Abstract: Certain embodiments of the invention may include systems and methods for predicting transient operational characteristics of a power plant. According to an example embodiment of the invention, a method is provided for predicting transient operational characteristics of a power plant. The method can include receiving operating information associated with the power plant; using a power plant dynamics model for determining a current operating state of the power plant based at least in part on the received operating information; and using the power plant dynamics model and a power plant controls model for predicting one or more future operating parameters associated with power plant based at least in part on the determined current operating state and the received operating information.

Abstract: A method for predicting a criteria of interest based upon tag location data, derived tag location data, statistical data or the like corresponding to an object. The method may include receiving an input that defines one or more objects and one or more criteria of interest. Further, the method may include accessing tag location data, derived tag location data or the like for the object. The method may also include generating a prediction for a criteria of interest for at least one of the one or more criteria of interest for at least one of the one or more objects, wherein the a prediction for a criteria of interest is generated based on at least one of tag location data, derived tag location data or the like for the one or more objects related to the one or more criteria of interest. An associated apparatus, computer program product and system are also provided.

Abstract: This disclosure is directed to a process for producing one or more predicted target sparkle values of a target coating composition. An artificial neural network can be used in the process. The process disclosed herein can be used for color and appearance matching in the coating industry including vehicle original equipment manufacturing (OEM) coatings and refinish coatings. A system for producing one or more predicted target sparkle values of a target coating composition is also disclosed.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a plurality of different types of predictive models using training data, wherein each of the predictive models implements a different machine learning technique. One or more weights are obtained wherein each weight is associated with an answer category in the plurality of examples. A weighted accuracy is calculated for each of the predictive models using the one or more weights.

Abstract: A prediction technique to predict an anomalous state of a processing environment at a future point in time. One or more values of one or more metrics of the processing system are obtained. For the one or more metrics, one or more predicted values are determined for one or more points in time in the future. Based the predicted values, one or more change values for one or more points in time are determined, and based on the one or more change values, a determination is made as to whether an anomalous state exists within the processing system.

Abstract: A steam turbine performance testing system, including at least one computer hardware device, including a neural network created using a dynamic steam turbine thermodynamic model and preliminary data collected from a steam turbine; a network tester for testing the neural network with testing data; a current performance calculator for calculating a current performance of the steam turbine from operation data of the steam turbine; and a projected performance calculator for calculating a projected performance of the steam turbine from the current performance.

Abstract: A system is provided for automatically predicting actions a user is likely to take in response to receiving data. The system may be configured to monitor and observe a user's interactions with incoming data and to identify patterns of actions the user may take in response to the incoming data. The system may enable a trainer component and a classifier component to determine the probability a user may take a particular action and to make predictions of likely user actions based on the observations of the user and the identified pattern of the user's actions. The system may also be configured to continuously observe the user's actions to fine-tune and adjust the identified patterns of user actions and to update the probabilities of likely user actions in order increase the accuracy of the predicted user action in response to incoming data.

Abstract: A method and system for validating energy measurement in a high pressure gas distribution network. The method comprises the steps of calculating a validation energy value using an artificial neural network (ANN) engine based on measured parameters associated with a gas flow in the gas distribution network; measuring an actual energy value of the gas flow; and comparing the validation energy value and the actual energy value, wherein the actual energy value is validated if the validation energy value and the actual energy value are substantially equal.

Abstract: A system and method for prefetching data. Address logs are separated into streams and a model associated with each stream. Each stream address is forecasted according to its respective model and pages corresponding to one or more forecasted stream addresses are retrieved from memory based on their respective models.

Abstract: A method and apparatus for predicting significant future events based on previous events. Plural messages representing events are received. Attributes of the messages are mapped to respective feature dimensions to define a multidimensional histogram. Co-occurrence of at least two event types are determined based on queries of the multidimensional histogram. Anomalous event types are detected from the messages by comparing feature dimensions of incoming messages to probability density functions of the cluster corresponding to the event type and highly anomalous event types are determined. Causal relationships between each pair of event types are determined and a Bayesian belief network of the pairs of event types is created and used to predict future events based on occurrence of additional events.

