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You searched for +publisher:"Old Dominion University" +contributor:("Andrew Collins"). Showing records 1 – 2 of 2 total matches.

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1. Beam, Aaron D. Civilians on the Battlefield: Creating a Realistic Training Aid for the United States Military.

Degree: MS, Modeling Simul & Visual Engineering, 2018, Old Dominion University

The United States and our allies and partners have adopted a humane approach to warfare based on established principle of the laws of war centered on the principles of Military Necessity, Humanity, Proportionality, Distinction, and Honor. These principles dictate that US Military forces conduct warfare with a careful consideration of our impact on civilian populations with a special duty to protect and limit harm as much as possible given the accomplishment of a mission. Likewise, the US Military has developed a sound counterinsurgency and unified action military model that recognizes that warfare is not fought simply with kinetic force, but rather is conducted across an array of areas, including the battle for “hearts and minds” of civilian populations to assist with military actions and legitimize lawful governments. These two factors contribute to a steady requirement to train military forces to respond properly when confronted with civilians on the battlefield. Unfortunately, the only viable method to provide this training is to employ large numbers of role-players – either in a live training setting or controlling entities in a wargame. These role-players must either be hired or be tasked from other military units. There are currently no viable autonomous solutions. The result is that commanders often choose to forego this training as too costly – which could have serious long-term ramifications for military forces confronting civilians in the real world. Can agent based modelling accurately represent civilians confronted with military operations to provide realistic training for military leaders and Soldiers? This thesis investigates this question and develops an agent-based model to explore the answer. Advisors/Committee Members: John Sokolowski, Andrew Collins, Rick McKenzie.

Subjects/Keywords: Agents; Autonomous; Civilians; Military; Training; Military and Veterans Studies; Science and Technology Studies; Social Psychology

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

APA (6th Edition):

Beam, A. D. (2018). Civilians on the Battlefield: Creating a Realistic Training Aid for the United States Military. (Thesis). Old Dominion University. Retrieved from 9780438022003 ; https://digitalcommons.odu.edu/msve_etds/13

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):

Beam, Aaron D. “Civilians on the Battlefield: Creating a Realistic Training Aid for the United States Military.” 2018. Thesis, Old Dominion University. Accessed September 22, 2018. 9780438022003 ; https://digitalcommons.odu.edu/msve_etds/13.

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

MLA Handbook (7th Edition):

Beam, Aaron D. “Civilians on the Battlefield: Creating a Realistic Training Aid for the United States Military.” 2018. Web. 22 Sep 2018.

Vancouver:

Beam AD. Civilians on the Battlefield: Creating a Realistic Training Aid for the United States Military. [Internet] [Thesis]. Old Dominion University; 2018. [cited 2018 Sep 22]. Available from: 9780438022003 ; https://digitalcommons.odu.edu/msve_etds/13.

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

Council of Science Editors:

Beam AD. Civilians on the Battlefield: Creating a Realistic Training Aid for the United States Military. [Thesis]. Old Dominion University; 2018. Available from: 9780438022003 ; https://digitalcommons.odu.edu/msve_etds/13

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

2. Ahmed, Umana. Algorithms for Constructing Vehicle Trajectories in Urban Networks Using Inertial Sensors Data from Mobile Devices.

Degree: PhD, Modeling Simul & Visual Engineering, 2017, Old Dominion University

Vehicle trajectories are an important source of information for estimating traffic flow characteristics. Lately, several studies have focused on identifying a vehicle’s trajectory in traffic network using data from mobile devices. However, these studies predominantly employed GPS coordinate information for tracking a vehicle’s speed and position in the transportation network. Considering the known limitations of GPS, such as, connectivity issues at urban canyons and underpasses, low precision of localization, high power consumption of device while GPS is in use, this research focuses on developing alternate methods for identifying a vehicle’s trajectory at an intersection and at a urban grid network using sensor data other than GPS in order to minimize GPS dependency. In particular, accelerometer and gyroscope data collected using smartphone’s inertial sensors, and speed data collected using an on-board diagnostics (OBD) device, are utilized to develop algorithms for maneuver (i.e., left/right turn and through), trip direction, and trajectory identification. Different algorithms using threshold of gyroscope and magnetometer readings, and machine learning techniques such as k-medoids clustering and dynamic time warping are developed for maneuver identification and their accuracy is tested on collected field data. It is found that, clustering based on maximum and minimum value of gyroscope readings is effective for maneuver identification. For trip direction identification at an intersection, two different methods are developed and tested. The first method utilizes accelerometer, gyroscope and OBD speed data, and the 2nd method employs magnetometer and acceleration data. The results demonstrate that the developed method using accelerometer, gyroscope and OBD speed data are effective in identifying a vehicle’s direction. An effective algorithm is developed using OBD speed information, maneuver and trip direction identification algorithms to identify vehicle’s trajectory at a grid network. Techniques for noise removal and orientation correction to transfer the raw data from phone’s local coordinate to global coordinate system are also demonstrated. Overall, this research eliminates the need for continuous GPS connectivity for trajectory identification. This research can be incorporated in methods developed by researchers to estimate traffic flow, delays, and queue lengths at intersections. This information can lead to better signal timings, travel recommendations, and traffic updates. Advisors/Committee Members: Mecit Cetin, Rick McKenzie, Roland Mielke, Andrew Collins, Rajesh Paleti.

Subjects/Keywords: Coordinate conversion; Direction identification; GPS; Inertial sensors; Maneuver identification; Trajectory identification; Computer Sciences; Transportation; Transportation Engineering

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

APA (6th Edition):

Ahmed, U. (2017). Algorithms for Constructing Vehicle Trajectories in Urban Networks Using Inertial Sensors Data from Mobile Devices. (Doctoral Dissertation). Old Dominion University. Retrieved from 9780355395419 ; https://digitalcommons.odu.edu/msve_etds/8

Chicago Manual of Style (16th Edition):

Ahmed, Umana. “Algorithms for Constructing Vehicle Trajectories in Urban Networks Using Inertial Sensors Data from Mobile Devices.” 2017. Doctoral Dissertation, Old Dominion University. Accessed September 22, 2018. 9780355395419 ; https://digitalcommons.odu.edu/msve_etds/8.

MLA Handbook (7th Edition):

Ahmed, Umana. “Algorithms for Constructing Vehicle Trajectories in Urban Networks Using Inertial Sensors Data from Mobile Devices.” 2017. Web. 22 Sep 2018.

Vancouver:

Ahmed U. Algorithms for Constructing Vehicle Trajectories in Urban Networks Using Inertial Sensors Data from Mobile Devices. [Internet] [Doctoral dissertation]. Old Dominion University; 2017. [cited 2018 Sep 22]. Available from: 9780355395419 ; https://digitalcommons.odu.edu/msve_etds/8.

Council of Science Editors:

Ahmed U. Algorithms for Constructing Vehicle Trajectories in Urban Networks Using Inertial Sensors Data from Mobile Devices. [Doctoral Dissertation]. Old Dominion University; 2017. Available from: 9780355395419 ; https://digitalcommons.odu.edu/msve_etds/8

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