Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Automation and Robotics: Latest Achievements, Challenges and Prospects

This SI presents the latest achievements, challenges and prospects for drives, actuators, sensors, controls and robot navigation with reverse validation and applications in the field of industrial automation and robotics. Automation, supported by robotics, can effectively speed up and improve production. The industrialization of complex mechatronic components, especially robots, requires a large number of special processes already in the pre-production stage provided by modelling and simulation. This area of research from the very beginning includes drives, process technology, actuators, sensors, control systems and all connections in mechatronic systems. Automation and robotics form broad-spectrum areas of research, which are tightly interconnected. To reduce costs in the pre-production stage and to reduce production preparation time, it is necessary to solve complex tasks in the form of simulation with the use of standard software products and new technologies that allow, for example, machine vision and other imaging tools to examine new physical contexts, dependencies and connections

Design and Analysis of Electric Powertrains for Offshore Drilling Applications

Doktorgradsavhandling ved Institutt for ingeniørvitenskap, Universitetet i Agder, 2016The global energy market is challenged with an ever increasing need for resources to meet the growing demands for electric power, transportation fuels, etc. Although we witness the expansion of the renewable energy industry, it is still the fossil fuels, with oil and gas dominating the scene of global energy supply sector, that provide majority of worldwide power generation.However, many of the easily accessible hydrocarbon reserves are depleted which requires from the producers of drilling equipment to focus on cost-effective operations and technology to compete in a challenging market. Particularly high level of activity is observed in both industry and academia in the field of electrical actuation systems of drilling machines, as control methods of alternating current (AC) motor drives have become an industrially mature technology over the past few decades. In addition, state-of-the-art AC motors manufacturing processes allow to conform to the strict requirements for safe operation of electrical equipment in explosive atmospheres. These two main reasons made electric actuation systems a tough competitor to hydraulic powertrains used traditionally by the industry. However, optimal design of induction motor drives and systematic analysis of factors associated with operation in harsh offshore conditions are still considered as a major challenge. In this thesis, effective methods for design and analysis of induction motor drives are proposed, including aspects of optimization and simulation-based engineering. The first part of the thesis is devoted to studying methods for modeling, control, and identification of induction machines operating in offshore drilling equipment with the focus to improve their reliability, extend lifetime, and avoid faults and damage, whereas the second part introduces more general approaches to the optimal selection of components of electric drivetrains and to the improvement of the existing dimensioning guidelines. A multidisciplinary approach to design of actuation systems is explored in this thesis by studying the areas of motion control, condition monitoring, and thermal modeling of electric powertrains with an aspiration to reach the level of design sophistication which goes beyond what is currently considered an industrial standard. We present a technique to reproduce operation of a full-scale offshore drilling machine on a scaled-down experimental setup to estimate the mechanical load that the designed powertrain must overcome to meet the specification requirements. The same laboratory setup is used to verify the accuracy of the estimation and control method of an induction motor drive based on the extended Kalman filter (EKF) to confirm that the sensorless control techniques can reduce the number of data acquisition devices in offshore machines, and thus decrease their failure rate without negatively affecting their functionality. To address the challenge of condition monitoring of induction motor drives, we propose a technique to assess the expected lifetime of electric drivetrain components when subjected to the desired duty cycles by comparing the effects of a few popular motion control signals on the cumulative damage and vibrations. As a result, the information about the influence of a given control strategy on drivetrain lifecycle is made available early in the design stage which can significantly affect the choice of the optimal powertrain components. The results show that some of the techniques that have a well-proven track record in other industries can be successfully applied to solve challenges associated with operation of offshore drilling machines. One of the most essential contributions of this thesis, optimal selection of drivetrain components, is based on formulating the drivetrain dimensioning problem as a mixed integer optimization program. The components of powertrain that satisfy the design constraints and are as cost-effective as possible are found to be the global optimum, contrary to the functionality offered by some commercially available drivetrain sizing software products. Another important drawback of the dimensioning procedures recommended by the motor drives manufacturers is the inability to assess if the permissible temperature limits given in the standards do not become violated when the actuation system experiences overloads different than these tabulated in the catalogs. Hence, the second most significant contribution is to propose a method to monitor thermal performance of induction motor drives that is based exclusively on publicly available catalog data and allows for evaluating whether the standard thermal performance limits are violated or not under arbitrary load conditions and at any ambient temperature. Both these solutions can effectively enrich the industrially accepted dimensioning procedures to satisfy the level of conservatism that is demanded by the offshore drilling business but, at the same time, provide improved efficiency and flexibility of the product design process and guarantee optimality (quantitatively, not qualitatively, measurable) of the final solution. An attractive direction for additional development is to further integrate knowledge from different fields relevant to electric powertrains to enable design of tailored solutions without compromising on their cost and performance

