Design of a Specialized UAV Platform for the Discharge of a Fire Extinguishing Capsule

Tato práce se zabývá návrhem systému specializovaného pro autonomní detekci a lokalizaci požárů z palubních senzorů bezpilotních helikoptér. Hašení požárů je zajištěno automatickým vystřelením ampule s hasící kapalinou do zdroje požáru z palubního vystřelovače. Hlavní část této práce se soustředí na detekci požárů v datech termální kamery a jejich následnou lokalizaci ve světě za pomoci palubní hloubkové kamery. Bezpilotní helikoptéra je poté optimálně navigována na pozici pro zajištění průletu ampule s hasící kapalinou do zdroje požáru. Vyvinuté metody jsou detailně analyzovány a jejich chování je testováno jak v simulaci, tak současně i při reálných experimentech. Kvalitativní a kvantitativní analýza ukazuje na použitelnost a robustnost celého systému.This thesis deals with the design of an unmanned multirotor aircraft system specialized for autonomous detection and localization of fires from onboard sensors, and the task of fast and effective fire extinguishment. The main part of this thesis focuses on the detection of fires in thermal images and their localization in the world using an onboard depth camera. The localized fires are used to optimally position the unmanned aircraft in order to effectively discharge an ampoule filled with a fire extinguishant from an onboard launcher. The developed methods are analyzed in detail and their performance is evaluated in simulation scenarios as well as in real-world experiments. The included quantitative and qualitative analysis verifies the feasibility and robustness of the system

A laboratory study on the effects of wind turbine noise on sleep: Results of the polysomnographic WiTNES study

Study Objectives: Assess the physiologic and self-reported effects of wind turbine noise (WTN) on sleep. Methods: Laboratory sleep study (n = 50 participants: n = 24 living close to wind turbines and n = 26 as a reference group) using polysomnography, electrocardiography, salivary cortisol, and questionnaire endpoints. Three consecutive nights (23:00-07:00): one habituation followed by a randomized quiet Control and an intervention night with synthesized 32 dB LAEq WTN. Noise in WTN nights simulated closed and ajar windows and low and high amplitude modulation depth. Results: There was a longer rapid eye movement (REM) sleep latency (+16.8 min) and lower amount of REM sleep (-11.1 min, -2.2%) in WTN nights. Other measures of objective sleep did not differ significantly between nights, including key indicators of sleep disturbance (sleep efficiency: Control 86.6%, WTN 84.2%; wakefulness after sleep onset: Control 45.2 min, WTN 52.3 min; awakenings: Control n = 11.4, WTN n = 11.5) or the cortisol awakening response. Self-reported sleep was consistently rated as worse following WTN nights, and individuals living close to wind turbines had worse self-reported sleep in both the Control and WTN nights than the reference group. Conclusions: Amplitude-modulated continuous WTN may impact on self-assessed and some aspects of physiologic sleep. Future studies are needed to generalize these findings outside of the laboratory and should include more exposure nights and further examine possible habituation or sensitization

Armstrong Flight Research Center Research Technology and Engineering Report 2015

I am honored to endorse the 2015 Neil A. Armstrong Flight Research Centers Research, Technology, and Engineering Report. The talented researchers, engineers, and scientists at Armstrong are continuing a long, rich legacy of creating innovative approaches to solving some of the difficult problems and challenges facing NASA and the aerospace community.Projects at NASA Armstrong advance technologies that will improve aerodynamic efficiency, increase fuel economy, reduce emissions and aircraft noise, and enable the integration of unmanned aircraft into the national airspace. The work represented in this report highlights the Centers agility to develop technologies supporting each of NASAs core missions and, more importantly, technologies that are preparing us for the future of aviation and space exploration.We are excited about our role in NASAs mission to develop transformative aviation capabilities and open new markets for industry. One of our key strengths is the ability to rapidly move emerging techniques and technologies into flight evaluation so that we can quickly identify their strengths, shortcomings, and potential applications.This report presents a brief summary of the technology work of the Center. It also contains contact information for the associated technologists responsible for the work. Dont hesitate to contact them for more information or for collaboration ideas

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Machine Learning and Data Mining Applications in Power Systems

This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries

Feature Papers of Drones - Volume I

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin

Machine Learning based Predictive Modeling of Stochastic Systems

Title from PDF of title page, viewed June 14, 2023Dissertation advisor: Amirfarhang MehdizadehVitaIncludes bibliographical references (pages 89-121)Dissertation (Ph.D.)--Department of Civil and Mechanical Engineering, Department of Mathematics and Statistics. University of Missouri--Kansas City, 2023Complex signals are ubiquitous in our daily lives, and interpreting and modeling them is vital for scientific advancement. Traditional methods for predictive modeling of complex signals include statistical signal processing and physics-based simulations. However, statistical signal processing methods often struggle to fully utilize complex and rich datasets, while physics-based simulations can be computationally demanding. As an alternative approach, machine learning (ML) offers a more effective method for the predictive modeling of complex signals. This research explores the applicability of ML-based predictive modeling to a biomedical and a mechanical system through two case studies. The first case study focuses on developing a machine learning-based model for early-stage glaucoma detection using electroretinogram signals, which has been a challenging problem in ophthalmology. By leveraging medically relevant information contained in ERG signals, the study aims to establish a novel and reliable predictive framework for the early detection of glaucoma using a machine-learning-based algorithm. The results demonstrate that machine-learning-based models, trained using advanced wavelet-based features, can effectively detect the early stage of glaucoma from ERG stochastic signals. The second case study centers on developing a machine learning-based model for stall delay correction in wind turbines. Existing stall delay correction models rely on 2D airfoil characteristics, which can lead to inaccuracies in predicting aerodynamic loads during design and, consequently, result in structural failure due to excessive load. To address this issue, the study proposes a novel stall delay correction model based on the soft computing technique of symbolic regression. The model offers high-level precise aerodynamic performance prediction through the blade element momentum process, making it a promising alternative for accurate and efficient stall delay correction in wind turbines.Introduction -- Case study 1: Novel machine-learning based framework using electroretinography data for the detection of early-stage glaucoma -- Case study 2: Novel machine-learning-based stall delay correction model for improving blade element momentum analysis in wind turbine performance prediction -- Conclusio