Relevant bibliographies by topics / Prediction interpolation

Contents

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

Academic literature on the topic 'Prediction interpolation'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Prediction interpolation"

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Ma, Hongyang, Qile Zhao, Sandra Verhagen, Dimitrios Psychas, and Han Dun. "Kriging Interpolation in Modelling Tropospheric Wet Delay." Atmosphere 11, no. 10 (2020): 1125. http://dx.doi.org/10.3390/atmos11101125.

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This contribution implements the Kriging interpolation in predicting the tropospheric wet delays using global navigation satellite system networks. The predicted tropospheric delays can be used in strengthening the precise point positioning models and numerical weather prediction models. In order to evaluate the performances of the Kriging interpolation, a sparse network with 8 stations and a dense network with 19 stations from continuously operating reference stations (CORS) of the Netherlands are selected as the reference. In addition, other 15 CORS stations are selected as users, which are divided into three blocks: 5 stations located approximately in the center of the networks, 5 stations on the edge of the networks and 5 stations outside the networks. The zenith tropospheric wet delays are estimated at the network and user stations through the ionosphere-free positioning model; meanwhile, the predicted wet delays at the user stations are generated by the Kriging interpolation in the use of the tropospheric estimations at the network. The root mean square errors (RMSE) are calculated by comparing the predicted wet delays and estimated wet delays at the same user station. The results show that RMSEs of the stations inside the network are at a sub-centimeter level with an average value of 0.74 cm in the sparse network and 0.69 cm in the dense network. The stations on edge and outside the network can also achieve 1-cm level accuracy, which overcomes the limitation that accurate interpolations can only be attained inside the network. This contribution also presents an insignificant improvement of the prediction accuracy from the sparse network to the dense network over 1-year’s data processing and a seasonal effect on the tropospheric wet delay predictions.
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Wang, Yanghua. "Seismic trace interpolation in the f‐x‐y domain." GEOPHYSICS 67, no. 4 (2002): 1232–39. http://dx.doi.org/10.1190/1.1500385.

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Seismic trace interpolation is implemented as a 2‐D (x, y) spatial prediction, performed separately on each frequency (f) slice. This so‐called f‐x‐y domain trace interpolation method is based on the relation that the linear prediction (LP) operator estimated at a given frequency may be used to predict data at a higher frequency but a smaller trace spacing. The relationship originally given for thef‐x domain trace interpolation is successfully extended to the f‐x‐y domain. The extension is achieved by masking the data samples selectively from the input frequency slice to design the LP operators. Two interpolation algorithms using the full‐step and the fractional‐step predictions, respectively, are developed. Both methods use an all‐azimuth prediction in the x‐y domain, but the fractional‐step prediction method is computationally more efficient. While the interpolation method can be applied to a common‐offset cube of 3‐D seismic, it can also be applied to 2‐D seismic traces for prestack data processing. Synthetic and real data examples demonstrate the capability of the interpolation method.
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Ni, Hepeng, Chengrui Zhang, Chao Chen, Tianliang Hu, and Yanan Liu. "A parametric interpolation method based on prediction and iterative compensation." International Journal of Advanced Robotic Systems 16, no. 1 (2019): 172988141982818. http://dx.doi.org/10.1177/1729881419828188.

