Bootstrap–CURE: A novel clustering approach for sensor data: an application to 3D printing industry

The agenda of Industry 4.0 highlights smart manufacturing by making machines smart enough to make data-driven decisions. Large-scale 3D printers, being one of the important pillars in Industry 4.0, are equipped with smart sensors to continuously monitor print processes and make automated decisions. One of the biggest challenges in decision autonomy is to consume data quickly along the process and extract knowledge from the printer, suitable for improving the printing process. This paper presents the innovative unsupervised learning approach, bootstrap–CURE, to decode the sensor patterns and operation modes of 3D printers by analyzing multivariate sensor data. An automatic technique to detect the suitable number of clusters using the dendrogram is developed. The proposed methodology is scalable and significantly reduces computational cost as compared to classical CURE. A distinct combination of the 3D printer’s sensors is found, and its impact on the printing process is also discussed. A real application is presented to illustrate the performance and usefulness of the proposal. In addition, a new state of the art for sensor data analysis is presented.This work was supported in part by KEMLG-at-IDEAI (UPC) under Grant SGR-2017-574 from the Catalan government.Peer ReviewedPostprint (published version

Automatic identification of the number of clusters in hierarchical clustering

Hierarchical clustering is one of the most suitable tools to discover the underlying true structure of a dataset in the case of unsupervised learning where the ground truth is unknown and classical machine learning classifiers are not suitable. In many real applications, it provides a perspective on inner data structure and is preferred to partitional methods. However, determining the resulting number of clusters in hierarchical clustering requires human expertise to deduce this from the dendrogram and this represents a major challenge in making a fully automatic system such as the ones required for decision support in Industry 4.0. This research proposes a general criterion to perform the cut of a dendrogram automatically, by comparing six original criteria based on the Calinski-Harabasz index. The performance of each criterion on 95 real-life dendrograms of different topologies is evaluated against the number of classes proposed by the experts and a winner criterion is determined. This research is framed in a bigger project to build an Intelligent Decision Support system to assess the performance of 3D printers based on sensor data in real-time, although the proposed criteria can be used in other real applications of hierarchical clustering.The methodology is applied to a real-life dataset from the 3D printers and the huge reduction in CPU time is also shown by comparing the CPU time before and after this modification of the entire clustering method. It also reduces the dependability on human-expert to provide the number of clusters by inspecting the dendrogram. Further, such a process allows applying hierarchical clustering in an automatic mode in real-life industrial applications and allows the continuous monitoring of real 3D printers in production, and helps in building an Intelligent Decision Support System to detect operational modes, anomalies, and other behavioral patterns.Peer ReviewedPostprint (author's final draft

Multi-scale cellular engineering: From molecules to organ-on-a-chip.

Recent technological advances in cellular and molecular engineering have provided new insights into biology and enabled the design, manufacturing, and manipulation of complex living systems. Here, we summarize the state of advances at the molecular, cellular, and multi-cellular levels using experimental and computational tools. The areas of focus include intrinsically disordered proteins, synthetic proteins, spatiotemporally dynamic extracellular matrices, organ-on-a-chip approaches, and computational modeling, which all have tremendous potential for advancing fundamental and translational science. Perspectives on the current limitations and future directions are also described, with the goal of stimulating interest to overcome these hurdles using multi-disciplinary approaches

Women in Artificial intelligence (AI)

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI

Dimensionality Reduction and Subspace Clustering in Mixed Reality for Condition Monitoring of High-Dimensional Production Data

