In large-scale construction projects such as naval refits or aircraft overhauls, project execution is subject to considerable uncertainty and a baseline schedule quickly becomes unachievable. For naval environments, high variability in work scope and duration occur at every stage. Furthermore, tools and equipment can fail or not be on-site when needed. Human resources are drawn from a pool of workers coming from a mix of fixed-capacity-unionized workers and contractors. To limit the effect of unexpected but inevitable schedule disruptions, resource (equipment and workers) buffers are us
Game-based learning tools often make use of questions to measure and encourage learning, but generating questions can be challenging, especially at the scale that companies like Axonify are required to do. In this project, the intern will design, implement, and evaluate a system that can apply machine-learning on a corpus of text (e.g., a textbook) to automatically generate questions that can be used in game-based learning tools.
The purpose of this project is to investigate self-adaptive forecasting and anomaly prediction algorithms based on deep neural networks (DNNs). DNNs present a compelling technology due to their wide-spread availability through open-source projects (e.g. TensorFlow, MXNet). However, usability of DNNs in scenarios outside of image, speech or text pattern recognition is mostly unproven. This project aims to reduce the knowledge gap that exists in the usage of DNNs in the context of pattern recognition with DNNs in network management and network equipment manufacturing.
By being moldable, durable, light, and inexpensive plastic packaging has seen a rapid growth in the use and its disposal is becoming a planetary challenge. Besides sustainable production and consumption globalized trade in waste plastics represents a significant option towards a circular economy. China, who so far represented the greatest importer of this material, recently (2017) implemented a policy banning the importation of most plastic waste and rising global concern regarding the destiny of this enormous displaced material flow.
The proposed research consists of two tiers to understand the volume change behaviour of problematic geomaterials. Engineered structures have been distressed by expansive soils because of heaving and subsidence. Likewise, mine waste tailings from mineral beneficiation process must be contained in-pit storage facilities to minimize environmental impacts.
AA6111 aluminum alloys possess a combination of excellent strength, good formability and good corrosion resistance that are widely use in the car panel manufacture. Direct chill (DC) casting process is typically employed for producing such alloy ingots. Despite its advantages, AA6111 alloys are considered as “hard-to-cast” alloy among 6xxx alloys because of high susceptibility to hot cracks. The present project will investigate the effect of chemical composition and grain refinement on hot crack susceptibility.
The integration of significant capacities of distributed energy resources (DERs) such as renewable wind and solar generation for a more sustainable energy future creates several challenges to the reliable and efficient operation of power distribution systems. These include: (i) Uncertain and intermittent nature of renewable generation compromises power quality for end-customers. (ii) Up-to-date distribution system network topologies are not well known and their real-time monitoring is limited. As a result, effective management of DERs is challenging.
In this project, a new method is developed to optimize the performance of an Unmanned Aerial Vehicle (UAV) for autonomous detection and on-the-job view-planning of infrastructure elements with the purpose of their accurate three-dimensional (3D) modeling. The existing view-planning approaches in the literature have mostly modeled non-complex or small-scale objects and have rarely been adapted to flying robots. In addition, the target object is often identified by human operators.
Data has become a central part of any organizationâs day-to-day operation. Organizations are looking to turn this information into actionable insight. The Dialysis Measurement, Analysis and Reporting System (DMAR) is designed to track healthcare quality indicators where changing practice will have high impact. The DMAR system applies rigorous methods to measure key health care performance measures and efficiently implement and measure change.
Clearpath Robotics has developed an accurate GPS navigation system that enables autonomous robots to move in outdoor environments. However, to move toward a target location and avoid obstacles along the path, a path planner is required. The objective of this project is to develop, tune and modify an efficient path planner for the robot in order to make it capable of moving in outdoor environments, then, the performance of the path planner and GPS navigation system will be checked by extensive real-world experiments.