Electronic Image Stabilization for Mobile Robotic Vision Systems

When a camera is affixed on a dynamic mobile robot, image stabilization is the first step towards more complex analysis on the video feed. This thesis presents a novel electronic image stabilization (EIS) algorithm for small inexpensive highly dynamic mobile robotic platforms with onboard camera systems. The algorithm combines optical flow motion parameter estimation with angular rate data provided by a strapdown inertial measurement unit (IMU). A discrete Kalman filter in feedforward configuration is used for optimal fusion of the two data sources. Performance evaluations are conducted by a simulated video truth model (capturing the effects of image translation, rotation, blurring, and moving objects), and live test data. Live data was collected from a camera and IMU affixed to the DAGSI Whegs™ mobile robotic platform as it navigated through a hallway. Template matching, feature detection, optical flow, and inertial measurement techniques are compared and analyzed to determine the most suitable algorithm for this specific type of image stabilization. Pyramidal Lucas-Kanade optical flow using Shi-Tomasi good features in combination with inertial measurement is the EIS algorithm found to be superior. In the presence of moving objects, fusion of inertial measurement reduces optical flow root-mean-squared (RMS) error in motion parameter estimates by 40%. No previous image stabilization algorithm to date directly fuses optical flow estimation with inertial measurement by way of Kalman filtering

Fault management in networks incorporating Superconducting Cables (SCs) using Artificial Intelligence (AI) techniques.

