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- Title
- In-Memory Computing Using Formal Methods and Paths-Based Logic.
- Creator
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Velasquez, Alvaro, Jha, Sumit Kumar, Leavens, Gary, Wu, Annie, Subramani, K., University of Central Florida
- Abstract / Description
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The continued scaling of the CMOS device has been largely responsible for the increase in computational power and consequent technological progress over the last few decades. However, the end of Dennard scaling has interrupted this era of sustained exponential growth in computing performance. Indeed, we are quickly reaching an impasse in the form of limitations in the lithographic processes used to fabricate CMOS processes and, even more dire, we are beginning to face fundamental physical...
Show moreThe continued scaling of the CMOS device has been largely responsible for the increase in computational power and consequent technological progress over the last few decades. However, the end of Dennard scaling has interrupted this era of sustained exponential growth in computing performance. Indeed, we are quickly reaching an impasse in the form of limitations in the lithographic processes used to fabricate CMOS processes and, even more dire, we are beginning to face fundamental physical phenomena, such as quantum tunneling, that are pervasive at the nanometer scale. Such phenomena manifests itself in prohibitively high leakage currents and process variations, leading to inaccurate computations. As a result, there has been a surge of interest in computing architectures that can replace the traditional CMOS transistor-based methods. This thesis is a thorough investigation of how computations can be performed on one such architecture, called a crossbar. The methods proposed in this document apply to any crossbar consisting of two-terminal connective devices. First, we demonstrate how paths of electric current between two wires can be used as design primitives in a crossbar. We then leverage principles from the field of formal methods, in particular the area of bounded model checking, to automate the synthesis of crossbar designs for computing arithmetic operations. We demonstrate that our approach yields circuits that are state-of-the-art in terms of the number of operations required to perform a computation. Finally, we look at the benefits of using a 3D crossbar for computation; that is, a crossbar consisting of multiple layers of interconnects. A novel 3D crossbar computing paradigm is proposed for solving the Boolean matrix multiplication and transitive closure problems and we show how this paradigm can be utilized, with small modifications, in the XPoint crossbar memory architecture that was recently announced by Intel.
Show less - Date Issued
- 2018
- Identifier
- CFE0007419, ucf:52720
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007419
- Title
- Quality Diversity: Harnessing Evolution to Generate a Diversity of High-Performing Solutions.
- Creator
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Pugh, Justin, Stanley, Kenneth, Wu, Annie, Sukthankar, Gita, Garibay, Ivan, University of Central Florida
- Abstract / Description
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Evolution in nature has designed countless solutions to innumerable interconnected problems, giving birth to the impressive array of complex modern life observed today. Inspired by this success, the practice of evolutionary computation (EC) abstracts evolution artificially as a search operator to find solutions to problems of interest primarily through the adaptive mechanism of survival of the fittest, where stronger candidates are pursued at the expense of weaker ones until a solution of...
Show moreEvolution in nature has designed countless solutions to innumerable interconnected problems, giving birth to the impressive array of complex modern life observed today. Inspired by this success, the practice of evolutionary computation (EC) abstracts evolution artificially as a search operator to find solutions to problems of interest primarily through the adaptive mechanism of survival of the fittest, where stronger candidates are pursued at the expense of weaker ones until a solution of satisfying quality emerges. At the same time, research in open-ended evolution (OEE) draws different lessons from nature, seeking to identify and recreate processes that lead to the type of perpetual innovation and indefinitely increasing complexity observed in natural evolution. New algorithms in EC such as MAP-Elites and Novelty Search with Local Competition harness the toolkit of evolution for a related purpose: finding as many types of good solutions as possible (rather than merely the single best solution). With the field in its infancy, no empirical studies previously existed comparing these so-called quality diversity (QD) algorithms. This dissertation (1) contains the first extensive and methodical effort to compare different approaches to QD (including both existing published approaches as well as some new methods presented for the first time here) and to understand how they operate to help inform better approaches in the future.It also (2) introduces a new technique for encoding neural networks for evolution with indirect encoding that contain multiple sensory or output modalities.Further, it (3) explores the idea that QD can act as an engine of open-ended discovery by introducing an expressive platform called Voxelbuild where QD algorithms continually evolve robots that stack blocks in new ways. A culminating experiment (4) is presented that investigates evolution in Voxelbuild over a very long timescale. This research thus stands to advance the OEE community's desire to create and understand open-ended systems while also laying the groundwork for QD to realize its potential within EC as a means to automatically generate an endless progression of new content in real-world applications.
Show less - Date Issued
- 2019
- Identifier
- CFE0007513, ucf:52638
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007513
- Title
- Analysis of Driver Behavior Modeling in Connected Vehicle Safety Systems Through High Fidelity Simulation.
- Creator
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Jamialahmadi, Ahmad, Pourmohammadi Fallah, Yaser, Rahnavard, Nazanin, Chatterjee, Mainak, University of Central Florida
- Abstract / Description
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A critical aspect of connected vehicle safety analysis is understanding the impact of human behavior on the overall performance of the safety system. Given the variation in human driving behavior and the expectancy for high levels of performance, it is crucial for these systems to be flexible to various driving characteristics. However, design, testing, and evaluation of these active safety systems remain a challenging task, exacerbated by the lack of behavioral data and practical test...
Show moreA critical aspect of connected vehicle safety analysis is understanding the impact of human behavior on the overall performance of the safety system. Given the variation in human driving behavior and the expectancy for high levels of performance, it is crucial for these systems to be flexible to various driving characteristics. However, design, testing, and evaluation of these active safety systems remain a challenging task, exacerbated by the lack of behavioral data and practical test platforms. Additionally, the need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly and time-consuming. As an alternative option, researchers attempt to use simulation platforms to study and evaluate their algorithms. In this work, we introduce a high fidelity simulation platform, designed for a hybrid transportation system involving both human-driven and automated vehicles. We decompose the human driving task and offer a modular approach in simulating a large-scale traffic scenario, making it feasible for extensive studying of automated and active safety systems. Furthermore, we propose a human-interpretable driver model represented as a closed-loop feedback controller. For this model, we analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics. Finally, we recreate a similarly dense traffic scenario within our simulator and conduct a thorough analysis of different human-specific and system-specific factors and study their effect on the performance and safety of the traffic network.
