Manufacturing Data Analytics for Manufacturing Quality Assurance

The authors acknowledge the European Commission for the support and funding under the scope of Horizon2020 i4Q Innovation Project (Agreement Number 958205) and the remaining partners of the i4Q Project Consortium.Nowadays, manufacturing companies are eager to access insights from advanced analytics, without requiring them to have specialized IT workforce or data science advanced skills. Most of current solutions lack of easy-to-use advanced data preparation, production reporting and advanced analytics and prediction. Thanks to the increase in the use of sensors, actuators and instruments, European manufacturing lines collect a huge amount of data during the manufacturing process, which is very valuable for the improvement of quality in manufacturing, but analyzing huge amounts of data on a daily basis, requires heavy statistical and technology training and support, making them not accessible for SMEs. The European i4Q Project, aims at providing an IoT-based Reliable Industrial Data Services (RIDS), a complete suite consisting of 22 i4Q Solutions, able to manage the huge amount of industrial data coming from cheap cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. This paper will present a set of i4Q services, for data integration and fusion, data analytics and data distribution. Such services, will be responsible for the execution of AI workloads (including at the edge), enabling the dynamic deployment industrial scenarios based on a cloud/edge architecture. Monitoring at various levels is provided in i4Q through scalable tools and the collected data, is used for a variety of activities including resource monitoring and management, workload assignment, smart alerting, predictive failure and model (re)training.publishersversionpublishe

Entropy, Age and Time Operator

The time operator and internal age are intrinsic features of entropy producing innovation processes. The innovation spaces at each stage are the eigenspaces of the time operator. The internal age is the average innovation time, analogous to lifetime computation. Time operators were originally introduced for quantum systems and highly unstable dynamical systems. Extending the time operator theory to regular Markov chains allows one to relate internal age with norm distances from equilibrium. The goal of this work is to express the evolution of internal age in terms of Lyapunov functionals constructed from entropies. We selected the Boltzmann–Gibbs–Shannon entropy and more general entropy functions, namely the Tsallis entropies and the Kaniadakis entropies. Moreover, we compare the evolution of the distance of initial distributions from equilibrium to the evolution of the Lyapunov functionals constructed from norms with the evolution of Lyapunov functionals constructed from entropies. It is remarkable that the entropy functionals evolve, violating the second law of thermodynamics, while the norm functionals evolve thermodynamically