With the increasing penetration of renewable energy sources, the immense growth in energy demand and the ageing of existing system infrastructure, future power systems have started to face reliability and resiliency challenges. To mitigate these issues, the need for bulk power corridors which enable the effective sharing of the available power capacity, between countries and from remote renewable energy sources, is rendered imperative. In this context, the deployment of multi-layer Superconducting Cables (SCs) with High Temperature Superconducting (HTS) tapes have been considered as a promising solution towards the modernisation of power systems. As opposed to conventional copper cables, SCs are characterised by a plethora of technically-attractive features such as compact structure, higher current-carrying capability, lower losses, higher power transfer at lower operating voltages and over longer distances, and reduced environmental impact. The performance of SCs is mainly determined by the structure of the cable and the electro-magneto-thermal properties of the HTS tapes, accounting for the critical current, critical temperature and critical magnetic field. Particularly, during steady state conditions, HTS tapes operate in superconducting mode, providing tangible benefits to power system operation such as a current-flowing path with approximately zero resistance. However, under certain transient conditions (e.g., electric faults), when the fault current flowing through HTS tapes reaches values higher than the critical current, HTS tapes start to quench. The quenching phenomenon is accompanied by a rapid increase in the equivalent resistance and temperature of SCs, the generation of Joule heating and the subsequent reduction in fault current magnitudes. Consequently, the transition of SCs from superconducting state to resistive state, during transient conditions, introduces many variables in the fault management of such cable technologies. Therefore, in order to exploit the technological advantages offered by SC applications, accommodate their wide-scale deployment within future energy grids, and accelerate their commercialisation, the detailed evaluation of their transient response and the consequent development of reliable fault management solutions are vital prerequisites. On that front, one of the main objectives of this thesis is to provide a detailed fault signature characterisation of AC and DC SCs and develop effective and practically feasible solutions for the fault management of AC and High Voltage Direct Current (HVDC) grids which incorporate SCs. As the fault management (i.e., fault detection, fault location, and protection) of SCs has proven to be a multi-variable problem, considering the complex structure, the unique features of SCs, and the quenching phenomenon, there is a need for advanced methods with immunity to these factors. In this context, the utilisation of Artificial Intelligence (AI) methods can be considered a very promising solution due to their capability to expose hidden patterns and acquire useful insights from the available data. Specifically, data-driven methods exhibit multifarious characteristics which allow them to provide innovative solutions for complex problems. Given their capacity for advanced learning and extensive data analysis, these methods merit thorough investigation for the fault management of SCs. Their inherent potential to adapt and uncover patterns in large datasets presents a compelling rationale for their exploration in enhancing the reliability and performance of superconducting cable systems. Therefore, this thesis proposes the development of novel, data-driven protection schemes which incorporate fault detection and classification elements for AC and multi-terminal HVDC systems with SCs, by exploiting the advantages of the latest trends in AI applications. In particular this thesis utilises cutting-edge developments and innovations in the field of AI, such as deep learning algorithms (i.e., CNN), and state-of-the-art techniques such as the XGBoost model which is a powerful ensemble learning algorithm. The developed schemes have been validated using simulation-based analysis. The obtained results confirm the enhanced sensitivity, speed, and discrimination capability of the developed schemes under various fault conditions and against other transient events, highlighting their superiority over other proposed methods or existing techniques. Furthermore, the generalisation capability of AI-assisted schemes has been verified against many adverse factors such as high values of fault resistance and noisy measurement. To further evaluate the practical feasibility and assess the time performance of the proposed schemes, real-time Software In the Loop (SIL) testing has been utilised. Another very important task for the effective fault management of AC and DC SCs is the estimation of the accurate fault location. Identifying the precise location of faults is crucial for SCs, given their complex structure and the challenging repair process. As such, this thesis proposes the design of a data-driven fault location scheme for AC systems with SCs. The developed scheme utilises pattern recognition techniques, such as image analysis, for feature extraction. It also incorporates AI algorithms in order to formulate the fault location problem as an AI regression problem. It is demonstrated that the scheme can accurately estimate the fault location along the SCs length and ensure increased reliability against a wide range of fault scenarios and noisy measurements. Further comparative analysis with other data-driven schemes validates the superiority of the proposed approach. In the final chapter the thesis summarises the key observations and outlines potential steps for further research in the field of fault management of superconducting-based systems.With the increasing penetration of renewable energy sources, the immense growth in energy demand and the ageing of existing system infrastructure, future power systems have started to face reliability and resiliency challenges. To mitigate these issues, the need for bulk power corridors which enable the effective sharing of the available power capacity, between countries and from remote renewable energy sources, is rendered imperative. In this context, the deployment of multi-layer Superconducting Cables (SCs) with High Temperature Superconducting (HTS) tapes have been considered as a promising solution towards the modernisation of power systems. As opposed to conventional copper cables, SCs are characterised by a plethora of technically-attractive features such as compact structure, higher current-carrying capability, lower losses, higher power transfer at lower operating voltages and over longer distances, and reduced environmental impact. The performance of SCs is mainly determined by the structure of the cable and the electro-magneto-thermal properties of the HTS tapes, accounting for the critical current, critical temperature and critical magnetic field. Particularly, during steady state conditions, HTS tapes operate in superconducting mode, providing tangible benefits to power system operation such as a current-flowing path with approximately zero resistance. However, under certain transient conditions (e.g., electric faults), when the fault current flowing through HTS tapes reaches values higher than the critical current, HTS tapes start to quench. The quenching phenomenon is accompanied by a rapid increase in the equivalent resistance and temperature of SCs, the generation of Joule heating and the subsequent reduction in fault current magnitudes. Consequently, the transition of SCs from superconducting state to resistive state, during transient conditions, introduces many variables in the fault management of such cable technologies. Therefore, in order to exploit the technological advantages offered by SC applications, accommodate their wide-scale deployment within future energy grids, and accelerate their commercialisation, the detailed evaluation of their transient response and the consequent development of reliable fault management solutions are vital prerequisites. On that front, one of the main objectives of this thesis is to provide a detailed fault signature characterisation of AC and DC SCs and develop effective and practically feasible solutions for the fault management of AC and High Voltage Direct Current (HVDC) grids which incorporate SCs. As the fault management (i.e., fault detection, fault location, and protection) of SCs has proven to be a multi-variable problem, considering the complex structure, the unique features of SCs, and the quenching phenomenon, there is a need for advanced methods with immunity to these factors. In this context, the utilisation of Artificial Intelligence (AI) methods can be considered a very promising solution due to their capability to expose hidden patterns and acquire useful insights from the available data. Specifically, data-driven methods exhibit multifarious characteristics which allow them to provide innovative solutions for complex problems. Given their capacity for advanced learning and extensive data analysis, these methods merit thorough investigation for the fault management of SCs. Their inherent potential to adapt and uncover patterns in large datasets presents a compelling rationale for their exploration in enhancing the reliability and performance of superconducting cable systems. Therefore, this thesis proposes the development of novel, data-driven protection schemes which incorporate fault detection and classification elements for AC and multi-terminal HVDC systems with SCs, by exploiting the advantages of the latest trends in AI applications. In particular this thesis utilises cutting-edge developments and innovations in the field of AI, such as deep learning algorithms (i.e., CNN), and state-of-the-art techniques such as the XGBoost model which is a powerful ensemble learning algorithm. The developed schemes have been validated using simulation-based analysis. The obtained results confirm the enhanced sensitivity, speed, and discrimination capability of the developed schemes under various fault conditions and against other transient events, highlighting their superiority over other proposed methods or existing techniques. Furthermore, the generalisation capability of AI-assisted schemes has been verified against many adverse factors such as high values of fault resistance and noisy measurement. To further evaluate the practical feasibility and assess the time performance of the proposed schemes, real-time Software In the Loop (SIL) testing has been utilised. Another very important task for the effective fault management of AC and DC SCs is the estimation of the accurate fault location. Identifying the precise location of faults is crucial for SCs, given their complex structure and the challenging repair process. As such, this thesis proposes the design of a data-driven fault location scheme for AC systems with SCs. The developed scheme utilises pattern recognition techniques, such as image analysis, for feature extraction. It also incorporates AI algorithms in order to formulate the fault location problem as an AI regression problem. It is demonstrated that the scheme can accurately estimate the fault location along the SCs length and ensure increased reliability against a wide range of fault scenarios and noisy measurements. Further comparative analysis with other data-driven schemes validates the superiority of the proposed approach. In the final chapter the thesis summarises the key observations and outlines potential steps for further research in the field of fault management of superconducting-based systems

