Applications of aerospace technology in biology and medicine

Utilization of National Aeronautics and Space Administration (NASA) technology in medicine is discussed. The objective is best obtained by stimulation of the introduction of new or improved commercially available medical products incorporating aerospace technology. A bipolar donor/recipient model of medical technology transfer is presented to provide a basis for the team's methodology. That methodology is designed to: (1) identify medical problems and NASA technology that, in combination, constitute opportunities for successful medical products; (2) obtain the early participation of industry in the transfer process; and (3) obtain acceptance by the medical community of new medical products based on NASA technology. Two commercial transfers were completed: the Stowaway, a lightweight wheelchair that provides mobility for the disabled and elderly in the cabin of commercial aircraft, and Micromed, a portable medication infusion pump for the reliable, continuous infusion of medications such as heparin or insulin. The marketing and manufacturing factors critical to the commercialization of the lightweight walker incorporating composite materials were studied. Progress was made in the development and commercialization of each of the 18 currently active projects

Voyager spacecraft phase B, task D. Volume 2 - System description. Book 5 - Final report

Voyager spacecraft design standards, and operational support and mission-dependent equipment requirement

An Investigation of Risk Management Approaches for NASA Piloted X-Plane Projects

NASA is resuming X-plane research. It plans to build a low-boom supersonic flight demonstrator (LBFD), an all-electric general aviation aircraft (X-57), and possibly an ultra-efficient subsonic transport (UEST) demonstrator. In an attempt to define what levels of risk are appropriate in piloted X-plane research, the NASA Office of the Chief Engineer (OCE) evaluated numerous NASA, Department of Defense (DoD), and industry project management and risk assessment tools. Provided are the results of the evaluations of NASA Procedural Requirements (NPR) 7120.5, 7120.8, and 8705.4; Langley Research Center (LaRC) Procedural Requirement (LPR) 7120.5; Dryden (Armstrong) Center Procedures S-002 and X-009; and Military Handbook 516C. Some of these were applied to the LBFD and X-57 aircraft. The impacts on risk of budgeting decisions and specialized flight conditions were also considered. None of the evaluated processes were found to be fully appropriate for governing experimental aircraft projects, but many useful elements were found in some of them

Expert system prototype for hydraulic system design focusing on concurent engineering aspects /

Tese (Doutorado)- Universidade Federal de Santa Catarina, Centro Tecnológico

Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping

Anomaly detection is critical in the smart industry for preventing equipment failure, reducing downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery, providing a rich source of information for Anomaly Detection. However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data. Besides, each technique has specific strengths and weaknesses based on the data nature and its corresponding systems. However, the current systematic mapping studies on Anomaly Detection primarily focus on addressing network and cybersecurity-related problems, with limited attention given to the industrial sector. Additionally, these studies do not cover the challenges involved in using ML for Anomaly Detection in industrial machinery within the context of the IoT ecosystems. This paper presents a systematic mapping study on Anomaly Detection for industrial machinery using IoT devices and ML algorithms to address this gap. The study comprehensively evaluates 84 relevant studies spanning from 2016 to 2023, providing an extensive review of Anomaly Detection research. Our findings identify the most commonly used algorithms, preprocessing techniques, and sensor types. Additionally, this review identifies application areas and points to future challenges and research opportunities

Self-aware reliable monitoring

Cyber-Physical Systems (CPSs) can be found in almost all technical areas where they constitute a key enabler for anticipated autonomous machines and devices. They are used in a wide range of applications such as autonomous driving, traffic control, manufacturing plants, telecommunication systems, smart grids, and portable health monitoring systems. CPSs are facing steadily increasing requirements such as autonomy, adaptability, reliability, robustness, efficiency, and performance. A CPS necessitates comprehensive knowledge about itself and its environment to meet these requirements as well as make rational, well-informed decisions, manage its objectives in a sophisticated way, and adapt to a possibly changing environment. To gain such comprehensive knowledge, a CPS must monitor itself and its environment. However, the data obtained during this process comes from physical properties measured by sensors and may differ from the ground truth. Sensors are neither completely accurate nor precise. Even if they were, they could still be used incorrectly or break while operating. Besides, it is possible that not all characteristics of physical quantities in the environment are entirely known. Furthermore, some input data may be meaningless as long as they are not transferred to a domain understandable to the CPS. Regardless of the reason, whether erroneous data, incomplete knowledge or unintelligibility of data, such circumstances can result in a CPS that has an incomplete or inaccurate picture of itself and its environment, which can lead to wrong decisions with possible negative consequences. Therefore, a CPS must know the obtained data’s reliability and may need to abstract information of it to fulfill its tasks. Besides, a CPS should base its decisions on a measure that reflects its confidence about certain circumstances. Computational Self-Awareness (CSA) is a promising solution for providing a CPS with a monitoring ability that is reliable and robust — even in the presence of erroneous data. This dissertation proves that CSA, especially the properties abstraction, data reliability, and confidence, can improve a system’s monitoring capabilities regarding its robustness and reliability. The extensive experiments conducted are based on two case studies from different fields: the health- and industrial sectors

