Genetic Algorithm Optimization Of Sos Meta-Architecture Attributes For Fuzzy Rule Based Assessments,
2016
Missouri University of Science and Technology
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) …
Using Neural Networks To Forecast Volatility For An Asset Allocation Strategy Based On The Target Volatility,
2016
Missouri University of Science and Technology
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 …
Jitim Volume 25 Issue 3 - Front Page,
2016
California State University, San Bernardino
Jitim Volume 25 Issue 3 - Front Page
Journal of International Technology and Information Management
JITIM volume 25, issue 3
Technology Trajectory Mapping Using Data Envelopment Analysis: The Ex-Ante Use Of Disruptive Innovation Theory On Flat Panel Technologies,
2016
Portland State University
Technology Trajectory Mapping Using Data Envelopment Analysis: The Ex-Ante Use Of Disruptive Innovation Theory On Flat Panel Technologies, Dong-Joon Lim, Timothy R. Anderson
Engineering and Technology Management Faculty Publications and Presentations
In this paper, we propose a technology trajectory mapping approach using Data Envelopment Analysis (DEA) that scrutinizes technology progress patterns from multidimensional perspectives. Literature reviews on technology trajectory mappings have revealed that it is imperative to identify key performance measures that can represent different value propositions and then apply them to the investigation of technology systems in order to capture indications of the future disruption. The proposed approach provides a flexibility not only to take multiple characteristics of technology systems into account but also to deal with various tradeoffs among technology attributes by imposing weight restrictions in the DEA model. …
Application Of An Artificial Neural Network To Predict Graduation Success At The United States Military Academy,
2016
Missouri University of Science and Technology
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,
2016
Missouri University of Science and Technology
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 …
Entity Resolution Using Convolutional Neural Network,
2016
Missouri University of Science and Technology
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,
2016
Missouri University of Science and Technology
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 …
Multiobjective System Of Systems Architecting With Performance Improvement Funds,
2016
Missouri University of Science and Technology
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 …
Shape Analysis Of Traffic Flow Curves Using A Hybrid Computational Analysis,
2016
Missouri University of Science and Technology
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,
2016
Missouri University of Science and Technology
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 …
Combined Nonlinear Visualization And Classification: Elmvis++C,
2016
Missouri University of Science and Technology
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 …
Clinical Narrative Classification Using Discriminant Word Embeddings With Elm,
2016
Missouri University of Science and Technology
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 …
Model Based Systems Engineeringfor Cubesats,
2016
University of Alabama in Huntsville
Model Based Systems Engineeringfor Cubesats, Lloyd Walker, Dale Thomas
Von Braun Symposium Student Posters
No abstract provided.
Cognitive Measure Of Workload In Multimodal User Interfaces,
2016
University of Alabama in Huntsville
Cognitive Measure Of Workload In Multimodal User Interfaces, Maliha Lubna, Paul Collopy, Jeff Hansberger
Von Braun Symposium Student Posters
No abstract provided.
Systems Generational Evolution,
2016
University of Alabama in Huntsville
Systems Generational Evolution, Katherine Burris, Dale Thomas
Von Braun Symposium Student Posters
No abstract provided.
Implementing Sysml To Reduce Ambiguity In The Life Cycle Management Of Unmanned Aerial Vehicles,
2016
University of Alabama in Huntsville
Implementing Sysml To Reduce Ambiguity In The Life Cycle Management Of Unmanned Aerial Vehicles, Zach Thomas, Dale Thomas
Von Braun Symposium Student Posters
No abstract provided.
Rotorcraft Cockpit Simulation For Early End-User Design Decisions,
2016
University of Alabama in Huntsville
Rotorcraft Cockpit Simulation For Early End-User Design Decisions, Derek Millard, Bryan Mesmer
Von Braun Symposium Student Posters
No abstract provided.
Storytelling In Engineering Preference Communication,
2016
University of Alabama in Huntsville
Storytelling In Engineering Preference Communication, Giulia E. Palma, Bryan L. Mesmer, Jeffrey E. Dyas
Von Braun Symposium Student Posters
No abstract provided.
Game-Based Learning Of Incentives In Systems Engineering,
2016
University of Alabama in Huntsville
Game-Based Learning Of Incentives In Systems Engineering, Joseph H. Clerkin, Bryan Mesmer
Von Braun Symposium Student Posters
No abstract provided.
