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Journal articles on the topic 'Gaussian grid'

Author: Grafiati
Published: 19 February 2023

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1

Ritchie, Harold. "Semi-Lagrangian Advection on a Gaussian Grid." Monthly Weather Review 115, no. 2 (February 1987): 608–19. http://dx.doi.org/10.1175/1520-0493(1987)115<0608:slaoag>2.0.co;2.

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2

Sanna, N., T. Castrignano, P. D’Onorio De Meo, D. Carrabino, A. Grandi, G. Morelli, P. Caruso, and V. Barone. "Gaussian grid: a computational chemistry experiment over a web service-oriented grid." Theoretical Chemistry Accounts 117, no. 5-6 (December 15, 2006): 1145–52. http://dx.doi.org/10.1007/s00214-006-0227-9.

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3

Zhou, Bing, Mark Greenhalgh, and Stewart Greenhalgh. "DC resistivity modelling with a Gaussian quadrature grid." ASEG Extended Abstracts 2006, no. 1 (December 2006): 1–4. http://dx.doi.org/10.1071/aseg2006ab205.

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4

Courtier, P., and M. Naughton. "A pole problem in the reduced Gaussian grid." Quarterly Journal of the Royal Meteorological Society 120, no. 519 (July 1994): 1389–407. http://dx.doi.org/10.1002/qj.49712051913.

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5

Li, Tanda, Guy R. Davies, Alexander J. Lyttle, Warrick H. Ball, Lindsey M. Carboneau, and Rafael A. García. "Modelling stars with Gaussian Process Regression: augmenting stellar model grid." Monthly Notices of the Royal Astronomical Society 511, no. 4 (February 21, 2022): 5597–610. http://dx.doi.org/10.1093/mnras/stac467.

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ABSTRACT Grid-based modelling is widely used for estimating stellar parameters. However, stellar model grid is sparse because of the computational cost. This paper demonstrates an application of a machine-learning algorithm using the Gaussian Process (GP) Regression that turns a sparse model grid on to a continuous function. We train GP models to map five fundamental inputs (mass, equivalent evolutionary phase, initial metallicity, initial helium fraction, and the mixing-length parameter) to observable outputs (effective temperature, surface gravity, radius, surface metallicity, and stellar age). We test the GP predictions for the five outputs using off-grid stellar models and find no obvious systematic offsets, indicating good accuracy in predictions. As a further validation, we apply these GP models to characterize 1000 fake stars. Inferred masses and ages determined with GP models well recover true values within one standard deviation. An important consequence of using GP-based interpolation is that stellar ages are more precise than those estimated with the original sparse grid because of the full sampling of fundamental inputs.
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Marasović, Ivan, Željka Milanović, and Tihomir Betti. "Resistance Fluctuations in GaAs Nanowire Grids." Journal of Nanomaterials 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/428390.

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We present a numerical study on resistance fluctuations in a series of nanowire-based grids. Each grid is made of GaAs nanowires arranged in parallel with metallic contacts crossing all nanowires perpendicularly. Electrical properties of GaAs nanowires known from previous experimental research are used as input parameters in the simulation procedure. Due to the nonhomogeneous doping, the resistivity changes along nanowire. Allowing two possible nanowire orientations (“upwards” or “downwards”), the resulting grid is partially disordered in vertical direction which causes resistance fluctuations. The system is modeled using a two-dimensional random resistor network. Transfer-matrix computation algorithm is used to calculate the total network resistance. It is found that probability density function (PDF) of resistance fluctuations for a series of nanowire grids changes from Gaussian behavior towards the Bramwell-Holdsworth-Pinton distribution when both nanowire orientations are equally represented in the grid.
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AGISHTEIN, M. E., R. BENAV, A. A. MIGDAL, and S. SOLOMON. "NUMERICAL STUDY OF A TWO-POINT CORRELATION FUNCTION AND LIOUVILLE FIELD PROPERTIES IN TWO-DIMENSIONAL QUANTUM GRAVITY." Modern Physics Letters A 06, no. 12 (April 20, 1991): 1115–31. http://dx.doi.org/10.1142/s0217732391001172.

