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Journal articles on the topic 'Prediction interpolation'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

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

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2

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 operator

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3

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 term

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4

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 seawat

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5

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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6

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 a

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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 c

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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 fo

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9

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 ecolo

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10

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 induc

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11

Ishikawa, Akio. "Inter-View Prediction and View Interpolation." Journal of the Institute of Image Information and Television Engineers 67, no. 4 (2013): 308–11. http://dx.doi.org/10.3169/itej.67.308.

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12

Yang, Zong-Chang. "Electric Load Movement Forecasting Based on the DFT Interpolation with Periodic Extension." Journal of Circuits, Systems and Computers 24, no. 08 (2015): 1550123. http://dx.doi.org/10.1142/s0218126615501236.

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Electric load forecasting is increasingly important for the industry. This study addresses the load forecasting based on the discrete Fourier transform (DFT) interpolation. As the most common analysis method in the frequency domain, the conventional Fourier analysis cannot be directly applied to prediction. From the perspective of time-series analysis, electric load movement influenced by various factors is also a time-series, which is usually subject to cyclical variations. Then with periodic extension for the load movement, a forecasting approach based on the DFT interpolation is proposed fo

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13

Sekulić, Aleksandar, Milan Kilibarda, Gerard B. M. Heuvelink, Mladen Nikolić, and Branislav Bajat. "Random Forest Spatial Interpolation." Remote Sensing 12, no. 10 (2020): 1687. http://dx.doi.org/10.3390/rs12101687.

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For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external drift and regression kriging have become basic techniques that benefit both from spatial autocorrelation and covariate information. More recently, machine learning techniques, such as random forest and gradient boosting, have become increasingly popular and are now often used for spatial interpolation. Some attempts have been made to explicitly take the spatial component i

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14

Riccio, Angelo. "A Bayesian Approach for the Spatiotemporal Interpolation of Environmental Data." Monthly Weather Review 133, no. 2 (2005): 430–40. http://dx.doi.org/10.1175/mwr-2862.1.

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Abstract Numerically based models are extensively used for many environmental applications, as for example, to assist in the prediction of weather phenomena (numerical weather prediction models), in risk assessment, or in pollutant emission control (air-quality models). These models often produce predictions for grid points over some temporal window, while observations are usually available as a set of spatially scattered values at individual locations (not coincident with the model grid points), so that the model assessment procedure, that is, the statistical evaluation of how well model outp

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15

Rew, Jehyeok, Sungwoo Park, Yongjang Cho, Seungwon Jung, and Eenjun Hwang. "Animal Movement Prediction Based on Predictive Recurrent Neural Network." Sensors 19, no. 20 (2019): 4411. http://dx.doi.org/10.3390/s19204411.

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Observing animal movements enables us to understand animal behavior changes, such as migration, interaction, foraging, and nesting. Based on spatiotemporal changes in weather and season, animals instinctively change their position for foraging, nesting, or breeding. It is known that moving patterns are closely related to their traits. Analyzing and predicting animals’ movement patterns according to spatiotemporal change offers an opportunity to understand their unique traits and acquire ecological insights into animals. Hence, in this paper, we propose an animal movement prediction scheme usin

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16

Naghizadeh, Mostafa, and Mauricio D. Sacchi. "f-x adaptive seismic-trace interpolation." GEOPHYSICS 74, no. 1 (2009): V9—V16. http://dx.doi.org/10.1190/1.3008547.

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We use exponentially weighted recursive least squares to estimate adaptive prediction filters for frequency-space [Formula: see text] seismic interpolation. Adaptive prediction filters can model signals where the dominant wavenumbers vary in space. This concept leads to an [Formula: see text] interpolation method that does not require windowing strategies for optimal results. In other words, adaptive prediction filters can be used to interpolate waveforms that have spatially variant dips. The interpolation method’s performance depends on two parameters: filter length and forgetting factor. We

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17

Picinbono, B., and J. M. Kerilis. "Some properties of prediction and interpolation errors." IEEE Transactions on Acoustics, Speech, and Signal Processing 36, no. 4 (1988): 525–31. http://dx.doi.org/10.1109/29.1556.

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18

Liu, Tieyan, Kwok-Tung Lo, Jian Feng, and Xudong Zhang. "Frame interpolation scheme using inertia motion prediction." Signal Processing: Image Communication 18, no. 3 (2003): 221–29. http://dx.doi.org/10.1016/s0923-5965(02)00141-8.

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19

Wright, William E., and Sakthivel Jeyaratnam. "Success rate of interpolation in subsegment prediction." Software: Practice and Experience 23, no. 3 (1993): 243–53. http://dx.doi.org/10.1002/spe.4380230303.

