Relevant bibliographies by topics / Prediction Motion

Contents

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

Academic literature on the topic 'Prediction Motion'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Prediction Motion"

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Fan, Hehe, Linchao Zhu, and Yi Yang. "Cubic LSTMs for Video Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8263–70. http://dx.doi.org/10.1609/aaai.v33i01.33018263.

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Predicting future frames in videos has become a promising direction of research for both computer vision and robot learning communities. The core of this problem involves moving object capture and future motion prediction. While object capture specifies which objects are moving in videos, motion prediction describes their future dynamics. Motivated by this analysis, we propose a Cubic Long Short-Term Memory (CubicLSTM) unit for video prediction. CubicLSTM consists of three branches, i.e., a spatial branch for capturing moving objects, a temporal branch for processing motions, and an output bra
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Rudenko, Andrey, Luigi Palmieri, Michael Herman, Kris M. Kitani, Dariu M. Gavrila, and Kai O. Arras. "Human motion trajectory prediction: a survey." International Journal of Robotics Research 39, no. 8 (2020): 895–935. http://dx.doi.org/10.1177/0278364920917446.

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With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots, and advanced surveillance systems. This article provides a survey of human motion trajectory prediction. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods
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Winkelstein, Beth A., and Barry S. Myers. "Importance of Nonlinear and Multivariable Flexibility Coefficients in the Prediction of Human Cervical Spine Motion." Journal of Biomechanical Engineering 124, no. 5 (2002): 504–11. http://dx.doi.org/10.1115/1.1504098.

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The flexibility matrix currently forms the basis for multibody dynamics models of cervical spine motion. While studies have aimed to determine cervical motion segment behavior, their accuracy and utility have been limited by both experimental and analytical assumptions. Flexibility terms have been primarily represented as constants despite the spine’s nonlinear stiffening response. Also, nondiagonal terms, describing coupled motions, of the matrices are often omitted. Currently, no study validates the flexibility approach for predicting vertebral motions; nor have the effects of matrix approxi
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Fernandes, J. M., and C. E. de Souza. "Ship Motion Prediction." IFAC Proceedings Volumes 26, no. 2 (1993): 881–85. http://dx.doi.org/10.1016/s1474-6670(17)48598-3.

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Ernst, Floris, Alexander Schlaefer, and Achim Schweikard. "Predicting the outcome of respiratory motion prediction." Medical Physics 38, no. 10 (2011): 5569–81. http://dx.doi.org/10.1118/1.3633907.

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Fridovich-Keil, David, Andrea Bajcsy, Jaime F. Fisac, et al. "Confidence-aware motion prediction for real-time collision avoidance1." International Journal of Robotics Research 39, no. 2-3 (2019): 250–65. http://dx.doi.org/10.1177/0278364919859436.

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One of the most difficult challenges in robot motion planning is to account for the behavior of other moving agents, such as humans. Commonly, practitioners employ predictive models to reason about where other agents are going to move. Though there has been much recent work in building predictive models, no model is ever perfect: an agent can always move unexpectedly, in a way that is not predicted or not assigned sufficient probability. In such cases, the robot may plan trajectories that appear safe but, in fact, lead to collision. Rather than trust a model’s predictions blindly, we propose t
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Gülerce, Zeynep, Bahadır Kargoığlu, and Norman A. Abrahamson. "Turkey-Adjusted NGA-W1 Horizontal Ground Motion Prediction Models." Earthquake Spectra 32, no. 1 (2016): 75–100. http://dx.doi.org/10.1193/022714eqs034m.

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The objective of this paper is to evaluate the differences between the Next Generation Attenuation: West-1 (NGA-W1) ground motion prediction models (GMPEs) and the Turkish strong ground motion data set and to modify the required pieces of the NGA-W1 models for applicability in Turkey. A comparison data set is compiled by including strong motions from earthquakes that occurred in Turkey and earthquake metadata of ground motions consistent with the NGA-W1 database. Random-effects regression is employed and plots of the residuals are used to evaluate the differences in magnitude, distance, and si
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Jin, Xin, Jia Guo, Zhong Li, and Ruihao Wang. "Motion Prediction of Human Wearing Powered Exoskeleton." Mathematical Problems in Engineering 2020 (December 21, 2020): 1–8. http://dx.doi.org/10.1155/2020/8899880.