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Two-point Green’s function is measured on the manifolds of a 2-dimensional quantum gravity. The recursive sampling technique is used to generate the triangulations, lattice sizes being up to hundred thousand triangles. The grid Laplacian was inverted by means of the algebraic multi-grid solver. The free field model of the Quantum Gravity assumes the Gaussian behavior of Liouville field and curvature. We measured histograms as well as six momenta of these fields. Our results support the Gaussian assumption.
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Chung, Wonil. "Grid-based Gaussian process models for longitudinal genetic data." Communications for Statistical Applications and Methods 29, no. 1 (January 31, 2022): 745–63. http://dx.doi.org/10.29220/csam.2022.29.1.745.

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Chung, Wonil. "Grid-based Gaussian process models for longitudinal genetic data." Communications for Statistical Applications and Methods 29, no. 1 (January 31, 2022): 65–83. http://dx.doi.org/10.29220/csam.2022.29.1.065.

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10

Rochas, Michel. "Comments on “Semi-Lagrangian Advection on a Gaussian Grid”." Monthly Weather Review 116, no. 4 (April 1988): 969–70. http://dx.doi.org/10.1175/1520-0493(1988)116<0969:colaoa>2.0.co;2.

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11

Rodi, William. "Grid-search event location with non-Gaussian error models." Physics of the Earth and Planetary Interiors 158, no. 1 (September 2006): 55–66. http://dx.doi.org/10.1016/j.pepi.2006.03.010.

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12

Xiao, Xiao Ping, Zi Sheng Li, and Wei Gong. "Fuzzy Fractal Interpolation Surface and its Applications." Advanced Materials Research 542-543 (June 2012): 1141–44. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1141.

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Tackling of uncertain data is a major problem in analysis, modeling and simulation. Fractal interpolation surface and fuzzy set method are employed to solve the issue of uncertainty in modeling irregular surface. Initial interpolation data grid point is used as the kernel of Gaussian fuzzy membership function and its fuzzy numbers can be calculated by specifying λ of λ-cut set. These fuzzy numbers are used as uncertain data, which are the boundaries of the fluctuation of initial grid, and defined as a new kind of fuzzy interpolation grids. With these interpolation grids fractal interpolation surface algorithm is applied to act on. By these definitions, experimental data for modeling rock surface is illustrated to show that how the interpolation scheme proposed in this paper enhances the controllability for manipulating uncertain data.
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13

Errera, Q., and R. Ménard. "Technical Note: Spectral representation of spatial correlations in variational assimilation with grid point models and application to the belgian assimilation system for chemical observations (BASCOE)." Atmospheric Chemistry and Physics Discussions 12, no. 7 (July 6, 2012): 16763–809. http://dx.doi.org/10.5194/acpd-12-16763-2012.

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Abstract. The formulation of the background error covariances represented in the spectral space is discussed in the context of univariate assimilation relying on a grid point model, leaving out all the aspects of balances between the different control variables needed in meteorological assimilation. The spectral transform operations are discussed in the case of a spherical harmonics basis and we stress that there is no need for an inverse spectral transform and of a Gaussian grid. The analysis increments are thus produced directly on the model grid. The practice of producing analysis increments on a horizontal Gaussian grid and then interpolating on a equally spaced grid is also shown to produce a degradation of the analysis. The method discussed in this paper allows the implementation of separable and non-separable spatial correlations. The separable formulation has been implemented in the Belgian Assimilation System for Chemical ObsErvations (BASCOE) and its impact on the assimilation of O3 observed by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) is shown. To promote the use of this method by other non-meteorological variational systems and in particular chemistry, the Fortran code developed is made available to the community.
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Errera, Q., and R. Ménard. "Technical Note: Spectral representation of spatial correlations in variational assimilation with grid point models and application to the Belgian Assimilation System for Chemical Observations (BASCOE)." Atmospheric Chemistry and Physics 12, no. 21 (November 1, 2012): 10015–31. http://dx.doi.org/10.5194/acp-12-10015-2012.

