21793 research outputs found
Sort by
Maritime Autonomous Surface Ships (MASS) and Energy Management System
No full textThe research and development of Maritime Autonomous Surface Ships (MASS) is underway in several countries, with operations either remotely controlled from a Shore Control Center (SCC) or fully autonomous, without the need for Officer of the Watch (OOW) supervision. This study focuses on integrating renewable energy systems, alternative fuels, and energy management strategies (EMS) to enhance the efficiency and sustainability of both conventional and fully autonomous vessels. In response to rising fuel costs and stringent International Maritime Organization (IMO) regulations, the research aims to optimize vessel performance, reduce emissions, and improve energy efficiency across various ship types.
The study begins by assessing conventional vessels before transitioning to fully autonomous operations. The research then examines the optimization of a hybrid renewable energy system (HRES) that incorporates photovoltaic (PV) arrays, vertical axis wind turbines (VAWTs), and battery storage into the existing ship power system. A comparative analysis is conducted between conventional and fully autonomous vessels using an artificial bee colony (ABC) algorithm. The optimal configuration for both vessel types is identified as Genset/PV/VAWT/Battery, minimizing the annualized cost of the system (ACS), while maximizing the renewable energy fraction and reducing carbon emissions. Notably, autonomous vessels demonstrate superior performance in terms of cost and emissions when compared to conventional vessels.
Further, the study investigates optimal marine alternative fuels for short-sea shipping, including hydrogen, LNG, and traditional fuels. Mathematical modeling in Python is used to evaluate key performance indicators (KPIs), with LNG proving to deliver the highest Net Present Value (NPV), especially for autonomous vessels. This provides insights for optimizing fuel selection and ensuring compliance with environmental regulations.
Finally, a multi-objective predictive energy management system is developed using nonlinear model predictive control (NMPC) combined with grey wolf optimization (GWO) to optimize energy distribution in autonomous vessels under dynamic wave conditions. The NMPC-GWO algorithm demonstrates robustness and adaptability, ensuring reliable performance in varying environmental and operational conditions.
In summary, this research offers a comprehensive framework for optimizing energy systems and fuel selection, driving improvements in operational efficiency and environmental sustainability in the maritime industry
Collision Avoidance for Non-Cooperative Multi-Swarm Coverage Control with Measurement Uncertainty
No full textThe main focus of this thesis is to provide strategies for collision-free motion in multi-swarm coverage control. Motivated by the diverse use of agent-based systems for various tasks, the scenario of multiple non-cooperating swarms independently covering a common area is presented. Using Voronoi tessellation in coverage control, collision-free motion of agents within the same swarm has been proven before. However, in the case of multiple swarms following their own objectives, these guarantees do not hold. To address this issue, the Optimal Reciprocal Collision Avoidance (ORCA) method used for safe navigation in multi-agent scenarios is applied to multi-swarm coverage control. Assuming knowledge regarding the positions of all agents, the proposed methodology is formally analyzed for planar motion and validated through Monte Carlo simulations. Subsequently, the collision-avoidance algorithm is investigated in environments where bounded disturbance measurement uncertainties are present. To account for these disturbances, an extension of ORCA is proposed. Formal guarantees are presented for motion without collisions between agents. This is done under the assumption that the input needed to counteract the disturbance can always be achieved. The theoretical results are applied to coverage control of multiple non-cooperating swarms and validated through MATLAB simulations
Modeling Tauopathy Progression in the Brain
No full textMotivated by the growing interest in modeling the nonlinear propagation dynamics of species, such as prion-like proteins, within complex networks like brain connectomes, this thesis introduces a comprehensive modeling framework comprising four key stages: (I) model development and analysis, (II) biologically informed parameterization, (III) a priori identifiability, and (IV) parameter estimation and practical identifiability, with a focus on modeling tauopathy progression in the brain. In Stage I, a generalization of the celebrated Fisher-Kolmogorov-Petrovsky-Piskunov (Fisher-KPP) reaction-diffusion (Re-Di) equation using fractional polynomial (FP) terms is proposed and rigorously analyzed, incorporating a nonlinear graph Laplacian to capture complex transport dynamics characterized by heterogeneity, directional bias, and subdiffusion over directed networks. In Stage II, we present an empirical framework for parameterizing the extended FP-Fisher-KPP Re-Di equations, grounded in a phenomenological description of tauopathy propagation, a leading hallmark of neurodegenerative diseases such as Alzheimer’s disease (AD), responsible for 60-80% of dementia cases worldwide. In Stage III, we propose a framework for a priori identifiability, formulated under idealized, noise-free experimental scenarios in a generic sense for the parameterized FP-Fisher-KPP Re-Di equations with multi-experimental designs, effortlessly adaptable to any analytic and meromorphic system of differential equations. This framework offers four main contributions: (i) highlighting the limitations and gaps in certain prior results by conducting a rigorous literature survey