2548 results about "Data prediction" patented technology

Systems and methods for time series data error detection, correction, and transformation may detect gaps and anomalies in time series data, such as from a meter device, and may correct the gaps and adjust the anomalies prior to long-term record storage. Data forecasting may be used to correct the errors in the time series data. The error corrected data may be regarded as an actual set of time series data and become a base data set against which additional heuristic projections are generated. In addition, the time series data may be transformed into any number of physical and virtual device hierarchies that represent the underlying data source configurations, and may then be stored in an analytical database for further analysis. The hierarchies may be irregular and may change over time.

Historical data is measured for a computer server system. Future demand for service in the computer server system is forecast based on the historical data, and the mapping of virtual machines to physical machines is updated based on the forecast of the future demand. Measurement, forecasting, and placement modules can be employed.

A method and apparatus for adaptively selecting a context model for entropy coding and a video decoder are provided. A method of coding a residual prediction flag indicating whether residual data for an enhancement layer block of a multi-layered video signal is predicted from residual data for a lower layer block corresponding to the residual data for the enhancement layer block includes the steps of calculating the energy of the residual data for the lower layer block, determining a coding method for the residual prediction flag according to the energy, and coding the residual prediction flag using the determined coding method.

A semantically organized domain space is created from a training corpus. Affective data are mapped onto the domain space to generate affective anchors for the domain space. A sentiment associated with an input text is determined based the affective anchors. A speech output may be generated from the input text based on the determined sentiment.

The present invention provides systems and methods for prosthesis fitting in joints that employ a trained neural network to predict at least one unknown set of data, such as position and contact force. The unknown data is predicted based on at least one known sensor value that is obtained intraoperatively. The predicted neural network data is made available to a physician and aids in the determination of whether to resect additional bone, release soft tissues, and/or select sizes for prosthetic components. Advantageously, increased data may be provided to a physician without the need to acquire numerous samples from a patient, and fewer sensors may be employed.

A data-driven lithium ion battery cycle life prediction method based on an AR (Autoregressive) model and an RPF (Regularized Particle Filtering) algorithm relates to a lithium ion battery cycle life prediction method and belongs to the technical field of data prediction. The invention solves the problems in the existing lithium ion battery cycle life prediction method that the model-based prediction method is complicated in modeling, and parameters are difficult to identify. The data-driven lithium ion battery cycle life prediction method combines time sequence analysis with particle filter method and comprises the following steps: the AR model is firstly utilized to realize the multi-step prediction on battery performance degradation process time sequence data; and then, aiming at the problem of uncertainty expression of the cycle life prediction result, the regularized particle filtering method is introduced, and a lithium ion battery cycle life prediction method framework is proposed. The method proposed by the invention can be used for effectively predicating the cycle life of a lithium ion battery and realizes the output of probability density distribution of the predication result, has good computational efficiency and uncertainty expression ability.

A visual analyzing and predicting method based on a virtual geological model is finished by depending on an interactive visual tool. The method mainly comprises steps of searching a multi-dimensional geological data model from a database and a file system; arranging or resetting a virtual geological scene by setting or regulating corresponding parameters and models; regulating coordinate, scale, data format and the like, of the geological model; sending results of visual calculating, analyzing, predicting and searching into a dual-display cache region; and integrally displaying geological data in a visual platform at real time. The method meets requirements of earth science application, enhances representability of geological data, improves comprehension and application environment of geological data, converts logical thinking of geologists into a trial ground having an imagery thinking of time and space in a virtual environment, helps strengthen deep recognition to complex geological phenomena, uncovers deep information and internal relation in geological data, digs out and extract knowledge unable to be obtained in a traditional mode, improves utilization rate and geospatial analytics of information, and provides a brand-new method for geologists to observe, explain, analyze and imitate geological phenomena in a three-dimensional space, predict and know geospatial distribution of geological structure in a studied area through known data, and obtain mine position, mine reserves and other important information. Furthermore, the human-computer interactive tool is simple and easy to learn to operate, saves cost, reduces blindness in application, reduces risk, conducts and makes decision for production and environment analysis and has significant economic and social benefits.