Show less - Date Issued
- 2018
- Identifier
- CFE0007573, ucf:52578
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007573
- Title
- Automated Synthesis of Memristor Crossbar Networks.
- Creator
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Chakraborty, Dwaipayan, Jha, Sumit Kumar, Leavens, Gary, Ewetz, Rickard, Valliyil Thankachan, Sharma, Xu, Mengyu, University of Central Florida
- Abstract / Description
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The advancement of semiconductor device technology over the past decades has enabled the design of increasingly complex electrical and computational machines. Electronic design automation (EDA) has played a significant role in the design and implementation of transistor-based machines. However, as transistors move closer toward their physical limits, the speed-up provided by Moore's law will grind to a halt. Once again, we find ourselves on the verge of a paradigm shift in the computational...
Show moreThe advancement of semiconductor device technology over the past decades has enabled the design of increasingly complex electrical and computational machines. Electronic design automation (EDA) has played a significant role in the design and implementation of transistor-based machines. However, as transistors move closer toward their physical limits, the speed-up provided by Moore's law will grind to a halt. Once again, we find ourselves on the verge of a paradigm shift in the computational sciences as newer devices pave the way for novel approaches to computing. One of such devices is the memristor -- a resistor with non-volatile memory.Memristors can be used as junctional switches in crossbar circuits, which comprise of intersecting sets of vertical and horizontal nanowires. The major contribution of this dissertation lies in automating the design of such crossbar circuits -- doing a new kind of EDA for a new kind of computational machinery. In general, this dissertation attempts to answer the following questions:a. How can we synthesize crossbars for computing large Boolean formulas, up to 128-bit?b. How can we synthesize more compact crossbars for small Boolean formulas, up to 8-bit?c. For a given loop-free C program doing integer arithmetic, is it possible to synthesize an equivalent crossbar circuit?We have presented novel solutions to each of the above problems. Our new, proposed solutions resolve a number of significant bottlenecks in existing research, via the usage of innovative logic representation and artificial intelligence techniques. For large Boolean formulas (up to 128-bit), we have utilized Reduced Ordered Binary Decision Diagrams (ROBDDs) to automatically synthesize linearly growing crossbar circuits that compute them. This cutting edge approach towards flow-based computing has yielded state-of-the-art results. It is worth noting that this approach is scalable to n-bit Boolean formulas. We have made significant original contributions by leveraging artificial intelligence for automatic synthesis of compact crossbar circuits. This inventive method has been expanded to encompass crossbar networks with 1D1M (1-diode-1-memristor) switches, as well. The resultant circuits satisfy the tight constraints of the Feynman Grand Prize challenge and are able to perform 8-bit binary addition. A leading edge development for end-to-end computation with flow-based crossbars has been implemented, which involves methodical translation of loop-free C programs into crossbar circuits via automated synthesis. The original contributions described in this dissertation reflect the substantial progress we have made in the area of electronic design automation for synthesis of memristor crossbar networks.
Show less - Date Issued
- 2019
- Identifier
- CFE0007609, ucf:52528
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007609
- Title
- Scalable Map Information Dissemination for Connected and Automated Vehicle Systems.
- Creator
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Gani, S M Osman, Pourmohammadi Fallah, Yaser, Vosoughi, Azadeh, Yuksel, Murat, Chatterjee, Mainak, Hasan, Samiul, University of Central Florida
- Abstract / Description
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Situational awareness in connected and automated vehicle (CAV) systems becomes particularly challenging in the presence of non-line of sight objects and/or objects beyond the sensing range of local onboard sensors. Despite the fact that fully autonomous driving requires the use of multiple redundant sensor systems, primarily including camera, radar, and LiDAR, the non-line of sight object detection problem still persists due to the inherent limitations of those sensing techniques. To tackle...
Show moreSituational awareness in connected and automated vehicle (CAV) systems becomes particularly challenging in the presence of non-line of sight objects and/or objects beyond the sensing range of local onboard sensors. Despite the fact that fully autonomous driving requires the use of multiple redundant sensor systems, primarily including camera, radar, and LiDAR, the non-line of sight object detection problem still persists due to the inherent limitations of those sensing techniques. To tackle this challenge, the inter-vehicle communication system is envisioned that allows vehicles to exchange self-status updates aiming to extend their effective field of view and thus compensate for the limitations of the vehicle tracking subsystem that relies substantially on onboard sensing devices. Tracking capability in such systems can be further improved through the cooperative sharing of locally created map data instead of transmitting only self-update messages containing core basic safety message (BSM) data. In the cooperative sharing of safety messages, it is imperative to have a scalable communication protocol to ensure optimal use of the communication channel. This dissertation contributes to the analysis of the scalability issue in vehicle-to-everything (V2X) communication and then addresses the range issue of situational awareness in CAV systems by proposing a content-adaptive V2X communication architecture. To that end, we first analyze the BSM scheduling protocol standardized in the SAE J2945/1 and present large-scale scalability results obtained from a high-fidelity simulation platform to demonstrate the protocol's efficacy to address the scalability issues in V2X communication. By employing a distributed opportunistic approach, the SAE J2945/1 congestion control algorithm keeps the overall offered channel load within an optimal operating range, while meeting the minimum tracking requirements set forth by upper-layer applications. This scheduling protocol allows event-triggered and vehicle-dynamics driven message transmits that further the situational awareness in a cooperative V2X context. Presented validation results of the congestion control algorithm include position tracking errors as the performance measure, with the age of communicated information as the evaluation measure. In addition, we examine the optimality of the default settings of the congestion control parameters. Comprehensive analysis and trade-off study of the control parameters reveal some areas of improvement to further the algorithm's efficacy. Motivated by the effectiveness of channel congestion control mechanism, we further investigate message content and length adaptations, together with transmit rate control. Reasonably, the content of the exchanged information has a significant impact on the map accuracy in cooperative driving systems. We investigate different content control schemes for a communication architecture aimed at map sharing and evaluate their performance in terms of position tracking error. This dissertation determines that message content should be concentrated to mapped objects that are located farther away from the sender to the edge of the local sensor range. This dissertation also finds that optimized combination of message length and transmit rate ensures the optimal channel utilization for cooperative vehicular communication, which in turn improves the situational awareness of the whole system.