and presenting illustrative counterexamples, (ii) formulating the a priori identifiability of parameters and the observability of states simultaneously for systems with partially known parameterized initial conditions in a generic sense, (iii) formalizing the concept of generic local minimal dependence using one-parameter Lie groups of transformations, and (iv) devising a decomposition method to explore this new concept. The Allen Mouse Brain Connectivity Atlas (AMBCA) dataset is also used to model tauopathy progression in the mouse brain for implementing the proposed framework for a priori identifiability. In Stage IV, parameter estimation and practical identifiability are carried out for the proposed tauopathy model using experimental data under realistic conditions. Practical identifiability is evaluated via singular value decomposition of the Fisher information matrix (FIM), accounting for noise and experimental constraints such as limited measurement time points
Intellectual Property Law Implementation in the Pharmaceutical Sector: The Influence of Pro-Stringent and Pro-Lax Actors
No full textIntellectual Property (IP) has shaped the landscape in the pharmaceutical sector. In this industry, these protections have resulted in varying sentiments ranging from pro-lax proponents like advocacy groups supporting less stringent IP so that medicines can be made affordable and widely available in emerging countries, such as in the form of generic drugs, while pro-stringent actors encompassing multinational pharmaceutical corporations and trade representatives espouse more IP safeguards for political and economic leverage. India and China are two emerging economies, yet they have achieved varying results in implementing IP law. This thesis examines the roles of international and domestic pro-lax and pro-stringent actors that have shaped the political and legal landscape surrounding IP in the pharmaceutical industry, analyzing their influence based on the theoretical underpinnings of framing, lobbying, venue shopping, and agenda-setting, with a focus on landmark court cases in both regions. Findings demonstrate that, given the greater influence of pro-lax actors in India domestically and internationally, there is less stringent implementation of IP law. In contrast, given the more pronounced influence of pro-stringent actors in China domestically and internationally, IP law implementation in the pharmaceutical sector contains greater IP legal protections
Development of Attention Guided U-Net for Medical Image Segmentation
No full textMedical image segmentation is a process of isolating or identifying an object of interest in medical images. It plays a pivotal role in clinical diagnostics, monitoring and treating diseases. The convolutional neural network, U-Net, was specifically developed for segmentation of medical images in view of its ability to accurately segment with limited training data. Existing U-Net based segmentation networks suffer from high computational complexity in order to provide a reasonable performance. This thesis presents five U-Net based schemes that significantly reduce the computational complexity without compromising the performance. In the first part, we develop a number of segmentation schemes referred to as MAGNet, MedSegNet, SSNet, and FFNet that utilize attention mechanism-enhanced multiscale feature fusion. The first two networks are developed for segmenting CT, colonoscopy, and non-mydriatic 3CCD images. SSNet is a semi-supervised network that effectively makes use of both labeled and unlabeled data for segmenting brain anatomical structures of tissues in MR images. FFNet is proposed for segmenting benign and malignant tumors from ultrasound images. In the second part, we present a lightweight attention-guided network with feature recalibration, referred to as LASegNet. The main idea used in designing LASegNet is on one hand to reduce the number of parameters by cutting on the number of filters used and on the other hand, restore the performance by combining the features of the encoder and decoder units through a judicious use of attention guided module. Extensive experiments are performed to demonstrate the effectiveness of each of the schemes proposed. Specifically, it is shown that LASegNet is robust across images from different modalities
The Christian Theology of the Urantia Book, Transpersonal Exo-consciouness & the Unity of Mind
No full textThought adjusters are described as fragments of the Universal Father’s spirit indwelling human minds, guiding individuals spiritually. The Urantia Book emphasizes that true knowledge of God arises from inner communion—divine essence both within and transcending individuals. This spiritual connection is personal, accounting for religious diversity. Although the divine spark is universal, individual interpretation shapes unique religious experiences. The article proposes the single universal consciousness theory as the framework to understand the nature of reality as this aligns with the ideas of Schrödinger, Bohm, and Penrose that proposes that all individual minds are expressions of one underlying quantum field. Exo-consciousness refers to direct contact with non-human intelligence in non-ordinary, non-local and transpersonal experiences that connect individuals to a universal unified field of consciousness. These experiences can be cultivated via Altered States of Consciousness (ASC)—for example, through holotropic breathwork—allowing individuals to transcend personal identity and reconnect with the thought adjuster within. Within Transpersonal Psychology, such practices may function as existential therapies, effectively addressing depression, anxiety, spiritual crises, and fostering self-awareness and spiritual intelligence
Advanced Techniques for Monitoring and Detecting Cyber Attacks on IEC 61850 Smart Grid Substations.
