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ACCEPT consists of an overall software infrastructure framework and two main software components. The software infrastructure framework consists of code written to pre-process data, pass information between the two main software components, learn models that will be shared by nearly all of the elements in one of the two software components (which will require calling third party open source software modules), and select which element/method should be used in each one of the two main software components. The two main software components can use interchangeable software elements that enable the regression and detection functionality. Some software elements are distributed with the initial release, while others need to be called separately as independent third party elements that have been open sourced already. Detailed installation instructions are provided in the enclosed ReadMe file. From a software standpoint, use of any standard publicly available open source machine learning code for model development to support both regression and detection modules, including fidelity analysis can be acquired and used.

Many natural or complex engineered systems rely upon critical functions or processes that can be measured with the aid of various sensors or other novel devices. As a result, sensor and measurement data can be used to learn a parametric or non-parametric model of the behavior for a given process or metric. For such processes or metrics, it may be critical to avoid or be forewarned of impending level-crossings that may characterize entry into extreme or potentially catastrophic operating regimes. Under certain circumstances, the metric to be monitored may represent the residual, or difference between an actual value and a predicted value generated by an independent regression method, rather than a physical process having a physically interpretable meaning. The state of the art in the prediction or forecasting of adverse events with respect to residual-based event detection is currently based upon the use of methods that have been derived from a technique called MSET (Multivariate State Estimation Technique). This technique was originally developed at Argonne National Laboratories, but has since been adapted for myriad applications spanning a broad range of disciplines. Some of the resulting variants have been patented. Some disadvantages of this method lie in the fact that certain technical conditions are required for the implementation of the detection portion of the method.

Novel features and advantages of the developed method include relaxation of the technical conditions required for robust early detection of the onset of adverse events. Rather than assume the residuals are white, or have been pre-whitened either with the aid of an optimal filter or by the use of an appropriate regression technique, serial correlations are retained to be learned using applicable data-driven or machine learning methods. Furthermore, the method attempts to characterize an adverse event via definition through various hypotheses to be tested that can be selected by the user, one which is based upon an extreme value level-crossing over a predefined prediction horizon, and the other an abrupt change in model parameters as is performed with MSET. Furthermore, the regression portion of methods derived from MSET have a limited ability to reduce residual error, and lack robust numerical stability properties and controls for complexity, which may be provided for with alternate regression techniques, some of which may be more scalable. The MCRScheduler uses the Matlab Compiler toolbox to emulate some of the core functionality in the Matlab Parallel Computing toolbox. The utilities provide the ability to create, submit, and manage jobs and tasks, collect results, as well as monitor the status of all jobs/tasks and clean up job folders.


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