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With the development of powered exoskeleton in recent years, one important limitation is the capability of collaborating with human. Human-machine interaction requires the exoskeleton to accurately predict the human motion of the upcoming movement. Many recent works implement neural network algorithms such as recurrent neural networks (RNN) in motion prediction. However, they are still insufficient in efficiency and accuracy. In this paper, a Gaussian process latent variable model (GPLVM) is employed to transform the high-dimensional data into low-dimensional data. Combining with the nonlinear
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Hadian Jazi, Marjan, Alireza Bab-Hadiashar, and Reza Hoseinnezhad. "Analytical Analysis of Motion Separability." Scientific World Journal 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/878417.

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Motion segmentation is an important task in computer vision and several practical approaches have already been developed. A common approach to motion segmentation is to use the optical flow and formulate the segmentation problem using a linear approximation of the brightness constancy constraints. Although there are numerous solutions to solve this problem and their accuracies and reliabilities have been studied, the exact definition of the segmentation problem, its theoretical feasibility and the conditions for successful motion segmentation are yet to be derived. This paper presents a simpli
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Dürichen, R., T. Wissel, F. Ernst, A. Schlaefer, and A. Schweikard. "Multivariate respiratory motion prediction." Physics in Medicine and Biology 59, no. 20 (2014): 6043–60. http://dx.doi.org/10.1088/0031-9155/59/20/6043.

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Dissertations / Theses on the topic "Prediction Motion"

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Lee, Suk Jin. "PREDICTION OF RESPIRATORY MOTION." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/336.

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Radiation therapy is a cancer treatment method that employs high-energy radiation beams to destroy cancer cells by damaging the ability of these cells to reproduce. Thoracic and abdominal tumors may change their positions during respiration by as much as three centimeters during radiation treatment. The prediction of respiratory motion has become an important research area because respiratory motion severely affects precise radiation dose delivery. This study describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delive
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Chibisov, Dmitry. "Design of algorithms for motion planning and motion prediction." kostenfrei, 2009. https://mediatum2.ub.tum.de/node?id=958521.

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Ranvik, Arne. "Slip Prediction Based on Manipulator Motion." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-26705.

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Predicting slip during robotic manipulation is of interest for a variety of appli-cations. Especially applications where weak grasps are applied. In this thesis, amodel for predicting slip for a two fingered grasping scenario is considered. Otherthan model parameters, the only measurements or sensor information assumed isof the manipulator joints. Soft objects that deform substantially under appliedforces are especially interesting in terms of frictional behaviour. A soft ball wasused as a test object and parameters for friction and deformation was experimen-tally determined. By grasping and m
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Braun, Jennifer L. "The Prediction of Motion Sickness Through People's Perception of Postural Motion." Miami University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=miami1353943941.

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Wiest, Jürgen [Verfasser]. "Statistical long-term motion prediction / Jürgen Wiest." Ulm : Universität Ulm, 2017. http://d-nb.info/1128728931/34.

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Chung, Hing-yip Ronald, and 鍾興業. "Fast motion estimation with search center prediction." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31220721.

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Tavakoli, Behrooz. "Prediction of Strong Ground Motion and Hazard Uncertainties." Doctoral thesis, Uppsala University, Department of Earth Sciences, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3535.

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<p>The purpose of this thesis is to provide a detailed description of recent methods and scientific basis for characterizing earthquake sources within a certain region with distinct tectonic environments. The focus will be on those characteristics that are most significant to the ground-shaking hazard and on how we can incorporate our current knowledge into hazard analyses for engineering design purposes. I treat two particular geographical areas where I think current hazard analysis methods are in need of significant improvement, and suggest some approaches that have proven to be effective in
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Fromreide, Mads. "Motion Prediction by Optimal Paths Through Disordered Landscapes." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for fysikk, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-24781.

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The ability to navigate safely and efficiently through a given landscape is relevant for any intelligent moving object. Examples range from robotic science and traffic analysis to the behavior within an ecosystem. Through this thesis, methods for finding traffic patterns and predicting future motion, have been constructed based on theory of optimal paths. The algorithms are applied to maritime traffic, in terms of recorded vessel coordinates. \newline By considering the structure of a given traffic situation as a disordered energy landscape, one can define optimal routes within the area. An al
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Backman, Anton. "Motion prediction of ego vehicle in complex scenarios." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278497.

