Operations Research, Systems Engineering and Industrial Engineering Commons^™

Particle Swarm Optimization Based Selective Ensemble Of Online Sequential Extreme Learning Machine, Yang Liu, Bo He, Diya Dong, Yue Shen, Tianhong Yan, Rui Nian, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

A Novel Particle Swarm Optimization based Selective Ensemble (PSOSEN) of Online Sequential Extreme Learning Machine (OS-ELM) is Proposed. It is based on the Original OS-ELM with an Adaptive Selective Ensemble Framework. Two Novel Insights Are Proposed in This Paper. First, a Novel Selective Ensemble Algorithm Referred to as Particle Swarm Optimization Selective Ensemble is Proposed, noting that PSOSEN is a General Selective Ensemble Method Which is Applicable to Any Learning Algorithms, Including Batch Learning and Online Learning. Second, an Adaptive Selective Ensemble Framework for Online Learning is Designed to Balance the Accuracy and Speed of the Algorithm. Experiments for Both …

Defensive Routines In Engineering Managers And Non-Engineering Managers -- A Case Analysis, Tyria Riley, Elizabeth A. Cudney

Engineering Management and Systems Engineering Faculty Research & Creative Works

There is a perception that engineers experience challenges in areas such as communication, conflict resolution, and leadership. Defensive routines are actions implemented as a result of being in an embarrassing or threatening situation. This research uses a case study approach to measure whether defensive routines are more common in engineering managers or non-engineering managers. Twenty-seven managers created case studies based on their unique experiences as managers. These case studies were scored, and the results of this research indicate that engineering managers employ defensive routines more commonly than non-engineering managers.

Detection And Recognition Of R/F Devices Based On Their Unintended Electromagnetic Emissions Using Stochastic And Computational Intelligence Methods, Shikhar Prasad Acharya

Doctoral Dissertations

"Radio Frequency (RF) devices produce some amount of Unintended Electromagnetic Emissions (UEEs). UEEs are generally unique to a device and can be thought of as a signature of the device. This property of uniqueness of UEEs can be used to detect and identify the device producing the emission. The problem with UEEs is that they are very low in power and are often buried deep inside the noise band which makes them difficult to detect. There are two types of UEE detection methods. The first one is called stimulated detection method where the UEEs of a device are enhanced using …

Quantum Inspired Algorithms For Learning And Control Of Stochastic Systems, Karthikeyan Rajagopal

Doctoral Dissertations

"Motivated by the limitations of the current reinforcement learning and optimal control techniques, this dissertation proposes quantum theory inspired algorithms for learning and control of both single-agent and multi-agent stochastic systems.

A common problem encountered in traditional reinforcement learning techniques is the exploration-exploitation trade-off. To address the above issue an action selection procedure inspired by a quantum search algorithm called Grover's iteration is developed. This procedure does not require an explicit design parameter to specify the relative frequency of explorative/exploitative actions.

The second part of this dissertation extends the powerful adaptive critic design methodology to solve finite horizon stochastic optimal …

Predicting Solar Irradiance Using Time Series Neural Networks, Ahmad Alzahrani, Jonathan W. Kimball, Cihan H. Dagli

Electrical and Computer Engineering Faculty Research & Creative Works

Increasing the accuracy of prediction improves the performance of photovoltaic systems and alleviates the effects of intermittence on the systems stability. A Nonlinear Autoregressive Network with Exogenous Inputs (NARX) approach was applied to the Vichy-Rolla National Airport's photovoltaic station. The proposed model uses several inputs (e.g. time, day of the year, sky cover, pressure, and wind speed) to predict hourly solar irradiance. Data obtained from the National Solar Radiation Database (NSRDB) was used to conduct simulation experiments. These simulations validate the use of the proposed model for short-term predictions. Results show that the NARX neural network notably outperformed the other …

Quantifying Economic Benefits For Rail Infrastructure Projects, Suzanna Long, Ruwen Qin, John Betak, Akhilesh Ojha, John J. Myers

Engineering Management and Systems Engineering Faculty Research & Creative Works

This project identifies metrics for measuring the benefit of rail infrastructure projects for key stakeholders. It is important that stakeholders with an interest in community economic development play an active role in the development of the rail network. Economic development activities in both rural and urban settings are essential if a nation is to realize growth and prosperity. Many communities have developed goals and visions to establish an economic development program, but they often fail to achieve their goals due to uncertainties during the project selection and planning process. Communities often select a project from a vast pool of ideas …

The Delta Test: The 1-Nn Estimator As A Feature Selection Criterion, Emil Eirola, Amaury Lendasse, Francesco Corona, Michel Verleysen

Engineering Management and Systems Engineering Faculty Research & Creative Works

Feature Selection is Essential in Many Machines Learning Problem, But It is Often Not Clear on Which Grounds Variables Should Be Included or Excluded. This Paper Shows that the Mean Squared Leave-One-Out Error of the First Nearest-Neighbour Estimator is Effective as a Cost Function When Selecting Input Variables for Regression Tasks. a Theoretical Analysis of the Estimator's Properties is Presented to Support its Use for Feature Selection. an Experimental Comparison to Alternative Selection Criteria (Including Mutual Information, Least Angle Regression, and the Rrelieff Algorithm) Demonstrates Reliable Performance on Several Regression Tasks.

