1764 results about "Prediction algorithms" patented technology

The present invention relates to methods to increase the number of analyte-related signals used to provide analyte measurement values, e.g., when two or more analyte-related signals are used to obtain a single analyte measurement value a “rolling” value based on the two or more signals can be employed. In another aspect, interpolation and/or extrapolation methods are used to estimate unusable, missing or error-associated analyte-related signals. Further, interpolation and extrapolation of values are employed in another aspect of the invention that reduces the incident of failed calibrations. Further, the invention relates to methods, which employ gradients and/or predictive algorithms, to provide an alert related to analyte values exceeding predetermined thresholds. The invention includes the above-described methods, one or more microprocessors programmed to execute the methods, one or more microprocessors programmed to execute the methods and control at least one sensing and/or sampling device, and monitoring systems employing the methods described herein.

A software platform in communication with networked distributed energy resource energy storage apparatus, configured to deliver various specific applications related to offset demand monitoring, methods of virtual power plant and orchestration, load shaping services, methods of reducing demand at aggregated level, prioritizing computer programs related to virtual energy pool, energy cloud controllers methods, charge discharge orchestration plans of electric vehicles, distributed energy resources, machine learning predictive algorithms, value optimizing algorithms, autonomous sensing event awareness, mode selection methods, capacity reservation monitoring, virtual power plant methods, advanced DER-ES apparatus features, energy management system for governing resources and methods, aggregated energy cloud methods, load shaping methods, marginal cost cycle-life degradation, load shaping API, forward event schedule, on demand request, and load service state request methods. Various rules, constraints of predictive algorithms for signal inputs to determine incremental storage cycles, cycle life degradation marginal cost, iterative and forward event schedule development, and load control.

Disclosed herein are systems and methods for demand forecasting that enable multiple-scenario comparisons and analyses by letting users create forecasts from multiple history streams (for example, shipments data, point-of-sale data, customer order data, return data, etc.) with various alternative forecast algorithm theories. The multiple model framework of the present invention enables users to compare statistical algorithms paired with various history streams (collectively referred to as “models”) so as to run various simulations and evaluate which model will provide the best forecast for a particular product in a given market. Once the user has decided upon which model it will use, it can publish forecast information provided by that model for use by its organization (such as by a downstream supply planning program). Embodiments of the present invention provide a system and method whereby appropriate demand responses can be dynamically forecasted whenever given events occur, such as when a competitor lowers the price on a particular product (such as for a promotion), or when the user's company is launching new sales and marketing campaigns. Preferred embodiments of the present invention use an automatic tuning feature to assist users in determining optimal parameter settings for a given forecasting algorithm to produce the best possible forecasting model.

A quality prognostics system and a quality prognostics method for predicting the product quality during manufacturing processes are disclosed. The present invention utilizes the current production tool parameters sensed during the manufacturing process and several previous quality data collected from the measurement tool to predict the future product quality. The quality prognostics system is composed of conjecture modeling means and prediction modeling means. The conjecture modeling means itself also can be applied for the purpose of virtual metrology. Further, the quality prognostics method possesses a self-searching means and a self-adjusting means for searching the best combination of various parameters/functions used by the conjecture algorithm or prediction algorithm; and meeting the requirements of new equipment parameters and conjecture/prediction accuracy.

An intrusion detection system (IDS) that uses application monitors for detecting application-based attacks against computer systems. The IDS implements application monitors in the form of a software program to learn and monitor the behavior of system programs in order to detect attacks against computer hosts. The application monitors implement machine learning algorithms to provide a mechanism for learning from previously observed behavior in order to recognize future attacks that it has not seen before. The application monitors include temporal locality algorithms to increased the accuracy of the IDS. The IDS of the present invention may comprise a string-matching program, a neural network, or a time series prediction algorithm for learning normal application behavior and for detecting anomalies.

A data processing system and method for developing predictions regarding future asset price movements, based on pattern detection in a time sequence of historical price data. The system includes computer implementation of a kernal regression to effect a smooth estimator of the non-linear price-time relationship. As tested against known patterns, the system provides an expectation regarding a future price movement.

A method of predicting disk drive failure at a central processing facility using an evolving drive failure prediction algorithm (DFPA) is disclosed. A set of quality metric values are transmitted from each of a plurality of remote disk drives to the central processing facility. The DFPA is executed at the central processing facility in response to the quality metric values to detect an impending failure of at least one of the remote disk drives. The DFPA is evolved at the central processing facility in response to a reference data base of quality metric values and a corresponding failure indicator. The processes is repeated so as to improve the accuracy of the DFPA over time.