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Parametric interpolation for spline plays an increasingly important role in modern manufacturing. It is critical to develop a fast parametric interpolator with high accuracy. To improve the computational efficiency while guaranteeing low and controllable feedrate fluctuation, a novel parametric interpolation method based on prediction and iterative compensation is proposed in this article. First, the feedrate fluctuation and Taylor’s expansion are analyzed that there are two main reasons to reduce the calculation accuracy including the truncation errors caused by neglecting the high-order terms and discrepancy errors between the original curve and the actual tool path. Then, to reduce these errors, a novel parametric interpolation method is proposed with two main stages, namely, prediction and iterative compensation. In the first stage, a quintic polynomial prediction algorithm is designed based on the historical interpolation knowledge to estimate the target length used in the second-order Taylor’s expansion, which can improve the calculation accuracy and the convergence rate of iterative process. In the second stage, an iterative compensation algorithm based on the second-order Taylor’s expansion and feedrate fluctuation is designed to approach the target point. Therefore, the calculation accuracy is controllable and can satisfy the specified value through several iterations. When finishing the interpolation of current period, the historical knowledge is updated to prepare for the following interpolation. Finally, a series of simulations are conducted to evaluate the good performance in accuracy and efficiency of the proposed method.
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TANJUNG, MAULINA, SAUMI SYAHREZA, and MUHAMMAD RUSDI. "Comparison of interpolation methods based on Geographic Information System (GIS) in the spatial distribution of seawater intrusion." Jurnal Natural 20, no. 2 (2020): 24–30. http://dx.doi.org/10.24815/jn.v20i2.16440.

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The study of monitoring seawater intrusion and groundwater quality in a coastal area needs to be done regularly to prevent the clean water crisis problems in the future. Accurate and reliable interpolation of seawater intrusion over a region is the requirement of an efficient monitoring. In this study, different interpolation methods were investigated and compared to determine the best interpolation method for predicting the spatial distribution of seawater intrusion in the coastal area of Banda Aceh. Groundwater electrical conductivity (EC) was analyzed to identify the contamination of seawater intrusion into the coastal aquifers. Four interpolation methods such as Empirical Bayesian Kriging (EBK), Global Polynomial Interpolation (GPI), Inverse Distance Weighting (IDW), and Local Polynomial Interpolation (LPI), were used to create the spatial distribution of the groundwater electrical conductivity. The accuracy of interpolation methods was evaluated by using a cross-validation technique through the coefficient of determination (R2) and the Root Mean Square Error (RMSE). The results showed that IDW performed the most accurate prediction values and the best surface which were indicated by the least RMSE and the highest R2 value. It can be concluded that IDW interpolation method is the best method for interpolating the groundwater electrical conductivity associated with seawater intrusion in the coastal area of Banda Aceh.
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Corsten, L. C. A. "Interpolation and optimal linear prediction." Statistica Neerlandica 43, no. 2 (1989): 69–84. http://dx.doi.org/10.1111/j.1467-9574.1989.tb01249.x.

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Cheng, Wen Feng, Xiang Long Yang, and Li Ren Wang. "Grey Prediction and Interpolation in WSN Greenhouse System." Advanced Materials Research 518-523 (May 2012): 4915–20. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.4915.

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To prolong the power life of greenhouse monitoring system based WSN (GMSW), and avoid worsening the control performance when sampling time is elongated, the algorithm of Grey Prediction and Interpolation was proposed. This algorithm predicts the changing trend of the system’s variables with current values, and provides the precise prediction of the following variables. After processing of interpolation for predictive value, feedback values provided for the controller would be smooth and precise. Thus the lagging problem due to the long sampling time is solved, control performance is improved and the power life of wireless node is elongated.
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Wang, Yang, Tian Huang, and Clement M. Gosselin. "Interpolation Error Prediction of a Three-Degree Parallel Kinematic Machine." Journal of Mechanical Design 126, no. 5 (2004): 932–37. http://dx.doi.org/10.1115/1.1767184.

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In this paper, an NC interpolation algorithm for a tripod-based parallel kinematic machine is investigated. The algorithm can be implemented in two steps, the rough interpolation in the Cartesian space and the precise interpolation in the actuator space. The upper bound of the theoretical interpolation error due to the interpolation algorithm in the precise interpolation and nonlinear mapping is analyzed. The representation of the interpolation error distribution within the Cartesian space is depicted in terms of the variations of the interpolation period and the programming velocity. It was concluded that this error is sufficiently small and may be neglected.
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Xing, Jinbo, Wenbo Hu, Yuechen Zhang, and Tien-Tsin Wong. "Flow-aware synthesis: A generic motion model for video frame interpolation." Computational Visual Media 7, no. 3 (2021): 393–405. http://dx.doi.org/10.1007/s41095-021-0208-x.