Variable Selection For Regression Problems Using Gaussian Mixture Models To Estimate Mutual Information, Emil Eirola, Amaury Lendasse, Juha Karhunen

Engineering Management and Systems Engineering Faculty Research & Creative Works

Variable Selection is a Crucial Part of Building Regression Models and is Preferably Done as a Filtering Method Independently from the Model Training. Mutual Information is a Popular Relevance Criterion for This, But It is Not Trivial to Estimate Accurately from a Limited Amount of Data. in This Paper, a Method is Presented Where a Gaussian Mixture Model is Used to Estimate the Joint Density of the Input and Output Variables, and Subsequently Used to Select the Most Relevant Variables by Maximizing the Mutual Information Which Can Be Estimated using the Model.

Rssi And Lqi Data Clustering Techniques To Determine The Number Of Nodes In Wireless Sensor Networks, Yanwen Wang, Ivan G. Guardiola, Xiaoling Wu

Engineering Management and Systems Engineering Faculty Research & Creative Works

With the rapid proliferation of wireless sensor networks, different network topologies are likely to exist in the same geographical region, each of which is able to perform its own functions individually. However, these networks are prone to cause interference to neighbor networks, such as data duplication or interception. How to detect, determine, and locate the unknown wireless topologies in a given geographical area has become a significant issue in the wireless industry. This problem is especially acute in military use, such as spy-nodes detection and communication orientation systems. In this paper, three different clustering methods are applied to classify the …

Study Of The Use Of A Genetic Algorithm To Improve Networked System-Of-Systems Resilience, Charles O. Adler, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Large scale failures or degradation resulting from smaller initial failures or disruptions in networked system-of-systems are an issue in multiple areas - for example cascading failures in electrical power grids or large disruptions in national air traffic due to local or regional weather conditions. The system architecture can have a significant impact on system-of-systems susceptibility to large scale failures. The study presented in this paper uses a simple interdependent networked system-of-systems failure model, integrated into a unique objective function that addresses both the overall level of failure and the rate of failure progression, and a genetic algorithm to demonstrate an …

Identifying And Overcoming The Barriers To Sustainable Construction, Cristin Szydlik

Doctoral Dissertations

"Sustainable construction has become increasingly more prevalent over the last decade. All federal and most state government buildings are mandated to achieve Leadership in Energy and Environmental Design (LEED) certification (at the silver level at a minimum). Although sustainable construction is increasingly more common, there are still barriers to the successful completion of a sustainable project, specifically with regards to projects achieving LEED certification. There are still holes in the guidance on how to successfully achieve LEED certification. Even though government agencies have mandated LEED certification, the existing barriers are preventing buildings from achieving certification, which can delay the building …

Workplace Sleepiness: Causes, Consequences And Countermeasures, Neda Khalafi

Masters Theses

"Sleep deprivation is an epidemic problem in our society that adversely affects the quality and safety of our daily lives. At home and on the job, inadequate sleep is associated with considerable social, financial, and health-related costs, due to instability of waking functions. Research has shown that, sleep deprivation among workers, whether from sleep disorders, lifestyle or shift work, can hinder the proper performance, and in extreme cases may pose hazard to the workers themselves and their environment. Deficits in daytime performance can have profound effects in the processing industry where workers are often required to perform monotonous tasks that …

The Use Of Statistics And Its Analysis In Resolving Transportation Related Problems, Venkat Sravan Kothapalli

Masters Theses

"Statistical methods are traditionally used to summarize or describe a collection of data. This can be termed as descriptive statistics. This research focuses on application of certain statistical approaches, tools to solve transportation related problems. This research focuses on usage of statistical analysis in determine some of the significant factors and assumptions which should be taken into consideration while developing safety and maintenance related measures by transportation agencies. This research mainly focuses on; testing null hypothesis and frequency analysis. The research therefore is presented in two different case studies: 1) Statistical analysis of the work zone crash historical raw data …

Hedge Fund Replication Using A Strategy Specific Modeling Approach, Sujit Subhash

