Abstract: Methods and systems for optimizing a solution set. A solution set is generated, and solutions in the solution set are evaluated. Desirable solutions from the solution set are selected. A structural model is created using the desirable solutions, and a surrogate fitness model is created based on the structural model and the desirable solutions. A new solution set may be generated and/or evaluated, based on analyzing at least one of the structural model and the surrogate fitness model, and determining a method for generating a new solution set and/or evaluating the new solution set based at least in part on the analyzing.

Abstract: The invention includes a tradeoff module having a range of values, a criteria behavior module that analyzes attributes including values of the criteria for a set of alternatives, generating a set of scores reflecting the desirability of the attributes using logic diagrams, and a scoring and ranking module that utilizes assigned values from the tradeoff module and the set of scores from the criteria behavior module to generate a ranked list of alternatives. The invention is used to facilitate a reverse auction, where suppliers submit bids to a purchaser desiring specific goods or services. The invention discloses a system for generating a ranked list of alternatives, including means for generating a homogeneous matrix of scores based on a heterogeneous matrix of attributes and alternatives, means for receiving a tradeoff value, and means for generating a ranked list of alternatives based on the homogeneous matrix of scores and the tradeoff value.

Abstract: Systems and methods that aggregate history data collected via embedded historians with additional data that is supplied by third parties. Triggering events can be defined for initiating aggregation of such history data with additional data, which enable a process/application to retrieve the operational metric data of the industrial unit/entity from any of a plurality of systems operatively coupled to such industrial unit/entity.

Abstract: A method for modeling data affinities and data structures. In one implementation, a contextual distance may be calculated between a selected data point in a data sample and a data point in a contextual set of the selected data point. The contextual set may include the selected data point and one or more data points in the neighborhood of the selected data point. The contextual distance may be the difference between the selected data point's contribution to the integrity of the geometric structure of the contextual set and the data point's contribution to the integrity of the geometric structure of the contextual set. The process may be repeated for each data point in the contextual set of the selected data point. The process may be repeated for each selected data point in the data sample. A digraph may be created using a plurality of contextual distances generated by the process.

Abstract: A method and apparatus based on transposition to speed up learning computations on sparse data are disclosed. For example, the method receives an support vector comprising at least one feature represented by one non-zero entry. The method then identifies at least one column within a matrix with non-zero entries, wherein the at least one column is identified in accordance with the at least one feature of the support vector. The method then performs kernel computations using successive list merging on the at least one identified column of the matrix and the support vector to derive a result vector, wherein the result vector is used in a data learning function.

Abstract: A system and method that facilitates and effectuates making an inference related to objects of interest within a context. A web projection component can generate sub graphs by projecting objects of interest onto a web graph representing a superset or partially overlapping set of objects to the objects of interest. An inference component can provide inferences relating to the quality of the objects of interest, adjustments to the context, and patterns recognized from graphical properties of the sub graphs.

Abstract: The embodiments of the invention provide a systems, methods, etc. for adaptive content processing and classification in a high-availability environment. More specifically, a system is provided having a plurality of processing engines and at least one server that classifies data objects on the computer system. The classification includes analyzing the data objects for the presence of a type of content. This can include assigning a score corresponding to the amount of the type of content in each of the data objects. Moreover, the server can remove a data object from the computer system based on the results of the analyzing. The results of the analyzing are stored and the computer system is updated with feedback information. This can include allowing a user to review the results of the analyzing and aggregating reviews of the user into the feedback information.

Abstract: A method of assessing a signal to identify particular signal characteristics comprises application of machine learning to multi-dimensional histograms derived from multi-tap sampling of the signal. The signal is sampled from at least two tap points to retrieve a sample set, and the at least two tap points are adapted to retrieve distinct samples from the signal, such as time spaced samples or spectrally distinct samples. Multiple sample sets are retrieved from the signal over time. The at least two dimensional histogram is built from the joint probability distribution of the plurality of sample sets. A machine learning algorithm then processes the multi-dimensional histogram, and is trained to predict a value of at least one characteristic of the signal.

Abstract: Methods, apparatuses and systems directed to pattern identification and pattern recognition. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In some implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multilevel pattern recognition schemes.

Abstract: Methods and apparatus for an intelligent human-machine interface comprising an interface shell, system agents, function agents, a dynamic documentation system, and a layering architecture. The system agents include dynamic, knowledge-based software object sub-agents that model and track the state of a work area. The function agents model, track, and facilitate work area functions. The interface shell provides a hardware and software interface between the system agents and the function agents. The layering architecture comprises a tracking layer, an equipment and supply management layer, a coordination layer, and a situational awareness layer, and an oversight layer. The oversight layer combines information from the situational awareness layer with process rule sets contained in the function agents to determine if processes are being performed correctly.

