Abstract: Embodiments presented herein provide techniques for generating test cases for transformation modules that are part of a larger data-integration workflow for an extract, transfer, and load (ETL) project. A test case generated according to the present disclosure can be executed independently of the application that generated it and independently of data sources and destinations referenced in the data-integration workflow. To achieve this independence, the test case can include code that simulates data sources and destinations that interact with the transformation module in the data-integration workflow. Furthermore, the test case can be a unit test based on a unit testing framework and can be compatible with a selected software framework.

Abstract: A method of specifying and configuring a causal relationship between the dynamics of a graphical model and the execution of components of the model is disclosed. Model component execution is tied to the occurrence of model events. Model events are first defined in the modeling environment. The occurrence of conditions in the model specified in the definition of the event causes the event to be “posted”. Model components that have been associated with the occurrence of the event “receive” the notice of the posting of the event and then execute. Random components within a subsystem may be designated to execute upon the occurrence of an event, as may non-contiguous components within a model. The association between model events and component execution may be specified without drawing graphical indicators connecting components in the view of the model.

Abstract: A method for generating address translation stimuli for post-silicon functional validation is provided. The method includes determining a plurality of memory configurations based on a plurality of translation tables used by a stimuli generator to solve a plurality of test templates, providing a test template from the plurality of test templates, selecting a memory configuration from the plurality of memory configurations based on the test template, a memory variable, and a set of testing parameters, identifying a translation table from the plurality of translation tables based on the test template, allocating a memory space for the translation table, and executing the test template on the stimuli generator based on the translation table, the memory space, and the set of testing parameters.

Abstract: A method for identifying root cause failure in a multi-parameter self learning machine application model is presented. At least one multi-function sensor having the capability to measure at least one of a voltage and current of the machine application model is provided. The method includes measuring voltages and currents of a multi-phase load with the multi-function sensors in a passive manor and calculating at least one of a time-varying variable KW, PF, kVAr, or Z out of the measured voltages and currents. The method further provides calculating a first, second or third order derivative of the time-varying variable and classifying segments of at least one of the time-varying variables depending on a state. Then, a step of choosing at least one of the variables and learning their normal behavior is undertaken. Finally, normal behavior is compared to a pattern difference and a root-cause meaning to the pattern difference is identified.

Abstract: A method and system for automatically determining an optimal re-training interval for a fault diagnoser based on online monitoring of the performance of a classifier. The classifier generates a soft measure of membership in association with a class based on a training data. The output of the classifier can be utilized to assign a label to new data and then the members associated with each class can be clustered into one or more core members and potential outliers. A statistical measure can be utilized to determine if the distribution of the outliers is sufficiently different than the core members after enough outliers have been accumulated. If the outliers are different with respect to the core members, then the diagnoser can be re-trained; otherwise, the output of the classifier can be fed to the fault diagnoser.

Abstract: A system for obtaining diagnostic information, such as evidence about a mechanism, within an algorithmic framework, including filtering and aggregating the information through, for instance, a stochastic process. The output may be an overall belief value relative to a presence of an item such as, for example, a fault in the mechanism.