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X-WR-CALDESC:Events for Penn Engineering Events
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190605T100000
DTEND;TZID=America/New_York:20190605T120000
DTSTAMP:20260408T170254
CREATED:20190523T153134Z
LAST-MODIFIED:20190523T153134Z
UID:10006225-1559728800-1559736000@seasevents.nmsdev7.com
SUMMARY:Doctoral Dissertation Defense: "Method of MRI-Based Assessment of Cortical Bone Matrix and Mineral Properties in a Clinical Setting”
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Drs. Hee Kwon Song & Felix Wehrli are pleased to announce the Doctoral Dissertation Defense of Xia Zhao. This event is open to the public. \n  \nLarge conference room\, 1st floor Founders Building\, MRI Education Center\, Department of Radiology\, 3400 Spruce Street
URL:https://seasevents.nmsdev7.com/event/doctoral-dissertation-defense-method-of-mri-based-assessment-of-cortical-bone-matrix-and-mineral-properties-in-a-clinical-setting/
LOCATION:Founders Building\, 3400 Spruce Street
CATEGORIES:Doctoral,Student,Dissertation or Thesis Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190610T103000
DTEND;TZID=America/New_York:20190610T123000
DTSTAMP:20260408T170254
CREATED:20190530T153821Z
LAST-MODIFIED:20190530T153821Z
UID:10006229-1560162600-1560169800@seasevents.nmsdev7.com
SUMMARY:CBE Doctoral Dissertation Defense: "Improving Performance of Infiltrated SOFC Cathodes via Scaffold Engineering and Catalyst Surface Engineering"
DESCRIPTION:Committee Members: Raymond J. Gorte and John M. Vohs\, Co-Advisors; Aleksandra Vojvodic and Donald Berry.
URL:https://seasevents.nmsdev7.com/event/cbe-doctoral-dissertation-defense-improving-performance-of-infiltrated-sofc-cathodes-via-scaffold-engineering-and-catalyst-surface-engineering/
LOCATION:Room 337\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Doctoral,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190610T110000
DTEND;TZID=America/New_York:20190610T120000
DTSTAMP:20260408T170254
CREATED:20190604T160021Z
LAST-MODIFIED:20190604T160021Z
UID:10006230-1560164400-1560168000@seasevents.nmsdev7.com
SUMMARY:NSF CAREER Awards Workshop
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/nsf-career-awards-workshop/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Panel Discussion
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190611T103000
DTEND;TZID=America/New_York:20190611T120000
DTSTAMP:20260408T170254
CREATED:20190607T135743Z
LAST-MODIFIED:20190607T135743Z
UID:10006232-1560249000-1560254400@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Hierarchical Task-Parameterized Learning from Demonstration "
DESCRIPTION:Many modern humanoid robots are designed to operate in human environments\, like homes and hospitals. Such robots could help humans accomplish tasks and lower their physical and/or mental workload. However\, robot users in homes and hospitals typically are not familiar with robotics or programming\, therefore it is difficult for them to adapt robots to their specific needs and environments. To remedy this situation\, many researchers turn to learning from demonstration (LfD)\, which enables a robot to emulate natural human movement as opposed to having an operator devise control policies and reprogram the robot for every new situation it encounters. \nWe suggest a hierarchical LfD structure of task-parameterized models\, particularly for object movement tasks that are ubiquitous in everyday life and could benefit from robotic support. Inspired by the task-parameterized Gaussian mixture model (TP-GMM) algorithm\, we develop the hierarchical structure and explicitly utilize task parameters to maximize the expected performance in a new situation from a few demonstrated situations. The robot can thus determine when it should request new demonstrations when the expected performance is too low. Other advantages of our approach include that a wider range of task situations can be modeled in the same framework without deteriorating performance and that adding or removing demonstrations incurs low computational load\, and thus the robot’s skill library can be built incrementally. We show these advantages in a simulated task and in the real world where naïve participants collaborated with a Willow Garage PR2 robot to move a handheld object. For most tested scenarios our hierarchical method achieved significantly better task performance and subjective ratings than both a passive model with only gravity compensation and a single TP-GMM encoding all demonstrations.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-hierarchical-task-parameterized-learning-from-demonstration/
LOCATION:Moore 216\, 200 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190618T103000
DTEND;TZID=America/New_York:20190618T120000
DTSTAMP:20260408T170254
CREATED:20190607T134252Z
LAST-MODIFIED:20190607T134252Z
UID:10006231-1560853800-1560859200@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Machine Learning for Robotics: Achieving Safety\, Performance and Reliability by Combining Models and Data in a Closed-Loop System Architecture"