Visual analytics are becoming more and more important in the light of big data and related scenarios. Along this trend, the field of immersive analytics has been variously furthered as it is able to provide sophisticated visual data analytics on one hand, while preserving user-friendliness on the other. Furthermore, recent hardware developments like smart glasses, as well as achievements in virtual-reality applications, have fanned immersive analytic solutions. Notably, such solutions can be very effective when they are applied to high-dimensional data sets. Taking this advantage into account, the work at hand applies immersive analytics to a high-dimensional production data set in order to improve the digital support of daily work tasks. More specifically, a mixed-reality implementation is presented that shall support manufactures as well as data scientists to comprehensively analyze machine data. As a particular goal, the prototype shall simplify the analysis of manufacturing data through the usage of dimensionality reduction effects. Therefore, five aspects are mainly reported in this paper. First, it is shown how dimensionality reduction effects can be represented by clusters. Second, it is presented how the resulting information loss of the reduction is addressed. Third, the graphical interface of the developed prototype is illustrated as it provides a (1) correlation coefficient graph, a (2) plot for the information loss, and a (3) 3D particle system. In addition, an implemented voice recognition feature of the prototype is shown, which was considered as being promising to select or deselect data variables users are interested in when analyzing the data. Fourth, based on a machine learning library, it is shown how the prototype reduces computational resources by the use of smart glasses. The main idea is based on a recommendation approach as well as the use of subspace clustering. Fifth, results from a practical setting are presented, in which the prototype was shown to domain experts. The latter reported that such a tool is actually helpful to analyze machine data on a daily basis. Moreover, it was reported that such system can be used to educate machine operators more properly. As a general outcome of this work, the presented approach may constitute a helpful solution for the industry as well as other domains like medicine

Semantic Exploration of Text Documents with Multi-Faceted Metadata Employing Word Embeddings: The Patent Landscaping Use Case

Die Menge der Veröentlichungen, die den wissenschaftlichen Fortschritt dokumentieren, wächst kontinuierlich. Dies erfordert die Entwicklung der technologischen Hilfsmittel für eine eziente Analyse dieser Werke. Solche Dokumente kennzeichnen sich nicht nur durch ihren textuellen Inhalt, sondern auch durch eine Menge von Metadaten-Attributen verschiedenster Art, unter anderem Beziehungen zwischen den Dokumenten. Diese Komplexität macht die Entwicklung eines Visualisierungsansatzes, der eine Untersuchung der schriftlichen Werke unterstützt, zu einer notwendigen und anspruchsvollen Aufgabe. Patente sind beispielhaft für das beschriebene Problem, weil sie in großen Mengen von Firmen untersucht werden, die sich Wettbewerbsvorteile verschaffen oder eigene Forschung und Entwicklung steuern wollen. Vorgeschlagen wird ein Ansatz für eine explorative Visualisierung, der auf Metadaten und semantischen Embeddings von Patentinhalten basiert ist. Wortembeddings aus einem vortrainierten Word2vec-Modell werden genutzt, um Ähnlichkeiten zwischen Dokumenten zu bestimmen. Darüber hinaus helfen hierarchische Clusteringmethoden dabei, mehrere semantische Detaillierungsgrade durch extrahierte relevante Stichworte anzubieten. Derzeit dürfte der vorliegende Visualisierungsansatz der erste sein, der semantische Embeddings mit einem hierarchischen Clustering verbindet und dabei diverse Interaktionstypen basierend auf Metadaten-Attributen unterstützt. Der vorgestellte Ansatz nimmt Nutzerinteraktionstechniken wie Brushing and Linking, Focus plus Kontext, Details-on-Demand und Semantic Zoom in Anspruch. Dadurch wird ermöglicht, Zusammenhänge zu entdecken, die aus dem Zusammenspiel von 1) Verteilungen der Metadatenwerten und 2) Positionen im semantischen Raum entstehen. Das Visualisierungskonzept wurde durch Benutzerinterviews geprägt und durch eine Think-Aloud-Studie mit Patentenexperten evaluiert. Während der Evaluation wurde der vorgestellte Ansatz mit einem Baseline-Ansatz verglichen, der auf TF-IDF-Vektoren basiert. Die Benutzbarkeitsstudie ergab, dass die Visualisierungsmetaphern und die Interaktionstechniken angemessen gewählt wurden. Darüber hinaus zeigte sie, dass die Benutzerschnittstelle eine deutlich größere Rolle bei den Eindrücken der Probanden gespielt hat als die Art und Weise, wie die Patente platziert und geclustert waren. Tatsächlich haben beide Ansätze sehr ähnliche extrahierte Clusterstichworte ergeben. Dennoch wurden bei dem semantischen Ansatz die Cluster intuitiver platziert und deutlicher abgetrennt. Das vorgeschlagene Visualisierungslayout sowie die Interaktionstechniken und semantischen Methoden können auch auf andere Arten von schriftlichen Werken erweitert werden, z. B. auf wissenschaftliche Publikationen. Andere Embeddingmethoden wie Paragraph2vec [61] oder BERT [32] können zudem verwendet werden, um kontextuelle Abhängigkeiten im Text über die Wortebene hinaus auszunutzen