Abstract: A method for an electronic messager to predict text while composing a reply message, including receiving an incoming message for a user, parsing the incoming message to identify questions, phone numbers and special phrases therein, and presenting, by an electronic messager having a memory and a processor, possible responses that the user may choose from while the user replies to the incoming message, based on the questions, phone numbers and special phrases identified by the parsing.

Abstract: Aspects of the invention provide for automatically selecting optimal fetch settings for business processes as a function of database query load and relational context by determining whether data loaded for data retrieval points is dependent upon a query result from another query process and automatically selecting an eager fetch setting if dependent upon a query result from another query process, or a lazy fetch setting if not. Usage of the data retrieval points is monitored with respect to defined units of work to define retrieval patterns and automatically update the fetch settings, including by revising selected eager fetch settings to lazy fetch settings if a data size of a defined retrieval pattern is larger than a permissible memory resource threshold.

Abstract: The invention relates to a controlling system for adjusting environmental conditions of at least one entity, wherein the entity has desired environmental conditions for at least two different states. The system comprises equipments controlled by controlling means for changing and/or maintaining the environmental condition of the entities. The controlling means is adapted to provide controlling parameters to equipments for adjusting the environmental condition of said entity so that at least one parameter used for controlling the environmental condition of said entity depends on at least one measured environmental condition parameter of another entity being different from the entity, which environmental condition is adjusted by said equipment.

Abstract: Data compression technology (“the technology”) is disclosed that can employ two or more prediction models contemporaneously. The technology receives data from one or more sources; shifts or re-sample one of more corresponding signals; creates a prediction model of uncompressed samples using at least two different individual or composite models; selects a subset of the models for prediction of samples; determines an order in which signals will be compressed; formulates a combined predictions model using the selected subset of models; predicts a future value for the data using the combined compression model; defines a function that has as parameters at least the predicted future values for the data and actual values; selects a compression method for the values of the function; and compresses the data using at least the predicted value of the function.

Abstract: Embodiments of systems and methods can determine evaluations of the quality of task results provided by judges. Certain embodiments can determine the quality evaluations with relatively little overlap of judges (e.g., two or three judges). The quality evaluations may include determining an error rate or a confidence index for a judge or for a particular judgment made by the judge. Certain embodiments may determine the quality evaluations by intercomparing judging results from multiple judges without requiring human intervention, comparison to evaluation data known (or assumed) to be accurate, or input from an external judge review panel. In some implementations, task results can be combined with a confidence score (based at least in part on the quality evaluation of the judge who provided the task result), and this information can be used as training data to improve performance of machine learning algorithms.

Abstract: The present invention provides systems and methods for data storage. A hierarchical storage management architecture is presented to facilitate data management. The disclosed system provides methods for evaluating the state of stored data relative to enterprise needs by using weighted parameters that may be user defined. Also disclosed are systems and methods evaluating costing and risk management associated with stored data.

Abstract: In the area of storage management, service automation can be realized through the use of “MAPE” loop(s). A Planner (P) interacts with the Monitoring (M), Analysis (A) and Execution (E) components in a closed loop. For each new option or potential planning action the Planner (P) invokes the Analysis (A) component. The correctness, as well as effectiveness, of the planning decision is dependent on the Analysis (A) component. Embodiments can utilize an adaptive Analysis (A) component (i.e., an analysis component that can be retrained) that also associates a value of confidence and a corresponding error in the evaluation along with a predicted impact. The Planner (P) component uses this additional information for quoting the final impact of a particular planning action as part of an adaptive MAPE loop to provide improved resource utilization and resource management.

