Digital Commons Network^™

Classification Of Radar Jammer Fm Signals Using A Neural Network Approach, Ariadna Estefania Mendoza

Open Access Theses & Dissertations

A Neural Network (NN) used to classify radar signals is proposed for the purpose of military survivability and lethality analysis. The goal of the NN is to correctly differentiate Frequency-Modulated (FM) signals from Additive White Gaussian Noise (AWGN) using limited signal pre-processing. The FM signals used to test the NN approach are the linear or chirp FM and the power-law FM. Preliminary simulations using the moments of the signals in the time and frequency domain yielded better results in the frequency domain, suggesting that time domain training would not be as effective frequency domain training. To test this hypoThesis, we …

Optimization Of Neural Network Architecture For Classification Of Radar Jamming Fm Signals, Alberto Soto

Open Access Theses & Dissertations

Radar jamming signal classification is valuable when situational awareness of radar systems is sought out for timely deployment of electronic support measures. Our Thesis shows that artificial neural networks can be utilized for effective and efficient signal classification. The goal is to optimize an artificial Neural Network (NN) approach capable of distinguishing between two common radar waveforms, namely bandlimited white Gaussian jamming noise (BWGN) and the ubiquitous linearly frequency modulated (LFM) signal. This is made possible by creating a theoretical framework for NN architecture testing that leads to a high probability of detection (PD) and a low probability of false …

Distress And Worry As Mediators In The Relationship Between Psychosocial Risks And Upper Body Musculosketal Complaints In Highly Automated Manufacturing, Fiona Wixted, Leonard O'Sullivan

H-Workload 2017: Models and Applications (Works in Progress)

As a result of an upward trend in automation, the requirement for supervisory monitoring and consequently, cognitive demand has increased in automated manufacturing. The incidence of musculoskeletal disorders has also increased in the manufacturing sector. A model was developed based on survey data to test if distress and worry mediate the relationship between psychosocial factors (job control, cognitive demand, social isolation and skill discretion), stress states and upper body musculoskeletal complaints in highly automated manufacturing companies (n=235). Cognitive demand was shown to be related to higher distress in employees. The data raise the question about the link between job control …

Petrochemical Plant Console Operator Workload:The Issues, David A. Strobhar

H-Workload 2017: Models and Applications (Works in Progress)

The console operators of certain petrochemical processes must maintain high levels of performance during process upsets or endanger personnel safety and the environment. Mismanagement of an upset can result in explosions, fires, and the release of hazardous chemicals to the environment. The change in workload from steady state to upset operation is significant, with alarms and control changes that are of an order of magnitude. This paper describes the state of console activity in process plants, particularly the increase with key upsets. Quantitative data on the nature of the console operator’s position, its workload during normal operation, and the requirements …

A Day In The Life Of A Sim: Making Meaning Of Video Game Avatars And Behaviors, Jessica Stark

Antioch University Dissertations & Theses

With video game usage--and criticism on its activity--on the rise, it may be helpful for the psychological community to understand what it actually means to play video games, and what the lived experience entails. This qualitative, phenomenological study specifically explores user behaviors and decisions in the simulated life video game, The Sims. Ten participants completed one- to two-hour long semi-structured interviews, and the data was transcribed, organized into 1,988 codes, which were clustered into 30 categories, and from which six themes ultimately emerged. These resulting themes are: self-representation; past, present, and future; purpose for play; self-reflection; co-creation; and familiarity. The …

Representing And Inferring Mental Workload Via Defeasible Reasoning: A Comparison With The Nasa Task Load Index And The Workload Profile, Lucas Middeldorf Rizzo, Luca Longo

Conference papers

The NASA Task Load Index (NASA − TLX) and the Workload Profile (WP) are likely the most employed instruments for subjective mental workload (MWL) measurement. Numerous areas have made use of these methods for assessing human performance and thusly improving the design of systems and tasks. Unfortunately, MWL is still a vague concept, with different definitions and no universal measure. This research investigates the use of defeasible reasoning to represent and assess MWL. Reasoning is defeasible when a conclusion, supported by a set of premises, can be retracted in the light of new information. In this empirical study, this type …

Cognition-Based Approaches For High-Precision Text Mining, George John Shannon

Doctoral Dissertations

"This research improves the precision of information extraction from free-form text via the use of cognitive-based approaches to natural language processing (NLP). Cognitive-based approaches are an important, and relatively new, area of research in NLP and search, as well as linguistics. Cognitive approaches enable significant improvements in both the breadth and depth of knowledge extracted from text. This research has made contributions in the areas of a cognitive approach to automated concept recognition in.

