Search results for: Dan Brennan

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Jalón</a>, <a href="https://publications.waset.org/search?q=Feargal%20Brennan"> Feargal Brennan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A non-stationary stochastic optimization methodology is applied to an OWC (oscillating water column) to find the design that maximizes the wave energy extraction. Different temporal cycles are considered to represent the long-term variability of the wave climate at the site in the optimization problem. The results of the non-stationary stochastic optimization problem are compared against those obtained by a stationary stochastic optimization problem. The comparative analysis reveals that the proposed non-stationary optimization provides designs with a better fit to reality. However, the stationarity assumption can be adequate when looking at averaged system response. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Non-stationary%20stochastic%20optimization" title="Non-stationary stochastic optimization">Non-stationary stochastic optimization</a>, <a href="https://publications.waset.org/search?q=oscillating%0D%0Awater%20column" title=" oscillating water column"> oscillating water column</a>, <a href="https://publications.waset.org/search?q=temporal%20variability" title=" temporal variability"> temporal variability</a>, <a href="https://publications.waset.org/search?q=wave%20energy." title=" wave energy."> wave energy.</a> </p> <a href="https://publications.waset.org/10007489/non-stationary-stochastic-optimization-of-an-oscillating-water-column" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10007489/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10007489/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10007489/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10007489/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10007489/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10007489/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10007489/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10007489/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10007489/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10007489/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10007489.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1392</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Detecting Older Drivers- Stress Level during Real-World Driving Tasks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Weihong%20Guo">Weihong Guo</a>, <a href="https://publications.waset.org/search?q=Dan%20Brennan"> Dan Brennan</a>, <a href="https://publications.waset.org/search?q=Phil%20Blythe"> Phil Blythe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the effect of driving a motor vehicle on the stress levels of older drivers, indicated by monitoring their hear rate increase whilst completing various everyday driving tasks. Results suggest that whilst older female drivers heart rate varied more significantly than males, the actual age of a participant did not result in a significant change in heart rate due to stress, within the age group tested. The analysis of the results indicates the most stressful manoeuvres undertaken by the older drivers and highlights the tasks which were found difficult with a view to implementing technologies to aid the more senior driver in automotive travel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Driver%20stress" title="Driver stress">Driver stress</a>, <a href="https://publications.waset.org/search?q=heart%20rate" title=" heart rate"> heart rate</a>, <a href="https://publications.waset.org/search?q=older%20driver" title=" older driver"> older driver</a>, <a href="https://publications.waset.org/search?q=road%20safety" title=" road safety"> road safety</a>, <a href="https://publications.waset.org/search?q=speeding." title=" speeding."> speeding.</a> </p> <a href="https://publications.waset.org/8728/detecting-older-drivers-stress-level-during-real-world-driving-tasks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8728/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8728/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8728/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8728/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8728/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8728/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8728/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8728/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8728/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8728/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8728.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2221</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Retrieval Augmented Generation against the Machine: Merging Human Cyber Security Expertise with Generative AI</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Brennan%20Lodge">Brennan Lodge</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Amidst a complex regulatory landscape, Retrieval Augmented Generation (RAG) emerges as a transformative tool for Governance Risk and Compliance (GRC) officers. This paper details the application of RAG in synthesizing Large Language Models (LLMs) with external knowledge bases, offering GRC professionals an advanced means to adapt to rapid changes in compliance requirements. While the development for standalone LLMs is exciting, such models do have their downsides. LLMs cannot easily expand or revise their memory, and they cannot straightforwardly provide insight into their predictions, and may produce “hallucinations.” Leveraging a pre-trained seq2seq transformer and a dense vector index of domain-specific data, this approach integrates real-time data retrieval into the generative process, enabling gap analysis and the dynamic generation of compliance and risk management content. We delve into the mechanics of RAG, focusing on its dual structure that pairs parametric knowledge contained within the transformer model with non-parametric data extracted from an updatable corpus. This hybrid model enhances decision-making through context-rich insights, drawing from the most current and relevant information, thereby enabling GRC officers to maintain a proactive compliance stance. Our methodology aligns with the latest advances in neural network fine-tuning, providing a granular, token-level application of retrieved information to inform and generate compliance narratives. By employing RAG, we exhibit a scalable solution that can adapt to novel regulatory challenges and cybersecurity threats, offering GRC officers a robust, predictive tool that augments their expertise. The granular application of RAG’s dual structure not only improves compliance and risk management protocols but also informs the development of compliance narratives with pinpoint accuracy. It underscores AI’s emerging role in strategic risk mitigation and proactive policy formation, positioning GRC officers to anticipate and navigate the complexities of regulatory evolution confidently.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Retrieval%20Augmented%20Generation" title="Retrieval Augmented Generation">Retrieval Augmented Generation</a>, <a href="https://publications.waset.org/search?q=Governance%20Risk%20and%20Compliance" title=" Governance Risk and Compliance"> Governance Risk and Compliance</a>, <a href="https://publications.waset.org/search?q=Cybersecurity" title=" Cybersecurity"> Cybersecurity</a>, <a href="https://publications.waset.org/search?q=AI-driven%20Compliance" title=" AI-driven Compliance"> AI-driven Compliance</a>, <a href="https://publications.waset.org/search?q=Risk%20Management" title=" Risk Management"> Risk Management</a>, <a href="https://publications.waset.org/search?q=Generative%20AI." title=" Generative AI."> Generative AI.</a> </p> <a href="https://publications.waset.org/10013734/retrieval-augmented-generation-against-the-machine-merging-human-cyber-security-expertise-with-generative-ai" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013734/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013734/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013734/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013734/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013734/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013734/json" target="_blank" rel="nofollow" class="btn 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Search results for: Dan Brennan