Show less - Date Issued
- 2019
- Identifier
- CFE0007634, ucf:52470
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007634
- Title
- Assessing the Safety and Operational Benefits of Connected and Automated Vehicles: Application on Different Roadways, Weather, and Traffic Conditions.
- Creator
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Rahman, Md Sharikur, Abdel-Aty, Mohamed, Eluru, Naveen, Hasan, Samiul, Yan, Xin, University of Central Florida
- Abstract / Description
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Connected and automated vehicle (CAV) technologies have recently drawn an increasing attention from governments, vehicle manufacturers, and researchers. Connected vehicle (CV) technologies provide real-time information about the surrounding traffic condition (i.e., position, speed, acceleration) and the traffic management center's decisions. The CV technologies improve the safety by increasing driver situational awareness and reducing crashes through vehicle-to-vehicle (V2V) and vehicle-to...
Show moreConnected and automated vehicle (CAV) technologies have recently drawn an increasing attention from governments, vehicle manufacturers, and researchers. Connected vehicle (CV) technologies provide real-time information about the surrounding traffic condition (i.e., position, speed, acceleration) and the traffic management center's decisions. The CV technologies improve the safety by increasing driver situational awareness and reducing crashes through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Vehicle platooning with CV technologies is another key element of the future transportation systems which helps to simultaneously enhance traffic operations and safety. CV technologies can also further increase the efficiency and reliability of automated vehicles (AV) by collecting real-time traffic information through V2V and V2I. However, the market penetration rate (MPR) of CAVs and the higher level of automation might not be fully available in the foreseeable future. Hence, it is worthwhile to study the safety benefits of CAV technologies under different MPRs and lower level of automation. None of the studies focused on both traffic safety and operational benefits for these technologies including different roadway, traffic, and weather conditions. In this study, the effectiveness of CAV technologies (i.e., CV /AV/CAV/CV platooning) were evaluated in different roadway, traffic, and weather conditions. To be more specific, the impact of CVs in reduced visibility condition, longitudinal safety evaluation of CV platooning in the managed lane, lower level of AVs in arterial roadway, and the optimal MPRs of CAVs for both peak and off-peak period are analyzed using simulation techniques. Currently, CAV fleet data are not easily obtainable which is one of the primary reasons to deploy the simulation techniques in this study to evaluate the impacts of CAVs in the roadway. The car following, lane changing, and the platooning behavior of the CAV technologies were modeled in the C++ programming language by considering realistic car following and lane changing models in PTV VISSIM. Surrogate safety assessment techniques were considered to evaluate the safety effectiveness of these CAV technologies, while the average travel time, average speed, and average delay were evaluated as traffic operational measures. Several statistical tests (i.e., Two sample t-test, ANOVA) and the modelling techniques (Tobit, Negative binomial, and Logistic regression) were conducted to evaluate the CAV effectiveness with different MPRs over the baseline scenario. The statistical tests and modeling results suggested that the higher the MPR of CAVs implemented, the higher were the safety and mobility benefits achieved for different roadways (i.e., freeway, expressway, arterials, managed lane), weather (i.e., clear, foggy), and traffic conditions (i.e., peak and off-peak period). Interestingly, from the safety and operation perspective, at least 30% and 20% MPR were needed to achieve both the safety and operational benefits of peak and off-peak period, respectively. This dissertation has major implications for improving transportation infrastructure by recommending optimal MPR of CAVs to achieve balanced mobility and safety benefits considering varying roadway, traffic, and weather condition.
Show less - Date Issued
- 2019
- Identifier
- CFE0007709, ucf:52442
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007709
- Title
- Data-Driven Modeling and Optimization of Building Energy Consumption.
- Creator
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Grover, Divas, Pourmohammadi Fallah, Yaser, Vosoughi, Azadeh, Zhou, Qun, University of Central Florida
- Abstract / Description
-
Sustainability and reducing energy consumption are targets for building operations. The installation of smart sensors and Building Automation Systems (BAS) makes it possible to study facility operations under different circumstances. These technologies generate large amounts of data. That data can be scrapped and used for the analysis. In this thesis, we focus on the process of data-driven modeling and decision making from scraping the data to simulate the building and optimizing the...
Show moreSustainability and reducing energy consumption are targets for building operations. The installation of smart sensors and Building Automation Systems (BAS) makes it possible to study facility operations under different circumstances. These technologies generate large amounts of data. That data can be scrapped and used for the analysis. In this thesis, we focus on the process of data-driven modeling and decision making from scraping the data to simulate the building and optimizing the operation. The City of Orlando has similar goals of sustainability and reduction of energy consumption so, they provided us access to their BAS for the data and study the operation of its facilities. The data scraped from the City's BAS serves can be used to develop statistical/machine learning methods for decision making. We selected a mid-size pilot building to apply these techniques. The process begins with the collection of data from BAS. An Application Programming Interface (API) is developed to login to the servers and scrape data for all data points and store it on the local machine. Then data is cleaned to analyze and model. The dataset contains various data points ranging from indoor and outdoor temperature to fan speed inside the Air Handling Unit (AHU) which are operated by Variable Frequency Drive (VFD). This whole dataset is a time series and is handled accordingly. The cleaned dataset is analyzed to find different patterns and investigate relations between different data points. The analysis helps us in choosing parameters for models that are developed in the next step. Different statistical models are developed to simulate building and equipment behavior. Finally, the models along with the data are used to optimize the building Operation with the equipment constraints to make decisions for building operation which leads to a reduction in energy consumption while maintaining temperature and pressure inside the building.
