Matching Items (149)
Description
The majority of drones are extremely simple, their functions include flight and sometimes recording video and audio. While drone technology has continued to improve these functions, particularly flight, additional functions have not been added to mainstream drones. Although these basic functions serve as a good framework for drone designs, it is now time to extend off from this framework. With this Honors Thesis project, we introduce a new function intended to eventually become common to drones. This feature is a grasping mechanism that is capable of perching on branches and carrying loads within the weight limit. This concept stems from the natural behavior of many kinds of insects. It paves the way for drones to further imitate the natural design of flying creatures. Additionally, it serves to advocate for dynamic drone frames, or morphing drone frames, to become more common practice in drone designs.
ContributorsMacias, Jose Carlos (Co-author) / Goldenberg, Edward Bradley (Co-author) / Downey, Matthew (Co-author) / Zhang, Wenlong (Thesis director) / Aukes, Daniel (Committee member) / Human Systems Engineering (Contributor) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.
ContributorsMerry, Tanner (Author) / Ren, Yi (Thesis director) / Zhang, Wenlong (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Overcrowding of Emergency Departments (EDs) put the safety of patients at risk. Decision makers implement Ambulance Diversion (AD) as a way to relieve congestion and ensure timely treatment delivery. However, ineffective design of AD policies reduces the accessibility to emergency care and adverse events may arise. The objective of this dissertation is to propose methods to design and analyze effective AD policies that consider performance measures that are related to patient safety. First, a simulation-based methodology is proposed to evaluate the mean performance and variability of single-factor AD policies in a single hospital environment considering the trade-off between average waiting time and percentage of time spent on diversion. Regression equations are proposed to obtain parameters of AD policies that yield desired performance level. The results suggest that policies based on the total number of patients waiting are more consistent and provide a high precision in predicting policy performance. Then, a Markov Decision Process model is proposed to obtain the optimal AD policy assuming that information to start treatment in a neighboring hospital is available. The model is designed to minimize the average tardiness per patient in the long run. Tardiness is defined as the time that patients have to wait beyond a safety time threshold to start receiving treatment. Theoretical and computational analyses show that there exists an optimal policy that is of threshold type, and diversion can be a good alternative to decrease tardiness when ambulance patients cause excessive congestion in the ED. Furthermore, implementation of AD policies in a simulation model that accounts for several relaxations of the assumptions suggests that the model provides consistent policies under multiple scenarios. Finally, a genetic algorithm is combined with simulation to design effective policies for multiple hospitals simultaneously. The model has the objective of minimizing the time that patients spend in non-value added activities, including transportation, waiting and boarding in the ED. Moreover, the AD policies are combined with simple ambulance destination policies to create ambulance flow control mechanisms. Results show that effective ambulance management can significantly reduce the time that patients have to wait to receive appropriate level of care.
ContributorsRamirez Nafarrate, Adrian (Author) / Fowler, John W. (Thesis advisor) / Wu, Teresa (Thesis advisor) / Gel, Esma S. (Committee member) / Limon, Jorge (Committee member) / Arizona State University (Publisher)
Created2011
Description
Hepatocellular carcinoma (HCC) is a malignant tumor and seventh most common cancer in human. Every year there is a significant rise in the number of patients suffering from HCC. Most clinical research has focused on HCC early detection so that there are high chances of patient's survival. Emerging advancements in functional and structural imaging techniques have provided the ability to detect microscopic changes in tumor micro environment and micro structure. The prime focus of this thesis is to validate the applicability of advanced imaging modality, Magnetic Resonance Elastography (MRE), for HCC diagnosis. The research was carried out on three HCC patient's data and three sets of experiments were conducted. The main focus was on quantitative aspect of MRE in conjunction with Texture Analysis, an advanced imaging processing pipeline and multi-variate analysis machine learning method for accurate HCC diagnosis. We analyzed the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Along with this we studied different machine learning algorithms and developed models using them. Performance metrics such as Prediction Accuracy, Sensitivity and Specificity have been used for evaluation for the final developed model. We were able to identify the significant features in the dataset and also the selected classifier was robust in predicting the response class variable with high accuracy.