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AbstractA popular and challenging task in video research, frame interpolation aims to increase the frame rate of video. Most existing methods employ a fixed motion model, e.g., linear, quadratic, or cubic, to estimate the intermediate warping field. However, such fixed motion models cannot well represent the complicated non-linear motions in the real world or rendered animations. Instead, we present an adaptive flow prediction module to better approximate the complex motions in video. Furthermore, interpolating just one intermediate frame between consecutive input frames may be insufficient for complicated non-linear motions. To enable multi-frame interpolation, we introduce the time as a control variable when interpolating frames between original ones in our generic adaptive flow prediction module. Qualitative and quantitative experimental results show that our method can produce high-quality results and outperforms the existing state-of-the-art methods on popular public datasets.
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Yu, Ze Yuan, Xiao Lin Chen, and Zhi Tao Qiu. "A New Prediction Method of Soil Pollution." Advanced Materials Research 908 (March 2014): 387–91. http://dx.doi.org/10.4028/www.scientific.net/amr.908.387.

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A new method for predicting soil pollution with lead pollution in a city as the research object is established. We use this particular method and the Kriging Interpolation method to simulate the spatial distribution of heavy metals in soil. Select the 20 sampling points as the cross-validation data set. Compare and analyze two kinds of interpolation methods. The results showed that: the new method is more suitable for urban areas contaminated with mutations since it has high prediction accuracy there. Analysis of soil with heavy metal contamination soil is premise of soil remediation and ecological restoration. Research results possess significant values for theories which choose different interpolation simulation methods according to different purposes.
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Highsmith, Max, and Jianlin Cheng. "Four-Dimensional Chromosome Structure Prediction." International Journal of Molecular Sciences 22, no. 18 (2021): 9785. http://dx.doi.org/10.3390/ijms22189785.

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Chromatin conformation plays an important role in a variety of genomic processes, including genome replication, gene expression, and gene methylation. Hi-C data is frequently used to analyze structural features of chromatin, such as AB compartments, topologically associated domains, and 3D structural models. Recently, the genomics community has displayed growing interest in chromatin dynamics. Here, we present 4DMax, a novel method, which uses time-series Hi-C data to predict dynamic chromosome conformation. Using both synthetic data and real time-series Hi-C data from processes, such as induced pluripotent stem cell reprogramming and cardiomyocyte differentiation, we construct smooth four-dimensional models of individual chromosomes. These predicted 4D models effectively interpolate chromatin position across time, permitting prediction of unknown Hi-C contact maps at intermittent time points. Furthermore, 4DMax correctly recovers higher order features of chromatin, such as AB compartments and topologically associated domains, even at time points where Hi-C data is not made available to the algorithm. Contact map predictions made using 4DMax outperform naïve numerical interpolation in 87.7% of predictions on the induced pluripotent stem cell dataset. A/B compartment profiles derived from 4DMax interpolation showed higher similarity to ground truth than at least one profile generated from a neighboring time point in 100% of induced pluripotent stem cell experiments. Use of 4DMax may alleviate the cost of expensive Hi-C experiments by interpolating intermediary time points while also providing valuable visualization of dynamic chromatin changes.
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Dissertations / Theses on the topic "Prediction interpolation"

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Björsell, Joachim. "Long Range Channel Predictions for Broadband Systems : Predictor antenna experiments and interpolation of Kalman predictions." Thesis, Uppsala universitet, Signaler och System, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-281058.

