Embry-Riddle Aeronautical University, Electrical Engineering & Computer Science Department的动态

Future of Flight! ?? ?? ? Safety assurance and certification challenges in artificial intelligence (AI) and machine learning (ML) were the topics of focus at a recent?Federal Aviation Administration?meeting in Virginia that GE Aerospace Research was invited to attend. The meeting brought together aerospace industry experts from across the country to share insights into key areas concerning AL/ML in safety-critical aerospace applications. ? Representing GE Aerospace Research, Computer Scientist?Saswata Paul, PhD?presented the results of an ongoing FAA-funded program on the assurance of AI/ML-enabled digital aerospace systems. This program is paving the foundation for defining a clear and standardized certification path for AI/ML-based intelligent digital aerospace systems in the future. #FutureOfFlight #AI #MachineLearning #GEAerospaceResearch

Hello Everyone , Artificial intelligence (AI) refers to the simulation of human intelligence in machines designed to think and learn like humans. This encompasses various technologies, including machine learning, natural language processing, and computer vision. AI can perform tasks like recognizing speech, making decisions, and understanding language. Today I completed AI module on space. AI?is?revolutionizing?space?exploration?in?many?exciting?ways!?Here?are?some?key?applications: 1.Data?Analysis:?AI?processes?vast?amounts?of?data?from?space?missions,?such?as?images?and?sensor?readings,?helping?scientists?better?understand?celestial?bodies?and?identify?potential?hazards. 2.Mission?Planning:?AI?helps?plan?space?missions?by?determining?optimal?trajectories?and?landing?strategies,?reducing?risks?and?ensuring?mission?success. 3.Robotics:?AI?controls?robots?exploring?space,?performing?tasks?too?dangerous?or?difficult?for?humans,?like?exploring?Mars'?surface. 4.Decision?Making:?AI?makes?real-time?decisions?during?missions,?such?as?aborting?a?mission?or?changing?course?to?ensure?safety?and?efficiency. 5.Autonomy:?AI?enhances?spacecraft?autonomy,?reducing?reliance?on?human?intervention?and?aiding?astronauts?with?navigation?and?operations. 6.Propulsion?Systems:?AI?is?used?to?develop?new,?more?efficient?propulsion?systems,?enabling?spacecraft?to?travel?farther?and?carry?heavier?payloads. AI's?potential?in?space?exploration?is?vast,?and?as?technology?advances,?we?can?expect?even?more?innovative?applications.??? Thank you JECRC University Intel Corporation

Imagine a world where artificial intelligence (AI) was introduced by Alan Turing as early as 1951. This thought experiment isn't just a leap into science fiction—it's a compelling exploration of how such an early advent of AI could have fundamentally reshaped our world. With AI emerging in the 1950s, technological advancements in healthcare, education, and even space exploration might have been significantly accelerated. By the late 20th century, we could have seen personalized learning in classrooms and AI-driven industrial processes that revolutionized the global economy. However, this early AI timeline wouldn't have been without its challenges. Rapid job displacement, heightened privacy concerns, and intensified Cold War tensions might have created a more precarious global landscape. The ethical and legal frameworks for AI, which are still evolving today, would have been forced to develop much earlier, potentially leading to significant societal dilemmas and disparities. Despite these potential pitfalls, the cultural and societal impacts of AI in this alternate timeline are intriguing. Imagine a world where autonomous vehicles have been the norm for decades, or where access to information has been dominated by AI concierges, fundamentally altering how we interact with technology and each other. The environmental implications would also be profound, with AI potentially both mitigating and exacerbating climate challenges. Reflecting on this scenario highlights the importance of thoughtful and responsible AI development as we continue to advance in this field. It serves as a reminder of the profound impact that technological innovation can have on society, urging us to consider the ethical and societal implications as we shape our future. Dive deeper into this fascinating alternate history by reading the full article on Forbes and explore how different our world might be if AI had emerged in 1951. Click this link https://lnkd.in/enuxwPz7 to continue the journey! #AI #AlanTuring #TechInnovation #EthicsInTech NASA - National Aeronautics and Space Administration, European Space Agency - ESA, Neil Sahota

Rory Nairn and Rafa? Lenczewski have just returned from the Dublin Tech Summit. ?? ???? The most popular topic of the talks at this conference was definitely the use of #AI and its business potential. The presentations that Rory and Rafa? enjoyed the most were ?? ?? "Where's my Flying Car?" – a comparison of past technological forecasts with contemporary innovations and an analysis of the reasons behind some of these unfulfilled promises. Speakers: Grzegorz Ombach from Airbus, Julie Garland from Avtrain, and Wassim Derguech from Future Mobility Campus Ireland. ?? "Hey AI, What’s Next?" on the future of artificial intelligence, its impact on industry and daily life, with a focus on potential ethical dilemmas and societal changes due to the growing popularity of AI. Speakers: Emerald de Leeuw from Logitech, Omar Hatamleh from NASA, and Jack McCauley from Berkeley University. Below you can find a photo from the trip to Dublin. ?? ?? #dublintechsummit #artificialintelligence

