Multivariable model predictive control of a thermal power plant with built-in classical regulation

Multivariable control systems are preferable to classical regulations based on frequency decoupling in plants where the dynamics of the controlled variables are strongly interacting. A multivariable solution generally lead to better performance, since the plant is considered as a whole, allowing the optimization of the control action. Nevertheless, such solution is scarcely adopted in practice. In fact, classical systems are preferred since they allow the operator to use his experience when necessary, acting manually on single parts of the plant. The design of a multivariable control system that corrects the action of classical regulation would allow both optimal performance and maximum flexibility. In this paper, a novel solution based on a multivariable control added to the traditional structure is proposed for a conventional power plant. Simulations show that the improvement in performance is significant, especially in the critical situation of a sudden change in the load request.     

Collaborative clustering of XML documents

Clustering XML documents is extensively used to organize large collections of XML documents in groups that are coherent according to structure and/or content features. The growing availability of distributed XML sources and the variety of high-demand environments raise the need for clustering approaches that can exploit distributed processing techniques. Nevertheless, existing methods for clustering XML documents are designed to work in a centralized way. In this paper, we address the problem of clustering XML documents in a collaborative distributed framework. XML documents are first decomposed based on semantically cohesive subtrees, then modeled as transactional data that embed both XML structure and content information. The proposed clustering framework employs a centroid-based partitional clustering method that has been developed for a peer-to-peer network. Each peer in the network is allowed to compute a local clustering solution over its own data, and to exchange its cluster representatives with other peers. The exchanged representatives are used to compute representatives for the global clustering solution in a collaborative way. We evaluated effectiveness and efficiency of our approach on real XML document collections varying the number of peers. Results have shown that major advantages with respect to the corresponding centralized clustering setting are obtained in terms of runtime behavior, although clustering solutions can still be accurate with a moderately low number of nodes in the network. Moreover, the collaborativeness characteristic of our approach has revealed to be a convenient feature in distributed clustering as found in a comparative evaluation with a distributed non-collaborative clustering method.     

Fusion of airborne LiDAR and satellite multispectral data for the estimation of timber volume in the Southern Alps

Remote sensing can be considered a key instrument for studies related to forests and their dynamics. At present, the increasing availability of multisensor acquisitions over the same areas, offers the possibility to combine data from different sensors (e.g., optical, RADAR, LiDAR). This paper presents an analysis on the fusion of airborne LiDAR and satellite multispectral data (IRS 1C LISS III), for the prediction of forest stem volume at plot level in a complex mountain area (Province of Trento, Southern Italian Alps), characterized by different tree species, complex morphology (i.e. altitude ranges from 65 m to 3700 m above sea level), and a range of different climates (from the sub-Mediterranean to Alpine type). 799 sample plots were randomly distributed over the 3000 km² of the forested areas of the Trento Province. From each plot, a set of variables were extracted from both LiDAR and multispectral data. A regression analysis was carried out considering two data sources (LiDAR and multispectral) and their combination, and dividing the plot areas into groups according to their species composition, altitude and slope. Experimental results show that the combination of LiDAR and IRS 1C LISS III data, for the estimation of stem volume, is effective in all the experiments considered. The best developed models comprise variables extracted from both of these data sources. The RMSE% on an independent validation set for the stem volume estimation models ranges between 17.2% and 26.5%, considering macro sets of tree species (deciduous, evergreen and mixed), between 17.5% and 29.0%, considering dominant species plots, and between 15.5% and 21.3% considering altitude and slope sets.     

Some problems in distributed computational geometry

In a planar geometric network vertices are located in the plane, and edges are straight line segments connecting pairs of vertices, such that no two of them intersect. In this paper we study distributed computing in asynchronous, failure-free planar geometric networks, where each vertex is associated to a processor, and each edge to a bidirectional message communication link. Processors are aware of their locations in the plane.We consider fundamental computational geometry problems from the distributed computing point of view, such as finding the convex hull of a geometric network and identification of the external face. We also study the classic distributed computing problem of leader election, to understand the impact that geometric information has on the message complexity of solving it.We obtain an O(nlog²n) message complexity algorithm to find the convex hull, and an O(nlogn) message complexity algorithm to identify the external face of a geometric network of n processors. We present a matching lower bound for the external face problem. We prove that the message complexity of leader election in a geometric ring is Ω(nlogn), hence geometric information does not help in reducing the message complexity of this problem.     

