Genetic algorithm optimization and control system design of flexible structures

This paper presents an investigation into the deployment of genetic algorithm (GA)-based controller design and optimization for vibration suppression in flexible structures. The potential of GA is explored in three case studies. In the first case study, the potential of GA is demonstrated in the development and optimization of a hybrid learning control scheme for vibration control of flexible manipulators. In the second case study, an active control mechanism for vibration suppression of flexible beam structures using GA optimization technique is proposed. The third case study presents the development of an effective adaptive command shaping control scheme for vibration control of a twin rotor system, where GA is employed to optimize the amplitudes and time locations of the impulses in the proposed control algorithm. The effectiveness of the proposed control schemes is verified in both an experimental and a simulation environment, and their performances are assessed in both the time and frequency domains

On the Reproducibility and Repeatability of Likelihood Ratio in Forensics: A case study using Face Biometrics

When using biometric technology in forensic applications, it is necessary to compute a Log-likelihood Ratio (LLR) for a given piece of evidence (E) under two competing hypotheses, namely the prosecution and the defence hypotheses. Although LLR is a quantity expressing uncertainty and intuitively quantifying its uncertainty would not make sense, in practice, it is computed under a set of assumptions and methods for a given data set. Therefore, it is essential to ask how well and how repeatable and/or reproducible it is that we can estimate LLR. More specifically, it is desirable to understand the behaviour of the confidence intervals of the estimated LLR for any feasible region since any incorrect estimate may lead to possible condemnation of innocent people. To this end, we have thus tackled the estimate of LLR which is fundamentally a Bayesian concept using a frequentist approach, via bootstraping, using two LLR estimators, namely Logistic Regression (LR) and Kernel Density Estimator (KDE). The experimental results, which are based on seven face recognition systems, show that LLR does have different confidence lengths, thus highlighting that LLR cannot be estimated with the same certainty everywhere. Moreover, for the two LLR estimators investigated, we found that there is a consistent region in which any LLR value can be estimated confidently. To our best knowledge, these two findings have never been systematically reported in literature. They thus advance our understanding of LLR when used in computing the strength of biometric evidence in forensic