Abstract: A multifunctional neural network system for prediction which includes memory components to store previous values of data within a network. The memory components provide the system with the ability to learn relationships/patterns existent in the data over time.

Abstract: An apparatus, system, and method are disclosed for defining normal usage of a computing system resource. A method for defining normal usage of a computing system resource includes receiving a repeating schedule that represents system usage of one or more computing resources and receiving one or more demand events that will affect the system usage of the one or more computer resources. The method also automatically creates a predictive temporal profile that represents the system usage of the one or more computer resources from information comprising the repeating schedule and the one or more demand events. The predictive temporal profile is displayed for the user to review.

Abstract: A system is provided for automatically predicting actions a user is likely to take in response to receiving data. The system may be configured to monitor and observe a user's interactions with incoming data and to identify patterns of actions the user may take in response to the incoming data. The system may enable a trainer component and a classifier component to determine the probability a user may take a particular action and to make predictions of likely user actions based on the observations of the user and the identified pattern of the user's actions. The system may also be configured to continuously observe the user's actions to fine-tune and adjust the identified patterns of user actions and to update the probabilities of likely user actions in order increase the accuracy of the predicted user action in response to incoming data.

Abstract: A method for predicting the risk of a pirate attack in a geographical area and for generating a distribution of probabilities of a pirate attack based on intelligence (INTEL) information regarding the pirates and pirate behavior combined with information regarding environmental and meteorological (METOC) conditions and information regarding shipping activity and shipping vulnerabilities. The method can also be used to predict the probability of other activities that are subject to METOC conditions, such as anti-narcotics efforts, undersea warfare operations, mine warfare operations, and human trafficking interdiction.

Abstract: Embodiments of the invention relate to predicting user activity in a social media application. In one embodiment, user activity information is collected for a user from a social media application and activity features of the user are determined, based on the collected activity information for the user. Then a model is created to predict a future activity of the user in the social media application. The model uses the determined activity features of the user and results obtained from running the created model, to determine future activity of the user in the social media application.

Abstract: Computerized contextual analysis systems and methods suitable for monitoring situations, regions, and groups characterized by volatility and uncertainty are provided. Via use of exemplary systems and methods, decision makers, for example politicians, warfighters, and analysts can gain insight into the cultures, attitudes, events, and relationships that may impact their missions.

Abstract: The disclosed techniques can provide users with a tool having an integrated, user-friendly interface and having automated mechanisms which can reveal correlations between data streams to the users in a clear and easily understandable way, thereby enabling the users to easily digest the vast amount of information contained in activities within one or more network, to understand the correlations among the activities, to stay informed and responsive to current or new trends, and even to predict future trends. Among other benefits, the disclosed techniques are especially useful in the context of discovering impacts of social networking activities on other types of commercial activities.

Abstract: Event prediction using hierarchical event features is described. In an embodiment a search engine monitors search results presented to users and whether users click on those search results. For example, features describing the search result events are universal resource locator prefix levels which are inherently hierarchically related. In an embodiment a graphical data structure is created and stored and used to represent the hierarchical relationships between features. An online training process is used in examples which enables knowledge to be propagated through the graphical data structure according to the hierarchical relations between features. In an example, the graphical data structure is used to predict whether a user will click on a search result and those predictions are used by the search engine to rank search results for future searches. In another example the events are advertisement impressions and the predictions are used by an online advertisement system.

Abstract: Biomarkers useful for diagnosing and assessing physiological age are provided, along with kits for measuring their expression. The invention also provides predictive models, based on the biomarkers, as well as computer systems, and software embodiments of the models for scoring and optionally classifying samples. In a preferred embodiment, the biomarkers include a group of biomarkers whose expression levels are highly correlated to each other. In a preferred embodiment, expression levels of CD248; CD248 and SLC 1A7; CD248 and one, two, three or four of the group consisting of CCR7, B3GAT1, VSIG4 and LRRN3; or CD248, SLC1A7 and one, two, three or four of the group consisting of CCR7, B3GAT1, VSIG4 and LRRN3 are determined.

