1158 results about "Real time prediction" patented technology

The Automated Healthcare Risk Management System is a real-time Software as a Service application which interfaces and assists investigators, law enforcement and risk management analysts by focusing their efforts on the highest risk and highest value healthcare payments. The system's Risk Management design utilizes real-time Predictive Models, a Provider Cost Index, Edit Analytics, Strategy Management, a Managed Learning Environment, Contact Management, Forensic GUI, Case Management and Reporting System for individually targeting, identifying and preventing fraud, abuse, waste and errors prior to payment. The Automated Healthcare Risk Management System analyzes hundreds of millions of transactions and automatically takes actions such as declining or queuing a suspect payment. Claim payment risk is optimally prioritized through a Managed Learning environment, from high risk to low risk for efficient resolution by investigators.

A real-time predictive intelligence platform comprises: receiving from a user through a meta API definitions for predictive intelligence (PI) artifacts that describe a domain of an online transaction system for least one business entity, each of the PI artifacts including types, component modules and behavior bundles; exposing an entity API based on the PI artifacts for receiving entity events from the online transaction system comprising records of interactions and transactions between customers and the online transaction system; responsive to receiving an entity event through the entity API, executing the component modules and behavior bundles to analyze relationships found between past entity events and metrics associated with the past entity events, and computing a probabilistic prediction and / or a score, which is then returned to the online transaction system in real-time; and processing entity event replicas using modified versions of the PI artifacts for experimentation.

A method and system for performing continuous (real-time) physics based prognostics analysis as a function of actual engine usage and changing operating environment. A rule-based mission profile analysis is conducted to determine the mission variability which yields variability in the type of thermal-mechanical loads that an engine is subjected to during use. This is followed by combustor modeling to predict combustion liner temperatures and combustion nozzle plane temperature distributions as a function of engine usage which is followed by off-design engine modeling to determine the pitch-line temperatures in hot gas path components and thermodynamic modeling to compute the component temperature profiles of the components for different stages of the turbine. This is automatically followed by finite element(FE) based non-linear stress-strain analysis using an real-time FE solver and physics based damage accumulation, life consumption and residual life prediction analyses using microstructural modeling based damage and fracture analysis techniques.

Various embodiments of the present invention are directed to a method and apparatus for improving the care of patients (e.g., chronically ill patients). In one example (which example is intended to be illustrative and not restrictive), the invention may be designed to improve such care through real time output (e.g., periodic, such as hourly, daily, weekly or monthly) to deter development of co-morbidities associated with many chronic diseases. Various embodiments of the invention may improve the care of chronically ill patients by: easing data collection, simplifying data transmission, efficiently interpreting chronically ill patient information, providing time series to form the basis of subsequent analysis, expanding the ability of the chronically ill patient or other user of the invention to understand the relationship between the medical practice and the patient's own lifestyle and / or easing the workload of healthcare providers (such as the patient's physician). In this regard, the invention may help in creating dialogue between chronically ill patients and healthcare providers that otherwise would be impossible.

Provided is an intelligent ammeter fault real time prediction method based on a decision-making tree, comprising the steps of: 1, pre-processing intelligent ammeter data of an electricity information acquisition system; 2, according to an intelligent ammeter fault determination model, screening the fault data of intelligent ammeters in the electricity information acquisition system and sending the fault data into an intelligent ammeter fault database; 3, dividing the historic data in the intelligent ammeter fault database into a training set and a test set, employing a decision-making tree algorithm to perform data excavation on the training set, and forming an intelligent ammeter fault decision-making tree and a preliminary classification rule; 4, through the data of the test set, performing accuracy assessment on the preliminary classification rule, determining the preliminary classification rule if the accuracy meets requirements, or else returning to the training set for training again; 5, generating an intelligent ammeter fault real time prediction model according to a finally determined classification rule; and 6, linking an intelligent ammeter real time fault database to the intelligent ammeter fault real time prediction model for real time prediction to obtain intelligent ammeter fault real time prediction results.

A method and system for real-time prediction of power usage for a change to another performance state provides input data for power management decision-making processes or for display to system operators. The unit(s) for which power usage is predicted may be a single processor in a uni-processor system or may extend up to the level of facilities within a complex of processing facilities. The method and system gather real-time data on the power consumption of the unit(s) and create a model, such as a regression model, of power versus performance. A resulting power usage change required by a prospective nominal performance state change is shown as display data, or is transmitted to a power budgeting controller to inform the controller as to potential changes that can enhance system operation, such as managing tradeoffs of power allocated to various sub-units of a processing system.