No full textThe increasing digitization and interconnection of power systems has improved their operational efficiency and flexibility, but has also introduced critical cyber vulnerabilities. Ensuring the security of smart grid substations is therefore crucial for maintaining reliable grid operation and power delivery. In this thesis, we address the critical challenge of detecting attacks against IEC 61850 substations. The research encompasses the development and validation of advanced security monitoring frameworks using machine learning techniques and system simulations. We first introduce an OpenStack-based Hardware-in-the-Loop (HIL) framework that supports both emulation and co-simulation. This environment enables controlled evaluation of smart grid components' resilience to cyber threats and facilitates testing of the proposed security solutions. We then leverage Network and System Management (NSM) based on IEC 62351-7 and propose a hybrid anomaly detection platform that combines rule-based methods and deep learning to detect threats within IEC 61850 substations. To this end, we introduce a two-stage deep learning architecture that integrates LSTM, RNN, and GRU models to further enhance the accuracy of NSM-based anomaly detection. We then validate these approaches through simulations on various standard IEEE test grids. Finally, we implement a Deep Packet Inspection (DPI) mechanism, in compliance with the IEC 62351-90-2 standard, to identify malicious activity targeting IEC 61850 substations. This mechanism employs a two-level architecture to identify anomalies and then determine whether they were caused by faults or attacks. We then test this approach on a realistic IEC 61850 substation model implemented in our real-time co-simulation testbed. Collectively, the contributions discussed within this thesis offer a strategy, based on the IEC 62351 standard, to secure substations in a smart grid
Open Licence Literacy: Given to Know, Give to Grow
No full textJoshua Chalifour will explore one of many ways that academic libraries can support researchers in adopting open practices. He will focus on the value of improving literacy in open licensing. Drawing from his experience as Digital Scholarship Librarian and founding member of Concordia's Open Science Working Group, he will discuss how open licensing bolsters the scholarly ecosystem and extends into public and private sectors. The rights granted through open licences have ramifications for peoples' information seeking, research, creation, and sharing practices, which underpin open scholarship. Although applying and using open access licences appears simple on the surface, people frequently misunderstand what these licences assert. Libraries can serve as catalysts for cultural change, providing essential technical and practical support for open scholarship. One way in which libraries support this is through an information literacy approach that helps people make sense of potential outcomes through conscientious decision-making around licences
Listening to Listening: Stories of SpokenWeb Ghost Readings
No full textGhost Readings are durational, performative, and collective practices of listening. The story of how their methods evolved – and exactly when the ghost entered – is a long story, or rather a series of stories, intertwined with the early years of SpokenWeb and with SpokenWeb’s series of events called “Performing the Archive.” Let’s begin with the durational, performative, and collective dimensions of this listening practice, specifically the question of what we are doing when we listen. How do we as literary scholars tend to listen to literary audio from the past
Evaluation of factors for adoption of alternative fuel-based vehicles
Get PDFThis thesis develops a system dynamics simulation model for evaluating five factors like customer awareness, government initiatives, cost of vehicles, cost of fuels, infrastructure developments to increase the adoption of alternative fuel vehicles like EV’s, Biofuel vehicles and Hydrogen vehicles. This model also integrates other modes of sustainable transportation like bikes and public buses. This helps in providing insights about the vehicle adoption over time and the reduction of greenhouse gases from the transportation sector. System dynamic simulation technique is used in this study to evaluate these fuels. Three scenarios were modeled: A baseline scenario that follows the existing trends, Scenario 1, which prioritizes higher adoption of electric vehicles (EVs) and biofuel-powered vehicles, Scenario 2 prioritizes hydrogen fuel-based vehicles and improved biking culture. The simulation findings show that all scenarios achieve reductions in GHG emissions compared to the baseline, with Scenario 2 showing the lowest emissions because of the near-zero emissions features of hydrogen fuel-based vehicles and the advantages of enhanced bicycle transportation. It was found that cost had a huge impact in the growth of adoption of these vehicles and modes of transportation. A combination of technology developments, government initiatives that are supportive of the effort, and significant investment in infrastructure shaping customer perception is essential in reducing the overall GHG emissions which also aligns with Canada’s net zero goals