Abstract: Peer-to-peer media item recommendations are provided by presenting a selected list of media items to a first user to consider for recommendation to a friend, receiving an indication from the first user that one or more of the presented media items are to be recommended to the friend, and presenting the one or more indicated media items to the friend. The presented list comprises media items the friend hasn't rated, and is selected using a media recommendation engine.

Abstract: This disclosure is directed to a process for producing one or more predicted target sparkle values of a target coating composition. An artificial neural network can be used in the process. The process disclosed herein can be used for color and appearance matching in the coating industry including vehicle original equipment manufacturing (OEM) coatings and refinish coatings. A system for producing one or more predicted target sparkle values of a target coating composition is also disclosed.

Abstract: Disclosed is an apparatus, method and computer program device which send a prediction request to a prediction service to construct a prediction result, receives a prediction result and an estimation of accuracy of the prediction result from the prediction service and configures an application to adapt to at least one of user preferences, behavior and habits based upon the prediction result and estimation of accuracy of the prediction result. The prediction service can include an over-the-air server which is coupled to a prediction server or a dedicated API residing on one or more user devices and configured to access a prediction server.

Abstract: A hierarchical based sequencing (HBS) machine learning model. In one example embodiment, a method of employing an HBS machine learning model to predict multiple interdependent output components of an MOD output decision may include determining an order for multiple interdependent output components of an MOD output decision. The method may also include sequentially training a classifier for each component in the selected order to predict the component based on an input and based on any previous predicted component(s).

Abstract: Disclosed herein is an evaluation predicting device including: an estimating section configured to define a plurality of first latent vectors, a plurality of second latent vectors, evaluation values, a plurality of first feature vectors, a plurality of second feature vectors, a first projection matrix, and a second projection matrix, express the first latent vectors and the second latent vectors, and perform Bayesian estimation with the first feature vectors, the second feature vectors, and a known the evaluation value as learning data, and calculate a posterior distribution of a parameter group including the first latent vectors, the second latent vectors, the first projection matrix, and the second projection matrix; and a predicting section configured to calculate a distribution of an unknown the evaluation value on a basis of the posterior distribution of the parameter group.

Abstract: A system(s) and method(s) for providing travel, asset, and personnel information accumulation, analysis, and reporting for improved risk assessment and management are disclosed. In various embodiments methods and systems may provide travel information to a consumer, by for example, acquiring information from at least one information source, characterizing the acquired information by its pertinence to travel, transforming the characterized travel information into a useable format, applying rules to this transformed travel information in connection with the consumer-specific travel profile, and delivering a report to the consumer. In various embodiments, risk assessments(s) may be provided and may include receiving intelligence from at least one intelligence source, receiving information about one or more asset(s), and generating a risk assessment report.

Abstract: A computer implemented method, computerized apparatus and computer program product for inconsistency detection between structured and non-structured data. The method comprising: automatically determining, by a computer, inconsistencies between fields in electronics records, the fields comprise at least a structured field and a non-structured field, the fields are designed to be able to include overlapping information in structured and non-structured form; and indicating, by the computer, to a user potential inconsistencies. Optionally, the indication uses a visual cue when displaying the electronic record to the user, wherein the visual cue indicates the fields which are determined to comprise inconsistent content.

Abstract: A method for controlling an intelligent lamp to work automatically according to people's habit includes generating a table in the memory, the table records values of a group of operational parameters of the intelligent lamp each time a user operates the intelligent lamp. Determining a frequent event and an accidental event in the predetermined time period in one day in last one week/month/year. Then obtaining all values of the operational parameters of the frequent events, and respectively calculating an accuracy value for each operational parameter according to an algorithm. In addition, controlling the lighting unit to work according to the accuracy values of the operational parameters, thus, an intelligent lamp for automatically working according to people's habit is also provided.

