Analysis of the use of industrial control systems in simulators: state of the art and basic guidelines

The use of industrial control systems in simulators facilitates the execution of engineering activities related with the installation and the optimization of the control systems in real plants. "Industrial control system" intends to be a valid term that would represent all the control systems which can be installed in an industrial plant, ranging from complex distributed control systems and SCADA packages to small single control devices. This paper summarizes the current alternatives for the development of simulators of industrial plants and presents an analysis of the process of integrating an industrial control system into a simulator, with the aim of helping in the installation of real control systems in simulators.     

Speeding up the evaluation phase of GP classification algorithms on GPUs

The efficiency of evolutionary algorithms has become a studied problem since it is one of the major weaknesses in these algorithms. Specifically, when these algorithms are employed for the classification task, the computational time required by them grows excessively as the problem complexity increases. This paper proposes an efficient scalable and massively parallel evaluation model using the NVIDIA CUDA GPU programming model to speed up the fitness calculation phase and greatly reduce the computational time. Experimental results show that our model significantly reduces the computational time compared to the sequential approach, reaching a speedup of up to 820×. Moreover, the model is able to scale to multiple GPU devices and can be easily extended to any evolutionary algorithm.     

Multi-objective approach based on grammar-guided genetic programming for solving multiple instance problems

Multiple instance learning (MIL) is considered a generalization of traditional supervised learning which deals with uncertainty in the information. Together with the fact that, as in any other learning framework, the classifier performance evaluation maintains a trade-off relationship between different conflicting objectives, this makes the classification task less straightforward. This paper introduces a multi-objective proposal that works in a MIL scenario to obtain well-distributed Pareto solutions to multi-instance problems. The algorithm developed, Multi-Objective Grammar Guided Genetic Programming for Multiple Instances (MOG3P-MI), is based on grammar-guided genetic programming, which is a robust tool for classification. Thus, this proposal combines the advantages of the grammar-guided genetic programming with benefits provided by multi-objective approaches. First, a study of multi-objective optimization for MIL is carried out. To do this, three different extensions of MOG3P-MI are designed and implemented and their performance is compared. This study allows us on the one hand, to check the performance of multi-objective techniques in this learning paradigm and on the other hand, to determine the most appropriate evolutionary process for MOG3P-MI. Then, MOG3P-MI is compared with some of the most significant proposals developed throughout the years in MIL. Computational experiments show that MOG3P-MI often obtains consistently better results than the other algorithms, achieving the most accurate models. Moreover, the classifiers obtained are very comprehensible.     

Birth of piglets after transferring of in vitro-produced embryos pre-matured with R-roscovitine

The objectives of this study were to evaluate: (1) the nuclear maturation, (2) the intracellular glutathione (GSH) content, (3) the normality of fertilization and (4) full development after transplantation of embryos derived from porcine oocytes pre-cultured with 50 micromol/l roscovitine (an inhibitor of p34cdc2/cyclin B kinase) for 22 h. After treatment with roscovitine, the nuclear configuration of oocytes (Hoechst staining) was comparable with those examined just after collection: the majority of oocytes were arrested at the germinal vesicle (GV) 1 stage (63.2%). Roscovitine-treated oocytes progressed through meiosis to the metaphase II stage in a conventional step-wise in vitro maturation (IVM) program for 44 h in a proportion similar to control ones (>85.0%). When roscovitine-treated oocytes and non-treated oocytes were matured for 44 h and then co-cultured with fresh spermatozoa for 18 h, no differences were observed in oocyte penetrability, proportion of monospermic penetration and male pronuclear formation (>87%). Roscovitine increased the GSH synthesis in oocytes at 22 h, whereas, after 44 h, roscovitine-treated oocytes had similar amounts of GSH to non-treated oocytes. Finally, surgical transfer of zygotes at 22-24 h post-insemination, derived from roscovitine-treated oocytes, resulted in one pregnancy with 12 piglets born; control non-treated zygotes resulted in one pregnancy and 10 piglets born. The full-term developmental ability of mammalian oocytes pre-cultured with roscovitine prior to IVM is thereby demonstrated. This validation is important before the introduction of roscovitine into routine procedures.     

