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Application Of The Fokker-Planck Equation For Quantifying Initial Condition Uncertainty Of Reversible Dynamic Systems, Troy S. Newhart

Mechanical & Aerospace Engineering Theses & Dissertations

Characterizing the behavior of dynamic systems requires the inclusion of initial conditions to propagate behavior forward in time. More realistic representations of system behavior quantify uncertainty about the initial conditions to assess sensitivity, reliability, and other stochastic response parameters. In many engineering applications, the uncertain initial conditions may be unknown given a desired response. This research applies the Fokker-Planck equation to reversible dynamic systems of select multi-dimensional nonlinear differential equations as a means for predicting the uncertainty about initial conditions. An alternating directions implicit numerical scheme is used to numerically solve the Fokker-Planck equation for both forward and reversed equations …

Optimal Scheduling Strategy Of Virtual Power Plant With Carbon Emission And Carbon Penalty Considering Uncertainty Of Wind Power And Photovoltaic Power, Jijun Shui, Daogang Peng, Yankan Song, Qiang Zhou

Journal of System Simulation

Abstract: To better meet the development needs of China's new power system, an optimal scheduling strategy of virtual power plant(VPP) with carbon emission and carbon penalty considering the uncertainty of wind power and photovoltaic power is proposed. The mathematical description of photovoltaic(PV), wind turbine(WT), combined heat and power(CHP) unit and energy storage system (ESS) is carried out, and a wind-solar output model considering the uncertainty is established. The scenario generation and reduction method is used to generate the typical scenario. To maximize the overall operation benefit of VPP, considering carbon emission cost and carbon penalty, an optimal scheduling model of …

Structured Invariant Subspace And Decomposition Of Systems With Time Delays And Uncertainties, Huan Phan-Van, Keqin Gu

SIUE Faculty Research, Scholarship, and Creative Activity

This article discusses invariant subspaces of a matrix with a given partition structure. The existence of a nontrivial structured invariant subspace is equivalent to the possibility of decomposing the associated system with multiple feedback blocks such that the feedback operators are subject to a given constraint. The formulation is especially useful in the stability analysis of time-delay systems using the Lyapunov-Krasovskii functional approach where computational efficiency is essential in order to achieve accuracy for large scale systems. The set of all structured invariant subspaces are obtained (thus all possible decompositions are obtained as a result) for the coupled differential-difference equations …

Multiple Imputation For Robust Cluster Analysis To Address Missingness In Medical Data, Arnold Harder, Gayla R. Olbricht, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Mathematics and Statistics Faculty Research & Creative Works

Cluster Analysis Has Been Applied To A Wide Range Of Problems As An Exploratory Tool To Enhance Knowledge Discovery. Clustering Aids Disease Subtyping, I.e. Identifying Homogeneous Patient Subgroups, In Medical Data. Missing Data Is A Common Problem In Medical Research And Could Bias Clustering Results If Not Properly Handled. Yet, Multiple Imputation Has Been Under-Utilized To Address Missingness, When Clustering Medical Data. Its Limited Integration In Clustering Of Medical Data, Despite The Known Advantages And Benefits Of Multiple Imputation, Could Be Attributed To Many Factors. This Includes Methodological Complexity, Difficulties In Pooling Results To Obtain A Consensus Clustering, Uncertainty Regarding …

Robust Uncertainty Estimation Framework In Deep Reinforcement Learning For Active Slam, Bryan Joseph Pedraza

Theses and Dissertations

Autonomous mobile robots are essential in various domains such as industry, manufacturing and healthcare. Navigating autonomously and avoiding obstacles are crucial tasks that involve localizing the robot to explore and map unknown environments without prior knowledge. Simultaneous localization and mapping (SLAM) present significant challenges. In this study, we introduce a new approach to address robust navigation and mapping of robot actions using Bayesian Actor-Critic (A2C) reinforcement learning. The A2C framework combines policy-based and value-based learning by dividing the model into two components: (1) the policy model (Actor) determines the actions based on the state, and (2) the value model (Critic) …

Uncertainty Quantification Framework For Design Optimization, Austin Ryan Williams

