Engineering Commons^™

Machine Learning Prediction Of Dod Personal Property Shipment Costs, Tiffany Tucker [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

U.S. Department of Defense (DoD) personal property moves account for 15% of all domestic and international moves - accurate prediction of their cost could draw attention to outlier shipments and improve budget planning. In this work 136,140 shipments between 13 personal property shipment hubs from April 2022 through March 2023 with a total cost of $1.6B were analyzed. Shipment cost was predicted using recursive feature elimination on linear regression and XGBoost algorithms, as well as through neural network hyperparameter sweeps. Modeling was repeated after removing 28 features related to shipment hub location and branch of service to examine their influence …

A Deep Reinforcement Learning Approach With Prioritized Experience Replay And Importance Factor For Makespan Minimization In Manufacturing, Jose Napoleon Martinez

LSU Doctoral Dissertations

In this research, we investigated the application of deep reinforcement learning (DRL) to a common manufacturing scheduling optimization problem, max makespan minimization. In this application, tasks are scheduled to undergo processing in identical processing units (for instance, identical machines, machining centers, or cells). The optimization goal is to assign the jobs to be scheduled to units to minimize the maximum processing time (i.e., makespan) on any unit.

Machine learning methods have the potential to "learn" structures in the distribution of job times that could lead to improved optimization performance and time over traditional optimization methods, as well as to adapt …

Characterizing Complex-Valued Neural Network Model Approximations Of 4-Input 4-Output Complex-Valued Reference Block Models, Larry C. Llewellyn Ii

Theses and Dissertations

System simulation models are often decomposed and abstracted as a collection of interconnected subsystem block models to facilitate system understanding, design, and analysis. Each subsystem block model contains mathematical functions that receive, process, and transmit signals that are modeled as real numbers, complex numbers, and/or logic values. This dissertation evaluates the use of a single two-layer complex-valued neural network model to approximate 4-input, 4-output subsystem reference block models in terms of accuracy, performance, and error. The research is novel in that it uses a neural network for continuous function approximation instead of data categorization; it uses a neural network designed …

Bayesian Convolutional Neural Network With Prediction Smoothing And Adversarial Class Thresholds, Noah M. Miller

Theses and Dissertations

Using convolutional neural networks (CNNs) for image classification for each frame in a video is a very common technique. Unfortunately, CNNs are very brittle and have a tendency to be over confident in their predictions. This can lead to what we will refer to as “flickering,” which is when the predictions between frames jump back and forth between classes. In this paper, new methods are proposed to combat these shortcomings. This paper utilizes a Bayesian CNN which allows for a distribution of outputs on each data point instead of just a point estimate. These distributions are then smoothed over multiple …

A Machine Learning Approach To Understanding Emerging Markets, Namita Balani

Graduate Theses and Dissertations

Logistic providers have learned to efficiently serve their existing customer bases with optimized routes and transportation resource allocation. The problem arises when there is potential for logistics growth in an emerging market with no previous data. The purpose of this work is to use industry data for previously known and well-documented markets to apply data analytic techniques such as machine learning to investigate the uncertainty in a new market. The thesis looks into machine learning techniques to predict miles per stop given historical data. It mainly focuses on Random Forest Regression Analysis, but concludes that additional techniques, such as Polynomial …

Meta-Heuristic Optimization Methods For Quaternion-Valued Neural Networks, Jeremiah P. Bill

Theses and Dissertations

In recent years, real-valued neural networks have demonstrated promising, and often striking, results across a broad range of domains. This has driven a surge of applications utilizing high-dimensional datasets. While many techniques exist to alleviate issues of high-dimensionality, they all induce a cost in terms of network size or computational runtime. This work examines the use of quaternions, a form of hypercomplex numbers, in neural networks. The constructed networks demonstrate the ability of quaternions to encode high-dimensional data in an efficient neural network structure, showing that hypercomplex neural networks reduce the number of total trainable parameters compared to their real-valued …

