Results of the Ontology Alignment Evaluation Initiative 2009

euzenat2009cInternational audienceOntology matching consists of finding correspondences between on- tology entities. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. Test cases can use ontologies of different nature (from expressive OWL ontologies to simple directories) and use different modal- ities, e.g., blind evaluation, open evaluation, consensus. OAEI-2009 builds over previous campaigns by having 5 tracks with 11 test cases followed by 16 partici- pants. This paper is an overall presentation of the OAEI 2009 campaign

Explainable AI for Machine Fault Diagnosis: Understanding Features' Contribution in Machine Learning Models for Industrial Condition Monitoring

Although the effectiveness of machine learning (ML) for machine diagnosis has been widely established, the interpretation of the diagnosis outcomes is still an open issue. Machine learning models behave as black boxes; therefore, the contribution given by each of the selected features to the diagnosis is not transparent to the user. This work is aimed at investigating the capabilities of the SHapley Additive exPlanation (SHAP) to identify the most important features for fault detection and classification in condition monitoring programs for rotating machinery. The authors analyse the case of medium-sized bearings of industrial interest. Namely, vibration data were collected for different health states from the test rig for industrial bearings available at the Mechanical Engineering Laboratory of Politecnico di Torino. The Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) diagnosis models are explained by means of the SHAP. Accuracies higher than 98.5% are achieved for both the models using the SHAP as a criterion for feature selection. It is found that the skewness and the shape factor of the vibration signal have the greatest impact on the models’ outcomes

A cybernetic approach to prediction with an outline of an adaptive optical computer

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Since the pioneering work of Kolmogoroff and Wiener the use of computing devices to solve problems concerned with - the prediction of future states of a time series has stimulated a large amount of research. Despite all this, however the results have been disappointing. If significant progress were to be made in this field it would lead not only to the possibility of forecasting economic events, the weather, earthquakes, epidemics, and so on, but also -to the possibility of simulating these system. Approaches involving the programming of a computer to carry out this task run into the difficulty of defining the variables involved in a precise enough manner, whereas using the computer -to investigate all the past events of that time series requires a large processing time and an enormous memory store. This thesis examines an approach to this problem which involves the use of a device for processing information in a parallel manner. The system envisaged consists of a holographic recognition device controlled by a digital computer -a combination of analogue and digital techniques. The principle of this device is that developed by Gabor and others, and allows the system to learn to predict the future of a time series. The system learns to predict the future of a time series by using a past length of time series as a training set. Using this training set it attempts to predict the next values of the time series, which can then be compared with the actual time series. The system, then, attempts to optimize its prediction by minimizing the error between the predicted and actual values

Experimental and observational geometry

Geometry has the distinction of being one of the oldest subjects given in the high-school. Its subject-matter was formulated and organized by the Greeks into a fine system of thought before the time of Christ. Since leaving the hands of the Greeks, geometry has received only a few minor changes, and these largely in recent years. Heretofore, the study of geometry has been made almost entirely dependent upon memory and reasoning. Geometricians have been slow in adopting the laboratory and observational methods. This thesis has been written to encourage the student in his work of observing geometrical forms, and in the construction of good designs and geometrical figures, and to obtain a better practical understanding of the figures and principles of geometry through the laboratory and observational work

