On the Easy Use of Scientific Computing Services for Large Scale Linear Algebra and Parallel Decision Making with the P-Grade Portal

Scientific research is becoming increasingly dependent on the large-scale analysis of data using distributed computing infrastructures (Grid, cloud, GPU, etc.). Scientific computing (Petitet et al. 1999) aims at constructing mathematical models and numerical solution techniques for solving problems arising in science and engineering. In this paper, we describe the services of an integrated portal based on the P-Grade (Parallel Grid Run-time and Application Development Environment) portal (http://www.p-grade.hu) that enables the solution of large-scale linear systems of equations using direct solvers, makes easier the use of parallel block iterative algorithm and provides an interface for parallel decision making algorithms. The ultimate goal is to develop a single sign on integrated multi-service environment providing an easy access to different kind of mathematical calculations and algorithms to be performed on hybrid distributed computing infrastructures combining the benefits of large clusters, Grid or cloud, when needed.     

The weak aggregating algorithm and weak mixability

This paper resolves the problem of predicting as well as the best expert up to an additive term of the order o(n), where n is the length of a sequence of letters from a finite alphabet. We call the games that permit this weakly mixable and give a geometrical characterisation of the class of weakly mixable games. Weak mixability turns out to be equivalent to convexity of the finite part of the set of superpredictions. For bounded games we introduce the Weak Aggregating Algorithm that allows us to obtain additive terms of the form .     

Generating vegetation leaf area index Earth system data record from multiple sensors. Part 2: Implementation,analysis and validation

The evaluation of a new global monthly leaf area index (LAI) data set for the period July 1981 to December 2006 derived from AVHRR Normalized Difference Vegetation Index (NDVI) data is described. The physically based algorithm is detailed in the first of the two part series. Here, the implementation, production and evaluation of the data set are described. The data set is evaluated both by direct comparisons to ground data and indirectly through inter-comparisons with similar data sets. This indirect validation showed satisfactory agreement with existing LAI products, importantly MODIS, at a range of spatial scales, and significant correlations with key climate variables in areas where temperature and precipitation limit plant growth. The data set successfully reproduced well-documented spatio-temporal trends and inter-annual variations in vegetation activity in the northern latitudes and semi-arid tropics. Comparison with plot scale field measurements over homogeneous vegetation patches indicated a 7% underestimation when all major vegetation types are taken into account. The error in mean values obtained from distributions of AVHRR LAI and high-resolution field LAI maps for different biomes is within 0.5 LAI for six out of the ten selected sites. These validation exercises though limited by the amount of field data, and thus less than comprehensive, indicated satisfactory agreement between the LAI product and field measurements. Overall, the inter-comparison with short-term LAI data sets, evaluation of long term trends with known variations in climate variables, and validation with field measurements together build confidence in the utility of this new 26 year LAI record for long term vegetation monitoring and modeling studies.     

Towards interactive simulation in automotive design

One of the important tasks in Mechanical Engineering is to increase the safety of the vehicle and decrease its production costs. This task is typically solved by means of Multiobjective Optimization, which formulates the problem as a mapping from the space of design variables to the space of target criteria and tries to find an optimal region in these multidimensional spaces. Due to high computational costs of numerical simulations, the sampling of this mapping is usually very sparse and scattered. Combining design of experiments methods, metamodeling, new interpolation schemes and innovative graphics methods, we enable the user to interact with simulation parameters, optimization criteria, and come to a new interpolated crash result within seconds. We denote this approach as Simulated Reality, a new concept for the interplay between simulation, optimization and interactive visualization. In this paper we show the application of Simulated Reality for solution of real life car design optimization problems.

Relating instance hardness to classification performance in a dataset: a visual approach

Machine Learning - Machine Learning studies often involve a series of computational experiments in which the predictive performance of multiple models are compared across one or more datasets. The...     

Closed-loop identification with routine operating data: Effect of time delay and sampling time

In industry, in order to store the reams of data that are collected from all the different flow, level, and temperature sensors, the fast-sampled data is very often downsampled before being stored in a data historian. This downsampled or even compressed data is, then, often used by process engineers to recover the appropriate process parameters. However, little has been written about the effects of the sampling on the quality of the model obtained. Therefore, in this paper, the effects of sampling time are investigated from both a theoretical and practical perspective using results that come out of the theory of closed-loop system identification with routine operating data. It is shown that if the discrete time delay in a process is sufficiently large or the sampling time is sufficiently small, then it is possible to recover the true system parameters. The most common industrial processes that fulfill this constraint are temperature control loops. These results suggest that the sampling time has an important bearing on the quality of the model estimated from routine operating data. Using both Monte Carlo simulations and an experimental set-up with a heated tank, the effect of discrete time delay on the identification of the true continuous time parameters was considered for different sampling times. It was shown that increasing the sampling time above a given threshold resulted in identifying an incorrect model. As well, the models obtained using a PID controller were less sensitive to sampling time than those obtained using a PI controller. However, the PID controller values were more sensitive to the effects of aliasing at larger sampling times.     

Hybrid‐impulsive second‐order sliding mode control: Lyapunov approach

Dynamic system of relative degree two controlled by discontinuous‐hybrid‐impulsive feedback in the presence of bounded perturbations is considered. The state feedback impulsive‐twisting control exhibits a uniform exact finite time convergence to the second‐order sliding mode with zero convergence time. The output feedback discontinuous control augmented by a simplified hybrid‐impulsive functions provides uniform exact convergence with zero convergence time of the system's states to a real second‐order sliding mode in the presence of bounded perturbations. Only ‘snap’ knowledge of the output derivative, that is, the knowledge of the output derivative in isolated time instants, is required. The output feedback hybrid‐impulsive control with practically implemented impulsive actions asymptotically drives the system's states to the origin. The Lyapunov analysis of the considered hybrid‐impulsive‐discontinuous system proves the system's stability. The efficacy of the proposed control technique is illustrated via computer simulations. Copyright © 2016 John Wiley & Sons, Ltd.