Embodiments of the invention describe thermostats that use model predictive controls and related methods. A method of controlling a thermostat using a model predictive control may involve determining a parameterized model. The parameterized model may be used to predicted ambient temperature values for an enclosure. A set of radiant heating system control strategies may be selected for evaluation to determine an optimal control strategy from the set of control strategies. To determine the optimal control strategy, a predictive algorithm may be executed, in which each control strategy is applied to the parameterized model to predict an ambient temperature trajectory and each ambient temperature trajectory is processed in view of a predetermined assessment function. Processing the ambient temperature trajectory in this manner may involve minimizing a cost value associated with the ambient temperature trajectory. The radiant heating system may subsequently be controlled according to the selected optimal control strategy.

A method of continuous real-time monitoring and positioning of multi-leaf collimators during on and off radiation exposure conditions of radiation therapy to account for target motion relative to a radiation beam is provided. A prediction algorithm estimates future positions of a target relative to the radiation source. Target geometry and orientation are determined relative to the radiation source. Target, treatment plan, and leaf width data, and temporal interpolations of radiation doses are sent to the controller. Coordinates having an origin at an isocenter of the isocentric plane establish initial aperture end positions of the leaves that is provided to the controller, where motors to position the MLC midpoint aperture ends according to the position and target information. Each aperture end intersects a single point of a convolution of the target and the isocenter of the isocentric plane. Radiation source hold-conditions are provided according to predetermined undesirable operational and/or treatment states.

The invention discloses an operation and maintenance automation system and method. The system comprises a data acquisition module, a pre-processing and storing module, a prediction module, an algorithm evaluation module and an operation and maintenance monitoring management module, wherein the data acquisition module is used for acquiring key performance indexes and running states of monitored units in an operation and maintenance system through a network management protocol or a log file; the pre-processing and storing module is used for performing pre-processing work and sorted storing on data acquired by the data acquisition module; the prediction module is used for performing predictions, including a CPU (Central Processing Unit) load prediction and a disk load prediction according to the data processed by the pre-processing and storing module; the algorithm evaluation module is used for establishing an evaluation criterion of a prediction algorithm and the prediction module, comparing an actual value with a predicted value of the prediction algorithm, and establishing a self-learning process; and the operation and maintenance monitoring management module is used for interacting with operation and maintenance management personnel. A load prediction mechanism and an algorithm prediction model are established in order to finish predictions specific to resource use situations of CPUs, memories, disks and the like. Alarm information is analyzed by further referring to a load prediction result in order to give a relevant auxiliary decision. Resource expansion and fault handling are realized in a way of using scripts, an API (Application Programming Interface) interface and the like.

An apparatus, system, and method are provided for developing failure prediction software for a storage system. The present invention allows a user to edit and revise a failure prediction algorithm that includes fuzzy logic rules. The failure prediction algorithm is generated in a human readable format and uses terms and operators familiar to experts in the field of storage systems. In addition, the present invention generates the machine-readable code necessary to implement or test a draft failure prediction algorithm. If the results of the failure prediction algorithm are unsatisfactory, the user may revise the failure prediction algorithm and re-run the tests until the results correspond to expected results. In addition, the present invention includes a performance monitor, processor, and determination module. The performance monitor gathers performance data for a storage system. The processor executes the failure prediction algorithm on the performance data to produce a result. The determination module selectively forecasts failure of one or more components of the storage system in response to the result.

An automated system and method for determining the value of an intangible asset or intellectual property and developing a fair remuneration structure for licensing or purchasing the intangible asset or intellectual property by comparison to a dissected database of prior licensing and sale transactions. Valuation determinants and remuneration structures from prior transactions are extracted, analyzed and weighted and loaded into a knowledge base. Remuneration structures are normalized and used to train predictive algorithms based on a market analysis of previous transactions. The algorithms are able both to learn from previous transactions and to assess the importance of particular valuation determinants in determining the value under particular circumstances. An equitable rate for a new transaction is determined by examining the knowledge base and varying the valuation determinants. An optional expert system and dynamic modeling environment are provided.

A system and method for supporting predictive text entry in software applications by sharing a common, predictive, software text-entry widget within a consumer device across multiple software applications and input contexts. The method comprises: a software application invoking an instance of a text-entry widget in a particular input context, the application optionally providing the widget a description of allowed symbols and a dictionary of expected symbol strings associated with the current context, the widget modifying a virtual keyboard display and predictive algorithm data according to the allowed symbols and dictionary, the user entering text via the widget, the widget returning the entered text to the application, and the application optionally including information derived from entered text in the associated dictionary to enhance the predictive capability of the widget on future invocations.