Ο τελεστής του χρόνου και η ηλικία εξελικτικών διαδικασιών

The Time Operator has originated in Quantum Mechanics and has been further elaborated in Dynamical Systems. In the present thesis, the Time Operator is generalized to several classes of evolutionary processes such as Stochastic Processes and Network Evolution models. We propose a new method for the construction of the Time Operator of Bernoulli processes so that it may be directly applied to the construction of the Time Operator of Markov chains and more general discrete-time Point Processes. The application of the Time Operator of Bernoulli processes to random walks obtains innovation probability and Age estimators from stock market data. The innovation probability is proved to be a variance ratio, so variance estimators are presented from Open, High, Low and Close values of an asset’s price which are efficient, drift-independent and able to estimate variance from one single trading day. The application to the Greek elections period of June 2012 shows that the innovation probabilities quantify the unpredictability of the complex financial environment. In case the Open, High, Low and Close values are not known yet to the observer, we employ Stochastic Variance (volatility squared) Models for the prediction of the intraday variance of the following trading day. Moreover, we construct the Time Operator of two-state Markov chains and demonstrate an algorithm which computes the internal Age of Markov chains. We determine a relation between the maximal total variation distance from equilibrium and the deviation of internal time from clock time, using a Monte Carlo method. The mixing time of a Markov chain is defined and compared to the Kemeny time (constant) and Goodman’s intrinsic time. Since the representation of the Kemeny constant has been discussed in the literature, we present another representation of the Kemeny constant in terms of internal Age of Markov chains and clock time. We illustrate the Mixing time, Kemeny time and Goodman’s time to two selected examples, one far from equilibrium and another very close to equilibrium. The relation of internal Age and mixing time is verified in both examples. We generalize the definition of mixing time of Markov chains using Lyapunov functionals, defined in terms of distances induced by an r-norm, in terms of Tsallis and Kaniadakis entropy and in terms of the classic Boltzmann-Shannon-Gibbs entropy. Based on the above developments we construct the Time Operator of Network Evolution and provide explicit Age formulas for generalized Erdős-Rényi graphs and Markov networks. We see that the internal Age is a function of the Tsallis Entropy (for q=2) of the attachment distribution. We also present the distribution of innovations in the Barabási-Albert network evolution model. We finally propose an “isochronic” inequality of Stochastic Processes, analogous to the isoperimetric inequality of closed curves on the plane.Σκοπός της διατριβής είναι η κατασκευή του τελεστή του χρόνου σε στοχαστικές διαδικασίες και σε δίκτυα, επεκτείνοντας προηγούμενες κατασκευές σε Δυναμικά Συστήματα. Οι εξελικτικές διαδικασίες μοντελοποιούνται σήμερα ως Δυναμικά Συστήματα, Στοχαστικές Διαδικασίες είτε χρονικά μεταβαλλόμενα δίκτυα (temporal/dynamical networks). Η ιδέα της αναπαράστασης του χρόνου ως τελεστή προέρχεται από την Κβαντομηχανική και την Στατιστική Φυσική και επεκτάθηκε σε Δυναμικά Συστήματα με θετική παραγωγή εντροπίας (Kolmogorov, Exact-Rohlin) και σε διαδικασίες καινοτομίας (Innovation Processes). Στην εργασία αυτή ο τελεστής του χρόνου επεκτείνεται και κατασκευάζεται σε διαδικασίες Bernoulli και Markov καθώς και σε διαδικασίες εξέλιξης δικτύων. Τα αποτελέσματα της θεωρητικής ανάλυσης εφαρμόζονται σε συγκεκριμένα οικονομικά μοντέλα και ένα μοντέλο πληθυσμιακής δυναμικής (Moran Process). Ο χρόνος μίξης διαδικασιών Markov ορίζεται και μέσω της εσωτερικής ηλικίας της διαδικασίας Markov, δηλαδή της μέσης τιμής του τελεστή του χρόνου. Ακολουθεί η επέκταση σε πολυδιάστατες τυχαίες μεταβλητές, όπως τετραγωνικοί τυχαίοι πίνακες με στοιχεία στο {0, 1}, που περιγράφουν την πιθανή εξέλιξη ενός τυχαίου γραφήματος. Η σχέση εσωτερικού χρόνου και χρόνου του ρολογιού στην παρατήρηση τυχαίων διαδικασιών μας προδιαθέτει να παρουσιάσουμε την «ισοχρονική» ανισότητα στοχαστικών διαδικασιών, σε πλήρη αντιστοιχία με τις έννοιες της ισοπεριμετρικής ανισότητας στο επίπεδο

Fast visual vocabulary construction for image retrieval using skewed-split k-d trees

Comunicació presentada a la 22nd International Conference on MultiMedia Modeling (MMM16), celebrada els dies 4 a 6 de gener 2016 a Miami (FL, EUA).Most of the image retrieval approaches nowadays are based on the Bag-of-Words (BoW) model, which allows for representing an image efficiently and quickly. The efficiency of the BoW model is related to the efficiency of the visual vocabulary. In general, visual vocabularies are created by clustering all available visual features, formulating specific patterns. Clustering techniques are k-means oriented and they are replaced by approximate k-means methods for very large datasets. In this work, we propose a faster construction of visual vocabularies compared to the existing method in the case of SIFT descriptors, based on our observation that the values of the 128-dimensional SIFT descriptors follow the exponential distribution. The application of our method to image retrieval in specific image datasets showed that the mean Average Precision is not reduced by our approximation, despite that the visual vocabulary has been constructed significantly faster compared to the state of the art methods.This work was supported by the projects MULTISENSOR (FP7-610411) and KRISTINA (H2020-645012), funded by the European Commission

Crater monitoring through social media observations

Pòster presentat a: European Planetary Science Congress 2017, celebrat a Riga, Letònia, del 17 al 22 de setembre de 2017.Lunar craters have attracted the attention of not only scientists but also citizens. Modern high-resolution cameras with zoom capabilities allow citizens to capture and share pictures of the Moon in Social Media platforms, such as Twitter. We have collected 69 pictures of the Moon, from 01-01-2017 to 17-04-2017, that have been uploaded on Twitter and have been associated with the keyword #crater. The lunar pictures are indexed using SIFT descriptors and are then clustered using density-based approaches to group them into the automatically detected levels of zoom.This work has supported by the EC-funded project KRISTINA (H2020-645012)