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The field of wireless communication is under massive development and the demands on the cellular system, especially, are constantly increasing as the utilizing devices are increasing in number and diversity. A key component of wireless communication is the knowledge of the channel, i.e, how the signal is affected when sent over the wireless medium. Channel prediction is one concept which can improve current techniques or enable new ones in order to increase the performance of the cellular system. Firstly, this report will investigate the concept of a predictor antenna on new, extensive measurements which represent many different environments and scenarios. A predictor antenna is a separate antenna that is placed in front of the main antenna on the roof of a vehicle. The predictor antenna could enable good channel prediction for high velocity vehicles. The measurements show to be too noisy to be used directly in the predictor antenna concept but show potential if the measurements can be noise-filtered without distorting the signal. The use of low-pass filter and Kalman filter to do this, did not give the desired results but the technique to do this should be further investigated. Secondly, a interpolation technique will be presented which utilizes predictions with different prediction horizon by estimating intermediate channel components using interpolation. This could save channel feedback resources as well as give a better robustness to bad channel predictions by letting fresh, local, channel predictions be used as quality reference of the interpolated channel estimates. For a linear interpolation between 8-step and 18-step Kalman predictions with Normalized Mean Square Error (NMSE) of -15.02 dB and -10.88 dB, the interpolated estimates had an average NMSE of -13.14 dB, while lowering the required feedback data by about 80 %. The use of a warning algorithm reduced the NMSE by a further 0.2 dB. It mainly eliminated the largest prediction error which otherwise could lead to retransmission, which is not desired.
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Islam, Tamanna. "Interpolation of linear prediction coefficients for speech coding." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0034/MQ64229.pdf.

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Curry, William. "Interpolation with prediction-error filters and training data /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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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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Degenhardt, Richard Kennedy III. "Self-collision avoidance through keyframe interpolation and optimization-based posture prediction." Thesis, University of Iowa, 2014. https://ir.uiowa.edu/etd/1446.

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Simulating realistic human behavior on a virtual avatar presents a difficult task. Because the simulated environment does not adhere to the same scientific principles that we do in the existent world, the avatar becomes capable of achieving infeasible postures. In an attempt to obtain realistic human simulation, real world constraints are imposed onto the non-sentient being. One such constraint, and the topic of this thesis, is self-collision avoidance. For the purposes of this topic, a posture will be defined solely as a collection of angles formed by each joint on the avatar. The goal of self-collision avoidance is to eliminate the formation of any posture where multiple body parts are attempting to occupy the exact same space. My work necessitates an extension of this definition to also include collision avoidance with objects attached to the body, such as a backpack or armor. In order to prevent these collisions from occurring, I have implemented an effort-based approach for correcting afflicted postures. This technique specifically pertains to postures that are sequenced together with the objective of animating the avatar. As such, the animation's coherence and defining characteristics must be preserved. My approach to this problem is unique in that it strategically blends the concept of keyframe interpolation with an optimization-based strategy for posture prediction. Although there has been considerable work done with methods for keyframe interpolation, there has been minimal progress towards integrating a realistic collision response strategy. Additionally, I will test this optimization-based approach through the use of a complex kinematic human model and investigate the use of the results as input to an existing dynamic motion prediction system.
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Davies, Paul Elliot. "Neural network prediction and interpolation of multi-channel seismic data." Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393158.

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Asfaw, Zeytu Gashaw. "Inference and Prediction in Non-stationary Stochastic Models: Survival Analysis and Kriging Interpolation." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-25982.

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Owaniyi, Kunle Meshach. "Geostatistical Interpolation and Analyses of Washington State AADT Data from 2009 – 2016." Thesis, North Dakota State University, 2019. https://hdl.handle.net/10365/31649.

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Annual Average Daily Traffic (AADT) data in the transportation industry today is an important tool used in various fields such as highway planning, pavement design, traffic safety, transport operations, and policy-making/analyses. Systematic literature review was used to identify the current methods of estimating AADT and ranked. Ordinary linear kriging occurred most. Also, factors that influence the accuracy of AADT estimation methods as identified include geographical location and road type amongst others. In addition, further analysis was carried out to determine the most apposite kriging algorithm for AADT data. Three linear (universal, ordinary, and simple), three nonlinear (disjunctive, probability, and indicator) and bayesian (empirical bayesian) kriging methods were compared. Spherical and exponential models were employed as the experimental variograms to aid the spatial interpolation and cross-validation. Statistical measures of correctness (mean prediction and root-mean-square errors) were used to compare the kriging algorithms. Empirical bayesian with exponential model yielded the best result.
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Oesting, Marco [Verfasser], Martin [Akademischer Betreuer] Schlather, and Robert [Akademischer Betreuer] Schaback. "Spatial Interpolation and Prediction of Gaussian and Max-Stable Processes / Marco Oesting. Gutachter: Martin Schlather ; Robert Schaback. Betreuer: Martin Schlather." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2012. http://d-nb.info/1042970890/34.

