Magnetic Flip Flops for Space Applications

The radiation environment of space presents a significant threat to the reliability of nonvolatile memory technologies. Ionizing radiation disturbs the charge stored on floating gates, and cosmic rays can permanently damage thin oxides. A new memory technology based on the magnetic tunneling junction (MTJ) appears to offer superior resistance to radiation effects and virtually unlimited write endurance. A magnetic flip flop has a number of potential applications, such as the configuration memory in field-programmable logic devices. However, using MTJs in a flip flop requires radically different circuitry for storing and retrieving data. New techniques are needed to insure that magnetic flip flops are reliable in the radiation environment of space. We propose a new radiation-tolerant magnetic flip flop that uses the inherent resistance of the MTJ to increase its immunity to single event upset and employs a robust “Pac-man” magnetic element.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.