Augmented Reality Uses and Applications in Aerospace and Aviation

Safi, Maryam; Chung, Joon

doi:10.1007/978-3-030-67822-7_20

Cited by 11 publications

(4 citation statements)

References 38 publications

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“…For example, AI-powered chatbots and virtual assistants can be used to provide passengers with realtime flight updates, baggage tracking, and other information (BR, 2022;Mayer, 2019). Additionally, generative AI can be used to create more realistic virtual reality environments for training pilots, flight attendants, and other airline staff (Safi & Chung, 2023).…”

Section: Edutrmentioning

confidence: 99%

Investigation of the Effects of Different Cooling/Lubrication Conditions and Cutting Parameters on Energy Consumption During Milling of Hybrid Aluminum Composites

Değirmenci¹

2023

Interdisciplinary Studies on Contemporary Research Practices in Engineering in the 21st Century II

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In the developing technological world, energy consumption is increasing day by day. Reducing energy consumption on a sectoral basis is very important for sustainable resource use and the improvement of environmental health. The manufacturing sector is one of the sectors where energy consumption is intense. Therefore, even a small improvement in the energy consumption in the machining sector will significantly affect the overall energy consumption, costs, and environmental performance of production systems. Since hybrid aluminum metal matrix composites (AMMC) are difficult to process due to their complex microstructure, energy consumption values during processing are high. For this reason, it is important to determine the most appropriate values by examining the cutting parameters and cooling lubrication conditions during processing to reduce energy consumption. In this article, Al-Gr reinforced hybrid composites containing different proportions of WC and Al2O3 reinforcements were produced. The focus is on the energy consumption when milling these composites under different cooling/lubrication conditions and different machinability parameters such as the dry, minimum amount of lubrication (MQL), and cryogenic-LN2. Taguchi statistical analysis was also used in the experiments. As a result, it was determined that the most effective parameter on the energy consumption values among the control parameters was the feed rate, with a contribution rate of 75.37%. In addition, for minimum energy consumption values, it was observed that the best cutting speed was 150 m/min, the best feed rate was 0.300 mm/rev, and the best cooling/lubrication medium was cryo-LN2 cooling medium.

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Section: Edutrmentioning

confidence: 99%

Investigation of the Effects of Different Cooling/Lubrication Conditions and Cutting Parameters on Energy Consumption During Milling of Hybrid Aluminum Composites

Değirmenci¹

2023

Interdisciplinary Studies on Contemporary Research Practices in Engineering in the 21st Century II

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“…Safi and Chung [3] provide a detailed exploration of AR applications and their uses in aerospace and aviation. Their chapter discusses the benefits and drawbacks of integrating AR into ATC operations.…”

Section: Air Traffic Control (Atc)mentioning

confidence: 99%

“…Both aircraft Head-Up Display (HUD) and Head-Worn Display (HWD) existed long before that. In this chapter, we attempt to discuss the evolution of these technologies slightly differently than several existing literatures [2,3] but also provide information on how it is evolving particularly in the navigation, engineering, and design sectors.…”

Section: Introductionmentioning

confidence: 99%

The state of Augmented Reality in aerospace navigation and engineering

Pradhan,

Rostami,

Kamoonpuri

et al. 2023

Applications of Augmented Reality - Current State of the Art

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The concept of Augmented Reality (AR) has existed in the field of aerospace for several decades in the form of Head-Up Display (HUD) or Head-Worn Display (HWD). These displays enhance Human-Machine Interfaces and Interactions (HMI2) and allow pilots to visualize the minimum required flight information while seeing the physical environment through a semi-transparent visor. Numerous research studies are still being conducted to improve pilot safety during challenging situations, especially during low visibility conditions and landing scenarios. Besides flight navigation, aerospace engineers are exploring many modern cloud-based AR systems to be used as remote and/or AI-powered assist tools for field operators, such as maintenance technicians, manufacturing operators, and Air Traffic Control Officers (ATCO). Thanks to the rapid advancement in computer vision and deep neural network architectures, modern AR technologies can also scan or reconstruct the 3D environment with high precision in real time. This feature typically utilizes the depth cameras onboard or independent from the AR devices, helping engineers rapidly identify problems during an inspection and implement the appropriate solutions. Some studies also suggest 3D printing of reconstructed models for additive manufacturing. This chapter covers several aspects and potentials of AR technology in the aerospace sector, including those already adopted by the companies and those currently under research.

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“…This growth can be primarily attributed to advancements in the physical devices utilized in this domain and the remarkable increase in processing power of computer systems. Various areas have benefited from these advances in artificial intelligence, including the medical, automotive, aerospace, and educational sectors [1][2][3][4][5][6][7][8]. Additionally, artificial intelligence has played a vital role in the development of smart cities [9,10], among other fields.…”

Section: Introductionmentioning

confidence: 99%

Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation

García-Domínguez

Galván-Tejada

Magallanes‐Quintanar

et al. 2023

Journal of Diabetes Research

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The development of medical diagnostic models to support healthcare professionals has witnessed remarkable growth in recent years. Among the prevalent health conditions affecting the global population, diabetes stands out as a significant concern. In the domain of diabetes diagnosis, machine learning algorithms have been widely explored for generating disease detection models, leveraging diverse datasets primarily derived from clinical studies. The performance of these models heavily relies on the selection of the classifier algorithm and the quality of the dataset. Therefore, optimizing the input data by selecting relevant features becomes essential for accurate classification. This research presents a comprehensive investigation into diabetes detection models by integrating two feature selection techniques: the Akaike information criterion and genetic algorithms. These techniques are combined with six prominent classifier algorithms, including support vector machine, random forest, k-nearest neighbor, gradient boosting, extra trees, and naive Bayes. By leveraging clinical and paraclinical features, the generated models are evaluated and compared to existing approaches. The results demonstrate superior performance, surpassing accuracies of 94%. Furthermore, the use of feature selection techniques allows for working with a reduced dataset. The significance of feature selection is underscored in this study, showcasing its pivotal role in enhancing the performance of diabetes detection models. By judiciously selecting relevant features, this approach contributes to the advancement of medical diagnostic capabilities and empowers healthcare professionals in making informed decisions regarding diabetes diagnosis and treatment.

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Augmented Reality Uses and Applications in Aerospace and Aviation

Cited by 11 publications

References 38 publications

Investigation of the Effects of Different Cooling/Lubrication Conditions and Cutting Parameters on Energy Consumption During Milling of Hybrid Aluminum Composites

Investigation of the Effects of Different Cooling/Lubrication Conditions and Cutting Parameters on Energy Consumption During Milling of Hybrid Aluminum Composites

The state of Augmented Reality in aerospace navigation and engineering

Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation

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