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Full-Text Articles in Engineering

Functional Object-Oriented Network: A Knowledge Representation For Service Robotics, David Andrés Paulius Ramos Mar 2020

Functional Object-Oriented Network: A Knowledge Representation For Service Robotics, David Andrés Paulius Ramos

Graduate Theses and Dissertations

In this dissertation, we discuss our work behind the development of the functional object-oriented network (abbreviated as FOON), a graphical knowledge representation for robotic manipulation and understanding of its own actions and (potentially) the intentions of humans in the household. Based on the theory of affordance, this representation captures manipulations and their effects on actions through the coupling of object and motion nodes as fundamental learning units known as functional units. The activities currently represented in FOON are cooking related, but this representation can be extended to other activities that involve manipulation of objects which result in observable changes of ...


Robotic Motion Generation By Using Spatial-Temporal Patterns From Human Demonstrations, Yongqiang Huang Mar 2019

Robotic Motion Generation By Using Spatial-Temporal Patterns From Human Demonstrations, Yongqiang Huang

Graduate Theses and Dissertations

Robots excel in manufacturing facilities because the tasks are repetitive and do not change. However, when the tasks change, which happens in almost all tasks that humans perform daily, such as cutting, pouring, and grasping, etc., robots perform much worse. We aim at teaching robots to perform tasks that are subject to change using demonstrations collected from humans, a problem referred to as learning from demonstration (LfD).

LfD consists of two parts: the data of human demonstrations, and the algorithm that extracts knowledge from the data to perform the same motions. Similarly, this thesis is divided into two parts. The ...


Towards Energy-Efficient Hardware Acceleration Of Memory-Intensive Event-Driven Kernels On A Synchronous Neuromorphic Substrate, Saunak Saha Jan 2019

Towards Energy-Efficient Hardware Acceleration Of Memory-Intensive Event-Driven Kernels On A Synchronous Neuromorphic Substrate, Saunak Saha

Graduate Theses and Dissertations

Spiking neural networks are increasingly becoming popular as low-power alternatives to deep learning architectures. To make edge processing possible in resource-constrained embedded devices, there is a requirement for reconfigurable neuromorphic accelerators that can cater to various topologies and neural dynamics typical to these networks. Subsequently, they also must consolidate energy consumption in emulating these dynamics. Since spike processing is essentially memory-intensive in nature, a significant proportion of the system's power consumption can be reduced by eliminating redundant memory traffic to off-chip storage that holds the large synaptic data for the network. In this work, I will present CyNAPSE, a ...


Deep Learning For Monitoring Cyber-Physical Systems, Tryambak Gangopadhyay Jan 2019

Deep Learning For Monitoring Cyber-Physical Systems, Tryambak Gangopadhyay

Graduate Theses and Dissertations

Different cyber-physical systems involving sequential data require accurate frameworks for predicting the state of the system leading to effective monitoring. If the framework is explanatory, the insights provided by the explanations can improve scientific understanding of the system. Detecting the transition to an impending instability is important to initiate effective control in a combustion system. Building robust frameworks is important in this context.

As one of the early applications of characterizing instability in a combustion system using Deep Neural Networks, we train our proposed deep convolutional neural network (CNN) model on sequential image frames extracted from hi-speed flame videos by ...


Freeway Traffic Incident Detection Using Large Scale Traffic Data And Cameras, Pranamesh Chakraborty Jan 2019

Freeway Traffic Incident Detection Using Large Scale Traffic Data And Cameras, Pranamesh Chakraborty

Graduate Theses and Dissertations

Automatic incident detection (AID) is crucial for reducing non-recurrent congestion caused by traffic incidents. In this paper, a data-driven AID framework is proposed that can leverage large-scale historical traffic data along with the inherent topology of the traffic networks to obtain robust traffic patterns. Such traffic patterns can be compared with the real-time traffic data to detect traffic incidents in the road network. Our AID framework consists of two basic steps for traffic pattern estimation. First, we estimate a robust univariate speed threshold using historical traffic information from individual sensors. This step can be parallelized using MapReduce framework thereby making ...


A Methodology For Rapid Hypersonic Flow Predictions Via Surrogate Modeling With Machine Learning And Deep Learning, Nathan Hemming Jan 2018

A Methodology For Rapid Hypersonic Flow Predictions Via Surrogate Modeling With Machine Learning And Deep Learning, Nathan Hemming

Graduate Theses and Dissertations

Generating and parsing through large amounts of wind tunnel,

ight test, or computational

uid dynamics (CFD) data can prove to be expensive. This makes, for example, the optimization

of aerothermal hypersonic components, which may contain a large number of independent variables,

challenging. Having a surrogate model to quickly and accurately approximate the data can help

with the optimal design process. A lower order model can be used instead of or in conjunction

with a higher order model to model a system with less computational eort. Typically, additional

assumptions are made to make a lower order model. These have the benet ...


A Study Of Interpretability Mechanisms For Deep Networks, Apurva Dilip Kokate Jan 2018

A Study Of Interpretability Mechanisms For Deep Networks, Apurva Dilip Kokate

Graduate Theses and Dissertations

Deep neural networks are traditionally considered to be “black-box” models where it is generally difficult to interpret a certain decision made by such models given a test instance. However, as deep learning is increasingly becoming the tool of choice in making many safety-critical and time-critical decisions such as perception for self-driving cars, the machine learning community has been extremely interested recently to build interpretation mechanisms for these so called black box deep learning models primarily to build users’ trust with the models. Many such mechanisms have been developed to explain behavior of deep models such as convolutional neural networks (CNNs ...