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20

Deng, X. P., W. Coles, G. Hobbs, et al. "Optimal interpolation and prediction in pulsar timing." Monthly Notices of the Royal Astronomical Society 424, no. 1 (2012): 244–51. http://dx.doi.org/10.1111/j.1365-2966.2012.21189.x.

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21

Borzone, G., C. Lanigan, and J. Moxham. "Prediction of Quadriceps Strength Using Twitch Interpolation." Clinical Science 70, s13 (1986): 93P. http://dx.doi.org/10.1042/cs070093pa.

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22

Qin, Chengjin, Jianfeng Tao, and Chengliang Liu. "A novel stability prediction method for milling operations using the holistic-interpolation scheme." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 13 (2019): 4463–75. http://dx.doi.org/10.1177/0954406218815716.

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Currently, accurate and efficient determination of chatter-free cutting conditions is becoming increasingly important. This paper proposes a semi-analytical stability prediction method for milling processes using the holistic-interpolation scheme. The dynamics considering regeneration effect for milling operations is formulated as delay differential equations with time-periodic coefficients. The period of milling dynamic system is divided into two time periods according to the value of the coefficient matrix. On each small time interval for the forced vibration time period, the holistic-interp

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23

Chen, Yangkang, Sergey Fomel, Hang Wang, and Shaohuan Zu. "5D dealiased seismic data interpolation using nonstationary prediction-error filter." GEOPHYSICS 86, no. 5 (2021): V419—V429. http://dx.doi.org/10.1190/geo2020-0540.1.

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The prediction-error filter (PEF) assumes that seismic data can be destructed to zero by applying a convolutional operation between the target data and the prediction filter in either the time-space or frequency-space domain. We have extended the commonly known PEF in 2D or 3D problems to its 5D version. To handle the nonstationary property of the seismic data, we formulate the PEF in a nonstationary way, which is called the nonstationary prediction-error filter (NPEF). In NPEF, the coefficients of a fixed-size PEF vary across the whole seismic data. In NPEF, we aim at solving a highly ill-pos

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24

Shuang, Qing, and Rui Ting Zhao. "Water Demand Prediction Using Machine Learning Methods: A Case Study of the Beijing–Tianjin–Hebei Region in China." Water 13, no. 3 (2021): 310. http://dx.doi.org/10.3390/w13030310.

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Predicting water demand helps decision-makers allocate regional water resources efficiently, thereby preventing water waste and shortage. The aim of this study is to predict water demand in the Beijing–Tianjin–Hebei region of North China. The explanatory variables associated with economy, community, water use, and resource availability were identified. Eleven statistical and machine learning models were built, which used data covering the 2004–2019 period. Interpolation and extrapolation scenarios were conducted to find the most suitable predictive model. The results suggest that the gradient

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25

Lu, Guanchen, Xiaoliang Shi, Ao Zhang, Yuchun Huang, and Xiyao Liu. "Prediction and assessment of working conditions of TiAl matrix composite containing MoO3 tabular crystals based on Newton interpolation." Industrial Lubrication and Tribology 70, no. 7 (2018): 1217–23. http://dx.doi.org/10.1108/ilt-10-2017-0316.

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Purpose This paper aims to predict and evaluate the wear rate of TiAl-2 Wt.% MoO3 tabular crystals (TMCs) using the Newton interpolation methods. Design/methodology/approach The friction and wear behaviors of TMC were examined using pin-on-disc apparatus at different times, namely, 1,200, 2,400, 3,600, 4,800 and 6,000 s. The wear rates of five different times as interpolation nodes were measured and calculated by electron probe microanalysis (EMPA) and field emission electron microscope (FESEM). Then, the prediction formula of wear rate was constructed using the Newton interpolation method. Th

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26

Ward, Rob, Burak Sencer, Bryn Jones, and Erdem Ozturk. "Accurate prediction of machining feedrate and cycle times considering interpolator dynamics." International Journal of Advanced Manufacturing Technology 116, no. 1-2 (2021): 417–38. http://dx.doi.org/10.1007/s00170-021-07211-2.

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AbstractThis paper presents an accurate machining feedrate prediction technique by modelling the trajectory generation behaviour of modern CNC machine tools. Typically, CAM systems simulate machines’ motion based on the commanded feedrate and the path geometry. Such approach does not consider the feed planning and interpolation strategy of the machine’s numerical control (NC) system. In this study, trajectory generation behaviour of the NC system is modelled and accurate cycle time prediction for complex machining toolpaths is realised. NC system’s linear interpolation dynamics and commanded a

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27

Porsani, Milton J. "Seismic trace interpolation using half‐step prediction filters." GEOPHYSICS 64, no. 5 (1999): 1461–67. http://dx.doi.org/10.1190/1.1444650.

