211 results about "Predictive power" patented technology

The invention may be embodied in a method for operating a disk drive in a mobile device to optimize power usage. The mobile device may be a hand held computing device such as a personal digital assistant (PDA) or a mobile terminal such as a cellular telephone. Immediately after responding to a disk access command, the disk drive is in first operating mode that consumes a relatively high level of power and may transition to a second operating mode that consumes a relatively low level of power. A transition from the second operating mode to the first operating mode consumes a transition quantity of power. A predictive technique tracks a user's usage pattern and adapts an operating mode transition time based on the usage pattern.

An apparatus comprising a power source, one or more sensors, a transceiver, and a memory. The power source may be configured to store energy to power the apparatus. The one or more sensors may be configured to receive captured data from one of a plurality of sources. The transceiver may be configured to send and receive data to and from a wireless network. The processor may be configured to execute computer readable instructions. The memory may be configured to store a set of instructions executable by the processor. The instructions may be configured to (A) evaluate an expected power usage budget calculated using a predictive model of future energy consumption and (B) (i) store the captured data in the memory in a first mode and (ii) transmit the captured data to a remote storage device in a second mode. The first mode or the second mode is selected based on characteristics of the captured data received from the sensors.

A prognostic processor for predicting machine failure in avionics electronics comprises prognostic capabilities in a single integrated circuit, with a processor, volatile and non-volatile memory, clock, on-chip and off-chip sensors and transducers, A / D converters, a common I / O interface adapted to be employed in a network of similar prognostic processors, and predictive Failure Analysis (FA) model software, which may be distributed throughout the network. The FA software employs a log file history, with the log file history storing data collected by the prognostic processor, real-time execution of a predictive model, with the ability to update the FA model with data from field failures. The prognostic processor network supports hierarchical processing to work with multiple prognostic processors. The prognostic processor system is applicable to FA monitoring of a wide range of avionics electronic equipment, in particular, Line Replacement Units (LRUs).

A computer-implemented method enables rack-level predictive power capping and power budget allocation to processing nodes in a rack-based IHS. A rack-level management controller receives node-level power-usage data and settings from several block controllers, including current power consumption and an initial power budget for each node. A power consumption profile is generated based on the power-usage data for each node. A total available system power of the IHS is identified. A system power cap is determined based on the power consumption profiles and the total available system power. A current power budget is determined for each node based on an analysis of at least one of the power consumption profile, the initial power budget, the current power consumption, the system power cap, and the total available system power. A power subsystem regulates power budgeted and supplied to each node based on the power consumption profiles and the system power cap.

The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human disease, but living systems more generally. The present invention provides novel gene network reconstruction algorithms that utilize naturally occurring genetic variations as a source of perturbations to elucidate the networks. The algorithms incorporate relative transcript abundance and genotypic data from segregating populations by employing a generalized scoring function of maximum likelihood commonly used in Bayesian network reconstruction problems. The utility of these novel algorithms can be demonstrated via application to gene expression data from a segregating mouse population. The network derived from such data using the novel network reconstruction algorithm is able to capture causal associations between genes that result in increased predictive power, compared to more classically reconstructed networks derived from the same data.

Predictive metric analysis for business management is divided into build time, corresponding to the business owner view of the enterprise, and run time, corresponding to the information technology view of the enterprise. The build time consists of a predictive model and a monitoring model. These models go through transformation processes to the components of the run time. The run time components are a Metric Value Prediction Service (MVPS), which receives as input predictive model transformation and outputs predicted metric values, and a monitoring engine, which receives as input monitoring model transformation, the predicted metric values and business events from the business process. Various analytical engines can be plugged in to provide the predictive capabilities. Input is provided to a framework from various business systems which results in predicting the value of the metrics across the future time horizons.

The present invention is related to methods of early diagnosis of ovarian cancer in a patient by determining serum levels of blood markers using a novel LabMAP™ technology (Luminex Corp., Austin, Tex.), which allows for simultaneous measurement of the blood markers in serum. The panel of blood markers offers extremely high predictive power for discrimination of ovarian cancer from both healthy control patients and from patients with benign pelvic / ovarian tumors. The methods of the present invention allow for rapid, early diagnosis of ovarian cancer with extremely high sensitivity and specificity to be clinically useful in disease diagnosis.

A battery control module for use with a battery includes a voltage measuring module that measures battery voltage and a current measuring module that measures battery current. A power limit module communicates with the current and voltage measuring modules and once every time period estimates a battery current limit that corresponds with a future time period. The battery current limit is based on a predetermined voltage limit of the battery and a battery current and a battery voltage that correspond with a time period that precedes the future time period.

