6179 results about "Predictive methods" patented technology

A method for controlling energy consumption within a building includes providing at least one environment sensing device and at least one energy consumption sensing device associated with the building. Current data is collected from the environment sensing device and the energy consumption sensing device along with associated time-of-day data. A value of a future energy consumption parameter is predicted based upon the collected current data, the associated time-of-day data, and historic data collected from the environment sensing device and the energy consumption sensing device. A profile of future costs per unit of energy consumption as a function of time is determined. Energy consumption is controlled dependent upon the predicted future energy consumption parameter value and the determined profile of energy consumption costs.

A predictive method and apparatus are disclosed for selecting an antenna to use in a multi-antenna wireless device. A predictive antenna selector predicts the best antenna (for both receiving and transmitting signals) based on the signal quality of the antenna for prior received frames. The quality of each antenna is evaluated, for example, in a random order, round robin fashion or according to some equal or weighted schedule. The signal quality can be evaluated for a given antenna during a preamble portion of a frame or for any frame up to an entire frame duration. A given antenna can be removed from the signal quality evaluation (for example, to a bad antenna list) if the given antenna fails to satisfy one or more predefined criteria, such as whether a signal quality of a given antenna is below a signal quality of a remainder of the plurality of antennas by a predefined amount. The signal quality of antennas on the bad antenna list can be reevaluated to determine when to return a removed antenna to the plurality of antennas that are evaluated.

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

A Customer Relationship Management (CRM) system that dynamically builds predictive models. The system is used by business users who are unfamiliar with the art of data mining. A model-building mechanism in a data mining subsystem is presented with a training segment consisting of records with appropriate input attributes and an output attribute to be predicted; the model-building mechanism builds a model in the form of a business measure that can subsequently be applied to make predictions against other like segments.

This invention relates to a management method, more particularly by logging and replay, for software operations performed during the execution of an application process executed on a computer. This method applies in particular to internal operations sending a result datum to the managed process, and comprises the following steps: carrying out a deterministic software processing, termed prediction function (FH), based on the state of a managed process or of the application (AOP, ASB), to provide a predicted result (RP) for said operation; comparative test to establish whether the value of the predicted result (RP) corresponds or does not correspond to the value of the actual result (DR, RRJ); performance of a complementary management phase (CH, DH) of said managed operation, this complementary management depending on the result of the preceding test.

A method for forecasting performance for and characterizing the properties of a multilayer low permeability gas reservoir. The method includes a coupled well / reservoir predictive model that accounts for pressure drop between layers, allowing accurate, rigorous, and rapid forecasting of reservoir performance. The method provides estimates of individual layer properties such as in-situ permeability, skin factor, fracture half-length, fracture conductivity, drainage area, etc. by simultaneously history matching production data and production log data using the coupled well / reservoir predictive model.

A system and method for creating a predictive model to select an object from a group of objects that can be associated with a requested web page, wherein a configuration of the requested web page defines a subgroup of one or more selected objects from the group of objects. Exemplary embodiments of the present invention seek to provide novel solutions for determining which content object, taken from a group of content objects, will be best suited for presentation in association with a link on a web page that has been requested by a certain surfer. Each web page can include one or more links to be associated with content objects from the group.For each content object presented over a requested web page, a predictive model with relevant predictive factors is processed such that the predicted objective, the probability of success for example, is calculated. A success is defined as a surfer responding to the presented content. For example, should a surfer select a relevant content object, the probability of the objects that can be presented is calculated. Subsequently, the objects with the highest predictive expected utility are selected to be associated with the links in the web page requested by the surfer.

The invention provides a method and a system for predicting cardiovascular and cerebrovascular disease risks. The method comprises the following steps: step 1, defining problems of cardiovascular and cerebrovascular disease prognosis risk prediction, step 2, collecting health medical data of cardiovascular and cerebrovascular disease patients; step 3, preprocessing data, including data integration, data cleaning, and processing missing data; step 4, constructing features and selecting features, identifying potential risk factors; step 5, the identified risk factors and rehabilitation outcomes forming an input-output sample set, inputting the input-output sample set to a random forest algorithm for model training, and evaluating prediction performance of a model. The method and the system can obtain health medical data of cardiovascular and cerebrovascular disease patients, the data being required by a clinician input model, and a predicted rehabilitation outcome of a patient in a certain time period in future is obtained through the model. The method and the system can preferably predict prognosis risks, so as to realize personalized accurate rehabilitation treatment.