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Abstract. The formulation of the background error covariances represented in the spectral space is discussed in the context of univariate assimilation relying on a grid point model, leaving out all the aspects of balances between the different control variables needed in meteorological assimilation. The spectral transform operations are discussed in the case of a spherical harmonics basis and we stress that there is no need for an inverse spectral transform and of a Gaussian grid. The analysis increments are thus produced directly on the model grid. The practice of producing analysis increments on a horizontal Gaussian grid and then interpolating to an equally spaced grid is also shown to produce a degradation of the analysis. The method discussed in this paper allows the implementation of separable and non-separable spatial correlations. The separable formulation has been implemented in the Belgian Assimilation System for Chemical ObsErvations (BASCOE) and its impact on the assimilation of O3 observed by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) is shown. To promote the use of this method by other non-meteorological variational systems and in particular chemistry, the Fortran code developed is made available to the community.
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15

D'Elia, Marta, and Max Gunzburger. "Coarse-Grid Sampling Interpolatory Methods for Approximating Gaussian Random Fields." SIAM/ASA Journal on Uncertainty Quantification 1, no. 1 (January 2013): 270–96. http://dx.doi.org/10.1137/120883311.

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16

Hrouda, Vojtěch, Martin Polášek, Petr Čársky, and Josef Michl. "Cubic-grid Gaussian basis sets for electron scattering calculations IV." Theoretica Chimica Acta 89, no. 5-6 (December 1994): 401–13. http://dx.doi.org/10.1007/bf01114110.

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17

Usta, Fuat, and Jeremy Levesley. "Multilevel quasi-interpolation on a sparse grid with the Gaussian." Numerical Algorithms 77, no. 3 (May 27, 2017): 793–808. http://dx.doi.org/10.1007/s11075-017-0340-y.

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18

Kieri, Emil, Gunilla Kreiss, and Olof Runborg. "Coupling of Gaussian Beam and Finite Difference Solvers for Semiclassical Schrödinger Equations." Advances in Applied Mathematics and Mechanics 7, no. 6 (September 9, 2015): 687–714. http://dx.doi.org/10.4208/aamm.2013.m411.

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AbstractIn the semiclassical regime, solutions to the time-dependent Schrödinger equation for molecular dynamics are highly oscillatory. The number of grid points required for resolving the oscillations may become very large even for simple model problems, making solution on a grid intractable. Asymptotic methods like Gaussian beams can resolve the oscillations with little effort and yield good approximations when the atomic nuclei are heavy and the potential is smooth. However, when the potential has variations on a small length-scale, quantum phenomena become important. Then asymptotic methods are less accurate. The two classes of methods perform well in different parameter regimes. This opens for hybrid methods, using Gaussian beams where we can and finite differences where we have to. We propose a new method for treating the coupling between the finite difference method and Gaussian beams. The new method reduces the needed amount of overlap regions considerably compared to previous methods, which improves the efficiency.
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19

Sharma, Pratibha, Alex W. Walker, Jeffrey F. Wheeldon, Karin Hinzer, and Henry Schriemer. "Enhanced Efficiencies for High-Concentration, Multijunction PV Systems by Optimizing Grid Spacing under Nonuniform Illumination." International Journal of Photoenergy 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/582083.

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The design of a triple junction solar cell’s front contact grid can significantly affect cell conversion efficiency under high concentration. We consider one aspect of grid design, choosing a linear grid within a distributed resistance cell model to optimize finger spacings at concentrations between 500 and 2500 suns under uniform and nonuniform illumination. Optimization for maximum efficiency under Gaussian irradiance profiles is done by SPICE analysis. Relative to the optimized uniform illumination designs, we find enhancements of 0.5% to 2% in absolute efficiencies for uniform spacing. Efficiency enhancement with nonuniform spacing under nonuniform illumination is also evaluated. Our model suggests that, at lower concentrations (<1000 suns), the penalty for using uniformly spaced fingers instead of nonuniformly spaced fingers is <0.1%. However, at a concentration of 2500 suns the penalty increases to 0.3%. Thus, relative to a uniform irradiance optimization, an absolute efficiency increase of 2.3% can be attained for an optimized nonuniform spacing given the Gaussian irradiance profile under consideration.
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20

Yalman, Yunus, Tayfun Uyanık, İbrahim Atlı, Adnan Tan, Kamil Çağatay Bayındır, Ömer Karal, Saeed Golestan, and Josep M. Guerrero. "Prediction of Voltage Sag Relative Location with Data-Driven Algorithms in Distribution Grid." Energies 15, no. 18 (September 11, 2022): 6641. http://dx.doi.org/10.3390/en15186641.

