IEEE Eastern Idaho Section

The second Virtual Town Hall opportunity for all members to connect, share ideas, and engage with the section leadership. 🗓 Date: June 18th, 2025 🕒 Time: 7PM to 8PM; MST 💻 Location: Online (Google Meet – https://meet.google.com/thb-uqbp-exy) This session will include: – Updates on section activities and initiatives – Opportunities for involvement and leadership – Open Q&A – Your feedback on how we can better serve you Whether you're a long-time member or new to IEEE, your voice matters. We hope you'll join us to help shape the future of our section. If you have any questions or topics you'd like to see addressed, feel free to email Scott Heller at the email below. We look forward to seeing you there! Warm regards, Scott Heller IEEE Eastern Idaho Section Chair [email protected] Virtual: https://events.vtools.ieee.org/m/487445

Join us for the upcoming technical talk —attend in person at Stockman’s Restaurant or virtually via (https://teams.microsoft.com/l/meetup-join/19%3ameeting_MGYxYTkzNmMtMzg0MS00MjdjLTkwYmMtOWNkMWMwNjMzOWFi%40thread.v2/0?context=%7b%22Tid%22%3a%224cf464b7-869a-4236-8da2-a98566485554%22%2c%22Oid%22%3a%2264d770c8-e8c2-4ef1-b2f4-1e7ebd03bd37%22%7d) Title: An Introduction to Uncertainty Quantification for Deep Learning-Based Models. Abstract: Deep learning has become increasingly popular in recent years, largely due to its ability to achieve state-of-the-art performance on many complex tasks. However, even well-trained models cannot detect incorrect predictions (e.g., ChatGPT cannot detect its own hallucinations), nor do they have the inherent ability to express what they “do not know”. Uncertainty quantification (UQ) is a recently emerging subfield of deep learning that provides tools for dealing with such issues. UQ methods estimate the uncertainty associated with a model's predictions, enabling the detection of misclassified samples or those from outside the semantic domain of the training data (i.e., samples far from the training distribution). UQ is crucial for models deployed in high-risk applications, such as medical diagnostics, materials science, nuclear engineering, and autonomous vehicles. This presentation gives a basic introduction to UQ in the context of deep learning and briefly details some important methods that have been consistently shown to perform well in a variety of tasks. Speaker: Kyle Lucke Kyle Lucke is a PhD student in the Computer Science Department at the University of Idaho, working in the Machine Intelligence and Data Analytics lab. He received his Bachelor of Science in Computer Science with a minor in Mathematics at the University of Montana in 2019. He graduated Summa Cum Laude and received the President's Outstanding Senior award. He then obtained his Master of Science in Computer Science at the University of Montana in 2021, where he developed novel statistical methods for protein inference. His current research focuses on developing novel uncertainty quantification approaches for high-stakes deep learning-based applications. In the past, he has worked on novel approaches for semantic image segmentation for biomedical images, physics-informed neural networks, and generative models for cybersecurity applications. His other research interests include robust AI, adversarial machine learning, and deep learning-based approaches for biomedical image processing. 1175 Pier View Dr, Idaho Falls, Idaho, United States, 83402, Virtual: https://events.vtools.ieee.org/m/490178

Join us for the upcoming technical talk —attend in person at Stockman’s Restaurant or virtually via (https://teams.microsoft.com/l/meetup-join/19%3ameeting_MGYxYTkzNmMtMzg0MS00MjdjLTkwYmMtOWNkMWMwNjMzOWFi%40thread.v2/0?context=%7b%22Tid%22%3a%224cf464b7-869a-4236-8da2-a98566485554%22%2c%22Oid%22%3a%2264d770c8-e8c2-4ef1-b2f4-1e7ebd03bd37%22%7d) Title: Enabling gid-forming control in unbalanced distribution networks. Abstract: Distribution networks are often unbalanced, causing oscillatory responses in inverter control designed for balanced conditions. To address this problem, this paper develops a grid-forming control effective under unbalanced conditions and meets specifications by setting and regulating frequency and voltage, providing voltage support, sharing active power, injecting negative sequence current, and providing fault ride-through. The proposed control features enhanced frequency and voltage droop controllers and nested current and voltage control loops that all nicely integrate an enhanced current limiter. To achieve the control design, a time-domain transformation appropriate for inverter control is proposed. This enables time-domain decomposition of unbalanced electrical signals into positive and negative synchronous reference frames. The effectiveness of the proposed transformation and grid-forming control is demonstrated through real-time simulation of a modified IEEE 123 distribution network on the Real-Time Digital Simulator. Speaker: Yemi Ojo Yemi Ojo earned his Master’s degree in electrical engineering and renewable energy systems from the University of Leeds, UK, and a PhD in electrical engineering from the University of Cambridge, UK. He is currently a postdoctoral researcher in the Grid Integration and Control group at Idaho National Laboratory. His research interests include control, analysis, and optimization of power networks with applications to inverter-based microgrids. 1175 Pier View Dr, Idaho Falls, Idaho, United States, 83402, Virtual: https://events.vtools.ieee.org/m/491508