Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks

We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset.Comment: Conference on Empirical Methods in Natural Language Processing (EMNLP 2017) September 2017, Copenhagen, Denmar

Exploiting Text and Network Context for Geolocation of Social Media Users

Research on automatically geolocating social media users has conventionally been based on the text content of posts from a given user or the social network of the user, with very little crossover between the two, and no bench-marking of the two approaches over compara- ble datasets. We bring the two threads of research together in first proposing a text-based method based on adaptive grids, followed by a hybrid network- and text-based method. Evaluating over three Twitter datasets, we show that the empirical difference between text- and network-based methods is not great, and that hybridisation of the two is superior to the component methods, especially in contexts where the user graph is not well connected. We achieve state-of-the-art results on all three datasets

Design, Fabrication, and Test of a Single Rotor Modular Unmanned Aerial Vehicle for Algae Bloom Monitoring of Lake Erie

Every summer, runoff pollution is causing algae in Lake Erie to grow out of control, impacting the health of the lake, suffocating fish, making water unsafe for swimming, deterring tourists, and damaging local economies. Given these facts, the current study proposed a swarm of single rotor unmanned aerial vehicles (SRUAV) for health monitoring of Lake Erie. Traditionally, for such a task, a single drone is designed with complicated structure and control modules resulting in high costs of design, construction and maintenance. A single unit design can be very vulnerable and costly to maintain. Robotic swarms can achieve the same ability through cooperation and have the advantage of reusability of the simple agents and the low cost of construction and maintenance. Robotic swarms also have the advantage of high parallelism, which is especially suitable for large scale tasks. In the present work, as the first phase of the overall project, design, fabrication and test of a single agent from the envisioned swarm is detailed. The simple agent will be equipped with a modular payload fitted with either a camera or sampling/dispenser device and will be responsible for the aerial photography and sampling of algae blooms in Lake Erie. The current practice for the research data collection is either relying on the US-based research centers data or conducting manual field investigations. The long-term goal of the proposed research is to provide an alternative low-cost solution for the health monitoring of Lake Erie, with other potential use cases, which could benefit local Canadian researchers including UWindsor’s Great Lakes Institute for Environmental Research and enhance the productivity and efficiency of the monitoring practices

Fault Isolation and Identification of a Four-Single-Gimbal Control Moment Gyro On-board a 3-axis Stabilized Satellite

Control moment gyros are known for their applications in attitude stabilization. These actuators are susceptible to malfunction, which results in faults and failures. Therefore, diagnosing the faults can improve the reliability of completing a mission while reducing maintenance costs. Thus, a model-based fault diagnosis method is proposed here. The intended algorithm is an enhanced version of previous work by the author. The enhancement employs a condensed approach to alleviate the delay caused by the filter’s confidence in its estimations. A case-study on a closed-loop controlled satellite is provided along with an extensive Monte Carlo simulation to evaluate the proposed method’s performance. The results show that the enhanced method can achieve superior performance while requiring less computational resources by eliminating extra grid search loops

A Neural Model for User Geolocation and Lexical Dialectology

We propose a simple yet effective text- based user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DAREDS, a dataset for evaluating dialect term detection methods