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Power quality (PQ) problems, including voltage sag, flicker, and harmonics, are the main concerns for the grid operator. Among these disturbances, voltage sag, which affects the sensitive loads in the interconnected system, is a crucial problem in the transmission and distribution systems. The determination of the voltage sag relative location as a downstream (DS) and upstream (US) is an important issue that should be considered when mitigating the sag problem. Therefore, this paper proposes a novel approach to determine the voltage sag relative location based on voltage sag event records of the power quality monitoring system (PQMS) in the real distribution system. By this method, the relative location of voltage sag is defined by Gaussian naive Bayes (Gaussian NB) and K-nearest neighbors (K-NN) algorithms. The proposed methods are compared with support vector machine (SVM) and artificial neural network (ANN). The results indicate that K-NN and Gaussian NB algorithms define the relative location of a voltage sag with 98.75% and 97.34% accuracy, respectively.
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21

Xiao, M. Y., and H. P. Hong. "Unconditional and conditional simulation of nonstationary and non-Gaussian vector and field with prescribed marginal and correlation by using iteratively matched correlation." Disaster Prevention and Resilience 1, no. 1 (2022): 5. http://dx.doi.org/10.20517/dpr.2022.01.

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In many probabilistic analysis problems, the homogeneous/nonhomogeneous non-Gaussian field is represented as a mapped Gaussian field based on the Nataf translation system. We propose a new sample-based iterative procedure to estimate the underlying Gaussian correlation for homogeneous/nonhomogeneous non-Gaussian vector or field. The numerical procedure takes advantage that the range of feasible correlation coefficients for non-Gaussian random variables is bounded if the translation system is adopted. The estimated underlying Gaussian correlation is then employed for unconditional as well as conditional simulation of the non-Gaussian vector or field according to the theory of the translation process. We then present the steps for augmenting the simulated non-Gaussian field through the Karhunen-Loeve expansion for a refined discretized grid of the field. In addition, the steps to extend the procedure described in the previous section to the multi-dimensional field are highlighted. The application of the proposed algorithms is presented through numerical examples.
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22

Guo, Hongjie, Guojun Dai, Jin Fan, Yifan Wu, Fangyao Shen, and Yidan Hu. "A Mobile Sensing System for UrbanPM2.5Monitoring with Adaptive Resolution." Journal of Sensors 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/7901245.

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This paper develops a mobile sensing system, the first system used in adaptive resolution urban air quality monitoring. In this system, we employ several taxis as sensor carries to collect originalPM2.5data and collect a variety of datasets, including meteorological data, traffic status data, and geographical data in the city. This paper also presents a novel method AG-PCEM (Adaptive Grid-Probabilistic Concentration Estimation Method) to infer thePM2.5concentration for undetected grids using dynamic adaptive grids. We gradually collect the measurements throughout a year using a prototype system in Xiasha District of Hangzhou City, China. Experimental data has verified that the proposed system can achieve good performance in terms of computational cost and accuracy. The computational cost of AG-PCEM is reduced by about 40.2% compared with a static grid method PCEM under the condition of reaching the close accuracy, and the accuracy of AG-PCEM is far superior as widely used artificial neural network (ANN) and Gaussian process (GP), enhanced by 38.8% and 14.6%, respectively. The system can be expanded to wide-range air quality monitor by adjusting the initial grid resolution, and our findings can tell citizens actual air quality and help official management find pollution sources.
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23

Liao, Tao, Yongjie Zhang, Peter M. Kekenes-Huskey, Yuhui Cheng, Anushka Michailova, Andrew D. McCulloch, Michael Holst, and J. Andrew McCammon. "Multi-core CPU or GPU-accelerated Multiscale Modeling for Biomolecular Complexes." Computational and Mathematical Biophysics 1 (July 19, 2013): 164–79. http://dx.doi.org/10.2478/mlbmb-2013-0009.