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A method to perform seismic trace interpolation known as the Spitz method handles spatially aliased events. The Spitz method uses the unit‐step prediction filter to estimate data spaced at Δx/2. The missing data are obtained by solving a complex linear system of equations whose unknowns are the coefficients at the interpolated location. We attack this problem by introducing a half‐step prediction filter that makes trace interpolation significantly more efficient and easier for implementation. A complex half‐step prediction filter at frequency f/2 is computed in the least‐squares sense to predi

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28

Hu, Rongxiang, Hsin-Ning Su, and Philip Nash. "Enthalpies of formation and lattice parameters of B2 phases in Al-Ni-X systems." Pure and Applied Chemistry 79, no. 10 (2007): 1653–73. http://dx.doi.org/10.1351/pac200779101653.

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Enthalpies of formation of Al-Ni-X (X: Fe, Ru, Pd, Pt, and Cu) alloys were measured by high-temperature calorimeter and compared with the calculated value from Miedema's model and interpolation models. The interpolation models generally provide better prediction than Miedama's model. No one interpolation model generated superior predictions. Lattice parameters of B2 phase compounds were determined by X-ray diffraction (XRD). The atomic volumes in the Al-Ni-Fe system were calculated and show that Fe substitutes preferentially on the Al sublattice. The heat content of Al0.5Ni0.2Ru0.3 and Al0.5Ni

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29

Wang, Fengtao, Xiaofei Liu, Chenxi Liu, Hongkun Li, and Qingkai Han. "Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) Model." Shock and Vibration 2018 (July 8, 2018): 1–10. http://dx.doi.org/10.1155/2018/3013684.

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A trend prediction method based on the Pchip-EEMD-GM(1,1) to predict the remaining useful life (RUL) of rolling bearings was proposed in this paper. Firstly, the dimension of the extracted features was reduced by the KPCA dimensionality reduction method, and the WPHM model parameters were estimated via the kernel principal components. Secondly, the hazard rate was calculated at each time, and the Pchip interpolation method was used to obtain the uniformly spaced interpolation data series. Then the main trend of signal was obtained through the EEMD method to fit the GM(1,1) prediction model. Fi

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30

Hussain, I., J. Pilz, and G. Spoeck. "Hierarchical Bayesian space-time interpolation versus spatio-temporal BME approach." Advances in Geosciences 25 (March 30, 2010): 97–102. http://dx.doi.org/10.5194/adgeo-25-97-2010.

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Abstract. The restrictions of the analysis of natural processes which are observed at any point in space or time to a purely spatial or purely temporal domain may cause loss of information and larger prediction errors. Moreover, the arbitrary combinations of purely spatial and purely temporal models may not yield valid models for the space-time domain. For such processes the variation can be characterized by sophisticated spatio-temporal modeling. In the present study the composite spatio-temporal Bayesian maximum entropy (BME) method and transformed hierarchical Bayesian space-time interpolat

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31

Müller, M. F., and S. E. Thompson. "Comparing statistical and process-based flow duration curve models in ungauged basins and changing rain regimes." Hydrology and Earth System Sciences 20, no. 2 (2016): 669–83. http://dx.doi.org/10.5194/hess-20-669-2016.

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Abstract. The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an adva

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32

Nurullaeli, Nurullaeli. "Alat Bantu Prediksi Kecepatan pada Gerak Lurus Berubah Beraturan Menggunakan Metode Interpolasi Berdimensi Satu." Navigation Physics : Journal of Physics Education 2, no. 2 (2020): 47–54. http://dx.doi.org/10.30998/npjpe.v2i2.473.

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The purpose of this study is to create a speed prediction tool in a uniformly accelerated linear motion using a single-dimensional interpolation method consisting of linear, cubic, cubic spline, and nearest neighbor interpolation methods. This tool is packaged in the form of Graphic User Interface (GUI) using Matlab programming language. This tool can be used as a medium of the analysis of the object speed experiencing a uniformly accelerated linear motion and a combination of several uniformly accelerated linear motions. Speed prediction results using linear, cubic, and cubic spline interpola

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33

Flores, Ester B., Brian P. Kinghorn, and Julius van der Werf. "Predicting lactation yields in dairy buffaloes by interpolation and multiple trait prediction." Livestock Science 151, no. 2-3 (2013): 97–107. http://dx.doi.org/10.1016/j.livsci.2012.10.017.

