Operations Research, Systems Engineering and Industrial Engineering Commons^™

Establishing Rules For Self-Organizing Systems-Of-Systems, David M. Curry, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Self-organizing systems-of-systems offer the possibility of autonomously adapting to new circumstances and tasking. This could significantly benefit large endeavors such as smart cities and national defense by increasing the probability that new situations are expediently handled. Complex self-organizing behaviors can be produced by a large set of individual agents all following the same simple set of rules. While biological rule sets have application in achieving human goals, other rules sets may be necessary as these goals are not necessarily mirrored in nature. To this end, a set of system, rather than biologically, inspired rules is introduced and an agent-based model …

Rethinking The Design Of Low-Cost Point-Of-Care Diagnostic Devices, Faith W. Kimani, Samuel M. Mwangi, Benjamin J. Kwasa, Abdi M. Kusow, Benjamin K. Ngugi, Jiahao Chen, Xinyu Liu, Rebecca Cademartiri, Martin M. Thuo

Engineering Management and Systems Engineering Faculty Research & Creative Works

Reducing the global diseases burden requires effective diagnosis and treatment. In the developing world, accurate diagnosis can be the most expensive and time-consuming aspect of health care. Healthcare cost can, however, be reduced by use of affordable rapid diagnostic tests (RDTs). In the developed world, low-cost RDTs are being developed in many research laboratories; however, they are not being equally adopted in the developing countries. This disconnect points to a gap in the design philosophy, where parameterization of design variables ignores the most critical component of the system, the point-of-use stakeholders (e.g., doctors, nurses and patients). Herein, we demonstrated that …

Instance Selection Using Genetic Algorithms For An Intelligent Ensemble Trading System, Youngmin Kim, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Instance selection is a way to remove unnecessary data that can adversely affect the prediction model, thereby selecting representative and relevant data from the original data set that is expected to improve predictive performance. Instance selection plays an important role in improving the scalability of data mining algorithms and has also proven to be successful over a wide range of classification problems. However, instance selection using an evolutionary approach, as proposed in this study, is different from previous methods that have focused on improving accuracy performance in the stock market (i.e., Up or Down forecast). In fact, we propose a …

Time Series Classification Using Deep Learning For Process Planning: A Case From The Process Industry, Nijat Mehdiyev, Johannes Lahann, Andreas Emrich, David Lee Enke, Peter Fettke, Peter Loos

Engineering Management and Systems Engineering Faculty Research & Creative Works

Multivariate time series classification has been broadly applied in diverse domains over the past few decades. However, before applying the classification algorithms, the vast majority of current studies extract hand-engineered features that are assumed to detect local patterns in the time series. Therefore, the efficiency and precision of these classification approaches are heavily dependent on the quality of variables defined by domain experts. Recent improvements in the deep learning domain offer opportunities to avoid such an intensive hand-crafted feature engineering which is particularly important for managing the processes based on time-series data obtained from various sensor networks. In our paper, …

Loading Time Flexibility In Cross-Docking Systems, Dincer Konur, Mihalis M. Golias

Engineering Management and Systems Engineering Faculty Research & Creative Works

In this study, we investigate truck-to-door assignment problem for loading outgoing trucks in a cross-docking system with flexible handling times. Specifically, a truck's loading time depends on the number of workers assigned to the outbound door, where the truck is being loaded. An optimization problem is formulated to jointly determine the number of workers and the trucks to be loaded at each door. The resulting problem is a nonlinear integer programming model. Due to the complexity of this model, two evolutionary heuristic methods are proposed for solution. First heuristic method is based on truck assignments while the second heuristic is …

A Low-Dimensional Vector Representation For Words Using An Extreme Learning Machine, Paula Lauren, Guangzhi Qu, Guang Bin Huang, Paul Watta, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

Word Embeddings Are a Low-Dimensional Vector Representation of Words that Incorporates Context. Two Popular Methods Are Word2vec and Global Vectors (Glove). Word2vec is a Single-Hidden Layer Feedforward Neural Network (Slfn) that Has an Auto-Encoder Influence for Computing a Word Context Matrix using Backpropagation for Training. Glove Computes the Word Context Matrix First Then Performs Matrix Factorization on the Matrix to Arrive at Word Embeddings. Backpropagation is a Typical Training Method for SLFN's, which is Time Consuming and Requires Iterative Tuning. Extreme Learning Machines (Elm) Have the Universal Approximation Capability of SLFN's, based on a Randomly Generated Hidden Layer Weight Matrix …

Brute-Force Missing Data Extreme Learning Machine For Predicting Huntington's Disease, Anton Akusok, Emil Eirola, Kaj Mikael Björk, Yoan Miche, Hans Johnson, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

This Paper Presents a Novel Procedure to Train Extreme Learning Machine Models on Datasets with Missing Values. in Effect, a Separate Model is Learned to Classify Every Sample in the Test Set, However, this is Accomplished in an Efficient Manner Which Does Not Require Accessing the Training Data Repeatedly. Instead, a Sparse Structure is Imposed on the Input Layer Weights, Which Enables Calculating the Necessary Statistics in the Training Phase. an application to Predicting the Progression of Huntington's Disease from Brain Scans is Presented. Experimental Comparisons Show Promising Results Equivalent to the State of the Art in Machine Learning with …

Using The Voice Of The Student To Evaluate Learning Management Systems, Elizabeth A. Cudney, Susan L. Murray, Brittany Groner, Katie M. Kaczmarek, Bonnie Wilt, Kamaria Blaney, Julie Phelps

Engineering Management and Systems Engineering Faculty Research & Creative Works

A learning management system is an educational tool employed in higher education to organize, document, track, report, and deliver courses. Selecting the appropriate learning management system is a critical decision for a university. This study explores the usability of two leading systems, Blackboard and Canvas, from the students’ perspective. The goal is to gather and analyze user preferences in order to select an appropriate learning management system. Data was collected through surveys of student’s experience with the two learning management systems. The survey evaluated the ease of the following tasks: finding course documents, viewing grades, ease of navigation, intuitiveness, and …

Work Zone Simulator Analysis: Driver Performance And Acceptance Of Missouri Alternate Lane Shift Configurations, Suzanna Long, Ruwen Qin, Dincer Konur, Ming-Chuan Leu, S. Moradpour, S. Thind, H. Nadathur

Engineering Management and Systems Engineering Faculty Research & Creative Works

The objective of this project is to evaluate MoDOT’s alternate lane shift sign configuration for work zones. The single signproposed by MoDOT provides the traveler with enough information to let them know that all lanes are available to shift around thework zone, whereas the MUTCD signs require drivers to see two signs. This research simulation project evaluates the drivers’ laneshifting performance and acceptance of the alternate lane shift sign proposed by MoDOT to be used on work zones as compared tothe MUTCD lane shift signs. Based on the study results, no difference was observed between MUTCD lane shift sign andMoDOT …

Trends In Engineering Management Education From 2011–2015, William Daughton

Engineering Management and Systems Engineering Faculty Research & Creative Works

The American Society for Engineering Education provides comprehensive data on enrollment and graduation rates for all engineering fields at the Bachelor's, Master's, and PhD levels. In addition, data are provided on the demographics of the various fields. This information, coupled with Accreditation Board for Engineering and Technology data, presents an opportunity to explore the trends in higher education in Engineering Management in the last five years. Contrasts to earlier times and implications for the future are presented as a result of the analysis of these trends.

A Practical Approach To Evaluating The Economic And Technical Feasibility Of Led Luminaires, Sean M. Schmidt, Suzanna Long

Engineering Management and Systems Engineering Faculty Research & Creative Works

LED roadway luminaires are currently under consideration for widespread implementation with departments of transportation, facilities managers, and city planners. This research focuses on a case study in Missouri and presents relevant research findings calculated by the authors as part of a project funded by the Missouri Department of Transportation. Although high-pressure sodium (HPS) luminaires have been the standard product for roadway illumination, advances in LED technologies have led many departments of transportation to consider them as viable options along state routes. For this case study, pilot sites were developed across the state of Missouri in sites assessed as moderately busy, …

Micro-Grid Implementation Of A Rooftop Photovoltaic System, Pranav Nitin Godse

Masters Theses

"In recent years, solar power has been a popular form of renewable energy. This research conducts a cost analysis in implementing a rooftop photovoltaic system as part of an energy management schema for a university campus. The proposed system would be installed on the roof of one of the largest buildings on campus at Missouri University of Science and Technology, Toomey Hall; the objective function of the research involves reducing dependence on conventional energy sources on campus. Toomey Hall houses the Department of Mechanical and Aerospace Engineering (MAE) and is the largest academic unit on campus. Considering the vast expanse …

Data Analysis Of Lane Merge And Lane Shift Sign Configurations In A Freeway Workzone, Satwinder Singh Thind

Masters Theses

"In this study, driver responses to alternative lane shift and lane merge signs are analyzed and compared using a driving simulation system. In particular, driver responses to the lane merge signs proposed by the Missouri Department of Transportation (MoDOT) are compared to the current lane merge signs recommended by the Manual on Uniform Traffic Control Devices (MUTCD) and driver responses to the lane shift signs proposed by MoDOT are compared to current lane shift signs recommended by MUTCD. The driving simulation system is composed of a driving simulator and a PC with data recording program such that the position coordinates, …

Shortest-Distance And Minimum-Cost Self-Charging Path Problems: Formulations And Application, Marc Monroe Teeter

Masters Theses

"In this study, self-charging paths for an electric bus are analyzed. Wireless-power-transfer technologies, when integrated on a road network, enable dynamic charging of electric vehicles. Roads implemented with a wireless-power-transfer technology are referred to as electric-roads in this study. Electric vehicles traversing on electric-roads, therefore, can be dynamically charged. This can further eliminate the need for static charging, i.e., the electric vehicle will not need to stop for charging.

