Relevant bibliographies by topics / Store Trajectory Prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Store Trajectory Prediction'

Author: Grafiati
Published: 6 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "Store Trajectory Prediction"

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Ching, Tan Kok. "Grid Survey Approach to Store Separation Trajectory Prediction." Journal of Aircraft 37, no. 4 (2000): 736–38. http://dx.doi.org/10.2514/2.2662.

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Hashemi, Seyed Mohammad, Seyed Ali Hashemi, Ruxandra Mihaela Botez, and Georges Ghazi. "Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl." Applied Sciences 13, no. 17 (2023): 9503. http://dx.doi.org/10.3390/app13179503.

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This paper introduces a novel and robust data-driven algorithm designed for Aircraft Trajectory Prediction (ATP). The approach employs a Neural Network architecture to predict future aircraft trajectories, utilizing input variables such as latitude, longitude, altitude, heading, speed, and time. The model’s foundation is rooted in the Generative Adversarial Network (GAN) framework, known for its inherent generative capabilities, rendering it remarkably resilient against Adversarial Attacks. To enhance its credibility, the Blockchain is employed as a Ledger Technology (LT) to securely store leg
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Lee, Seungsoo, Minwoo Park, Kum Won Cho, and Jang Hyuk Kwon. "New Fully Automated Procedure for the Prediction of Store Trajectory." Journal of Aircraft 37, no. 6 (2000): 1038–49. http://dx.doi.org/10.2514/2.2709.

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KANDIEL, M., and M. ESSAWf. "PREDICTION OE STORE TRAJECTORY DROPPED 'FROM A HIGH SPEED AIRCRAFT." International Conference on Aerospace Sciences and Aviation Technology 1, CONFERENCE (1985): 1–13. http://dx.doi.org/10.21608/asat.1985.26482.

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Razzaghi, Mohammad Mehdi. "Numerical study on trajectory of released store with manipulation of grid connections." Aircraft Engineering and Aerospace Technology 93, no. 5 (2021): 768–75. http://dx.doi.org/10.1108/aeat-07-2020-0140.

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Purpose This study aims to present a moving grid method based on the manipulation of connections. Design/methodology/approach In this study, the grid’s connections were manipulated to simulate a released store’s displacement. The selected model in this research is the EGLIN test case. In the introduced method, connections are modified in specific nodes of the grid. Governing flow equations were solved with the finite volume method. The major characteristic of this technique is using the averaging method for calculating the flux of cells. Findings This method maintains the grid’s quality even i
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Lijewski, Lawrence E., and Norman E. Suhs. "Time-accurate computational fluid dynamics approach to transonic store separation trajectory prediction." Journal of Aircraft 31, no. 4 (1994): 886–91. http://dx.doi.org/10.2514/3.46575.

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Chakraborty, Debasis, K. Anandhanarayanan, Ankit Raj, Vaibhav Shah, and R. Krishnamurthy. "Separation Dynamics of Air-to-Air Missile and Validation with Flight Data." Defence Science Journal 68, no. 1 (2017): 5. http://dx.doi.org/10.14429/dsj.68.11480.

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<p class="p1">Prediction of flight characteristics of a store in the vicinity of an aircraft is vitally important for ensuring the safety of the aircraft and effectiveness of the store to meet the mission objective. Separation dynamics of an agile air-to-air-Missile from a fighter aircraft is numerically simulated using an integrated store separation dynamics suite. Chimera cloud of points along with a grid-free Euler solver is used to obtain aerodynamic force on the missile and the force is integrated using a rigid body dynamics code to obtain the missile position. In the present work,
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Wang, Shanshan. "Enterprise Management Optimization by Using Artificial Intelligence and Edge Computing." International Journal of Distributed Systems and Technologies 13, no. 3 (2022): 1–9. http://dx.doi.org/10.4018/ijdst.307994.

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In the internet era, huge data is generated every day. With the help of cloud computing, enterprises can store and analyze these data more conveniently. With the emergence of the internet of things, more hardware devices have accessed the network and produced massive data. The data heavily relies on cloud computing for centralized data processing and analysis. However, the rapid growth of data volume has exceeded the network throughput capacity of cloud computing. By deploying computing nodes at the edge of the local network, edge computing allows devices to complete data collection and prepro
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Velychko, Dmytro, Benjamin Knopp, and Dominik Endres. "Making the Coupled Gaussian Process Dynamical Model Modular and Scalable with Variational Approximations." Entropy 20, no. 10 (2018): 724. http://dx.doi.org/10.3390/e20100724.