Show less - Date Issued
- 2019
- Identifier
- CFE0007810, ucf:52335
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007810
- Title
- Real-time SIL Emulation Architecture for Cooperative Automated Vehicles.
- Creator
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Gupta, Nitish, Pourmohammadi Fallah, Yaser, Rahnavard, Nazanin, Vosoughi, Azadeh, University of Central Florida
- Abstract / Description
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This thesis presents a robust, flexible and real-time architecture for Software-in-the-Loop (SIL) testing of connected vehicle safety applications. Emerging connected and automated vehicles (CAV) use sensing, communication and computing technologies in the design of a host of new safety applications. Testing and verification of these applications is a major concern for the automotive industry. The CAV safety applications work by sharing their state and movement information over wireless...
Show moreThis thesis presents a robust, flexible and real-time architecture for Software-in-the-Loop (SIL) testing of connected vehicle safety applications. Emerging connected and automated vehicles (CAV) use sensing, communication and computing technologies in the design of a host of new safety applications. Testing and verification of these applications is a major concern for the automotive industry. The CAV safety applications work by sharing their state and movement information over wireless communication links. Vehicular communication has fueled the development of various Cooperative Vehicle Safety (CVS) applications. Development of safety applications for CAV requires testing in many different scenarios. However, the recreation of test scenarios for evaluating safety applications is a very challenging task. This is mainly due to the randomness in communication, difficulty in recreating vehicle movements precisely, and safety concerns for certain scenarios. We propose to develop a standalone Remote Vehicle Emulator (RVE) that can reproduce V2V messages of remote vehicles from simulations or from previous tests, while also emulating the over the air behavior of multiple communicating nodes. This is expected to significantly accelerate the development cycle. RVE is a unique and easily configurable emulation cum simulation setup to allow Software in the Loop (SIL) testing of connected vehicle applications in a realistic and safe manner. It will help in tailoring numerous test scenarios, expediting algorithm development and validation as well as increase the probability of finding failure modes. This, in turn, will help improve the quality of safety applications while saving testing time and reducing cost.The RVE architecture consists of two modules, the Mobility Generator, and the Communication emulator. Both of these modules consist of a sequence of events that are handled based on the type of testing to be carried out. The communication emulator simulates the behavior of MAC layer while also considering the channel model to increase the probability of successful transmission. It then produces over the air messages that resemble the output of multiple nodes transmitting, including corrupted messages due to collisions. The algorithm that goes inside the emulator has been optimized so as to minimize the communication latency and make this a realistic and real-time safety testing tool. Finally, we provide a multi-metric experimental evaluation wherein we verified the simulation results with an identically configured ns3 simulator. With the aim to improve the quality of testing of CVS applications, this unique architecture would serve as a fundamental design for the future of CVS application testing.
Show less - Date Issued
- 2018
- Identifier
- CFE0007185, ucf:52280
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007185
- Title
- Machine Learning Methods for Multiparameter Flow Cytometry Analysis and Visualization.
- Creator
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Sassano, Emily, Jha, Sumit Kumar, Pattanaik, Sumanta, Hughes, Charles, Moore, Sean, University of Central Florida
- Abstract / Description
-
Flow cytometry is a popular analytical cell-biology instrument that uses specific wavelengths of light to profile heterogeneous populations of cells at the individual level. Current cytometers have the capability of analyzing up to 20 parameters on over a million cells, but despite the complexity of these datasets, a typical workflow relies on subjective labor-intensive manual sequential analysis. The research presented in this dissertation provides two machine learning methods to increase...
Show moreFlow cytometry is a popular analytical cell-biology instrument that uses specific wavelengths of light to profile heterogeneous populations of cells at the individual level. Current cytometers have the capability of analyzing up to 20 parameters on over a million cells, but despite the complexity of these datasets, a typical workflow relies on subjective labor-intensive manual sequential analysis. The research presented in this dissertation provides two machine learning methods to increase the objectivity, efficiency, and discovery in flow cytometry data analysis. The first, a supervised learning method, utilizes previously analyzed data to evaluate new flow cytometry files containing similar parameters. The probability distribution of each dimension in a file is matched to each related dimension of a reference file through color indexing and histogram intersection methods. Once a similar reference file is selected the cell populations previously classified are used to create a tailored support vector machine capable of classifying cell populations as an expert would. This method has produced results highly correlated with manual sequential analysis, providing an efficient alternative for analyzing a large number of samples. The second, a novel unsupervised method, is used to explore and visualize single-cell data in an objective manner. To accomplish this, a hypergraph sampling method was created to preserve rare events within the flow data before divisively clustering the sampled data using singular value decomposition. The unsampled data is added to the discovered set of clusters using a support vector machine classifier, and the final analysis is displayed as a minimum spanning tree. This tree is capable of distinguishing rare subsets of cells comprising of less than 1% of the original data.
Show less - Date Issued
- 2018
- Identifier
- CFE0007243, ucf:52241
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007243
- Title
- E.A.I. Anxiety: Technopanic and Post-Human Potential.
- Creator
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Mandell, Zachary, Brenckle, Martha, Jones, Natasha, Scott, Blake, University of Central Florida
- Abstract / Description
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Robots have been a part of the imagination of Western culture for centuries. The possibility for automation and artificial life has inspired the curiosity of thinkers like Leonardo Da Vinci who once designed a mechanical knight. It wasn't until the 19th century that automated machinery has become realized. The confrontation between human and automation has inspired a fear, referred to as (")technopanic("), that has been exacerbated in tandem with the evolution of technology. This thesis seeks...