Crater monitoring through social media observations

Pòster presentat a: European Planetary Science Congress 2017, celebrat a Riga, Letònia, del 17 al 22 de setembre de 2017.Lunar craters have attracted the attention of not only scientists but also citizens. Modern high-resolution cameras with zoom capabilities allow citizens to capture and share pictures of the Moon in Social Media platforms, such as Twitter. We have collected 69 pictures of the Moon, from 01-01-2017 to 17-04-2017, that have been uploaded on Twitter and have been associated with the keyword #crater. The lunar pictures are indexed using SIFT descriptors and are then clustered using density-based approaches to group them into the automatically detected levels of zoom.This work has supported by the EC-funded project KRISTINA (H2020-645012)

Fast visual vocabulary construction for image retrieval using skewed-split k-d trees

Comunicació presentada a la 22nd International Conference on MultiMedia Modeling (MMM16), celebrada els dies 4 a 6 de gener 2016 a Miami (FL, EUA).Most of the image retrieval approaches nowadays are based on the Bag-of-Words (BoW) model, which allows for representing an image efficiently and quickly. The efficiency of the BoW model is related to the efficiency of the visual vocabulary. In general, visual vocabularies are created by clustering all available visual features, formulating specific patterns. Clustering techniques are k-means oriented and they are replaced by approximate k-means methods for very large datasets. In this work, we propose a faster construction of visual vocabularies compared to the existing method in the case of SIFT descriptors, based on our observation that the values of the 128-dimensional SIFT descriptors follow the exponential distribution. The application of our method to image retrieval in specific image datasets showed that the mean Average Precision is not reduced by our approximation, despite that the visual vocabulary has been constructed significantly faster compared to the state of the art methods.This work was supported by the projects MULTISENSOR (FP7-610411) and KRISTINA (H2020-645012), funded by the European Commission

Topic detection using the DBSCAN-Martingale and the time operator

Comunicació presentada a: The 17th Conference of the Applied Stochastic Models and Data Analysis (ASMDA), celebrada del 6 al 9 de juny de 2017 a Londres, Regne Unit.Topic detection is usually considered as a decision process implemented in some relevant context, for example clustering. In this case, clusters correspond to topics that should be identifed. Density-based clustering, for example, uses only a density level E and a lower bound for the number of points in a cluster. As the density level is hard to be estimated, a stochastic process, called the DBSCANMartingale, is constructed for the combination of several outputs of DBSCAN for various randomly selected values of E in a predefned closed interval [0; Emax] from the uniform distribution. We have observed that most of the clusters are extracted in the interval [0; Emax=2], and moreover in the interval [Emax=2; Emax] the DBSCANMartingale stochastic process is less innovative, i.e. extracts only a few or no clusters. Therefore, non-symmetric skewed distributions are needed to generate density levels for the extraction of all clusters in a fast way. In this work we show that skewed distributions may be used instead of the uniform, so as to extract all clusters as quickly as possible. Experiments on real datasets show that the average innovation time of the DBSCAN-Martingale stochastic process is reduced when skewed distributions are employed, so less time is needed to extract all clusters.The first author would like to thank the Research Committee of the Aristo- tle University of Thessaloniki for awarding him the \Aristeia" postdoctoral scholarship 2016. Moreover, this work has been partially supported by the EC-funded project KRISTINA (H2020-645012)

Fusion of Compound Queries with Multiple Modalities for Known Item Video Search

Multimedia collections are ubiquitous and very often contain hundreds of hours of video information. The retrieval of a particular scene of a video (Known Item Search) in a large collection is a difficult problem, considering the multimodal character of all video shots and the complexity of the query, either visual or textual. We tackle these challenges by fusing, first, multiple modalities in a nonlinear graph-based way for each subtopic of the query. In addition, we fuse the top retrieved video shots per sub-query to provide the final list of retrieved shots, which is then re-ranked using temporal information. The framework is evaluated in popular Known Item Search tasks in the context of video shot retrieval and provides the largest Mean Reciprocal Rank scores