Engineering Commons^™

Recursive Subspace-Based Model Refinement Method For Digital Twin Of Thermal Power Unit, Yanbo Zhao, Yuanli Cai, Huaizhong Hu

Journal of System Simulation

Abstract: Due to factors such as simplified assumptions or equipment characteristic deviation, modeling errors are inevitable in the mechanism modeling of thermal power units. To deal with the problem, this paper proposes a novel model refinement method based on recursive subspace for the digital twin of thermal power units. Firstly, the digital twin models are built based on mechanism analysis and combined with small sample data of typical conditions, ensuring interpretability and generalization performance. Secondly, based on the recursive subspace identification method, the refinement model is built and updated online in real time to compensate for the modeling error, improving …

Predictability Of Mechanical Behavior Of Additively Manufactured Particulate Composites Using Machine Learning And Data-Driven Approaches, Steven Malley, Crystal Reina, Somer Nacy, Jérôme Gilles, Behrad Koohbor

Henry M. Rowan College of Engineering Faculty Scholarship

Additive manufacturing and data analytics are independently flourishing research areas, where the latter can be leveraged to gain a great insight into the former. In this paper, the mechanical responses of additively manufactured samples using vat polymerization process with different weight ratios of magnetic microparticles were used to develop, train, and validate a neural network model. Samples with six different compositions, ranging from neat photopolymer to a composite of photopolymer with 4 wt.% of magnetic particles, were manufactured and mechanically tested at quasi-static strain rate and ambient environmental conditions. The experimental data were also synthesized using a data-driven approach based …

Machine Learning In Requirements Elicitation: A Literature Review, Cheligeer Cheligeer, Jingwei Huang, Guosong Wu, Nadia Bhuiyan, Yuan Xu, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords …

The Method Summary Of Generating Large-Scale Artificial Population In An Artificial Society, Yuanzheng Ge, Zhichao Song, Rongqing Meng

Journal of System Simulation

Abstract: The generation of large-scale artificial population is the fundamental data of high-resolution agent-based artificial society. By the comparison of typical artificial population models, the basic elements, generating methods, and evolving rules, the development of artificial society, generation of population database, the establishment of spatial-temporal relationship, evolving rules of behavior, and reconstruction of social relationship are analyzed. Furthermore, data-driven method and open interface framework of artificial population are discussed, and the future direction and development are listed.

A Data Driven Approach To Identify Journalistic 5ws From Text Documents, Venkata Krishna Mohan Sunkara

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Textual understanding is the process of automatically extracting accurate high-quality information from text. The amount of textual data available from different sources such as news, blogs and social media is growing exponentially. These data encode significant latent information which if extracted accurately can be valuable in a variety of applications such as medical report analyses, news understanding and societal studies. Natural language processing techniques are often employed to develop customized algorithms to extract such latent information from text.

Journalistic 5Ws refer to the basic information in news articles that describes an event and include where, when, who, what and why …

Data-Driven Simulation Modeling Of Construction And Infrastructure Operations Using Process Knowledge Discovery, Reza Akhavian

Electronic Theses and Dissertations

Within the architecture, engineering, and construction (AEC) domain, simulation modeling is mainly used to facilitate decision-making by enabling the assessment of different operational plans and resource arrangements, that are otherwise difficult (if not impossible), expensive, or time consuming to be evaluated in real world settings. The accuracy of such models directly affects their reliability to serve as a basis for important decisions such as project completion time estimation and resource allocation. Compared to other industries, this is particularly important in construction and infrastructure projects due to the high resource costs and the societal impacts of these projects. Discrete event simulation …

Structural Health Monitoring Using Novel Sensing Technologies And Data Analysis Methods, Seyedmasoud Malekzadeh

Electronic Theses and Dissertations

The main objective of this research is to explore, investigate and develop the new data analysis techniques along with novel sensing technologies for structural health monitoring applications. The study has three main parts. First, a systematic comparative evaluation of some of the most common and promising methods is carried out along with a combined method proposed in this study for mitigating drawbacks of some of the techniques. Secondly, nonparametric methods are evaluated on a real life movable bridge. Finally, a hybrid approach for non-parametric and parametric method is proposed and demonstrated for more in depth understanding of the structural performance. …

A Framework For Process Data Collection, Analysis, And Visualization In Construction Projects, Reza Akhavian

Electronic Theses and Dissertations

Automated data collection, simulation and visualization can substantially enhance the process of designing, analysis, planning, and control of many engineering processes. In particular, managing processes that are dynamic in nature can significantly benefit from such techniques. Construction projects are good examples of such processes where a variety of equipment and resources constantly interact inside an evolving environment. Management of such settings requires a platform capable of providing decision-makers with updated information about the status of project entities and assisting site personnel making critical decisions under uncertainty. To this end, the current practice of using historical data or expert judgments as …