Cognitive approaches to search, also called concept-based search, have been shown to improve search precision. Given the tremendous amount of electronic text generated in our digital …

A Bounded Actor-Critic Algorithm For Reinforcement Learning, Ryan Jacob Lawhead

Masters Theses

"This thesis presents a new actor-critic algorithm from the domain of reinforcement learning to solve Markov and semi-Markov decision processes (or problems) in the field of airline revenue management (ARM). The ARM problem is one of control optimization in which a decision-maker must accept or reject a customer based on a requested fare. This thesis focuses on the so-called single-leg version of the ARM problem, which can be cast as a semi-Markov decision process (SMDP). Large-scale Markov decision processes (MDPs) and SMDPs suffer from the curses of dimensionality and modeling, making it difficult to create the transition probability matrices (TPMs) …

Towards A Continuous Assessment Of Cognitive Workload For Smartphone Multitasking Users, Angel Jimenez-Molina, Hernan Lira

H-Workload 2017: Models and Applications (Works in Progress)

The intermeshing of Smartphone interactions and daily activities depletes the availability of cognitive resources. This excessive demand may lead to several undesirable cognitive states, which can be avoided by continuously assessing the user cognitive workload. Recently, many attempts have emerged to assess this workload by using psycho physiological signals. This paper provides evidence that it is possible to train models that accurately identify in short time windows such cognitive workload by processing heart rate and blood oxygen saturation signals. This assessment could be applied in Smartphone notification delivery, interface adaptations or cognitive capabilities evaluation.

Human Performance Modelling In Manufacturing: Mental Workload And Task Complexity, Maria Chiara Leva, Lorenzo Comberti, Micaela Demichela, Rebecca Duane

H-Workload 2017: Models and Applications (Works in Progress)

No abstract provided.

A Workload-Centered Perspective On Reduced Crew Operations In Commercial Aviation, Daniela Schmid

H-Workload 2017: Models and Applications (Works in Progress)

Mental workload of a pilot, in short workload, depends on various characteristics of different accumulated tasks on the flight deck. Exogenous task demands and endogenous supply of attentional or information processing resources determine workload [1]. Performance is expect to drop if the demand exceeds the available resources of the pilot. Expertise and experience modulate the endogenous sup- ply of resources like perceiving, updating memory, planing, making a decision, and executing and processing a response. Subsequently, workload manifests in performance variables, subjective experience, and physiological parameters [2]. This is how we can summarize workload very brie y to introduce a model …

Reducing Peak Workload In The Cockpit: A Human In The Loop Simulation Evaluating New Runway Selection Tool, Tanja Bos, Rolf Zon, Wilfred Rouwhorst

H-Workload 2017: Models and Applications (Works in Progress)

In efforts to increase safety and reduce peak workload situations in the cockpit, a tool with a different interaction style was developed for use in case of a runway change instructed by Air Traffic Control during approach. In an experiment a workload comparison was made between the new tool and the conventional cockpit. Workload was measured by means of a self-rating after each experiment run, as well as eye blink frequency during each run. Results show that the self-rated workload decreases with the new tool for one of the two crew members and the blink frequency suggests a workload decrease …

Smart Workload Balancing, Ferdinand Coster

H-Workload 2017: Models and Applications (Works in Progress)

The cognitive workload of operators working with automated systems should neither be too high nor too low. A static level of automation is unable to cope with systems that produce large fluctuations in cognitive workload, therefore a method for adaptive automation is proposed that could balance workload by intelligently choosing what to automate and when. To this end the concept of the Cognitive Workload Value factor is introduced, which takes into account both workload and situation awareness. This initial work introduces a possible framework for categorizing and using different workload and situation awareness measures.

Managing Operator Mental Workload With Standards Based Decision Support, Maurice Wilkins

H-Workload 2017: Models and Applications (Works in Progress)

H-Workload 2017: The first international symposium on human mental workload, Dublin Institute of Technology, Dublin, Ireland, June 28-30.

Facing Human Workload: The Resilient Ego: A Psychoanalytic Point Of View, Glauco Maria Genga, Maria Gabriella Pediconii

H-Workload 2017: Models and Applications (Works in Progress)

The paper aims to show new connections among Human Factors, Human Workload and Resilience. We intend: 1) to highlight the role of subject in facing the human workload, inflected as demanding tasks and emergency situations; 2) to show how psychoanalysis can provide novel insights, not only into human errors, but also into human resilience. They have a common denominator, at least in part: the role of subjective contributions even in demanding situations. Human workload includes a work for satisfaction. We recall also the case study of US Airways Flight 1549 water landing (the so called “Miracle on the Hudson”), which …

System Identification Of Motion Artifact: Noise In Eeg Headsets From Locomotion, Kaela Shea, James Tung

H-Workload 2017: Models and Applications (Works in Progress)

Fall prevention for geriatric populations is a growing concern among clinicians and researchers due to severe risk of morbidity and loss of independence. Emerging evidence has demonstrated that mental workload while walking influences gait stability and the risk for falling. Electroencephalography (EEG) presents a potential method to provide objective measures of mental workload, particularly during daily activities. Noise introduced to the EEG signal during motion, however, is restrictive. The study presented in the following paper isolates EEG signal noise attained from gait for a commercially accessible EEG system, the "Emotiv" Time and spectral system identification techniques were applied to model …

Online Measuring Of Available Resources, Enrique Munoz-De-Escalona, José Juan Canas

H-Workload 2017: Models and Applications (Works in Progress)

This paper present a proposal for measuring available mental resources during the accomplishment of a task. Our proposal consists in measuring emotions provoked by perceived self-efficacy in the execution of the task. Self-efficacy is one of the most important factors that affect the resources that a person puts at the disposal of the execution of the task. When a person perceives that he/she is not being effective he/she will activate more resources to improve his performance. This self-efficacy will be reflected in the emotions that the person experiences. A good efficacy will provoke positive emotions and a bad efficacy negative …

A Validated Description Of How Crew Manage Flight Operations For Two-Pilot And Reduced Crew Operations, Nick Mcdonnell, Alison Kay, Margaret Ryan, Rabea Morrison, Rolf Zon

H-Workload 2017: Models and Applications (Works in Progress)

This research provides a rich validated description of how crew manage workload for both two-pilot and reduced crew operations. It outlines flight operations modelling, operational narratives, requirements and scenarios validated with expert advisers from the EU-FP7 ACROSS Project. The crew are considered to be the managers of the operation who receive integrated technical support to help them manage flight operations across of three configurations i.e. 1) standard two-crew configuration, 2) reduced crew under normal operations 3) reduced-crew under non-normal operations developed within the FP7 EU-funded ACROSS (Advanced Cockpit for the Reduction Of Stress and Workload) project.

Neural Network Predictions Of A Simulation-Based Statistical And Graph Theoretic Study Of The Board Game Risk, Jacob Munson

Murray State Theses and Dissertations

We translate the RISK board into a graph which undergoes updates as the game advances. The dissection of the game into a network model in discrete time is a novel approach to examining RISK. A review of the existing statistical findings of skirmishes in RISK is provided. The graphical changes are accompanied by an examination of the statistical properties of RISK. The game is modeled as a discrete time dynamic network graph, with the various features of the game modeled as properties of the network at a given time. As the network is computationally intensive to implement, results are produced …

Deep Models For Engagement Assessment With Scarce Label Information, Feng Li, Guangfan Zhang, Wei Wang, Roger Xu, Tom Schnell, Jonathan Wen, Frederic Mckenzie, Jiang Li

Electrical & Computer Engineering Faculty Publications

Task engagement is defined as loadings on energetic arousal (affect), task motivation, and concentration (cognition) [1]. It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overfitting. In this paper, we proposed two deep models (i.e., a deep classifier and a deep autoencoder) for engagement assessment with scarce label information. We recruited 15 pilots to conduct a 4-h flight simulation from Seattle to Chicago and recorded their electroencephalograph (EEG) signals during the simulation. Experts carefully examined the EEG signals and labeled …

A New Reinforcement Learning Algorithm With Fixed Exploration For Semi-Markov Decision Processes, Angelo Michael Encapera

Masters Theses

"Artificial intelligence or machine learning techniques are currently being widely applied for solving problems within the field of data analytics. This work presents and demonstrates the use of a new machine learning algorithm for solving semi-Markov decision processes (SMDPs). SMDPs are encountered in the domain of Reinforcement Learning to solve control problems in discrete-event systems. The new algorithm developed here is called iSMART, an acronym for imaging Semi-Markov Average Reward Technique. The algorithm uses a constant exploration rate, unlike its precursor R-SMART, which required exploration decay. The major difference between R-SMART and iSMART is that the latter uses, in addition …

Rationality, Parapsychology, And Artificial Intelligence In Military And Intelligence Research By The United States Government In The Cold War, Guy M. Lomeo

Theses and Dissertations

A study analyzing the roles of rationality, parapsychology, and artificial intelligence in military and intelligence research by the United States Government in the Cold War. An examination of the methodology behind the decisions to pursue research in two fields that were initially considered irrational.