An apparatus optimizes a hybrid power system with respect to long-term characteristics of the hybrid power system. The apparatus includes a real-time controller of the hybrid power system and a processor. The processor cooperates with the real-time controller and is structured to input current measurements of information from the hybrid power system and hybrid dynamics information including continuous dynamics and discrete time dynamics that model the hybrid power system. The processor provides online optimization of the hybrid power system based upon the input, and outputs a power flow reference and a number of switch controls to the real-time controller based upon the online optimization. The processor is further structured to provide at least one of: real-time forecasts or real-time prediction of future information operatively associated with the hybrid power system as part of the input, and real-time processing of the online optimization.

The invention discloses a convolutional neural network structure-based traffic flow prediction method. The method comprises the following steps of 1) establishing a traffic flow data set and preprocessing the data set: establishing the traffic flow data set according to obtained traffic flow data, preprocessing the data set, constructing a data set sample matrix, and dividing the data set into a training set and a test set; 2) establishing a single-layer conventional convolutional neural network, removing a pooling layer, constructing a feature extraction network of a road traffic flow matrix,adding a sigmoid nonlinear regression layer to a full connection layer, and constructing a road traffic flow nonlinear regression prediction network; and 3) training the convolutional neural networkand realizing real-time prediction of short-term traffic flow: defining a model objective function, taking the training set as an input of a convolutional neural network model, solving an optimal parameter of the model to finish model training, and performing real-time traffic flow prediction on the test set by utilizing the trained convolutional neural network model. The short-term prediction accuracy of the traffic flow is improved.

A system for making real-time predictions about an arc flash event on an electrical system is disclosed. The system includes a data acquisition component, an analytics server and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the electrical system. The analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and an arc flash simulation engine.

The invention is directed, in part, to method of using self-reported health data in online communities to predict significant health events in life-changing illnesses to improve the lives of individuals and to improve patient self-management. The invention provides a method for providing real-time personalized medical predictions for an individual patient. The method includes: providing a database containing patient information for a plurality of other patients including one or more attributes for each patient in the database; constructing a model of a disease based on disease progressions for the plurality of patients; receiving a request from the individual patient, the patient associated with one or more attributes; and making a real-time prediction for the individual patient based on the mode and the individual patient's attributes.

The invention belongs to the control field of electric power electron converters and relates to a model prediction control method for a three-level voltage-source-type converter. The model prediction control method for the three-level voltage-source-type converter comprises the following steps: step one, building models of relations between currents of the alternating current side of the three-level voltage-source-type converter and switch functions; step two, building models of relations between capacitance voltage offsets of the direct current side and switch functions; step three, obtaining current instruction values through outer voltage loops and obtaining a current instruction value of the next moment by calculating current instruction values of several moments before; step four, setting value functions of a model prediction control algorithm for the three-level voltage-source-type converter; step five, calculating on-off state subsets; step six, achieving real-time prediction control. The model prediction control method for the three-level voltage-source-type converter is simple in algorithm, easy to achieve and capable of being adopted in a higher-level converter, and has generality.

A system and method for maximizing the standby time of mobile communication devices that have WiFi or other high energy-consuming network interfaces, by predicting in real time actionable silent periods (ASPs) of the interface and shutting the interface down during these ASPs. Standby times are significantly increased, resulting in longer periods of operation before battery charging is required, while keeping minimal the probabilities of missing incoming data packets when the interface is turned off.

The invention discloses a tool wear condition prediction method of a numerical control machine tool based on a parallel deep neural network. A dynamometer, an acceleration sensor and an acoustic sensor are installed on a workbench and a fixture of the numerical control machine tool; a milling experiment is conducted, the cutting force and vibration and acoustic signals of a milling process are collected so as to obtain multisensor data, and the wear capacity of a tool is collected; pretreatment is performed so as to obtain training data and to-be-tested data; a parallel deep neural network model is established; the treated training data and the label of the wear capacity of the tool are input into an offline training model in the parallel deep neural network model; and the to-be-tested multisensor data are introduced into the trained model so as to predict the wear capacity of the tool in real time and on line. According to the method, the implied characteristics during tool processingof the numerical control machine tool are fully mined, and the wear capacity of the tool can be predicted in real time. The method has the advantage of wide applicability and can be widely applied tovarious numerical control machine tools.