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Pospíšil, Jiří. "Pokročilé metody interpolace zvukových signálů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220598.

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This diploma thesis deals with the theoretical analysis of the predictive methods of signal interpolation and signal modeling using sinusoidal model. On the basis of this theory the algorithm for the reconstruction of the missing sections in the audio signal is implemented in computing environment MATLAB. Results of mass testing reconstructions are displayed using objective methods SNR and PEMO-Q. Further experiments are carried out on single signals and their evaluation is described.
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Books on the topic "Prediction interpolation"

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Franke, Richard H. The use of observed data for the initial value problem in numerical weather prediction. Naval Postgraduate School, 1987.

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Schmidt, Alexandra, Jennifer Hoeting, João Batista M. Pereira, and Pedro Paulo Vieira. Mapping malaria in the Amazon rain forest: A spatio-temporal mixture model. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.5.

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This article focuses on the use of a spatio-temporal mixture model for mapping malaria in the Amazon rain forest. The spatio-temporal model was developed to study malaria outbreaks over a four year period in the state of Amazonas, Brazil. The goal is to predict malaria counts for unobserved municipalities and future time periods with the aid of a free-form spatial covariance structure and a methodology that allows temporal prediction and spatial interpolation for outbreaks of malaria over time. The proposed structure is unique in that it is not a distance- or neighbourhood-based covariance model. Instead, spatial correlation is allowed among all locations to be estimated freely. To model the temporal correlation between observations, a Bayesian dynamic linear model is incorporated into one level of the spatio-temporal mixture model. The model also provides sensible ways of malaria mapping for municipalities which were not observed.
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Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.

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The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.
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Book chapters on the topic "Prediction interpolation"

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Gusak, Dmytro, Alexander Kukush, Alexey Kulik, Yuliya Mishura, and Andrey Pilipenko. "Prediction and interpolation." In Theory of Stochastic Processes. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-87862-1_9.

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Liu, Shu-Tang, Yu-Pin Wang, Zhi-Min Bi, and Yin Wang. "Interpolation Prediction of Mesoscale Eddies." In Mathematical Principle and Fractal Analysis of Mesoscale Eddy. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1839-0_13.

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Jochum, Peter. "One and Multidimensional Numerical Interpolation Methods." In Physical Property Prediction in Organic Chemistry. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-74140-1_13.

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Possolo, Antonio. "Model-Based Interpolation, Prediction, and Approximation." In IFIP Advances in Information and Communication Technology. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32677-6_13.

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Agusa, Yu, Keiichi Endo, Hisayasu Kuroda, and Shinya Kobayashi. "Examination of Water Temperature Interpolation Method for Prediction." In Progress in Image Processing, Pattern Recognition and Communication Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81523-3_33.

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Harba, Rachid, Hassan Douzi, and Mohamed El Hajji. "Maximum Likelihood Estimation, Interpolation and Prediction for Fractional Brownian Motion." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31254-0_37.

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Ding, Weilong, Zhe Wang, Yanqing Xia, and Kui Ma. "An Efficient Interpolation Method Through Trends Prediction in Smart Power Grid." In EAI/Springer Innovations in Communication and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50184-6_5.

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Baik, Sung, and Ran Baik. "Adaptive Texture Recognition in Image Sequences with Prediction through Features Interpolation." In Computational Science and Its Applications – ICCSA 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24767-8_44.

