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You searched for +publisher:"U of Denver" +contributor:("Mohammad H. Mahoor"). Showing records 1 – 3 of 3 total matches.

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1. Mollahosseini, Ali. Developing an Affect-Aware Rear-Projected Robotic Agent.

Degree: PhD, Computer Science and Engineering, 2018, U of Denver

Social (or Sociable) robots are designed to interact with people in a natural and interpersonal manner. They are becoming an integrated part of our daily lives and have achieved positive outcomes in several applications such as education, health care, quality of life, entertainment, etc. Despite significant progress towards the development of realistic social robotic agents, a number of problems remain to be solved. First, current social robots either lack enough ability to have deep social interaction with human, or they are very expensive to build and maintain. Second, current social robots have yet to reach the full emotional and social capabilities necessary for rich and robust interaction with human beings. To address these problems, this dissertation presents the development of a low-cost, flexible, affect-aware rear-projected robotic agent (called ExpressionBot), that is designed to support verbal and non-verbal communication between the robot and humans, with the goal of closely modeling the dynamics of natural face-to-face communication. The developed robotic platform uses state-of-the-art character animation technologies to create an animated human face (aka avatar) that is capable of showing facial expressions, realistic eye movement, and accurate visual speech, and then project this avatar onto a face-shaped translucent mask. The mask and the projector are then rigged onto a neck mechanism that can move like a human head. Since an animation is projected onto a mask, the robotic face is highly flexible research tool, mechanically simple, and low-cost to design, build and maintain compared with mechatronic and android faces. The results of our comprehensive Human-Robot Interaction (HRI) studies illustrate the benefits and values of the proposed rear-projected robotic platform over a virtual-agent with the same animation displayed on a 2D computer screen. The results indicate that ExpressionBot is well accepted by users, with some advantages in expressing facial expressions more accurately and perceiving mutual eye gaze contact. To improve social capabilities of the robot and create an expressive and empathic social agent (affect-aware) which is capable of interpreting users' emotional facial expressions, we developed a new Deep Neural Networks (DNN) architecture for Facial Expression Recognition (FER). The proposed DNN was initially trained on seven well-known publicly available databases, and obtained significantly better than, or comparable to, traditional convolutional neural networks or other state-of-the-art methods in both accuracy and learning time. Since the performance of the automated FER system highly depends on its training data, and the eventual goal of the proposed robotic platform is to interact with users in an uncontrolled environment, a database of facial expressions in the wild (called AffectNet) was created by querying emotion-related keywords from different search engines. AffectNet contains more than 1M images with faces and 440,000 manually annotated… Advisors/Committee Members: Mohammad H. Mahoor.

Subjects/Keywords: Affect perception; Empathic robot; Facial expressions; Human robot interaction; Rear projected robot; Social robots; Artificial Intelligence and Robotics; Computer Engineering; Computer Sciences; Robotics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Mollahosseini, A. (2018). Developing an Affect-Aware Rear-Projected Robotic Agent. (Doctoral Dissertation). U of Denver. Retrieved from https://digitalcommons.du.edu/etd/1413

Chicago Manual of Style (16th Edition):

Mollahosseini, Ali. “Developing an Affect-Aware Rear-Projected Robotic Agent.” 2018. Doctoral Dissertation, U of Denver. Accessed October 15, 2019. https://digitalcommons.du.edu/etd/1413.

MLA Handbook (7th Edition):

Mollahosseini, Ali. “Developing an Affect-Aware Rear-Projected Robotic Agent.” 2018. Web. 15 Oct 2019.

Vancouver:

Mollahosseini A. Developing an Affect-Aware Rear-Projected Robotic Agent. [Internet] [Doctoral dissertation]. U of Denver; 2018. [cited 2019 Oct 15]. Available from: https://digitalcommons.du.edu/etd/1413.

Council of Science Editors:

Mollahosseini A. Developing an Affect-Aware Rear-Projected Robotic Agent. [Doctoral Dissertation]. U of Denver; 2018. Available from: https://digitalcommons.du.edu/etd/1413

2. Shekhar, Diwanshu. A Bi-Encoder LSTM Model for Learning Unstructured Dialogs.

Degree: MS, Computer Science, 2018, U of Denver

Creating a data-driven model that is trained on a large dataset of unstructured dialogs is a crucial step in developing a Retrieval-based Chatbot systems. This thesis presents a Long Short Term Memory (LSTM) based Recurrent Neural Network architecture that learns unstructured multi-turn dialogs and provides implementation results on the task of selecting the best response from a collection of given responses. Ubuntu Dialog Corpus Version 2 (UDCv2) was used as the corpus for training. Ryan et al. (2015) explored learning models such as TF-IDF (Term Frequency-Inverse Document Frequency), Recurrent Neural Network (RNN) and a Dual Encoder (DE) based on Long Short Term Memory (LSTM) model suitable to learn from the Ubuntu Dialog Corpus Version 1 (UDCv1). We use this same architecture but on UDCv2 as a benchmark and introduce a new LSTM based architecture called the Bi-Encoder LSTM model (BE) that achieves 0.8%, 1.0% and 0.3% higher accuracy for [email protected], [email protected] and [email protected] respectively than the DE model. In contrast to the DE model, the proposed BE model has separate encodings for utterances and responses. The BE model also has a different similarity measure for utterance and response matching than that of the benchmark model. We further explore the BE model by performing various experiments. We also show results on experiments performed by using several similarity functions, model hyper-parameters and word embeddings on the proposed architecture. Advisors/Committee Members: Mohammad H. Mahoor, Ph.D..

