A robust fault diagnosis and accommodation scheme for robot manipulators

This paper investigates an algorithm for robust fault diagnosis (FD) in uncertain robotic systems by using a neural sliding mode (NSM) based observer strategy. A step by step design procedure will be discussed to determine the accuracy of fault estimation. First, an uncertainty observer is designed to estimate the uncertainties based on a first neural network (NN1). Then, based on the estimated uncertainties, a fault diagnosis scheme will be designed by using a NSM observer which consists of both a second neural network (NN2) and a second order sliding mode (SOSM), connected serially. This type of observer scheme can reduce the chattering of sliding mode (SM) and guarantee finite time convergence of the neural network (NN). The obtained fault estimations are used for fault isolation as well as fault accommodation to self-correct the failure systems. The computer simulation results for a PUMA560 robot are shown to verify the effectiveness of the proposed strategy.     

Inverse modelling of an aneurysm’s stiffness using surrogate-based optimization and fluid-structure interaction simulations

Characterization of the mechanical properties of arterial tissues is highly relevant. In this work, we apply an inverse modelling approach to a model accounting for an aneurysm and the distal part of the circulation which can be modified using two independent stiffness parameters. For given values of these parameters, the position of the arterial wall as a function of time is calculated using a forward simulation which takes the fluid-structure interaction (FSI) into account. Using this forward simulation, the correct values of the stiffness parameters are obtained by minimizing a cost function, which is defined as the difference between the forward simulation and a measurement. The minimization is performed by means of surrogate-based optimization using a Kriging model combined with the expected improvement infill criterion. The results show that the stiffness parameters converge to the correct values, both for a zero-dimensional and for a three-dimensional model of the aneurysm.     

The effect of alkyd resins on the properties of AA‐NC semi‐IPNs as binders in wood finish

Wood coatings of AA‐NC semi‐interpenetrating polymer networks (semi‐IPNs), made from acid curing amino‐alkyd resins (AA) and nitrocellulose (NC), were prepared by sequential polymerization method. To investigate the effects of oil length on the properties of AA‐NC semi‐IPNs, three grades of alkyd resins (Alkyd) containing 38, 48, and 58% oil were synthesized with phthalic anhydride, glycerol, and soybean oil, employing alcoholysis method. The butylated urea formaldehyde resin (UF) and melamine formaldehyde resin (MF) were also prepared in this study. The AA‐NC semi‐IPNs were maintained at a weight ratio of AA : NC of 25 : 75, where the AA was the composition of MF : UF : Alkyd of 7.5 : 22.5 : 70 (by weight), and 10% of p‐toluene sulfonic acid solution (concentration, 25% in isopropyl alcohol) based on the weight of amino resins was added as acid catalyst. The properties of coatings such as viscosity, drying time, and gel time, and the properties of films including adhesion, hardness, abrasion resistance, impact resistance, tensile strength, released formaldehyde, lightfastness, solvent resistance, and durability were examined. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 93: 1923–1927, 2004     

Nonlinear state affine identification methods: Applications to electrical power plants

A nonlinear identification method by simple state-space models is proposed in the discrete-time case. This approach is suggested by theoretical results similar to the approximation property known for a long time for Volterra series. The proposed technique uses extensions with polynomial inputs of bilinear systems called state-affine systems. This modelling requires a small computing time and enables us to obtain nonlinear models which can be easily implemented on microprocessors and remain valid over a wide range of operating conditions. Several physical examples concerning electric power plants are also reported.     

Dynamic Monte Carlo Simulations of Oscillatory Reactions

The dynamic Monte Carlo method has been used to simulate the 2 A + B₂ → 2 AB reaction catalyzed by a reconstructing substrate. Oscillatory behavior and spatio-temporal is studied as a function of grid size. Spatio-temporal pattern formation has been simulated in various forms: cellular patterns, target patterns, rotating spirals, and turbulent patterns. Cellular patterns are a manifestation of a local synchronization mechanism in which all reaction fronts periodically extinguish each other. This illustrates that dynamic Monte Carlo simulations form a promising technique and can be used to predict macroscopic kinetic phenomena on a molecular basis.     

Chaotic,fluid and unstable: an exploration of the complex housing trajectories of homeless people in Flanders,Belgium

This article analyses the housing and homeless pathways of (ex)homeless persons in the coastal city of Ostend. After a short review of the literature on the causes and meaning of the vulnerability of homeless persons, we describe how our case study with (former) homeless persons in Ostend was organised. We deal with some methodological issues and the analytic results, revealing a very complex housing trajectory. We focus on these dynamic and complex housing pathways and look at the role of relationships and relationship breakdown, work and unemployment, eviction after rent arrears and moving as an escape strategy. We also deal with the searching process for housing and the role of social networks. We end with some conclusions and interest points for policy.     

Automated Modeling of Three-Dimensional Structural Components Using Irregular Lattices

Abstract:   Irregular lattices are used to model three-dimensional (3D) structural components consisting of a bulk material, curvilinear reinforcement, and their interface. Domain discretization is highly automated and involves the semi-random placement of nodal points within the domain, followed by Voronoi tessellation of the nodal point set. A technique is given for the Voronoi partitioning of nonconvex domains. For discretizing nonconvex domains, and for effective gradation of nodal point density, a minimum allowable distance between nodes is maintained and the domain is saturated with nodes. To accelerate this computationally expensive operation, a partitioned domain search is used during the filling process. Reinforcement, and its interface with the bulk material, are discretely modeled and freely positioned in the domain, irrespective of the geometry of the irregular lattice representing the bulk material. This method of discretization facilitates model construction, results interpretation, and possible revisions to the model. While the focus is on automated domain discretization and the modeling of reinforcement, elastic properties of the model are demonstrated through examples involving nodal stress calculations and deflection analyses of prestressed concrete beams.