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Engineering Management and Systems Engineering Faculty Research & Creative Works

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Articles 31 - 60 of 220

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Preface To The Special Issue: "Complex Adaptive Systems," Malvern, Pennsylvania, November 13-15, 2019, Nil Kilicay-Ergin, Cihan H. Dagli May 2020

Preface To The Special Issue: "Complex Adaptive Systems," Malvern, Pennsylvania, November 13-15, 2019, Nil Kilicay-Ergin, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

No abstract provided.

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Exploring The Relationship Between Sustainable Projects And Institutional Isomorphisms: A Project Typology, Rakan Alyamani, Suzanna Long, Mohammad Nurunnabi May 2020

Exploring The Relationship Between Sustainable Projects And Institutional Isomorphisms: A Project Typology, Rakan Alyamani, Suzanna Long, Mohammad Nurunnabi

Engineering Management and Systems Engineering Faculty Research & Creative Works

With the increase in awareness about the wide range of issues and adverse effects associated with the use of conventional energy sources came an increase in project management research related to sustainability and sustainable development. Part of that research is devoted to the development of sustainable project typologies that classify projects based on a variety of external factors that can significantly impact these projects. This research focuses on developing a sustainable project typology that classifies sustainable projects based on the external institutional influences. The typology explores the influence of the coercive, normative, and mimetic institutional isomorphisms on the expected level …

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Flood Prediction And Uncertainty Estimation Using Deep Learning, Vinayaka Gude, Steven Corns, Suzanna Long Mar 2020

Flood Prediction And Uncertainty Estimation Using Deep Learning, Vinayaka Gude, Steven Corns, Suzanna Long

Engineering Management and Systems Engineering Faculty Research & Creative Works

Floods are a complex phenomenon that are difficult to predict because of their non-linear and dynamic nature. Therefore, flood prediction has been a key research topic in the field of hydrology. Various researchers have approached this problem using different techniques ranging from physical models to image processing, but the accuracy and time steps are not sufficient for all applications. This study explores deep learning techniques for predicting gauge height and evaluating the associated uncertainty. Gauge height data for the Meramec River in Valley Park, Missouri was used to develop and validate the model. It was found that the deep learning …

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Hedge Fund Replication Using Strategy Specific Factors, Sujit Subhash, David Lee Enke Dec 2019

Hedge Fund Replication Using Strategy Specific Factors, Sujit Subhash, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Hedge funds have traditionally served wealthy individuals and institutional investors with the promise of delivering protection of capital and uncorrelated positive returns irrespective of market direction, allowing them to better manage portfolio risk. However, the financial crisis of 2008 has heightened investor sensitivity to the high fees, illiquidity, lack of transparency, and lockup periods typically associated with hedge funds. Hedge fund replication products, or clones, seek to answer these challenges by providing daily liquidity, transparency, and immediate exposure to a desired hedge fund strategy. Nonetheless, although lowering cost and adding simplicity by using a common set of factors, traditional replication …

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Predicting The Daily Return Direction Of The Stock Market Using Hybrid Machine Learning Algorithms, X. Zhong, David Lee Enke Dec 2019

Predicting The Daily Return Direction Of The Stock Market Using Hybrid Machine Learning Algorithms, X. Zhong, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 …

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Better Beware: Comparing Metacognition For Phishing And Legitimate Emails, Casey I. Canfield, Baruch Fischhoff, Alex Davis Dec 2019

Better Beware: Comparing Metacognition For Phishing And Legitimate Emails, Casey I. Canfield, Baruch Fischhoff, Alex Davis

Engineering Management and Systems Engineering Faculty Research & Creative Works

Every electronic message poses some threat of being a phishing attack. If recipients underestimate that threat, they expose themselves, and those connected to them, to identity theft, ransom, malware, or worse. If recipients overestimate that threat, then they incur needless costs, perhaps reducing their willingness and ability to respond over time. In two experiments, we examined the appropriateness of individuals' confidence in their judgments of whether email messages were legitimate or phishing, using calibration and resolution as metacognition metrics. Both experiments found that participants had reasonable calibration but poor resolution, reflecting a weak correlation between their confidence and knowledge. These …

