Operations Research, Systems Engineering and Industrial Engineering Commons^™

Integrating Resilience Into Military Infrastructure Mission Assurance Assessments And Decision Making, John Richards

Doctoral Dissertations

“This research created the Mission Assurance Resilience Matrix, a decision framework that integrates existing infrastructure assessment methods with emerging resilience research to model resilience under uncertainty as part of a detailed infrastructure management system. This framework enables military decision makers to easily visualize deficiencies in infrastructure resilience and assess where to most efficiently allocate resources. This research further extends results by including modules on training and education as a component of the scope of work.

There are three significant contributions of this research. The first identifies the gaps of how and where modeling under uncertainty, infrastructure systems management, and resilient …

Balancing Labor Requirements In A Manufacturing Environment, Patrick Bernard Dwyer

Doctoral Dissertations

“This research examines construction environments within manufacturing facilities, specifically semiconductor manufacturing facilities, and develops a new optimization method that is scalable for large construction projects with multiple execution modes and resource constraints. The model is developed to represent real-world conditions in which project activities do not have a fixed, prespecified duration but rather a total amount of work that is directly impacted by the level of resources assigned. To expand on the concept of resource driven project durations, this research aims to mimic manufacturing construction environments by allowing a non-continuous resource allocation to project tasks. This concept allows for resources …

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.

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 …

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 …

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 …

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 …

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 …

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 …

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 …

Action Recognition In Manufacturing Assembly Using Multimodal Sensor Fusion, Md. Al-Amin, Wenjin Tao, David Doell, Ravon Lingard, Zhaozheng Yin, Ming-Chuan Leu, Ruwen Qin

Computer Science Faculty Research & Creative Works

Production innovations are occurring faster than ever. 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 operation performance in near real-time will achieve better performance than peers. Action recognition can serve this purpose. Despite that human action recognition has been an active field of study in machine learning, limited work has been done for recognizing worker actions in performing manufacturing tasks that involve complex, intricate operations. Using data captured by one sensor or a single type of sensor to recognize …

Joint Manufacturing And Onsite Microgrid System Control Using Markov Decision Process And Neural Network Integrated Reinforcement Learning, Wenqing Hu, Zeyi Sun, Y. Zhang, Y. Li

Mathematics and Statistics Faculty Research & Creative Works

Onsite microgrid generation systems with renewable sources are considered a promising complementary energy supply system for manufacturing plant, especially when outage occurs during which the energy supplied from the grid is not available. Compared to the widely recognized benefits in terms of the resilience improvement when it is used as a backup energy system, the operation along with the electricity grid to support the manufacturing operations in non-emergent mode has been less investigated. In this paper, we propose a joint dynamic decision-making model for the optimal control for both manufacturing system and onsite generation system. Markov Decision Process (MDP) is …

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 …

Preface, Cihan H. Dagli, Gursel A. Suer

Engineering Management and Systems Engineering Faculty Research & Creative Works

No abstract provided.

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 …

Local Receptive Fields Based Extreme Learning Machine With Hybrid Filter Kernels For Image Classification, Bo He, Yan Song, Yuemei Zhu, Qixin Sha, Yue Shen, Tianhong Yan, Rui Nian, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

In This Paper, an Innovative Method Called Extreme Learning Machine with Hybrid Local Receptive Fields (Elm-Hlrf) is Presented for Image Classification. in This Method, Filters Generated by Gabor Functions and the Randomly Generated Convolution Filters Are Incorporated into the Convolution Filter Kernels of Local Receptive Fields based Extreme Learning Machine (Elm-Lrf). Extreme Learning Machine (Elm) is Derived from Single Hidden Layer Feed-Forward Neural Networks, and the Parameters of its Hidden Layer Can Be Generated Randomly. as Locally Connected Elm, Elm-Lrf Directly Processes Information with Strong Correlations Such as Images and Speech. in This Paper, Two Main Contributions Are Proposed to …

Spiking Networks For Improved Cognitive Abilities Of Edge Computing Devices, Anton Akusok, Yoan Miche, Kaj Mikael Björk, Renjie Hu, Leonardo Espinosa Leal, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

This Concept Paper Highlights a Recently Opened Opportunity for Large Scale Analytical Algorithms to Be Trained Directly on Edge Devices. Such Approach is a Response to the Arising Need of Processing Data Generated by Natural Person (A Human Being), Also Known as Personal Data. Spiking Neural Networks Are the Core Method Behind It: Suitable for a Low Latency Energy-Constrained Hardware, Enabling Local Training or Re-Training, While Not Taking Advantage of Scalability Available in the Cloud.

Complementary Use Of Glider Data, Altimeter For Exploring Vertical Structure Of Mesoscale Eddies In The New England Coast, Yu Cai, Xue Geng, Hui He, Rui Nian, Qiang Yuan, Xiaoyu Li, Bo He, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

Mesoscale Eddies Are Ubiquitous Features in the Global Oceans, Having Long Been Recognized to Influence the Distribution of Physical, and Biogeochemical Properties in the Marine Environment. They Have Also Been Shown to Contribute to Shelf-Slope Exchange and to Strongly Influence Biological Processes, Either by Inducing Vertical Fluxes of Nutrients that Can Support New Production or by Passively Advecting Phytoplankton. Observations from the Ocean Observatories Initiative (Ooi) Pioneer Array Suggest that Coastal Ocean Dynamics Are Changing Rapidly New England, Changes to Shelf-Slope Exchange May Significantly Affect Nutrient Transport between the Deep Ocean and the Continental Shelf, with Consequences for Higher Trophic …

Towards Developing Visual Statistical Cues For Biodiversity, Abundance, Biomass Around Mariana Trench In An Embeddable Smart Module, Xiaoyu Li, Shidong Ren, Xue Geng, Hui He, Amaury Lendasse, Rui Nian

Engineering Management and Systems Engineering Faculty Research & Creative Works

Most Ecosystems in Mariana Trench, Still Remain Unexplored or Poorly Known. Recently, Advanced Underwater Vision Systems, Especially Those Deployed by the Remotely Operated Vehicles (Rov) and Autonomous Underwater Vehicles (AUV), Provide Us More Possibilities to Have a Closer Observation on the Deep-Sea Environments, Communities, Lifestyles, Even Behaviors of the Inhabitants. However, Online Visual Understanding is Still a Big Challenge in These Deepest Spots of the Ocean, Due to the Lack of Prior Knowledge, Poor Visibility, and Dynamic Investigation. in This Paper, We Are Trying to Make a Further Step to Develop Ecological Indicators in an Embeddable Smart Module, Capturing Visual …

Exploring Seafloor Stretching In Mariana Trench Arc Via The Squeeze And Excitation Network With High-Resolution Multibeam Bathymetric Survey, Shasha Liu, Jie Wang, Lina Zang, Rui Nian, Xiaoyu Li, Bo He, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

Multibeam Bathymetry Data Could Represent Nearly Continuous Coverage Depth Measurements of the Seafloor and Reveal Geomorphological Regions. Recent Studies Have Utilized Multibeam Bathymetry Data to Provide Geological Maps, but their Delineations Were Done Manually. Manual Classification and Delineation Are Inherently Subjective and Therefore Can Be Inaccurate. in This Paper, We Try to Develop One Strategy to Explore Seafloor Stretching in Mariana Trench Arc Via Squeeze and Excitation Network, Combining Data Clustering, Slope and Gradient. in Our Experiments, We Use the High-Resolution Multibeam Bathymetric Data Collected by Noaa Office of Ocean Exploration and Research (Oer). the Geomorphological Seabed in the Mariana …

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 …

Domain Adaption Via Feature Selection On Explicit Feature Map, Wan Yu Deng, Amaury Lendasse, Yew Soon Ong, Ivor Wai Hung Tsang, Lin Chen, Qing Hua Zheng

Engineering Management and Systems Engineering Faculty Research & Creative Works

In Most Domain Adaption Approaches, All Features Are Used for Domain Adaption. However, Often, Not Every Feature is Beneficial for Domain Adaption. in Such Cases, Incorrectly Involving All Features Might Cause the Performance to Degrade. in Other Words, to Make the Model Trained on the Source Domain Work Well on the Target Domain, it is Desirable to Find Invariant Features for Domain Adaption Rather Than using All Features. However, Invariant Features Across Domains May Lie in a Higher Order Space, instead of in the Original Feature Space. Moreover, the Discriminative Ability of Some Invariant Features Such as Shared Background Information …

Segmentation Of Sidescan Sonar Imagery Using Markov Random Fields And Extreme Learning Machine, Yan Song, Bo He, Ying Zhao, Guangliang Li, Qixin Sha, Yue Shen, Tianhong Yan, Rui Nian, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

As a Widely Used Segmentation Scheme, Markov Random Field (Mrf) Utilizes K-Means Clustering to Calculate the Initial Model for Sidescan Sonar Image Segmentation. However, for the Noise and Intensity Inhomogeneity Nature of the Sidescan Sonar Images, the Segmentation Results of K-Means Clustering Have Low Accuracy, Motivating Us to Use Machine Learning Methods to Initialize Mrf. Meanwhile, an Extreme Learning Machine (Elm), a Supervised Learning Algorithm Derived from the Single-Hidden-Layer Feedforward Neural Networks, Learns Faster Than Randomly Generated Hidden-Layer Parameters and is Superior to a Support Vector Machine (Svm). Therefore, in This Paper, We Proposed a Novel Method for Sidescan Sonar …

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 …

Embedded Online Fish Detection And Tracking System Via Yolov3 And Parallel Correlation Filter, Shasha Liu, Xiaoyu Li, Mingshan Gao, Yu Cai, Rui Nian, Peiliang Li, Tianhong Yan, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

Nowadays, Ocean Observatory Networks, Which Gather and Provide Multidisciplinary, Long-Term, 3d Continuous Marine Observations at Multiple Temporal Spatial Scales, Play a More and More Important Role in Ocean Investigations. in This Paper, We First Perform Image Enhancement to Produce Depth Information and Benefit Many Vision Algorithms and Advanced Image Editing. We Try to Develop a Novel Underwater Fish Detection and Tracking Strategies Combining You Only Look Once (Yolo) Latest Detection Algorithm Yolov3 Algorithm and Parallel Correlation Filter. We Demonstrated on the Nvidia Jetson Tx2 for Online Fish Detection and Tracking, Enabling a Fast System and Rapid Experimentation. It Has Been …

Freeform Extrusion Fabrication Of Advanced Ceramics And Ceramic-Based Composites, Wenbin Li

Doctoral Dissertations

"Ceramic On-Demand Extrusion (CODE) is a recently developed freeform extrusion fabrication process for producing dense ceramic components from single and multiple constituents. In this process, aqueous paste of ceramic particles with a very low binder content ( < 1 vol%) is extruded through a moving nozzle to print each layer sequentially. Once one layer is printed, it is surrounded by oil to prevent undesirable water evaporation from the perimeters of the part. The oil level is regulated just below the topmost layer of the part being fabricated. Infrared radiation is then applied to uniformly and partially dry the top layer so that the yield stress of the paste increases to avoid part deformation. By repeating the above steps, the part is printed in a layer-wise fashion, followed by post-processing. Paste extrusion precision of different extrusion mechanisms was compared and analyzed, with an auger extruder determined to be the most suitable paste extruder for the CODE system. A novel fabrication system was developed based on a motion gantry, auger extruders, and peripheral devices. Sample specimens were then produced from 3 mol% yttria stabilized zirconia using this fabrication system, and their properties, including density, flexural strength, Young's modulus, Weibull modulus, fracture toughness, and hardness were measured. The results indicated that superior mechanical properties were achieved by the CODE process among all the additive manufacturing processes. Further development was made on the CODE process to fabricate ceramic components that have external/internal features such as overhangs by using fugitive support material. Finally, ceramic composites with functionally graded materials (FGMs) were fabricated by the CODE process using a dynamic mixing device"--Abstract, page iv.

Quantifying Restoration Costs In The Aftermath Of An Extreme Event Using System Dynamics And Dynamic Mathematical Modeling Approaches, Akhilesh Ojha

Doctoral Dissertations

"Extreme events such as earthquakes, hurricanes, and the like, lead to devastating effects that may render multiple supply chain critical infrastructure elements inoperable. The economic losses caused by extreme events continue well after the emergency response phase has ended and are a key factor in determining the best path for post-disaster restoration. It is essential to develop efficient restoration and disaster management strategies to ameliorate the losses from such events. This dissertation extends the existing knowledge base on disaster management and restoration through the creation of models and tools that identify the relationship between production losses and restoration costs. The …

Biofuel Supply Chain Restructuring -- An Economic Viability And Environmental Sustainability Investigation For Enhancing Second Generation Biofuel Adoption, Rajkamal Kesharwani

Doctoral Dissertations

"Biofuel is a promising clean alternative to fossil fuels. Currently, first generation biofuels are commercially produced by using corn grain as biomass feedstock. However, the use of edible matter of crops, may lead to a competition between food and fuel. Therefore, there is a significant push in both industry and academia to commercialize second generation biofuel manufacturing technology, which uses non-edible matter from crops. Most research focuses on individual manufacturing processes for producing second generation biofuel, but the economic and environmental impacts of a large-scale adoption of second generation biofuel manufacturing have been less widely reported.

This work investigates the …

Application Of Computational Intelligence To Explore And Analyze System Architecture And Design Alternatives, Gene Lesinski

Doctoral Dissertations

"Systems Engineering involves the development or improvement of a system or process from effective need to a final value-added solution. Rapid advances in technology have led to development of sophisticated and complex sensor-enabled, remote, and highly networked cyber-technical systems. These complex modern systems present several challenges for systems engineers including: increased complexity associated with integration and emergent behavior, multiple and competing design metrics, and an expansive design parameter solution space. This research extends the existing knowledge base on multi-objective system design through the creation of a framework to explore and analyze system design alternatives employing computational intelligence. The first research …

Routing Algorithm For The Ground Team In Transmission Line Inspection Using Unmanned Aerial Vehicle, Yu Li

Masters Theses

"With the rapid development of robotics technology, robots are increasingly used to conduct various tasks by utility companies. An unmanned aerial vehicle (UAV) is an efficient robot that can be used to inspect high-voltage transmission lines. UAVs need to stay within a data transmission range from the ground station and periodically land to replace the battery in order to ensure that the power system can support its operation. A routing algorithm must be used in order to guide the motion and deployment of the ground station while using UAV in transmission line inspection. Most existing routing algorithms are dedicated to …