The invention discloses a traffic flow prediction method based on a bidirectionally nested LSTM neural network. The method comprises the steps of acquiring traffic flow data of a prediction road section and K most related road sections, building a road traffic flow space-time matrix data set and performing data serialization treatment; then building a bidirectionally nested LSTM neural network prediction model, defining a prediction model loss function, and combining with the training set data to complete model training; and at last, using the data of a test set as the input of the trained model, achieving the real-time prediction of the traffic flow state of the test set and defining a model assessment standard, to perform error analysis. According to the method provided by the invention,through improving the LSTM unit time hierarchical effect and the linkage between the future, historical traffic flow states and the current state, the time feature extraction capacity of the road traffic flow data is improved, and thus the prediction accuracy of the road traffic flow is improved.

In a plasma reactor having an electrostatic chuck, wafer voltage may be determined from RF measurements at the bias input using previously determined constants based upon transmission line properties of the bias input, and this wafer voltage may be used to accurately control the DC wafer clamping voltage.

The invention provides a multiple-target operation optimizing and coordinating control method and a device of a garbage power generator. The multiple-target operation optimizing and coordinating control method includes the following steps. Operational parameters are downloaded from a data communication system (DCS), data judged as reasonable based on a threshold value are transmitted to a database. In terms of environmental protection, economy and safety of the power generator, three models are respectively set up by means of a support vector machine and a fuzzy neural network. A modified strength PARETO genetic algorithm is used for comprehensively optimizing multiple targets and then optimum operation parameters under the present working condition are worked out. Operational staff can adjust operation of corresponding parts based on the optimum operation parameters. The device comprises a data collecting module, a data filtering module, a database module, a data modeling module, an optimizing module, a forecasting module, a remote monitoring module, a monitor, an alarming module and a manual alarming module. The multiple-target operation optimizing and coordinating control method and the device of the garbage power generator achieve multiple functions of real-time forecasting, offline simulation, dynamic optimizing and the like and have the advantages of being strong in adaptability, good in self-learning ability, high in fitting precision, obvious in optimizing effect and the like.

The invention provides a vehicle driving risk prediction method based on time varying state transition probability markov chain. Firstly, an offline vehicle driving risk prediction model training: based on samples of accidents and near accidents, real-time vehicle driving risk states are divided by clustering time window characteristics parameters and regarded as countable states of the markov chain, and a multiterm logistic model of vehicle driving risk states transition in different vehicle driving risk states is built. Secondly, an online vehicle driving risk model real-time prediction: under the circumstance of car networking, the variable parameters required by a prediction model are collected in real time, through a risk state clustering center position and markov property, an original state probability distribution vector and a markov chain n steps transition probability at any time in the future are calculated, and the prediction result of the vehicle risk states in the futureis obtained. According to the invention, by means of a recurrence algorithm, the estimation of markov chain n steps time varying state transition probability is achieved, which can reflect the characteristics of the vehicle driving risk states changing with the characteristics of the transportation system, and can meet the requirement of early warning in real time.

The invention relates to a wind turbine generator fault early warning method based on data mining. A method based on the minimal redundancy of maximum weight is adopted to select a fault feature signal and carry out dimensionality reduction on the fault feature signal, so that decision accuracy is guaranteed, and the calculated amount of data processing is reduced. Meanwhile, on the basis of the historical data of an SCADA (Supervisory Control And Data Acquisition) system, the early warning model of each equipment component of a wind turbine generator is established, and a nonlinear state estimation technology is adopted to obtain the real-time prediction value of each piece of equipment and each component. On the basis, an adaptive threshold value is designed, and the false alarm of the system due to interference including environment temperature, wind speed change and the like can be avoided. By use of the method, an abnormal state is identified before faults happen so as to be convenient in adopting a corresponding measure in time, so that preventive repair is carried out, and the method has an important practical application value.

The invention relates to a computerized system for predicting completions to input text which is typed by a user to a text-oriented active application within a client computer, which comprises: (a) an off-line server that gathers texts from textual repositories, analyzes and processes the same to produce entries for an off-line database, said off-line server updates periodically a real time database of a real time server; (b) a real-time prediction server for receiving from an agent at said client computer serial requests for predictions, extracting in response to each of said requests one or more text predictions from tables within said real time database, and for conveying said one or more text predictions to said agent, wherein each of said text predictions comprises one or more words; (c) said real time database which comprises one or more tables containing word combination entries of various sizes, wherein each of said entries is associated with a weight which is used to estimate the probability for this combination to complete the typed text as included in the respective request for prediction; and (d) said agent which extracts in real time each present user typing to said text-oriented application, produces requests for prediction for new typing, sends the same to said prediction server, receives in response said one or more text predictions, and presents to said user said one or more text predictions for selection.