Masters Theses

"Institutional investors and wealthy individuals have in the past allocated a significant portion of their portfolios to hedge funds with the expectation of unconditional and uncorrelated returns to the market. However, the financial crisis of 2008 has heightened investor sensitivity to the high fees, illiquidity, and lockup periods typically associated with hedge funds. Hedge fund indexes showing excellent returns and low volatility contain funds that are closed to new investments, while the performance of investable funds have been shown to be inferior to their non-investable counterparts. The lack of transparency and extreme variation in the performance of hedge funds make …

Economic And Environmental Comparison Of Different Ordering Policies For An Integrated Inventory Control And Supplier Selection Problem, Sepideh Almasi Monfared

Masters Theses

"This study analyzes an integrated inventory control and supplier selection problem in stochastic demand environment under carbon emissions regulations. In particular, a continuous review inventory model with multiple suppliers is investigated under carbon taxing and carbon trading regulations. We analyze and compare the optimal supplier selection and order splitting decisions with single sourcing and two alternative delivery structures for multi-sourcing, namely, sequential ordering and sequential delivery. For each of the three ordering policies, a solution method is proposed and these policies are compared in terms of their economic as well as environmental performances. A numerical study is conducted to demonstrate …

Conquering Complexity: Challenges And Opportunities: Preface, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

No abstract provided.

Part Viii: Health Care Analytics: Preface, Cilan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

No abstract provided.

How To Rein In The Volatile Actor: A New Bounded Perspective, Abhijit Gosavi

Engineering Management and Systems Engineering Faculty Research & Creative Works

Actor-critic algorithms are amongst the most well-studied reinforcement learning algorithms that can be used to solve Markov decision processes (MDPs) via simulation. Unfortunately, the parameters of the so-called "actor" in the classical actor-critic algorithm exhibit great volatility - getting unbounded in practice, whence they have to be artificially constrained to obtain solutions in practice. The algorithm is often used in conjunction with Boltzmann action selection, where one may have to use a temperature to get the algorithm to work, but the convergence of the algorithm has only been proved when the temperature equals 1. We propose a new actor-critic algorithm …

Ieee Cibcb 2014-Computational Intelligence In Bioinformatics And Computational Biology [Conference Reports], Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

The 2014 Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) was held at the Hilton Hawaiian Village, Honolulu, from May 21-25. The papers presented at the conference covered the full range of computational intelligence techniques and applications. James Hughes from Brock University received the best student paper award from General Chair Steven Corns for his paper titled, 'Recentering and Restarting Genetic Algorithms Variations for DNA Fragment Assembly.' The best overall paper was awarded to Jennifer Hallinan, Owen Gilfellon, Goksel Misirli and Anil Wipat, all of Newcastle University, for their work 'Tuning Receiver Characteristics in Bacterial Quorum Communication: An …

Carbon Constrained Integrated Inventory Control And Truckload Transportation With Heterogeneous Freight Trucks, Dinçer Konur

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper analyzes an integrated inventory control and transportation problem with environmental considerations. Particularly, explicit transportation modeling is included with inventory control decisions to capture per truck costs and per truck capacities. Furthermore, a carbon cap constraint on the total emissions is formulated by considering emission characteristics of various trucks that can be used for inbound transportation. Due to complexity of the resulting optimization problem, a heuristic search method is proposed based on the properties of the problem. Numerical studies illustrate the efficiency of the proposed method. Furthermore, numerical examples are presented to show that both costs and emissions can …

Nonlinear Modeling Using Neural Networks For Trading The Soybean Complex, Phoebe S. Wiles, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Recently, there has been a spike in the prices and popularity of commodities. On a macroeconomic level, developing countries are increasing production; while on a microeconomic level, speculative traders are becoming more involved in the market. Agricultural products have a diverse array of factors that can affect the price (i.e. political, government, population, weather, supply and demand). Commodity prices can suffer from extreme volatility in the short term, changing as much as 50% in one year. This research uses the soybean crush spread as a model. The soybean complex adds an interesting component as the underlying soybean product can be …

Computational Complexity Measures For Many-Objective Optimization Problems, David M. Curry, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Multi-objective Optimization Problems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the number of objectives grows beyond three, Pareto dominance alone is no longer satisfactory. These problems are termed "Many-Objective Optimization Problems (MaOPs)". While most MaOP algorithms are modifications of common MOP algorithms, determining the impact on their computational complexity is difficult. This paper defines computational complexity measures for these algorithms and applies these measures to a Multi-Objective Evolutionary Algorithm (MOEA) and its MaOP counterpart.

A Hybrid Neuro-Fuzzy Model To Forecast Inflation, David Lee Enke, Nijat Mehdiyev

Engineering Management and Systems Engineering Faculty Research & Creative Works

One of the key issues in constructing monetary policy is accurate prediction of the inflation level. The complex behavior and non-linear nature of the financial markets makes it hard to forecast the inflation rate precisely. This paper introduces a hybrid model that attempts to forecast the inflation rate with a combination of a subtractive clustering technique and a fuzzy inference neural network to overcome the shortcomings of the individual methodologies. Selected macroeconomic factors were used to predict the historical CPI data from the US Markets. The results of the proposed hybrid model are measured in RMSE.