Abstract: Methods, apparatuses and systems directed to pattern identification and pattern recognition. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In some implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes.

Abstract: An air conditioner for a vehicle includes an air-conditioning unit for supplying conditioned air into a vehicle, an information acquiring unit for acquiring state information indicating a state of the vehicle, a control information modifying unit having at least one probabilistic model for calculating the probability that a passenger will perform a predetermined setting operation, calculating the probability by inputting the state information into the at least one probabilistic model, and modifying the setting information or the control information related to the predetermined setting operation according to the probability so that the predetermined setting operation is performed, and an air-conditioning control unit for controlling the air-conditioning unit according to the modified setting information or modified control information.

Abstract: Systems and methods that provide for adaptive processes in an industrial setting. Historian data, in conjunction with current collected data, can be converted into decision making information that is subsequently employed for modifying a process in real time. A process trend component, which is associated with a controller, can access historian data (e.g., trends collected via historians) to determine/predict an outcome of a current industrial process. Such enables a tight control and short reaction time to correcting process parameters.

Abstract: Data for a first time period in a primary data sequence is compared with data for a second time period in each of a set of secondary data sequences. The durations of the first and second time periods are correlated, and the first time period is different from the second time period. A ranking is assigned to each secondary data sequence based on the corresponding data for the second time period. The ranking for each of the set of secondary data sequences is written to a computer useable medium. The primary data sequence and the set of secondary data sequences are adjusted based on a request to change the primary data sequence to one of the set of secondary data sequences. The comparing, assigning, and writing are repeated for the adjusted primary data sequence and set of secondary data sequences.

Abstract: A method and system for responding to an alert pertaining to an event. A unique processor of a first micro grid apparatus of at least one micro grid apparatus detects an alert data packet that includes the alert. Each micro grid apparatus includes at least two processors that contain a unique processor. Each processor of each micro grid apparatus has its own operating system. The unique processor of each micro grid apparatus has a unique operating system. Each unique processor selects at least one processor from each micro grid apparatus. Each selected processor is designated as a macro grid processor of a respective macro grid by altering the operating system of each selected processor. An artificial intelligence is generated for each macro grid. The event is responded to and quenched by implementing the artificial intelligence of each macro grid, after which each macro grid is extinguished.

Abstract: A method and system for controlling an in-process software distribution to computing devices. A time of a disturbance in an environment of a computing device is predicted based on a change in a pressure exerted on the computing device. A checkpoint in a time interval of a distribution of a set of software upgrade increments to the computing device is automatically determined. The checkpoint is prior to the time of the disturbance. The determination of the checkpoint utilizes length(s) of increment(s) of the set of increments. Prior to the checkpoint, the computing device receives the increment(s). The computing device requests an interruption of the distribution beginning at the checkpoint. In one embodiment, the computing device is a buoy-like energy capture and generation device.

Abstract: Embodiments of the present invention are employ dynamical, nanoscale devices, including memristive connections between nanowires, for constructing parallel, distributed, dynamical computational networks and systems, including perceptron networks and neural networks. In many embodiments of the present invention, neuron-like computational devices are constructed from silicon-based microscale and/or submicroscale components, and interconnected with one another by dynamical interconnections comprising nanowires and memristive connections between nanowires. In many massively parallel, distributed, dynamical computing systems, including the human brain, there may be a far greater number of interconnections than neuron-like computational nodes. Use of dynamical nanoscale devices for these connections results in enormous design, space, energy, and computational efficiencies.

Abstract: The TRIZ decision process of the clustering method proposed by this invention uses the characteristics and invention rules from the contradiction matrix table resulting from massive quantities of patent inferences to find a similar or approximate character group and invention rule group of the physical meanings, and also applies statistics to calculate the number of display times of the groups to be the basic foundation. Apart from the number of display times, Bayes probability, fuzzy object oriented method and Bayes probability combined with fuzzy object oriented method can be used as the system. The reading value is utilized as a foundation for prioritizing the sequence of consideration for the groups, in which the system reading value constructed by different models gives designers lots of options to perform the reading, so as to acquire the undesired result features of the prioritized consideration.

Abstract: A computer implemented method and system for analysing a first set of data records where each data record comprises attribute values for one or more attributes, by expanding the first set of data records into a second set of data records by creating for at least one of the attributes of the first set of data records at least two redundant attributes with corresponding redundant attribute values, assigning different generalization rules to the at least two redundant attributes, and performing a generalization of the second set of data records by means of an attribute-oriented induction (AOI)-algorithm.

Abstract: A method of categorizing the relationship of individuals from a collection of photos taken in multiple sets of events includes: searching the photo collection to identify individuals in the photo collection, determining the gender and age range of the identified individuals; and using the identified individuals, their occurrences and co-occurrences, their gender, and their ages to infer social relationships between the individuals based on a set of rules.