283 results about "Predictive function" patented technology

A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.

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 computing device including an indicator predicting use of a network resource. The indicator may provide an indication that a threshold associated with a budget for the network resource has or will be reached. When a user requests a network operation that will consume the network resource, an amount of the network resource to be consumed by the operation is predicted, if possible. If the prior use of the network resource and the amount to be used to complete the network operation exceeds a threshold the user may be warned and given an opportunity to cancel the operation or adjust the way in which the operation will be performed. If the amount of network resource a network operation will consume is unknown, a rate of use of the network resource is determined and a time until the budget is reached is estimated. An administrative tool is provided for managing network resources and setting budgets for resource consumption by users, devices, or applications.

A system and method for predicting price fluctuations in financial markets. Our approach utilizes both market history and public news articles, published before the beginning of trading each day, to produce a set of recommended investment actions. We empirically show that these markets are surprisingly predictable, even by purely market-historical techniques. Furthermore, analyzing relevant news articles captures information features independent of the markets history, and combining the two methods significantly improves results. Capturing usable features from news articles requires some linguistic sophistication the standard naïve bag-f-words approach does not yield predictive features. Instead, we use part-of-speech tagging, dependency parsing and semantic role labeling to generate features that improve system accuracy. We evaluate our system on eight political prediction markets from 2004 and show that we can make effective investment decisions based on our systems predictions, whose profits greatly exceed those generated by a baseline system.

The invention discloses a clustering-analysis-based wind power short-term prediction system and prediction method. The prediction system comprises a short-term prediction server and a real-time data acquisition apparatus; and a power prediction function unit and a prediction database are installed at the short-term prediction server. According to the prediction method, a daily correlation analysis is carried out by using a pearson product moment correlation coefficient to determine consistency of the daily correlation of the wind power and daily similar situation of the available weather information; clustering analysis pretreatment is carried out on historical weather database by using a K mean value clustering method; historical day data similar to a weather feature parameter of a prediction day are selected by using a method using an Euclidean distance as a similarity measure and the data are used as the training samples for neural network prediction model establishment; after training based on the similar samples after clustering, a wind power prediction model based on the cluster analysis is obtained; and the prediction day NWP information is used as input parameter of the model and the wind power is used as the model output, so that prediction power data of the prediction day are obtained.

The invention discloses a network security situation awareness model and method based on an attack graph. The model comprises a data preprocessing module, an attack feature library construction module, an attack state identification module, a security situation evaluation module and a security situation prediction module. The method comprises: firstly, collecting data sets of different sources, and obtaining asset vulnerability threat data and asset attack threat data after preprocessing; further analyzing and training by utilizing the asset attack threat data and the principal component dataof the attack information, and establishing an attack feature library; matching the attack portrait, and determining the attack state of the current equipment; then, calculating a security situation value of the whole network by utilizing the asset attack threat data; and finally, predicting the next attack state through the attack and defense game matrix of the attack state diagram so as to predict the network security situation. A good situation awareness effect and a network security situation evaluation and prediction function are realized, and the model and the method have relatively goodpracticability, high efficiency and expansibility.

A technique for efficiently compressing rendered three-dimensional images in a remote rendering system adds a novel render-assisted prediction function to an existing video compression framework, such as the standard H.264 / 5 framework. Auxiliary rendering information is separated from rendering information used to describe a reference image by a server system. A client system may alter the auxiliary data and generate a new image based on the reference image and rendered scene information from the auxiliary data without creating additional network bandwidth or server workload.

A technique for efficiently compressing rendered three-dimensional images in a remote rendering system adds a novel render-assisted prediction function to an existing video compression framework, such as the standard H.264 / 5 framework. Auxiliary rendering information is separated from rendering information used to describe a reference image by a server system. A client system may alter the auxiliary data and generate a new image based on the reference image and rendered scene information from the auxiliary data without creating additional network bandwidth or server workload.

This method implements a transition from i) moving state in which the drone is flying at speed and tilt angle that are not zero to ii) hovering state in which the drone has speed and tilt angle that are both zero. The method comprises: a) measuring horizontal linear speed, tilt angles, and angular speeds at the initial instant; b) setting stopping time value; c) on the basis of initial measurements and set stopping time, parameterizing a predetermined predictive function that models optimum continuous decreasing variation of horizontal linear speed as a function of time; d) applying setpoint values to a loop for controlling motors of the drone, which values correspond to target horizontal linear speed precalculated from said parameterized predictive function; and e) once hovering state has been reached, activating a hovering flight control loop for maintaining drone at speed and tilt angle that are zero relative to the ground.

