Complexity of adaptive testing in scenarios defined extensionally

In this paper,we consider a testing setting where the set of possible definitions of the Implementation Under Test(IUT),as well as the behavior of each of these definitions in all possible interactions,are extensionally defined,i.e.,on an element-by-element and case-by-case basis.Under this setting,the problem of finding the minimum testing strategy such that collected observations will necessarily let us decide whether the IUT is correct or not(i.e.,whether it necessarily belongs to the set of possible correct definitions or not)is studied in four possible problem variants:with or without non-determinism;and with or without more than one possible definition in the sets of possible correct and incorrect definitions.The computational complexity of these variants is studied,and properties such as PSPACE-completeness and Log-APX-hardness are identified.

Study on grid adaptability testing methodology for wind turbines

The use of wind power has grown rapidly in recent years.Wind power is a clean source of energy,but can have negative impacts on the distribution grid.The influence of large-scale wind power integration on the safe and stable operation of a power system cannot be ignored.It is necessary and urgent to achieve grid adaptability for wind turbines in China.Using a 35 kV/6 MVA grid simulator,the performance of a grid is investigated by simulation.Typical grid disturbances such as voltage deviation,frequency fluctuation,voltage unbalance,and distortion can be simulated.A grid adaptability testing methodology was developed and applied to a doubly fed wind turbine with a focus on analyzing real test data to ascertain its three-phase voltage unbalance adaptability,which was successfully demonstrated.The methodology can also be used to guide other grid adaptability tests.

An adaptive lack of fit test for big data

New technological advancements combined with powerful computer hardware and high-speed network make big data available.The massive sample size of big data introduces unique computational challenges on scalability and storage of statistical methods.In this paper,we focus on the lack of fit test of parametric regression models under the framework of big data.We develop a computationally feasible testing approach via integrating the divide-and-conquer algorithm into a powerful nonparametric test statistic.Our theory results show that under mild conditions,the asymptotic null distribution of the proposed test is standard normal.Furthermore,the proposed test benefits fromthe use of data-driven bandwidth procedure and thus possesses certain adaptive property.Simulation studies show that the proposed method has satisfactory performances,and it is illustrated with an analysis of an airline data.