Show moreRobots have been a part of the imagination of Western culture for centuries. The possibility for automation and artificial life has inspired the curiosity of thinkers like Leonardo Da Vinci who once designed a mechanical knight. It wasn't until the 19th century that automated machinery has become realized. The confrontation between human and automation has inspired a fear, referred to as (")technopanic("), that has been exacerbated in tandem with the evolution of technology. This thesis seeks to discover the historical precedence for these fears. I explore three modes of knowledge (Philosophy, Economics, and Film Theory) to examine the agendas behind the messages on the topic of Artificial Life, specifically Robots. I then advocate for an alternative philosophy called Post-Humanism. I argue that what is needed to alleviate the fears and anxieties of Western culture is a shift in how humanity views itself and its relation to the natural world. By structuring my thesis in this way, I identify the roots of Western humanity's anthropocentric ontology first, explore the economic implications of automation second, analyze the cultural anticipations of artificial life in Western media third, and finally offer an alternative attitude and ethic as a way out of the pre-established judgments that do little to protect Western culture from E.A.I.
Show less - Date Issued
- 2018
- Identifier
- CFE0007049, ucf:52022
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007049
- Title
- A Multiagent Q-learning-based Restoration Algorithm for Resilient Distribution System Operation.
- Creator
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Hong, Jungseok, Sun, Wei, Zhou, Qun, Zheng, Qipeng, University of Central Florida
- Abstract / Description
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Natural disasters, human errors, and technical issues have caused disastrous blackouts to power systems and resulted in enormous economic losses. Moreover, distributed energy resources have been integrated into distribution systems, which bring extra uncertainty and challenges to system restoration. Therefore, the restoration of power distribution systems requires more efficient and effective methods to provide resilient operation.In the literature, using Q-learning and multiagent system (MAS...
Show moreNatural disasters, human errors, and technical issues have caused disastrous blackouts to power systems and resulted in enormous economic losses. Moreover, distributed energy resources have been integrated into distribution systems, which bring extra uncertainty and challenges to system restoration. Therefore, the restoration of power distribution systems requires more efficient and effective methods to provide resilient operation.In the literature, using Q-learning and multiagent system (MAS) to restore power systems has the limitation in real system application, without considering power system operation constraints. In order to adapt to system condition changes quickly, a restoration algorithm using Q-learning and MAS, together with the combination method and battery algorithm is proposed in this study. The developed algorithm considers voltage and current constraints while finding system switching configuration to maximize the load pick-up after faults happen to the given system. The algorithm consists of three parts. First, it finds switching configurations using Q-learning. Second, the combination algorithm works as a back-up plan in case of the solution from Q-learning violates system constraints. Third, the battery algorithm is applied to determine the charging or discharging schedule of battery systems. The obtained switching configuration provides restoration solutions without violating system constraints. Furthermore, the algorithm can adjust switching configurations after the restoration. For example, when renewable output changes, the algorithm provides an adjusted solution to avoid violating system constraints.The proposed algorithm has been tested in the modified IEEE 9-bus system using the real-time digital simulator. Simulation results demonstrate that the algorithm offers an efficient and effective restoration strategy for resilient distribution system operation.
Show less - Date Issued
- 2017
- Identifier
- CFE0006746, ucf:51856
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006746
- Title
- Verification and Automated Synthesis of Memristor Crossbars.
- Creator
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Pourtabatabaie, Arya, Jha, Sumit Kumar, Chatterjee, Mainak, Pattanaik, Sumanta, University of Central Florida
- Abstract / Description
-
The Memristor is a newly synthesized circuit element correlating differences in electrical charge and magnetic flux, which effectively acts as a nonlinear resistor with memory. The small size of this element and its potential for passive state preservation has opened great opportunities for data-level parallel computation, since the functions of memory and processing can be realized on the same physical device.In this research we present an in-depth study of memristor crossbars for...
Show moreThe Memristor is a newly synthesized circuit element correlating differences in electrical charge and magnetic flux, which effectively acts as a nonlinear resistor with memory. The small size of this element and its potential for passive state preservation has opened great opportunities for data-level parallel computation, since the functions of memory and processing can be realized on the same physical device.In this research we present an in-depth study of memristor crossbars for combinational and sequential logic. We outline the structure of formulas which they are able to produce and henceforth the inherent powers and limitations of Memristive Crossbar Computing.As an improvement on previous methods of automated crossbar synthesis, a method for symbolically verifying crossbars is proposed, proven and analysed.
Show less - Date Issued
- 2016
- Identifier
- CFE0006840, ucf:51765
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006840
- Title
- The Impact of Automation and Stress on Human Performance in UAV Operation.
- Creator
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Lin, Jinchao, Matthews, Gerald, Reinerman, Lauren, Szalma, James, Funke, Gregory, University of Central Florida
- Abstract / Description
-
The United States Air Force (USAF) has increasing needs for unmanned aerial vehicle (UAV) operators. Automation may enable a single operator to manage multiple UAVs at the same time. Multi-UAV operation may require a unique set of skills and the need for new operators calls for targeting new populations for recruitment. The objective of this research is to develop a simulation environment for studying the role of individual differences in UAV operation under different task configurations and...
Show moreThe United States Air Force (USAF) has increasing needs for unmanned aerial vehicle (UAV) operators. Automation may enable a single operator to manage multiple UAVs at the same time. Multi-UAV operation may require a unique set of skills and the need for new operators calls for targeting new populations for recruitment. The objective of this research is to develop a simulation environment for studying the role of individual differences in UAV operation under different task configurations and investigate predictors of performance and stress. Primarily, the study examined the impact of levels of automation (LOAs), as well as task demands, on task performance, stress and operator reliance on automation. Two intermediate LOAs were employed for two surveillance tasks included in the simulation of UAV operation. Task demand was manipulated via the high and low frequency of events associated with additional tasks included in the simulation. The task demand and LOA manipulations influenced task performance generally as expected. The task demand manipulations elicited higher subjective distress and workload. LOAs did not affect operator workload but affected reliance behavior. Also, this study examined the role of individual differences in simulated UAV operation. A variety of individual difference factors were associated with task performance and with subjective stress response. Video gaming experience was linked to lower distress and better performance, suggesting possible transfer of skills. Some gender differences were revealed in stress response, task performance, but all the gender effects became insignificant with gaming experience controlled. Generally, the effects of personality were consistent with previous studies, except some novel findings with the performance metrics. Additionally, task demand was found to moderate the influence of personality factors on stress response and performance metrics. Specifically, conscientiousness was associated with higher subjective engagement and performance when demands were higher. This study supports future research which aims to improve the dynamic interfaces in UAV operation, optimize operator reliance on automation, and identify individuals with the highest aptitude for multi-UAV control.
