Min-Max MPC based on an upper bound of the worst case cost with guaranteed stability. Application to a pilot plant

Min-Max MPC (MMMPC) offers the possibility to consider disturbances and uncertainties in the mathematical model used to predict the future trajectory of the system. The explicit consideration of disturbances and uncertainties in order to obtain a more robust control performance complicates the practical implementation of MMMPC due to the high computational burden required to compute the control law. The computational complexity of the optimization problem can be reduced by using approximate solutions or upper bounds of the worst case cost of the objective function. A computationally efficient MMMPC strategy based on such an upper bound was presented in a previous work also published in this journal (see Section 1). One of the main drawbacks of that strategy is the lack of a stability guarantee. In this paper it is shown that input-to-state practical stability of the MMMPC strategy can be guaranteed if a certain initial condition and a semi-feedback approach are used. Furthermore, the MMMPC strategy is validated in experiments with a continuous stirred tank reactor in which the temperature of the reactor is controlled. The behavior of the system and the controller are illustrated by means of experimental results.     

Classifying the typefaces of the Gutenberg 42-line bible

We have measured the dissimilarities among several printed characters of a single page in the Gutenberg 42-line bible, and we prove statistically the existence of several different matrices from which the metal types were constructed. This is in contrast with the prevailing theory, which states that only one matrix per character was used in the printing process of Gutenberg’s greatest work. The main mathematical tool for this purpose is cluster analysis, combined with a statistical test for outliers. We carry out the research with two letters, i{texttt{i}} and a{texttt{a}}. In the first case, an exact clustering method is employed; in the second, with more specimens to be classified, we resort to an approximate agglomerative clustering method. The results show that the letters form clusters according to their shape, with significant shape differences among clusters, and allow to conclude, with a very small probability of error, that indeed the metal types used to print them were cast from several different matrices.     

The 3-D gravity inversion package GROWTH2.0 and its application to Tenerife Island, Spain

We present the gravity inversion software GROWTH2.0 and its application to recently obtained gravity data from the volcanic island of Tenerife (Canary Islands, Spain) to inform on its subsurface density structure. GROWTH2.0 is an inversion tool which enables the user to obtain, in a nearly automatic and nonsubjective mode, a 3D model of the subsurface density anomalies based on observed gravity anomaly data. The package is composed of three parts: (a) GRID3D to generate a 3D partition of the subsurface volume into parallelepiped elements, (b) GROWTH to perform the inversion routine and to obtain a 3D anomalous density model, and (c) VIEW for visual representation of the input data, the inversion model, and modeling residuals. The current version of the tool has been developed from an earlier code (Camacho et al., 2002) and now incorporates several novelties: (1) a Graphical User Interface (GUI), (2) an optional automated routine for determination of parameter λ, which controls the balance between model fitness and smoothness, (3) optional determination of values for minimum density contrast, (4) a robust handling of outlier data, and (5) improved automated data reduction for terrain effects based on anticorrelation with topographic data. The new capabilities and applicability of GROWTH2.0 for 3-D gravity inversion are demonstrated by a case example using new gravity data from the volcanic island of Tenerife. In a nearly automatic approach, the software provides a 3-D model informing on the location and shape of the main structural building blocks of the island. Our model results allow us to shed light on the low-density structure of the islands dominant Pico Viejo-Pico Teide (PV-PT) volcanic complex and the identification of an intrusive structure (the east bulge volcano) embedded in Teide's east flank. A low-density body located at around 5.8 km depth beneath PT's summit may represent a current magma or hybrid reservoir.     

An analytical evaluation of search by content and interaction patterns on multimodal meeting records

It has been suggested that combining content-based indexing with automatically generated temporal metadata might help improve search and browsing of recordings of computer-mediated collaborative activities such as on-line meetings, which are characterised by extensive multimodal communication. This paper presents an analytical evaluation of the effectiveness of these techniques as implemented through automatic speech recognition and temporal mapping. In particular, it assesses the extent to which this strategy can help uncover contextual relationships between audio and text segments in recorded remote meetings. Results show that even simple temporal mapping can effectively support retrieval of recorded audio segments, improve retrieval performance in situations where speech recognition alone would have exhibited prohibitively high word error rates, and provide a basic form of semantic adaptation. The authors are listed in alphabetical order.     

Finite population estimation under generalized linear model assistance

Finite population estimation is the overall goal of sample surveys. When information regarding auxiliary variables are available, one may take advantage of general regression estimators (GREG) to improve sample estimates precision. GREG estimators may be derived when the relationship between interest and auxiliary variables is represented by a normal linear model. However, in some cases, such as when estimating class frequencies or counting processes means, Bernoulli or Poisson models are more suitable than linear normal ones. This paper focuses on building regression type estimators under a model-assisted approach, for the general case in which the relationship between interest and auxiliary variables may be suitably described by a generalized linear model. The finite population distribution of the variable of interest is viewed as if generated by a member of the exponential family, which includes Bernoulli, Poisson, gamma and inverse Gaussian distributions, among others. The resulting estimator is a generalized linear model regression estimator (GEREG). Its general form and basic statistical properties are presented and studied analytically and empirically, using Monte Carlo simulation experiments. Three applications are presented in which the GEREG estimator shows better performance than the GREG one.     

Automated Derivation of Translators From Annotated Grammars

In this paper we propose a technique to automate the process of building translators between operations languages, a family of DSLs used to program satellite operations procedures. We exploit the similarities between those languages to semi-automatically build a transformation schema between them, through the use of annotated grammars. To improve the overall translation process even more, reducing its complexity, we also propose an intermediate representation common to all operations languages. We validate our approach by semi-automatically deriving translators between some operations languages, using a prototype tool which we implemented for that purpose.     

Cyberbullying impacts on victims’ satisfaction with information and communication technologies: The role of Perceived Cyberbullying Severity

This study aims to understand how individuals’ perceptions of the severity of cyberbullying they endure affects their experience with the Information and Communication Technology (ICT) medium through which cyberbullying occurs. To this end, it proposes a theoretical model based on Transactional Theory of Stress and Coping and Expectation-Confirmation Theory. A survey-based study involving 115 cyberbullying victims is employed to empirically validate the proposed model. Results indicate that victims’ perceptions of the severity of a cyberbullying episode negatively impact their satisfaction with ICT. Implications of these results for academics and practitioners are discussed and directions for future research are outlined.     

ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e., cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e., support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e., there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis.