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 branch for combining the first two branches to generate predicted frames. Stacking multiple CubicLSTM units along the spatial branch and output branch, and then evolving along the temporal branch can form a cubic recurrent neural network (CubicRNN). Experiment shows that CubicRNN produces more accurate video predictions than prior methods on both synthetic and real-world datasets.
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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 (June 7, 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 based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.
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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 (September 30, 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 approximations and simplifications been quantified. Therefore, the purpose of this study is to quantify flexibility relationships for cervical motion segments, examine the importance of nonlinear components of the flexibility matrix, and determine the extent to which multivariable relationships may alter motion prediction. To that end, using unembalmed human cervical spine motion segments, a full battery of flexibility tests were performed for a neutral orientation and also following an axial pretorque. Primary and coupled matrix components were described using linear and piecewise nonlinear incremental constants. A third matrix approach utilized multivariable incremental relationships. Measured motions were predicted using structural flexibility methods and evaluated using RMS error between predicted and measured responses. A full set of flexibility relationships describe primary and coupled motions for C3-C4 and C5-C6. A flexibility matrix using piecewise incremental responses offers improved predictions over one using linear methods (p<0.01). However, no significant improvement is obtained using nonlinear terms represented by a multivariable functional approach (p<0.2). Based on these findings, it is suggested that a multivariable approach for flexibility is more demanding experimentally and analytically while not offering improved motion prediction.
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Fernandes, J. M., and C. E. de Souza. "Ship Motion Prediction." IFAC Proceedings Volumes 26, no. 2 (July 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 (September 22, 2011): 5569–81. http://dx.doi.org/10.1118/1.3633907.

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Fridovich-Keil, David, Andrea Bajcsy, Jaime F. Fisac, Sylvia L. Herbert, Steven Wang, Anca D. Dragan, and Claire J. Tomlin. "Confidence-aware motion prediction for real-time collision avoidance1." International Journal of Robotics Research 39, no. 2-3 (June 24, 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 that the robot should use the model’s current predictive accuracy to inform the degree of confidence in its future predictions. This model confidence inference allows us to generate probabilistic motion predictions that exploit modeled structure when the structure successfully explains human motion, and degrade gracefully whenever the human moves unexpectedly. We accomplish this by maintaining a Bayesian belief over a single parameter that governs the variance of our human motion model. We couple this prediction algorithm with a recently proposed robust motion planner and controller to guide the construction of robot trajectories that are, to a good approximation, collision-free with a high, user-specified probability. We provide extensive analysis of the combined approach and its overall safety properties by establishing a connection to reachability analysis, and conclude with a hardware demonstration in which a small quadcopter operates safely in the same space as a human pedestrian.
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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 (February 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 site amplification scaling. Incompatibilities between the NGA-W1 GMPEs and Turkish data set in small-to-moderate magnitude, large distance, and site effects scaling are encountered. The NGA-W1 GMPEs are modified for the misfit between the actual ground motions and the model predictions using adjustments functions. Turkey-adjusted NGA-W1 models are compatible with the regional strong ground motion characteristics and preserve the well-constrained features of the global models.
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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 autoregressive (NAR) neural network, the GPLVM-NAR method is proposed to predict human motions. Experiments with volunteers wearing powered exoskeleton performing different types of motion are conducted. Results validate that the proposed method can forecast the future human motion with relative error of 2%∼5% and average calculation time of 120 s∼155 s, depending on the type of different motions.
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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 simplified theoretical framework for the prediction of feasibility, of segmentation of a two-dimensional linear equation system. A statistical definition of a separable motion (structure) is presented and a relatively straightforward criterion for predicting the separability of two different motions in this framework is derived. The applicability of the proposed criterion for prediction of the existence of multiple motions in practice is examined using both synthetic and real image sequences. The prescribed separability criterion is useful in designing computer vision applications as it is solely based on the amount of relative motion and the scale of measurement noise.
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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 (September 25, 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 delivery systems. In the first part of our study we review three prediction approaches of respiratory motion, i.e., model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the second part of our work we propose respiratory motion estimation with hybrid implementation of extended Kalman filter. The proposed method uses the recurrent neural network as the role of the predictor and the extended Kalman filter as the role of the corrector. In the third part of our work we further extend our research work to present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the fourth part of our work we retrospectively categorize breathing data into several classes and propose a new approach to detect irregular breathing patterns using neural networks. We have evaluated the proposed new algorithm by comparing the prediction overshoot and the tracking estimation value. The experimental results of 448 patients’ breathing patterns validated the proposed irregular breathing classifier.
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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 moving the ball with an industrial manipulator,slip and object loss was induced in order to compare these observations againstmodel predictions.It was found that the models prediction of slip was reasonable when compared tothe observations. However, the model could not be fully tested and validated be-cause the simple geometry of the test object did not excite any frictional behaviourfrom the soft characteristics.
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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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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 past applications elsewhere. A combined hazard procedure is used to estimate seismicity in northern Central America, where there appear to be four tectonic environments for modeling the seismogenic sources and in Iran, where the large earthquakes usually occur on known faults. A preferred seismic hazard model for northern Central America and the western Caribbean plate based on earthquake catalogs, geodetic measurements, and geological information is presented. I used the widely practiced method of relating seismicity data to geological data to assess the various seismic hazard parameters and test parameter sensitivities.