The invention discloses an establishing method for an energy consumption model of a base station. The establishing method comprises the following steps of: a, presetting an independent variable and obtaining historical energy consumption data and base station service and configuration data corresponding to the variable; b, fitting a straight line for the obtained data by using multivariate linear regression analysis, returning regression analysis parameters that describe the fitted straight line and establishing the energy consumption model; c, carrying out model validation; and d, carrying out model modification according to a model validation result until the model validation result indicates that the obtained energy consumption model achieves the expected result. The invention further discloses an energy consumption predicating method of the base station, a corresponding establishing device for the energy consumption model of the base station, and an energy consumption predicating device of the base station. Compared with the conventional energy consumption management system, the energy consumption model obtained by the establishing method provided by the invention has an accurate and effective energy consumption predicating function.

A successive approximation register (SAR) analog-to-digital converter (ADC) is disclosed. A first and second capacitor DACs receive a first and second input signals respectively. A first coarse comparator compares an output of the first capacitor DAC with a window reference voltage, a second coarse comparator compares an output of the second capacitor DAC with the window reference voltage, and a fine comparator compares the output of the first capacitor DAC with the output of the second capacitor DAC. A SAR controller receives outputs of the first and second coarse comparators to determine whether the outputs of the first and second capacitor DACs are within a predictive window determined by the window reference voltage. The SAR controller bypasses at least one phase of analog-to-digital conversion of the SAR ADC when the outputs of the first capacitor DAC and the second capacitor DAC are determined to be within the predictive window. The SAR controller decodes the outputs of the first and second coarse comparators and the fine comparator to obtain a converted output of the SAR ADC.

This method implements a transition from i) moving state in which the drone is flying at speed and tilt angle that are not zero to ii) hovering state in which the drone has speed and tilt angle that are both zero. The method comprises: a) measuring horizontal linear speed, tilt angles, and angular speeds at the initial instant; b) setting stopping time value; c) on the basis of initial measurements and set stopping time, parameterizing a predetermined predictive function that models optimum continuous decreasing variation of horizontal linear speed as a function of time; d) applying setpoint values to a loop for controlling motors of the drone, which values correspond to target horizontal linear speed precalculated from said parameterized predictive function; and e) once hovering state has been reached, activating a hovering flight control loop for maintaining drone at speed and tilt angle that are zero relative to the ground.

The invention provides a portable fruit quality nondestructive testing device based on an LED combined light source, comprising a hardware unit and a software system, wherein the hardware unit comprises a lighting module, a spectrum acquisition module and a spectrum processing module; the software system comprises softwares with functions of reading, storing, displaying, parameter setting and forecasting for spectrum by spectrum acquisition and spectrum analysis; the lighting module comprises a handheld probe interface (1), optical fibres (2), a switch (7), a handheld probe (8), the LED combined light source, and a constant flow source; the spectrum acquisition module comprises an optical fibre spectrograph (29) and USB wires; and the spectrum processing module comprises a support (3), a host machine (4), a display screen (6), a mainboard, data wires, a data storage, power cords, a power supply and USB wires. The invention can be applied to on-site fast nondestructive testing for internal quality of the fruit, such as sugar degree, acidness and vitamin C.

Embodiments generally relate to methods of accurately predicting seasonal fluctuations in precipitation or other approximate functionals of a climate state space, such as the number of heating or cooling degree days in a season, maximum river flow rates, water table levels and the like. In one embodiment, a method for predicting climate comprises: deriving a climate attractor from a global climate model, wherein a tuning parameter for the climate attractor comprises a value of total energy for moving air and water on the earth's surface; estimating a predictive function for each of a plurality of computational cells within the global climate model; and predicting an approximate climate functional of interest for a given specific location utilizing a combination of the predictive functions from each of the plurality of computational cells geographically proximate the location, where at all stages, predictive functions are selected in part by comparison to historical data.

The invention discloses a PEMFC (proton exchange membrane fuel cell) temperature control method. The method comprises the following steps of using a PID (proportion integration differentiation) controller as a common feedback controller to stabilize the temperature of a fuel cell stack to achieve an initial control effect, and then obtaining a variation trend of the temperature of a fuel cell system according to the forward predictive function of a grey model, wherein a fuzzy controller is utilized to obtain predictive information to compensate the uncertainty and external interference of thesystem so that the control accuracy of the system is further improved. Feedforward compensation control and feedback control function together to realize accurate control of PEMFC temperature.

The present invention provides a method and system for storing information into a folder / location using a predictive function to select the name for the newly stored information. The predictive functions use names of applications, documents or words within these items to select and present a name to a user. The present invention introduces a new function within document processing applications, (also potentially operating systems), whereby each time a document is saved to a storage medium, a predictive function will cause the selection of the most probable folder / location in which the document would be stored.

