Gotowe bibliografie tematyczne / Store Trajectory Prediction

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Gotowa bibliografia na temat „Store Trajectory Prediction”

Autor: Grafiati

Data publikacji: 6 września 2023

Data aktualizacji: 19 lipca 2025

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Artykuły w czasopismach na temat "Store Trajectory Prediction"

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 legitimate predicted values utilized in subsequent trajectory predictions. The Blockchain ensures that only authorized and non-adversarial samples are stored in the blocks, rejecting any adversarial predictions. In the validation process, trajectory data for training the GAN model were generated through the UAS-S4 Ehécatl simulation model. The performance evaluation relies on the model’s resistance to adversarial attacks, measured by fooling rates. The results acquired affirm the excellent efficacy of the GAN model, Secured by Blockchain, approaching against adversarial attacks.

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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 in large displacements of the released store. The three-dimensional simulation was carried out in transonic and supersonic regimes. Comparison of the results with experimental data were highly satisfactory. Research limitations/implications Using this moving grid method is recommended for simulating other models. Practical implications Prediction of store trajectory released from air vehicles is one of the most critical issues under study especially in the design of new stores. Originality/value The most prominent advantage of this method is maintaining the grid quality simultaneous with large displacements of the released store.

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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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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, the suite is applied to a flight test case and sensitivity of trajectory variables on launch parameters is studied. Further, the results of the suite are compared with the flight data. The predicted body rates and Euler angles of missile compare well with the flight data.

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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 preprocessing in the local network. Thus, it can overcome the problems of low efficiency and large transmission delay of cloud computing for massive native data. This paper designs a human trajectory training system for enterprise management. The simulation demonstrates that the system can support human trajectory tracing and prediction for enterprise management.

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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 crucial, to allow for the storage of a large library of movements. We first derive the variational approximation, illustrate it on toy data, test its predictions against a range of other MP models and finally compare movements produced by the model against human perceptual expectations. We show that the variational CGPDM outperforms several other MP models on movement trajectory prediction. Furthermore, human observers find its movements nearly indistinguishable from replays of natural movement recordings for a very compact parameterization of the approximation.

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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-based framework that incorporates edge computing features. The framework consists of pre-training, an input module, an encoding–decoding module, and an output module. Compared with four sequential deep learning methods, namely a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), a Gated Recurrent Unit (GRU), and the Transformer, the LTransformer achieves the highest accuracy, reaching 80.1% on the real dataset. In addition, the LTransformer achieves 0.008 s when predicting a single trajectory, fully satisfying the fundamental requirements of real-time prediction and enabling task offloading optimization.

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Rozprawy doktorskie na temat "Store Trajectory Prediction"

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 obtained from Cartesian grids. The Cartesian grid with its immense potential for process automation and the LSFD-U method with its ability to discretize the conservation equations on any arbitrary point distribution, form a natural pair for solving complex engineering problems in an automated process. The thesis presents a number of complex configurations of industrial relevance where the point distribution for the meshless solver are obtained from Cartesian grids in short turn-around times and without any human intervention. The grid convergence of the 3D inviscid solver is also established on a sequence of Cartesian point distributions. The automation capability is one of the key requirements for solving multi-body dynamics, moving body and optimization problems. The CFD process on such problems primarily involves repetitive grid generation. Any need for human intervention and expertise in the CFD process seriously hampers the overall performance and productivity. The meshless LSFD-U solver offers complete automation in the CFD process regardless of the complexity in the configurations. This aspect has been demonstrated in this thesis by predicting the store trajectory using quasi-steady simulations. In order to understand these results better, the work has also been extended to include the viscous effects in the trajectory prediction (although within a finite volume framework) and the sensitivities of the 6-DOF model integration. An automated CFD process to determine the optimal flap location has also been included in the demonstrations. Mesh adaptivity is one of the important areas of focus in a CFD work-flow for obtaining high resolution CFD solutions. Adopting such methodology for the meshless LSFD-U solver is attempted in this thesis work. A residual-based grid adaptive strategy in which an estimate of the local truncation error is used to define length scales for adequately resolving the flow in a given region is developed in the context of the LSFD-U solver. An attempt has been made to evolve an automated termination of the grid adaptation, which establishes the efficacy of the proposed adaptive strategy. For the flows with discontinuities, a hybrid strategy is employed in which the smooth flow regions are adapted using the R-parameter and the limiter operational regions are adapted using the divergence of velocity based indicator. A critical milestone for the success of the meshless methods is their ability to simulate turbulent flows by the way of solving RANS equations using highly anisotropic point distribution. The LSFD-U RANS solver makes use of a wall resolved hybrid Cartesian grid for the viscous turbulent flow computations. The Spalart-Allmaras turbulence model implementation within the meshless framework is discussed in detail. A combination of high aspect ratio grids (in a finite volume parlance) exhibiting grid folding, which is common in domains with wall slope discontinuity, results in loss in accuracy and robustness of the meshless solver. In order to handle such issues, we have proposed a point adaptive strategy which detects such regions with grid folding and improves the grid quality by introducing points along the rays exhibiting grid folding. The 2D LSFD-U RANS solver is validated for complex high lift cases. The work also includes some attempts towards achieving a successful 3D LSFD-U RANS solver.

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Książki na temat "Store Trajectory Prediction"

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 of the imminent demise of the public company appear to be wide of the mark. That means the transformation of the public company the book has described should end up being part of a larger story yet to be written rather than being a public company epitaph.

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Części książek na temat "Store Trajectory Prediction"

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 feature expressions of past trajectories and future intentions; during prediction, the controller finds similar scenes that have occurred in history in the memory bank based on the past trajectories and contextual information for trajectory prediction, and ultimately obtains a multimodal trajectory prediction through K-means clustering, and while predicting, the model can keep updating the content of the memory bank to learn, thus iteratively optimizing the model prediction performance. Thus, the model prediction performance is iteratively optimized. Compared with baseline models such as Kalman filter and Memory Augmented Networks for Multiple Trajectory Prediction (MANTRA) model, the method in this paper has significant improvement in Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics.

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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 illustrates speech trajectory and voice shaping. Clear Audio is a biologically inspired framework for studying natural language. Like the story behind Jurassic Park, Clear Audio attempts to make predictions about data from existing data, inspired by biological processes. Its main goal is to give feedback for speech pathology purposes.

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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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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 American champion, Carl Van Vechten, led to his publication in Great Britain, then in the United States. Sitwell’s predictions that Purdy would receive great recognition in the future did not happen, and the Introduction explores various reasons why he remained a cult writer instead of becoming canonical, after a boom of critical praise and academic attention in the early-to-mid-1960s.

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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 the arrival of computers, databases, and spreadsheets. The emergence of advanced analytics techniques like data mining and predictive modelling paved the way for the eras of big data and cloud computing. The combination of Artificial intelligence (AI) and machine learning have created novel prospects, ushering in the present era of augmented analytics and automation. In addition, the chapter sheds light on developing trends and novel solutions for solving real-world challenges, as well as discussing the data processing stages. As the business landscape evolves, data analytics remains a vital tool for organisations, allowing them to negotiate difficulties, capture opportunities, and chart a road towards long-term development and success.

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Streszczenia konferencji na temat "Store Trajectory Prediction"

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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