An electronic messager with a predictive text editor, including a storage unit for storing a data structure associating, for each one of a plurality of a user's contacts, usage data for the user's history of usage of words in communications with the user contact, a data manager coupled with the storage unit for generating the data structure in the storage unit, and for updating the data structure as additional communications with each user contact are performed and additional usage data is obtained therefrom, and a text predictor coupled with the storage unit, for receiving as input a character string and a designated user contact, and for generating as output an ordered list of predicted words, based on usage data in the data structure associated with the designated user contact. A method is also described and claimed.

The invention discloses a prediction method and visualization method of traffic jam. The methods comprise the following steps: GPS data sent back by a taxicab is utilized for being associated to roads in an electronic map through the map matching method; the road section speed calculated in terms of the matched data is utilized for judging the traffic state of road sections; the evolution rule of the traffic jam, including formation and dissipation of the traffic jam, is extracted with the utilization of the historical data; the traffic jam prediction is performed with the utilization of the sliding time window scheme after a real-time traffic information database is associated; the congestion intensity and the influence range of congested road sections can be presented by a thermodynamic chart after the congestion intensity of the congested road sections is calculated based on the prediction result. The prediction method provided by the invention can realize highly accurate traffic jam prediction, and the visualization method of the thermodynamic chart enables the traffic jam to be visualized and understandable, so that the location and the influence range of the traffic jam can be discerned conveniently.

The invention relates to a CNN and LSTM fused neural network-based air PM2.5 concentration prediction method. The method comprises the steps of S1: building a city PM2.5 concentration prediction modelbased on a deep learning principle, a CNN and LSTM; S2: for the built model, selecting training data and test data from environment monitoring data, and finishing initialization of the prediction model; S3: training the model by utilizing the training data; S4: by utilizing the trained model, obtaining a test prediction result according to test data; S5: judging the accuracy of the test prediction result, and if the accuracy exceeds a threshold, executing the step S6, or otherwise, returning to the step S2; and S6: performing prediction by utilizing the trained model. Compared with the priorart, the prediction accuracy of the prediction method is higher than that of a conventional prediction method; and under the same working duration and working conditions, a better result can be generated.

The invention discloses a wind power forecasting method based on a genetic algorithm optimization BP neural network, comprising the steps: acquiring forecasting reference data from a data processing module of a wind power forecasting system; establishing a forecasting model of the BP neural network to the reference data, adopting a plurality of population codes corresponding to different structures of the BP neural network, encoding the weight number and threshold of the neural network by every population to generate individuals with different lengths, evolving and optimizing every population by using selection, intersection and variation operations of the genetic algorithm, and finally judging convergence conditions and selecting optimal individual; then initiating the neural network, further training the network by using momentum BP algorithm with variable learning rate till up to convergence, forecasting wind power by using the network; and finally, repeatedly using a forecasted valve to carry out a plurality of times of forecasting in a circle of forecast for realizing multi-step forecasting with spacing time interval. In the invention, the forecasting precision is improved, the calculation time is decreased, and the stability is enhanced.

The present invention generally relates to computer databases and, more particularly, to data mining and knowledge discovery. The invention specifically relates to a method for constructing segmentation-based predictive models, such as decision-tree classifiers, wherein data records are partitioned into a plurality of segments and separate predictive models are constructed for each segment. The present invention contemplates a computerized method for automatically building segmentation-based predictive models that substantially improves upon the modeling capabilities of decision trees and related technologies, and that automatically produces models that are competitive with, if not better than, those produced by data analysts and applied statisticians using traditional, labor-intensive statistical techniques. The invention achieves these properties by performing segmentation and multivariate statistical modeling within each segment simultaneously. Segments are constructed so as to maximize the accuracies of the predictive models within each segment. Simultaneously, the multivariate statistical models within each segment are refined so as to maximize their respective predictive accuracies.

This invention proposes a more efficient way to quantize temporal envelope shaping of high band signal by benefiting from energy relationship between low band signal and high band signal; if low band signal is well coded or it is coded with time domain codec such as CELP, temporal envelope shaping information of low band signal can be used to predict temporal envelope shaping of high band signal; the temporal envelope shaping prediction can bring significant saving of bits to precisely quantize temporal envelope shaping of high band signal. This prediction approach can be combined with other specific approach to further increase the efficiency and save mores bits.

