More Like this

1. IEEE Transactions on Power Systems

   Kancherla, Sameera; Dobson, Ian (January 2018, IEEE transactions on power systems)

   null (Ed.)

   The empirical probability distribution of transmission line restoration times is obtained from 14 years of field data from a large utility. The distribution of restoration times has a heavy tail that indicates that long restoration times, although less frequent, routinely occur. The heavy tail differs from the convenient assumption of exponentially distributed restoration times, impacts power system resilience, and makes estimates of the mean restoration time highly variable. 

   more » « less
2. Uplink Resource Allocation in IEEE 802.11ax

   https://doi.org/10.1109/ICC.2019.8761594  

   Bhattarai, Sudeep; Naik, Gaurang; Park, Jung-Min Jerry (May 2019, IEEE International Conference on Communications (ICC))
3. Wi-Fi Sensing Based on IEEE 802.11bf

   https://doi.org/10.1109/MCOM.007.2200347  

   Chen, Cheng; Song, Hao; Li, Qinghua; Meneghello, Francesca; Restuccia, Francesco; Cordeiro, Carlos (January 2023, IEEE Communications Magazine)
4. Stonebraker receives IEEE John von Neumann Medal

   https://doi.org/10.1145/1058150.1058153  

   DeWitt, David; Carey, Michael; Hellerstein, Joseph M. (March 2005, ACM SIGMOD Record)

   null (Ed.)

   In December 2004, Michael Stonebraker was selected to receive the 2005 IEEE John von Neumann Medal for his "contributions to the design, implementation, and commercialization of relational and object-relational database systems." Mike is the first person from the database field selected to receive this award. He joins an illustrious group of former recipients, including Barbara Liskov (2004), Alfred Aho (2003), Ole-Johan Dahl and Kristen Nygaard (2002), Butler Lampson (2001), John Hennessy and David Patterson (2000), Douglas Engelbart (1999), Ivan Sutherland (1998), Maurice Wilkes (1997), Carver Mead (1996), Donald Knuth (1995), John Cocke (1994), Fred Brooks (1993), and Gordon Bell (1992). 

   more » « less
5. 2024 IEEE International Communications Energy Conference (INTELEC)

   https://doi.org/10.1109/INTELEC60315.2024  

   Li, Yingpei; Bohara, Bharat; Krishnamoorthy, Harish; Seshadrinath, Jeevanand (January 2024, IEEE)

   This work introduces a machine learning approach for developing Digital Twins (DTs) for DC-DC converters, focusing on in-situ implementation in real-world operational conditions. A system based on a boost converter has been developed in MATLAB Simulink. To mirror real-world scenarios, commercial datasheets along with a range of input parameters, health degradation elements, temperature influence, and random noises have been considered. The study employs Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) for predicting critical circuit responses of the boost converter, including inductor current, output voltage, and efficiency. Investigations show that MLP performs relatively poorly in the presence of noise. The CNN and RNN outperform the MLP under various noise levels, with the RNN exhibiting the best performance. This work advances DTs technology in power electronics, aiming to improve converter system optimization and enable predictive maintenance. 

   more » « less