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In this thesis, we study the trajectory prediction problem of an ego vehicle,i.e. "predicting" the location of the ego vehicle in the short future. Instead oftraditional methods, we use Machine Learning (ML) techniques since they easilyincorporate features, such as contextual information from the environment, theprediction process The contextual features signicantly improve the predictionquality since they provide important information about the driving environmentand scenarios.The Long Short-Term Memory (LSTM) model is used to develop variouspredictors which utilize dierent features. The pred
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Vatis, Yuri. "Non-symmetric adaptive interpolation filter for motion compensated prediction /." Düsseldorf : VDI-Verl, 2009. http://d-nb.info/998470724/04.

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Books on the topic "Prediction Motion"

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Lee, Suk Jin, and Yuichi Motai. Prediction and Classification of Respiratory Motion. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41509-8.

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Bernd, Girod, ed. Multi-frame motion-compensated prediction for video transmission. Kluwer Academic Publishers, 2001.

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Wiegand, Thomas, and Bernd Girod. Multi-Frame Motion-Compensated Prediction for Video Transmission. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1487-9.

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Wiegand, Thomas. Multi-Frame Motion-Compensated Prediction for Video Transmission. Springer US, 2001.

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Gibson, David Michael. Trajectory-based multi-frame motion estimation with applications to motion compensated prediction. University of Birmingham, 2001.

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Govea, Alejandro Dizan Vasquez. Incremental Learning for Motion Prediction of Pedestrians and Vehicles. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13642-9.

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Chaos: The science of predictable random motion. Oxford University Press, 2011.

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Kautz, Richard. Chaos: The science of predictable random motion. Oxford University Press, 2011.

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Chaos: The science of predictable random motion. Oxford University Press, 2011.

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Cronin, Meghan. Mooring motion correction of SYNOP central array current meter data. University of Rhode Island, Graduate School of Oceanography, 1992.

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Book chapters on the topic "Prediction Motion"

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Schweikard, Achim, and Floris Ernst. "Motion Prediction." In Medical Robotics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22891-4_8.

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Govea, Alejandro Dizan Vasquez. "Intentional Motion Prediction." In Springer Tracts in Advanced Robotics. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13642-9_3.

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Dobie, Thomas G. "Prediction of Susceptibility to Motion Sickness." In Motion Sickness. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-97493-4_8.

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Lau, Rynson W. H., and Addison Chan. "Motion Prediction for Online Gaming." In Motion in Games. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89220-5_11.

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Weng, Juyang, Thomas S. Huang, and Narendra Ahuja. "Motion Modeling and Prediction." In Motion and Structure from Image Sequences. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77643-4_8.

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Lee, Suk Jin, and Yuichi Motai. "Customized Prediction of Respiratory Motion." In Prediction and Classification of Respiratory Motion. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41509-8_5.

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Lee, Suk Jin, and Yuichi Motai. "Review: Prediction of Respiratory Motion." In Prediction and Classification of Respiratory Motion. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41509-8_2.

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Boore, David M. "The Prediction of Strong Ground Motion." In Strong Ground Motion Seismology. Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-017-3095-2_5.

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Sznaier, Mario, and Octavia Camps. "Motion Prediction for Continued Autonomy." In Encyclopedia of Complexity and Systems Science. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_340.

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Artuñedo, Antonio. "Motion Prediction and Manoeuvre Planning." In Decision-making Strategies for Automated Driving in Urban Environments. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45905-5_5.

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Conference papers on the topic "Prediction Motion"

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Li, Zhen, and Edward J. Delp III. "Universal motion prediction." In Electronic Imaging 2004, edited by Sethuraman Panchanathan and Bhaskaran Vasudev. SPIE, 2004. http://dx.doi.org/10.1117/12.526156.

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Zang, Chuanqi, Mingtao Pei, and Yu Kong. "Few-shot Human Motion Prediction via Learning Novel Motion Dynamics." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/118.