Show less - Date Issued
- 2017
- Identifier
- CFE0006951, ucf:51630
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006951
- Title
- The Impact of Automation Reliability and Fatigue on Reliance.
- Creator
-
Wohleber, Ryan, Matthews, Gerald, Reinerman, Lauren, Szalma, James, Funke, Gregory, Jentsch, Florian, University of Central Florida
- Abstract / Description
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The objective of this research is to inform th(&)#172;(&)#172;e design of dynamic interfaces to optimize unmanned aerial vehicle (UAV) operator reliance on automation. A broad goal of the U.S. military is to improve the ratio of UAV operators to UAVs controlled. Accomplishing this goal requires the use of automation; however, the benefits of automation are jeopardized without appropriate operator reliance. To improve reliance on automation, this effort sought to accomplish several objectives...
Show moreThe objective of this research is to inform th(&)#172;(&)#172;e design of dynamic interfaces to optimize unmanned aerial vehicle (UAV) operator reliance on automation. A broad goal of the U.S. military is to improve the ratio of UAV operators to UAVs controlled. Accomplishing this goal requires the use of automation; however, the benefits of automation are jeopardized without appropriate operator reliance. To improve reliance on automation, this effort sought to accomplish several objectives organized into phases. The first phase aimed to validate metrics that could be used to gauge operator fatigue online, to understand how the reliability of automated systems influences subjective and objective responses, and to understand how the impact of automation reliability changes with different levels of fatigue. To that end, this study employed a multiple UAV simulation containing several tasks. Findings for a challenging Image Analysis task indicated a decrease in accuracy and reliance with time. Both accuracy and reliance were lower with an unreliable automated decision making aid (60% reliability) than with a reliable automated decision making aid (86.7% reliability). Further, a significant interaction indicated that reliance diminished more quickly when the automated aid was less reliable. Concerning the identification of possible eye tracking measures for fatigue, metrics for percentage of eye closure (PERCLOS), blinks, fixations, and dwell time registered changes with time on task. Fixation metrics registered reliability differences. The second phase sought to use outcomes from the first phase to build two algorithms, based on eye tracking, to drive continuous diagnostic monitoring, one simple and another complex. These algorithms were intended to diagnose the passive fatigue state of UAV operators and used subjective task engagement as the dependent variable. The simple algorithm used PERCLOS and total dwell time within the automated tasking area. The complex algorithm added percent of cognitive fixations and frequency of express fixations. The complex algorithm successfully predicted task engagement, primarily on the strength of percentage of cognitive fixations and express fixation frequency metrics.
Show less - Date Issued
- 2016
- Identifier
- CFE0006548, ucf:51323
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006548
- Title
- Techniques for automated parameter estimation in computational models of probabilistic systems.
- Creator
-
Hussain, Faraz, Jha, Sumit, Leavens, Gary, Turgut, Damla, Uddin, Nizam, University of Central Florida
- Abstract / Description
-
The main contribution of this dissertation is the design of two new algorithms for automatically synthesizing values of numerical parameters of computational models of complexstochastic systems such that the resultant model meets user-specified behavioral specifications.These algorithms are designed to operate on probabilistic systems (-) systems that, in general,behave differently under identical conditions. The algorithms work using an approach thatcombines formal verification and...
Show moreThe main contribution of this dissertation is the design of two new algorithms for automatically synthesizing values of numerical parameters of computational models of complexstochastic systems such that the resultant model meets user-specified behavioral specifications.These algorithms are designed to operate on probabilistic systems (-) systems that, in general,behave differently under identical conditions. The algorithms work using an approach thatcombines formal verification and mathematical optimization to explore a model's parameterspace.The problem of determining whether a model instantiated with a given set of parametervalues satisfies the desired specification is first defined using formal verification terminology,and then reformulated in terms of statistical hypothesis testing. Parameter space explorationinvolves determining the outcome of the hypothesis testing query for each parameter pointand is guided using simulated annealing. The first algorithm uses the sequential probabilityratio test (SPRT) to solve the hypothesis testing problems, whereas the second algorithmuses an approach based on Bayesian statistical model checking (BSMC).The SPRT-based parameter synthesis algorithm was used to validate that a given model ofglucose-insulin metabolism has the capability of representing diabetic behavior by synthesizingvalues of three parameters that ensure that the glucose-insulin subsystem spends at least 20minutes in a diabetic scenario. The BSMC-based algorithm was used to discover the valuesof parameters in a physiological model of the acute inflammatory response that guarantee aset of desired clinical outcomes.These two applications demonstrate how our algorithms use formal verification, statisticalhypothesis testing and mathematical optimization to automatically synthesize parameters ofcomplex probabilistic models in order to meet user-specified behavioral properties
Show less - Date Issued
- 2016
- Identifier
- CFE0006117, ucf:51200
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006117
- Title
- Applied Error Related Negativity: Single Electrode Electroencephalography in Complex Visual Stimuli.
- Creator
-
Sawyer, Benjamin, Karwowski, Waldemar, Hancock, Peter, Xanthopoulos, Petros, University of Central Florida
- Abstract / Description
-
Error related negativity (ERN) is a pronounced negative evoked response potential (ERP) that follows a known error. This neural pattern has the potential to communicate user awareness of incorrect actions within milliseconds. While the implications for human-machine interface and augmented cognition are exciting, the ERN has historically been evoked only in the laboratory using complex equipment while presenting simple visual stimuli such as letters and symbols. To effectively harness the...