The invention discloses a traffic flow prediction method of a divergence convolution recurrent neural network based on a space-time diagram. The traffic flow prediction method is characterized in thata directed weighted graph of a road network is constructed based on spatial features of a traffic network, then a traffic flow prediction model of a graphic divergence convolution recurrent neural network is constructed by taking the directed weighted graph as a basic unit of prediction, deep learning is carried out by means of time-space characteristics of the traffic network, time-space prediction is carried out on the traffic flow of the traffic road network, a final traffic flow prediction model is constructed, and real-time prediction of the traffic flow is realized. The traffic flow prediction method has the advantages of precise prediction and high fitting degree.

A wind generating set fault prediction method based on D-S evidence fusion is disclosed. In the method, for two kinds of signals, two support vector machines after parameter optimization are constructed, the two support vector machines are taken as two evidences, and after D-S fusion, a final prediction fault type is given. The method has advantages that (1) in a traditional vibration method, only a vibration signal is analyzed, and a neural network, a decision tree and other machine learning algorithm models are constructed according to a vibration energy characteristic vector; but the vibration signal is only observed so that some fault states can be misclassified, for instance, a bearing damage and rotor eccentricity can cause a vibration signal abnormity, and at this time, a current signal can be used to distinguish two kinds of fault states; and (2) a prediction model established in the method can be stored, historical data does not need to be repeatedly extracted and trained, and under a real-time prediction environment of a wind field, a prediction result can be quickly provided.

The invention relates to the technical field of coal mine safety, in particular to a continuous prediction method of gas emission dynamic characteristic outburst of a tunneling surface, which utilizes the development state and the development trend of three factors of outburst comprehensive hypothesis of the tunneling surface of gas emission dynamic characteristic reaction of the tunneling surface and realizes the non-contact type continuous prediction technology of coal of the tunneling surface and gas outburst; and the method comprises the following steps: acquiring real-time gas emission monitoring data of an underground gas sensor from a coal mine gas monitoring system; extracting dynamic characteristics of gas emission of the tunneling surface, including average value of frequency ofthe monitoring data of the gas emission, the maximum value of the frequency per minute of the monitoring data of the gas emission and the movement minimum value of the frequency per t minutes of the gas emission; and acquiring the shape design characteristic parameters of a lane, the original desorbable gas content of a coal layer, wind rate and frequency time, sequentially acquiring characteristic index of the gas emission rate of the tunneling surface, gas desorption index and gas dividing source characteristic index, and carrying out real-time prediction and forecast of outburst risk of a working surface according to the states and the trends of the three characteristic indexes.

The invention relates to an on-line position prediction method based on mass of space trajectory excavation and belongs to the field of space trajectory excavation. The method comprises the steps of firstly, excavating frequent sequential patterns from the mass of space trajectory excavation, secondly, using the sequence patterns to establish a model based on a prefix tree structure, thirdly, establishing a model based on distance and popularity to solve the zero frequency problem, and finally, using the established predication models to predict the next position of a moving target according to the current trajectory information of the moving target. The accuracy of the prediction method is improved greatly compared with the existing methods, the calculation complexity is low, real-time prediction can be carried out on the position of the moving target, only the position information of the moving target is needed, and therefore the on-line position prediction method based on mass of space trajectory excavation can be widely applied to the fields such as intelligent traffic and services based on geographic positions.

The invention discloses a rear-end collision real-time prediction method aimed at a frequently jammed section of an expressway. Firstly, a traffic flow detector used for collecting real-time traffic flow data of all sections of the expressway is installed on the expressway (generally the upstream section of a bottleneck area). When a rear-end collision occurs, vehicle following running tracks are analyzed, a rear-end collision risk real-time prediction logistic model of the frequently jammed section of the expressway is built, and model parameters are calibrated according to traffic flow data by five minutes before the collision occurs. The method overcomes the defect that effective real-time prediction of the rear-end collision of frequently jammed section of the expressway cannot be conducted in the past, rear-end collision risks can be estimated in real time according to the traffic data collected by the traffic flow detector on the expressway, and moreover, dynamic traffic control is utilized to control and prevent the rear-end collision. The method has important time application value and wide application prospects in China.

The invention provides a method for predicting the internal temperature of a battery in real time, and relates to the real-time prediction methods for the battery. The problem that the actual internal working temperature of the battery can not be embodied by battery surface temperature monitoring is solved. According to the method, the battery is divided into an inner core and an outer shell, temperature prediction models are established respectively, the specific heat capacity of the internal material and the surface material of the battery, the thermal-resistance parameter of the inner core-outer shell of the battery, the thermal-resistance parameter of the outer shell-the outside of the battery, the open-circuit voltage curve of the battery, the curve of the open-circuit voltage variation with temperature and the like are acquired by an experiment method. A Kalman filtering method is used for tracking and correcting the internal temperature of the battery in real time, real-time battery surface and environment temperatures are input into the predication models, and the internal temperature of the battery is predicted in real time. The method is suitable for predicting the internal temperature of the battery in an electrical automobile and an energy storage system.