The invention provides a method and system for tracking wind and electric output plans through energy storage based on predictive power of wind and electricity. The method comprises the following steps: reading the relevant data of a wind power field and an energy storage system; establishing a charging and discharging control strategy of the energy storage system; confirming a target function; confirming charging and discharging control coefficients through a particle-swarm optimization algorithm; confirming the charging and discharging power of the energy storage system according to the charging and discharging control coefficients. The system comprises an acquisition unit, a control unit, a calculating unit, an optimizing unit and a result outputting unit. According to the method and system, each predictive point performs roll poling at a time, and the purpose of optimizing the charging and discharging control coefficients of an energy storage power station in real time is achieved through the particle-swarm optimization algorithm, so that the limited capacity of the energy storage system can be sufficiently utilized; in addition, the target function is set, so that the stage of charge of the energy storage system is kept in an appropriate range as much as possible, the charging and discharging capabilities of the energy storage system are further improved, and finally the capability of tracking planning output, of a wind-storage association system, is improved.

An improved method is provided for managing workload on a multi-server computer system. In one embodiment, a subset of servers is selected according to an anticipated net workload. The remaining servers in the system may be powered off to conserve energy and prolong equipment life. Workload is dynamically apportioned among the subset of servers at selected intervals to more uniformly distribute the mean and variance of the workload among the subset of servers. More particularly, the mean and the variance for each of a plurality of workload units are equally weighed in determining a ranking of the workload units. The workload units may be ordered according to a mathematical combination of the mean and variance, such as the sum or product of mean and variance for each workload unit. The workload units are allocated among the subset of servers in according to rank, such as by assigning the workload units to the servers in a reverse round-robin fashion according to rank. Predictive power management schemes such as DVS and DVFS may then be used to control power to the servers.

Methods and systems consistent with the principles of some embodiments of the present invention provide for determining a set of variables that together have strong predictive power relative to some target variable(s); identifying a subset of variables that together describe the majority of the information in the database; statistically segmenting the database; predicting unknown values in the target variables; combining prediction and VIVa; filling gaps in databases for further analysis or database completion; predicting probabilities; and outputting information based on the processed data. The outputs may relate to optimizing price to maximize return, producing a database based on postal code, identifying a trade area for sale of goods; and / or optimizing the distribution of marketing funds across various marketing channels.

The invention relates to an ultrashort-term slide prediction method for wind power. An atomic sparse decomposition method with quite high non-stable signal tracking and prediction capacity is used as a front decomposition method of a neural network. A wind power time sequence is decomposed into an atomic component and a residual error component, the atomic component is automatically predicted, the residual error component is predicted by the neural network, atomic decomposition results are updated by adding the latest wind power real-time data, and further the wind power of a next moment is slidably predicted. Actual wind field data prove that the model can effectively avoid non-stability of the wind power, sparser decomposition effects are achieved, and statistical intervals of absolute average error and root mean square error computation values can be remarkably reduced. Therefore, the ultrashort-term slide prediction method has the advantages that non-stability of the wind power can be effectively avoided, the sparser decomposition effects are achieved, and the statistical intervals of the absolute average error and root mean square error computation values can be remarkably reduced.

Embodiments of the present disclosure provide a hybrid vehicle predictive power control system. The system is mainly aimed at long-distance freight heavy truck application scenarios; Based on the vehicle configuration parameters and the current operating conditions, with the aid of electronic navigation 3D map of vehicle expressway, the system can accurately and real-timely predict the dynamic road load power time-space function within the range of tens of kilometers of the electronic horizon in front of the vehicle, the electric power shunt is commanded by the vehicle controller, the direction and amplitude of the flow of the 100 kilowatt-level electric power are accurately and continuously adjusted among engine-driven generator set capable of being driven in tens of millisecond system response time, the battery pack and the drive motor;, keeping the engine working stably in its high efficiency area for a long time, the transient power balance of road load required by vehicle dynamicsequation can be satisfied in real time by fast charging and discharging of battery pack of several hundred kilowatts, Compared with traditional diesel heavy trucks, hybrid heavy trucks can greatly reduce the overall fuel consumption and emissions in real world operation under the premise of ensuring vehicle power, freight timeliness and driving safety.

The invention relates to HMD virtual visual delay compensation system and working procedure, based on the body surface electromyography information, belongs to virtual reality technology. The system includes collecting electrode, reference electrode, collection equipment of multi-channel surface electromyography information, computers, HMD.The work steps of the system are: place electrode on the neck of operator, and make signal amplification and filtering connecting with multi-channel surface electromyography information collection devices, put the digital signal treatment into the computer, first initial calibration procedures by the computer, by we can initially study the typical head movement according to voice prompt, then move the head to forecast operating procedures in the virtual HMD visual signal delay compensation, while tracking data with the original data integration, improving accuracy of head tracking. The characteristic of this invention is small in size, light weight, strong predictive power, and high sensitivity.

