Using reversible computing to achieve fail-safety

This paper describes a fail-safe design approach that can be used to achieve a high level of fail-safety with conventional computing equipment which may contain design flaws. The method is based on the well-established concept of reversible computing. Conventional programs destroy information and hence cannot be reversed. However it is easy to define a virtual machine that preserves sufficient intermediate information to permit reversal. Any program implemented on this virtual machine is inherently reversible. The integrity of a calculation can therefore be checked by reversing back from the output values and checking for the equivalence of intermediate values and original input values. By using different machine instructions on the forward and reverse paths, errors in any single instruction execution can be revealed. Random corruptions in data values are also detected. An assessment of the performance of the reversible computer design for a simple reactor trip application indicates that it runs about ten times slower than a conventional software implementation and requires about 20 kilobytes of additional storage. The trials also show a fail-safe bias of better than 99.998% for random data corruptions, and it is argued that failures due to systematic flaws could achieve similar levels of fail-safe bias. Potential extensions and applications of the technique are discussed

MC/DC based estimation and detection of residual faults in PLC logic networks

A logic coverage measure related to MC/DC testing is used to estimate residual faults. The residual fault prediction method is evaluated on an industrial PLC logic example. A randomized form of MC/DC testing is used to maximize coverage growth and fault detection efficiency

Overcoming non-determinism in testing smart devices: how to build models of device behaviour

Justification of smart instruments has become an important topic in the nuclear industry. In practice, however, the publicly available artefacts are often the only source of information about the device. Therefore, in many cases independent black-box testing may be the only way to increase the confidence in the device. In this paper we provide a set of recommendations, which we consider to be the best practices for performing black-box assessments. We present our method of testing smart instruments, in which we use the publicly available artefacts only. We present a test harness and describe a method of test automation. We focus on the analysis of test results, which is made particularly complex by the inherent non determinism in the testing of analogue devices. In the paper we analyse the sources of non-determinism, which for instance may arise from inaccuracy in an analogue measurement made by the device when two alternative actions are possible. We propose three alternative ideas on how to build models of device behaviour, which can cope with this kind of non-determinism. We compare and contrast these three solutions, and express our recommendations. Finally, we use a case study, in which a black box assessment of two similar smart instruments is performed to illustrate the differences between the solutions

Low-energy parameters and spin gap of a frustrated spin-ss Heisenberg antiferromagnet with s≤32s \leq \frac{3}{2} on the honeycomb lattice

The coupled cluster method is implemented at high orders of approximation to investigate the zero-temperature $(T=0)$ phase diagram of the frustrated spin-$s$ $J_{1}$--$J_{2}$--$J_{3}$ antiferromagnet on the honeycomb lattice. The system has isotropic Heisenberg interactions of strength $J_{1}>0$, $J_{2}>0$ and $J_{3}>0$ between nearest-neighbour, next-nearest-neighbour and next-next-nearest-neighbour pairs of spins, respectively. We study it in the case $J_{3}=J_{2}\equiv \kappa J_{1}$, in the window $0 \leq \kappa \leq 1$ that contains the classical tricritical point (at $\kappa_{{\rm cl}}=\frac{1}{2}$) of maximal frustration, appropriate to the limiting value $s \to \infty$ of the spin quantum number. We present results for the magnetic order parameter $M$, the triplet spin gap $\Delta$, the spin stiffness $\rho_{s}$ and the zero-field transverse magnetic susceptibility $\chi$ for the two collinear quasiclassical antiferromagnetic (AFM) phases with N\'{e}el and striped order, respectively. Results for $M$ and $\Delta$ are given for the three cases $s=\frac{1}{2}$, $s=1$ and $s=\frac{3}{2}$, while those for $\rho_{s}$ and $\chi$ are given for the two cases $s=\frac{1}{2}$ and $s=1$. On the basis of all these results we find that the spin-$\frac{1}{2}$ and spin-1 models both have an intermediate paramagnetic phase, with no discernible magnetic long-range order, between the two AFM phases in their $T=0$ phase diagrams, while for $s > 1$ there is a direct transition between them. Accurate values are found for all of the associated quantum critical points. While the results also provide strong evidence for the intermediate phase being gapped for the case $s=\frac{1}{2}$, they are less conclusive for the case $s=1$. On balance however, at least the transition in the latter case at the striped phase boundary seems to be to a gapped intermediate state

Worst Case Reliability Prediction Based on a Prior Estimate of Residual Defects

In this paper we extend an earlier worst case bound reliability theory to derive a worst case reliability function R(t), which gives the worst case probability of surviving a further time t given an estimate of residual defects in the software N and a prior test time T. The earlier theory and its extension are presented and the paper also considers the case where there is a low probability of any defect existing in the program. For the "fractional defect" case, there can be a high probability of surviving any subsequent time t. The implications of the theory are discussed and compared with alternative reliability models

Rural soldiers continue to account for disproportionately high share of U.S. casualties in Iraq and Afghanistan

When the nation goes to war, all Americans are expected to make sacrifices. Today\u27s rural Americans, however, have fewer job opportunities within their communities, and are joining the military at higher rates. In turn, rural communities are facing military losses in disproportionate numbers to their urban counterparts

Using a Log-normal Failure Rate Distribution for Worst Case Bound Reliability Prediction

Prior research has suggested that the failure rates of faults follow a log normal distribution. We propose a specific model where distributions close to a log normal arise naturally from the program structure. The log normal distribution presents a problem when used in reliability growth models as it is not mathematically tractable. However we demonstrate that a worst case bound can be estimated that is less pessimistic than our earlier worst case bound theory