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We describe a sparse, variational posterior approximation to the Coupled Gaussian Process Dynamical Model (CGPDM), which is a latent space coupled dynamical model in discrete time. The purpose of the approximation is threefold: first, to reduce training time of the model; second, to enable modular re-use of learned dynamics; and, third, to store these learned dynamics compactly. Our target applications here are human movement primitive (MP) models, where an MP is a reusable spatiotemporal component, or “module” of a human full-body movement. Besides re-usability of learned MPs, compactness is
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Yang, Yichi, Ruibin Yan, and Yijun Gu. "LTransformer: A Transformer-Based Framework for Task Offloading in Vehicular Edge Computing." Applied Sciences 13, no. 18 (2023): 10232. http://dx.doi.org/10.3390/app131810232.

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Vehicular edge computing (VEC) is essential in vehicle applications such as traffic control and in-vehicle services. In the task offloading process of VEC, predictive-mode transmission based on deep learning is constrained by limited computational resources. Furthermore, the accuracy of deep learning algorithms in VEC is compromised due to the lack of edge computing features in algorithms. To solve these problems, this paper proposes a task offloading optimization approach that enables edge servers to store deep learning models. Moreover, this paper proposes the LTransformer, a transformer-bas
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Dissertations / Theses on the topic "Store Trajectory Prediction"

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Yousuf, Mohamed Amali Uthuman. "Automated Meshless CFD Process using Cartesian Point Distribution." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4398.

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The thesis deals with the meshless methods based on generalized finite difference procedure operating on the mere distribution of points. The work per se focuses on maturing the meshless LSFD-U solver as a standard industrial tool for Aerospace CFD. One of the purported advantages of this class of methods as opposed to finite- volume methods is that they can considerably ease the need for generating grids. This aspect has been truly exploited in this thesis by projecting the meshless LSFD-U solver as a Cartesian grid methodology. The point distribution required by the LSFD-U solver is obt
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Books on the topic "Store Trajectory Prediction"

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Cheffins, Brian R. The Future of the Public Company. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190640323.003.0007.

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The seventh and concluding chapter of The Public Company Transformed extrapolates from trends the previous chapters have identified to speculate on the future trajectory of the public company. Salient developments from the 2010s are taken into account, with particular emphasis being placed on those implying a path different from what would be anticipated given events occurring from the mid-twentieth century through to the opening decade of the twenty-first century. This chapter argues radical departures from present day arrangements are unlikely any time soon. For instance, recent predictions
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Book chapters on the topic "Store Trajectory Prediction"

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Zhang, Wenxu, and Xuxiu Zhang. "Multimodal Trajectory Prediction Based on Sustainable Learning from Memory Networks." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde240513.

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In the field of trajectory prediction, when new scene data is continuously introduced, the system may forget the historical trajectory information that has been learned before, leading to the phenomenon of decreasing the prediction accuracy of previous scenes, which is called catastrophic forgetting. The Multi-Memo Net framework proposed in this paper is an instance-based approach that stores typical trajectories through a memory bank thereby establishing a direct link between the current and historical scenes. During training, self-encoders and decoders are used to efficiently memorize the fe
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Wahlstedt, Robert. "How Games Improve Language in People With Language Dysfunctions." In Ubiquitous Machine Learning and Its Applications. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2545-5.ch010.

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Many people as they age face a greater challenge of muscular dexterity around their facial muscles. This results in difficulty producing certain sounds, and sometimes the problem is so severe that they are unintelligible. People who could benefit from the methods in this chapter are those who are hard of hearing and do not have feedback readily accessible and people with ALS. This chapter describes a method that uses a computer learning algorithm that predicts what people are about to say based on earlier content and learns what the natural sound of their voice sounds like. This chapter illust
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Wahlstedt, Robert. "How Games Improve Language in People With Language Dysfunctions." In Natural Language Processing. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0951-7.ch071.

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Many people as they age face a greater challenge of muscular dexterity around their facial muscles. This results in difficulty producing certain sounds, and sometimes the problem is so severe that they are unintelligible. People who could benefit from the methods in this chapter are those who are hard of hearing and do not have feedback readily accessible and people with ALS. This chapter describes a method that uses a computer learning algorithm that predicts what people are about to say based on earlier content and learns what the natural sound of their voice sounds like. This chapter illust
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Snyder, Michael. "Introduction." In James Purdy. Oxford University PressNew York, 2023. http://dx.doi.org/10.1093/oso/9780197609729.003.0001.