A system and a method for providing a determination of predicted cognitive performance of an individual preferably based the time of day and on factors including sleep history based on activity data from an actigraph and the individual's activities. The system and the method provide a numerical representation of the predicted cognitive performance. Both may be used to optimize the work schedule of the actigraph wearer to maximize the cognitive capacity during working hours.

The invention discloses a method for online prediction on cooling load of a central air conditioner in marketplace buildings. The method comprises the following steps of: continuously acquiring various parameters; when the quantity of acquired data meets the needs, respectively building prediction models of the air conditioner cooling load and all input parameter values in three types including working day, weekend and festival and holiday by using an Online SVR (Support Vector Regression) method; then, predicting the input parameter value of 24 hours of that very day according to the historical data of outdoor meteorological parameter and air conditioner operation input parameter; finally, predicting the air conditioner cooling load within 24 hours of that very day by using the air conditioner load prediction model of corresponding date type and taking the predicted value of each input parameter within 24 hours of that very day as the input, and compensating by a residual error sequence of the actual value and the predicted value of the air conditioner cooling load in the previous day of the corresponding date type; and simultaneously, dynamically correcting the prediction model of the air conditioner cooling load with online addition of new samples. The method provided by the invention effectively realizes the dynamic and accurate prediction on the air conditioner cooling load.

The present invention includes a method and system for creating a price-forecasting tool for a commodity. The method and system comprise providing price data and industry capacity utilization data related to the commodity, and establishing the price-forecasting tool based on a statistical relationship between the price data and the industry capacity utilization data. A method of doing business is disclosed comprising creating a price forecasting tool based on a statistical relationship between price data and industry capacity utilization data of the commodity, and utilizing the price forecasting tool to generate a plurality of scenario prices for the commodity for a plurality of forecast horizons. A computer program product is disclosed for creating a price forecasting tool comprising a computer usable medium having computer readable program means for causing a computer to perform the steps of providing price data and industry capacity utilization data related to the commodity, and establishing the price forecasting tool based on a statistical relationship between the price data and the industry capacity utilization data.

The invention relates to the technical field of computer application engineering, in particular to a method for using an improved neural network model based on particle swarm optimization for data prediction. The method includes the steps of firstly, expressing data samples; secondly, pre-processing data; thirdly, initiating the parameters of an RBF neural network; fourthly, using the binary particle swarm optimization to determine the number of neurons of a hidden layer and the center of the radial basis function of the hidden layer; fifthly, initiating the parameters of the local particle swarm optimization. By the method for using the improved neural network model based on particle swarm optimization for data prediction, the number of the neurons of the hidden layer of the RBF neural network model can be determined easily, RBF neural network performance is improved, and data prediction accuracy is increased. In addition, the improved neural network model based on particle swarm optimization is low in model complexity, high in robustness and good in expandability.

A thermal management of a computer system may be advantageously obtained by predicting temperature variations based upon past temperature readings. Such a system may, in preferred embodiments, filter the past temperature data to provide a more precise value, allow for a floating trip level that will change with the rate of temperature change and even allow for prediction of the amount of time left before a system shutdown may occur.

Systems and methods for vendor managed inventory (VMI) that include a demand management system that receives customer product usage data and forecasts customer demand for products in accordance with the product usage data. The VMI system also includes an order management system that receives orders from the demand management system and sends the order for fulfillment. A relational database stores customer information for later retrieval and ad hoc querying. An aspect integrator platform is provided for collaboratively presenting the customer information.

The invention belongs to the technical field of cloud computing and network multimedia, and particularly provides an auto-expanding/shrinking cost-optimized content distribution service method based on a hybrid cloud scheduling model. The method comprises: a future number of user visits is predicted on the basis of historical data and provides basis for auto-expanding/shrinking of resources; according to the predicted value and a long-term scheduling algorithm, a rough plan of resource booking strategy is obtained through calculation; wherein a short-term scheduling model is introduced to reduce the prediction error and to improve the precision of resource supply and the quality of service. In the long-term scheduling algorithm, a locality-aware booking model is set up to derive the locality-aware resource booking algorithm. A resource prediction algorithm employs the ARIMA model. In a short-term adjustment algorithm, virtual machine status parameters are designed and a content missing algorithm is provided, so that the user experience of the entire system is further improved. The method enables hybrid cloud technology to support streaming media content distribution applications efficiently with auto-expanding/shrinking functions and optimized costs.

