1,721,067 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Adaptive Response Surface Method for Efficient Bayesian Reliability Based Design Optimization
No full textPaper presented to the 7th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Marcus Welcome Center, Wichita State University, May 4, 2011.Research completed at the Department of Industrial and Manufacturing EngineeringTo tackle engineering design problems engaging both aleatory and epistemic uncertainties, Reliability-Based Design
Optimization (RBDO) has been integrated with Bayes Theorem, referred to as Bayesian RBDO. However, Bayesian RBDO
becomes expensive when employing the First- or Second-Order Reliability Methods for reliability predictions. This paper
proposes an Adaptive Response Surface Method (ARSM) for efficient Bayesian reliability analysis and design optimization. The
ARSM integrates the iterative design optimization process with the local response surface methodology through an adaptive
sampling scheme. Through this integration, the information for reliability analysis generated at early design stages can be used
adaptively to construct local response surfaces for later design iterations. Thus, the computational efficiency of the Bayesian
RBDO can be improved as substantially fewer experiments are required in the overall design process. The proposed methodology
is demonstrated with a ground vehicle lower control arm design case study
Cost benefit analysis of condition monitoring systems for optimal maintenance decision making
No full textThesis (M.S.)--Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing EngineeringTremendous advances in high performance sensing and signal processing technology enable the development of condition monitoring systems (CMS) for complex engineered systems to detect, diagnose, and predict the system-wide effects of failure events. Although employing CMS in preventing catastrophic system failures and reducing the operation and maintenance (O&M) costs have been acknowledged, the cost and benefit of CMS have not been well studied and further the advantages of CMS have not been fully recognized for the optimal maintenance decision making, mainly due to the lack of valid theoretical modeling addressing the interrelationship between the CMS effectiveness and system downtime due to system failures. In this study, a Poisson Process model will be developed for the modeling of occurrence of the system-wide failure events and study the potential benefits provided by the CMS in preventing these failure events. With the developed Poisson process model, the cost benefit analysis (CBA) will then be implemented by considering the CMS system reliability and costs varying with its failure detection effectiveness presented by the probabilistic detectability measure. Facilitated by CBA of the CMS, break-even points (BEP) between expected lifecycle benefits and the required CMS detectability level can be found to select optimal CMS for different system failure modes. Moreover, with the help of the CBA results, optimal maintenance strategies can be determined to minimize the O&M costs. The presented CBA methodology for the CMS systems will be demonstrated with an aircraft maintenance case study and the efficacy will be validated
Resilience quantification and allocation for design of complex engineered systems
No full textPresented to the 11th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Heskett Center, Wichita State University, April 24, 2015.Research completed at Department of Industrial and Manufacturing Engineering, College of EngineeringContinued growth in terms of scale, complexity, and prolonged system useful lives has become increasingly apparent for complex engineered systems. This growing global trend has challenged system designers to design affordable and effective complex engineered systems. Previous research efforts have been focused on protecting an engineered system against failure events, in other words, ensuring high reliability. Improving reliability in a system is often associated with the exponential behavior of improvement costs. At one point, it is not affordable to improve system reliability because the improvement costs increase substantially as the system reliability level approaches the maximum achievable reliability. Therefore, most recent research have given attention towards developing an adaptive engineered system that is able to response to and recover from adverse disruptive events, such as natural disasters, man-made accidents, and vicious attacks. This type of system is also known as a resilient system. Resilience in an engineered system implies the capability of a system to autonomously sense adverse changes in health conditions, withstand failure events, and to recover from the effects of these unpredicted events.
