Fuzzy fault tolerant predictive control for a diesel engine air path

This paper proposes a Fuzzy Fault Tolerant Predictive Control (FFTPC) with integral action method for a class of nonlinear systems. The Takagi-Sugeno (T-S) fuzzy approach is introduced as a modelling technique in order to consider the active control methods adapted to linear models. The proposed control strategy is based on a combination between Parallel Distributed Compensation (PDC) control law and Model Predictive Control (MPC) where the T-S fuzzy aspect uses the Unmeasurable Premise Variables (UPV). A T-S fuzzy observer provides an L2 norm estimation of system state vector and faults. The controller and observer gains are obtained by solving Linear Matrix Inequalities (LMIs) derived from the Lyapunov theory. The validity of the proposed Fault Tolerant Control (FTC) strategy is illustrated through an application to a Diesel Engine Air Path (DEAP) system.     

Effect of temperature on raw whole milk density and its potential impact on milk payment in the dairy industry

The objective of this study was to determine the effect of temperature on whole milk density measured at four different temperatures: 5, 10, 15, and 20 °C. A total of ninety-three individual milk samples were collected from morning milking of thirty-two Holstein Friesian dairy cows, of national average genetic merit, once every two weeks over a period of 4 weeks and were assessed by Fourier transform infrared spectroscopy for milk composition analysis. Density of the milk was evaluated using two different analytical methods: a portable density meter DMA35 and a standard desktop model DMA4500M (Anton Paar GmbH, UK). Milk density was analysed with a linear mixed model with the fixed effects of sampling period, temperature and analysis method; triple interaction of sampling period x analysis method x temperature; and the random effect of cow to account for repeated measures. The effect of temperature on milk density (ρ) was also evaluated including temperature (t) as covariate with linear and quadratic effects within each analytic method. The regression equation describing the curvature and density–temperature relationship for the DMA35 instrument was ρ = 1.0338−0.00017T−0.0000122T² (R² = 0.64), while it was ρ = 1.0334 + 0.000057T−0.00001T² (R² = 0.61) for DMA4500 instrument. The mean density determined with DMA4500 at 5 °C was 1.0334 g cm⁻³, with corresponding figures of 1.0330, 1.0320 and 1.0305 g cm⁻³ at 10, 15 and 20 °C, respectively. The milk density values obtained in this study at specific temperatures will help to address any bias in weight–volume calculations and thus may also improve the financial and operational control for the dairy processors in Ireland and internationally.     

Dynamic voltage scaling under EDF revisited

Basic algorithms have been proposed in the field of low-power (Yao, F., et al. in Proceedings of lEEE annual foundations of computer science, pp. 374–382, 1995) which compute the minimum energy-schedule for a set of non-recurrent tasks (or jobs) scheduled under EDF on a dynamically variable voltage processor. In this study, we propose improvements upon existing algorithms with lower average and worst-case complexities. They are based on a new EDF feasibility test that helps to identify the “critical intervals”. The complexity of this feasibility test depends on structural characteristics of the set of jobs. More precisely, it depends on how tasks are included one in the other. The first step of the algorithm is to construct the Hasse diagram of the set of tasks where the partial order is defined by the inclusion relation on the tasks. Then, the algorithm constructs the shortest path in a geometrical representation at each level of the Hasse diagram. The optimal processor speed is chosen according to the maximal slope of each path.

EASEA: specification and execution of evolutionary algorithms on GPGPU

EASEA is a framework designed to help non-expert programmers to optimize their problems by evolutionary computation. It allows to generate code targeted for standard CPU architectures, GPGPU-equipped machines as well as distributed memory clusters. In this paper, EASEA is presented by its underlying algorithms and by some example problems. Achievable speedups are also shown onto different NVIDIA GPGPUs cards for different optimization algorithm families.     

Some approximate Godunov schemes to compute shallow-water equations with topography

We study here the computation of shallow-water equations with topography by Finite Volume methods, in a one-dimensional framework (though all methods introduced may be naturally extended in two dimensions). All methods are based on a discretisation of the topography by a piecewise function constant on each cell of the mesh, from an original idea of Le Roux et al. Whereas the Well-Balanced scheme of Le Roux is based on the exact resolution of each Riemann problem, we consider here approximate Riemann solvers. Several single step methods are derived from this formalism, and numerical results are compared to a fractional step method. Some test cases are presented: convergence towards steady states in subcritical and supercritical configurations, occurrence of dry area by a drain over a bump and occurrence of vacuum by a double rarefaction wave over a step. Numerical schemes, combined with an appropriate high-order extension, provide accurate and convergent approximations.     

Morphological and stance interpolations in database for simulating bipedalism of virtual humans

We present a computer tool for testing walk hypotheses for human beings. This tool aims to generate plausible walking movements according to anatomical knowledge. To this end, we introduce an interpolation method based, on one hand, on morphological data and, on the other hand, on stance hypotheses and on footprint hypotheses. We want to test these hypotheses for application to the reconstruction of early hominid walking. We interpolate from a specific representation of the movement—a characteristic relative displacement. First, we use a motion capture system to acquire real movements of a walk cycle, and we propose to represent them by using a generic parametric model. Thus, we create a database of movements. The interpolation process produces, thanks to this database, a retargeted motion adapted to the morphology of the considered targeted skeleton. The interpolation is done according to three main hypotheses. The first concerns the reference stance, the second the lateral spacing between the feet, and the third the length of the step. In the introduction, we refer to related work. Then we propose the two following points of our method: the 3D representation of our motion representation and the multidimensional interpolation method applied to this representation. The interpolation method addresses morphological adaptation, and the use of an inverse kinematics solver addresses the computation of skeleton movements. The self-coherent validation process aims to test the coherence of the proposed method. The results propose an application to a virtual skeleton of Lucy (Australopithecus afarensis A.L. 288-1) reconstructed from real data. Finally, the relevance of the method for anthropological investigations and for animation purposes is discussed and future work is discussed with respect to the limitations of the proposed method.