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X-WR-CALDESC:Events for Northeastern University College of Engineering
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DTSTART;VALUE=DATE:20210317
DTEND;VALUE=DATE:20210422
DTSTAMP:20260414T051008
CREATED:20210318T134829Z
LAST-MODIFIED:20210318T134829Z
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SUMMARY:Study Recruitment: Ancient Techniques and Mental Health Today
DESCRIPTION:Northeastern Department of Philosophy & Religion  \nHave you been experiencing stress and anxiety? \nYou may be eligible to participate in our study! \nHelp us investigate the impact of mindfulness on various life outcomes! All components of this study will take place virtually; participants will be asked to attend two 30-minute Zoom sessions in addition to up to 5 weeks of short\, daily smartphone tasks. \nYou must be 18 years or older\, a Boston-based Northeastern undergraduate student\, and a native English speaker to be eligible to participate. \nParticipants will receive $80 in compensation. \nContact us at pwolstudy@gmail.com if you’re interested and to see if you are eligible! \nThis study has been reviewed and approved by the Northeastern University Institutional Review Board (#21-02-21).
URL:https://coe.northeastern.edu/event/study-recruitment-ancient-techniques-and-mental-health-today/
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DTSTART;TZID=America/New_York:20210419T090000
DTEND;TZID=America/New_York:20210419T100000
DTSTAMP:20260414T051008
CREATED:20210412T145223Z
LAST-MODIFIED:20210412T145223Z
UID:25392-1618822800-1618826400@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Ahmet Oner
DESCRIPTION:PhD Dissertation Defense: Improving the Resilience of the Power Grid \nAhmet Oner \nLocation: Teams Meeting \nAbstract: The power grid constitutes one of the most critical infrastructures that have significant interdependencies with various others such as communication\, transportation\, emergency\, and health-care delivery systems. A disruption in the operation of the power grid may affect the operation of all others in an undesirable manner. Therefore\, improving the resiliency of power grids can also help increase the resiliency of other critical infrastructures. This dissertation presents methods to improve the resiliency of power grids against extreme events and/or system changes. \nFirst\, generation dispatch\, adaptable load shedding strategy\, and pro-active line switching are combined in order to maximize the resiliency of the overall power grid against extreme events. The moving event is monitored\, and the control actions are adjusted accordingly to improve the resilience under changing conditions affected by the natural disaster during its active period. Then\, that study is further extended and made it robust against voltage instability. The details of the methodology and its implementation are presented. \nTo reduce the probability of voltage problems and line flow limit violations\, and to improve power quality\, distributed generators (DG) are placed strategically ahead of the event using outage forecasts based on historical outage data. Therefore\, a possible set of outage scenarios is considered\, and a minimum number of required DG placements are determined to maintain system feasibility for all considered scenarios. \nLastly\, reactive power sources are placed to solve the voltage instability problems\, which are caused by the lack of reactive power in the system. The computational burden of optimal placement problem presents a practical limitation for applying it to very large scale systems considering multi-contingency cases. This part presents a practical and easily implementable solution that will address this limitation.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-ahmet-oner/
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DTSTART;TZID=America/New_York:20210419T150000
DTEND;TZID=America/New_York:20210419T160000
DTSTAMP:20260414T051008
CREATED:20210420T140007Z
LAST-MODIFIED:20210420T140007Z
UID:25489-1618844400-1618848000@coe.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Kaier Liang
DESCRIPTION:MS Thesis Defense: Rough-Terrain Locomotion and Unilateral Contact Force Regulations With a Multi-Modal Legged Robot \nKaier Liang \nLocation: Zoom Link \nAbstract: The study for legged locomotion has made lots of achievements. However\, the stability of the state-of-the-art bipedal robots are still vulnerable to external perturbation\, cannot negotiate extreme rough terrains\, and cannot directly regulate unilateral contact force.\nThis thesis will introduce a thruster-assisted bipedal walking robot called Harpy. The objective is to integrate the merits of legged and aerial robots in a single platform. The robot’s dynamics is simulated with simplifying assumptions. Furthermore\, this research will show that the employment of thruster allows to stabilize the robot’s frontal dynamics and apply model predictive control (MPC) to jump over obstacles to achieve multi-modal locomotion. In addition\, we will capitalize the thruster actions to demonstrate an optimization-free approach by regulating contact forces using an Explicit Reference Governor (ERG). Then\, we will focus on ERG-based fine-tuning of the joint’s desired trajectories to satisfy unilateral contact force constraints.
URL:https://coe.northeastern.edu/event/ece-ms-thesis-defense-kaier-liang/
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DTSTART;TZID=America/New_York:20210419T173000
DTEND;TZID=America/New_York:20210419T183000
DTSTAMP:20260414T051008
CREATED:20210303T144634Z
LAST-MODIFIED:20210303T144634Z
UID:24860-1618853400-1618857000@coe.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Ilkay Yildiz
DESCRIPTION:PhD Dissertation Defense: Spectral Ranking Regression \nIlkay Yildiz \nLocation: Zoom Link \nAbstract: We consider learning from ranking labels generated as follows: given a query set of samples in a dataset\, a labeler ranks the samples w.r.t.~her preference. Such ranking labels scale exponentially with query set size; most importantly\, in practice\, they often exhibit lower variance compared to class labels. \nWe propose a new neural network architecture based on siamese networks to incorporate both class and comparison labels\, i.e.\, rankings of sample pairs\, in the same training pipeline using Bradley-Terry and Thurstone loss functions. Our architecture leads to a significant improvement in predicting both class and comparison labels\, increasing classification AUC by as much as 35% and comparison AUC by as much as 6% on several real-life datasets. We further show that\, by incorporating comparisons\, training from few samples becomes possible: a deep neural network of 5.9 million parameters trained on 80 images attains a 0.92 AUC when incorporating comparisons. \nFurthermore\, we tackle the problem of accelerating learning over the exponential number of rankings. We consider a ranking regression problem in which we learn Plackett-Luce scores as functions of sample features. We solve the maximum likelihood estimation problem by using the Alternating Directions Method of Multipliers (ADMM)\, effectively separating the learning of scores and model parameters. This separation allows us to express scores as the stationary distribution of a continuous-time Markov Chain. Using this equivalence\, we propose two spectral algorithms for ranking regression that learn shallow regression model parameters up to 579 times faster than the Newton’s method. \nFinally\, we bridge the gap between deep neural networks (DNNs) and efficient spectral algorithms that regress rankings under the Plackett-Luce model. We again solve the ranking regression problem using ADMM\, and thus\, express scores as the stationary distribution of a Markov chain. Moreover\, we replace the standard l_2-norm proximal penalty of ADMM with Kullback-Leibler (KL) divergence. This is a more suitable distance metric for Plackett-Luce scores\, which form a probability distribution\, and significantly improves prediction performance. Our resulting spectral algorithm is up to 175 times faster than siamese networks over four real-life datasets comprising ranking observations. At the same time\, it consistently attains equivalent or better prediction performance than siamese networks\, by up to 26% higher Top-1 Accuracy and 6% higher Kendall-Tau correlation.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-ilkay-yildiz/
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