ContributorsBansal, Gaurav (Author) / Wu, Teresa (Thesis advisor) / Mitchell, Ross (Thesis advisor) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2013
Description
A Pairwise Comparison Matrix (PCM) is used to compute for relative priorities of criteria or alternatives and are integral components of widely applied decision making tools: the Analytic Hierarchy Process (AHP) and its generalized form, the Analytic Network Process (ANP). However, a PCM suffers from several issues limiting its application to large-scale decision problems, specifically: (1) to the curse of dimensionality, that is, a large number of pairwise comparisons need to be elicited from a decision maker (DM), (2) inconsistent and (3) imprecise preferences maybe obtained due to the limited cognitive power of DMs. This dissertation proposes a PCM Framework for Large-Scale Decisions to address these limitations in three phases as follows. The first phase proposes a binary integer program (BIP) to intelligently decompose a PCM into several mutually exclusive subsets using interdependence scores. As a result, the number of pairwise comparisons is reduced and the consistency of the PCM is improved. Since the subsets are disjoint, the most independent pivot element is identified to connect all subsets. This is done to derive the global weights of the elements from the original PCM. The proposed BIP is applied to both AHP and ANP methodologies. However, it is noted that the optimal number of subsets is provided subjectively by the DM and hence is subject to biases and judgement errors. The second phase proposes a trade-off PCM decomposition methodology to decompose a PCM into a number of optimally identified subsets. A BIP is proposed to balance the: (1) time savings by reducing pairwise comparisons, the level of PCM inconsistency, and (2) the accuracy of the weights. The proposed methodology is applied to the AHP to demonstrate its advantages and is compared to established methodologies. In the third phase, a beta distribution is proposed to generalize a wide variety of imprecise pairwise comparison distributions via a method of moments methodology. A Non-Linear Programming model is then developed that calculates PCM element weights which maximizes the preferences of the DM as well as minimizes the inconsistency simultaneously. Comparison experiments are conducted using datasets collected from literature to validate the proposed methodology.
ContributorsJalao, Eugene Rex Lazaro (Author) / Shunk, Dan L. (Thesis advisor) / Wu, Teresa (Thesis advisor) / Askin, Ronald G. (Committee member) / Goul, Kenneth M (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study explores the impact of feedback and feedforward and personality on computer-mediated behavior change. The impact of the effects were studied using subjects who entered information relevant to their diet and exercise into an online tool. Subjects were divided into four experimental groups: those receiving only feedback, those receiving only feedforward, those receiving both, and those receiving none. Results were analyzed using regression analysis. Results indicate that both feedforward and feedback impact behavior change and that individuals with individuals ranking low in conscientiousness experienced behavior change equivalent to that of individuals with high conscientiousness in the presence of feedforward and/or feedback.
ContributorsMcCreless, Tamuchin (Author) / St. Louis, Robert (Thesis advisor) / St. Louis, Robert D. (Committee member) / Goul, Kenneth M (Committee member) / Shao, Benjamin B (Committee member) / Arizona State University (Publisher)
Created2012
Description
Social media offers a powerful platform for the independent digital content producer community to develop, disperse, and maintain their brands. In terms of information systems research, the broad majority of the work has not examined hedonic consumption on Social Media Sites (SMS). The focus has mostly been on the organizational perspectives and utilitarian gains from these services. Unlike through traditional commerce channels, including e-commerce retailers, consumption enhancing hedonic utility is experienced differently in the context of a social media site; consequently, the dynamic of the decision-making process shifts when it is made in a social context. Previous research assumed a limited influence of a small, immediate group of peers. But the rules change when the network of peers expands exponentially. The assertion is that, while there are individual differences in the level of susceptibility to influence coming from others, these are not the most important pieces of the analysis--unlike research centered completely on influence. Rather, the context of the consumption can play an important role in the way social influence factors affect consumer behavior on Social Media Sites. Over the course of three studies, this dissertation will examine factors that influence consumer decision-making and the brand personalities created and interpreted in these SMS. Study one examines the role of different types of peer influence on consumer decision-making on Facebook. Study two observes the impact of different types of producer message posts with the different types of influence on decision-making on Twitter. Study three will conclude this work with an exploratory empirical investigation of actual twitter postings of a set of musicians. These studies contribute to the body of IS literature by evaluating the specific behavioral changes related to consumption in the context of digital social media: (a) the power of social influencers in contrast to personal preferences on SMS, (b) the effect on consumers of producer message types and content on SMS at both the profile level and the individual message level.
ContributorsSopha, Matthew (Author) / Santanam, Raghu T (Thesis advisor) / Goul, Kenneth M (Committee member) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2013
Description
Creative design lies at the intersection of novelty and technical feasibility. These objectives can be achieved through cycles of divergence (idea generation) and convergence (idea evaluation) in conceptual design. The focus of this thesis is on the latter aspect. The evaluation may involve any aspect of technical feasibility and may be desired at component, sub-system or full system level. Two issues that are considered in this work are: 1. Information about design ideas is incomplete, informal and sketchy 2. Designers often work at multiple levels; different aspects or subsystems may be at different levels of abstraction Thus, high fidelity analysis and simulation tools are not appropriate for this purpose. This thesis looks at the requirements for a simulation tool and how it could facilitate concept evaluation. The specific tasks reported in this thesis are: 1. The typical types of information available after an ideation session 2. The typical types of technical evaluations done in early stages 3. How to conduct low fidelity design evaluation given a well-defined feasibility question A computational tool for supporting idea evaluation was designed and implemented. It was assumed that the results of the ideation session are represented as a morphological chart and each entry is expressed as some combination of a sketch, text and references to physical effects and machine components. Approximately 110 physical effects were identified and represented in terms of algebraic equations, physical variables and a textual description. A common ontology of physical variables was created so that physical effects could be networked together when variables are shared. This allows users to synthesize complex behaviors from simple ones, without assuming any solution sequence. A library of 16 machine elements was also created and users were given instructions about incorporating them. To support quick analysis, differential equations are transformed to algebraic equations by replacing differential terms with steady state differences), only steady state behavior is considered and interval arithmetic was used for modeling. The tool implementation is done by MATLAB; and a number of case studies are also done to show how the tool works. textual description. A common ontology of physical variables was created so that physical effects could be networked together when variables are shared. This allows users to synthesize complex behaviors from simple ones, without assuming any solution sequence. A library of 15 machine elements was also created and users were given instructions about incorporating them. To support quick analysis, differential equations are transformed to algebraic equations by replacing differential terms with steady state differences), only steady state behavior is considered and interval arithmetic was used for modeling. The tool implementation is done by MATLAB; and a number of case studies are also done to show how the tool works.