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Correction To: Better Beware: Comparing Metacognition For Phishing And Legitimate Emails (Metacognition And Learning, (2019), 14, 3, (343-362), 10.1007/S11409-019-09197-5), Casey I. Canfield, Baruch Fischhoff, Alex Davis Dec 2019

Correction To: Better Beware: Comparing Metacognition For Phishing And Legitimate Emails (Metacognition And Learning, (2019), 14, 3, (343-362), 10.1007/S11409-019-09197-5), Casey I. Canfield, Baruch Fischhoff, Alex Davis

Engineering Management and Systems Engineering Faculty Research & Creative Works

The article "Better beware: comparing metacognition for phishing and legitimate emails", written by Casey Inez Canfield, Baruch Fischhoff and Alex Davis, was originally published electronically on the publisher's internet portal (currently SpringerLink) on 20 July 2019 without open access.

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Opportunities And Challenges For Rural Broadband Infrastructure Investment, Casey I. Canfield, Ona Egbue, Jacob Hale, Suzanna Long Oct 2019

Opportunities And Challenges For Rural Broadband Infrastructure Investment, Casey I. Canfield, Ona Egbue, Jacob Hale, Suzanna Long

Engineering Management and Systems Engineering Faculty Research & Creative Works

Insufficient internet access is holding back local economies, reducing educational outcomes, and creating health disparities in rural areas of the U.S. At present, federal and state funding is available for rural broadband infrastructure deployment, but existing efforts have not invested in analytical work to maximize efficiency and minimize cost. In this study, we use a state-of-the-art matrix (SAM) to identify key challenges and opportunities facing rural broadband infrastructure from previous research and government reports. We focus on six themes: (1) technology, (2) hardware costs, (3) financing, (4) adoption, (5) regulatory/legal, and (6) management. We highlight key issues to be addressed …

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A Mixed Method Study Of Infrastructure Resilience Education And Instruction, John Richards, Suzanna Long Oct 2019

A Mixed Method Study Of Infrastructure Resilience Education And Instruction, John Richards, Suzanna Long

Engineering Management and Systems Engineering Faculty Research & Creative Works

As the frequency and severity of natural and man-made disasters increases, the importance of improving the resilience of complex infrastructure systems in an uncertain environment is increasingly critical. Proper training and education are key components to addressing this issue, but it is unclear how and where modeling under uncertainty, infrastructure systems management, and resilient systems are integrated into the standard undergraduate and graduate engineering management curriculum. This research uses a mixed method to determine whether and at what level engineering managers receive instruction regarding the implementation of tools and techniques to improve infrastructure resilience. A review of current courses and …

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Flood Management Deep Learning Model Inputs: A Review Of Necessary Data And Predictive Tools, Jacob Hale, Suzanna Long, Steven Corns, Tom Shoberg Oct 2019

Flood Management Deep Learning Model Inputs: A Review Of Necessary Data And Predictive Tools, Jacob Hale, Suzanna Long, Steven Corns, Tom Shoberg

Engineering Management and Systems Engineering Faculty Research & Creative Works

Current flood management models are often hampered by the lack of robust predictive analytics, as well as incomplete datasets for river basins prone to heavy flooding. This research uses a State-of-the-Art matrix (SAM) analysis and integrative literature review to categorize existing models by method and scope, then determines opportunities for integrating deep learning techniques to expand predictive capability. Trends in the SAM analysis are then used to determine geospatial characteristics of the region that can contribute to flash flood scenarios, as well as develop inputs for future modeling efforts. Preliminary progress on the selection of one urban and one rural …

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Risk Awareness Enhancement Systems For Hazmat Transportation: Prototyping And Technology Evaluation, Jian Xue, Katherine Linville, Yu Li, Pranav Nitin Godse, Suzanna Long, Ruwen Qin Oct 2019