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34

Ali, Arshad, Lyudmila Mihaylova, Bamidele Adebisi, and Augustine Ikpehai. "Location prediction optimisation in WSNs using Kriging interpolation." IET Wireless Sensor Systems 6, no. 3 (2016): 74–81. http://dx.doi.org/10.1049/iet-wss.2015.0079.

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35

Rabinowicz, Assaf, and Saharon Rosset. "Assessing prediction error at interpolation and extrapolation points." Electronic Journal of Statistics 14, no. 1 (2020): 272–301. http://dx.doi.org/10.1214/19-ejs1666.

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Li, Fangyi, Ying Li, Changjing Shang, and Qiang Shen. "Fuzzy Knowledge-Based Prediction Through Weighted Rule Interpolation." IEEE Transactions on Cybernetics 50, no. 10 (2020): 4508–17. http://dx.doi.org/10.1109/tcyb.2018.2887340.

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37

Tsaknakis, Haralampos, Dimitri Kazakos, and P. Papantoni-Kazakos. "Robust prediction and interpolation for vector stationary processes." Probability Theory and Related Fields 72, no. 4 (1986): 589–602. http://dx.doi.org/10.1007/bf00344722.

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38

Niewȩgłowski, Jacek, and Petri Haavisto. "Temporal image sequence prediction using motion field interpolation." Signal Processing: Image Communication 7, no. 4-6 (1995): 333–53. http://dx.doi.org/10.1016/0923-5965(95)00007-5.

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39

Li, Lun, and Sam Anand. "Fast additive manufacturing distortion prediction using backward interpolation." Additive Manufacturing 41 (May 2021): 101955. http://dx.doi.org/10.1016/j.addma.2021.101955.

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40

WANG, HONG-YONG, HONG LI, and JIN-YE SHEN. "A NOVEL HYBRID FRACTAL INTERPOLATION-SVM MODEL FOR FORECASTING STOCK PRICE INDEXES." Fractals 27, no. 04 (2019): 1950055. http://dx.doi.org/10.1142/s0218348x19500555.

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Forecasting stock price indexes has been regarded as a challenging task in financial time series analysis. In order to improve the prediction accuracy, a novel hybrid model that integrates fractal interpolation with support vector machine (SVM) models has been developed in this paper to forecast the time series of stock price indexes. For this, a new method to calculate the vertical scaling factors of the fractal interpolation iterated function system is first proposed and an improved fractal interpolation model is then established. The improved fractal interpolation model and the SVM model ar

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Klen, K. S., M. K. Yaremenko, and V. Ya Zhuykov. "THE INFLUENCE OF THE WIND SPEED PREDICTION ERROR ON THE SIZE OF THE STORAGE CONTROLLED OPERATION ZONE IN THE SYSTEM WITH THE WIND GENERATOR." Praci elektrodinamiki Nacionalanoi akademii nauk Ukraini Institutu 2020, no. 57 (2020): 35–41. http://dx.doi.org/10.15407/publishing2020.57.035.

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The article analyzes the influence of wind speed prediction error on the size of the controlled operation zone of the storage. The equation for calculating the power at the output of the wind generator according to the known values of wind speed is given. It is shown that when the wind speed prediction error reaches a value of 20%, the controlled operation zone of the storage disappears. The necessity of comparing prediction methods with different data discreteness to ensure the minimum possible prediction error and determining the influence of data discreteness on the error is substantiated.

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OSCAR, T. P. "Development and Validation of Primary, Secondary, and Tertiary Models for Growth of Salmonella Typhimurium on Sterile Chicken†." Journal of Food Protection 68, no. 12 (2005): 2606–13. http://dx.doi.org/10.4315/0362-028x-68.12.2606.

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Models are used in the food industry to predict pathogen growth and to help assess food safety. However, criteria are needed to determine whether models provide acceptable predictions. In the current study, primary, secondary, and tertiary models for growth of Salmonella Typhimurium (104.8 CFU/g) on sterile chicken were developed and validated. Kinetic data obtained at 10 to 40°C were fit to a primary model to determine initial density (N0), lag time (λ), maximum specific growth rate (μmax), and maximum population density (Nmax). Secondary models for N0, λ, μmax, and Nmax as a function of temp

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Müller, M. F., and S. E. Thompson. "Stochastic or statistic? Comparing flow duration curve models in ungauged basins and changing climates." Hydrology and Earth System Sciences Discussions 12, no. 9 (2015): 9765–811. http://dx.doi.org/10.5194/hessd-12-9765-2015.

