IEEE Access Special Section: Cyber-Physical Systems

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Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

Current, January 31, 2011

https://irl.umsl.edu/current2010s/1068/thumbnail.jp

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

An Innovation-driven IT Governance Framework for Benefits Realisation and its Application to Public Sector

Information and communication technology (ICT or IT) provides benefits to an organisation. However, a large number of IT projects fail. The research literature shows extant governance frameworks are not adequately protecting against project failures and are not as effective on many of these IT projects as they should be. This thesis developed a new IT governance framework using an agile benefit management approach, aimed to achieve benefits realisation for any scale of IT projects, particularly for projects in the public sector such as Defence. The framework objectively targets digital transformation and technological agility, however, is shown in the thesis to assist in other enterprise challenges with IT acquisition and through-life management, such as cyber-resilience. The framework is based on many theories, principles, and practices, such as: Transaction Cost Economics Theory, Prospect Theory (Decision-Making Under Uncertainty), Reference Class Forecasting, Stratification and the Incremental Enlargement Principle and Fuzzy Logic. The framework is shown to be effective primarily through better informed decision-making. Benefits realisation is critical for information technology project success. The framework provides a systematic methodology on how to define economic benefit, technical benefit, and strategic benefit. It provides benefit realisation measures through fuzzy inference system, and it provides decision support based on the benefit performance measures and dis-benefit risk management. When compared to industry-based frameworks for IT acquisition and sustainment this new framework is unique because it includes all internal and external stakeholders such as users, providers, industry, and academia to continuously collaborate for innovating, iterating, and evaluating technology for realisation of benefits to achieve the organisational goals and objectives. The proof of concept has been conducted through a detailed case study in the Defence public sector, and several critiques of IT reform in other public sector applications where difficulties are occurring. Organisations will be able to use this framework for a more rapid and assured uptake of emerging technologies of the Fourth Industrial Revolution into their technology stack. Examples of some of these emerging technologies are AI, machine learning, geo-spatial, block chain, cognitive and brain computing, cloud computing, data echo system, and cybersecurity

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Machine Learning Methods for Product Quality Monitoring in Electric Resistance Welding

Elektrisches Widerstandsschweißen (Englisch: Electric Resistance Welding, ERW) ist eine Gruppe von vollautomatisierten Fertigungsprozessen, bei denen metallische Werkstoffe durch Wärme verbunden werden, die von elektrischem Strom und Widerstand erzeugt wird. Eine genaue Qualitätsüberwachung von ERW kann oft nur teilweise mit destruktiven Methoden durchgeführt werden. Es besteht ein großes industrielles und wirtschaftliches Potenzial, datengetriebene Ansätze für die Qualitätsüberwachung in ERW zu entwickeln, um die Wartungskosten zu senken und die Qualitätskontrolle zu verbessern. Datengetriebene Ansätze wie maschinelles Lernen (ML) haben aufgrund der enormen Menge verfügbarer Daten, die von Technologien der Industrie 4.0 bereitgestellt werden, viel Aufmerksamkeit auf sich gezogen. Datengetriebene Ansätze ermöglichen eine zerstörungsfreie, umfassende und präzise Qualitätsüberwachung, wenn eine bestimmte Menge präziser Daten verfügbar ist. Dies kann eine umfassende Online-Qualitätsüberwachung ermöglichen, die ansonsten mit herkömmlichen empirischen Methoden äußerst schwierig ist. Es gibt jedoch noch viele Herausforderungen bei der Adoption solcher Ansätze in der Fertigungsindustrie. Zu diesen Herausforderungen gehören: effiziente Datensammlung, die dasWissen von erforderlichen Datenmengen und relevanten Sensoren für erfolgreiches maschinelles Lernen verlangt; das anspruchsvolle Verstehen von komplexen Prozessen und facettenreichen Daten; eine geschickte Selektion geeigneter ML-Methoden und die Integration von Domänenwissen für die prädiktive Qualitätsüberwachung mit inhomogenen Datenstrukturen, usw. Bestehende ML-Lösungen für ERW liefern keine systematische Vorgehensweise für die Methodenauswahl. Jeder Prozess der ML-Entwicklung erfordert ein umfassendes Prozess- und Datenverständnis und ist auf ein bestimmtes Szenario zugeschnitten, das schwer zu verallgemeinern ist. Es existieren semantische Lösungen für das Prozess- und Datenverständnis und Datenmanagement. Diese betrachten die Datenanalyse als eine isolierte Phase. Sie liefern keine Systemlösungen für das Prozess- und Datenverständnis, die Datenaufbereitung und die ML-Verbesserung, die konfigurierbare und verallgemeinerbare Lösungen für maschinelles Lernen ermöglichen. Diese Arbeit versucht, die obengenannten Herausforderungen zu adressieren, indem ein Framework für maschinelles Lernen für ERW vorgeschlagen wird, und demonstriert fünf industrielle Anwendungsfälle, die das Framework anwenden und validieren. Das Framework überprüft die Fragen und Datenspezifitäten, schlägt eine simulationsunterstützte Datenerfassung vor und erörtert Methoden des maschinellen Lernens, die in zwei Gruppen unterteilt sind: Feature Engineering und Feature Learning. Das Framework basiert auf semantischen Technologien, die eine standardisierte Prozess- und Datenbeschreibung, eine Ontologie-bewusste Datenaufbereitung sowie halbautomatisierte und Nutzer-konfigurierbare ML-Lösungen ermöglichen. Diese Arbeit demonstriert außerdem die Übertragbarkeit des Frameworks auf einen hochpräzisen Laserprozess. Diese Arbeit ist ein Beginn des Wegs zur intelligenten Fertigung von ERW, der mit dem Trend der vierten industriellen Revolution korrespondiert