This paper is dedicated to exploring the gap between quantitative and qualitative assessments of engineering resilience in the domain of designing complex engineered systems, thus optimally allocating resilience into subsystems level could be achieved. Engineering resilience can be quantified based on the probabilities of passive survival rate (Reliability) and proactive survival rate (Restoration). As the assessment tool of engineering resilience, Bayesian Network approach is proposed. The optimization of engineering resilience allocations are further employed at the subsystems level so that the system development cost could be minimized while satisfying a system target resilience level. A supply chain resilience allocation case study is employed to demonstrate the proposed approach. The proposed resilience quantification and allocation approach using Bayesian Networks would empower system designers in the conceptual design stage, to have a better grasp of the weakness and strength of their own systems against disruptions. This research also aims to provide a fundamental methodology to develop a more effective, readily-used design tool that can optimally allocate resilience attributes for complex engineered systems.Graduate School, Academic Affairs, University Librarie
Advanced data-driven prognostics and health management for complex dynamic systems
No full textThesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing EngineeringPrognostics and health management (PHM) is an emerging engineering discipline that diagnoses and predicts how an engineered system will degrade its performance and when it will lose its partial or whole functionality. With monitored parameters from the system and observations from its operating conditions, PHM can significantly enhance the reliability, availability, and predictability of the system. In this dissertation, contributions have been made to address the challenges of PHM for complex dynamic systems applied to lithium-ion batteries, as outlined in the following three major research thrusts:
- Adaptive Dynamic System Modeling for PHM: in this thrust, a new self-cognizant dynamic system (SCDS) approach has been developed to address the challenge of dynamic system modeling considering the deterioration of system performance over time so that system inherent parameters can be accurately identified and system health states can be assessed. The SCDS approach has been applied to battery health management and also generalized for PHM of general complex dynamics systems.
- Lithium-Plating Diagnosis: In this thrust, a novel internal state variable (SV) mapping approach has been developed to address the challenging of diagnosing lithium-plating with only operational measurements such as voltage and current information.
- Lithium-Plating prognosis: In this thrust, a multi-scale filtering technique is developed based on the ISV mapping approach for the remaining useful life prediction of lithium-plating induced battery system failures.
This dissertation consists of four journal articles that have been either published or submitted for publication in chapters two to five, whereas chapter one provides an overview of the research background and chapter six summarizes the dissertation with conclusions and future work
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
A Generic Reliability and Risk Centered Maintenance Framework for Wind Turbines
No full textPaper presented to the 7th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Marcus Welcome Center, Wichita State University, May 4, 2011.Research completed at the Department of industrial and Manufacturing EngineeringOperation and maintenance (O&M) are significant contributors to the cost of energy in wind industry. To reduce the cost, effective maintenance strategies have become an indispensable part of operational decision-making for wind turbines. This research presents a generic framework for the maintenance planning of wind turbines. Within the proposed framework, the performance degradation of wind turbines over time is characterized with stochastic damage
growth models. Costs incurred during the life span of wind turbines are modeled mathematically to compute the accumulated risks involved in the O&M process. Probabilistic models are used to characterize the maintenance activities and a unified maintenance optimization platform is then formulated to derive optimal maintenance strategies. A case study of offshore wind turbines is used to demonstrate the proposed methodology
Adaptive surrogate modeling for high dimensional problems using Autoencoder Gaussian Process
No full textSubmission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01The student, Jiayi Zhao, accepted the attached license on 2024-12-09 at 09:42.The student, Jiayi Zhao, submitted this Thesis for approval on 2024-12-09 at 09:50.This Thesis was approved for publication on 2024-12-12 at 09:58.DSpace SAF Submission Ingestion Package generated from Vireo submission #21546 on 2025-03-28 at 14:57:05High-dimensional surrogate modeling poses significant challenges, particularly when data is limited, as traditional Gaussian Process (GP) models struggle with scalability and computational efficiency. This paper addresses these issues by proposing a framework for optimizing the latent dimension in an Autoencoder-Gaussian Process (AE-GP) model, ensuring both accuracy and scalability. Using 10 representative benchmark functions, the study evaluates the GP’s performance in terms of Mean Squared Error (MSE) under 5-fold cross-validation, with latent dimensions ranging from 1 to 20. The experiments are conducted across varying combinations of dataset dimensions D0 and sample sizes N, identifying the best-performing specific values and ranges of latent dimensions. These optimal dimensions are then applied to high-dimensional case studies with unknown x-y relationships to validate the model’s practical applicability. By proposing an adaptive framework for high-dimensional surrogate modeling, this work provides actionable insights for selecting AE latent dimensions under resource constraints and demonstrates its effectiveness in improving model scalability and accuracy across diverse scenarios
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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