Semantically defined Analytics for Industrial Equipment Diagnostics

In this age of digitalization, industries everywhere accumulate massive amount of data such that it has become the lifeblood of the global economy. This data may come from various heterogeneous systems, equipment, components, sensors, systems and applications in many varieties (diversity of sources), velocities (high rate of changes) and volumes (sheer data size). Despite significant advances in the ability to collect, store, manage and filter data, the real value lies in the analytics. Raw data is meaningless, unless it is properly processed to actionable (business) insights. Those that know how to harness data effectively, have a decisive competitive advantage, through raising performance by making faster and smart decisions, improving short and long-term strategic planning, offering more user-centric products and services and fostering innovation. Two distinct paradigms in practice can be discerned within the field of analytics: semantic-driven (deductive) and data-driven (inductive). The first emphasizes logic as a way of representing the domain knowledge encoded in rules or ontologies and are often carefully curated and maintained. However, these models are often highly complex, and require intensive knowledge processing capabilities. Data-driven analytics employ machine learning (ML) to directly learn a model from the data with minimal human intervention. However, these models are tuned to trained data and context, making it difficult to adapt. Industries today that want to create value from data must master these paradigms in combination. However, there is great need in data analytics to seamlessly combine semantic-driven and data-driven processing techniques in an efficient and scalable architecture that allows extracting actionable insights from an extreme variety of data. In this thesis, we address these needs by providing: • A unified representation of domain-specific and analytical semantics, in form of ontology models called TechOnto Ontology Stack. It is highly expressive, platform-independent formalism to capture conceptual semantics of industrial systems such as technical system hierarchies, component partonomies etc and its analytical functional semantics. • A new ontology language Semantically defined Analytical Language (SAL) on top of the ontology model that extends existing DatalogMTL (a Horn fragment of Metric Temporal Logic) with analytical functions as first class citizens. • A method to generate semantic workflows using our SAL language. It helps in authoring, reusing and maintaining complex analytical tasks and workflows in an abstract fashion. • A multi-layer architecture that fuses knowledge- and data-driven analytics into a federated and distributed solution. To our knowledge, the work in this thesis is one of the first works to introduce and investigate the use of the semantically defined analytics in an ontology-based data access setting for industrial analytical applications. The reason behind focusing our work and evaluation on industrial data is due to (i) the adoption of semantic technology by the industries in general, and (ii) the common need in literature and in practice to allow domain expertise to drive the data analytics on semantically interoperable sources, while still harnessing the power of analytics to enable real-time data insights. Given the evaluation results of three use-case studies, our approach surpass state-of-the-art approaches for most application scenarios.Im Zeitalter der Digitalisierung sammeln die Industrien überall massive Daten-mengen, die zum Lebenselixier der Weltwirtschaft geworden sind. Diese Daten können aus verschiedenen heterogenen Systemen, Geräten, Komponenten, Sensoren, Systemen und Anwendungen in vielen Varianten (Vielfalt der Quellen), Geschwindigkeiten (hohe Änderungsrate) und Volumina (reine Datengröße) stammen. Trotz erheblicher Fortschritte in der Fähigkeit, Daten zu sammeln, zu speichern, zu verwalten und zu filtern, liegt der eigentliche Wert in der Analytik. Rohdaten sind bedeutungslos, es sei denn, sie werden ordnungsgemäß zu verwertbaren (Geschäfts-)Erkenntnissen verarbeitet. Wer weiß, wie man Daten effektiv nutzt, hat einen entscheidenden Wettbewerbsvorteil, indem er die Leistung steigert, indem er schnellere und intelligentere Entscheidungen trifft, die kurz- und langfristige strategische Planung verbessert, mehr benutzerorientierte Produkte und Dienstleistungen anbietet und Innovationen fördert. In der Praxis lassen sich im Bereich der Analytik zwei unterschiedliche Paradigmen unterscheiden: semantisch (deduktiv) und Daten getrieben (induktiv). Die erste betont die Logik als eine Möglichkeit, das in Regeln oder Ontologien kodierte Domänen-wissen darzustellen, und wird oft sorgfältig kuratiert und gepflegt. Diese Modelle sind jedoch oft sehr komplex und erfordern eine intensive Wissensverarbeitung. Datengesteuerte Analysen verwenden maschinelles Lernen (ML), um mit minimalem menschlichen Eingriff direkt ein Modell aus den Daten zu lernen. Diese Modelle sind jedoch auf trainierte Daten und Kontext abgestimmt, was die Anpassung erschwert. Branchen, die heute Wert aus Daten schaffen wollen, müssen diese Paradigmen in Kombination meistern. Es besteht jedoch ein großer Bedarf in der Daten-analytik, semantisch und datengesteuerte Verarbeitungstechniken nahtlos in einer effizienten und skalierbaren Architektur zu kombinieren, die es ermöglicht, aus einer extremen Datenvielfalt verwertbare Erkenntnisse zu gewinnen. In dieser Arbeit, die wir auf diese Bedürfnisse durch die Bereitstellung: • Eine einheitliche Darstellung der Domänen-spezifischen und analytischen Semantik in Form von Ontologie Modellen, genannt TechOnto Ontology Stack. Es ist ein hoch-expressiver, plattformunabhängiger Formalismus, die konzeptionelle Semantik industrieller Systeme wie technischer Systemhierarchien, Komponenten-partonomien usw. und deren analytische funktionale Semantik zu erfassen. • Eine neue Ontologie-Sprache Semantically defined Analytical Language (SAL) auf Basis des Ontologie-Modells das bestehende DatalogMTL (ein Horn fragment der metrischen temporären Logik) um analytische Funktionen als erstklassige Bürger erweitert. • Eine Methode zur Erzeugung semantischer workflows mit unserer SAL-Sprache. Es hilft bei der Erstellung, Wiederverwendung und Wartung komplexer analytischer Aufgaben und workflows auf abstrakte Weise. • Eine mehrschichtige Architektur, die Wissens- und datengesteuerte Analysen zu einer föderierten und verteilten Lösung verschmilzt. Nach unserem Wissen, die Arbeit in dieser Arbeit ist eines der ersten Werke zur Einführung und Untersuchung der Verwendung der semantisch definierten Analytik in einer Ontologie-basierten Datenzugriff Einstellung für industrielle analytische Anwendungen. Der Grund für die Fokussierung unserer Arbeit und Evaluierung auf industrielle Daten ist auf (i) die Übernahme semantischer Technologien durch die Industrie im Allgemeinen und (ii) den gemeinsamen Bedarf in der Literatur und in der Praxis zurückzuführen, der es der Fachkompetenz ermöglicht, die Datenanalyse auf semantisch inter-operablen Quellen voranzutreiben, und nutzen gleichzeitig die Leistungsfähigkeit der Analytik, um Echtzeit-Daten-einblicke zu ermöglichen. Aufgrund der Evaluierungsergebnisse von drei Anwendungsfällen Übertritt unser Ansatz für die meisten Anwendungsszenarien Modernste Ansätze

The Supreme Court Case That the Federal Circuit Overruled: Westinghouse v. Boyden Power Brake Co.

Can a federal court of appeals overrule Supreme Court precedent? Not overtly. But if nobody takes notice, a circuit court can undermine Supreme Court precedent, vacating lower court decisions that rely on the precedent and announcing in published opinions that a once robust doctrine has somehow suddenly become archaic, disfavored, and rarely applied. This is how the Court of Appeals for the Federal Circuit has caused an important Supreme Court patent law doctrine to vanish: the reverse doctrine of equivalents, as announced by the Court in the 1898 case Westinghouse v. Boyden Power Brake Co. Hence Westinghouse represents forgotten precedent in a different sense than is conventionally thought: the leading patent court in the nation has requested that we forget this precedent, with the result that the case is receding from memory and relevance, unless and until the Supreme Court intervenes. For nearly one hundred years following Westinghouse, the reverse doctrine of equivalents was a necessary safety valve in patent law to ensure that granting the patent monopoly did not impede the progress sought by the Intellectual Property Clause of the Constitution. In cases where the accused product represents a leap forward in the technology far beyond what is disclosed in the asserted patent, society benefits from access to that superior innovation unimpeded and untaxed by the asserted patent. There are over thirty published opinions finding or affirming noninfringement under the reverse doctrine of equivalents between 1898 and the 1982 creation of the Federal Circuit and uncounted additional unpublished dispositions as well as opinions denying or vacating summary judgment of infringement due to a dispute of fact regarding reverse equivalency. In at least the Second, Fifth, Sixth, Seventh, and Ninth Circuits, and indeed as stated by the Supreme Court itself in Westinghouse, reverse equivalency always had to be considered as part of the principal infringement case. The reverse doctrine of equivalents enjoyed steady application by the courts in the decades since the 1898 Westinghouse case until the 1980s, when the Federal Circuit was created and proceeded to stamp out the doctrine. In the 1980s the Federal Circuit regularly vacated or reversed findings of noninfringement under the reverse doctrine of equivalents to the point where such findings of noninfringement have disappeared altogether.\u27 In 2002 the Federal Circuit proclaimed final victory over the reverse doctrine of equivalents, mischaracterizing it as an anachronistic exception, long mentioned but rarely applied ... Subsequently, the Federal Circuit warned the lower courts that this court has never affirmed a finding of noninfringement under the reverse doctrine of equivalents. And it likely never will, unless an intrepid petitioner for certiorari someday convinces the Supreme Court to revive the doctrine

