Control Reconfiguration of Dynamical Systems for Improved Performance via Reverse-engineering and Forward-engineering

This paper presents a control reconfiguration approach to improve the performance of two classes of dynamical systems. Motivated by recent research on re-engineering cyber-physical systems, we propose a three-step control retrofit procedure. First, we reverse-engineer a dynamical system to dig out an optimization problem it actually solves. Second, we forward-engineer the system by applying a corresponding faster algorithm to solve this optimization problem. Finally, by comparing the original and accelerated dynamics, we obtain the implementation of the redesigned part (i.e., the extra dynamics). As a result, the convergence rate/speed or transient behavior of the given system can be improved while the system control structure maintains. Three practical applications in the two different classes of target systems, including consensus in multi-agent systems, Internet congestion control and distributed proportional-integral (PI) control, show the effectiveness of the proposed approach.Comment: 27 pages, 8 figure

TRANSFORMING A CIRCULAR ECONOMY INTO A HELICAL ECONOMY FOR ADVANCING SUSTAINABLE MANUFACTURING

The U.N. projects the world population to reach nearly 10 billion people by 2050, which will cause demand for manufactured goods to reach unforeseen levels. In order for us to produce the goods to support an equitable future, the methods in which we manufacture those goods must radically change. The emerging Circular Economy (CE) concept for production systems has promised to drastically increase economic/business value by significantly reducing the world’s resource consumption and negative environmental impacts. However, CE is inherently limited because of its emphasis on recycling and reuse of materials. CE does not address the holistic changes needed across all of the fundamental elements of manufacturing: products, processes, and systems. Therefore, a paradigm shift is required for moving from sustainment to sustainability to “produce more with less” through smart, innovative and transformative convergent manufacturing approaches rooted in redesigning next generation manufacturing infrastructure. This PhD research proposes the Helical Economy (HE) concept as a novel extension to CE. The proposed HE concepts shift the CE’s status quo paradigm away from post-use recovery for recycling and reuse and towards redesigning manufacturing infrastructure at product, process, and system levels, while leveraging IoT-enabled data infrastructures and an upskilled workforce. This research starts with the conceptual overview and a framework for implementing HE in the discrete product manufacturing domain by establishing the future state vision of the Helical Economy Manufacturing Method (HEMM). The work then analyzes two components of the framework in detail: designing next-generation products and next-generation IoT-enabled data infrastructures. The major research problems that need to be solved in these subcomponents are identified in order to make near-term progress towards the HEMM. The work then proceeds with the development and discussion of initial methods for addressing these challenges. Each method is demonstrated using an illustrative industry example. Collectively, this initial work establishes the foundational body of knowledge for the HE and the HEMM, provides implementation methods at the product and IoT-enabled data infrastructure levels, and it shows a great potential for HE’s ability to create and maximize sustainable value, optimize resource consumption, and ensure continued technological progress with significant economic growth and innovation. This research work then presents an outlook on the future work needed, as well as calls for industry to support the continued refinement and development of the HEMM through relevant prototype development and subsequent applications

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world

Impacts of Autonomous Cars from a Traffic Engineering Perspective

The era of autonomous vehicles infer new challenges in several fields. When autonomous vehicles take over the road in a large volume, consumer preferences around car-ownership will transform, traffic modeling and control will need correction, and moreover hackers will appear. These are just a few impacts which are expectable in the near future. Accordingly, the paper’s aim is to enlighten trends and upcoming challenges of driverless vehicles and automated transportation system from a transportation engineering perspective

2018 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

Excerpt: As an academic institution, we strive to meet and exceed the expectations for graduate programs and laud our values and contributions to the academic community. At the same time, we must recognize, appreciate, and promote the unique non-academic values and accomplishments that our faculty team brings to the national defense, which is a priority of the Federal Government. In this respect, through our diverse and multi-faceted contributions, our faculty, as a whole, excel, not only along the metrics of civilian academic expectations, but also along the metrics of military requirements, and national priorities

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligence

The potential of digital twin technology is yet to be fully realized due to its diversity and untapped potential. Digital twins enable systems' analysis, design, optimization, and evolution to be performed digitally or in conjunction with a cyber-physical approach to improve speed, accuracy, and efficiency over traditional engineering methods. Industry 4.0, factories of the future, and digital twins continue to benefit from the technology and provide enhanced efficiency within existing systems. Due to the lack of information and security standards associated with the transition to cyber digitization, cybercriminals have been able to take advantage of the situation. Access to a digital twin of a product or service is equivalent to threatening the entire collection. There is a robust interaction between digital twins and artificial intelligence tools, which leads to strong interaction between these technologies, so it can be used to improve the cybersecurity of these digital platforms based on their integration with these technologies. This study aims to investigate the role of artificial intelligence in providing cybersecurity for digital twin versions of various industries, as well as the risks associated with these versions. In addition, this research serves as a road map for researchers and others interested in cybersecurity and digital security.Comment: 60 pages, 8 Figures, 15 Table

Air Force Institute of Technology Research Report 2014

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics