Data Science Commons^™

Assessing Organizational Effectiveness Through The Competing Values Framework A Data Envelopment Approach, Raghavender Macherla

Engineering Management & Systems Engineering Theses & Dissertations

This study proposes a model to diagnose organizations using the mathematical principles of data envelopment analysis (DEA) to the variables generated using competing values framework (CVF) in order to evaluate overall organizational effectiveness. The notion of organizational effectiveness is abstract and difficult to measure due to its complexity and multi-functional nature. Over the years, measurement of organizational effectiveness has remained a challenge due to the lack of agreement on the factors that should be assessed to determine effectiveness. This research is aimed at shedding some light into this topic by using data envelopment analysis as a tool to measure relative …

Information In Biological Systems And The Fluctuation Theorem, Yaşar Demirel

Department of Chemical and Biomolecular Engineering: Faculty Publications

Some critical trends in information theory, its role in living systems and utilization in fluctuation theory are discussed. The mutual information of thermodynamic coupling is incorporated into the generalized fluctuation theorem by using information theory and nonequilibrium thermodynamics. Thermodynamically coupled dissipative structures in living systems are capable of degrading more energy, and processing complex information through developmental and environmental constraints. The generalized fluctuation theorem can quantify the hysteresis observed in the amount of the irreversible work in nonequilibrium regimes in the presence of information and thermodynamic coupling.

Modeling Cyber Situational Awareness Through Data Fusion, Evan L. Raulerson

Theses and Dissertations

Cyber attacks are compromising networks faster than administrators can respond. Network defenders are unable to become oriented with these attacks, determine the potential impacts, and assess the damages in a timely manner. Since the observations of network sensors are normally disjointed, analysis of the data is overwhelming and time is not spent efficiently. Automation in defending cyber networks requires a level of reasoning for adequate response. Current automated systems are mostly limited to scripted responses. Better defense tools are required. This research develops a framework that aggregates data from heterogeneous network sensors. The collected data is correlated into a single …

Tropical Cyclone Intensity Estimation Using Temporal And Spatial Features From Satellite Data, Gholamreza Fetanat Haghighi

Dissertations

Accurate intensity estimation of tropical cyclones (TC) is an important topic of research due to its economic impact and public safety concerns. An accurate measure of the current wind strength is necessary to accurately predict TC intensity. We have developed and tested automated method to estimate TC intensity based on the existing historical satellite images alone. The Hurricane Satellite data (HURSAT-B1) is used to develop the algorithm, which focuses on the North Atlantic from 1978-2009. The algorithm is trained and validated using aircraft reconnaissance-based data. Here, the data is restricted to include only fixes that are over water and are …

Hyperspectral Image Classification Using A Spectral-Spatial Sparse Coding Model, Ender Oguslu, Guoqing Zhou, Jiang Li, Lorenzo Bruzzone (Ed.)

Electrical & Computer Engineering Faculty Publications

We present a sparse coding based spectral-spatial classification model for hyperspectral image (HSI) datasets. The proposed method consists of an efficient sparse coding method in which the l₁/l_q regularized multi-class logistic regression technique was utilized to achieve a compact representation of hyperspectral image pixels for land cover classification. We applied the proposed algorithm to a HSI dataset collected at the Kennedy Space Center and compared our algorithm to a recently proposed method, Gaussian process maximum likelihood (GP-ML) classifier. Experimental results show that the proposed method can achieve significantly better performances than the GP-ML classifier when training data …

Model Individualization For Real-Time Operator Functional State Assessment, Guangfan Zhang, Roger Xu, Wei Wang, Aaron A. Pepe, Feng Li, Jiang Li, Frederick Mckenzie, Tom Schnell, Nick Anderson, Dean Heitkamp

Electrical & Computer Engineering Faculty Publications

Proper assessment of Operator Functional State (OFS) and appropriate workload modulation offer the potential to improve mission effectiveness and aviation safety in both overload and under-load conditions. Although a wide range of research has been devoted to building OFS assessment models, most of the models are based on group statistics and little or no research has been directed towards model individualization, i.e., tuning the group statistics based model for individual pilots. Moreover, little emphasis has been placed on monitoring whether the pilot is disengaged during low workload conditions. The primary focus of this research is to provide a real-time engagement …

Real-Time Anomaly Detection In Full Motion Video, Glenn Konowicz,, Jiang Li, Donnie Self (Ed.)

Electrical & Computer Engineering Faculty Publications

Improvement in sensor technology such as charge-coupled devices (CCD) as well as constant incremental improvements in storage space has enabled the recording and storage of video more prevalent and lower cost than ever before. However, the improvements in the ability to capture and store a wide array of video have required additional manpower to translate these raw data sources into useful information. We propose an algorithm for automatically detecting anomalous movement patterns within full motion video thus reducing the amount of human intervention required to make use of these new data sources. The proposed algorithm tracks all of the objects …

Semi-Automatic Management Of Knowledge Bases Using Formal Ontologies, Andreas Textor

Theses

This thesis presents an approach that deals with the ever-growing amount of data in knowledge bases, especially concerning knowledge interoperability and formal representation of domain knowledge. There arc multiple issues that must be addressed with current systems. A multitude of different formats, sources and tools exist in a domain, and it is desirable to develop their use further towards a standardised environment. Such an environment should support both the representation and processing of data from this domain, and the connection to other domains, where necessary. In order to manage large amounts of data, it should be possible to perform whatever …

Artificial Intelligence – I: A Two-Step Approach For Improving Efficiency Of Feedforward Multilayer Perceptrons Network, Shoukat Ullah, Zakia Hussain

International Conference on Information and Communication Technologies

An artificial neural network has got greater importance in the field of data mining. Although it may have complex structure, long training time, and uneasily understandable representation of results, neural network has high accuracy and is preferable in data mining. This research paper is aimed to improve efficiency and to provide accurate results on the basis of same behaviour data. To achieve these objectives, an algorithm is proposed that uses two data mining techniques, that is, attribute selection method and cluster analysis. The algorithm works by applying attribute selection method to eliminate irrelevant attributes, so that input dimensionality is reduced …

Sensitivity Analysis Framework For Large And Complex Simulation Models, Ghaith Rabadi, Shannon Bowling, Charles Keating, Resit Unal

Engineering Management & Systems Engineering Faculty Publications

In this paper, a framework for conducting Sensitivity Analysis (SA) on large and complex simulation models is introduced. The framework consists of components that are designed to make the SA a systematic process that is easy to manage and follow by simulation analysts and practitioners. Unlike local SA (one-variable-at-a-time SA), the method presented here is variance-based and it is rooted in the field of Design of Experiments (DoE) where Input Variables are varied and Output Variables are measured. Based on the DoE results, a risk scoring system is developed to identify the sensitivity of the Input Variables, and as a …

Distributed Cluster-Based Outlier Detection In Wireless Sensor Networks, Swetha Gali

Electrical & Computer Engineering Theses & Dissertations

Wireless sensor networks find several potential applications in a variety of fields, such as environmental monitoring and control, battlefields, surveillance, smart buildings, human health monitoring, etc. These sensor networks consist of a large number of very tiny, inexpensive, and low power sensor nodes, which are deployed in a variety of harsh environments that may result in the sensor data getting corrupted. It is thus critical to detect and report these abnormal values in the sensor data, in order to have a better understanding of the monitored environment. Detection of the abnormal values is of special interest for the sensor network …

Information Visualization Methods And Techniques Using Web Services, Kevin Dupigny

Computational Modeling & Simulation Engineering Theses & Dissertations

Data, even in small amounts, can be difficult to interpret quickly and efficiently. Data domains are continually expanding and making this data available and usable in collaborative data environment is a challenge that was not addressed by legacy systems. Such a framework would make the existence of a global information sharing system feasible. Visualizing this complex dataset presents a complex task for current static interface solutions. A flexible layered service oriented model is presented in this work to approach this challenge. The visual scope of human perception is largely untapped by today's data visualization applications. Human mental processes are adept …

A Component Based Formal Ontology Model, A Method For Evaluating Such A Model, And The Results Of An Application Of That Method, Charles Tumitsa

Computational Modeling & Simulation Engineering Theses & Dissertations

For systems to truly communicate effectively, more than just data interchange is necessary. The systems must achieve conceptual interoperability, which implies communicating with understanding.

The LCIM (Levels of Conceptual Interoperability Model) describes a number of levels of interoperability, each of which shows systems with an increased amount of conceptual understanding of the other systems they are communicating with. The lowest levels of this model, technical and syntactic interoperability, are achieved (and there are many existing examples) via data exchange in a number of different ways. Research has shown, however, that to attain higher levels of conceptual understanding, that there must …

Surveying Cost Growth, Michael A. Greiner, Vince Sipple, Edward D. White

Faculty Publications

Cost growth that weapon systems incur throughout their acquisition life cycle concerns those who work in the acquisition environment. One way to reduce the amount of unexpected cost growth is to develop better cost estimates. In attaining better cost estimates though, it is often helpful to understand and account for potential cost drivers. Several cost studies, some of which specifically focus on the aircraft industry, have been performed documenting and investigating these growth factors. Overviews of these various cost growth studies are presented as other tools for the cost estimators and program managers.

Autoassociative-Heteroassociative Neural Network, Claudia V. Kropas-Hughes, Steven K. Rogers, Mark E. Oxley, Matthew Kabrisky

AFIT Patents

An efficient neural network computing technique capable of synthesizing two sets of output signal data from a single input signal data set. The method and device of the invention involves a unique integration of autoassociative and heteroassociative neural network mappings, the autoassociative neural network mapping enabling a quality metric for assessing the generalization or prediction accuracy of the heteroassociative neural network mapping.

Designing Human-Centered Distributed Information Systems, Jiagie Zhang, Vimla L. Patel, Kathy A. Johnson, Jane Malin, Jack W. Smith

Faculty, Staff and Student Publications

Many computer systems are designed according to engineering and technology principles and are typically difficult to learn and use. The fields of human-computer interaction, interface design, and human factors have made significant contributions to ease of use and are primarily concerned with the interfaces between systems and users, not with the structures that are often more fundamental for designing truly human-centered systems. The emerging paradigm of human-centered computing (HCC)-which has taken many forms-offers a new look at system design. HCC requires more than merely designing an artificial agent to supplement a human agent. The dynamic interactions in a distributed system …

Data Reduction With Least Squares Differential Correction Using Equinoctial Elements, Michael S. Wasson

Theses and Dissertations

This study investigates earth satellite orbit estimation on a track of range, azimuth, and elevation data from a single tracking station. The estimation routine is a least squares batch filter based solely on two-body orbital motion. Using equinoctial elements for the reference orbit avoids the numerical difficulties of the classical elements at eccentricities near zero and inclinations near zero or 90 degrees. Orbits for Mir, DMSP, Explorer, Cosmos, and GPS are investigated. The goal of this study is to reduce orbit information from observations (range, azimuth, and elevation) to an element set and a covariance matrix without considering perturbation effects. …