This thesis analyzes the design of transit routes for an electric-bus so that the electric-bus is charged by only electric-roads. Specifically, the focus is on designing a path, which passes through …

Developing Restoration Schemes For A Road Transportation Network In The Event Of A Disaster, Ebin Antony

Masters Theses

"Transportation systems such as rail, road, and waterways are key component of critical infrastructure systems, providing connectivity between other components to enable the production and distribution of goods and services. During large scale disasters such as earth quakes and floods, this connectivity is disrupted, restricting or completely halting the flow of goods and services. To ensure that the connectivity between the different modes of transportation are restored in an aftermath of these disruptions, the interdependence between them and the importance of individual elements to the overall connectivity have to be studied and formulated to develop a system-level restoration plan. This …

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

Masters Theses

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

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

Masters Theses

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

Attitudes Towards Face-To-Face Meetings In Virtual Engineering Teams: Perceptions From A Survey Of Defense Projects, Lawrence R. Blenke, Abhijit Gosavi, William Daughton

Engineering Management and Systems Engineering Faculty Research & Creative Works

Modes of communication used in virtual defense projects have changed dramatically over the years with tools such as email and video-conferencing dominating face-to-face (FTF) meetings. We conducted a survey at a defense firm with an aim to test current attitudes towards FTF meetings – with respect to significant problems faced, project success, transfer of technical requirements, preference for FTF vis-à-vis virtual meetings, differences between virtual and co-located environments, criticality of various forms of communication, and whether FTF meetings were scheduled as often as desired. Our survey participants, about one hundred in number, were experienced engineers, technicians, and program managers – …

Multi-Objective Combinatorial Optimization Problems In Transportation And Defense Systems, Hadi Farhangi

Doctoral Dissertations

"Multi-objective Optimization problems arise in many applications; hence, solving them efficiently is important for decision makers. A common procedure to solve such problems is to generate the exact set of Pareto efficient solutions. However, if the problem is combinatorial, generating the exact set of Pareto efficient solutions can be challenging. This dissertation is dedicated to Multi-objective Combinatorial Optimization problems and their applications in system of systems architecting and railroad track inspection scheduling. In particular, multi-objective system of systems architecting problems with system flexibility and performance improvement funds have been investigated. Efficient solution methods are proposed and evaluated for not only …

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

Doctoral Dissertations

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

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

Programming Problems On Time Scales: Theory And Computation, Rasheed Basheer Al-Salih

Doctoral Dissertations

"In this dissertation, novel formulations for several classes of programming problems are derived and proved using the time scales technique. The new formulations unify the discrete and continuous programming models and extend them to other cases "in between." Moreover, the new formulations yield the exact optimal solution for the programming problems on arbitrary isolated time scales, which solve an important open problem. Throughout this dissertation, six distinct classes of programming problems are presented as follows. First, the primal as well as the dual time scales linear programming models on arbitrary time scales are formulated. Second, separated linear programming primal and …

Underwater Scene Search Scheme Via Similarity Measure And Sparse Representation For Autonomous Underwater Vehicle, Zhiyuan Wang, Yue Geng, Congcong Shi, Rui Nian, Bo He, Tianhong Yan, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

Underwater Scene Search Turns Out to Be One of the Most Challenging Topics in the Underwater Image Analysis. in This Paper, We Present One Underwater Scene Search Scheme Combined with Similarity Measure and Sparse Representation. the Color Histogram is First Adopted to Classify the Candidate Image Patches for Each Kind of the Underwater Scene. at the Same Time, the Feature Similarity (FSIM) Considers a Full Reference of the Complementary Terms, I.e., the Phase Congruency (Pc) and the Image Gradient Magnitude (Gm), to Reflect and Generate the Similarity Map between the Query Image Patch and the Reference One. Sparse Representation is …

Underwater Image Enhancement Strategy With Virtual Retina Model And Image Quality Assessment, Yaomin Wang, Ruijie Chang, Ruinian, Bo He, Xunfei Liu, Jen Hwa Guo, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

Underwater Image Enhancement is One of the Most Essential and Fundamental Tasks in Ocean Investigations Recent Years. in This Paper, We Made an Attempt to Develop One Adaptive Underwater Image Enhancement Approach with the Help of the Virtual Retina Model and the Image Quality Assessment (IQA). the Virtual Retina Model, Which Yields Comparatively High Correlation with the Human Vision System, is First Taken to Achieve Simultaneous Ambiguity Removing and Detail Enhancing of Single Image Due to the Specific Mechanisms of Different Retinal Sub-Layers. after This, an Adaptive Image Enhancement Strategy is Taken with One Kind of No-Reference Image Quality Assessment …

