Operations Research, Systems Engineering and Industrial Engineering Commons^™

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 …

Cockpit In The Systems Engineering Lenses, Aysen K. Taylor, Charles B. Keating, T. Steven Cotter

Engineering Management & Systems Engineering Faculty Publications

The commercial transport aircraft of today vary greatly from early aircraft in regards to how they are controlled and the feedback provided from the machine to the human operator. Automation has improved operational precision and efficiency but at the cost of providing less feedback. Pilots are the last line of defense and current technology cannot provide the human ability to solve novel problems for which no computer logic can be written. The automated cockpits of today have may sub-components that interact in a manner often opaque and unpredictable when a sensor or sub-component fails or even in situations where no …

A Multivariate Quality Control Approach For Automated Manufacturing Systems, Hisham Naim Ashur

Engineering Management & Systems Engineering Theses & Dissertations

In today's competitive manufacturing environment, effective and practical statistical quality control approaches are essential. A successful process control approach needs to provide on line real time monitoring of quality related characteristics.

No longer acceptable is an approach that analyzes quality related data only after the product is produced. A statistical process control approach that monitors the process during production and that reports trouble spots before bad products are made is necessary in an automated manufacturing environment.

An automated manufacturing environment is characterized by high volume production runs and short production cycles. Traditional statistical process control approaches are not capable of …