The Autonomous Attack Aviation Problem, John C. Goodwill

Theses and Dissertations

An autonomous unmanned combat aerial vehicle (AUCAV) performing an air-to-ground attack mission must make sequential targeting and routing decisions under uncertainty. We formulate a Markov decision process model of this autonomous attack aviation problem (A3P) and solve it using an approximate dynamic programming (ADP) approach. We develop an approximate policy iteration algorithm that implements a least squares temporal difference learning mechanism to solve the A3P. Basis functions are developed and tested for application within the ADP algorithm. The ADP policy is compared to a benchmark policy, the DROP policy, which is determined by repeatedly solving a deterministic orienteering problem as …

Fault Diagnosis For Automobile Coating Equipments Based On Extension Neural Network, Yongwei Ye, Shedong Ren, Lianqiang Ye, Shenhao Ge, Zhiqin Qian

Journal of System Simulation

Abstract: Aiming at the difficulty in discovering and eliminating the system faults of automobile coating equipments in time, a new method of fault diagnosis based on extension neural network was proposed. The feature of extension theory was used in managing the structured information through qualitative and quantitative description, and it was also combined by the characteristic of parallel construct in neural network. So the extension reasoning process was completed by means of the parallel distributed processing construct of the network. Matter-element input and output models were established according to the equipment monitoring parameters and fault types for the heating system. …

Analysis Of Information Security Methods In Biosystems And Application Of Intelligent Tools In Information Security Systems, Sherzod Sayfullaev

Chemical Technology, Control and Management

In this paper, the methods of information protection in bio systems are studied. The paper considers the use of intelligent tools in information security systems and the use of adaptive information security systems. Several articles on the field of information protection in bio systems are analyzed. Disadvantages and advantages of neural network technologies in modern information security systems are described. The characteristics of bio systems and the specificity of DNA, the main features of the DNA code that provide information security and functional stability of bio systems data protection structure. Application of intelligent tools to create a comprehensive adaptive protection …

Research On Asymptotic Stability For Markovian Jumping Neural Network With Unknown Transition Probabilities, Lu Yang, Shujuan Yi, Weijian Ren, Jiandong Liu

Journal of System Simulation

Abstract: The analysis problem of asymptotic stability for a class of uncertain neural networks with Markovian jumping parameters and time delays was addressed. The general representative dynamic stochastic neural network model was established. The considered transition probabilities were assumed to be partially unknown. The parameter uncertainties were considered to be norm-bounded. Based on Lyapunov stability theory, by constructing a suitable Lyapunov-Krasovskii function and using the stochastic analysis method, some sufficient criteria for the stability of discrete Markovian neural networks was derived. Through the Matlab LMI toolbox, solving a set of linear matrix inequalities to test criterion, the new criterion reduced …

Research On Image Description Method Based On Neural Network, Kong Rui, Xie Wei, Lei Tai

Journal of System Simulation

Abstract: The automatic recognition and automatically describing image content is an important research direction to the artificial intelligence to connect the computer vision and the natural language processing. A method of describing the image content is proposed to generate the natural language by using the deep neural network model. The model consists of a convolutional neural network (CNN) and a recurrent neural network (RNN). The CNN is used to extract features of the input image to generate a fixed-length feature vector, which initializes the RNN to generate the sentences. Experimental results on the MSCOCO image description dataset show the syntactic …

Ground Weather Radar Signal Characterization Through Application Of Convolutional Neural Networks, Stephen M. Lee

Theses and Dissertations

The 45th Weather Squadron supports the space launch efforts out of the Kennedy Space Center and Cape Canaveral Air Force Station for the Department of Defense, NASA, and commercial customers through weather assessments. Their assessment of the Lightning Launch Commit Criteria (LLCC) for avoidance of natural and rocket triggered lightning to launch vehicles is critical in approving space shuttle and rocket launches. The LLCC includes standards for cloud formations, which requires proper cloud identification and characterization methods. Accurate reflectivity measurements for ground weather radar are important to meet the LLCC for rocket triggered lightning. Current linear interpolation methods for ground …

Predicting Upper Atmospheric Weather Conditions Utilizing Long-Short Term Memory Neural Networks For Aircraft Fuel Efficiency, Garrett A. Alarcon