Underwater Non-Rigid 3d Shape Reconstruction Via Structure From Motion For Fish Ethology Research, Renzheng Che, Xiao Xu, Rui Nian, Bo He, Meimei Chen, Cheng Zhang, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

In This Paper, We Try to Develop a General Framework of 3d Shape Reconstruction Strategy with Extremely Rare Point Cloud Extracted for Fish Ethology Research. Particle Filter is First Taken to Focus on Fish Trajectory Tracking from Monocular Video Sequence. the Speeded Up Robust Features (Surf) Technique Will Be Adopted to Match the Same Tracking Fish Across the overlapping View Fields with More Stable and Accurate Features. Non-Rigid 3d Shape Reconstruction Will Be Finally Developed with the Help of Expectation Maximization (Em) Model and Linear Dynamical System (LDS). It is Shown from Our Simulation Experiment that the Developed Scheme of …

Using Neural Networks To Forecast Volatility For An Asset Allocation Strategy Based On The Target Volatility, Youngmin Kim, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

The objective of this study is to use artificial neural networks for volatility forecasting to enhance the ability of an asset allocation strategy based on the target volatility. The target volatility level is achieved by dynamically allocating between a risky asset and a risk-free cash position. However, a challenge to data-driven approaches is the limited availability of data since periods of high volatility, such as during financial crises, are relatively rare. To resolve this issue, we apply a stability-oriented approach to compare data for the current period to a past set of data for a period of low volatility, providing …

Shape Analysis Of Traffic Flow Curves Using A Hybrid Computational Analysis, Wasim Irshad Kayani, Shikhar P. Acharya, Ivan G. Guardiola, Donald C. Wunsch, B. Schumacher, Isaac Wagner-Muns

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper highlights and validates the use of shape analysis using Mathematical Morphology tools as a means to develop meaningful clustering of historical data. Furthermore, through clustering more appropriate grouping can be accomplished that can result in the better parameterization or estimation of models. This results in more effective prediction model development. Hence, in an effort to highlight this within the research herein, a Back-Propagation Neural Network is used to validate the classification achieved through the employment of MM tools. Specifically, the Granulometric Size Distribution (GSD) is used to achieve clustering of daily traffic flow patterns based solely on their …

Genetic Algorithm Optimization Of Sos Meta-Architecture Attributes For Fuzzy Rule Based Assessments, Andrew Renault, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The analysis of an acknowledged systems of systems (SoS) meta-architecture requires a preliminary method for potential trade space exploration to ensure compliance to evolving capability requirements. It is important to assess the SoS meta-architecture concept to ensure that it satisfies all stakeholder needs and requirements in the early stages of development. There are numerous linguistic terms called key performance attributes (KPAs) that could be used to assess the different aspects of the architectures capabilities, however, too many KPAs could complicate the assessment. The initial population of suitable KPAs is reduced through non-derivative based optimization employed by a genetic algorithm (GA) …

Application Of Neural Network In Shop Floor Quality Control In A Make To Order Business, Rajkamal Kesharwani, Cihan H. Dagli, Zeyi Sun

Engineering Management and Systems Engineering Faculty Research & Creative Works

A make to order business has to produce the products that are customized to the customer's current need. The customization can be realized by assembling different standard parts with various 'configurations'. The oil field service industry is a typical example where most products produced are cylindrical assemblies made up of standard parts customized in their size, material specifications, coating specifications, and threading suited for the particular load rating and environment. As business cycles go up and down, hiring and firing of personnel is the routine of the day. Thus, it is very hard to keep experienced inspectors due to high …

Application Of An Artificial Neural Network To Predict Graduation Success At The United States Military Academy, Gene Lesinski, Steven Corns, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper presents a neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A multi-layer feedforward network with backpropagation learning is used as the model framework. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. Nine input variables consist of categorical and numeric data elements including: high school rank, high school quality, standardized test scores, high school faculty assessments, extra-curricular activity score, parent's education status, and time since high school graduation. These inputs and the multi-layer neural network model are used …

Combining Max-Min And Max-Max Approaches For Robust Sos Architecting, Hadi Farhangi, Dincer Konur, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

A System of Systems (SoS) architecting problem requires creating a selection of systems in order to provide a set of capabilities. SoS architecting finds many applications in military/defense projects. In this paper, we study a multi-objective SoS architecting problem, where the cost of the architecture is minimized while its performance is maximized. The cost of the architecture is the summation of the costs of the systems to be included in the SoS. Similarly, the performance of the architecture is defined as the sum of the performance of the capabilities, where the performance of a capability is the sum of the …