Operations Research, Systems Engineering and Industrial Engineering Commons^™

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 …

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 …

Engineering Cyber Physical Systems: Applying Theory To Practice Preface, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

No abstract provided.

Entity Resolution Using Convolutional Neural Network, Ram Deepak Gottapu, Cihan H. Dagli, Bharami Ali

Engineering Management and Systems Engineering Faculty Research & Creative Works

Entity resolution is an important application in field of data cleaning. Standard approaches like deterministic methods and probabilistic methods are generally used for this purpose. Many new approaches using single layer perceptron, crowdsourcing etc. are developed to improve the efficiency and also to reduce the time of entity resolution. The approaches used for this purpose also depend on the type of dataset, labeled or unlabeled. This paper presents a new method for labeled data which uses single layered convolutional neural network to perform entity resolution. It also describes how crowdsourcing can be used with the output of the convolutional neural …

Evaluating Forecasting Methods By Considering Different Accuracy Measures, Nijat Mehdiyev, David Lee Enke, Peter Fettke, Peter Loos

Engineering Management and Systems Engineering Faculty Research & Creative Works

Choosing the appropriate forecasting technique to employ is a challenging issue and requires a comprehensive analysis of empirical results. Recent research findings reveal that the performance evaluation of forecasting models depends on the accuracy measures adopted. Some methods indicate superior performance when error based metrics are used, while others perform better when precision values are adopted as accuracy measures. As scholars tend to use a smaller subset of accuracy metrics to assess the performance of forecasting models, there is a need for a concept of multiple accuracy dimensions to assure the robustness of evaluation. Therefore, the main purpose of this …

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) …

Multiobjective System Of Systems Architecting With Performance Improvement Funds, Hadi Farhangi, Dincer Konur, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

A System of Systems architecting problem aims to determine a selection of systems, which are capable of providing a set of desired capabilities. A SoS architect usually has multiple objectives in generating efficient architectures such as minimization of the total cost and maximization the overall performance of the SoS. This study formulates a biobjective SoS architecting problem with these two objectives. Here, we consider that, by allocating funds to the systems, the SoS architect can improve the performance of the capabilities the systems can provide. The resulting architecting problem is a biobjective mixed-integer linear programming model. Specifically, the system selection …

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 …

Clinical Narrative Classification Using Discriminant Word Embeddings With Elm, Paula Lauren, Guangzhi Qu, Feng Zhang, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

Clinical Texts Are Inherently Complex Due to the Medical Domain Expertise Required for Content Comprehension. in Addition, the Unstructured Nature of These Narratives Poses a Challenge for Automatically Extracting Information. in Natural Language Processing, the Use of Word Embeddings Are an Effective Approach to Generate Word Representations (Vectors) in a Low Dimensional Space. in This Paper We Use a Log-Linear Model (A Type of Neural Language Model) and Linear Discriminant Analysis with a Kernel-Based Extreme Learning Machine (Elm) to Map the Clinical Texts to the Medical Code. Experimental Results on Clinical Texts Indicate Improvement with Elm in Comparison to Svm …

Combined Nonlinear Visualization And Classification: Elmvis++C, Andrey Gritsenko, Anton Akusok, Yoan Miche, Kaj Mikael Björk, Stephen Baek, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

This Paper Presents an Improvement of the ELMVIS+ Method that is Proposed for Fast Nonlinear Dimensionality Reduction. the ELMVIS++C Has an Additional Supervised Learning Component Compared to ELMVIS+, Which is Originally an Unsupervised Method as Like the Majority of the Other Dimensionality Reduction Method. This Component Prevents Samples under the Same Class Being Separated Apart from Each Other. in This Improved Method, the Importance of the Supervised Component Can Be Further Tuned to Have Different Level of Influence. the Test Results on Four Datasets Indicate that the Proposed Improvement Not Only Maintains the Performance of ELMVIS+, But Also is Extremely …

Fuzzy-Genetic Algorithm Approach To Generate An Optimal Meta-Architecture For A Smart, Safe & Efficient City Transportation System Of Systems, Rahul Alaguvelu, David M. Curry, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

A city's transportation infrastructure deeply affects its citizens' quality of life-from the freshness of food to the amount of frustration felt while commuting to work. Providing an optimal infrastructure has the possibility of dramatically improving the population's well-being. Since the transportation infrastructure is a system-of-systems (SoS) [1], it may be modelled and optimized for a given set of objectives [2]. In this manner, the city resources can be optimized for multiple objectives such as commute time, overall throughput, and sustainability. This is made possible by using a fuzzy assessor to map the individual objectives into a single overall fitness value …