Theses and Dissertations

Aviation fuel is a major component of the Air Force (AF) budget, and vital for the core mission of the AF. This study investigated the viability of LSTMs to increase the accuracy of deterministic NWP models, while also investigating the ability to reduce model generation time. Increased forecast accuracy for wind speeds could be implemented into existing flight path models to further increase fuel efficiency, while reduced modeling times would allow flight planners to generate a flight plan in rapid response situations. The most viable model consisted of an ensemble of six LSTMs trained o six coordinates. The model's error …

Neuro-Fuzzy Modeling For Predictive Control Systems With Complex Technological Processes And Production, Yusupbekov Nodirbek, Shukhrat Gulyamov, Malika Doshchanova

Chemical Technology, Control and Management

The paper implements a modification of a fuzzy neural network, which is suitable for predictive control purposes. Adaptation of a multidimensional programmable controller based on a neural algorithm for the back propagation of forecasting errors is proposed, as well as neural parametric identification of a fuzzy mathematical model of complex technological processes and production based on experimental data and expert estimates.

Application Of Artificial Neural Networks To Assess Student Happiness, Gokhan Egilmez, Nadiye O. Erdil, Omid Mohammadi Arani, Mana Vahid

Mechanical and Industrial Engineering Faculty Publications

The purpose of this study is to develop an analytical assessment approach to identify the main factors that affect graduate students' happiness level. The two methods, multiple linear regression (MLR) and artificial neural networks (ANN), were employed for analytical modelling. A sample of 118 students at a small non-profit private university constituted the survey pool. Various factors including education, school facilities, health, social activities, and family were taken into consideration as a result of literature review in happiness assessment. A total of 32 inputs and one output variables were identified during survey design phase. The following survey conduction, data collection, …

Evasive Maneuvers Against Missiles For Unmanned Combat Aerial Vehicle In Autonomous Air Combat, Xizhong Yang, Jianliang Ai

Journal of System Simulation

Abstract: For UCAV having the capability to deal with autonomous air combat, the flight dynamics model with the overload input and the 3-dimensional proportional navigation guidance model were established. Based on artificial neural network, an evasive maneuver decision was presented for avoiding incoming missiles. The degrees of freedom for UCAV-missile system were reduced by coordinate transformation, which simplified the complicated model as a non-linear model with relatively small amount of input and a single output. After the neural network samples were generated and trained, evasive results could be directly predicted from the relationship of positions between UCAV and missile …

Robust Engineering Of Dynamic Structures In Complex Networks, Walter Botongo Bomela

McKelvey School of Engineering Theses & Dissertations

Populations of nearly identical dynamical systems are ubiquitous in natural and engineered systems, in which each unit plays a crucial role in determining the functioning of the ensemble. Robust and optimal control of such large collections of dynamical units remains a grand challenge, especially, when these units interact and form a complex network. Motivated by compelling practical problems in power systems, neural engineering and quantum control, where individual units often have to work in tandem to achieve a desired dynamic behavior, e.g., maintaining synchronization of generators in a power grid or conveying information in a neuronal network; in this dissertation, …

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 …

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 …

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 …

Self-Organizing Neural Network For Adaptive Operator Selection In Evolutionary Search, Teck Hou Teng, Stephanus Daniel Handoko, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Evolutionary Algorithm is a well-known meta-heuristics paradigm capable of providing high-quality solutions to computationally hard problems. As with the other meta-heuristics, its performance is often attributed to appropriate design choices such as the choice of crossover operators and some other parameters. In this chapter, we propose a continuous state Markov Decision Process model to select crossover operators based on the states during evolutionary search. We propose to find the operator selection policy efficiently using a self-organizing neural network, which is trained offline using randomly selected training samples. The trained neural network is then verified on test instances not used for …

Noise Canceling In Volatility Forecasting Using An Adaptive Neural Network Filter, Soheil Almasi Monfared, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Volatility forecasting models are becoming more accurate, but noise looks to be an inseparable part of these forecasts. Nonetheless, using adaptive filters to cancel the noise should help improve the performance of the forecasting models. Adaptive filters have the advantage of changing based on the environment. This feature is vital when they are used along with a model for volatility forecasting and error cancellation in the financial markets. Nonlinear Autoregressive (NAR) neural networks have simple structures, but they are efficient tools in error cancelation systems when working with non-stationary and random walk noise processes. For this research, an adaptive threshold …

