The attribute hierarchy method (AHM) is a psychometric procedure for classifying examinees’ test item responses into a set of structured attribute patterns associated with different components from a cognitive model of task performance. Results from an AHM analysis yield information on examinees’ cognitive strengths and weaknesses. Hence, the AHM can be used for cognitive diagnostic assessment. The purpose of this study is to introduce and evaluate a new concept for assessing attribute reliability using the ratio of true score variance to observed score variance on items that probe specific cognitive attributes. This reliability procedure is evaluated and illustrated using both simulated data and student response data from a sample of algebra items taken from the March 2005 administration of the SAT. The reliability of diagnostic scores and the implications for practice are also discussed.
This study proposes a new real-time diagnosis method for an in-wheel motor (IWM) of an electric vehicle (EV) based on dynamic Bayesian networks (DBNs). Since the electrical signal of the vehicle power supply is unstable because of the interference resulting from the EV's frequent acceleration and deceleration, the IWM's vibration signal is focused. Symptom parameters (SPs) in the time and frequency domains are used to represent different features of the vibration signals in the actual operating conditions of the EV. To select highly sensitive SPs, stable average discrimination rate (SADR) is proposed, which consists of the average discrimination rate (ADR) and the stability coefficient of the group (SCG). Moreover, DBNs are employed to establish a model for the real-time diagnosis of the IWM's mechanical faults, in which the parameter of road-speed-time slice (RSTS) is used to solve the problem that the state transition probability distribution between two continuous time slices cannot be obtained. Finally, the effectiveness of the proposed methods is verified by experiments using the IWM test bench. INDEX TERMS Dynamic Bayesian networks, electric vehicle, in-wheel motor, real-time diagnosis, roadspeed-time slice.
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