IEMS 2023
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Item Proceedings of the 2023 International IEMS Conference, March 5-7, 2023(Industry, Engineering & Conference Management Systems Conference, 2023-03) International Conference on Industry, Engineering, and Management Systems; Moscoso-Kingsley, WilfredoIt is with pleasure that we present to you the Proceedings of the 29th International Conference on Industry, Engineering and Management Systems (IEMS). The papers presented this year consistently represented high quality scholarship in the authors' respective fields. The papers covered a wide range of topics in the business and engineering disciplines, integrating concepts that further the mission of the IEMS Conference. We present these Proceedings to you to enable your own thought leadership so that you may share your presentations and papers in the future at our IEMS conference. These proceedings would not have been made possible without the valuable contributions of our Track Chairs and reviewers for the time and effort they spent reviewing the papers. We look forward to seeing you virtually at IEMS 2024!Item The Relationship Between Pilot Experience and Aviation Safety Attitudes(Industry, Engineering & Conference Management Systems Conference, 2023-03) Piasecki, Isabella; Le Gall, Steven; Wheeler, BrookeAlthough the influence of safety attitudes on pilot performance has been documented and research has been conducted on how training can increase safety attitudes, little research has been conducted to understand the relationship between experience and a pilot's safety attitudes. The purpose of this study was to determine whether there is a relationship between pilot experience and pilot attitudes toward safety issues. Pilot attitudes towards safety issues were measured using Hunter's Aviation Safety Attitudes Scale (ASAS). A survey was distributed that included both ASAS and demographic questions. The relationship between flight hours (pilot experience) and the ASAS score was examined.Item Willingness to Fly in an Electric Aircraft(Industry, Engineering & Conference Management Systems Conference, 2023-03) Wheeler, Brooke; Binhalil, Saud; Mallah, Mohammed; Tobar, LuisThis study examined US consumers' willingness to fly in an electric aircraft and an aircraft with an internal combustion engine for either one hour or 30-minute flight durations. The participants were provided with four different scenarios: electric engine 30-minute flight, electric aircraft one hour, combustion engine 30 minute, and combustion engine one hour. The scenarios were delivered in a random order to avoid order effects, and each flight scenario explained that the weather was clear and calm and had a knowledgeable pilot. The U.S. Amazon Mechanical Turk (MTurk) consumers' willingness to fly showed no significant differences between type of engine or duration of flight. On average, the participants were willing to fly in electric aircraft, which is promising for the aviation industry.Item On-Machine Coordinate Measuring for In-Situ Quality Control(Industry, Engineering & Conference Management Systems Conference, 2023-03) Goheen, Jr., William D.; Towner, Ridge D.; Moscoso-Kingsley, Wilfredo; Carstens, Deborah S.The capability of a computer numerical control (CNC) machine tool equipped with direct computer control coordinate measuring software and high accuracy contact probing is validated for in-situ machine health monitoring and in-situ part quality control. The validation conforms to international standards for performance evaluation of commercially available coordinate measuring machines (CMMs). The capability of the CNC system to perform CMM-type measurements is demonstrated via a case study. The equivalency between part dimensional measurements obtained directly from the use of the CNC machine tool as a CMM and from part dimensional measurements performed using commercially available CMMs is established via a correlation study.Item ThreshNet: a Novel Machine Learning Technique to Optimize Sensitivity and Specificity Performance(Industry, Engineering & Conference Management Systems Conference, 2023-03) Xu, ShirleyIn image classification applications for medical diagnosis, sensitivity and specificity are important performance metrics that are often inversely related. Both high sensitivity and high specificity are not always achievable for a given neural network; the trade-off and balance between them are not easily controllable. This paper proposes "ThreshNet", a novel method to address this dilemma. ThreshNet is composed of an ensemble of different neural networks. Many well-known networks were leveraged through transfer learning. With custom-designed dense layers, network parameters were tuned to optimize performance and enhance the diversity of members in the ThreshNet networks ensemble. To yield the ThreshNet system's decision, a threshold-based algorithm is proposed. Demonstrated with a brain tumor MRI dataset, ThreshNet systems consistently outperform individual networks. Specific sensitivity-specificity trade-offs and optimization goals can conveniently be achieved by adjusting the threshold parameter. Performance variance among ThreshNet systems is smaller than those among individual networks. To locate tumor(s) predicted by ThreshNet, a ResUNet-based image segmentation model was developed, achieving a Tversky index of 90.49% in predicting pixel-wise masks to mark tumor locations.