ContributorsKhorshidi, Maryam (Author) / Shah, Jami J. (Thesis advisor) / Wu, Teresa (Committee member) / Gel, Esma (Committee member) / Arizona State University (Publisher)
Created2014
Description
Identifying important variation patterns is a key step to identifying root causes of process variability. This gives rise to a number of challenges. First, the variation patterns might be non-linear in the measured variables, while the existing research literature has focused on linear relationships. Second, it is important to remove noise from the dataset in order to visualize the true nature of the underlying patterns. Third, in addition to visualizing the pattern (preimage), it is also essential to understand the relevant features that define the process variation pattern. This dissertation considers these variation challenges. A base kernel principal component analysis (KPCA) algorithm transforms the measurements to a high-dimensional feature space where non-linear patterns in the original measurement can be handled through linear methods. However, the principal component subspace in feature space might not be well estimated (especially from noisy training data). An ensemble procedure is constructed where the final preimage is estimated as the average from bagged samples drawn from the original dataset to attenuate noise in kernel subspace estimation. This improves the robustness of any base KPCA algorithm. In a second method, successive iterations of denoising a convex combination of the training data and the corresponding denoised preimage are used to produce a more accurate estimate of the actual denoised preimage for noisy training data. The number of primary eigenvectors chosen in each iteration is also decreased at a constant rate. An efficient stopping rule criterion is used to reduce the number of iterations. A feature selection procedure for KPCA is constructed to find the set of relevant features from noisy training data. Data points are projected onto sparse random vectors. Pairs of such projections are then matched, and the differences in variation patterns within pairs are used to identify the relevant features. This approach provides robustness to irrelevant features by calculating the final variation pattern from an ensemble of feature subsets. Experiments are conducted using several simulated as well as real-life data sets. The proposed methods show significant improvement over the competitive methods.
ContributorsSahu, Anshuman (Author) / Runger, George C. (Thesis advisor) / Wu, Teresa (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2013
Description
Buildings (approximately half commercial and half residential) consume over 70% of the electricity among all the consumption units in the United States. Buildings are also responsible for approximately 40% of CO2 emissions, which is more than any other industry sectors. As a result, the initiative smart building which aims to not only manage electrical consumption in an efficient way but also reduce the damaging effect of greenhouse gases on the environment has been launched. Another important technology being promoted by government agencies is the smart grid which manages energy usage across a wide range of buildings in an effort to reduce cost and increase reliability and transparency. As a great amount of efforts have been devoted to these two initiatives by either exploring the smart grid designs or developing technologies for smart buildings, the research studying how the smart buildings and smart grid coordinate thus more efficiently use the energy is currently lacking. In this dissertation, a "system-of-system" approach is employed to develop an integrated building model which consists a number of buildings (building cluster) interacting with smart grid. The buildings can function as both energy consumption unit as well as energy generation/storage unit. Memetic Algorithm (MA) and Particle Swarm Optimization (PSO) based decision framework are developed for building operation decisions. In addition, Particle Filter (PF) is explored as a mean for fusing online sensor and meter data so adaptive decision could be made in responding to dynamic environment. The dissertation is divided into three inter-connected research components. First, an integrated building energy model including building consumption, storage, generation sub-systems for the building cluster is developed. Then a bi-level Memetic Algorithm (MA) based decentralized decision framework is developed to identify the Pareto optimal operation strategies for the building cluster. The Pareto solutions not only enable multiple dimensional tradeoff analysis, but also provide valuable insight for determining pricing mechanisms and power grid capacity. Secondly, a multi-objective PSO based decision framework is developed to reduce the computational effort of the MA based decision framework without scarifying accuracy. With the improved performance, the decision time scale could be refined to make it capable for hourly operation decisions. Finally, by integrating the multi-objective PSO based decision framework with PF, an adaptive framework is developed for adaptive operation decisions for smart building cluster. The adaptive framework not only enables me to develop a high fidelity decision model but also enables the building cluster to respond to the dynamics and uncertainties inherent in the system.
ContributorsHu, Mengqi (Author) / Wu, Teresa (Thesis advisor) / Weir, Jeffery (Thesis advisor) / Wen, Jin (Committee member) / Fowler, John (Committee member) / Shunk, Dan (Committee member) / Arizona State University (Publisher)
Created2012