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Abstract. The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an adva

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44

Liu, Xinrong, and Zhonghou Wang. "Analysis of Contact Part of Error Tooth Surface and Dynamic Performance Prediction for Involute Gear." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/6143054.

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Aiming at the problem of constructing digital model of involute gear with error, the method of linear interpolation combined with area weight interpolation is proposed. Based on the non-feature discrete data block technique, the true tooth surface discrete data obtained by the coordinate measuring instrument is divided into blocks, and then the interpolation method is used to interpolate the nonmeasurement area to construct the real tooth surface with errors. The contact part and dynamic performance of the gear are predicted by using the constructed error tooth surface. The contact error of th

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Shen, Feifan, Jiaqi Zheng, Lingjian Ye, and Nael El-Farra. "Online local modeling and prediction of batch process trajectories using just-in-time learning and LSTM neural network." Journal of Computational Methods in Sciences and Engineering 20, no. 3 (2020): 715–26. http://dx.doi.org/10.3233/jcm-194086.

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This paper deals with the online sample trajectory prediction problem of batch processes considering complex data characteristics and batch-to-batch variations. Although some methods have been proposed to implement the trajectory interpolation problem for quality prediction and monitoring applications, the accuracy and reliability are not ensured due to data nonlinearity, dynamics and other complicated feature. To improve the data interpolation performance, an improved JITL-LSTM approach is designed in this work. Firstly, an improved trajectory-based JITL strategy is developed to extract simil

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Kang, Ziqiu, Cagatay Catal, and Bedir Tekinerdogan. "Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks." Sensors 21, no. 3 (2021): 932. http://dx.doi.org/10.3390/s21030932.

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Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipme

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Ijaware, Victor Ayodele, and Adebayo T. Adeboye. "Assessment of Spatial Prediction Techniques Accuracy for Elevation Determination in Akure South Local Government, Ondo State." European Journal of Engineering Research and Science 5, no. 5 (2020): 550–53. http://dx.doi.org/10.24018/ejers.2020.5.5.1805.

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The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a cooperative effort between NASA and Japan's Ministry of Economy Trade and Industry (METI), with the collaboration of scientific and industry organizations in both countries. The ASTER instrument provides a more robust remote sensing imaging capability when compared to the older Landsat Thematic Mapper. This paper deals with the accuracy assessment of elevation data obtained using ASTER from each of the eleven (11) selected extrapolation/interpolation algorithms: Inverse Distance Weighting, Natural Neighbor, Spline

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Wang, Xinyi, Shaozhang Niu, and Jiwei Zhang. "Digital Image Forensics Based on CFA Interpolation Feature and Gaussian Mixture Model." International Journal of Digital Crime and Forensics 11, no. 2 (2019): 1–12. http://dx.doi.org/10.4018/ijdcf.2019040101.

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According to the characteristics of the color filter array interpolation in a camera, an image splicing forgery detection algorithm based on bi-cubic interpolation and Gaussian mixture model is proposed. The authors make the assumption that the image is acquired using a color filter array, and that tampering removes the artifacts due to a demosaicing algorithm. This article extracts the image features based on the variance of the prediction error and create image feature likelihood map to detect and locate the image tampered areas. The experimental results show that the proposed method can det

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Jian-Kai, Liang, Carlo Cattani, and Song Wan-Qing. "Power Load Prediction Based on Fractal Theory." Advances in Mathematical Physics 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/827238.

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The basic theories of load forecasting on the power system are summarized. Fractal theory, which is a new algorithm applied to load forecasting, is introduced. Based on the fractal dimension and fractal interpolation function theories, the correlation algorithms are applied to the model of short-term load forecasting. According to the process of load forecasting, the steps of every process are designed, including load data preprocessing, similar day selecting, short-term load forecasting, and load curve drawing. The attractor is obtained using an improved deterministic algorithm based on the f

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OSCAR, T. P. "Predictive Model for Survival and Growth of Salmonella Typhimurium DT104 on Chicken Skin during Temperature Abuse†." Journal of Food Protection 72, no. 2 (2009): 304–14. http://dx.doi.org/10.4315/0362-028x-72.2.304.

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To better predict risk of Salmonella infection from chicken subjected to temperature abuse, a study was undertaken to develop a predictive model for survival and growth of Salmonella Typhimurium DT104 on chicken skin with native flora. For model development, chicken skin portions (2.14 cm2) were inoculated with 0.85 log of Salmonella Typhimurium DT104 (ATCC 700408) and then stored at 5 to 50°C for 8 h. Kinetic data from the storage trials were fit to a primary model to determine lag time (λ), specific growth rate (μ), and the 95% prediction interval (PI). Secondary models for λ, μ, and PI as a

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