The embodiment of the invention discloses a traffic information predicting method based on time series and a device thereof, relating to the communication field. In order to fast, effectively and accurately predict the traffic information, the technical scheme of the invention comprises the following steps: obtaining a statistic value of road condition data of each time window of each road in characteristic days every week according to memorized history road condition data; removing a trend term of reference road condition data of a predictive road according to the statistic value of the predictive road; obtaining a predictive equation of the predictive road according to the reference road condition data of the predictive road after removing the trend term; and obtaining predictive road condition data of the predictive road according to the predictive equation of the predictive road and real-time road condition data of the predictive road.

A method and apparatus for predicting future data values based on past data values. Past data values such as past product demand values are received. Leading indicators are identified based on the past data values. The future data values are generated based on the leading indicators.

Systems and methods are provided for knowledge discovery in spatial data as well as to systems and methods for optimizing recipes used in spatial environments such as may be found in precision agriculture. A spatial data analysis and modeling module is provided which allows users to interactively and flexibly analyze and mine spatial data. The spatial data analysis and modeling module applies spatial data mining algorithms through a number of steps. The data loading and generation module obtains or generates spatial data and allows for basic partitioning. The inspection module provides basic statistical analysis. The preprocessing module smoothes and cleans the data and allows for basic manipulation of the data. The partitioning module provides for more advanced data partitioning. The prediction module applies regression and classification algorithms on the spatial data. The integration module enhances prediction methods by combining and integrating models. The recommendation module provides the user with site-specific recommendations as to how to optimize a recipe for a spatial environment such as a fertilizer recipe for an agricultural field.

The invention provides a power transmission line disaster monitoring and risk assessment platform based on satellite and weather information in order to effectively warn disasters. The platform comprises a power transmission line geographic information sub-platform, a satellite remote sensing data receiving sub-platform, a data modeling and risk assessment sub-platform, a disaster warning and treatment sub-platform, and a support and operation sub-platform. The data modeling and risk assessment sub-platform comprises a fire development trend prediction submodule; the submodule predicts time sequence of fire data by a time prediction method, auto-regressive integrated moving average, captures cross-fire hidden spatial correlation by a spatial prediction method through dynamic regression neural network, simulates stochastic disturbance by a Markov chain model, and acquires space-time integrated and disturbance-removing prediction results by means of statistical regression.

The present invention relates generally to wireless, remote, physiological monitoring in the evaluation of health and disease state specifically with respect to cardiac and pulmonary pathologies, including heart failure and sleep apnea. Personalized thresholds are used to enhance performance of classification and prediction methods by utilizing patient clinical history and past empiric sensed data, such as through an initialization period, to learn the biological variation present in each sensed individual.Data from an adherent patch undergoes disseminated processing as data is securely sent from a sensing device ultimately to a remote server. Monitoring, classification, and prediction results are hosted and made accessible through an authenticated system to members of the individual's healthcare team, family, or other caregivers.Learning algorithms are used to characterize physiological attributes and to classify a health or disease state based on an aggregate of features. Classification models and feature selection are optimized with validation algorithms.

The invention discloses a power equipment infrared image fault positioning, identification and prediction method. The power equipment infrared image fault positioning, identification and prediction method comprises the following steps: 1) collecting power equipment infrared thermal image data; 2) classifying the infrared images to form a data set; 3) constructing a convolutional neural network model; 4) separating out faulty power equipment; 5) monitoring faulty power equipment in real time, and longitudinally collecting temperature data; 6) positioning a fault part, segmenting the infrared image of the power equipment, and extracting a fault area; 7) diagnosing a fault area, and judging a fault level; 8) predicting an equipment state trend; 9) uniformly outputting and displaying the information; 10) storing the fault level; 11) making four types of infrared image data sets; 12) building a target detection model and training; 13) directly detecting an infrared image of power equipmentto be detected through a target to obtain a fault position and a fault level; 14) repeating the step (5); 15) repeating the step (8); and 16) repeating the step (9) facilitating positioning of the fault position, fault level judgment and prediction of the fault equipment and giving a maintenance suggestion.

The invention discloses a density clustering-based self-adaptive trajectory prediction method which comprises a trajectory modeling stage and a trajectory updating stage, wherein in the trajectory modeling stage, rasterizing treatment is carried out on a newly generated movement report, so that moving points can be obtained and are divided into six moving point subsets; the six moving point subsets are clustered by adopting a limited area data sampling-based density clustering algorithm, so that a new trajectory cluster can be formed; the new trajectory cluster and an old trajectory cluster in the same period of time are merged with each other according to the similarity of the trajectory points, and the trajectory points of the merged trajectory cluster and the area of influence are updated; the trajectory points are combined according to the time sequence, so that a complete user movement trajectory can be obtained; in the trajectory updating stage, the user movement trajectory generated in the trajectory modeling stage is corrected. The density clustering-based self-adaptive trajectory prediction method is used for user movement trajectory prediction in the mobile communication scene; furthermore, when the new user movement trajectory is generated, the whole trajectory data is not needed to be modeled again.