Masters Theses

Difficulty to obtain neutron sources of interest have driven the need for optimization
techniques to tailor a neutron generator as a replacement. A proposed solution uses
off-the-shelf neutron sources coupled with an energy-tuning assembly to mimic the
source of interest (i.e. AmLi, AmBe, thermonuclear fission spectra, etc.). These
energy-tuning assemblies have been designed with complex optimization algorithms
coupled with Monte Carlo simulations. These new system surrogate designs often
do not have an experimental counterpart for validation and comparison, and lack
non-statistical uncertainties. This work aims to improve confidence in the predictions
by providing a tool for fast uncertainty quantification to …

Design Space Visualization And Exploration For Many Goal Problems Under Uncertainity, Niharika Balaji

Theses and Dissertations

ABSTRACT

Designing a complex engineered system is challenging due to many conflicting goals, uncertainties, and multiple interactions. Traditional optimization approaches often yield single-point solutions, which may not be suitable for early design stages due to their susceptibility to changes in conditions and uncertainties. To address this challenge, a satisficing approach is employed. This approach enables designers to effectively navigate the design space and identify satisficing solutions that balance conflicting goals in the face of uncertainties and changes in conditions. From a systems design perspective, we view design as an iterative process that involves making informed decisions based on available information …

A Bayesian Network-Based Methodology For Improved Bridge Load Rating And Asset Management, Jeffery Mark Roberts

Dissertations and Theses

From the day a new structure is made available for use, to the day that the structure is no longer able to fulfill an intended purpose, structural safety is a vital interest. Managing a portfolio of structures can be a difficult undertaking for an asset manager, particularly if different types of structures are being maintained. The goal is to manage assets in the most efficient manner which can be influenced by, at a minimum, safety and financial concerns.

A potential tool for an asset manager or owner is the use of Bayesian Networks (BNs). When a BN is used to …

Between Flexibility And Relativism: How Students Deal With Uncertainty In Sustainability Challenges, Nina Lotte Bohm, Renate Klaassen, Ellen Van Bueren, Perry Den Brok

Research Papers

Universities open their doors to society, inviting the complexity of the world to enter engineering education through challenge-based courses. While working on complex issues, engineering students learn to deal with different kinds of uncertainty: uncertainty about the dynamics of a real-world challenge, the knowledge gaps in the problem, or the conflicting perspectives amongst the people involved. Although we know from previous research that students are likely to encounter these uncertainties in sustainability challenges, which metacognitive strategies they use to deal with them is unclear. We interviewed nine MSc students at the end of a challenge-based course at a Dutch university …

Uncertainties In Retrieval Of Remote Sensing Reflectance From Ocean Color Satellite Observations, Eder I. Herrera Estrella

Dissertations, Theses, and Capstone Projects

Ocean Color radiometry uses remote sensing to interpret ocean dynamics by retrieving remote sensing reflectance (𝑅𝑟𝑠) from satellite imagery at different scales and over different time periods. 𝑅𝑟𝑠 spectrum characterizes the ocean color that we observe, and from which we can discern concentrations of chlorophyll, organic and inorganic particles, and carbon fluxes in the ocean and atmosphere. 𝑅𝑟𝑠 is derived from the total radiance at the top of the atmosphere (TOA). However, it only represents up to ten percent of the total signal. Hence, the retrieval of 𝑅𝑟𝑠 from the total radiance at TOA involves the application of atmospheric correction …

Research On Period Emergency Supply Distribution Optimization Under Uncertainty, Li Zhang, Mingling He, Qiushuang Yin, Ning Li, Le'an Yu

Journal of System Simulation

Abstract: Aiming at the uncertainty and multi-periodicity of emergency supply distribution, a novel period vehicle routing problem(PVRP) multi-objective optimization model is built and a three-step optimization method is proposed. A triangular fuzzy number is used to eliminate the uncertainty. An AHP approach is used to transform the multi-objective function into the single objective function. An improved ACO algorithm is proposed to solve the single objective optimization problem. By classical data set, the time effectiveness of proposed method on emergency supply distribution problem is verified. The computational advantage in convergence speed is proved by the comparative analysis of the proposed …

Statistical Uncertainty Of The Ignition Time, Burning Rate, And Extinction Characteristics Of Engineered Timber Products, Jacob David