Risk Awareness Enhancement Systems For Hazmat Transportation: Prototyping And Technology Evaluation, Jian Xue, Katherine Linville, Yu Li, Pranav Nitin Godse, Suzanna Long, Ruwen Qin

Engineering Management and Systems Engineering Faculty Research & Creative Works

Workers of hazardous material (hazmat) transportation have a higher chance than other workers to be exposed to various risks in their workplace. Assisting them to safely operate in their workplace in a near real-time manner is in particular need. This paper presents a study of designing, prototyping and developing feedback systems to help increase the risk awareness of workers in the loading and uploading phases of hazmat transportation. The first system was prototyped on an Arduino board, serving as the reference for system development. Then, the second system, named a Bluetooth Low Energy (BLE) beacon based system, was designed as …

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A Framework Of Integrating Manufacturing Plants In Smart Grid Operation: Manufacturing Flexible Load Identification, Md. Monirul Islam, Zeyi Sun, Wenqing Hu, Cihan H. Dagli Aug 2019

A Framework Of Integrating Manufacturing Plants In Smart Grid Operation: Manufacturing Flexible Load Identification, Md. Monirul Islam, Zeyi Sun, Wenqing Hu, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

In the deregulated electricity markets run by Independent System Operator (ISO), a two-settlement (day-ahead and real-time) process is typically used to determine the electricity price to the end-use customers at different buses. In the day-ahead settlement, the demand is predicted at each bus based on the previous consumption behavior of the consumers and thus, Locational Marginal Price (LMP) can be determined and shared to the consumers. A significant gap is usually observed between the planned and real-time demands due to the uncertainties of the weather (temperature, wind-speed etc.), the intensity of business, and everyday activities. Therefore, a large price variation …

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Preface, Cihan H. Dagli, Gursel A. Suer Aug 2019

Preface, Cihan H. Dagli, Gursel A. Suer

Engineering Management and Systems Engineering Faculty Research & Creative Works

No abstract provided.

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System Of Systems (Sos) Architecture For Digital Manufacturing Cybersecurity, Lirim Ashiku, Cihan H. Dagli Aug 2019

System Of Systems (Sos) Architecture For Digital Manufacturing Cybersecurity, Lirim Ashiku, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Technology advancements of real time connectivity and computing powers has evolved the way people manage activities triggering heavy reliance on smart devices. This has reshaped the ability to memorize crucial information, instead accumulate the information into devices allowing real-time fingertip access when needed. Inability to access such information when needed is routinely assumed with device malfunctioning bypassing the probability of compromise, but what if the information is now being accessed by adversaries depriving the data-owner access to crucial information? Cyber manufacturing systems are not immune from these issues. It is possible to approach this problem as generating SoS meta-architecture. In …

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Vision Sensor Based Action Recognition For Improving Efficiency And Quality Under The Environment Of Industry 4.0, Zipeng Wang, Ruwen Qin, Jihong Yan, Chaozhong Guo May 2019

Vision Sensor Based Action Recognition For Improving Efficiency And Quality Under The Environment Of Industry 4.0, Zipeng Wang, Ruwen Qin, Jihong Yan, Chaozhong Guo

Engineering Management and Systems Engineering Faculty Research & Creative Works

In the environment of industry 4.0, human beings are still an important influencing factor of efficiency and quality which are the core of product life cycle management. Hence, monitoring and analyzing humans' actions are essential. This paper proposes a vision sensor based method to evaluate the accuracy of operators' actions. Each action of operators is recognized in real time by a Convolutional Neural Network (CNN) based classification model in which hierarchical clustering is introduced to minimize the effects of action uncertainty. Warnings are triggered when incorrect actions occur in real time and applications of action analysis of workers on a …

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System Architecting Approach For Designing Deep Learning Models, Ram Deepak Gottapu, Cihan H. Dagli Apr 2019

System Architecting Approach For Designing Deep Learning Models, Ram Deepak Gottapu, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Deep Learning (DL) models have proven to be very effective in solving many challenging problems, especially, those related to computer vision, text, and speech. However, the design of such models is challenging because of the vast search space and computational complexity that needs to be explored. Our goal in this paper is to reduce the human effort required to design architectures by using a system architecture development process that allows the exploration of large design space by automating certain model construction, alternative generation, and assessment. The proposed framework is generic and targeted at all deep learning architectures that can be …

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Supply Chain Infrastructure Restoration Calculator Software Tool -- Developer Guide And User Manual, Akhilesh Ojha, Bhanu Kanwar, Suzanna Long, Thomas G. Shoberg, Steven Corns Jan 2019

Supply Chain Infrastructure Restoration Calculator Software Tool -- Developer Guide And User Manual, Akhilesh Ojha, Bhanu Kanwar, Suzanna Long, Thomas G. Shoberg, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

This report describes a software tool that calculates costs associated with the reconstruction of supply chain interdependent critical infrastructure in the advent of a catastrophic failure by either outside forces (extreme events) or internal forces (fatigue). This tool fills a gap between search and recover strategies of the Federal Emergency Management Agency (or FEMA) and construction techniques under full recovery. In addition to overall construction costs, the tool calculates reconstruction needs in terms of personnel and their required support. From these estimates, total costs (or the cost of each element to be restored) can be calculated. Estimates are based upon …

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Analysis Of Parkinson's Disease Data, Ram Deepak Gottapu, Cihan H. Dagli Nov 2018

Analysis Of Parkinson's Disease Data, Ram Deepak Gottapu, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

In this paper, we investigate the diagnostic data from patients suffering with Parkinson's disease (PD) and design classification/prediction model to simplify the diagnosis. The main aim of this research is to open possibilities to be able to apply deep learning algorithms to help better understand and diagnose the disease. To our knowledge, the capabilities of deep learning algorithms have not yet been completely utilized in the field of Parkinson's research and we believe that by having an in-depth understanding of data, we can create a platform to apply different algorithms to automate the Parkinson's Disease diagnosis to certain extent. We …

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Densenet For Anatomical Brain Segmentation, Ram Deepak Gottapu, Cihan H. Dagli Nov 2018

Densenet For Anatomical Brain Segmentation, Ram Deepak Gottapu, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Automated segmentation in brain magnetic resonance image (MRI) plays an important role in the analysis of many diseases and conditions. In this paper, we present a new architecture to perform MR image brain segmentation (MRI) into a number of classes based on type of tissue. Recent work has shown that convolutional neural networks (DenseNet) can be substantially more accurate with less number of parameters if each layer in the network is connected with every other layer in a feed forward fashion. We embrace this idea and generate new architecture that can assign each pixel/voxel in an MR image of the …

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Early Detection Of Disease Using Electronic Health Records And Fisher's Wishart Discriminant Analysis, Sijia Yang, Jian Bian, Zeyi Sun, Licheng Wang, Haojin Zhu, Haoyi Xiong, Yu Li Nov 2018

Early Detection Of Disease Using Electronic Health Records And Fisher's Wishart Discriminant Analysis, Sijia Yang, Jian Bian, Zeyi Sun, Licheng Wang, Haojin Zhu, Haoyi Xiong, Yu Li

Engineering Management and Systems Engineering Faculty Research & Creative Works

Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and "linear inseparability" of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher's Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of "potential" inverse …

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Multi-Objective Evolutionary Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns Nov 2018

Multi-Objective Evolutionary Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A pareto-based, multi-objective evolutionary algorithm utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) fitness evaluation scheme simultaneously evolves connection weights and identifies the neural network topology using network complexity and classification accuracy as objective functions. A combined vector-matrix representation scheme and differential evolution recombination operators are employed. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. The inputs to the evolutionary neural network model are used to classify …

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System Of Systems Architecting Problems: Definitions, Formulations, And Analysis, Hadi Farhangi, Dincer Konur Nov 2018

System Of Systems Architecting Problems: Definitions, Formulations, And Analysis, Hadi Farhangi, Dincer Konur

Engineering Management and Systems Engineering Faculty Research & Creative Works

The system of systems architecting has many applications in transportation, healthcare, and defense systems design. This study first presents a short review of system of systems definitions. We then focus on capability-based system of systems architecting. In particular, capability-based system of systems architecting problems with various settings, including system flexibility, fund allocation, operational restrictions, and system structures, are presented as Multi-Objective Nonlinear Integer Programming problems. Relevant solution methods to analyze these problems are also discussed.

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Study Of Cost Overrun And Delays Of Department Of Defense (Dod)'S Space Acquisition Program, Nazareen Sikkandar Basha, Benjamin J. Kwasa, Christina Bloebaum Oct 2018

Study Of Cost Overrun And Delays Of Department Of Defense (Dod)'S Space Acquisition Program, Nazareen Sikkandar Basha, Benjamin J. Kwasa, Christina Bloebaum

Engineering Management and Systems Engineering Faculty Research & Creative Works

Defense and Aerospace Systems Acquisition projects, just like any other Large-Scale Complex Engineered Systems (LSCES) experience delays and cost overrun during the acquisition process. Cost overrun and delays in LSCES are due, in part, to high complexity, size of the project, involvement of various stakeholders, organizations, political disruptions, changes in requirements and scope. These uncertainties, due to the exogenous factors, have cost the federal government billions of dollars and delays in completion of the programs. Cost estimation of federal programs is usually based on previous generations of systems produced and almost all the time the costs are underestimated. Underestimation of …

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A Retention Model For Community College Stem Students, Jennifer Snyder, Elizabeth A. Cudney Jun 2018

A Retention Model For Community College Stem Students, Jennifer Snyder, Elizabeth A. Cudney

Engineering Management and Systems Engineering Faculty Research & Creative Works

The number of students attending community colleges that take advantage of transfer pathways to universities continues to rise. Therefore, there is a need to engage in academic research on these students and their attrition in order to identify areas to improve retention. Community colleges have a very diverse population and provide entry into science, technology, engineering, and math (STEM) programs, regardless of student high school preparedness. It is essential for these students to successfully transfer to universities and finish their STEM degrees to meet the global workforce demands. This research develops a predictive model for community college students for degree …

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Classification Of Rest And Active Periods In Actigraphy Data Using Pca, Isaac W. Muns, Yogesh Lad, Ivan G. Guardiola, Matthew S. Thimgan Nov 2017

Classification Of Rest And Active Periods In Actigraphy Data Using Pca, Isaac W. Muns, Yogesh Lad, Ivan G. Guardiola, Matthew S. Thimgan

Engineering Management and Systems Engineering Faculty Research & Creative Works

In this paper we highlight a clustering algorithm for the purpose of identifying sleep and wake periods directly from actigraphy signals. The paper makes use of statistical Principal Component Analysis to identify periods of rest and activity. The aim of the proposed methodology is to develop a quick and efficient method to determine the sleep duration of an individual. In addition, a robust method that can identify sleep periods in the accelerometer data when duration, time of day varies by individual. A selected group of 10 individual's sensor data consisting of actigraphy from an accelerometer (3-axis), near body temperature, and …

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A General Algorithm For Assessing Product Architecture Performance Considering Architecture Extension In Cyber Manufacturing, Md Monirul Islam, Zeyi Sun, Cihan H. Dagli Nov 2017

A General Algorithm For Assessing Product Architecture Performance Considering Architecture Extension In Cyber Manufacturing, Md Monirul Islam, Zeyi Sun, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

In modern manufacturing, the product architecture design options are usually restricted to those that can be produced with 100% confidence using those proven technologies to satisfy the existing customer requirement. As a result, the inefficiencies of architecture design are considerable due to such limitations. This issue is of particular interests in cyber manufacturing when exploring the tradeoff between generality and feasibility in product design and manufacturing. It can be expected that the improvement and extension of the existing product architecture may be required to meet new customer requirement when new technologies become available. An effective system performance assessment algorithm is …

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Energy Consumption Modeling Of Stereolithography-Based Additive Manufacturing Toward Environmental Sustainability, Yiran Yang, Lin Li, Yayue Pan, Zeyi Sun Nov 2017

Energy Consumption Modeling Of Stereolithography-Based Additive Manufacturing Toward Environmental Sustainability, Yiran Yang, Lin Li, Yayue Pan, Zeyi Sun

Engineering Management and Systems Engineering Faculty Research & Creative Works

Additive manufacturing (AM), also referred as three-dimensional printing or rapid prototyping, has been implemented in various areas as one of the most promising new manufacturing technologies in the past three decades. In addition to the growing public interest in developing AM into a potential mainstream manufacturing approach, increasing concerns on environmental sustainability, especially on energy consumption, have been presented. To date, research efforts have been dedicated to quantitatively measuring and analyzing the energy consumption of AM processes. Such efforts only covered partial types of AM processes and explored inadequate factors that might influence the energy consumption. In addition, energy consumption …

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Learning Curve Analysis Using Intensive Longitudinal And Cluster-Correlated Data, Xiao Zhong, Zeyi Sun, Haoyi Xiong, Neil Heffernan, Md Monirul Islam Nov 2017

Learning Curve Analysis Using Intensive Longitudinal And Cluster-Correlated Data, Xiao Zhong, Zeyi Sun, Haoyi Xiong, Neil Heffernan, Md Monirul Islam

Engineering Management and Systems Engineering Faculty Research & Creative Works

Intensive longitudinal and cluster-correlated data (ILCCD) can be generated in any situation where numerical or categorical characteristics of multiple individuals or study units are observed and measured at tens, hundreds, or thousands of occasions. The spacing of measurements in time for each individual can be regular or irregular, fixed or random, and the number of characteristics measured at each occasion may be few or many. Such data can also arise in situations involving continuous-time measurements of recurrent events. Generalized linear models (GLMs) are usually considered for the analysis of correlated non-normal data, while multivariate analysis of variance (MANOVA) is another …

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Reward/Penalty Design In Demand Response For Mitigating Overgeneration Considering The Benefits From Both Manufacturers And Utility Company, Md Monirul Islam, Zeyi Sun, Cihan H. Dagli Nov 2017

Reward/Penalty Design In Demand Response For Mitigating Overgeneration Considering The Benefits From Both Manufacturers And Utility Company, Md Monirul Islam, Zeyi Sun, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The high penetration of renewable sources in electricity grid has led to significant economic, environmental, and societal benefits. However, one major side effect, overgeneration, due to the uncontrollable property of renewable sources has also emerged, which becomes one of the major challenges that impedes the further large-scale adoption of renewable technology. Electricity demand response is an effective tool that can balance the supply and demand of the electricity throughout the grid. In this paper, we focus on the design of reward/penalty mechanism for the demand response programs aiming to mitigate the overgeneration. The benefits for both manufacturers and utility companies …

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Analysis Of Autonomous Unmanned Aerial Systems Based On Operational Scenarios Using Value Modelling, Akash Vidyadharan, Robert Philpott Iii, Benjamin J. Kwasa, Christina L. Bloebaum Nov 2017

Analysis Of Autonomous Unmanned Aerial Systems Based On Operational Scenarios Using Value Modelling, Akash Vidyadharan, Robert Philpott Iii, Benjamin J. Kwasa, Christina L. Bloebaum

Engineering Management and Systems Engineering Faculty Research & Creative Works

In recent years, the use of UAS (Unmanned Aerial Systems) has moved beyond the realm of military operations and has made its way into the hands of consumers and commercial industries. Although the applications of UAS in commercial industries are virtually endless, there are many issues regarding their operations that need to be considered before these valuable pieces of equipment are allowed for widespread civil use. Currently, UAS operations in the public domain are guided and controlled by the FAA Part 107 rules after overwhelming public pressure caused by the earlier 333 exemption. In order to approach such larger issues, …

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