Revolutionizing Aircraft Refueling with AI: My Master's Thesis Journey ???? Today, I'm proud to present my Master's thesis at Cranfield University, marking the culmination of months of intensive research and innovation. Here's a glimpse into my work: ?? Topic: Enhancing Automated Ground Refueling Systems for Smart Airports ?? Collaboration: Airbus, UK Research and Innovation, and Aerospace Technology Institute ?? Tech Stack: Advanced Machine Learning framework combining YOLOv10 and a novel deep learning sequence model (SizPos-GRU) ?? Goal: Accurately predict the future position of commercial aircraft's refueling port in video streams Key Achievements: - Developed a cutting-edge AI solution for a critical aviation challenge - Bridged the gap between academic research and industry application - Potential to significantly improve airport efficiency and sustainability This project has been a thrilling journey of pushing the boundaries of AI in aerospace. I'm grateful for the guidance of my advisors, the support from industry partners, and the resources provided by Cranfield University. As I move from academia to industry, I'm excited to continue innovating in the AI field! #CranfieldUniversity #Innovation #Tech #GenerativeAI #DataAnalytics #AI #ArtificialIntelligence #MachineLearning #DataScience #AIEngineer #NewGrad #OpenToWork Interested in discussing AI applications in aerospace or exploring collaboration opportunities? Let's connect!

A claimed nanosecond response time for artificial neurons built using glass could change the AI training game “Because PSG (phosphosilicate glass) can withstand high voltages without breaking, it allows the protons to travel at ludicrous speeds while also being incredibly energy-efficient. The material is both common and easy to fabricate, making it not only the fastest option, but also a practical one. "Once you have an analog processor, you will no longer be training networks everyone else is working on," said lead author Murat Onen in the statement. "You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all." "In other words, this is not a faster car, this is a spacecraft," he added.” https://lnkd.in/g6VV7bHq

I am very pleased to share my Master's project: Spacecraft Position and Attitude Estimation with Deep Learning. In this project, state-of-the-art artificial intelligence models are explored as a means of informing the position and attitude of a spacecraft through the data captured by its onboard camera. Namely, AlexNet is trained using transfer learning to classify images of nearby planets and estimate the altitude of the spacecraft relative to the planet. Additionally, an optical flow estimation algorithm, RAFT, is proposed in a novel methodology for spacecraft attitude estimation based on the relative motion of the star field and surrounding celestial bodies captured by the spacecraft camera. To simulate a realistic space environment, the open-source software OpenSpace is used to gather synthetic image data and the corresponding planet and altitude labels for training purposes. The position estimation model is able to perform planetary classification with a test accuracy of about 98% and an average altitude error of 2800 kilometers. Though this project offers a brief glimpse of the greater complexities within AIML as a science, the possibility of leveraging advanced models for autonomous spacecraft GNC is established. Overall, this project was a fruitful learning experience that offered insight on how AIML algorithms can be (and are) used within aerospace engineering.

Our PhD Fellow Muhammad Hamza Zafar presented our recent work. Syed Muhammad Salman Bukhari, Muhammad Hamza Zafar, Syed Kumayl Raza Moosavi, Noman Mujeeb Khan, and Filippo Sanfilippo. "FireNet: A Hybrid Deep Learning Approach for Enhanced Fire Detection in Remote Sensing Imagery." In Intelligent Systems Conference, pp. 1-15. Cham: Springer Nature Switzerland, 2024. We are especially proud of the work conducted by our talented team member Syed Muhammad Salman Bukhari! In this study, we address the critical task of early bushfire detection in remote areas through UAV imaging, a vital effort for protecting inhabitants, infrastructure, and ecosystems within the smart cities paradigm. We introduce FireNet, a deep learning framework that employs a hybrid Inception Residual Neural Network (Inception-ResNet) model, specifically designed for the rapid and precise classification of fire and non-fire regions in real-time image processing scenarios. FireNet synergises the strengths of both Inception and ResNet architectures, enhancing the model’s feature extraction capabilities while significantly reducing computational overhead. A distinctive aspect of FireNet is the adoption of the HardSwish activation function, demonstrating superior performance over the conventional Rectified Linear Unit (ReLU) in our fire detection cases. Through rigorous evaluation of a robust dataset, FireNet achieves an impressive accuracy of 96%, with a corresponding AUC of approximately 96%. These results not only affirm FireNet’s efficacy in accurately identifying fire and non-fire areas but also underscore its importance as a crucial tool for real-time fire monitoring and rescue operations, especially in the context of smart cities. With its outstanding efficiency and accuracy, FireNet represents a significant leap forward in the domain of fire detection technologies, highlighting the role of deep learning in enhancing urban resilience against fires. Artificial Intelligence, Biomechatronics, and Collaborative Robotics UiA - Mechatronics / Mekatronikk CIEM - Centre for Integrated Emergency Management UiA - Faculty of Engineering and Science University of Agder (UiA) #DeepLearning #FireDetection #UAV #Imaging #InceptionResNet #SmartCities

??? The Moonshot’s Legacy for AI Join us for breakthrough new research that shows how the stringent safety standards and trust protocols of the Apollo space program can inform the development of secure AI systems. Vital lessons from space exploration are essential for ensuring the reliability and safety of AI. ? FDL-X Heliolab: AI SOTA (State of the Art) Showcase ? ?? WHEN: 16th August 2024 ?? WHERE: fdl.ai To find out how we are making AI a tool for discovery, join us for the SOTA Showcase:?https://lnkd.in/gvF2Khs3 Meticulous attention to detail is essential in AI development. From algorithm design to data preprocessing and model training, every aspect must be scrutinized to ensure accuracy and reliability. These principles and others are behind the results of this year’s FDL-X | Heliolab. #AIEngineering #Precision #Development

The Impact of AI on Aerospace Engineering