A system, method, and program product for enhancing keyword contribution to a code-completion facility used in association with an Integrated Development Environment source code editor. A code-completion facility supporting a source code editor includes a look-up module that retrieves keywords from a keyword compare pool responsive to a code-completion prompt. The code-completion facility further includes a contribution mechanism for contributing keywords derived from system artifacts to the keyword compare pool. The contribution mechanism includes a remote artifact contribution module for obtaining or deriving keywords from remote object interface definitions and contributing the same to the keyword compare pool. In a preferred embodiment, the remote artifact contribution module includes a keyword prediction module for accessing a remote object interface repository, predicting one or more remote object based keywords in accordance with remote object interface definition data, and contributing one or more of the predicted keywords to the code-completion facility.

An image coding apparatus for efficient coding on image data having discrete pixel values such as a CG image or a limited color image as well as a natural image where pixel value changes are mild. For this purpose, a tile dividing unit divides multi-valued image data into tiles. An occurred level count unit outputs the result of counting of the number of brightness values in the tile to a comparison unit and outputs the existing brightness values to an index table generation unit. The index table generation unit allocates continuous index values to the existing brightness values, and stores the result of allocation into an index memory table. An index conversion unit replaces the input pixel values in the tile with index values, and outputs the index values to a selector. The selector selects one of the brightness values in the tile or the index values based on the result of comparison by the comparison unit, and outputs the selected values to a tile data predictive coding unit so as to perform predictive coding processing.

The invention discloses a travel time prediction method for optimizing an LSTM neural network through a particle swarm optimization algorithm, and the method comprises the following steps: S1, collecting travel time data, performing data normalization, and dividing the data into a training set and a test set proportionally; S2, optimizing each parameter of an LSTM neural network prediction model by using the particle swarm optimization algorithm; S3, inputting the parameters, optimized through the particle swarm optimization algorithm, and the training set, and performing the iterative optimization of the LSTM neural network prediction model; S4, predicting the test set through the trained LSTM neural network model, and evaluating a model error. The method is quick in optimization. Compared with a random forest, SVM and KNN in the traditional prediction algorithm, the method of the invention has the least mean square error and square error for the data prediction, and the model reducesthe calculation burden, so the method shows better prediction performance.

The invention discloses a system for forecasting traffic flow of urban ring-shaped roads, relating the related technical fields of database management, data analysis and processing and data speculation. The system comprises a traffic flow data management system, a traffic flow data characteristic analysis system and a traffic flow data speculation system. The traffic flow data management system is used for maintaining the traffic flow database, realizing reading of real-time traffic flow data and input of predicted data; the traffic flow data characteristic analysis system is used for realizing characteristic analysis of traffic flow data and pre-processing traffic flow data; the traffic flow data speculation system is used for selecting traffic flow prediction models and analyzing predicting outcomes, realizing prediction method comparison and display and saving of the predicting outcomes. The system can solve the quality problem of traffic flow data and deviated prediction in the traditional traffic flow prediction, introduces various influencing factors of upper sections and lower sections of roads for predicting road traffic flow and realize real-time and accurate traffic flow prediction of the road network.

A particular method includes receiving aircraft state data associated with an aircraft at an air traffic control system. The aircraft state data includes a detected position of the aircraft, a velocity of the aircraft and an orientation of the aircraft. The method also includes predicting at least one future position of the aircraft based on the aircraft state data. The method further includes generating an alert in response to comparing the predicted future position to an air traffic navigation constraint assigned to the aircraft.

The invention relates to a grid load predicting method based on a support vector regression machine. The method comprises the following steps: firstly, carrying out automatic regression (AR) modelingon history property data of nodes by a time sequence method; evaluating dimensions of the input vector in SVR according to orders of an AR model; performing SVR learning on the history data, and constructing a regression function of SVR; predicting the property of the node at the next time according to the regression function and the measured history property data, and regulating the regression function of SVR on line according to the regression function. The method can provide a data reference for dispatch, property optimization and the like of grid resources, avoid passive and blind task dispatch and enhance the efficiency of the grid environment.