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DTSTART;TZID=America/Denver:20250812T130000
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SUMMARY:Dynamic Modelling for Analysis of Wind Farm and Grid Interaction - Bikash Pal
DESCRIPTION:Abstract of Seminar\nElectrical generation\, transmission and distribution systems all over the world have entered a period of significant renewal and technological change. There have been phenomenal changes/deployments in technology of generation driven by the worldwide emphasis on energy from wind and solar as a sustainable solution to our energy need. Increasingly energy demand from heating and transportation will be met by electricity. So\, to accommodate changes in either end the transmission grid is required to operate in more responsive manner. This is the most credible challenge in smart transmission grid operation today. Some of the recent wind farm operations have grabbed media headlines of not being connectable to the grid. While the debate is on whether it is the wind farm or the grid is the cause\, the balance of the debate is shifting towards the integration and control aspect of these two technologies.\nThis seminar will briefly mention the recent major problems in connecting big wind farms to the grid. It will then identify few possible specific technical reasons supported by the general technical insights gathered from detailed technical study conducted at Bikash Pal’s research group at Imperial College London. Future research challenges and opportunities will be highlighted.\nCo-sponsored by: IEEE Task Force on IoT for Power Systems\nSpeaker(s): Bikash Pal\,\nVirtual: https://events.vtools.ieee.org/m/495135
URL:https://svec.org/event/dynamic-modelling-for-analysis-of-wind-farm-and-grid-interaction-bikash-pal/
LOCATION:Virtual: https://events.vtools.ieee.org/m/495135
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DTSTART;TZID=America/Los_Angeles:20250812T180000
DTEND;TZID=America/Los_Angeles:20250812T191500
DTSTAMP:20260422T042343
CREATED:20250718T094838Z
LAST-MODIFIED:20250718T094838Z
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SUMMARY:Distinguished Lecture: Machine Learning in NextG Networks via Generative Adversarial Networks
DESCRIPTION:Generative Adversarial Networks (GANs) implement Machine Learning (ML) algorithms that can address competitive resource allocation problems\, together with detection and mitigation of anomalous behavior. In this talk\, the speaker will discuss their use in next-generation (NextG) communications within the context of cognitive networks to address i) spectrum sharing\, ii) detecting anomalies\, and iii) mitigating security attacks. GANs have the following advantages. First\, they can learn and synthesize field data\, which can be costly\, time-consuming\, and non-repeatable. Second\, they enable pre-training classifiers by using semisupervised data. Third\, they facilitate increased resolution. Fourth\, they enable recovering corrupted bits in the spectrum. The talk will provide basics of GANs\, a comparative discussion on different kinds of GANs\, performance measures for GANs in computer vision and image processing as well as wireless applications\, a number of datasets for wireless applications\, performance measures for general classifiers\, a survey of the literature on GANs for i)–iii) above\, some simulation results\, and future research directions. In the spectrum sharing problem\, connections to cognitive wireless networks are established. Simulation results show that a particular GAN implementation is better than a convolutional autoencoder for an outlier detection problem in spectrum sensing.\nCo-sponsored by: Vishnu S. Pendyala\, SJSU\nSpeaker(s): Dr. Vishnu S. Pendyala\, Prof. Ender Ayanoglu\nVirtual: https://events.vtools.ieee.org/m/493301
URL:https://svec.org/event/distinguished-lecture-machine-learning-in-nextg-networks-via-generative-adversarial-networks/
LOCATION:Virtual: https://events.vtools.ieee.org/m/493301
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