The invention relates to a method for predicting the space-time traveling track of a blacklisted vehicle. The method comprises the prediction of the traveling route of the blacklisted vehicle and the prediction of the traveling time of the blacklisted vehicle on the predicted traveling route. The prediction of the traveling route is achieved with a traveling route predicting method based on historical route similarity. The prediction of the traveling time of the blacklisted vehicle on the predicted traveling route is achieved with a traveling time predicting method based on road traffic condition evaluation. The method for predicting the space-time traveling track of the blacklisted vehicle is high in efficiency. According to the method for predicting the space-time traveling track of a blacklisted vehicle, the traveling route of the blacklisted vehicle can be predicted in real time after the blacklisted vehicle appears, the next position where the blacklisted vehicle might appear and the time when the blacklisted vehicle appears at the position can be determined, video surveillance and control can be conducted on the corresponding position in advance according to the predicted traveling route and the predicted traveling time of the blacklisted vehicle, and decision aids are provided for the arrest of the blacklisted vehicle.

An approach is provided for automatically predicting an event occurrence based on a question from an end user presented using a near-real-time natural language processing (NLP) analysis to generate, score and rank a plurality of event occurrences based on a plurality of question context parameters extracted from the question, one or more user profile parameters for the end user, and the one or more historical questions, answers, and events having a specified spatial and / or temporal proximity to the question which are identified by an information handling system. In the approach, performed by an information handling system, a top ranked event occurrence from the ranked plurality of event occurrences is selected for inclusion in a notification message that is communicated or broadcast to the end user, as well as other users engaged with the information handling system and / or first responders in the affected area.

The invention discloses a micro-grid robustness multi-target operation optimization method containing renewable energy resources. The method comprises the steps of: collecting micro-grid operation information, generating an uncertainty implementation scene in micro-grid operation, constructing a robustness multi-target model according to micro-grid attributes, inputting the robustness multi-target model and the uncertainty implementation scene into a two-stage solving strategy, carrying out iterative solving respectively on an internal layer maximum optimization problem under the uncertainty scene and an external layer minimum optimization problem under an operation scheme until an ending condition is met and circulation is stopped, forming an optimal operation scheme set, and selecting an optimal operation strategy according to real-time prediction data. The optimal robustness non-domination leading edge of economy and environment can be obtained under a worst uncertainty condition. Compared with an existing operation optimization method, the operation optimization method provided by the invention realizes interference inhibition of uncertainty in the micro-grid operation under a multi-target framework.

The invention discloses a multi-AUV self-adaptive target searching and obstacle avoidance method oriented to an unknown environment, and the method is suitable for multi-AUV target searching under anunknown complex underwater environment. The method is mainly divided into three modes including a target mode, a non-target mode, and an obstacle avoidance mode. In the target mode, the self-adaptivesearching is achieved through dynamic real-time prediction according to the target information for sensing the outside; in the non-target mode, full-area coverage searching and collaborative planningtasks are achieved through a sub-area strategy; and in the obstacle avoidance mode, the barrier threat is avoided in real time based on the improved dynamic window method. According to different underwater environment information, a multi-AUV target searching task is executed through alternate mode switching among the three modes, and the uncertain information under the unknown water can be dealtwith, the credible interval of the target state information is guaranteed, and environment adaptability and searching efficiency are achieved.

The invention provides a gas concentration real-time prediction method based on a dynamic neural network. Firstly, the neural network is trained by means of data in a mine gas concentration historical database, activeness of hidden nodes of the network and learning ability of each hidden node are dynamically judged in the network training process, splitting and deletion of the hidden nodes of the network are achieved, and a network preliminary prediction model is built; secondly, mine gas concentration information is continuously collected in real time and input into the prediction model of the neutral network to predict the change tendency of gas concentration in the future, and the network is trained timely through predicted real-time data according to the first-in first-out queue sequence to update a neutral network structure in real time, so that the neutral network structure can be adjusted according to real-time work conditions to improve gas concentration real-time prediction precision. According to the method, the neural network structure can be adjusted timely on line according to the real-time gas concentration data, so that gas concentration prediction precision is improved, and the technical requirements of a mine gas concentration information management system are met.