An apparatus for determining a condition parameter of a battery, receives measurement signals related to the battery, determines input data such as electrical impedance from the measurement signals, and provides the input data to a plurality of different prediction algorithms, wherein each prediction algorithm provides a condition parameter estimate. A plurality of condition parameter estimates are then provided to a decision fusion algorithm, allowing a more accurate prediction of the condition parameter.

A system and a method of retrieving information is described. In a system according to the invention, software modules may be used to provide the user with information that is most likely to be the information desired.

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.

The location of a mobile wireless communication unit in the service area of a CDMA communications system is predicted utilizing two likelihood functions that define maximum likelihood estimators of the mobile unit's location, based on attribute measurements, such as but not limited to pilot signal strength, being made at the location of the mobile unit and reported back to a base station. One of the likelihood functions comprises a frequentist likelihood function and the other comprises a Bayesian-modified likelihood function. The likelihood functions are based on the assumption that there is an RF model which provides the probability a mobile unit is able to detect one or more attributes associated with an arbitrary base station, given it is located at an arbitrary location within the service area. Each of the likelihoods are also incorporated into a sequential Bayesian procedure which outputs a posterior distribution indicative of the location of the mobile unit.

A predictive tuning system enables a user to easily and efficiently find desired digital content among a plurality of content streams. Using a data collector, analyzer, and distributed tuning service, users may specify one or more particular items of interest, and the system, through the use of predictive algorithms, determines a subset of the plurality of content streams that should be monitored in order to optimize along one or more dimensions, such as the length of time that the user must wait in order to receive their desired digital content. Various strategies can be employed to find the desired content in the data streams, and a combination of strategies can provide the most efficient approach to achieving the desired content. Once found, a desired content can be accessed contemporaneously, stored for later access, or can be input to another application.

A method is provided for deriving optimized operating parameter settings for industrial furnaces of different designs as commonly used in power generation that will achieve robust and desirable operations (for example, low NOx and low CO emissions while maintaining specific furnace exit gas temperatures). The method includes the application of recursive partitioning algorithms to historical process data to identify critical combinations of ranges of operational parameter (combinations of settings) that will result in robust (low-variability) desirable (optimized) boiler performance, based on empirical evidence in the historical data. The method may include the application of various algorithms for recursive partitioning of data, as well as the consecutive application of recursive partitioning methods to prediction residuals of previous models (a methodology also known as boosting), as well as the application of other prediction algorithms that rely on the partitioning of data (support vector machines, naive Bayes classifiers, k-nearest neighbor methods).

An anomaly detection engine monitors network traffic to detect orders placed by users from an electronic catalog of items, aggregates data about the detected orders by time period, and analyzes the aggregated data to detect anomalies in activity levels associated with specific items in the catalog. To detect whether an anomaly exists in the activity data associated with a given item, a forecasting algorithm, such as an exponential smoothing algorithm, is used to generate an expected order volume for a current time period, and the expected order volume is compared to an actual order volume. Other criteria may also be taken into consideration. If an anomaly is detected, such as a sharp increase in the item's order volume, the anomaly detection engine generates an alert message to notify a catalog administrator, who may then determine whether the anomaly is attributable to an erroneous item description in the catalog.

A subset of quality metrics as input to a disk drive failure prediction algorithm (DFPA) may be selected using a genetic algorithm. The DFPA is executed for an initially selected generation of subset quality metrics using quality metric values stored in a reference data base. At least one DFPA setting is adjusted and the DFPA executed again for the selected subset. After training the DFPA, the best DFPA setting is saved for the selected subset. A fitness score is generated for the selected subset, representing an accuracy of the DFPA relative to failure indicators stored in the reference data base. At least one genetic operator is applied in response to the fitness scores to generate a new generation of subsets. The process is repeated until a best subset of quality metrics and corresponding DFPA setting are found.

The invention discloses a combination forecast modeling method of wind farm power by using gray correlation analysis, belonging to the technical field of wind power generation modeling. In particular, the invention is related to a weighted combination forecast method of wind power based on a least square support vector machine and an error back propagation neural network. The forecast method comprises that forecasted values of wind speed and wind direction are acquired in advance from meteorological departments while real-time output power is acquired from a wind farm data acquiring system; that the forecasted values of wind speed and wind direction and the real-time output power are inputted into a data processing module for data analyzing extraction and data normalization, and then normalized data is loaded to a database server; processed data in the database server is extracted by a combination forecast algorithm server to carry out model training and power forecast, and the wind farm sends running data to the data processing module in real time to realize rolling forecasting. The method of the invention achieves the goal of combination forecast of wind farm output in a short time. The method not only maximally utilizes advantages of two algorithms but also increases forecast efficiency by saving computing resources and shortening computing time.