EXPERIMENTAL IMPLEMENTATION OF AN ARTIFICIAL IMMUNE SYSTEM ON A ROBOTIC MANIPULATOR

An artificial immune system (AIS) has been used to realize robust control of a robotic manipulator. The AIS recognizes “self” and “non-self” operation of a closed-loop system, where self is defined as a condition where controller gains are appropriate for a given manipulator configuration. As configuration changes occur, the changing performance of the system indicates a transition to non-self. When non-self operation is first detected, the corresponding dynamic response is defined as a receptor and a genetic algorithm (GA) is called to optimize the controller for the new configuration. A library of receptors is built as additional configuration changes are experienced. For subsequent self to non-self transitions, new and recorded receptors are compared. In the event of a high correlation between the receptors, previously determined controller gains are implemented without calling the GA. The system is agile and robust and can recognize and respond to recognized receptors within a single reference step.     

Observations of wind and ocean wave fields using ERS Synthetic Aperture Radar imagery

The aim of the research reported here is to evaluate Synthetic Aperture Radar (SAR) capability to estimate the wind vector and associated directional wave spectrum. Two ERS–2 SAR images of the Mediterranean Sea, one over the Sicily Channel and one over the Ligurian Sea, were selected as case studies. Wind speed was estimated using SAR calibrated backscatter response, in conjunction with empirically derived ERS scatterometer models such as CMOD4 and CMOD–IFREMER. The predictions of these models were then compared with the actual sea surface wave spectra either provided by in situ measurements or resulting from the inversion of the SAR image spectrum. SAR-detected effects of both wind and wave features, induced either by atmospheric boundary layer instability or by land shadowing, were also used as reliable indicators of wind direction.     

Modeling failure of heterogeneous viscoelastic solids under dynamic/impact loading due to multiple evolving cracks using a two-way coupled multiscale model

This paper presents a model for predicting damage evolution in heterogeneous viscoelastic solids under dynamic/impact loading. Some theoretical developments associated with the model have been previously reported. These are reviewed briefly, with the main focus of this paper on new developments and applications. A two-way coupled multiscale approach is employed and damage is considered in the form of multiple cracks evolving in the local (micro) scale. The objective of such a model is to develop the ability to consider energy dissipation due to both bulk dissipation and the development of multiple cracks occurring on multiple length and time scales. While predictions of these events may seem extraordinarily costly and complex, there are multiple structural applications where effective models would save considerable expense. In some applications, such as protective devices, viscoelastic materials may be preferred because of the considerable amount of energy dissipated in the bulk as well as in the fracture process. In such applications, experimentally based design methodologies are extremely costly, therefore suggesting the need for improved models. In this paper, the authors focus on the application of the newly developed multiscale model to the solution of some example problems involving dynamic and impact loading of viscoelastic heterogeneous materials with growing cracks at the local scale.     

A constrained optimization approach to preserving prior knowledge during incremental training

In this paper, a supervised neural network training technique based on constrained optimization is developed for preserving prior knowledge of an input-output mapping during repeated incremental training sessions. The prior knowledge, referred to as long-term memory (LTM), is expressed in the form of equality constraints obtained by means of an algebraic training technique. Incremental training, which may be used to learn new short-term memories (STMs) online, is then formulated as an error minimization problem subject to equality constraints. The solution of this problem is simplified by implementing an adjoined error gradient that circumvents direct substitution and exploits classical backpropagation. A target application is neural network function approximation in adaptive critic designs. For illustrative purposes, constrained training is implemented to update an adaptive critic flight controller, while preserving prior knowledge of an established performance baseline that consists of classical gain-scheduled controllers. It is shown both analytically and numerically that the LTM is accurately preserved while the controller is repeatedly trained over time to assimilate new STMs.     

Comparison of FDTD and SPICE simulations for lossy and dispersivenonlinear transmission lines

For the first time, a simulation has been carried out of lossy and dispersive nonlinear transmission lines (NLTLs) used for pulse compression, by two different time-domain approaches: SPICE and a full wave 3D finite difference time domain method. Results show good agreement between the two approaches. The output pulse risetime is strongly affected by DC and skin-effect losses     

Fluorescence line narrowing study of Cr ions in cordierite glass nucleating MgAl2O4 nanocrystals

The luminescence of Cr³⁺ ions in cordierite glass nucleating MgAl₂O₄ nanocrystallites has been investigated. The time resolved fluorescence line narrowing measurements and the temperature dependence of the homogeneous line width show that most Cr³⁺ ions are inside the nanocrystallites. Cr³⁺ ions occupy non-equivalent crystal sites, due to the Mg²⁺–Al³⁺ inversion effect. The values of the homogeneous line width compare well with those of previous studies in crystals. No surface effect has been observed due to the large size of the crystals.     

Influence of a biopolymer admixture on corrosion behaviour of steel rebars in concrete

Among the multitude of concrete structure pathologies, corrosion of rebars is one of the most important problems of concrete durability. In the context of sustainable development, it appears of primary importance to develop new means to protect the rebars against corrosion. This study aims to develop a new eco‐friendly and corrosion‐inhibiting admixture based on EPS 180 exopolysaccharides, biopolymers used in coatings already studied for the corrosion inhibition on steel in seawater. C15 rebars embedded in CEMI and CEMV cement paste containing EPS 180 were immersed in natural seawater and their electrochemical behaviour was studied using open circuit potential measurements and electrochemical impedance spectroscopy. These tests highlight the decrease of the cathodic reaction kinetics due to the EPS 180 action at the rebars surface, and the absence of effect on the passive layer. Capillary imbibition tests carried out on cement paste and mortars showed that although limiting the imbibition kinetics for cement pastes, the EPS 180 did not influence the water imbibition of mortars. Tests comparing capillary imbibition of soaked cement pastes and mortars with EPS 180 solution and the same samples containing the EPS 180 admixture highlight that the corrosion inhibition induced by EPS 180 admixture is more due to the modification of the cement – rebars interface than to the clogging of the cement porous network.