A New Application Of Machine Learning In Health Care, Kaj Mikael Björk, Yoan Miche, Emil Eirola, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

In Our Ever More Complex World, the Field of Analytics Has Dramatically Increased its Importance. Gut Feeling is No Longer Sufficient in Decision Making, But Intuition Has to Be Combined with Support from the Huge Amount of Data Available Today. Even If the Amount of Data is Enormous, the Quality of the Data is Not Always Good. Problems Arise in at Least Two Situations: I) the Data is Imprecise by Nature and Ii) the Data is Incomplete (Or There Are Missing Parts in the Data Set). Both Situations Are Problematic and Need to Be Addressed Appropriately. If These Problems Are …

Underwater Object Detection With Efficient Shadow-Removal For Side Scan Sonar Images, Ruijie Chang, Yaomin Wang, Jiaru Hou, Shuqi Qiu, Rui Nian, Bo He, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

Side Scan Sonar Has Been Widely Used in Ocean Investigations, Underwater Object Detection by Side Scan Sonar is One of the Most Essential and Fundamental Tasks These Years. in This Paper, We Present One Simplified Underwater Object Detection Scheme with the Help of Shadow Removal of Side Scan Sonar Images. the Fuzzy C-Mean Clustering (FCM) Algorithm is First Taken to Partition All Pixels from the Side Scan Sonar Images into a Collection of C Fuzzy Clusters, Which Makes Shadow Regions Be Segmented. the Criminisi Algorithm based on Isophote-Driven Image Sampling Process is Then Made Full Use of to Undertake the …

Seafloor Visual Saliency Evaluation For Navigation With Bow And Dbscan, Yue Geng, Zhiyuan Wang, Congcong Shi, Rui Nian, Cheng Zhang, Bo He, Yue Shen, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

In This Paper, We Try to Combine Bag-Of-Words (Bow) with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Together for One Kind of Sparse Representation in the Seafloor Visual Saliency Evaluation. Properties in the Water, Due to the Large Amount of Acoustic Noises, Sonar Signals Are Easily Polluted and Interfered during Image Collection, and the Sonar Images Usually Diverge from the True Underwater Environment or Degrade the Accuracy of the Measure, So Sparse Representation Has Been Taken into Consideration for Underwater Sonar Image Visual Saliency Evaluation. Our Method is a Simple and Computationally Efficient Extension of an Order less Bow …

Underwater 3d Object Reconstruction With Multiple Views In Video Stream Via Structure From Motion, Xiao Xu, Renzheng Che, Rui Nian, Bo He, Meimei Chen, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

Underwater 3d Object Reconstruction is One of the Most Essential and Fundamental Tasks in Ocean Investigations. in This Paper, We Try to Capture the Inherent Geometrical Variation of 3d Objects at Multiple Visual Angels with the Underwater Vehicles and Develop a Novel Underwater 3d Object Reconstruction Model for the Continuous Video Stream. Image Enhancement Will Be First Taken into Consideration by Guided Image Filtering to Acquire Clearer Images for Object Tracking Which Can Be Completed by the Particle Filter. and Then, We Use Scale Invariant Feature Transform (Sift) and Random Sample Consensus (Ransac) to Detect and Correspond the Features of …

Work Zone Simulator Analysis: Driver Performance And Acceptance Of Alternate Merge Sign Configurations, Suzanna Long, Ruwen Qin, Dincer Konur, Ming-Chuan Leu, S. Moradpour, S. Wu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Improving work zone road safety is an issue of great interest due to the high number of crashes observed in work zones. Departments of Transportation (DOTs) use a variety of methods to inform drivers of upcoming work zones. One method used by DOTs is work zone signage configuration. It is necessary to evaluate the efficiency of different configurations, by law, before implementation of new signage designs that deviate from national standards. This research presents a driving simulator based study, funded by the Missouri Department of Transportation (MoDOT) that evaluates a driver’s response to work zone sign configurations. This study has …

Lean Six Sigma Journey In A Uk Higher Education Institute: Challenges, Projects, And Key Lessons Learned, Jiju Antony, Elizabeth A. Cudney

Engineering Management and Systems Engineering Faculty Research & Creative Works

Lean Six Sigma is a powerful methodology for achieving process efficiency and effectiveness resulting in enhanced customer satisfaction and improved bottom line results. Although a number of manufacturing and service organizations are utilizing the power of this integrated methodology, Higher Education Institutions have been slow to introduce and develop this process excellence methodology. The purpose of the paper is to critically evaluate Lean Six Sigma as a powerful business improvement methodology for improving the efficiency and effectiveness of Higher Education Institutions. The paper will explore the fundamental challenges and critical success factors encountered with the introduction and development of Lean …

Air Quality Simulations Using Big Data Programming Models, Haripriya Ayyalasomayajula, Edgar Gabriel, Peggy Lindner, Daniel Price

Engineering Management and Systems Engineering Faculty Research & Creative Works

Forecasts of daily pollutant levels have become a standard part of weather predictions in television, on-line, and in newspapers. Research groups also need to analyze larger timeframes across more locations to correlate long term developments for different pollutants with multiple serious health effects such as asthma. This paper presents a comparison of the Hadoop MapReduce and Spark programing models for air quality simulations, guiding future code development for the research groups interested in these analyses. Two use cases have been used, namely (i) calculating the eight-hour rolling average of pollutants in a restricted region, (ii) identifying clusters of sensors showing …

A R-Som Analysis Of The Link Between Financial Market Conditions And A Systemic Risk Index Based On Ica-Factors Of Systemic Risk Measures, Patrick Kouontchou, Amaury Lendasse, Yoan Miche, Alejandro Modesto, Peter Sarlin, Bertrand Maillet

Engineering Management and Systems Engineering Faculty Research & Creative Works

Due to the Recent Financial Crisis, Several Systemic Risk Measures Have Been Proposed in the Literature for Quantifying Financial System Wide Distress. in This Note We Propose an Aggregated Index for Financial Systemic Risk Measurement based on EOF and Ica Analyses on the Several Systemic Risk Measures Released in the Recent Literature. We Use This Index to Further Identify the States of the Market as Suggested in Kouontchou Et Al. [18]. We Show, by Characterizing Markets Conditions with a Robust Kohonen Self-Organizing Maps Algorithm that This Measure is Directly Linked to Crises Markets States and There is a Strong Link …

Hsr: L 1/2-Regularized Sparse Representation For Fast Face Recognition Using Hierarchical Feature Selection, Bo Han, Bo He, Tingting Sun, Tianhong Yan, Mengmeng Ma, Yue Shen, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

In This Paper, We Propose a Novel Method for Fast Face Recognition Called L1/2-Regularized Sparse Representation using Hierarchical Feature Selection. by Employing Hierarchical Feature Selection, We Can Compress the Scale and Dimension of Global Dictionary, Which Directly Contributes to the Decrease of Computational Cost in Sparse Representation that Our Approach is Strongly Rooted In. It Consists of Gabor Wavelets and Extreme Learning Machine Auto-Encoder (Elm-Ae) Hierarchically. for Gabor Wavelets' Part, Local Features Can Be Extracted at Multiple Scales and Orientations to Form Gabor-Feature-Based Image, Which in Turn Improves the Recognition Rate. Besides, in the Presence of Occluded Face Image, the …

Auto-Detection Of Anisakid Larvae In Cod Fillets By Uv Fluorescent Imaging With Os-Elm, Wenqiang Cai, Limin Cao, Hong Lin, Jianxin Sui, Rui Nian, Jidong Hu, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

In This Paper, One Auto-Detection Scheme of Anisakid Larvae in Cod Fillets is Developed on the Basis of Online Sequential Extreme Learning Machine (OS-ELM) in a Single Hidden Layer Feedforward Neural Networks (SLFN). One Uv Fluorescent Imaging System is First Set Up to Collect and Extract the Typical Image Patches with and Without Anisakid Larvae Inside the Fish Muscles, the Uv Fluorescent Image Patches Are Then Fed into SLFN Sequentially to Learn How to Nondestructively Identify the Parasites in Real-Time, particularly for a Growing Size of the Training Set with New Observations Arrived Again and Again. It Has Been Shown …

Multi-Level Evolutionary Algorithms Resource Allocation Utilizing Model-Based Systems Engineering, Bhanuchander Reddy Poreddy

Doctoral Dissertations

"This research presents an innovative approach to solve the resource allocation problems using Multi-level Evolutionary Algorithms. Evolutionary Algorithms are used to solve resource allocation problems in different domains and their results are then incorporated into a higher level system solution using another Evolutionary Algorithm to solve base camp planning problems currently faced by the U.S. Department of Defense.