Master's Theses

The characterization of flammability parameters such as time to ignition, mass loss rate (MLR), and extinction criteria is critical for understanding ignition and burning behavior of timber products. These parameters, often determined with bench scale experiments, have previously been presented in literature. However, standard test methods generally use relatively low trial quantities (e.g., n=3) which can potentially cause large variation in reported values. This study investigates the influence of trial quantity on observed statistical variation in key flammability metrics for timber products (e.g., ignition time, peak MLR, MLR at extinction). Using a conical heater, 100 repeat trials were conducted …

Addressing The Challenged Of Dcop Based Decision-Making Algorithms In Modern Power Systems, Luis Daniel Ramirez Burgueno

Open Access Theses & Dissertations

Natural disasters have been determined as the leading cause of power outages, causing not only huge economic losses, but also the interruption of crucial welfare activities and the arise of security concerns. Because of the later, decision-making considering grid modernization, power system economics, and system resiliency has been a crucial theme in power systemsâ?? research. The need to better withstand catastrophic events and reducing the dependency of bulky generating units has propelled the development and better management of behind-the-meter generation or distributed energy resources (DERs). DERs can assist in the grid in different manners, not only by meeting energy demand …

A Value-Based Sequential Optimization Framework For Efficient Materials Design Considering Uncertainty And Variability, Maher Alghalayini

All Dissertations

Many problems in engineering and science can be framed as decision problems in which we choose values for decision variables that lead to desired outcomes. Notable examples include maximizing lift in airplane wing design, improving the efficiency of a power plant, or identifying processing protocols resulting in structural materials with desired mechanical properties. These problems typically involve a significant degree of uncertainty about the often-complex underlying relationships between the decision variables and the outcomes. Solving such decision problems involves the use of computational models or physical experimentation to generate data to make predictions and test hypotheses. As a result, both …

Advances And Applications Of Dsmt For Information Fusion. Collected Works, Volume 5, Florentin Smarandache, Jean Dezert, Albena Tchamova

Branch Mathematics and Statistics Faculty and Staff Publications

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 (available at fs.unm.edu/DSmT-book4.pdf or www.onera.fr/sites/default/files/297/2015-DSmT-Book4.pdf) in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.

First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of …

Evaluation Of Lidar Uncertainty And Applications Towards Slam In Off-Road Environments, Zachary D. Jeffries

Dissertations, Master's Theses and Master's Reports

Safe and robust operation of autonomous ground vehicles in all types of conditions and environment necessitates complex perception systems and unique, innovative solutions. This work addresses automotive lidar and maximizing the performance of a simultaneous localization and mapping stack. An exploratory experiment and an open benchmarking experiment are both presented. Additionally, a popular SLAM application is extended to use the type of information gained from lidar characterization, demonstrating the performance gains and necessity to tightly couple perception software and sensor hardware. The first exploratory experiment collects data from child-sized, low-reflectance targets over a range from 15 m to 35 m. …

A Resilience-Oriented Multi-Stage Adaptive Distribution System Planning Considering Multiple Extreme Weather Events, Siyuan Wang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Climate Change May Increase the Risk of an Area Being Hit by Multiple Extreme Weather Events, Which Brings Significant Challenges for Distribution System Planners in an Increasing Renewable Penetration Era. There is an Urgent Need for Planning Approaches to Be More Flexible and Allow for Adaptive Adjustments in the Future to Hedge Against High Uncertainties in Extreme Weather Event Scenarios. in This Work, We Propose a Resilience-Oriented Distribution System Planning Approach that Considers Multiple Extreme Weather Events. a Multi-Stage Hybrid-Stochastic-And-Robust Formulation is Developed to Model Decisions Not Only for Initial Investments, But Also for Adaptive Investments and Emergent Operations in …

Path Planning For Aircraft Under Threat Of Detection From Ground-Based Radar With Uncertainty In Aircraft And Radar States, Austin D. Costley

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Mission planners for manned and unmanned aircraft operating within the detection range of ground-based radar systems are often concerned with the probability of detection. Several factors influence the probability of detection, including aircraft position and orientation, radar position, and radar performance parameters. Current path planning algorithms assume that these factors are known with certainty, but in practice, these factors are estimated and have some uncertainty.