A metabolic network of a phosphate-accumulating organism provides new insights into enhanced biological phosphorous removal

Here we present a metabolic network representing the central carbon metabolism as well as the synthesis of polyhydroxyalcanohates and the polyphosphate accumulation mechanisms of the bacterium Candidatus Accumulibacter phosphatis, which was previously identified from metagenomic studies in enhanced biological phosphorous removal sludges. The reconstructed metabolic network, together with flux balance analysis can be used to provide new insights into controversial aspects of the metabolism of phosphate-accumulating organisms and is also a tool that can be used in to help enhanced biological phosphorous removal (EBPR) process design and operation.     

Apertium: a free/open-source platform for rule-based machine translation

Apertium is a free/open-source platform for rule-based machine translation. It is being widely used to build machine translation systems for a variety of language pairs, especially in those cases (mainly with related-language pairs) where shallow transfer suffices to produce good quality translations, although it has also proven useful in assimilation scenarios with more distant pairs involved. This article summarises the Apertium platform: the translation engine, the encoding of linguistic data, and the tools developed around the platform. The present limitations of the platform and the challenges posed for the coming years are also discussed. Finally, evaluation results for some of the most active language pairs are presented. An appendix describes Apertium as a free/open-source project.     

Cyclic maximization of non-Gaussianity for blind signal extraction of complex-valued sources

This article presents a new algorithm for the blind extraction of communications sources (complex-valued sources) through the maximization of negentropy approximations based on nonlinearities. A criterion based on the square modulus of a nonlinearity of the output is used. We decouple the arguments of the criterion so that the algorithm maximizes it cyclically with respect to each argument by means of the Cauchy-Schwarz inequality. A proof of the ascent of the objective function after each iteration is also provided. Numerical simulations corroborate the good performance of the proposed algorithm in comparison with the existing methods.     

Fabrication of large-area microfluidics structures on glass by imprinting and diode-pumped solid state laser writing techniques

A micro structured master prototype for imprint lithography in glass has been successfully fabricated by using a Q-switched Nd:YVO₄ Diode-pumped solid state laser writing in Nickel-based alloy. The micro-structures in the prototype were characterized using an optical microscope, scanning electron microscopy, surface profilometer and field emission scanning electron microscopy. The surface quality of the fabricated master prototype was further improved through the process of polishing and cleaning. Then the master prototype was used for imprinting a soda lime glass substrate under the optimized imprinting conditions that have been previously studied for lab-on-chip devices. The results showed that very good quality transfer of the micro structures from the prototype master to the glass substrate was obtained.     

Automated metamorphic testing on the analyses of feature models

Context

A feature model (FM) represents the valid combinations of features in a domain. The automated extraction of information from FMs is a complex task that involves numerous analysis operations, techniques and tools. Current testing methods in this context are manual and rely on the ability of the tester to decide whether the output of an analysis is correct. However, this is acknowledged to be time-consuming, error-prone and in most cases infeasible due to the combinatorial complexity of the analyses, this is known as the oracle problem.

Objective

In this paper, we propose using metamorphic testing to automate the generation of test data for feature model analysis tools overcoming the oracle problem. An automated test data generator is presented and evaluated to show the feasibility of our approach.

Method

We present a set of relations (so-called metamorphic relations) between input FMs and the set of products they represent. Based on these relations and given a FM and its known set of products, a set of neighbouring FMs together with their corresponding set of products are automatically generated and used for testing multiple analyses. Complex FMs representing millions of products can be efficiently created by applying this process iteratively.

Results

Our evaluation results using mutation testing and real faults reveal that most faults can be automatically detected within a few seconds. Two defects were found in FaMa and another two in SPLOT, two real tools for the automated analysis of feature models. Also, we show how our generator outperforms a related manual suite for the automated analysis of feature models and how this suite can be used to guide the automated generation of test cases obtaining important gains in efficiency.

Conclusion

Our results show that the application of metamorphic testing in the domain of automated analysis of feature models is efficient and effective in detecting most faults in a few seconds without the need for a human oracle.