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Human motion prediction is a task where we anticipate future motion based on past observation. Previous approaches rely on the access to large datasets of skeleton data, and thus are difficult to be generalized to novel motion dynamics with limited training data. In our work, we propose a novel approach named Motion Prediction Network (MoPredNet) for few-short human motion prediction. MoPredNet can be adapted to predicting new motion dynamics using limited data, and it elegantly captures long-term dependency in motion dynamics. Specifically, MoPredNet dynamically selects the most informative p
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Fucile, Fabio, Gabriele Bulian, and Claudio Lugni. "Prediction Error Statistics in Deterministic Linear Ship Motion Forecasting." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77456.

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Deterministic ship motions predictions methodologies represent a promising emerging approach, which could be embedded in decision support systems for certain types of operation. The typically envisioned prediction chain starts from the remote sensing of the wave elevation through wave radar technology. An estimated wave field is then fitted to the data, it is propagated in space and time, and it is finally fed to a ship motion prediction model. Prediction time horizons, typically, are practically limited to the order of minutes. Deterministic predictions are, however, inevitably associated wit
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Eslami, Abouzar, and Massoud Babaeizadeh. "Adaptive Block Motion Prediction." In 2006 IEEE International Symposium on Signal Processing and Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/isspit.2006.270927.

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Faraway, Julian J. "Data-Based Motion Prediction." In Digital Human Modeling for Design and Engineering Conference and Exhibition. SAE International, 2003. http://dx.doi.org/10.4271/2003-01-2229.

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Shoujun Zhou, Qubo Zheng, Hongliang Li, Yueqian Zhou, and Yuan Hong. "Probabilistic respiratory motion prediction." In 2010 International Conference of Medical Image Analysis and Clinical Application (MIACA). IEEE, 2010. http://dx.doi.org/10.1109/miaca.2010.5528497.

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Xie, Kan, Luc Van Eycken, and Andre J. Oosterlinck. "Motion-compensated interframe prediction." In San Diego '90, 8-13 July, edited by Andrew G. Tescher. SPIE, 1990. http://dx.doi.org/10.1117/12.23527.

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Lasota, Przemyslaw A., and Julie A. Shah. "A multiple-predictor approach to human motion prediction." In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017. http://dx.doi.org/10.1109/icra.2017.7989265.

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Oliva, Carmine, and Hannes Högni Vilhjálmsson. "Prediction in social path following." In MIG '14: Motion in Games. ACM, 2014. http://dx.doi.org/10.1145/2668064.2668103.

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Wang, Ziyou, Shengpeng Liu, and Yan Xu. "Human motion prediction based on hybrid motion model." In 2017 IEEE International Conference on Information and Automation (ICIA). IEEE, 2017. http://dx.doi.org/10.1109/icinfa.2017.8079038.

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Reports on the topic "Prediction Motion"

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Silver, A. L., M. J. Hughes, R. E. Conrad, S. S. Lee, J. T. Klamo, and J. T. Park. Evaluation of Multi-Vessel Ship Motion Prediction Codes. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada493241.

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Larmat, Carene, Ting Chen, and Zhou Lei. DAG-4 ground motion prediction LANL – Part 2. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1526940.

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Cicci, David A., John E. Cochran, and Jr. Identification and Motion Prediction of Tethered Satellite Systems. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada387974.

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Weinacht, Paul. Prediction of Projectile Performance, Stability, and Free-Flight Motion Using Computational Fluid Dynamics. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada417123.

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Yang, Xiaoning, Howard John Patton, and Ting Chen. SPE-5 Ground-Motion Prediction at Far-Field Geophone and Accelerometer Array Sites and SPE-5 Moment and Corner-Frequency Prediction. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1244326.

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Walck, M. C. Summary of ground motion prediction results for Nevada Test Site underground nuclear explosions related to the Yucca Mountain project. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/399667.

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Elsberry, Russell L. Advances in Dynamical Predictions and Modelling of Tropical Cyclone Motion. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada264500.

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O'Dea, John F. Correlation of VERES Predictions for Multihull Ship Motions. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada440212.

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Ruiz, Javier Matias. Predictive Sampling-Based Robot Motion Planning in Unmodeled Dynamic Environments. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1573326.

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Kitazawa, Yukihito. Effective Detection of Low-luminosity GEO Objects Using Population and Motion Predictions. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada590261.

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