Show moreError related negativity (ERN) is a pronounced negative evoked response potential (ERP) that follows a known error. This neural pattern has the potential to communicate user awareness of incorrect actions within milliseconds. While the implications for human-machine interface and augmented cognition are exciting, the ERN has historically been evoked only in the laboratory using complex equipment while presenting simple visual stimuli such as letters and symbols. To effectively harness the applied potential of the ERN, detection must be accomplished in complex environments using simple, preferably single-electrode, EEG systems feasible for integration into field and workplace-ready equipment. The present project attempted to use static photographs to evoke and successfully detect the ERN in a complex visual search task: motorcycle conspicuity. Drivers regularly fail to see motorcycles, with tragic results. To reproduce the issue in the lab, static pictures of traffic were presented, either including or not including motorcycles. A standard flanker letter task replicated from a classic ERN study (Gehring et al., 1993) was run alongside, with both studies requiring a binary response. Results showed that the ERN could be clearly detected in both tasks, even when limiting data to a single electrode in the absence of artifact correction. These results support the feasibility of applied ERN detection in complex visual search in static images. Implications and opportunities will be discussed, limitations of the study explained, and future directions explored.
Show less - Date Issued
- 2014
- Identifier
- CFE0005885, ucf:50886
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005885
- Title
- An Integrated Framework for Automated Data Collection and Processing for Discrete Event Simulation Models.
- Creator
-
Rodriguez, Carlos, Kincaid, John, Karwowski, Waldemar, O'Neal, Thomas, Kaup, David, Mouloua, Mustapha, University of Central Florida
- Abstract / Description
-
Discrete Events Simulation (DES) is a powerful tool of modeling and analysis used in different disciplines. DES models require data in order to determine the different parameters that drive the simulations. The literature about DES input data management indicates that the preparation of necessary input data is often a highly manual process, which causes inefficiencies, significant time consumption and a negative user experience.The focus of this research investigation is addressing the manual...
Show moreDiscrete Events Simulation (DES) is a powerful tool of modeling and analysis used in different disciplines. DES models require data in order to determine the different parameters that drive the simulations. The literature about DES input data management indicates that the preparation of necessary input data is often a highly manual process, which causes inefficiencies, significant time consumption and a negative user experience.The focus of this research investigation is addressing the manual data collection and processing (MDCAP) problem prevalent in DES projects. This research investigation presents an integrated framework to solve the MDCAP problem by classifying the data needed for DES projects into three generic classes. Such classification permits automating and streamlining the preparation of the data, allowing DES modelers to collect, update, visualize, fit, validate, tally and test data in real-time, by performing intuitive actions. In addition to the proposed theoretical framework, this project introduces an innovative user interface that was programmed based on the ideas of the proposed framework. The interface is called DESI, which stands for Discrete Event Simulation Inputs.The proposed integrated framework to automate DES input data preparation was evaluated against benchmark measures presented in the literature in order to show its positive impact in DES input data management. This research investigation demonstrates that the proposed framework, instantiated by the DESI interface, addresses current gaps in the field, reduces the time devoted to input data management within DES projects and advances the state-of-the-art in DES input data management automation.
Show less - Date Issued
- 2015
- Identifier
- CFE0005878, ucf:50861
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005878
- Title
- The Effects of Diagnostic Aiding on Situation Awareness under Robot Unreliability.
- Creator
-
Schuster, David, Jentsch, Florian, Szalma, James, Mouloua, Mustapha, Shumaker, Randall, University of Central Florida
- Abstract / Description
-
In highly autonomous robotic systems, human operators are able to attend to their own, separate tasks, but robots still need occasional human intervention. In this scenario, it may be difficult for human operators to determine the status of the system and environment when called upon to aid the robot. The resulting lack of situation awareness (SA) is a problem common to other automated systems, and it can lead to poor performance and compromised safety. Existing research on this problem...
Show moreIn highly autonomous robotic systems, human operators are able to attend to their own, separate tasks, but robots still need occasional human intervention. In this scenario, it may be difficult for human operators to determine the status of the system and environment when called upon to aid the robot. The resulting lack of situation awareness (SA) is a problem common to other automated systems, and it can lead to poor performance and compromised safety. Existing research on this problem suggested that reliable automation of information processing, called diagnostic aiding, leads to better operator SA. The effects of unreliable diagnostic aiding, however, were not well understood. These effects are likely to depend on the ability of the operator to perform the task unaided. That is, under conditions in which the operator can reconcile their own sensing with that of the robot, the influence of unreliable diagnostic aiding may be more pronounced. When the robot is the only source of information for a task, these effects may be weaker or may reverse direction. The purpose of the current experiment was to determine if SA is differentially affected by unreliability at different levels of unaided human performance and at different stages of diagnostic aiding. This was accomplished by experimentally manipulating the stage of diagnostic aiding, robot reliability, and the ability of the operator to build SA unaided. Results indicated that while reliable diagnostic aiding is generally useful, unreliable diagnostic aiding has effects that depend on the amount of information available to operators in the environment. This research improves understanding of how robots can support operator SA and can guide the development of future robots so that humans are most likely to use them effectively.
Show less - Date Issued
- 2013
- Identifier
- CFE0005247, ucf:50577
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005247
- Title
- Analytical study of computer vision-based pavement crack quantification using machine learning techniques.
- Creator
-
Mokhtari, Soroush, Yun, Hae-Bum, Nam, Boo Hyun, Catbas, Necati, Shah, Mubarak, Xanthopoulos, Petros, University of Central Florida
- Abstract / Description
-
Image-based techniques are a promising non-destructive approach for road pavement condition evaluation. The main objective of this study is to extract, quantify and evaluate important surface defects, such as cracks, using an automated computer vision-based system to provide a better understanding of the pavement deterioration process. To achieve this objective, an automated crack-recognition software was developed, employing a series of image processing algorithms of crack extraction, crack...