Project for the analysis of technology transfer Quarterly report, 13 Jul. - 12 Oct. 1968

Statistical characteristics of transfer data bank users, and outline of technology transfer and utilization instruction cours

IoT Applications Computing

The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing

Volume 2 – Conference

We are pleased to present the conference proceedings for the 12th edition of the International Fluid Power Conference (IFK). The IFK is one of the world’s most significant scientific conferences on fluid power control technology and systems. It offers a common platform for the presentation and discussion of trends and innovations to manufacturers, users and scientists. The Chair of Fluid-Mechatronic Systems at the TU Dresden is organizing and hosting the IFK for the sixth time. Supporting hosts are the Fluid Power Association of the German Engineering Federation (VDMA), Dresdner Verein zur Förderung der Fluidtechnik e. V. (DVF) and GWT-TUD GmbH. The organization and the conference location alternates every two years between the Chair of Fluid-Mechatronic Systems in Dresden and the Institute for Fluid Power Drives and Systems in Aachen. The symposium on the first day is dedicated to presentations focused on methodology and fundamental research. The two following conference days offer a wide variety of application and technology orientated papers about the latest state of the art in fluid power. It is this combination that makes the IFK a unique and excellent forum for the exchange of academic research and industrial application experience. A simultaneously ongoing exhibition offers the possibility to get product information and to have individual talks with manufacturers. The theme of the 12th IFK is “Fluid Power – Future Technology”, covering topics that enable the development of 5G-ready, cost-efficient and demand-driven structures, as well as individual decentralized drives. Another topic is the real-time data exchange that allows the application of numerous predictive maintenance strategies, which will significantly increase the availability of fluid power systems and their elements and ensure their improved lifetime performance. We create an atmosphere for casual exchange by offering a vast frame and cultural program. This includes a get-together, a conference banquet, laboratory festivities and some physical activities such as jogging in Dresden’s old town.:Group 1 | 2: Digital systems Group 3: Novel displacement machines Group 4: Industrial applications Group 5: Components Group 6: Predictive maintenance Group 7: Electro-hydraulic actuatorsDer Download des Gesamtbandes wird erst nach der Konferenz ab 15. Oktober 2020 möglich sein.:Group 1 | 2: Digital systems Group 3: Novel displacement machines Group 4: Industrial applications Group 5: Components Group 6: Predictive maintenance Group 7: Electro-hydraulic actuator

RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness promises a comprehensive assessment of the system state for sensible and well-informed actions and resource management. Computational self-awareness concepts can be used in many applications such as automated manufacturing plants, telecommunication systems, autonomous driving, traffic control, smart grids, and wearable health monitoring systems. Developing self-aware systems from scratch for each application is the most common practice currently, but this is highly redundant, inefficient, and uneconomic. Hence, we propose a framework that supports modeling and evaluation of various self-aware concepts in hierarchical agent systems, where agents are made up of self-aware functionalities. This paper presents the Research on Self-Awareness (RoSA) framework and its design principles. In addition, self-aware functionalities abstraction, data reliability, and confidence, which are currently provided by RoSA, are described. Potential use cases of RoSA are discussed. Capabilities of the proposed framework are showcased by case studies from the fields of healthcare and industrial monitoring. We believe that RoSA is capable of serving as a common framework for self-aware modeling and applications and thus helps researchers and engineers in exploring the vast design space of hierarchical agent-based systems with computational self-awareness