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AbstractMulti-scale modeling plays an important role in understanding the structure and biological functionalities of large biomolecular complexes. In this paper, we present an efficient computational framework to construct multi-scale models from atomic resolution data in the Protein Data Bank (PDB), which is accelerated by multi-core CPU and programmable Graphics Processing Units (GPU). A multi-level summation of Gaussian kernel functions is employed to generate implicit models for biomolecules. The coefficients in the summation are designed as functions of the structure indices, which specify the structures at a certain level and enable a local resolution control on the biomolecular surface. A method called neighboring search is adopted to locate the grid points close to the expected biomolecular surface, and reduce the number of grids to be analyzed. For a specific grid point, a KD-tree or bounding volume hierarchy is applied to search for the atoms contributing to its density computation, and faraway atoms are ignored due to the decay of Gaussian kernel functions. In addition to density map construction, three modes are also employed and compared during mesh generation and quality improvement to generate high quality tetrahedral meshes: CPU sequential, multi-core CPU parallel and GPU parallel. We have applied our algorithm to several large proteins and obtained good results.
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24

Sun, Kang, Lei Zhu, Karen Cady-Pereira, Christopher Chan Miller, Kelly Chance, Lieven Clarisse, Pierre-François Coheur, et al. "A physics-based approach to oversample multi-satellite, multispecies observations to a common grid." Atmospheric Measurement Techniques 11, no. 12 (December 18, 2018): 6679–701. http://dx.doi.org/10.5194/amt-11-6679-2018.

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Abstract. Satellite remote sensing of the Earth's atmospheric composition usually samples irregularly in space and time, and many applications require spatially and temporally averaging the satellite observations (level 2) to a regular grid (level 3). When averaging level 2 data over a long period to a target level 3 grid that is significantly finer than the sizes of level 2 pixels, this process is referred to as “oversampling”. An agile, physics-based oversampling approach is developed to represent each satellite observation as a sensitivity distribution on the ground, instead of a point or a polygon as assumed in previous methods. This sensitivity distribution can be determined by the spatial response function of each satellite sensor. A generalized 2-D super Gaussian function is proposed to characterize the spatial response functions of both imaging grating spectrometers (e.g., OMI, OMPS, and TROPOMI) and scanning Fourier transform spectrometers (e.g., GOSAT, IASI, and CrIS). Synthetic OMI and IASI observations were generated to compare the errors due to simplifying satellite fields of view (FOVs) as polygons (tessellation error) and the errors due to discretizing the smooth spatial response function on a finite grid (discretization error). The balance between these two error sources depends on the target grid size, the ground size of the FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for fine-grid oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grids finer than ∼16 km. The generalized 2-D super Gaussian function also enables smoothing of the level 3 results by decreasing the shape-determining exponents, which is useful for a high noise level or sparse satellite datasets. This physical oversampling approach is especially advantageous during smaller temporal windows and shows substantially improved visualization of trace gas distribution and local gradients when applied to OMI NO2 products and IASI NH3 products. There is no appreciable difference in the computational time when using the physical oversampling versus other oversampling methods.
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25

Eaton, Miller, Carlos González-Arciniegas, Rafael N. Alexander, Nicolas C. Menicucci, and Olivier Pfister. "Measurement-based generation and preservation of cat and grid states within a continuous-variable cluster state." Quantum 6 (July 20, 2022): 769. http://dx.doi.org/10.22331/q-2022-07-20-769.

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We present an algorithm to reliably generate various quantum states critical to quantum error correction and universal continuous-variable (CV) quantum computing, such as Schrödinger cat states and Gottesman-Kitaev-Preskill (GKP) grid states, out of Gaussian CV cluster states. Our algorithm is based on the Photon-counting-Assisted Node-Teleportation Method (PhANTM), which uses standard Gaussian information processing on the cluster state with the only addition of local photon-number-resolving measurements. We show that PhANTM can apply polynomial gates and embed cat states within the cluster. This method stabilizes cat states against Gaussian noise and perpetuates non-Gaussianity within the cluster. We show that existing protocols for breeding cat states can be embedded into cluster state processing using PhANTM.
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26

Ospina, Ana M., Yue Chen, Andrey Bernstein, and Emiliano Dall’Anese. "Learning-based demand response in grid-interactive buildings via Gaussian Processes." Electric Power Systems Research 211 (October 2022): 108406. http://dx.doi.org/10.1016/j.epsr.2022.108406.

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27

Samikannu, Ravi, K. Vinoth, Narasimha Rao Dasari, and Senthil Kumar Subburaj. "Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System." Intelligent Automation & Soft Computing 35, no. 1 (2023): 983–95. http://dx.doi.org/10.32604/iasc.2023.026069.