This dissertation explores methods to consider the uncertainty in the detection factors for an aircraft path planner. First, the detection model is extended to include uncertainty in the aircraft position and orientation, radar position, and …

Stochastic Seismic Response Analysis Of Engineering Site Considering Correlations Of Critical Soil Dynamic Parameters, Zi-Lan Zhong, Yue-Bo Shi, Jin-Qiang Li, Mi Zhao, Xiu-Li Du

Rock and Soil Mechanics

This paper presents a method to generate random samples of soil dynamic shear modulus and dynamic damping curves with full consideration of the correlations of critical soil dynamic parameters to investigate the influence of their uncertainties on the engineering site seismic response in the implementation of equivalent linear method. A one-dimensional (1D) equivalent linear site seismic response analysis program, which serves stochastic dynamic response analysis of the engineering site, has been developed in MATLAB. A 1D free field model for the typical layered engineering sites of site class II is established in this study. The target response spectra, which are …

Optimal Global Supply Chain And Warehouse Planning Under Uncertainty, Avnish Kishor Malde

All Dissertations

A manufacturing company's inbound supply chain consists of various processes such as procurement, consolidation, and warehousing. Each of these processes is the focus of a different chapter in this dissertation.

The manufacturer depends on its suppliers to provide the raw materials and parts required to manufacture a finished product. These suppliers can be located locally or overseas with respect to the manufacturer's geographic location. The ordering and transportation lead times are shorter if the supplier is located locally. Just In Time (JIT) or Just In Sequence (JIS) inventory management methods could be practiced by the manufacturer to procure the raw …

Selected Interdiction Games With Uncertain, Risk-Averse, And Simultaneous Play Considerations, Di H. Nguyen

All Dissertations

This dissertation examines two network interdiction problems: a shortest-path interdiction problem under uncertainty and a network interdiction problem in a simultaneous game. Both problems happen in two stages over a directed network, and involve a leader and a follower who have opposing interests.

In the first problem, the leader acts first to lengthen a subset of arcs, and a follower acts second to select a shortest path across the network. The cost for a follower’s arc consists of a base cost if the arc is not interdicted, plus an additional cost that is incurred if the arc is interdicted. The …

A Parallelizable Algorithm For Stabilizing Large Sparse Linear Systems With Uncertain Interconnections, Aleksandar Zečević, Maryam Khanbaghi

Electrical and Computer Engineering

This paper proposes a new method for permuting sparse matrices into an upper block triangular from. The algorithm is highly parallelizable, which makes it suitable for large-scale systems with uncertain interconnection patterns. In such cases, the proposed decomposition can be used to develop flexible decentralized control strategies that produce a different gain matrix whenever the configuration changes. Applications to interconnected microgrids and supply and demand networks are provided to illustrate the versatility of the proposed approach.

Uncertainties In The Projected Patterns Of Wave-Driven Longshore Sediment Transport Along A Non-Straight Coastline, Amin Reza Zarifsanayei, José A. A. Antolínez, Amir Etemad-Shahidi, Nick Cartwright, Darrell Strauss, Gil Lemos

Research outputs 2022 to 2026

This study quantifies the uncertainties in the projected changes in potential longshore sediment transport (LST) rates along a non-straight coastline. Four main sources of uncertainty, including the choice of emission scenarios, Global Circulation Model-driven offshore wave datasets (GCM-Ws), LST models, and their non-linear interactions were addressed through two ensemble modelling frameworks. The first ensemble consisted of the offshore wave forcing conditions without any bias correction (i.e., wave parameters extracted from eight datasets of GCM-Ws for baseline period 1979–2005, and future period 2081–2100 under two emission scenarios), a hybrid wave transformation method, and eight LST models (i.e., four bulk formulae, four …

Uncertainty Simulation Method Based On Deep Bayesian Networks Learning, Nie Kai, Kejun Zeng, Qinghai Meng

Journal of System Simulation

Abstract: There are lots of uncertain elements in battlefields situation assessment and the uncertainty simulation would enhance the ability of situation assessment. A deep variational autoencoder bayesian networks (BN) model with memory module is proposed aiming at the problem of being unable to represent the uncertainties exactly caused by the various combat objects and more uncertain elements. Based on the deep BN learning, the situation assessment model is designed from the deep generative model. The principle of deep generative model mixing with the memory module is discussed and the leaning and reasoning process of the model is explained. The proposed …