Constrained Nonlinear‐Based Optimisation Applied to Fuzzy PID Controllers Tuning

This paper aims at studying the optimal Fuzzy Proportional–Integral– Derivative controllers' tuning problem by considering two different nonlinear constrained optimisation techniques. One relying on a Hessian‐based analytical approach, and the other based on a differential evolutionary method. In the case of offline implementation, two basic frameworks are under assessment, depending on the controller parameters to be adjusted. For online scaling factors and membership functions' width tuning, its implementation is based on the parallel computation paradigm. The performance index is described by a quadratic cost function, taking as arguments control errors and the increment of control actions. Constraints on the scaling factors, membership functions' width, as well as on the system inputs and outputs are also included in the optimisation problem. Experiments carried out on a benchmark system favour the offline joint optimisation based on the differential evolutionary approach of scaling factors and membership functions' width.     

Scalable CAIM discretization on multiple GPUs using concurrent kernels

Class-attribute interdependence maximization (CAIM) is one of the state-of-the-art algorithms for discretizing data for which classes are known. However, it may take a long time when run on high-dimensional large-scale data, with large number of attributes and/or instances. This paper presents a solution to this problem by introducing a graphic processing unit (GPU)-based implementation of the CAIM algorithm that significantly speeds up the discretization process on big complex data sets. The GPU-based implementation is scalable to multiple GPU devices and enables the use of concurrent kernels execution capabilities of modern GPUs. The CAIM GPU-based model is evaluated and compared with the original CAIM using single and multi-threaded parallel configurations on 40 data sets with different characteristics. The results show great speedup, up to 139 times faster using four GPUs, which makes discretization of big data efficient and manageable. For example, discretization time of one big data set is reduced from 2 h to \(<\) 2 min.     

Effects of Backward Speech and Speaker Variability in Language Discrimination by Rats.

Human infants use prosodic cues present in speech to extract language regularities, and it has been suggested that this capacity is anchored in more general mechanisms that are shared across mammals. This study explores the extent to which rats can generalize prosodic cues that have been extracted from a training corpus to new sentences and how this discrimination process is affected by the normalization of the sentences when multiple speakers are introduced. Conditions 1 and 2 show rats' abilities to use prosodic cues present in speech, allowing them to discriminate between sentences not previously heard. But this discrimination is not possible when sentences are played backward. Conditions 3 and 4 show that language discrimination by rats is also taxed by the process of speaker normalization. These findings have remarkable parallels with data from human adults, human newborns, and cotton-top tamarins. Implications for speech perception by humans are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Experimental verification of a theoretical model for erbium-doped fibre ring lasers

A simple theoretical model for erbium-doped fibre ring lasers is presented. This model is experimentally verified by means of the comparison between theoretical values and measurements of efficiency, threshold pump power and output laser power employing a wide set of experimental situations. This model provides semi-analytical equations, which characterise the erbium-doped fibre, determining its absorption and emission spectral coefficients.     

Development and validation of the high performance liquid chromatography-ion exclusion method for detection of lactic acid in milk

The present work reports the development and validation of an analytical method for lactic acid quantification in milk by ion exclusion-HPLC with UV detection. This method showed good precision (variation coefficient <7.0%) and good accuracy (97.6-99.6%). Calibration curves were linear (R(2)>0.996) at the tested range of 5.0-25.0mmolL(-1); and the detection and quantification limits were 0.5 and 1.0mmolL(-1), respectively. Analysis of lactic acid in fermented milk (FM) or NaOH neutralised fermented milk (FNM) was performed using samples obtained during 9h of fermentation. An ANOVA analysis combined with the Tukey test was used to show that the method was insensitive to the differences between FM and FNM. The proposed method therefore proved to be a useful quality control tool in laboratories because it was able to determine milk acidity even in adulterated samples.