Least squaresThe method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems (sets of equations in which there are more equations than unknowns) by minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each individual equation. The most important application is in data fitting.
Power-system automationPower-system automation is the act of automatically controlling the power system via instrumentation and control devices. Substation automation refers to using data from Intelligent electronic devices (IED), control and automation capabilities within the substation, and control commands from remote users to control power-system devices. Since full substation automation relies on substation integration, the terms are often used interchangeably. Power-system automation includes processes associated with generation and delivery of power.
Nonlinear regressionIn statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations. In nonlinear regression, a statistical model of the form, relates a vector of independent variables, , and its associated observed dependent variables, . The function is nonlinear in the components of the vector of parameters , but otherwise arbitrary.
Point-of-care testingPoint-of-care testing (POCT), also called near-patient testing or bedside testing, is defined as medical diagnostic testing at or near the point of care—that is, at the time and place of patient care. This contrasts with the historical pattern in which testing was wholly or mostly confined to the medical laboratory, which entailed sending off specimens away from the point of care and then waiting hours or days to learn the results, during which time care must continue without the desired information.
Dynamic program analysisDynamic program analysis is analysis of computer software that involves executing the program in question (as opposed to static program analysis, which does not). Dynamic program analysis includes familiar techniques from software engineering such as unit testing, debugging, and measuring code coverage, but also includes lesser-known techniques like program slicing and invariant inference. Dynamic program analysis is widely applied in security in the form of runtime memory error detection, fuzzing, dynamic symbolic execution, and taint tracking.