The invention proposes a traffic flow prediction method based on firefly algorithm and RBF neural network. The method comprises: performing normalization to the sample data so that the input data and output data are on the same order of magnitude; initializing the firefly algorithm parameters; utilizing the random method to initialize the firefly populations and encoding each individual in the populations; using the firefly algorithm to train the RBF neural network to obtain the best individual in the populations; decoding the best individual in the populations to obtain the trained RBF neural network; and utilizing the trained RBF neural network to predict the traffic flow data sample. Compared with the traditional traffic flow prediction method, the method of the invention makes full use of the advantages of the firefly algorithm in the RBF neural network training so that the RBF network possesses a more accurate prediction capability, achieves even faster training efficiency and better generalization capability. The invention belongs to the traffic transportation information engineering technology field and can be used for the predictions of road traffic flows in an intelligent traffic system.

A stochastic algorithm has been developed for predicting the drug-likeness of molecules. It is based on optimization of ranges for a set of descriptors. Lipinski's “rule-of-5”, which takes into account molecular weight, logP, and the number of hydrogen bond donor and acceptor groups for determining bioavailability, was previously unable to distinguish between drugs and non-drugs with its original set of ranges. The present invention demonstrates the predictive power of the stochastic approach to differentiate between drugs and non-drugs using only the same four descriptors of Lipinski, but modifying their ranges. However, there are better sets of 4 descriptors to differentiate between drugs and non-drugs, as many other sets of descriptors were obtained by the stochastic algorithm with more predictive power to differentiate between databases (drugs and non-drugs). A set of optimized ranges constitutes a “filter”. In addition to the “best” filter, additional filters (composed of different sets of descriptors) are used that allow a new definition of “drug-like” character by combining them into a “drug like index” or DLI. In addition to producing a DLI (drug-like index), which permits discrimination between populations of drug-like and non-drug-like molecules, the present invention may be extended to be combined with other known drug screening or optimizing methods, including but not limited to, high-throughput screening, combinatorial chemistry, scaffold prioritization and docking.

The invention discloses a congestion index prediction method combining a road network topological structure and semantic association. The method comprises the following steps: (1) establishing an undirected graph based on a space topological structure of a road network; (2) firstly calculating the similarity between the historical congestion index data of the road, then establishing a weighted undirected graph based on the similarity, and finally embedding the weighted undirected graph to obtain a semantic vector for representing the road; And (3) extracting short-term congestion index changecharacteristics on the basis of the graph convolutional network, extracting long-term congestion index change characteristics on the basis of the recurrent neural network, and fusing road semantic vectors on the basis to establish a prediction model. According to the method, spatial topology association and historical semantic association of the road network are considered at the same time, and the prediction capability of the model is improved; A graph convolutional network is adopted to model a road network topological structure, and graph embedding is adopted to model road network semanticassociation, so that the road network topological structure and the semantic association can be processed by a deep neural network.

A system comprising a plurality of self-powered devices and at least one remote device. The plurality of self-powered devices may be configured to (i) perform one or more tasks and (ii) select one of a plurality of modes of operation. The remote device may be configured to (a) determine scheduling data for one or more activities based on a resource capacity of the self-powered devices and (b) communicate with the self-powered devices. The activities may comprise one or more of the tasks. The self-powered devices may determine a computational cost of performing the tasks.

Provides a method for optimizing the language model, including using the maximum matching technique to establish an initial language model based on the lexicon and segmentation obtained from the received corpus, and by dynamically updating the dictionary and re-segmenting the corpus according to statistical principles, iteratively improves Initialize the language model until a threshold of predictive power is reached.

The invention relates to the technical field of Internet information analysis, in particular to a network public opinion tendency prediction analysis method. The method includes the following steps that S101, an index system is determined, network public opinion information is gathered in crawling mode, and the time sequence of needed indexes is acquired through preprocessing; S102, models are established, wherein candidate models are established on the acquired time sequence; S103, an optimal model is selected, wherein the optical model is selected from the established candidate models through comparison; S104, network public opinion tendency prediction analysis is performed, wherein network public opinion tendency prediction analysis is performed based on the selected optimal model. By means of the method, adjustability of lag parameters can flexibly adapt to actual requirements, meanwhile, an MVE is introduced to serve as a model selection judgment criteria, and prediction capacity on fluctuation of special public opinion development tendency is improved. Finally, the models are corrected through new data, and long-term tracing prediction becomes possible.