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Chung, Tae-Young, Il-Lyong Jung, Kwanwoong Song, and Chang-Su Kim. "Virtual View Interpolation and Prediction Structure for Full Parallax Multi-view Video." In Advances in Multimedia Information Processing - PCM 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10467-1_48.

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Prus, Maryna, and Rainer Schwabe. "Interpolation and Extrapolation in Random Coefficient Regression Models: Optimal Design for Prediction." In mODa 11 - Advances in Model-Oriented Design and Analysis. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31266-8_24.

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Conference papers on the topic "Prediction interpolation"

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Yao Chunlian, Jiang Dong, and Li Wei. "Intra prediction based on interpolation." In 2011 International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2011. http://dx.doi.org/10.1109/iccsnt.2011.6182188.

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Lakshman, Haricharan, Heiko Schwarz, Thierry Blu, and Thomas Wiegand. "Generalized interpolation for motion compensated prediction." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6115649.

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Mazzuchelli, P., F. Rocca, U. Spognolini, and S. Spitz. "Wavefield Interpolation - Continuation or Prediction Filter Techniques?" In 60th EAGE Conference and Exhibition. European Association of Geoscientists & Engineers, 1998. http://dx.doi.org/10.3997/2214-4609.201408278.

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Crawley, Sean, Robert Clapp, and Jon Claerbout. "Interpolation with smoothly nonstationary prediction‐error filters." In SEG Technical Program Expanded Abstracts 1999. Society of Exploration Geophysicists, 1999. http://dx.doi.org/10.1190/1.1820707.

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Ji, Jun. "Interpolation using prediction‐error filtering simulation (PEFS)." In SEG Technical Program Expanded Abstracts 1993. Society of Exploration Geophysicists, 1993. http://dx.doi.org/10.1190/1.1822325.

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Gao, Jun, and Peter Revesz. "Voting prediction using new spatiotemporal interpolation methods." In the 2006 national conference. ACM Press, 2006. http://dx.doi.org/10.1145/1146598.1146678.

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Calin, Simu. "Signal strength prediction using digital maps interpolation." In 2012 10th International Symposium on Electronics and Telecommunications (ISETC). IEEE, 2012. http://dx.doi.org/10.1109/isetc.2012.6408093.

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Jie Dong and King Ngi Ngan. "Parametric interpolation filter for motion compensated prediction." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5413822.

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Rui Bo, Fangxing Li, and Chaoming Wang. "Congestion prediction for ACOPF framework using quadratic interpolation." In Energy Society General Meeting. IEEE, 2008. http://dx.doi.org/10.1109/pes.2008.4596607.

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Dong, Jie, and King Ngi Ngan. "Adaptive pre-interpolation filter for motion-compensated prediction." In 2011 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2011. http://dx.doi.org/10.1109/iscas.2011.5938141.

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Reports on the topic "Prediction interpolation"

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Jiang, Wenping, and Jin Li. The effects of spatial reference systems on the predictive accuracy of spatial interpolation methods. Geoscience Australia, 2014. http://dx.doi.org/10.11636/record.2014.001.

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2

Allen, Luke, Joon Lim, Robert Haehnel, and Ian Detwiller. Rotor blade design framework for airfoil shape optimization with performance considerations. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41037.

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Abstract:
A framework for optimizing rotor blade airfoil shape is presented. The framework uses two digital workflows created within the Galaxy Simulation Builder (GSB) software package. The first is a workflow enabling the automated creation of a surrogate model for predicting airfoil performance coefficients. An accurate surrogate model for the rapid generation of airfoil coefficient tables has been developed using linear interpolation techniques that is based on C81Gen and ARC2D CFD codes. The second workflow defines the rotor blade optimization problem using GSB and the Dakota numerical optimization library. The presented example uses a quasi-Newton optimization algorithm to optimize the tip region of the UH-60A main rotor blade with respect to vehicle performance. This is accomplished by morphing the blade tip airfoil shape for optimum power, subject to a constraint on the maximum pitch link load.
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