Show moreImage-based techniques are a promising non-destructive approach for road pavement condition evaluation. The main objective of this study is to extract, quantify and evaluate important surface defects, such as cracks, using an automated computer vision-based system to provide a better understanding of the pavement deterioration process. To achieve this objective, an automated crack-recognition software was developed, employing a series of image processing algorithms of crack extraction, crack grouping, and crack detection. Bottom-hat morphological technique was used to remove the random background of pavement images and extract cracks, selectively based on their shapes, sizes, and intensities using a relatively small number of user-defined parameters. A technical challenge with crack extraction algorithms, including the Bottom-hat transform, is that extracted crack pixels are usually fragmented along crack paths. For de-fragmenting those crack pixels, a novel crack-grouping algorithm is proposed as an image segmentation method, so called MorphLink-C. Statistical validation of this method using flexible pavement images indicated that MorphLink-C not only improves crack-detection accuracy but also reduces crack detection time.Crack characterization was performed by analysing imagerial features of the extracted crack image components. A comprehensive statistical analysis was conducted using filter feature subset selection (FSS) methods, including Fischer score, Gini index, information gain, ReliefF, mRmR, and FCBF to understand the statistical characteristics of cracks in different deterioration stages. Statistical significance of crack features was ranked based on their relevancy and redundancy. The statistical method used in this study can be employed to avoid subjective crack rating based on human visual inspection. Moreover, the statistical information can be used as fundamental data to justify rehabilitation policies in pavement maintenance.Finally, the application of four classification algorithms, including Artificial Neural Network (ANN), Decision Tree (DT), k-Nearest Neighbours (kNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is investigated for the crack detection framework. The classifiers were evaluated in the following five criteria: 1) prediction performance, 2) computation time, 3) stability of results for highly imbalanced datasets in which, the number of crack objects are significantly smaller than the number of non-crack objects, 4) stability of the classifiers performance for pavements in different deterioration stages, and 5) interpretability of results and clarity of the procedure. Comparison results indicate the advantages of white-box classification methods for computer vision based pavement evaluation. Although black-box methods, such as ANN provide superior classification performance, white-box methods, such as ANFIS, provide useful information about the logic of classification and the effect of feature values on detection results. Such information can provide further insight for the image-based pavement crack detection application.
Show less - Date Issued
- 2015
- Identifier
- CFE0005671, ucf:50186
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005671
- Title
- Objectively Defining Scenario Complexity: Towards Automated, Adaptive Scenario-Based Training.
- Creator
-
Dunn, Robert, Sivo, Stephen, Hoffman, Bobby, Hartshorne, Richard, Bowers, Clint, University of Central Florida
- Abstract / Description
-
Effective Scenario-Based Training (SBT) is sequenced in an efficient trajectory from novice to mastery and is well-grounded in pedagogically sound instructional strategies and learning theory. Adaptive, automated SBT attempts to sequence scenarios according to the performance of the student and implement the sequence without human agency. The source of these scenarios may take the form of a matrix constructed by Instructional Systems Designers (ISD), software engineers or trainers. The domain...
Show moreEffective Scenario-Based Training (SBT) is sequenced in an efficient trajectory from novice to mastery and is well-grounded in pedagogically sound instructional strategies and learning theory. Adaptive, automated SBT attempts to sequence scenarios according to the performance of the student and implement the sequence without human agency. The source of these scenarios may take the form of a matrix constructed by Instructional Systems Designers (ISD), software engineers or trainers. The domain being instructed may contain procedures or concepts that are easily differentiated thus allowing quick and accurate determination of difficulty. In this instance, the sequencing of the SBT is relatively simple. However, in complex, domain-integrated instructional environments accurate and efficient sequencing may be extremely difficult as ISD, software engineers and trainers, without an objective means to calculate a scenario's complexity must rely on subjectivity. In the Military, where time, fiscal and manpower constraints may lead to ineffective, inefficient and, perhaps, negative training SBT is a growing alternative to live training due to the significant cost avoidance demonstrated by such systems as the United States Marine Corps' (USMC) Abrams Main Battle Tank (M1A1) Advanced Gunnery Training System (AGTS). Even as the practice of simulation training grows, leadership such as the Government Accountability Office asserts that little has been done to demonstrate simulator impact on trainee proficiency. The M1A1 AGTS instructional sub system, the Improved Crew Training Program (ICTP), employs an automated matrix intended to increase Tank Commander (TC) and Gunner (GNR) team proficiency. This matrix is intended to guide the team along a trajectory of ever-increasing scenario difficulty. However, as designed, the sequencing of the matrix is based on subjective evaluation of difficulty, not on empirical or objective calculations of complexity. Without effective, automated SBT that adapts to the performance of the trainee, gaps in combat readiness and fiscal responsibility could grow large.In 2010, the author developed an algorithm intended to computationally define scenario complexity (Dunne, Schatz, Fiore, Martin (&) Nicholson, 2010) and conducted a proof of concept study to determine the algorithm's effectiveness (Dunne, Schatz, Fiore, Nicholson (&) Fowlkes, 2010). Based on results of that study, and follow-on analysis, revisions were made to that Scenario Complexity (SC) algorithm. The purpose of this research was to examine the efficacy of the revised SC algorithm to enable Educators and Trainers, ISDs, and software engineers to objectively and computationally define SC. The research process included a period of instruction for Subject Matter Experts (SME) to receive instruction on how to identify the base variables that comprise SC. Using this knowledge SMEs then determined the values of the scenarios base variables. Once calculated, these values were ranked and compared to the ICTP matrix sequence.Results indicate that the SMEs were very consistent in their ratings of the items across scenario base variables. Due to the highly proceduralized process underlying advanced gunnery skills, this high degree of agreement was expected. However, the significant lack of correlation to the matrix sequencing is alarming and while a recent study has shown the AGTS to increase TC and GNR team proficiency (PM TRASYS, 2014a), this research's findings suggests that redesign of the ICTP matrix is in order.
Show less - Date Issued
- 2014
- Identifier
- CFE0005789, ucf:50062
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005789