Abstract: Mechanisms for predicting a remaining useful life of a cutter head of a milling machine that includes a plurality of flutes are disclosed. Features are extracted from reference data associated with a plurality of reference cutter heads. The reference data includes reference vibration data and reference wear data. At least two Neural Network predictive models are trained in parallel for predicting the remaining life of a new cutter head based upon the extracted features. Operational data associated with the new cutter head is obtained. The operational data includes operational vibration data and operational wear data. Features extracted from the operational data are input into an optimal predictive model of the at least two Neural Network predictive models. A remaining useful life of the new cutter head is estimated by the optimal predictive model.

Abstract: This disclosure is directed to a process for producing one or more predicted target sparkle values of a target coating composition. An artificial neural network can be used in the process. The process disclosed herein can be used for color and appearance matching in the coating industry including vehicle original equipment manufacturing (OEM) coatings and refinish coatings. A system for producing one or more predicted target sparkle values of a target coating composition is also disclosed.

Abstract: Embodiments of the present invention provide a method and device for synthesis of network traffic. The method includes extracting a first real traffic composition parameter sequence and a second real traffic composition parameter sequence from real traffic. A first synthetic traffic composition parameter sequence is generated. Relational coefficients between first traffic composition parameters and second traffic composition parameters are obtained. A second synthetic traffic composition parameter sequence is generated and synthetic traffic is generated according to the first synthetic traffic composition parameter sequence and the second synthetic traffic composition parameter sequence.

Abstract: An occupancy estimator calculates an occupancy estimate (x) of a region based on sensor data (z) provided by one or more sensor devices and a model-based occupancy estimate generated by an occupant traffic model (f). The occupant traffic model (f) is based on predicted movement of occupants throughout a region. The occupancy estimation system includes an occupancy estimator algorithm (20) that combines the sensor data (z) and the model-based occupancy estimate generated by the occupant traffic model (f) to generate an occupancy estimate (x) for the region.

Abstract: Provided are techniques for incrementally integrating and persisting context over an available observational space. At least one feature associated with a new observation is used to create at least one index key. The at least one index key is used to query one or more reverse lookup tables to locate at least one previously persisted candidate observation. The new observation is evaluated against the at least one previously persisted candidate observation to determine at least one relationship. In response to determining the at least one relationship, a threshold is used to make a new assertion about the at least one relationship. The new observation is used to review previous assertions to determine whether a previous assertion is to be reversed. In response to reversing the previous assertion, the new observation, the new assertion, and the reversed assertion are incrementally integrated into persistent context.

Abstract: Predicting the remaining life of individual aircraft, fleets of aircraft, aircraft components and subpopulations of these components. This is accomplished through the use of precomputed databases of response that are generated from a model for the nonlinear system behavior prior to the time that decisions need to be made concerning the disposition of the system. The database is calibrated with a few data points, to account for unmodeled system variables, and then used with an input variable to predict future system behavior. These methods also permit identification of the root causes for observed system behavior. The use of the response databases also permits rapid estimations of uncertainty estimates for the system behavior, such as remaining life estimates, particularly, when subsets of an input variable distribution are passed through the database and scaled appropriately to construct the output distribution.

Abstract: Methods, apparatuses and systems that facilitate the design, production and/or measurement tasks associated with manufacturing and other processes. In one embodiment, the present invention provides an understanding of how the multiple characteristics of a given process output are related to each other and to process inputs. This knowledge facilitates a reduction in measurement costs. It also facilitates an understanding of the sometimes complex interrelationships between design targets, design tolerances, process inputs, process control variables, average process output and variation in the process output. As discussed in more detail below, embodiments of the present invention facilitate 1.) determination of design target values, 2.) determination of design specification limits, 3.) design of process inputs, 4.) determination of process control variable settings, and/or 5.) reduction of measurement costs.