DESCRIPTION:The ultimate promise of robotics is to design devices that can physically interact with the world. To date\, robots have been primarily deployed in highly structured and predictable environments. However\, we envision the next generation of robots (ranging from self-driving and -flying vehicles to robot assistants) to operate in unpredictable and generally unknown environments alongside humans. This challenges current robot algorithms\, which have been largely based on a-priori knowledge about the system and its environment. While research has shown that robots are able to learn new skills from experience and adapt to unknown situations\, these results have been limited to learning single tasks\, and demonstrated in simulation or lab settings. The next challenge is to enable robot learning in real-world application scenarios. This will require versatile\, data-efficient and online learning algorithms that guarantee safety when placed in a closed-loop system architecture. It will also require to answer the fundamental question of how to design learning architectures for dynamic and interactive agents. This talk will highlight our recent progress in combining learning methods with formal results from control theory. By combining models with data\, our algorithms achieve adaptation to changing conditions during long-term operation\, data-efficient multi-robot\, multi-task transfer learning\, and safe reinforcement learning. We demonstrate our algorithms in vision-based off-road driving and drone flight experiments\, as well as on mobile manipulators.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-machine-learning-for-robotics-achieving-safety-performance-and-reliability-by-combining-models-and-data-in-a-closed-loop-system-architecture/
LOCATION:Moore 216\, 200 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190625T103000
DTEND;TZID=America/New_York:20190625T120000
DTSTAMP:20260408T170254
CREATED:20190610T205631Z
LAST-MODIFIED:20190610T205631Z
UID:10006233-1561458600-1561464000@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: “Thermalization of Bulk Magnetic Materials in Spin-Lattice Dynamics Simulations”
DESCRIPTION:Spin-lattice dynamics (SLD)\, an increasingly popular simulation method which simultaneously computes both atomic displacements and spins\, offer new possibilities for modeling the temporal evolution of systems where the coupling between these atomic features are relevant such as spin caloritronics\, heat assisted magnetic recording\, magnetocaloric responses and magnetic nanoparticle hyperthermia. To accurately model and understand these magnetic materials\, SLD must capture spin-spin and spin-lattice interactions in a physically meaningful way. Recent work indicates that including a local magnetic anisotropy term in SLD simulations may be necessary to appropriately couple the magnetic spins to the atomic system to allow for thermal transport between the systems. A key obstacle to the adoption of this term is the lack of knowledge of its parameters for a broad range of materials. Ab initio calculations can obtain these parameters\, but these calculations are limited by energy scales\, system size and computational cost. A new framework using bulk experimental properties is introduced to obtain these parameters which avoids some or all of these challenges of the ab initio method. Results from this framework are discussed for iron along with the validation procedure for the found parameters.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-thermalization-of-bulk-magnetic-materials-in-spin-lattice-dynamics-simulations/
LOCATION:Moore 216\, 200 S. 33rd Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20190626T153000
DTEND;TZID=America/New_York:20190626T170000
DTSTAMP:20260408T170254
CREATED:20190624T193409Z
LAST-MODIFIED:20190624T193409Z
UID:10006234-1561563000-1561568400@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Effect of Nanoparticle Size on the Mechanical Properties of Nanoparticle (NP) Assemblies"
DESCRIPTION:Nanoparticle assemblies (NPAs) have attracted tremendous interests of various research communities. The particle-size-effect on mechanical properties of NPAs is systematically studied. With decreasing the particle size d from 300 nm to 10 nm\, the SiO2 NPAs become drastically harder (∼39×)\, stiffer (∼15×)\, and tougher (>3.5×). The results are consistent with the data scattered in the literature for various nanoparticle (NP) systems\, indicating a fundamentally universal d-effect for all NPAs. A model is developed to correlate the hardness and the NP junction (NPJ) strength f. Here\, f is mainly due to van der Waals and capillary interactions\, roughly a constant (140 nN) for d = 100–300 nm\, and then f decreases with decreasing d from ∼100 nm. The deformation mechanism of NPAs (for indentation depth ≫d) is shear plasticity involving shear breaking of NPJs. The fundamental mechanism for the d-effect is that\, with decreasing d\, the NPJ’s planar density increases much faster than the decrease of f. Moreover\, three deformation mechanisms of NPAs\, (1) nanoparticle dislodging\, (2) shear-band formation\, and (3) cracking are naturally d-dependent. These new findings can provide important insights into the fundamental understanding of the inter-NP interaction\, the mechanical behavior of the NPAs\, and the design of robust NP-based devices. If time allows\, as an independent topic\, the experimental characterization of freestanding membrane will also be briefly discussed.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-effect-of-nanoparticle-size-on-the-mechanical-properties-of-nanoparticle-np-assemblies/
LOCATION:Moore 212
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
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