Scientific Advances in STEM: From Professor to Students

This book collects the publications of the special Topic Scientific advances in STEM: from Professor to students. The aim is to contribute to the advancement of the Science and Engineering fields and their impact on the industrial sector, which requires a multidisciplinary approach. University generates and transmits knowledge to serve society. Social demands continuously evolve, mainly because of cultural, scientific, and technological development. Researchers must contextualize the subjects they investigate to their application to the local industry and community organizations, frequently using a multidisciplinary point of view, to enhance the progress in a wide variety of fields (aeronautics, automotive, biomedical, electrical and renewable energy, communications, environmental, electronic components, etc.). Most investigations in the fields of science and engineering require the work of multidisciplinary teams, representing a stockpile of research projects in different stages (final year projects, master’s or doctoral studies). In this context, this Topic offers a framework for integrating interdisciplinary research, drawing together experimental and theoretical contributions in a wide variety of fields

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.Comment: 29 pages,2 figures and 18 Table

Six Decades of Flight Research: An Annotated Bibliography of Technical Publications of NASA Dryden Flight Research Center, 1946-2006

Titles, authors, report numbers, and abstracts are given for nearly 2900 unclassified and unrestricted technical reports and papers published from September 1946 to December 2006 by the NASA Dryden Flight Research Center and its predecessor organizations. These technical reports and papers describe and give the results of 60 years of flight research performed by the NACA and NASA, from the X-1 and other early X-airplanes, to the X-15, Space Shuttle, X-29 Forward Swept Wing, X-31, and X-43 aircraft. Some of the other research airplanes tested were the D-558, phase 1 and 2; M-2, HL-10 and X-24 lifting bodies; Digital Fly-By-Wire and Supercritical Wing F-8; XB-70; YF-12; AFTI F-111 TACT and MAW; F-15 HiDEC; F-18 High Alpha Research Vehicle, F-18 Systems Research Aircraft and the NASA Landing Systems Research aircraft. The citations of reports and papers are listed in chronological order, with author and aircraft indices. In addition, in the appendices, citations of 270 contractor reports, more than 200 UCLA Flight System Research Center reports, nearly 200 Tech Briefs, 30 Dryden Historical Publications, and over 30 videotapes are included

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018

Fundamental concepts and models for the direct problem

This book series is an initiative of the Post Graduate Program in Integrity of Engineering Materials from UnB, organized as a collaborative work involving researchers, engineers, scholars, from several institutions, universities, industry, recognized both nationally and internationally. The book chapters discuss several direct methods, inverse methods and uncertainty models available for model-based and signal based inverse problems, including discrete numerical methods for continuum mechanics (Finite Element Method, Boundary Element Method, Mesh-Free Method, Wavelet Method). The different topics covered include aspects related to multiscale modeling, multiphysics modeling, inverse methods (Optimization, Identification, Artificial Intelligence and Data Science), Uncertainty Modeling (Probabilistic Methods, Uncertainty Quantification, Risk & Reliability), Model Validation and Verification. Each book includes an initial chapter with a presentation of the book chapters included in the volume, and their connection and relationship with regard to the whole setting of methods and models

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words