Convolutional Neural Networks as Efficient Emulators for Atmospheric Models

Author: Berlin Chen Mentors: Hai Nguyen, Derek Posselt Editor: Michael Yao Abstract We used Convolutional Neural Networks (CNNs) to emulate the physics of the atmosphere in order to bypass solving partial-differential equations (PDEs) explicitly, which cuts down on computational cost. This is important because in the past, the models used to produce reliable weather forecasts … Continue reading Convolutional Neural Networks as Efficient Emulators for Atmospheric Models

Detection of Volume Changes in Greenland’s Marine Terminating Glaciers

Increased melting of marine-terminating glaciers of the Greenland Ice Sheet could lead to unstable dynamic ice mass loss, further accelerating global sea level rise. To better understand the historical context of the present-day widespread glacier front retreat, we mapped frontal positions for two years, 1994 and 2017, for 146 and 251 major outlet glaciers, respectively. Front position locations were identified using optical remote sensing imagery from the Landsat 5 (1994) and Landsat 8 (2017) satellites. Of the fronts surveyed, we find that 86% retreated between 2017 and 1994 and 62% retreated between 2017 and 2015, when the most recent glacial front positions were recorded. In addition, ice surface elevation differences for four dynamically-thinning glaciers were calculated using data collected from NASA’s Oceans Melting Greenland (OMG) mission in 2016 and 2017 by the Glacier and Ice Surface Topography Interferometer (GLISTIN-A). In each case, ice surface lowering was observed in excess of 10 m within the 10km-wide swath of GLISTIN-A near the glacier front. This evaluation of glacier change advances our understanding of ocean-driven Greenland ice mass loss.

Machine Learning for Cybersecurity: Network-based Botnet Detection Using Time-Limited Flows

Author: Stephanie Ding Mentor: Julian Bunn Editor: Sherry Wang Abstract Botnets are collections of connected, malware-infected hosts that can be controlled by a remote attacker. They are one of the most prominent threats in cybersecurity, as they can be used for a wide variety of purposes including denial-of-service attacks, spam or bitcoin mining. We propose … Continue reading Machine Learning for Cybersecurity: Network-based Botnet Detection Using Time-Limited Flows

Interview with Professor Maxwell J. Robb

Interview by Jonathan Chan, Associate Editor Fun trivia: Favorite food: sushi Favorite genre of music: punk rock Favorite molecule: naphthopyran Favorite TV show: obliged to say Breaking Bad Favorite scientist(s): Craig Hawker and Jeffrey Moore (former research advisers) 1. What type of research does your lab work on, and how might this research change the … Continue reading Interview with Professor Maxwell J. Robb

Development and Implementation of Captive Trajectories in the NOAH Water Tunnel Laboratory

Abstract: The NOAH Water Tunnel Laboratory is currently used to improve our understanding of various aspects of turbulence and fluid mechanics. The goal of this project is to understand the capabilities of a newly installed technology and the opportunities it presents for enhancing the the use of the NOAH Laboratory in the future. The recently installed Captive Trajectory System (CTS) , a cyber-mechanical system capable of moving and rotating within the water tunnel, allows us to explore new methods of simulating objects moving in complex, turbulent fluids. By harnessing the ability to program how the CTS behaves, the system was shown effective in modeling the motion of an object in real time as variable forces were applied to it. Through simple, controlled examples, we discovered the ability of the CTS to accurately model various types of motion, such as that of a mass-spring-damper system, or a planet orbiting a sun. In more complex examples, the CTS was able to simulate the general behavior of an airfoil in the wake of a cylinder with vortex shedding. The examples explored over the course of the project have proven that the CTS can be used as a useful experimental tool and will open the door to new methods of studying turbulence and unsteady aerodynamics in the future.

Eye Measurements as Objective Measures of Flow Experience

Author: Salma Elnagar Mentors: Shinsuke Shimojo and Mohammad Shehata Editor: Jagath Vytheeswaran Abstract Flow experience is achieved when a person is said to be “in the zone” as they achieve a fit between skill-and-challenge level in a certain activity. Many of these activities, such as video games, music, or athletic competitions, involve the participation or … Continue reading Eye Measurements as Objective Measures of Flow Experience