Subjects/Keywords: Bi-Encoder; Learning; LSTM; Machine; RNN; Ubuntu; Computer Sciences; Physical Sciences and Mathematics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shekhar, D. (2018). A Bi-Encoder LSTM Model for Learning Unstructured Dialogs. (Thesis). U of Denver. Retrieved from https://digitalcommons.du.edu/etd/1508

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Shekhar, Diwanshu. “A Bi-Encoder LSTM Model for Learning Unstructured Dialogs.” 2018. Thesis, U of Denver. Accessed October 15, 2019. https://digitalcommons.du.edu/etd/1508.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Shekhar, Diwanshu. “A Bi-Encoder LSTM Model for Learning Unstructured Dialogs.” 2018. Web. 15 Oct 2019.

Vancouver:

Shekhar D. A Bi-Encoder LSTM Model for Learning Unstructured Dialogs. [Internet] [Thesis]. U of Denver; 2018. [cited 2019 Oct 15]. Available from: https://digitalcommons.du.edu/etd/1508.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Shekhar D. A Bi-Encoder LSTM Model for Learning Unstructured Dialogs. [Thesis]. U of Denver; 2018. Available from: https://digitalcommons.du.edu/etd/1508

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

3. Lu, Qiang. Safe and Efficient Intelligent Intersection Control of Autonomous Vehicles.

Degree: PhD, Computer Science and Engineering, 2019, U of Denver

In this dissertation, we address a problem of safe and efficient intersection crossing traffic management of autonomous and connected ground traffic. Toward this objective, we propose several algorithms to handle different traffic environments. First, an algorithm that is called the Discrete-time occupancies trajectory (DTOT) based Intersection traffic Coordination Algorithm (DICA) is proposed. All vehicles in the system are Connected and Autonomous Vehicles (CAVs) and capable of wireless Vehicle-to-Intersection communication. The main advantage of DICA is that it enables us to utilize the intersection space more efficiently resulting in less delay for vehicles to cross the intersection. In the proposed framework, an intersection coordinates the motions of CAVs based on their proposed DTOTs to let them cross the intersection efficiently while avoiding collisions. In case when there is a potential collision between vehicles' DTOTs, the intersection modifies conflicting DTOTs to avoid the collision and requests CAVs to approach and cross the intersection according to the modified DTOTs. We also prove that the basic DICA is deadlock free and starvation free. We show that the basic DICA has a computational complexity of <em>O(n2 L3m)</em> where n is the number of vehicles granted to cross an intersection and Lm is the maximum length of intersection crossing routes. To improve the overall computational efficiency of the algorithm, the basic DICA is enhanced by several computational techniques. The enhanced algorithm has a reduced computational complexity of O(n2 <em>Lm</em> log2 <em>Lm)</em>. The problem of evacuating emergency vehicles as quickly as possible through autonomous and connected intersection traffic is also addressed in this dissertation. The proposed Reactive DICA aims to determine an efficient vehicle-passing sequence which allows the emergency vehicle to cross an intersection as soon as possible while the travel times of other normal vehicles are minimally affected. When there are no emergency vehicles within the intersection area, the vehicles are controlled by DICA. When there are emergency vehicles entering communication range, we prioritize emergency vehicles through the optimal ordering of vehicles. Since the number of possible vehicle-passing sequences increases rapidly with the number of vehicles, finding an efficient sequence of vehicles in a short time is the main challenge of the study. A genetic algorithm is proposed to solve the optimization problem which finds the optimal vehicle sequence in real time that gives the emergency vehicles the highest priority. We then address an optimization problem of autonomous intersection control which provides the optimal trajectory for every entering vehicle. Based on the algorithm DICA, we improve the conservative way of trajectory generation which is the key part of DICA to be an optimization approach using mixed integer… Advisors/Committee Members: Duan Zhang, Ph.D., Mohammad H. Mahoor, Ph.D..

Subjects/Keywords: Algorithm; Autonomous vehicles; Intersection control; Optimization; Controls and Control Theory; Electrical and Computer Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lu, Q. (2019). Safe and Efficient Intelligent Intersection Control of Autonomous Vehicles. (Doctoral Dissertation). U of Denver. Retrieved from https://digitalcommons.du.edu/etd/1541

Chicago Manual of Style (16th Edition):

Lu, Qiang. “Safe and Efficient Intelligent Intersection Control of Autonomous Vehicles.” 2019. Doctoral Dissertation, U of Denver. Accessed October 15, 2019. https://digitalcommons.du.edu/etd/1541.

MLA Handbook (7th Edition):

Lu, Qiang. “Safe and Efficient Intelligent Intersection Control of Autonomous Vehicles.” 2019. Web. 15 Oct 2019.

Vancouver:

Lu Q. Safe and Efficient Intelligent Intersection Control of Autonomous Vehicles. [Internet] [Doctoral dissertation]. U of Denver; 2019. [cited 2019 Oct 15]. Available from: https://digitalcommons.du.edu/etd/1541.

Council of Science Editors:

Lu Q. Safe and Efficient Intelligent Intersection Control of Autonomous Vehicles. [Doctoral Dissertation]. U of Denver; 2019. Available from: https://digitalcommons.du.edu/etd/1541

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