Evaluating Machine Learning Classifiers For Defensive Cyber Operations, Michael D. Rich, Robert F. Mills, Thomas E. Dube, Steven K. Rogers

Military Cyber Affairs

Today’s defensive cyber sensors are dominated by signature-based analytical methods that require continuous maintenance and lack the ability to detect unknown threats. Anomaly detection offers the ability to detect unknown threats, but despite over 15 years of active research, the operationalization of anomaly detection and machine learning for Defensive Cyber Operations (DCO) is lagging. This article provides an introduction to machine learning concepts with a focus on the unique challenges to using machine learning for DCO. Traditional machine learning evaluation methods are challenged in favor of a value-focused evaluation method that incorporates evaluator-specific weights for classifier and sensitivity threshold selection …

Real-Time Online Chinese Character Recognition, Wenlong Zhang

Master's Projects

In this project, I built a web application for handwritten Chinese characters recognition in real time. This system determines a Chinese character while a user is drawing/writing it. The techniques and steps I use to build the recognition system include data preparation, preprocessing, features extraction, and classification. To increase the accuracy, two different types of neural networks ared used in the system: a multi-layer neural network and a convolutional neural network.

Argumentation For Knowledge Representation, Conflict Resolution, Defeasible Inference And Its Integration With Machine Learning, Luca Longo

Conference papers

Modern machine Learning is devoted to the construction of algorithms and computational procedures that can automatically improve with experience and learn from data. Defeasible argumentation has emerged as sub-topic of artificial intelligence aimed at formalising common-sense qualitative reasoning. The former is an inductive approach for inference while the latter is deductive, each one having advantages and limitations. A great challenge for theoretical and applied research in AI is their integration. The first aim of this chapter is to provide readers informally with the basic notions of defeasible and non-monotonic reasoning. It then describes argumentation theory, a paradigm for implementing defeasible …

Review Classification, Balraj Aujla

Computer Science and Software Engineering

The goal of this project is to find a way to analyze reviews and determine the sentiment of a review. It uses various machine learning techniques in order to achieve its goals such as SVMs and Naive Bayes. Overall the purpose is to learn many different machine learning techniques, determine which ones would be useful for the project, then compare the results. Research is the foremost goal of the project, and it is able to determine the better algorithm for review classification, naive bayes or an SVM. In addition, an SVM which actually gave review’s scores rather than just classifying …

On Path Consistency For Binary Constraint Satisfaction Problems, Christopher G. Reeson

School of Computing: Dissertations, Theses, and Student Research

Constraint satisfaction problems (CSPs) provide a flexible and powerful framework for modeling and solving many decision problems of practical importance. Consistency properties and the algorithms for enforcing them on a problem instance are at the heart of Constraint Processing and best distinguish this area from other areas concerned with the same combinatorial problems. In this thesis, we study path consistency (PC) and investigate several algorithms for enforcing it on binary finite CSPs. We also study algorithms for enforcing consistency properties that are related to PC but are stronger or weaker than PC.

We identify and correct errors in the literature …

Towards Building A Review Recommendation System That Trains Novices By Leveraging The Actions Of Experts, Shilpa Khanal

School of Computing: Dissertations, Theses, and Student Research

Online reviews increase consumer visits, increase the time spent on the website, and create a sense of community among the frequent shoppers. Because of the importance of online reviews, online retailers such as Amazon.com and eOpinions provide detailed guidelines for writing reviews. However, though these guidelines provide instructions on how to write reviews, reviewers are not provided instructions for writing product-specific reviews. As a result, poorly-written reviews are abound and a customer may need to scroll through a large number of reviews, which could be up to 6000 pixels down from the top of the page, in order to find …

Validating Social Media Data For Automatic Persona Generation, Jisun An, Haewoon Kwak, Bernard J Jansen

Research Collection School Of Computing and Information Systems

Using personas during interactive design has considerable potential for product and content development. Unfortunately, personas have typically been a fairly static technique. In this research, we validate an approach for creating personas in real time, based on analysis of actual social media data in an effort to automate the generation of personas. We validate that social media data can be implemented as an approach for automating generating personas in real time using actual YouTube social media data from a global media corporation that produces online digital content. Using the organization's YouTube channel, we collect demographic data, customer interactions, and topical …

Lexicon Knowledge Extraction With Sentiment Polarity Computation, Zhaoxia Wang, Vincent Joo Chuan Tong, Pingcheng Ruan, Fang Li

Research Collection School Of Computing and Information Systems

Sentiment analysis is one of the most popular natural language processing techniques. It aims to identify the sentiment polarity (positive, negative, neutral or mixed) within a given text. The proper lexicon knowledge is very important for the lexicon-based sentiment analysis methods since they hinge on using the polarity of the lexical item to determine a text's sentiment polarity. However, it is quite common that some lexical items appear positive in the text of one domain but appear negative in another. In this paper, we propose an innovative knowledge building algorithm to extract sentiment lexicon knowledge through computing their polarity value …