Computer Simulation

The Louisiana coastal ecosystem is experiencing increasing threats from human flood control construction, sea-level rise (SLR), and subsidence. Louisiana lost about 4,833 km2 of coastal wetlands from 1932 to 2016, and concern exists whether remaining wetlands will persist while facing the highest rate of relative sea-level rise (RSLR) in the world. Restoration aimed at rehabilitating the ongoing and future disturbances is currently underway through the implementation of the Coastal Wetlands Planning Protection and Restoration Act of 1990 (CWPPRA). To effectively monitor the progress of projects in CWPPRA, the Coastwide Reference Monitoring System (CRMS) was established in 2006. To date, more than a decade of valuable coastal, environmental, and ground elevation data have been collected and archived. This dataset offers a unique opportunity to evaluate the wetland ground elevation dynamics by linking the Rod Surface Elevation Table (RSET) measurements with environmental variables like water salinity and biophysical variables like canopy coverage. This dissertation research examined the effects of the environmental and biophysical variables on wetland terrain elevation by developing innovative machine learning based models to quantify the contribution of each factor using the CRMS collected dataset. Three modern machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were assessed and cross-compared with the commonly used Multiple Linear Regression (MLR). The results showed that RF had the best performance in modeling ground elevation with Root Mean Square Error (RMSE) of 10.8 cm and coefficient of coefficient (r) = 0.74. The top four factors contributing to ground elevation are the distance from monitoring station to closest water source, water salinity, water elevation, and dominant vegetation height.

The change point problem is a problem where a process changes regimes because a parameter changes at a point in time called the change point. The objective of this problem is to estimate the change point and each of the parameters of the stochastic process. In this thesis, we examine the change point problem for two classes of stochastic processes. First, we consider the volatility change point problem for stochastic diffusion processes driven by Brownian motions. Then, we consider the drift change point problem for Ornstein-Uhlenbeck processes driven by _-stable Levy motions. In each problem, we establish the consistency of the estimators, determine asymptotic behavior for the changing parameters, and finally, we perform simulation studies to computationally assess the convergence of parameters.

Software quality is crucial both to software makers and customers. However, in reality, improvement of quality and reduction of costs are often at odds. Software modeling can help us to detect fault-prone software modules based on software metrics, so that we can focus our limited resources on fewer modules and lower the cost but still achieve high quality. In the present study, a tree classification modeling technique---TREEDISC was applied to three case studies. Several major contributions have been made. First, preprocessing of raw data was adopted to solve the computer memory problem and improve the models. Secondly, TREEDISC was thoroughly explored by examining the roles of important parameters in modeling. Thirdly, a generalized classification rule was introduced to balance misclassification rates and decrease type II error, which is considered more costly than type I error. Fourthly, certainty of classification was addressed. Fifthly, TREEDISC modeling was validated over multiple releases of software product.

Computer modeling has become indispensable to the engineering process, from initially refining an idea with computer-aided tools to implementing the final steps in manufacturing a product. This thesis addresses the issue of design of a housing of LCD watch. An approach is developed to determine an optimal LCD watch design. An analysis and development of a design of experiment is performed to identify the major controllable variables to performed a statistical significance analysis on different shapes for LCD glass. A housing of LCD watch is modeled using Pro/Engineer (a parametric-based solid modeling system), and different shapes of LCD glass are tested using P3/Patran. A non-destructive static experiment is performed on the LCD. This experiment consisted of measuring the maximum displacement and equivalent stress. Taguchi method was used to analyze this experiment.

Multiprocessor systems have demonstrated great potential for meeting the ever increasing demand for higher performance. In this thesis, we develop simulation models with fewer and more realistic assumptions to evaluate the performance of the circuit-switched cluster-based multiprocessor system. We then introduce a packet-switched variation of the cluster-based architecture and develop simulation models to evaluate its performance. The analysis of the cluster-based systems is performed for both uniform and non-uniform memory reference models. We conducted similar analysis for the crossbar and multiple-bus systems. Finally, the results of the cluster-based systems are compared to those obtained for the crossbar and the multiple-bus systems.

This thesis was prepared as an introductory text in business systems
simulation using the General Purpose Simulation System/360 computer
programming language. The material requires no background in computer
programming or simulation. A knowledge of elementary probability and
statistics and a course in operations research would be prerequisites.
A number of examples illustrate an approach to simulation problems
and the use of the computer language.

Sorensen's model of glucose metabolism and regulation is reconstructed using SimulinkRTM. Most of the existing glucose metabolism models consist of several mass balance equations that interact with each others. Graphical format used by SimulinkRTM provides a visualized perspective of such relations so that it is easier to modify the model on ad hoc basis. Type-I and Type-II diabetes with relevant clinical details are simulated. Further, a control strategy is introduced in order to simulate the control of exogenous insulin pump. Simulated results are consistent with available clinical data. Living systems in general, exhibit both stochastical and deterministic characteristics. Activities such as glucose metabolism traditionally modeled do not include stochastical properties, nor that they are viewed in the large framework of complex system with explicit interaction details. Currently, a complexity system model is developed to describe the glucose metabolism related activities. The simulation results obtained thereof illustrate the bounding domain of variations in some clinically observed details.