A Heuristic Simulation and Optimization Algorithm for Large Scale Natural Gas Storage Valuation under Uncertainty

Natural gas storage valuation is an optimal scheduling of natural gas storage facilities. It is a complex predictive decision making research problem since it involves the financial decisions and the physical storage facility characteristics. The challenge arises from large scale stochastic input data sets and complex mathematical models. Research in the literature has been heavily focused on the financial facet of the valuation with little emphasis on the physical storage facility characteristics. The mathematical models and the solution approaches provided in the literature so far are also either overly simplified or are only relevant for very small scale problems. The contribution of this research is on the physical storage facility characteristics in combination with the financial aspect of the natural gas storage valuation. A large scale stochastic non-linear natural gas storage valuation problem that includes underground and aboveground storage facilities is formulated and solved efficiently. A new heuristic simulation and optimization natural gas storage valuation algorithm that handles a very complex and large size problems is proposed. The algorithm (i) decreases significantly the computation time from hundreds of days to fractions of a second, (ii) provides a reasonable solution quality, and (iii) incorporates all the possible underground and aboveground physical gas storage facility complexities. The research has both practical applications and mathematical significance. Practically, natural gas storage facility managers can use the models developed in this research as decision support tools to make a predictive storage decision under uncertainty within a reasonable time. Mathematically, a novel perspective to solving a non-linear natural gas storage facilities valuation problem is provided. Such approach can be used in a variety of applications; for instance, the algorithm can be applied to a high penetration of renewables to electric power grid and fluid flow network optimization among others

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Learning to Disagree in a Game of Experimentation

We analyse strategic experimentation in which information arrives through fully revealing, publicly observable “breakdowns.” With hidden actions, there exists a unique equilibrium that involves randomization over stopping times. This randomization induces belief disagreement on the equilibrium path. When actions are observable, the equilibrium is pure, and welfare improves. We analyse the role of policy interventions such as subsidies for experimentation and risk-sharing agreements. We show that the optimal risk-sharing agreement restores the first-best outcome, independent of the monitoring structure

Fracking Patents: The Emergence of Patents as Information-Containment Tools in Shale Drilling

The advantages of new sources of energy must be weighed against environmental, health, and safety concerns related to new production technology. The rapid development of unconventional oil and gas fields, such as the Barnett and Marcellus Shales, provide an excellent context for these contrasting goals. Information about extraction hazards is an extremely important issue. In general, patents are viewed as a positive force in this regard, providing a vehicle for disseminating information in exchange for a limited property right over an invention. However, by limiting the evaluation of an invention by third parties, patents might also be used to control the creation of new information. Such control is more likely in situations where third-party use and assessment may produce information damaging to the patent owner. This Article explores the relationship between patents and information control in the context of natural gas extraction. Understanding the role of a patent as an information-control mechanism is critical to the safe employment of new technology. If patents substantially limit information creation or disclosure, government intervention may be necessary to permit non-patentee experimental use along with environmental, health, and safety testing. Before patent rights are encumbered, however, options that exist under current law should be considered

Advanced Mobile Robotics: Volume 3

Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective