Operations Research, Systems Engineering and Industrial Engineering Commons^™

Quantifying Time-Frequency Co-Movement Impact Of Covid-19 On U.S. And China Stock Market Toward Investor Sentiment Index, Rui Nian, Yijin Xu, Qiang Yuan, Chen Feng, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

The Worldwide Spread of Covid-19 Dramatically Influences the World Economic Landscape. in This Paper, We Have Quantitatively Investigated the Time-Frequency Co-Movement Impact of Covid-19 on U.S. and China Stock Market Since Early 2020 in Terms of Daily Observation from National Association of Securities Dealers Automated Quotations Index (Ndx), Dow Jones Industrial Average (Djia), Standard & Poor's 500 Index (Spx), Shanghai Securities Composite Index (Ssec), Shenzhen Securities Component Index (Szi), in Favor of Spatiotemporal Interactions over Investor Sentiment Index, and Propose to Explore the Divisibility and the Predictability to the Volatility of Stock Market during the Development of Covid-19. We Integrate …

Evaluating Decision Making In Sustainable Project Selection Between Literature And Practice, Rakan Alyamani, Suzanna Long, Mohammad Nurunnabi

Engineering Management and Systems Engineering Faculty Research & Creative Works

A robust project selection process is critical for the selection of sustainable projects that meet the needs of an organization or community. There are multiple factors or criteria that can be considered in the selection of the appropriate sustainable project, but it can be challenging to find sufficient depth of expert opinion to perform a strong evaluation of these criteria. Several researchers have turned to the sustainable project literature as a source of expert opinion to evaluate the criteria used in sustainable project selection and rank them based on importance using different multi-criteria decision-making (MCDM) methodologies. However, using the literature …

Comparing Behavioral Theories To Predict Consumer Interest To Participate In Energy Sharing, Julia Morgan, Casey I. Canfield

Engineering Management and Systems Engineering Faculty Research & Creative Works

Consumer investment in distributed energy resources (DERs) is increasing the penetration of renewable energy in the grid. In some cases, DERs produce more electricity than needed by the owner and this excess electricity is sold to the utility (e.g., net metering). In contrast, energy sharing allows a facilitator, which may or may not be the utility, to redistribute excess renewable electricity to fellow community members directly. However, little is known about consumer interest in participating in this type of arrangement. This preregistered study uses structural equation modeling to compare two behavioral theories, Value-Belief-Norm and Diffusion of Innovation, to predict consumer …

Handwriting Features Based Detection Of Fake Signatures, Anton Akusok, Leonardo Espinosa Leal, Kaj Mikael Bjxc3xb6rk, Amaury Lendasse, Renjie Hu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Detection of Fake Signatures is a Hard Task. in This Paper, We Present a Novel Method for Detecting Trained Forgeries using Features Extracted from Sliding Windows with Different overlaps on a Publicly Available Dataset of Static Images of Signatures. using a Linear Machine Learning Model Named Extreme Learning Machine (Elm), Our Methodology Achieves, in Average, an Equal Error Rates (Eer) of 2.31% for an overlap of 90%. in Line with the State-Of-The-Art Results Available in the Scientific Literature.

Sos Explorer Application With Fuzzy-Genetic Algorithms To Assess An Enterprise Architecture -- A Healthcare Case Study, Josh Goldschmid, Vinayaka Gude, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

Kevin Dooley (1997), defined Complex Adaptive System (CAS) as a group of semi-autonomous agents who interact in interdependent ways to produce system-wide patterns, such that those patterns then influence behavior of the agents. A healthcare system is considered as a Complex Adaptive System of system (SoS) with agents composed of strategies, people, process, and technology. Healthcare systems are fragmented with independent systems and information. The enterprise architecture (EA) aims to address these fragmentations by creating boundaries around the business strategy and key performance attributes that drive integration across multiple systems of processes, people, and technology. This paper uses a SoS …

Single-Image Super Resolution Using Convolutional Neural Network, William Symolon, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Increasing threats to U.S. national security satellite constellations have resulted in an increased interest in constellation resilience and satellite redundancy. CubeSats have contributed to commercial, scientific and government applications in remote sensing, communications, navigation and research and have the potential to enhance satellite constellation resilience. However, the inherent size, weight and power limitations of CubeSats enforce constraints on imaging hardware; the small lenses and short focal lengths result in imagery with low spatial resolution. Low resolution limits the utility of CubeSat images for military planning purposes and national intelligence applications. This paper implements a super-resolution deep learning architecture and proposes …

Network Intrusion Detection System Using Deep Learning, Lirim Ashiku, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The widespread use of interconnectivity and interoperability of computing systems have become an indispensable necessity to enhance our daily activities. Simultaneously, it opens a path to exploitable vulnerabilities that go well beyond human control capability. The vulnerabilities deem cyber-security mechanisms essential to assume communication exchange. Secure communication requires security measures to combat the threats and needs advancements to security measures that counter evolving security threats. This paper proposes the use of deep learning architectures to develop an adaptive and resilient network intrusion detection system (IDS) to detect and classify network attacks. The emphasis is how deep learning or deep neural …

The Identification And Prediction In Abundance Variation Of Atlantic Cod Via Long Short-Term Memory With Periodicity, Time–Frequency Co-Movement, And Lead-Lag Effect Across Sea Surface Temperature, Sea Surface Salinity, Catches, And Prey Biomass From 1919 To 2016, Rui Nian, Qiang Yuan, Hui He, Xue Geng, Chi Wei Su, Bo He, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

The Population of Atlantic Cod Significantly Contributes to the Prosperity of Fishery Production in the World. in This Paper, We Quantitatively Investigate the Global Abundance Variation in Atlantic Cod from 1919 to 2016, in Favor of Spatiotemporal Interactions over Manifold Impact Factors at Local Observation Sites and Propose to Explore the Predictive Mechanism with the Help of its Periodicity, Time–frequency Co-Movement, and Lead-Lag Effects, Via Long Short-Term Memory (Lstm). We First Integrate Evidence Yielded from Wavelet Coefficients, to Suggest that the Abundance Variation Potentially Follows a 36-Year Major Cycle and 24-Year Secondary Cycle at the Time Scales of 55 Years …

Co-Designing Ai Tools To Reduce Kidney Discard, Casey Hines

Undergraduate Research Conference at Missouri S&T

Currently, there are about 94,000 people on the kidney transplant waiting list. Less than one-third will receive a new kidney this year. Unfortunately, some kidneys that could have been transplanted into a new recipient, end up being discarded. The largest group of discarded kidneys are ones that are high-risk. At present, an organ procurement organization will send out offers for an available kidney to transplant centers. These offers go out in chronological order following the kidney transplant waiting list. A single high-risk kidney could potentially go through hundreds of offers before being accepted by a transplant center. The longer the …

Behavioral Science Can Increase Zipper Merge Usage, Maria Galbraith

Undergraduate Research Conference at Missouri S&T

Research demonstrates that zipper merges (or late merges), under heavy traffic conditions, are safer and faster than a traditional early merge. In implementation, zipper merges can be less efficient due to a lack of compliance on the part of drivers, often more accustomed to early merging. Behavioral science has been applied to a many transportation-related challenges, such as increasing seat belt usage and decreasing drinking and driving but has not yet been applied to the zipper merge. We have identified six relevant behavioral science strategies including (1) social norms, (2) appeals to reason, (3) emotional appeals, (4) humor, (5) memory …

A Multi-Elm Model For Incomplete Data, Baichuan Chi, Amaury Lendasse, Edward Ratner, Renjie Hu

Engineering Management and Systems Engineering Faculty Research & Creative Works

This Paper Presents a Novel Model of Extreme Learning Machines (Elms) for Incomplete Data. Elms Are Fast Accurate Randomized Neural Networks. Nevertheless, Elm Can Only Be Applied on the Complete Dataset. Therefore, a Novel Multi-Elm Model for Incomplete Data is Proposed, Consisting of Multiple Secondary Elms and One Primary Elm. the Secondary Elms Are Approximating the Primary Elm's Hidden Neurons' Outputs for the Data with Missing Values. as Summarized in the Experimental Section, This Model Can Be Applied on Data with Any Missing Patterns, without using Imputations and Can Outperform the Traditional Imputation Methods within a Reasonable Fraction of Missing …

A Fuzzy Clustering Methodology To Analyze Interfaces And Assess Integration Risks In Large-Scale Systems, Josh Henry Goldschmid

Doctoral Dissertations

“Interface analysis and integration risk assessment for a large-scale, complex system is a difficult systems engineering task, but critical to the success of engineering systems with extraordinary capabilities. When dealing with large-scale systems there is little time for data gathering and often the analysis can be overwhelmed by unknowns and sometimes important factors are not measurable because of the complexities of the interconnections within the system. This research examines the significance of interface analysis and management, identifies weaknesses in literature on risk assessment for a complex system, and exploits the benefits of soft computing approaches in the interface analysis in …

Nnbmss: A Novel And Fast Method For Model Structure Selection, Amaury Lendasse, Kallin Khan, Edward Ratner

Engineering Management and Systems Engineering Faculty Research & Creative Works

In This Paper, We Present a New Method to Perform Model Structure Selection. This Proposed Method Can Be Used to Select the Complexity of Any Continuous Regression Method. We Also Present an Asymptotic Mathematical Proof of the Proposed Method and the New Method is Illustrated on a Benchmark. Compared to the Well-Known 10-Fold Cross-Validation, the Computational Time Associated to Our New Method is Approximately Divided by a Factor 8 as Illustrated on the Benchmark.

Establishing Links Between Safety Culture, Climate, Behaviors, And Outcomes Of Long-Haul Truck Drivers, Carlton Washburn

Doctoral Dissertations

“This research examines the safety relationships between safety culture, safety influences, safety climate, and safety outcomes for long-haul truck drivers. The relationships focus on the intersection of the electronic logging device (ELD) technology, regulations, and truck drivers that fall into the lone-worker category. Truck drivers were interviewed to understand their beliefs, attitudes, practices, values, and behavior patterns aligned with the phase in of the ELD system. Large truck crashes during the same time period were analyzed to understand associations. Outcomes included both a safety culture and climate were established for long-haul truck drivers. Both positive and negative safety behaviors were …

Agent-Based Model Of Broadband Adoption In Unserved And Underserved Areas, Ankit Agarwal

Masters Theses

"In the last two decades, demand for broadband internet has far outpaced its availability. The Federal Communications Commission’s (FCC) 2020 Broadband Deployment report suggests that at least 22 million Americans living in rural areas lack access to broadband internet. With the COVID-19 pandemic affecting normal life, there is an overwhelming need to enable unserved and underserved communities to adapt to the “new normal”. To address this challenge, federal and state agencies are funding internet service providers (ISPs) to deploy infrastructure in rural communities. However, policymakers and ISPs need open-source tools to predict take-rates of broadband service and formulate effective strategies …

The Role Of Psychological Reactance In Smart Home Energy Management Systems, Matthew Thomas Heatherly

Masters Theses

“With an ever-growing demand for energy, our increasing consumption is producing more greenhouse gases and other pollutants, impacting climate change. One approach to reducing residential energy consumption is through the use of smart energy management systems. However, automation from smart technology inherently removes a certain amount of control from the user. If loss of control is perceived as a loss of freedom, this may lead users to experience psychological reactance when using these products. A set of experiments was conducted to assess how three features of a message notification from smart home energy management systems may induce reactance in users. …

The Effects Of Rigid Polyurethane Foam As A Confinement Material On Breaching Charge Detonations, Nathan Franz Paerschke-O'Brien

Masters Theses

"The effects of a rigid polyurethane foam used as a confinement material on four types of breaching explosives were tested, focusing on the changes in shockwave peak pressures, detonation load compression forces, and brisance cratering abilities. The Plate Dent testing procedure was modified to incorporate a load cell force sensor, and two air overpressure sensors were included adjacent to the blast to quantify each test result. The testing variables focused on the polyurethane foam cure times and thickness volumes around the breaching explosives to determine the breaching charges' optimal energy output capabilities when confined by the foam material. The rigid …

Machine Learning For Measuring And Analyzing Online Social Communications, Chris Bronk, Amaury Lendasse, Peggy Lindner, Dan S. Wallach, Barbara Hammer

Engineering Management and Systems Engineering Faculty Research & Creative Works

In This Paper, We Propose a Framework for Application of a Novel Machine Learning-Based System for Analyzing Online Social Communications. as an Example, We Are Targeting Anti-Semitic Graphical Memes Posted to Social Media. We Presented Very Promising Preliminary Results on a Facebook Dataset that Consists of a Total of 10000 Labeled Memes. We Can Conclude that Machine Learning Will Soon Be Able to Successfully Analyze and Monitor Complex Social Communications.

The Implementation Of Energy Sharing Using A System Of Systems Approach, Julia Morgan

Doctoral Dissertations

"There is an increasing demand for renewable energy and consumers need more procurement options to meet their needs. Energy sharing provides a peer-to-peer (P2P) marketplace where prosumer electricity is redistributed to fellow energy-sharing community participants. This redistribution of prosumer electricity provides consumers with additional electricity suppliers, while also decreasing the load on the utility company. Though significant progress has been made regarding research and implementation of energy sharing, there is still room for growth when evaluating energy-sharing communities and defining appropriate community coordination based on end-user needs. The first contribution in this work identified nine characteristics of energy-sharing communities as …

Infrastructure Systems Modeling Using Data Visualization And Trend Extraction, Jacob Marshal Hale

Doctoral Dissertations

“Current infrastructure systems modeling literature lacks frameworks that integrate data visualization and trend extraction needed for complex systems decision making and planning. Critical infrastructures such as transportation and energy systems contain interdependencies that cannot be properly characterized without considering data visualization and trend extraction.

This dissertation presents two case analyses to showcase the effectiveness and improvements that can be made using these techniques. Case one examines flood management and mitigation of disruption impacts using geospatial characteristics as part of data visualization. Case two incorporates trend analysis and sustainability assessment into energy portfolio transitions.

Four distinct contributions are made in this …

Sensor Data Based Adaptive Models For Assembly Worker Training In Cyber Manufacturing, Md. Al-Amin

Doctoral Dissertations

“Production innovations are occurring faster than ever leading conventional production systems towards cyber manufacturing. Manufacturing workers thus need to frequently learn new methods and skills. In fast-changing, largely uncertain production systems, manufacturers with the ability to comprehend workers’ behavior and assess their operational performance in near real-time will achieve better performance than peers. Recognizing worker actions in near real-time while performing the assembly can serve this purpose. However, reliably recognizing the assembly actions performed by the workers is challenging, because the actions for assembly are complex and workers are not only heterogeneous but sensitive to the variation of the work …

A Time Series Sustainability Assessment Of A Partial Energy Portfolio Transition, Jacob Hale, Suzanna Long

Engineering Management and Systems Engineering Faculty Research & Creative Works

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a …

A Markov Chain Approach For Forecasting Progression Of Opioid Addiction, Abhijit Gosavi, Susan L. Murray, N. Karagiannis

Engineering Management and Systems Engineering Faculty Research & Creative Works

The U.S. is currently facing an opioid crisis. Naltrexone is a common treatment for drug addiction; it reduces the desire to take opiates. However, addicts often stop treatment or continue to use opioids while in treatment. This results in increased fatalities and associated costs. A Markov-chain model is presented to analyze the progression of opioid addiction to assist the medical community in developing appropriate treatments. The model includes patients who continue opiate use while on naltrexone (blocked patients) and those who use opiates after missing naltrexone doses (unblocked patients). The other types of patients are abstinent (the best-case scenario) and …

Rural Access To Industry 4.0: Barriers From The Infrastructure Planning Front Lines, Javier Valentin-Sivico, Casey I. Canfield, Ona Egbue

Engineering Management and Systems Engineering Faculty Research & Creative Works

Many rural communities lack adequate broadband infrastructure, which limits the economic development potential in these regions. They are not able to attract new businesses, and established businesses are unable to use tools and services that require high-speed internet. Broadband access is a requirement for the Internet of Things, robotics, and big data, which are part of Industry 4.0 and the future economy. Such technological advances are not only transforming the manufacturing environments and the service industry, but also finding applications in the food supply chain, such as precision agriculture. In this study, we conducted 17 semi-structured interviews (11 reported here) …

Communicating Uncertain Information From Deep Learning Models In Human Machine Teams, Harishankar V. Subramanian, Casey I. Canfield, Daniel Burton Shank, Luke Andrews, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The role of human-machine teams in society is increasing, as big data and computing power explode. One popular approach to AI is deep learning, which is useful for classification, feature identification, and predictive modeling. However, deep learning models often suffer from inadequate transparency and poor explainability. One aspect of human systems integration is the design of interfaces that support human decision-making. AI models have multiple types of uncertainty embedded, which may be difficult for users to understand. Humans that use these tools need to understand how much they should trust the AI. This study evaluates one simple approach for communicating …

The Application Of Fuzzy Analytic Hierarchy Process In Sustainable Project Selection, Rakan Alyamani, Suzanna Long

Engineering Management and Systems Engineering Faculty Research & Creative Works

The project selection process is a crucial step in sustainable development. Effective sustainable development depends on the ability to select the appropriate sustainable project to implement to ensure that the desired goals are met. Some of the most common characteristics or criteria used in evaluating sustainable projects include novelty, uncertainty, skill and experience, technology information transfer, and project cost. Prioritizing these criteria based on relative importance helps project managers and decision makers identify elements that require additional attention, better allocate resources, as well as improve the selection process when evaluating different sustainable project alternatives. The aim of this research is …

A Machine-Learning-Enhanced Hierarchical Multiscale Method For Bridging From Molecular Dynamics To Continua, Shaoping Xiao, Renjie Hu, Zhen Li, Siamak Attarian, Kaj Mikael Björk, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

In the Community of Computational Materials Science, One of the Challenges in Hierarchical Multiscale Modeling is Information-Passing from One Scale to Another, especially from the Molecular Model to the Continuum Model. a Machine-Learning-Enhanced Approach, Proposed in This Paper, Provides an Alternative Solution. in the Developed Hierarchical Multiscale Method, Molecular Dynamics Simulations in the Molecular Model Are Conducted First to Generate a Dataset, Which Represents Physical Phenomena at the Nanoscale. the Dataset is Then Used to Train a Material Failure/defect Classification Model and Stress Regression Models. Finally, the Well-Trained Models Are Implemented in the Continuum Model to Study the Mechanical Behaviors …

Transceivers As A Resource: Scheduling Time And Bandwidth In Software-Defined Radio, Nathan D. Price, Maciej Jan Zawodniok, Ivan G. Guardiola

Electrical and Computer Engineering Faculty Research & Creative Works

In the future, software-defined radio may enable a mobile device to support multiple wireless protocols implemented as software applications. These applications, often referred to as waveform applications, could be added, updated, or removed from a software-radio device to meet changing demands. Current software-defined radio solutions grant an active waveform exclusive ownership of a specific transceiver or analog front-end. Since a wireless device has a limited number of front-ends, this approach puts a hard constraint on the number of concurrent waveform applications a device can support. A growing trend in software-defined radio research is to virtualize front-ends to allow sharing and …

Deep Learning Based Poisson Solver In Particle Simulation Of Pn Junction With Transient Esd Excitation, Ling Zhang, Wenchang Huang, Zeyi Sun, Nicholas Erickson, Ryan From, Jun Fan

Engineering Management and Systems Engineering Faculty Research & Creative Works

In particle simulations of semiconductor devices for electro-static discharge (ESD) study at the microscopic level, solving Poisson's equation is an inevitable but time-consuming step. In this work, a deep learning technique is utilized to resolve Poisson's equation for a PN junction under an ESD event, namely using a trained deep neural network (DNN) to predict the potential distribution according to the charge distribution and the boundary condition under a transient ESD excitation. To improve the generalization performance of the DNN, multiple typical ESD curves with different parameters are used as the excitation boundary to generate large amounts of training data …

Feature Bagging And Extreme Learning Machines: Machine Learning With Severe Memory Constraints, Kallin Khan, Edward Ratner, Robert Ludwig, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

With the Onset of Easy Access to Supercomputers with High Amounts of Memory Available, Machine Learning Algorithms Have Continued to Increase the Resources Necessary to Perform their Data Analysis. This Paper Aims to Show Development in the Other Direction, by Showing that through the Use of a Combination of Feature Bagging and Ensembles of Extreme Learning Machines (Elms) It is Possible to Leverage Machine Learning, Without Loss of Accuracy, on Devices Where Flash Memory is Very Scarce, and Random-Access Memory (Ram) is Even Scarcer, Such as on Embedded Systems. This Novel Strategy is Called Feature Bagged Extreme Learning Machines (Fb-Elms).