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This book describes the challenges that critical infrastructure systems face, and presents state of the art solutions to address them. How can we design intelligent systems or intelligent agents that can make appropriate real-time decisions in the management of such large-scale, complex systems? What are the primary challenges for critical infrastructure systems? The book also provides readers with the relevant information to recognize how important infrastructures are, and their role in connection with a society’s economy, security and prosperity. It goes on to describe state-of-the-art solutions to address these points, including new methodologies and instrumentation tools (e.g. embedded software and intelligent algorithms) for transforming and optimizing target infrastructures. The book is the most comprehensive resource to date for professionals in both the private and public sectors, while also offering an essential guide for students and researchers in the areas of modeling and analysis of critical infrastructure systems, monitoring, control, risk/impact evaluation, fault diagnosis, fault-tolerant control, and infrastructure dependencies/interdependencies. The importance of the research presented in the book is reflected in the fact that currently, for the first time in human history, more people live in cities than in rural areas, and that, by 2050, roughly 70% of the world’s total population is expected to live in cities.
Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems.
Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 3 describes how the resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how machine learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from machine learning, for example by uncovering hidden characteristics of the wireless channel.
Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more. - Synthesizes the newest developments in data-driven transportation science - Includes case studies and examples in each chapter that illustrate the application of methodologies and technologies employed - Useful for both theoretical and technically-oriented researchers
Erstmals eine umfassende und einheitliche Wissensbasis und Grundlage für weiterführende Studien und Forschung im Bereich der Automobiltechnik. Die Encyclopedia of Automotive Engineering ist die erste umfassende und einheitliche Wissensbasis dieses Fachgebiets und legt den Grundstein für weitere Studien und tiefgreifende Forschung. Weitreichende Querverweise und Suchfunktionen ermöglichen erstmals den zentralen Zugriff auf Detailinformationen zu bewährten Branchenstandards und -verfahren. Zusammenhängende Konzepte und Techniken aus Spezialbereichen lassen sich so einfacher verstehen. Neben traditionellen Themen des Fachgebiets beschäftigt sich diese Enzyklopädie auch mit "grünen" Technologien, dem Übergang von der Mechanik zur Elektronik und den Möglichkeiten zur Herstellung sicherer, effizienterer Fahrzeuge unter weltweit unterschiedlichen wirtschaftlichen Rahmenbedingungen. Das Referenzwerk behandelt neun Hauptbereiche: (1) Motoren: Grundlagen; (2) Motoren: Design; (3) Hybrid- und Elektroantriebe; (4) Getriebe- und Antriebssysteme; (5) Chassis-Systeme; (6) Elektrische und elektronische Systeme; (7) Karosserie-Design; (8) Materialien und Fertigung; (9) Telematik. - Zuverlässige Darstellung einer Vielzahl von Spezialthemen aus dem Bereich der Automobiltechnik. - Zugängliches Nachschlagewerk für Jungingenieure und Studenten, die die technologischen Grundlagen besser verstehen und ihre Kenntnisse erweitern möchten. - Wertvolle Verweise auf Detailinformationen und Forschungsergebnisse aus der technischen Literatur. - Entwickelt in Zusammenarbeit mit der FISITA, der Dachorganisation nationaler Automobil-Ingenieur-Verbände aus 37 Ländern und Vertretung von über 185.000 Ingenieuren aus der Branche. - Erhältlich als stets aktuelle Online-Ressource mit umfassenden Suchfunktionen oder als Print-Ausgabe in sechs Bänden mit über 4.000 Seiten. Ein wichtiges Nachschlagewerk für Bibliotheken und Informationszentren in der Industrie, bei Forschungs- und Schulungseinrichtungen, Fachgesellschaften, Regierungsbehörden und allen Ingenieurstudiengängen. Richtet sich an Fachingenieure und Techniker aus der Industrie, Studenten höherer Semester und Studienabsolventen, Forscher, Dozenten und Ausbilder, Branchenanalysen und Forscher.
Pavement Engineering will cover the entire range of pavement construction, from soil preparation to structural design and life-cycle costing and analysis. It will link the concepts of mix and structural design, while also placing emphasis on pavement evaluation and rehabilitation techniques. State-of-the-art content will introduce the latest concepts and techniques, including ground-penetrating radar and seismic testing. This new edition will be fully updated, and add a new chapter on systems approaches to pavement engineering, with an emphasis on sustainability, as well as all new downloadable models and simulations.