Computational Intelligence Based Complex Adaptive System-Of-Systems Architecture Evolution Strategy, Siddharth Agarwal

Doctoral Dissertations

The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving …

Modeling Small Unmanned Aerial System Mishaps Using Logistics Regression And Artificial Neural Networks, Sean E. Wolf

Theses and Dissertations

A dataset of 854 small unmanned aerial system (SUAS) flight experiments from 2005-2009 is analyzed to determine significant factors that contribute to mishaps. The data from 29 airframes of different designs and technology readiness levels were aggregated. 20 measured parameters from each flight experiment are investigated, including wind speed, pilot experience, number of prior flights, pilot currency, etc. Outcomes of failures (loss of flight data) and damage (injury to airframe) are classified by logistic regression modeling and artificial neural network analysis. From the analysis, it can be concluded that SUAS damage is a random event that cannot be predicted with …

A Structure First Image Inpainting Approach Based On Self-Organizing Map (Som), Bo Chen, Zhaoxia Wang, Ming Bai, Quan Wang, Zhen Sun

Research Collection School Of Computing and Information Systems

This paper presents a structure first image inpainting method based on self-organizing map (SOM). SOM is employed to find the useful structure information of the damaged image. The useful structure information which includes relevant edges of the image is used to simulate the structure information of the lost or damaged area in the image. The structure information is described by distinct or indistinct curves in an image in this paper. The obtained target curves separate the damaged area of the image into several parts. As soon as each part of the damaged image is restored respectively, the damaged image is …

Multilayer Image Inpainting Approach Based On Neural Networks, Quan Wang, Zhaoxia Wang, Che Sau Chang, Ting Yang

Research Collection School Of Computing and Information Systems

This paper describes an image inpainting approach based on the self-organizing map for dividing an image into several layers, assigning each damaged pixel to one layer, and then restoring these damaged pixels by the information of their respective layer. These inpainted layers are then fused together to provide the final inpainting results. This approach takes advantage of the neural network's ability of imitating human's brain to separate objects of an image into different layers for inpainting. The approach is promising as clearly demonstrated by the results in this paper.

The Effect Of Model Formulation On The Comparative Performance Of Artificial Neural Networks And Regression, Michael F. Cochrane

Engineering Management & Systems Engineering Theses & Dissertations

Multiple linear regression techniques have been traditionally used to construct predictive statistical models, relating one or more independent variables (inputs) to a dependent variable (output). Artificial neural networks can also be constructed and trained to learn these complex relationships, and have been shown to perform at least as well as linear regression on the same data sets. Research on the use of neural network models as alternatives to multivariate linear regression has focused predominantly on the effects of sample size, noise, and input vector size on the comparative performance of these two modeling techniques. However, research has also shown that …

Feature Selection For Predicting Pilot Mental Workload, Julia A. East

Theses and Dissertations

Advances in technology have the cockpits of the aircraft in the Air Force inventory increasingly complex. Consequently, mental demands on the pilot have risen. In some cases, mental demands were so overwhelming that pilots have forgotten basic flying techniques, such as G-straining maneuvers. The results have been fatal. Recent research in this area has involved collecting psychophysiological features, such as electroencephalography (EEG), heart, eye and respiration measures, in an attempt to identify pilot mental workload. This thesis focuses on feature selection and reduction of the psycophysiological features and subsequent classification of pilot mental workload on multiple subjects over multiple days. …

Study Of Human Factors Variables In Battle Outcome Prediction Models, David Andrew Glovier

Engineering Management & Systems Engineering Theses & Dissertations

Over time there have been many improvements in models that are used to predict the outcome of battles. Currently there is much supposition and speculation surrounding the use of human performance related factors as additional inputs to battle simulation models to improve their accuracy. However there is no conclusive scientific evidence which shows that these factors do make a significant difference. This study investigates the use of factors that may impact on the human performance directly or indirectly in battle prediction models. These factors consist of traditional human factors and external factors that may influence the human performance. The research …