hybrid model parameters of the characteristic values lambda 1, lambda 2 and lambda 3 of the homogeneous regions by adopting an expectation-maximization (EM) algorithm respectively, and solving a probability density distribution function of the characteristic values; 4) solving a joint probability distribution function of three characteristic values of each homogeneous region; and 5) performing Bayesian classification on the pixel points in the homogeneous regions, and outputting the classification results. The method has the advantage of remarkable classification effect on the polarized SAR images, and can be used for target detection and target identification of the polarized SAR images.

The invention discloses a surrounding vehicle behavior identification method based on V2V communication and an HMM-GBDT hybrid model and belongs to the intelligent vehicle driving field. The method comprises steps that a, an offline training link, typical surrounding vehicle behaviors are concluded and divided, for each type of typical behaviors, based on real vehicle platform, the driving information of the surrounding vehicles under real traffic scenarios is collected, trajectory characteristic data is extracted, and parameter learning for the HMM-GBDT hybrid model is carried out. And b, anonline detection link, the acquired self driving information of a tracked target vehicle is transmitted to a driver in real time, a new characteristic observation sequence is constructed by the driverin combination with trajectory characteristic data of two vehicles, and the trained HMM-GBDT hybrid model is utilized to identify belonging behavior modes of the tracked vehicles. The method is advantaged in that the historical trajectory characteristics of vehicle are acquired in a passive information reception mode, influence of the traffic status and environmental factors on active detection is avoided, the method is not dependent on a fixed base station in a common vehicle network system, instant information transmission is guaranteed, and the target vehicle behaviors can be accurately identified.

The present invention provides novel techniques for graphically modeling, displaying, and interacting with parametric hybrid models used to optimize and control components of industrial plants and enterprises. In particular, a graphical modeling tool of a control / optimization system for controlling a plant or enterprise is configured to transmit a graphical user interface to a user, wherein the graphical user interface enables a plurality of command inputs relating to a plurality of parametric hybrid models based on a security access level of the user. The parametric hybrid models may be displayed by the graphical user interface as nodes of a network with connections connecting the nodes. The user may graphically manipulate the nodes and connections associated with the parametric hybrids models to either modify optimization constraints of the model network, or actually modify the manner in which the parametric hybrid models function (e.g., inputs, outputs, parameters, and so forth, of the parametric hybrid models), depending on the access level of the user.

The invention relates to the technical field of transformer fault on-line monitoring, in particular to a transformer maintenance decision method based on a hybrid model. According to the method, a hybrid modeling mode of an RBF neural network and a PHM proportion failure model is adopted, so the advantage that the proportion failure model can be maintained according to needs is fully developed compared with a traditional maintenance mode, the screening function of the RBF neural network method and an MIV method on characteristic quantities is fully utilized, and accordingly the accuracy and reliability of maintenance decision schemes of the proportion failure model(PHM) are further improved.

The invention provides a comment emotion classification method and system based on deep hybrid model transfer learning. The comment emotion classification method comprises the following steps: Step S1, collecting a commodity comment and preprocessing a source domain data sample set of the commodity comment; Step S2, mapping the preprocessed data into a word vector; Step S3, pre-training the sourcedomain data sample set of the commodity review with the depth mixing model; Step S4, fine-tuning the depth mixing model for the target domain data sample set of the commodity review; and Step S5, classifying the emotion of the commodity comment in the target domain. Tthe training speed is fast and the training difficulty is low, the invention only need several rounds of training to obtain high classification accuracy, and also can obtain good classification effect when the data set with more noise or less quantity is trained, and has little dependence on the data set and good robustness. Theinvention also effectively improves the transferability, and achieves the purpose of improving the classification accuracy after the transfer learning.