Extraction robuste de primitives géométriques 3D dans un nuage de points et alignement basé sur les primitives

Dans ce projet, nous étudions les problèmes de rétro-ingénierie et de contrôle de la qualité qui jouent un rôle important dans la fabrication industrielle. La rétro-ingénierie tente de reconstruire un modèle 3D à partir de nuages de points, qui s’apparente au problème de la reconstruction de la surface 3D. Le contrôle de la qualité est un processus dans lequel la qualité de tous les facteurs impliqués dans la production est abordée. En fait, les systèmes ci-dessus nécessitent beaucoup d’intervention de la part d’un utilisateur expérimenté, résultat souhaité est encore loin soit une automatisation complète du processus. Par conséquent, de nombreux défis doivent encore être abordés pour atteindre ce résultat hautement souhaitable en production automatisée. La première question abordée dans la thèse consiste à extraire les primitives géométriques 3D à partir de nuages de points. Un cadre complet pour extraire plusieurs types de primitives à partir de données 3D est proposé. En particulier, une nouvelle méthode de validation est proposée pour évaluer la qualité des primitives extraites. À la fin, toutes les primitives présentes dans le nuage de points sont extraites avec les points de données associés et leurs paramètres descriptifs. Ces résultats pourraient être utilisés dans diverses applications telles que la reconstruction de scènes on d’édifices, la géométrie constructive et etc. La seconde question traiée dans ce travail porte sur l’alignement de deux ensembles de données 3D à l’aide de primitives géométriques, qui sont considérées comme un nouveau descripteur robuste. L’idée d’utiliser les primitives pour l’alignement arrive à surmonter plusieurs défis rencontrés par les méthodes d’alignement existantes. Ce problème d’alignement est une étape essentielle dans la modélisation 3D, la mise en registre, la récupération de modèles. Enfin, nous proposons également une méthode automatique pour extraire les discontinutés à partir de données 3D d’objets manufacturés. En intégrant ces discontinutés au problème d’alignement, il est possible d’établir automatiquement les correspondances entre primitives en utilisant l’appariement de graphes relationnels avec attributs. Nous avons expérimenté tous les algorithmes proposés sur différents jeux de données synthétiques et réelles. Ces algorithmes ont non seulement réussi à accomplir leur tâches avec succès mais se sont aussi avérés supérieus aux méthodes proposées dans la literature. Les résultats présentés dans le thèse pourraient s’avérér utilises à plusieurs applications.In this research project, we address reverse engineering and quality control problems that play significant roles in industrial manufacturing. Reverse engineering attempts to rebuild a 3D model from the scanned data captured from a object, which is the problem similar to 3D surface reconstruction. Quality control is a process in which the quality of all factors involved in production is monitored and revised. In fact, the above systems currently require significant intervention from experienced users, and are thus still far from being fully automated. Therefore, many challenges still need to be addressed to achieve the desired performance for automated production. The first proposition of this thesis is to extract 3D geometric primitives from point clouds for reverse engineering and surface reconstruction. A complete framework to extract multiple types of primitives from 3D data is proposed. In particular, a novel validation method is also proposed to assess the quality of the extracted primitives. At the end, all primitives present in the point cloud are extracted with their associated data points and descriptive parameters. These results could be used in various applications such as scene and building reconstruction, constructive solid geometry, etc. The second proposition of the thesis is to align two 3D datasets using the extracted geometric primitives, which is introduced as a novel and robust descriptor. The idea of using primitives for alignment is addressed several challenges faced by existing registration methods. This alignment problem is an essential step in 3D modeling, registration and model retrieval. Finally, an automatic method to extract sharp features from 3D data of man-made objects is also proposed. By integrating the extracted sharp features into the alignment framework, it is possible implement automatic assignment of primitive correspondences using attribute relational graph matching. Each primitive is considered as a node of the graph and an attribute relational graph is created to provide a structural and relational description between primitives. We have experimented all the proposed algorithms on different synthetic and real scanned datasets. Our algorithms not only are successful in completing their tasks with good results but also outperform other methods. We believe that the contribution of them could be useful in many applications