Two models are introduced to solve two domain specific models: a logistics model and a power model. The logistic model evaluates routes for logistics vehicles on a daily basis with a goal of reducing fuel usage by delivery trucks. The evaluation includes …

A Meta-Architecture Analysis For A Coevolved System-Of-Systems, George Anthony Muller Iv

Masters Theses

"Modern engineered systems are becoming increasingly complex. This is driven in part by an increase in the use of systems-of-systems and network-centric concepts to improve system performance. The growth of systems-of-systems allows stakeholders to achieve improved performance, but also presents new challenges due to increased complexity. These challenges include managing the integration of asynchronously developed systems and assessing SoS performance in uncertain environments.

Many modern systems-of-systems must adapt to operating environment changes to maintain or improve performance. Coevolution is the result of the system and the environment adapting to changes in each other to obtain a performance advantage. The complexity …

Modeling Resilience In System Of Systems Architecture, Paulette Acheson, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

System of Systems (SoS) can be represented by the SoS architecture which defines the constituent systems and their interfaces. SoS delivers capabilities beyond what the systems working independently can provide. Resiliency of the SoS is becoming increasingly more important and necessary for mission success. Resiliency can be defined as the ability of the SoS architecture to defend against emerging threats. This defense means adapting the SoS architecture to handle the new threat. As a new threat is uncovered, the SoS architecture evolves to a new SoS architecture resilient to the new threat. This paper presents a novel approach to modeling …

Analysis Of Va Sacramento Medical Center Capacity Using Discrete Event Simulation, Tatiana Alejandra Cardona

Masters Theses

"The development of simulation models continues to provide effective solutions to a wide range of problems in healthcare systems. In the research presented within this thesis is the development of a representative and validated discrete event simulation model for the purpose of evaluating additional capacity. The study consists of a detailed exploratory analysis, verification and validation tests of the simulation results, and a thorough design of experiments. The exploratory analysis consisted of developing simulation models that provide similar characteristics found in the data. The design of experiments consisted of generating scenarios of various bed additions in the hospital units of …

Data Analysis For Driving Pattern Identification And Driver's Behavior Modeling In A Freeway Work Zone, Hari Narayanan Vijaya Raghavan Nadathur

Masters Theses

"A variety of methods are used by Departments of Transportation (DOT) for informing drivers about upcoming work zones. One such method is work zone signage configuration. Signage plays an important role in work zones to provide guidance to drivers when conditions on the road vary from normal. Therefore, it is necessary to evaluate the effectiveness of different configurations, by law, before implementation of new signage designs that deviate from the national standards.

The Manual on Uniform Traffic Control Devices (MUTCD) is a compilation of national standards for all traffic control devices, including road markings, highway signs, and traffic signals. In …

Applications Of Simulation And Optimization Techniques In Optimizing Room And Pillar Mining Systems, Angelina Konadu Anani

Doctoral Dissertations

"The goal of this research was to apply simulation and optimization techniques in solving mine design and production sequencing problems in room and pillar mines (R&P). The specific objectives were to: (1) apply Discrete Event Simulation (DES) to determine the optimal width of coal R&P panels under specific mining conditions; (2) investigate if the shuttle car fleet size used to mine a particular panel width is optimal in different segments of the panel; (3) test the hypothesis that binary integer linear programming (BILP) can be used to account for mining risk in R&P long range mine production sequencing; and (4) …

Post-Disaster Supply Chain Interdependent Critical Infrastructure System Restoration: A Review Of Data Necessary And Available For Modeling, Varun Ramachandran, Suzanna Long, Tom Shoberg, Steven Corns, Hector J. Carlo

Engineering Management and Systems Engineering Faculty Research & Creative Works

The majority of restoration strategies in the wake of large-scale disasters have focused on short-term emergency response solutions. Few consider medium- to long-term restoration strategies to reconnect urban areas to national supply chain interdependent critical infrastructure systems (SCICI). These SCICI promote the effective flow of goods, services, and information vital to the economic vitality of an urban environment. To re-establish the connectivity that has been broken during a disaster between the different SCICI, relationships between these systems must be identified, formulated, and added to a common framework to form a system-level restoration plan. To accomplish this goal, a considerable collection …