Fast high-dimensional Bayesian classification and clustering

We introduce a fast approach to classification and clustering applicable to high-dimensional continuous data, based on Bayesian mixture models for which explicit computations are available. This permits us to treat classification and clustering in a single framework, and allows calculation of unobserved class probability. The new classifier is robust to adding noise variables as a drawback of the built-in spike-and-slab structure of the proposed Bayesian model. The usefulness of classification using our method is shown on metabololomic example, and on the Iris data with and without noise variables. Agglomerative hierarchical clustering is used to construct a dendrogram based on the posterior probabilities of particular partitions, to provide a dendrogram with a probabilistic interpretation. An extension to variable selection is proposed which summarises the importance of variables for classification or clustering and has probabilistic interpretation. Having a simple model provides estimation of the model parameters using maximum likelihood and therefore yields a fully automatic algorithm. The new clustering method is applied to metabolomic, microarray, and image data and is studied using simulated data motivated by real datasets. The computational difficulties of the new approach are discussed, solutions for algorithm acceleration are proposed, and the written computer code is briefly analysed. Simulations shows that the quality of the estimated model parameters depends on the parametric distribution assumed for effects, but after fixing the model parameters to reasonable values, the distribution of the effects influences clustering very little. Simulations confirms that the clustering algorithm and the proposed variable selection method is reliable when the model assumptions are wrong. The new approach is compared with the popular Bayesian clustering alternative, MCLUST, fitted on the principal components using two loss functions in which our proposed approach is found to be more efficient in almost every situation

Monitoring system for long-distance pipelines subject to destructive attack

In an era of terrorism, it is important to protect critical pipeline infrastructure, especially in countries where life is strongly dependent on water and the economy on oil and gas. Structural health monitoring (SHM) using acoustic waves is one of the common solutions. However, considerable prior work has shown that pipes are cylindrical acoustic waveguides that support many dispersive, lossy modes; only the torsional T(0, 1) mode has zero dispersion. Although suitable transducers have been developed, these typically excite several modes, and even if they do not, bends and supports induce mode conversion. Moreover, the high-power transducers that could in principle be used to overcome noise and attenuation in long distance pipes present an obvious safety hazard with volatile products, making it difficult to distinguish signals and extract pipeline status information. The problem worsens as the pipe diameter increases or as the frequency rises (due to the increasing number of modes), if the pipe is buried (due to rising attenuation), or if the pipe carries a flowing product (because of additional acoustic noise). Any system is therefore likely to be short-range. This research proposes the use of distributed active sensor network to monitor long-range pipelines, by verifying continuity and sensing small disturbances. A 4-element cuboid Electromagnetic Acoustic Transducer (EMAT) is used to excite the longitudinal L(0,1) mode. Although the EMAT also excites other slower modes, long distance propagation allows their effects to be separated. Correlation detection is exploited to enhance signal-to-noise ratio (SNR), and code division multiplexing access (CDMA) is used to distinguish between nodes in a multi-node system. An extensive numerical search for multiphase quasi-orthogonal codes for different user numbers is conducted. The results suggest that side lobes degrade performance even with the highest possible discrimination factor. Golay complementary pairs (which can eliminate the side lobes completely, albeit at the price of a considerable reduction in speed) are therefore investigated as an alternative. Pipeline systems are first reviewed. Acoustic wave propagation is described using standard theory and a freeware modeling package. EMAT modeling is carried out by numerical calculation of electromagnetic fields. Signal propagation is investigated theoretically using a full system simulator that allows frequency-domain description of transducers, dispersion, multi-mode propagation, mode conversion and multiple reflections. Known codes for multiplexing are constructed using standard algorithms, and novel codes are discovered by an efficient directed search. Propagation of these codes in a dispersive system is simulated. Experiments are carried out using small, unburied air-filled copper pipes in a frequency range where the number of modes is small, and the attenuation and noise are low. Excellent agreement is obtained between theory and experiment. The propagation of pulses and multiplexed codes over distances up to 200 m are successfully demonstrated, and status changes introduced by removable reflectors are detected.Open Acces

Optical non-classicality as a Quantum Resource in Continuous-Variable Quantum Information

In this thesis we address the problem of building a Quantum Resource Theory in infinite dimension. In particular, we study bosonic non-classicality as a Quantum Resource in continuous-variable Quantum Information. After reviewing the formalism of open quantum systems and Quantum Optics, we introduce the framework of Quantum Resource Theories and we discuss the case of non-classicality, and its applications in Quantum Optics and Quantum Technologies. Finally, we study a Resource Theory of non-classicality based on the standard and measured relative entropies of non-classicality as resource monotones and we prove, for the first time in an infinite-dimensional Resource Theory, a bound for asymptotic conversion rates