A method for controlling transmission of video data in a network, including: transmitting video data to a receiver via the network; receiving channel parameter information measured by the receiver; applying a predictive function to the channel parameter information to compensate for delay times in receiving the channel parameter information from the receiver, and generating a feedback function; and adjusting the video data to be transmitted in response to the feedback function to compensate for network conditions.

The present application discloses a monitoring indicator optimization method of a business operation analysis early warning system. An early warning analysis method comprises three stages which are a threshold design phase, a static early warning stage and a dynamic early warning stage. The threshold design is based on large sample historical data, a Monte Carlo simulation method is used, and a threshold can be set according to the development change trend of an operational performance indicator. According to static early warning, a multi-layer radar chart analysis method is statically used, and the identification of a key indicator which generates unusual action is facilitated. According to dynamic early warning analysis, a prediction of combining logic regression and a neural network is used, and the indicator change trend can be predicted. Thus, according to the method, the corresponding reaction of the change trend of the operational performance indicator can be effectively carried out, and the change trend of the operational performance indicator can be quantitatively analyzed. In addition, through the method, the problems of insufficient unusual action identification and prediction functions of the key indicator and weak support ability of large sample data in the previous comprehensive early warning can be solved.

The invention belongs to the technical field of network traffic resource situation prediction and particularly relates to a network traffic resource situation predication method based on a long short-term memory LSTM model. The invention aims to provide a Long Short-Term Memory LSTM cyclic neural network predictive model suitable for a traffic space-time nonlinear characteristic. The model combines the characteristics of strong burstiness and long-range dependence of actual traffic data for model training and quantitative prediction. The method in the invention has the following beneficial effects that the network traffic predication function is realized through the LSTM model, thereby effectively improving the prediction accuracy; and the advantages of the LSTM predication model are moreobvious when the difference of the data samples is large.

The invention provides a capacitive sub memreactive element and inductive sub memreactive element filter which can be used to realize a novel circuit component capable fractional-order nonlinear memory and prediction, and belongs to the technical field of interdiscipline of circuit and system, modern signal processing and applied mathematics. The fractional order v is not a traditional positive integer but a positive real number, a fraction or a rational decimal tends to be used in engineering application, v=m+p, m is a positive integer, and p is greater than or equivalent to 0 and lower than or equivalent to 1. The filter is formed by connected the input point, a fractional-order differentiator, a convolver, a memresistor, a (1-p) order power arithmetic unit, a Laplace inverse converter, a multiplier and the output point in a cascaded manner. The filter is especially suitable for the application occasion in which the novel circuit component capable fractional-order nonlinear memory and prediction is realized.

The invention discloses a support vector machine (SVM) based prediction method for the degradation tendency of super-capacitor capacitance value. The prediction method utilizes the regression function of the support vector machine to predict the degradation tendency of the super capacitor capacitance value and comprises: 1) pre-processing the input value and the output value; 2) carrying out trainings to the training set data for a regression estimation function; 3) using the particle swarm optimization algorithm to automatically optimize the relevant parameters of the support vector machine; 4) according to the optimization result, configuring the corresponding parameter values of the support vector machine; substituting the training set data into a correlation vector machine model to obtain a regression prediction model for the degradation tendency of the capacitance value; and 5) substituting the training set data into the regression prediction model to obtain the degradation tendency of the capacitance value. According to the invention, it is possible to conduct online prediction to the degradation tendency of the capacitance value. Through the introduction of a particle swarm optimization algorithm to modify the parameter optimization method, the prediction efficiency and accuracy of the algorithm are increased so that it can be applied in a larger scope.

An injection molding control method that uses a neural network is disclosed. The control method determines an injection condition according to a prediction function, a first value, and a second value. The prediction function is implemented in test molding, and the first value and second value are implemented in a measurement step that has a time margin. The injection condition predicted for mass-production molding is determined prior to the injection step, and the need for computation during the injection step is thereby eliminated.

The invention discloses a tunnel event detection method based on integrated learning time sequence prediction, which mainly solves the problem that similar methods fail to accurately predict values of a sensor and cannot effectively control tunnel ventilation. The method comprises the detection steps of: pre-processing acquired highway tunnel data to form a training data set; training a plurality of basic predictors according to the training data set, and forming a strong predictor by the weighted combination of the basic predictors; utilizing the strong predictor to calculate a predictive value of smoke concentration of a tunnel at t+1 time according to a value of a tunnel sensor at the current t time, and dynamically adjusting the basic predictors which take part in the integration according to prediction error; comparing the predictive value of the smoke concentration of the tunnel at t+1 time with a smoke concentration threshold, and judging whether the smoke concentration is an over-standard event; and for the over-standard event, calculating control parameters of a ventilation controller, and reducing the smoke concentration. The method has the advantages of strong prediction function and high control accuracy of the ventilation controller, and is used for operation monitoring, energy conservation and emission reduction of highway tunnels.