Accuracy And Uncertainty In Traffic And Transit Ridership Forecasts, Jawad Mahmud Hoque

Theses and Dissertations--Civil Engineering

Investments of public dollars on highway and transit infrastructure are influenced by the anticipated demands for highways and public transportations or traffic and transit ridership forecasts. The purpose of this study is to understand the accuracy of road traffic forecasts and transit ridership forecasts, to identify the factors that affect their accuracy, and to develop a method to estimate the uncertainty inherent in those forecasts. In addition, this research investigates the pre-pandemic decline in transit ridership across the US metro areas since 2012 and its influence on the accuracy of transit forecasts.

The sample of 1,291 road projects from the …

Simulation-Based Optimization: Implications Of Complex Adaptive Systems And Deep Uncertainty, Andreas Tolk

VMASC Publications

Within the modeling and simulation community, simulation-based optimization has often been successfully used to improve productivity and business processes. However, the increased importance of using simulation to better understand complex adaptive systems and address operations research questions characterized by deep uncertainty, such as the need for policy support within socio-technical systems, leads to the necessity to revisit the way simulation can be applied in this new area. Similar observations can be made for complex adaptive systems that constantly change their behavior, which is reflected in a continually changing solution space. Deep uncertainty describes problems with inadequate or incomplete information about …

Home Energy Management System Considering Effective Demand Response Strategies And Uncertainties, Marcos Tostado-Véliz, Paul Arévalo, Salah Kamel, Hossam Zawbaa, Francisco Jurado

Articles

Nowadays, load serving entities require more active participation from consumers. In this context, demand response programs and home energy management systems play a crucial role in achieving multiple goals such as peak clipping. However, the adoption of demand response initiatives typically has a negative impact on the monetary expenditures of the users. This way, a demand response program should be as effective as possible to make the different goals more easily achievable without compromising the financial requirements of the users. This paper develops a home energy management system that incorporates three novel effective demand response strategies. The effectiveness of the …

Jointly-Learnt Networks For Future Action Anticipation Via Self-Knowledge Distillation And Cycle Consistency, Md Moniruzzaman, Zhaozheng Yin, Zhihai He, Ming-Chuan Leu, Ruwen Qin

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Future action anticipation aims to infer future actions from the observation of a small set of past video frames. In this paper, we propose a novel Jointly learnt Action Anticipation Network (J-AAN) via Self-Knowledge Distillation (Self-KD) and cycle consistency for future action anticipation. In contrast to the current state-of-the-art methods which anticipate the future actions either directly or recursively, our proposed J-AAN anticipates the future actions jointly in both direct and recursive ways. However, when dealing with future action anticipation, one important challenge to address is the future's uncertainty since multiple action sequences may come from or be followed by …

Probabilistic Space Weather Modeling And Forecasting For The Challenge Of Orbital Drag In Space Traffic Management, Richard J. Licata Iii

Graduate Theses, Dissertations, and Problem Reports

In the modern space age, private companies are crowding the already-congested low Earth orbit (LEO) regime with small satellite mega constellations. With over 25,000 objects larger than 10 cm already in LEO, this rapid expansion is forcing us towards the enterprise on Space Traffic Management (STM). STM is an operational effort that focuses on conjunction assessment and collision avoidance between objects. While the equations of motion for objects in orbit are well-known, there are many uncertain parameters that result in the uncertainty of an object's future position. The force that the atmosphere exerts on satellite - known as drag - …

Sources Of Variability And Uncertainty In Food-Energy-Water Nexus Systems, Heydi Calderon-Ambelis, Deepak R. Keshwani

Department of Biological Systems Engineering: Papers and Publications

A nexus approach contributes to the strategic allocation of resources to secure food, energy, and water for the world population. Integrated models considering the complex interactions across food, energy, and water (FEW) enhance decision-making and strategic planning towards resilience. However, a significant number of the existing integrated models leave unaddressed the inherent variability and uncertainty present in the FEW sectors. Here, we review the importance of characterizing variability over spatial and temporal scales and the importance of decreasing the uncertainty present within a FEW nexus systems. The review also discusses existing modeling tools that address variability and uncertainty on single …