The invention provides an interpretable short critical extreme rainfall prediction method based on an attention mechanism. The interpretable short critical extreme rainfall prediction method includesthe steps: firstly, screening use parameters through a random forest; calculating a standard precipitation index (SPI) of the to-be-measured station; forming a new matrix by the standard precipitationindexes and the selected parameters and inputting the new matrix into a long-term and short-term memory network for training; adding an attention mechanism into the neural network; visualizing weightcalculation of the attention mechanism in the training process; and finally, predicting the rainfall of the target area in the future 3, 6, 9 and 12 hours by taking 3 hours as a unit, wherein an attention mechanism is used in the prediction process to optimize the situation of short-term heavy rainfall, so that the prediction capability of the model for extreme weather is improved, and meanwhile,the interpretability of the model is also enhanced.

The present embodiment is able to find the optimal or near optimal variables composition of multivariate models by an evolutionary process within acceptable amount of time and resources that are less than using full variables permutation methodology. Subjected to any data, it adaptively identifies and constructs the most effective combination of the relevant variables to achieve one or more objectives. The objective could be for high explanatory power, high predictive power, response measure, or other objectives that the user defines. The present embodiment solves the sequential F-test problem by conducting non-sequential and non-linear search. The algorithm also solves partial F-test dilemma by evaluating all candidate variables membership intact, maintaining fidelity of full variables membership test throughout its permutation. Furthermore, the stochastic nature of the algorithm neutralizes the prejudices of manual decisions in variables identification and membership construction.

The invention discloses a PM2.5 concentration prediction method which combines stack-type self-coding and support vector regression. Steps are as follows: selecting influential factors with strong correlation as independent variables of the PM2.5 concentration prediction model, continuously collecting the selected independent variables and corresponding PM2.5 concentration data for N hours, and constructing a training set; a deep learning network model of stack self-encoder with K hidden layers being constructed; the training set being input into the model for training and the weights of eachvariable being obtained; the SVR model being trained by using the eigenvalues of the learning network model as input vectors, and the output of the SVR model being obtained; the test set being obtained, the test sample being predicted by the predictive model, and the precision of the predictive result being calculated. The invention has the advantages of SAE extracting data set features and SVR excellent prediction ability, and the prediction performance and precision are better than widely used ANN algorithm and SVR algorithm.

The invention discloses a power customer power outage sensitivity score card implementation method based on a logistic regression model. The method comprises steps: firstly, customer power outage sensitivity related attribute data are extracted from the power grid company marketing business system and the 95598 system, and properties with high predictive power are selected through information values; then, numerical value attributes are converted to ordinal number attributes through histogram analysis, and categories of the attributes are merged to reduce the cardinal number; then, based on an evidence weight conversion value of each attribute of the user, the logistic regression model is used to build a customer power outage sensitivity analysis model; and finally, based on the output parameters of the power outage sensitivity analysis model and the evidence weight conversion value of each attribute, a customer power outage sensitivity score card is built. The customer sensitivity model can be converted to a list form, the main factors affecting the customer power outage sensitivity and the affecting degree can be expressed intuitively, and the power outage sensitivity score of each customer can be calculated conveniently.

The invention discloses a three-level inverter model predictive power control method based on two-vector synthesis. The instantaneous active and reactive power of a grid-connected inverter is adjustedvia the model predictive power control based on two-vector synthesis, and a neutral-point voltage is balanced; the three-level inverter model predictive power control method based on two-vector synthesis is divided into two steps as follows: establishing two cost functions, firstly figuring out a small sector where an ideal voltage vector is located, then selecting two vectors through a reasonable cost function, and adjusting the duty ratio thereof to control switching elements of a three-level inverter. By adoption of the method, the switching frequency can be fixed, the calculation amount of the algorithm can be reduced, the computational resources can be saved, and the steady state and dynamic performance of the inverter controlled by the method are good.

The invention relates to a mode-switching-based symmetrical prediction control method of bilateral teleoperation of a robot. A symmetrical prediction control strategy based on mode switching is put forward based on a neural network technology with the estimation capability of the neural network for estimating the uncertain gravity term and the prediction capability for constructing the predictor kernel, an adaptation theory for eliminating various estimation and prediction errors, a homomorphic model prediction idea being an additional condition needing to be met for prediction outputting andbeing used for improving the prediction accuracy, and a proportional differential control algorithm for designing master and slave controllers. Therefore, stable, real-time, continuous, precise, and synchronous control of master and slave robots is realized and the expected teleoperation task is completed.

Predictive metric analysis for business management is divided into build time, corresponding to the business owner view of the enterprise, and run time, corresponding to the information technology view of the enterprise. The build time consists of a predictive model and a monitoring model. These models go through transformation processes to the components of the run time. The run time components are a Metric Value Prediction Service (MVPS), which receives as input predictive model transformation and outputs predicted metric values, and a monitoring engine, which receives as input monitoring model transformation, the predicted metric values and business events from the business process. Various analytical engines can be plugged in to provide the predictive capabilities. Input is provided to a framework from various business systems which results in predicting the value of the metrics across the future time horizons.