Volatility Forecasting Using A Hybrid Gjr-Garch Neural Network Model, Soheil Almasi Monfared, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Volatility forecasting in the financial markets, along with the development of financial models, is important in the areas of risk management and asset pricing, among others. Previous testing has shown that asymmetric GARCH models outperform other GARCH family models with regard to volatility prediction. Utilizing this information, three popular Neural Network models (Feed-Forward with Back Propagation, Generalized Regression, and Radial Basis Function) are implemented to help improve the performance of the GJR (1,1) method for estimating volatility over the next forty-four trading days. During training and testing, four different economic cycles have been considered between 1997-2011 to represent real and …

Timekeeping Issues In Ultra-Quality Metering Systems, David D. Haynes, Steven M. Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

The improvements made to smart metering systems in terms of performance and accuracy have in many cases created a gap in the requirements for timekeeping performance. When clock errors accumulate in different metrology systems, data collected from one system cannot be compared to data collected from another system. This paper recommends the use of a common time base for all data gathering systems, and that systems clocks are corrected with numerous minor corrections when possible, rather than major corrections. © 2013 IEEE.

On The Flexibility Of Systems In System Of Systems Architecting, Dincer Konur, Hadi Farhangi, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

System of Systems (SoS) architecting requires analyzing a set of individual but interconnected systems simultaneously in order to build a communicating SoS, which can provide the capabilities needed. In general, the systems can provide a set of capabilities and the SoS architect needs to decide which systems to include in the SoS so that each capability is provided by at least one system. In this case, the systems are inflexible, i.e., a selected system will contribute to the SoS with all the capabilities it can provide. On the other hand, if SoS architect can incentivize systems to contribute specific capabilities …

A Hybrid Genetic Algorithm And Particle Swarm Optimization With Type-2 Fuzzy Sets For Generating Systems Of Systems Architectures, Siddhartha Agarwal, Louis E. Pape, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Both modeling and simulating a system of systems (SoS) are difficult due not only to a changing environment but also the unique behavior that is linked to different participating systems. Generating architectures for a SoS is a multi-objective optimization problem with large number of variables and constraints. The paper presents several of computational intelligence techniques that can generate SoS architectures, such as genetic algorithms (GA), and particle swarm optimization (PSO) combined with Type 2 Fuzzy logic nets. The Maritime search and rescue (SAR) were used as a SoS domain scenario to both implement and demonstrate the architecting methodology. SAR utilizes …

Joint Decisions On Inventory Replenishment And Emission Reduction Investment Under Different Emission Regulations, Ayşegül Toptal, Haşim Özlü, Dinçer Konur

Engineering Management and Systems Engineering Faculty Research & Creative Works

Carbon emission regulation policies have emerged as mechanisms to control firms' carbon emissions. To meet regulatory requirements, firms can make changes in their production planning decisions or invest in green technologies. In this study, we analyze a retailer's joint decisions on inventory replenishment and carbon emission reduction investment under three carbon emission regulation policies. Particularly, we extend the economic order quantity model to consider carbon emissions reduction investment availability under carbon cap, tax and cap-and-trade policies. We analytically show that carbon emission reduction investment opportunities, additional to reducing emissions as per regulations, further reduce carbon emissions while reducing costs. We …

Multi-Agent Based Architecture For Acknowledged System Of Systems, Siddhartha Agarwal, Lou E. Pape, Nil Kilicay-Ergin, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Negotiation and collaboration issues in large organizations, with centralized control, across the globe, are becoming more complex with each passing day. The Acknowledged System of Systems (SoS) is a new approach that addresses some of these issues in a systematic, efficient manner. This paper proposes a hierarchical architectural framework to support Acknowledged SoS architecting and analysis for a Department of Defense (DoD) Acquisition process. A major challenge of the successful planning and evolution of an Acknowledged SoS is the lack of understanding of the impact of presence or absence of a system and its interface with another constituent system on …

Sustainability Analysis In Integrated Inventory Control And Transportation Systems, Brian Joseph Schaefer

Doctoral Dissertations

"Due to the importance of costs as well as environmental effects of logistical activities throughout supply chains, such as inventory holding, freight transportation, and warehousing activities, this dissertation models and analyzes four integrated inventory control and transportation problems that account for economic and environmental aspects of a supply chain agents related decisions.

The first model presents an integrated inventory control and transportation problem in a single item deterministic demand setting. A supply chain agents inventory control and transportation mode selection problem is solved under carbon cap, carbon cap and trade, carbon cap and offset, and carbon tax regulations. The second …