A Tiny Convolutional Neural Network driven by System Identification for Vibration Anomaly Detection at the Extreme Edge

Vibration data analysis is the driving tool for the Structural Health Monitoring (SHM) of structures in the dynamic regime, i.e., structures showing important oscillatory behaviours, which largely dominate the transportation back-bone: from terrestrial/aerial vehicles (e.g., trains, aircraft, etc.) to the supporting infrastructures (e.g., bridges, viaducts, etc.). Outstanding opportunities have recently been disclosed in the field of Intelligent Transportation Systems (ITS) by the advent of sensor-near processing functionalities, eventually empowered by Artificial Intelligence (AI). The latter allow for the extraction of damage-sensitive features at the extreme edge, without the need of transmitting long time series over the monitoring network. In this work, we explore for the first time a novel anomaly detection workflow for on-sensor vibration diagnostics, which combines the unique advantages of embedded System Identification (eSysId) as a data compression strategy with the computational/energy advantages of Tiny Machine Learning (TinyML). Experimental results conducted on a representative SHM dataset demonstrate that the proposed pipeline can achieve high classification scores (above 90%) for the health assessment of the well-known Z24 bridge. In particular, the minimal inference time (less than 44 ms) and power consumption performed while running on three different general-purpose microprocessors make it a promising solution for the development of the next generation of SHM-oriented ITS

Expressive N+N combinations in Polish and the coordination/attribution cline

W niniejszym artykule zbadano zestawienia składające się z rzeczowników pozostających w związku zgody w języku polskim, takie jak kobieta anioł, praca marzenie, dziecko geniusz oraz kierowca cham. Stwierdzono, że mają one cechy wyrażeń ekspresywnych, podobnie do wyrażeń w języku angielskim analizowanych przez Pottsa (2007) oraz złożeń w języku niemieckim badanych przez Meibauera (2013). Zaproponowano podział polskich zestawień ekspresywnych na dwa typy. Przedstawiono argumenty za traktowaniem zestawień pierwszego typu, np. kierowca cham, jako wyrażeń o strukturze współrzędnej (tj. składających się z elementów równorzędnych pod względem semantycznym). Wykazano, że zestawienia drugiego typu, np. praca marzenie, zachowują się jak połączenia rzeczownikowe o semantycznej strukturze nadrzędno-podrzędnej, tj. jako zestawienia atrybutywno-apozycyjne (por. Scalise i Bisetto 2009). W artykule postuluje się istnienie strefy pośredniej pomiędzy obiema grupami zestawień ekspresywnych w języku polskim.In this paper I will examine N+N juxtapositions in Polish, such as kobieta anioł (woman angel) 'an angel of a woman', praca marzenie (job dream) 'dream job', dziecko geniusz (child genius) 'prodigy child', and kierowca cham (driver lout) 'a lout of a driver'. I will demonstrate that they exhibit properties of expressive combinations, as discussed for English by Potts (2007) and for German by Meibauer (2013). It will be proposed that Polish expressive N+N juxtapositions under analysis fall into two groups. Juxtapositions belonging to the first group, e.g. kierowca cham 'a lout of a driver', behave like coordinate compound-like units. Juxtapositions which form the second group of expressive complexes, such as kobieta anioł 'an angel of a woman' and praca marzenie 'dream job', can be treated as attributive-appositive (ATAP) combinations (in Scalise and Bisetto's 2009 classification). The occurrence of a cline between coordinate and attributive multi-word units is postulated