Reducing Labeling Complexity In Streaming Data Mining, Yesdaulet Izenov Jan 2018

Reducing Labeling Complexity In Streaming Data Mining, Yesdaulet Izenov

Graduate Theses and Dissertations

Supervised machine learning is an approach where an algorithm estimates a mapping

function by using labeled data i.e. utilizing data attributes and target values. One of the major

obstacles in supervised learning is the labeling step. Obtaining labeled data is an expensive

procedure since it typically requires human effort. Training a model with too little data tends

to overfit therefore in order to achieve a reasonable accuracy of prediction we need a minimum

number of labeled examples. This is also true for streaming machine learning models. Maintaining

a model without rebuilding and performing a prediction task without ever storing ...


Graph-Based Latent Embedding, Annotation And Representation Learning In Neural Networks For Semi-Supervised And Unsupervised Settings, Ismail Ozsel Kilinc Nov 2017

Graph-Based Latent Embedding, Annotation And Representation Learning In Neural Networks For Semi-Supervised And Unsupervised Settings, Ismail Ozsel Kilinc

Graduate Theses and Dissertations

Machine learning has been immensely successful in supervised learning with outstanding examples in major industrial applications such as voice and image recognition. Following these developments, the most recent research has now begun to focus primarily on algorithms which can exploit very large sets of unlabeled examples to reduce the amount of manually labeled data required for existing models to perform well. In this dissertation, we propose graph-based latent embedding/annotation/representation learning techniques in neural networks tailored for semi-supervised and unsupervised learning problems. Specifically, we propose a novel regularization technique called Graph-based Activity Regularization (GAR) and a novel output layer ...


Scene-Dependent Human Intention Recognition For An Assistive Robotic System, Kester Duncan Jan 2014

Scene-Dependent Human Intention Recognition For An Assistive Robotic System, Kester Duncan

Graduate Theses and Dissertations

In order for assistive robots to collaborate effectively with humans for completing everyday tasks, they must be endowed with the ability to effectively perceive scenes and more importantly, recognize human intentions. As a result, we present in this dissertation a novel scene-dependent human-robot collaborative system capable of recognizing and learning human intentions based on scene objects, the actions that can be performed on them, and human interaction history. The aim of this system is to reduce the amount of human interactions necessary for communicating tasks to a robot. Accordingly, the system is partitioned into scene understanding and intention recognition modules ...


Behavior-Grounded Multi-Sensory Object Perception And Exploration By A Humanoid Robot, Jivko Sinapov Jan 2013

Behavior-Grounded Multi-Sensory Object Perception And Exploration By A Humanoid Robot, Jivko Sinapov

Graduate Theses and Dissertations

Infants use exploratory behaviors to learn about the objects around them. Psychologists have theorized that behaviors such as touching, pressing, lifting, and dropping enable infants to form grounded object representations. For example, scratching an object can provide information about its roughness, while lifting it can provide information about its weight. In a sense, the exploratory behavior acts as a ``question'' to the object, which is subsequently ``answered" by the sensory stimuli produced during the execution of the behavior. In contrast, most object representations used by robots today rely solely on computer vision or laser scan data, gathered through passive observation ...


Intelligence Tests For Robots: Solving Perceptual Reasoning Tasks With A Humanoid Robot, Connor Schenck Jan 2013

Intelligence Tests For Robots: Solving Perceptual Reasoning Tasks With A Humanoid Robot, Connor Schenck

Graduate Theses and Dissertations

Intelligence test scores have long been shown to correlate with a wide variety of other abilities. The goal of this thesis is to enable a robot to solve some of the common tasks from intelligence tests with the intent of improving its performance on other real-world tasks. In other words, the goal of this thesis is to make robots more intelligent. We used an upper-torso humanoid robot to solve three common perceptual reasoning tasks: the object pairing task, the order completion task, and the matrix completion task. Each task consisted of a set of objects arranged in a specific configuration ...


Robotic Swarming Without Inter-Agent Communication, Daniel Jonathan Standish Jan 2013

Robotic Swarming Without Inter-Agent Communication, Daniel Jonathan Standish

Graduate Theses and Dissertations

Many physical and algorithmic swarms utilize inter-agent communication to achieve advanced swarming behaviors. These swarms are inspired by biological swarms that can be seen throughout nature and include bee swarms, ant colonies, fish schools, and bird flocks. These biological swarms do not utilize inter-agent communication like their physical and algorithmic counterparts. Instead, organisms in nature rely on a local awareness of other swarm members that facilitates proper swarm motion and behavior. This research aims to pursue an effective swarm algorithm using only line-of-sight proximity information and no inter-agent communication. It is expected that the swarm performance will be lower than ...


A Location-Aware Architecture Supporting Intelligent Real-Time Mobile Applications, Sean J. Barbeau Jan 2012

A Location-Aware Architecture Supporting Intelligent Real-Time Mobile Applications, Sean J. Barbeau

Graduate Theses and Dissertations

This dissertation presents LAISYC, a modular location-aware architecture for intelligent real-time mobile applications that is fully-implementable by third party mobile app developers and supports high-precision and high-accuracy positioning systems such as GPS. LAISYC significantly improves device battery life, provides location data authenticity, ensures security of location data, and significantly reduces the amount of data transferred between the phone and server. The design, implementation, and evaluation of LAISYC using real mobile phones include the following modules: the GPS Auto-Sleep module saves battery energy when using GPS, maintaining acceptable movement tracking (approximately 89% accuracy) with an approximate average doubling of battery life ...