The sensitivity and overall uncertainty in peak ground acceleration (PGA) estimates are calculated for northwestern Iran by using a specific randomized blocks design. A Monte Carlo approach is utilized to evaluate the ground motion hazard and its uncertainties in northern Central America. A set of new seismic hazard maps, exhibiting probabilistic values of peak ground acceleration (PGA) with 50%, 10%, and 5% probabilities of exceedance (PE) in 50 years, is presented for the area of relevance. Disaggregation of seismic hazard is carried out for cities of San Salvador and Guatemala by using a spatial distribution of epicenters around these sites to select design ground motion for seismic risk decisions.

In conclusion, consideration of the effect of parameters such as seismic moment, fault rupture, rupture directivity and stress drop are strongly recommended in estimating the near field ground motions. The rupture process of the 2002 Changureh earthquake (Mw = 6.5), Iran, was analyzed by using the empirical Green’s function (EGF) method. This method simulates strong ground motions for future large earthquakes at particular sites where no empirical data are available.

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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 algorithm for finding hierarchies of optimal paths in a disordered energy landscape is implemented. The algorithms are used in two settings, one for detecting patterns of motion within a given area, and a method for estimating single vessel trajectories. The results found in the thesis, show that the methods have great potential for analyzing traffic patterns and predict future motion.
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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 predictors are evaluated againsta Zero-order hold (ZOH) model as a baseline. All models are evaluated ona complete test data set as well as specic, complex, test cases. The resultsshow that the proposed predictors generally outperform the ZOH model. Furthermore,the Root Mean Square Error (RMSE) is halved in many complexscenarios, suggesting more reliable predictions.
Detta examensarbete handlar om att tackla problemet med att prediktera bratrajektorier av egofordonet, det vill säga att ”gissa” vart egofordonet kommer attåka i nära framtiden. Genom att använda maskininlärning istället för att göra modeller på det traditionella sättet så har det blivit enklare att använda andra kännetäcken, såsom kontextuell information från omgivningen, i modellerna. Dessa kännetäcken hjälper modellerna att göra bra prediktioner eftersom de ger ledtrådar om vilken situation egofordonet är i. Maskininlärning kan också vara det bättre alternativet eftersom maskininlärningsmodeller kan potentiellt se mönster som de traditionella metoderna inte kan se.LSTM modeller som använder olika kännetäcken har skapats. Dessa har evaluerats för att undersöka vilka kännetäcken som, till synes, verkar vara de viktigaste för prediktionerna. Modellerna har ställts mot en nollte ordningens model som är satt som en baslinje. Alla modeller har evaluerats på ett helt test dataset. De har också blivit evaluerade på specfika, komplexa, testfall. Resultaten visar att de förslagna modellerna är generellt bättre än nollte ordningens modellen samt att maskininlärningmodellernas RMSE är halverad i många kom-plexa testfall, vilket antyder en förbättring.
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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. Berlin, Heidelberg: 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. Boston: Kluwer Academic Publishers, 2001.

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Wiegand, Thomas, and Bernd Girod. Multi-Frame Motion-Compensated Prediction for Video Transmission. Boston, MA: 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. Boston, MA: Springer US, 2001.

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

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Govea, Alejandro Dizan Vasquez. Incremental Learning for Motion Prediction of Pedestrians and Vehicles. Berlin, Heidelberg: 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. New York: Oxford University Press, 2011.

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

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

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Cronin, Meghan. Mooring motion correction of SYNOP central array current meter data. Narragansett, R.I: 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, 277–309. Cham: 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, 27–43. Berlin, Heidelberg: 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, 147–64. Cham: 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, 104–14. Berlin, Heidelberg: 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, 381–422. Berlin, Heidelberg: 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, 91–107. Berlin, Heidelberg: 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, 7–37. Berlin, Heidelberg: 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, 109–41. Dordrecht: 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, 5702–18. New York, NY: 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, 69–89. Cham: 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}. California: 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 poses in the streaming motion data as masked poses. In addition, MoPredNet improves its encoding capability of motion dynamics by adaptively learning spatio-temporal structure from the observed poses and masked poses. We also propose to adapt MoPredNet to novel motion dynamics based on accumulated motion experiences and limited novel motion dynamics data. Experimental results show that our method achieves better performance over state-of-the-art methods in motion prediction.
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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 with prediction uncertainty which is seldom quantified. This paper, therefore, presents a semi-analytical methodology for the estimation of ship motion prediction error statistics in ensemble domain as function of the forecasting time, assuming linear Gaussian irregular waves and stationary linear ship motions. This information can be used, for instance, to supplement deterministic forecasting with corresponding confidence intervals. The paper describes the theoretical background of the developed methodology and reports some numerical application examples.
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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. 400 Commonwealth Drive, Warrendale, PA, United States: 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. New York, NY, USA: 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. Fort Belvoir, VA: Defense Technical Information Center, September 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), June 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. Fort Belvoir, VA: Defense Technical Information Center, May 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. Fort Belvoir, VA: Defense Technical Information Center, July 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), March 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), October 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. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada264500.

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O'Dea, John F. Correlation of VERES Predictions for Multihull Ship Motions. Fort Belvoir, VA: Defense Technical Information Center, September 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), October 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. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada590261.

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