One embodiment generally pertains to a method of prediction. The method includes generating a set of affixes from a selected input sequence and comparing the set of affixes with a predictive set of affixes. The method also includes selecting an affix from the predictive set of affixes. The invention uses various input data sets and allows the ability to perfectly render the original data set and the minimal size of the predictive set of affixes.

The invention relates to an online predicting method of damage and service life of a high temperature pipeline. The method comprises the following implementing steps of: (1) carrying out finite element simulation analysis of damage and coupling to the high temperature pipeline; (2) finding out important monitoring parts according to the analysis results, arranging a sensor and monitoring the strain of the sensor; (3) carrying out finite element analysis (including analytical subprogram of a constitutive equation) for different working conditions and establishing database with damage, strain and residual life and strain; and (4) carrying out online inquiry and comparison to strain values detected online and the value of the load working condition and the data in the database so as to obtain the assessment value of corresponding damage and residual life. The online predicting method has the advantages of being capable of carrying out real-time monitoring to the high temperature pipeline in operation while production is carried out normally, reflecting the deformation and damage of the important parts and key parts in time, making correct estimation to the use life and residual life of the pipeline, being beneficial to guaranteeing safe production, adjusting the production load, planning maintenance reasonably and effectively prolonging the service life of production equipment.

The invention discloses a short-term load prediction method based on similar day segmentation and an LM-BP network. According to the method, through quantitative calculation of comprehensive correlative coefficients between meteorological factors and a historical load curve corresponding to a to-be-predicted day, the load curve of the to-be-predicted day is segmented, and according to prediction load curves within different time intervals, corresponding similar days are separately figured out; selection of the similar days is conducted through comprehensive consideration of a multi-feature similarity judgement standard of tendency similarity and shape similarity which are based on historical day meteorological similarity and historical load data. A similar day sample with the highest similarity is selected form historical data of the same kind; different neural network models are constructed through different training samples within different load prediction time intervals, and therefore the prediction precision of the neural network models is further improved. By means of the method, the calculation speed and convergence speed of a prediction algorithm are increased.

In a state deciding apparatus, an information acquiring unit acquires pieces of information of different types related to a state of an image forming apparatus. An index value calculating unit calculates an index value based on the pieces of information acquired. A state change deciding unit decides a change in a subsequent state of the image forming apparatus, based on a temporal change in the index value calculated.

This invention teaches that a single auxiliary key (1006) can support a multiplicity of ordered groups (1003-1004) and that only a subset of each group (1003-1004) needs to be visually represented on the face of a key, and that either multi-tap or predictive methods, or both, can be used to input members of each groups, and that auxiliary keys (1005-1007) are a convenient and effective method to select ordered groups, and to perform the multi-tap function. This invention teaches: limited multi-tap depth, reduced keystrokes per symbol, discoverability, graduated discoverability, compact presentation and seamless integration of multi-tap and predictive methods.

The invention provides a ship intelligent identification tracking method. For the ship intelligent identification tracking method, based on a deep learning algorithm based on computer vision, a basicclassification network structure and a multi-scale target prediction method in traditional deep learning are improved, and a Darknet network and a YOLOv3 algorithm are combined to track a ship and detect and identify the type of the ship in real time. According to the ship intelligent identification tracking method, the idea of a residual error network is introduced; a full convolution structure is adopted; the network depth is increased; the data feature learning capability is improved; and local feature interaction between the feature maps is achieved in a convolution kernel mode through a YOLOv3 algorithm, and matching and positioning of targets are conducted, and target area prediction and category prediction are integrated into a single neural network model on the basis, so that global information of the images is recognized as the targets. Experimental results show that compared with a traditional method, the algorithm provided by the invention not only has better real-time performance and accuracy, but also has better robustness for various environmental changes.

An output of a photovoltaic (PV) device is predicted by applying Fourier analysis to historical data to obtain frequencies and a mean of the frequencies in the data. Regression analysis is applied to the data to obtain a regression coefficient. Then, the prediction is a sum of the mean at the time step and a deviation from the mean at a previous time step, wherein the means are represented and approximated by selected frequencies, and the deviation for the previous time step is weighted by the regression coefficient.