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Abstract Tracing out the trajectory of Purdy’s career, the Introduction begins with a prologue describing Dame Edith Sitwell’s rapturous encounter in 1956 with the stories of an unknown American writer. It summarizes the strategy by which Purdy, who struggled to get published, was aided by two friends who privately published a story collection and a novella. Purdy mailed them to writers, poets, and critics with whom he thought his work might resonate and received copious responses from notables like Tennessee Williams. The support of recipients like Edith Sitwell and his greatest early America
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Shmirthaa Santhosh, U. S., Dr S. Balakrishnan, and I. Anantraj. "DATA ALCHEMY: UNLEASHING BUSINESS TERRAIN WITH DATA ANALYTICS." In Futuristic Trends in Artificial Intelligence Volume 3 Book 4. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3biai4p3ch2.

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This book chapter delves into the evolution and impact of data analytics in the corporate sector which tell a compelling story of revolutionary progress and strategic significance. This chapter digs into data analytics’ historical trajectory and deep influence, offering light on its dynamic evolution and crucial role in creating current business practices. The chapter begins with an introduction to developing data analytics in the business sector and goes on to discuss the voyage of data analytics as it develops through several stages, from manual data processing in the pre-computer period to
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Conference papers on the topic "Store Trajectory Prediction"

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Stokes, S., J. Chappell, and M. Leatham. "Efficient numerical store trajectory prediction for complex aircraft/store configurations." In 30th Fluid Dynamics Conference. American Institute of Aeronautics and Astronautics, 1999. http://dx.doi.org/10.2514/6.1999-3712.

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Bore, Clifford L. "The Aerodynamics of Store Drag and Trajectory Prediction." In Aerospace Technology Conference and Exposition. SAE International, 1987. http://dx.doi.org/10.4271/871791.

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Nelson, Chris, and Alan Cain. "Prediction of Store Trajectory Response to Unsteady Aerodynamic Loads." In 47th AIAA Aerospace Sciences Meeting including The New Horizons Forum and Aerospace Exposition. American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-548.

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Pandey, Navdeep, Jaysinh J. Patel, and Aniruddha Sinha. "Store Trajectory Prediction Using Domain-Decomposed Reduced-Order Modelling." In AIAA AVIATION FORUM AND ASCEND 2024. American Institute of Aeronautics and Astronautics, 2024. http://dx.doi.org/10.2514/6.2024-4444.

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Lee, Seungsoo, Minwoo Park, Kum Cho, and Jang Kwon. "A new automated Chimera method for the prediction of store trajectory." In 17th Applied Aerodynamics Conference. American Institute of Aeronautics and Astronautics, 1999. http://dx.doi.org/10.2514/6.1999-3131.

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Berglind, Torsten, and Lars Tysell. "Time-Accurate CFD Approach to Numerical Simulation of Store Separation Trajectory Prediction." In 29th AIAA Applied Aerodynamics Conference. American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-3958.

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Coley, Christopher. "Artificial Neural Network Surrogate Modeling for Cavity-Released Store Separation Trajectory Prediction." In AIAA AVIATION FORUM AND ASCEND 2024. American Institute of Aeronautics and Astronautics, 2024. http://dx.doi.org/10.2514/6.2024-4616.

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Das, Arpan, Errol Hale, Pier Marzocca, and Michael J. Candon. "A Data-Driven Approach in Real-Time Trajectory Prediction of Store Release From Cavity." In AIAA SCITECH 2025 Forum. American Institute of Aeronautics and Astronautics, 2025. https://doi.org/10.2514/6.2025-1261.

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Cenko, Alexis. "Store Aeparation Trajectory Predictions for Maritime Search and Rescue (SAR)." In 35th AIAA Applied Aerodynamics Conference. American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-3251.

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Jolly, Bruce A., Wali Aziz, Clapp Andrew, and Austin Amour. "J-factor Theory & Explanation for Predicting Store Trajectory Repeatability." In AIAA AVIATION 2022 Forum. American Institute of Aeronautics and Astronautics, 2022. http://dx.doi.org/10.2514/6.2022-3451.

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Journal articles
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