Abstract: A system for training a neural network. A switch is linked to feature detectors in at least some of the layers of the neural network. For each training case, the switch randomly selectively disables each of the feature detectors in accordance with a preconfigured probability. The weights from each training case are then normalized for applying the neural network to test data.

Abstract: Various embodiments may provide methods and systems capable of evaluating physiological parameter data. The methods and systems may include a receiver capable of collecting a signal representative of a physiological status of a patient, a plurality of data analysis components, wherein each of the plurality of data analysis components is capable of generating a metric based on the signal, and an arbitrator communicatively coupled to each of the plurality of data analysis components and capable of generating a single metric from the metrics generated by the plurality of data analysis components.

Abstract: A system and a 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. 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, one or more 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 according to the preferences of the site owner. Each predicted model can be associated with a key-performance indicator (KPI). Further, a predictive model can reflect the number of times the surfer requested the web page during the surfer's visit.

Abstract: The approaches described herein provide an efficient way to process certain kinds of queries that retrieve XML data stored in an object-relational database. An XML query may include a predicate condition that joins two row sources. One or both of the row sources required for predicate evaluation may be path-based; that is, consume and produce rows of XMLType data. Such a path-based row source may be modified to use the predicate condition to filter the rows it generates so that only rows satisfying the predicate condition need to be further processed. A small cache structure introduced into the path-based row source may be used to optimize the evaluation of the predicate row. Once populated with row data, the cache structure may be reorganized to optimize the performance of predicate evaluation.

Abstract: A system and related method for identifying a trigger event to a patient health-related exacerbation. The system includes at least one sensor configured to collect data related to the patient health-related exacerbation, an analyzer configured to validate the collected data, a patient interface device configured to receive patient inputs to at least one question, and an identifier device configured to receive the patient inputs and collected data and identify the trigger event. The system may also include an expert system configured to receive the patient inputs and the validated collected data and identify a primary patient disease, and a reporting device configured to generate reports related to the identified primary patient disease, patient inputs, and sensed patient conditions.

Abstract: For each item, a factorial effect value is derived that represents the SN ratio of the prediction object to each data including the data of the item relative to the SN ratio of the prediction object to each data excluding the data of the item. The strength of the SN ratio of the comprehensive estimated value to the data of a plurality of items selected in descending order of the derived factorial effect value is calculated for each value of the number of items. On the basis of the calculated SN ratio of the comprehensive estimated value, the number of items is determined. In descending order of the derived factorial effect value, items in the determined number of items are selected. On the basis of the data of the selected items, a change of the prediction object is predicted by using a method such as a T-method.

Abstract: Provided is a method for predicting location of an object by using a learning model which includes an input layer, a hidden layer, and an output layer each including one or more nodes, that are associated with each other by a weight. When a definitive time value is input to the input layer, the output layer is configured to output a probability value that the object is located at a specific place.

Abstract: An embodiment in accordance with the present invention provides a method of forecasting a demand for a particular hospital unit. The method can be executed by programming the steps into a computer readable medium. One step includes logging a total number of beds in the particular hospital unit and available nursing slots to determine a capacity for the particular hospital unit. The method also includes analyzing data for patients scheduled to stay in the particular hospital unit data to predict stochastic arrivals in order to estimate a total inflow. A length of stay of a patient in the particular hospital unit is predicted using a survival analysis based on physician orders to estimate a total outflow. Additionally, the method includes executing an algorithm designed to use the capacity, total inflow, and total outflow to determine the demand for the particular hospital unit.

Abstract: To facilitate the estimation of relatedness between nodes of a graph, implementations estimate relatedness between nodes in a graph by pre-computing for a subset of sample nodes (e.g., center nodes) a plurality of transition probabilities between each sample node and each of the other nodes in the graph, and then later when queried the implementations calculate in real-time the resultant estimated transition probability between the first node and the second node through the at least one sample node based on the pre-computed transition probabilities.