The invention belongs to the field of the analysis of nonlinear time series, in particular to a time series predicting model. The network model comprises an input layer, a middle layer and an output layer, wherein the middle layer unit consists of a chaos operator with the chaos characteristic. The parameter of the chaos operator is trained and adjusted through a chaos optimization algorithm. By learning and training, the network predicts the value at a certain time in future with the previous known value of the time series, and gradually modifies the parameter of the chaos operator according a predicted error, thereby gradually having the information which is accordant with the regularity in the time series and completing the predicting function of the time series. Particularly, the model can effectively realize the multi-step prediction of the time series. The time series is mainly used for the field of the prediction and the analysis of the time series in the practical engineering.

The invention discloses a cold-chain logistics management device with the shelf life prediction function, a method of the cold-chain logistics management device and application of the cold-chain logistics management device. The cold-chain logistics management device comprises a temperature sensor, a humidity sensor, a carbon dioxide sensor, an ethylene sensor, a microprocessor unit, a display unit, an RFID storage unit and an RFID read-write unit. A user sends information such as the start record, the temperature collection interval, the product type and the initial shelf life to a cold-chain logistics management RFID card through any handset with an RFID data format before a product is placed on a shelf, and then the cold-chain logistics management RFID card is attached to the target product. After the cold-chain logistics management device is started, the display unit displays the initial shelf life, and the sensors transmit collected data to the microprocessor unit, calculate the remaining shelf life according to a shelf life prediction model by combining the specific product type, and display the remaining shelf life on the display unit. The cold-chain logistics management device with the shelf life prediction function, the method of the cold-chain logistics management device and application of the cold-chain logistics management device can achieve direct prediction of the real-time remaining shelf life of food or agricultural products.

A conversation-simulating system facilitates simulating an intelligent conversation with a human user. During operation, the system can receive a user-statement from the user during a simulated conversation, and generates a set of automatic-statements that each responds to the user-statement. The system then determines a set of behavior-characteristics for the user, and computes relevance scores for the automatic-statements based on the behavior-characteristics. Each relevance score indicates an outcome quality that the user is likely to perceive for the automatic-statement as a response to the user-statement. The system selects an automatic-statement that has a highest relevance score from the set of automatic-statements, and provides the selected automatic-statement to the user.

The invention discloses a network attack target prediction method based on neighbor similarity. In the existing threat prediction methods, prediction and analysis are performed based on attack behaviors, and an attack target is not further predicted. The technical scheme adopted in the invention is as follows: firstly, preprocessing a safety event, performing normalization processing, and removing redundancy and misinformation; then matching the preprocessed safety event with a pre-defined rule library, performing correlation analysis, and reconstructing an attack scene; and finally calculating the similarity of a host address, an open port and an operating system of the attack target with these attributes of a neighbor host, and predicting the next step network attack target. The network attack target prediction method disclosed by the invention provides reference for an administrator to prepare handling strategies, achieves a prediction function of network attacks and improves the overall safety of the network.

The invention discloses a community carbon emission monitoring and predicting system and method. The system comprises a carbon emission monitoring module and a carbon emission prediction module. The carbon emission monitoring module collects community electric power, fuel gas, liquefied petroleum gas, gasoline, diesel oil and other energy consumption activity data and garbage, waste water and other solid waste activity data, the carbon emission of a community is calculated through an emission factor method, and the function of monitoring community carbon emission is achieved. The monitoring module collects per capita resident consumption expenditure, community permanent resident population quantity and local resident consumption price index input parameters. The carbon emission predictionmodule firstly nondimensionalizes input parameters, and then inputs the converted parameters to an improved support vector machine to carry out training modeling, thereby realizing a function of predicting community carbon emission. According to the invention, all carbon emission sources in the community boundary range can be comprehensively and accurately monitored, the future carbon emission ofthe community can be predicted, and a basis is provided for the community to formulate carbon emission reduction measures.

The present invention relates to method of coding blocks of video data for a handheld apparatus comprising a battery. Said method comprising a step of computing a residual error block from the use of a set of prediction functions having different power consumption levels. It also comprises a step of enabling or disabling a prediction function of the set depending on its associated power consumption level for a predetermined level of the battery. It finally comprises a step of selecting a prediction function among a set of enabled prediction functions to code the residual error block.