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28

Samikannu, Ravi, K. Vinoth, Narasimha Rao Dasari, and Senthil Kumar Subburaj. "Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System." Intelligent Automation & Soft Computing 35, no. 1 (2023): 983–95. http://dx.doi.org/10.32604/iasc.2023.026069.

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29

Simpson, D., J. B. Illian, F. Lindgren, S. H. Sørbye, and H. Rue. "Going off grid: computationally efficient inference for log-Gaussian Cox processes." Biometrika 103, no. 1 (February 5, 2016): 49–70. http://dx.doi.org/10.1093/biomet/asv064.

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30

Oladeji, Ifedayo, Ramon Zamora, and Tek Tjing Lie. "An Online Security Prediction and Control Framework for Modern Power Grids." Energies 14, no. 20 (October 14, 2021): 6639. http://dx.doi.org/10.3390/en14206639.

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The proliferation of renewable energy sources distributed generation (RES-DG) into the grid results in time-varying inertia constant. To ensure the security of the grid under varying inertia, techniques for fast security assessment are required. In addition, considering the high penetration of RES-DG units into the modern grids, security prediction using varying grid features is crucial. The computation burden concerns of conventional time-domain security assessment techniques make it unsuitable for real-time security prediction. This paper, therefore, proposes a fast security monitoring model that includes security prediction and load shedding for security control. The attributes considered in this paper include the load level, inertia constant, fault location, and power dispatched from the renewable energy sources generator. An incremental Naïve Bayes algorithm is applied on the training dataset developed from the responses of the grid to transient stability simulations. An additive Gaussian process regression (GPR) model is proposed to estimate the load shedding required for the predicted insecure states. Finally, an algorithm based on the nodes’ security margin is proposed to determine the optimal node (s) for the load shedding. The average security prediction and load shedding estimation model training times are 1.2 s and 3 s, respectively. The result shows that the proposed model can predict the security of the grid, estimate the amount of load shed required, and determine the specific node for load shedding operation.
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31

Oladeji, Ifedayo, Ramon Zamora, and Tek Tjing Lie. "An Online Security Prediction and Control Framework for Modern Power Grids." Energies 14, no. 20 (October 14, 2021): 6639. http://dx.doi.org/10.3390/en14206639.

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The proliferation of renewable energy sources distributed generation (RES-DG) into the grid results in time-varying inertia constant. To ensure the security of the grid under varying inertia, techniques for fast security assessment are required. In addition, considering the high penetration of RES-DG units into the modern grids, security prediction using varying grid features is crucial. The computation burden concerns of conventional time-domain security assessment techniques make it unsuitable for real-time security prediction. This paper, therefore, proposes a fast security monitoring model that includes security prediction and load shedding for security control. The attributes considered in this paper include the load level, inertia constant, fault location, and power dispatched from the renewable energy sources generator. An incremental Naïve Bayes algorithm is applied on the training dataset developed from the responses of the grid to transient stability simulations. An additive Gaussian process regression (GPR) model is proposed to estimate the load shedding required for the predicted insecure states. Finally, an algorithm based on the nodes’ security margin is proposed to determine the optimal node (s) for the load shedding. The average security prediction and load shedding estimation model training times are 1.2 s and 3 s, respectively. The result shows that the proposed model can predict the security of the grid, estimate the amount of load shed required, and determine the specific node for load shedding operation.
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32

Hamd-Allah, Samira Mohammed, Osamah Shareef Abbas, and Mohammed K. Dhaidan. "Distribution of Petrophysical Properties Based on Conceptual Facies Model, Mishrif Reservoir/South of Iraq." Journal of Petroleum Research and Studies 12, no. 3 (September 11, 2022): 51–70. http://dx.doi.org/10.52716/jprs.v12i3.556.

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A 3D geological model is an essential step to reveal reservoir heterogeneity and reservoir properties distribution. In the present study, a three-dimensional geological model for the Mishrif reservoir was built based on data obtained from seven wells and core data. The methodology includes building a 3D grid and populating it with petrophysical properties such as (facies, porosity, water saturation, and net to gross ratio). The structural model was built based on a base contour map obtained from 2D seismic interpretation along with well tops from seven wells. A simple grid method was used to build the structural framework with 234x278x91 grid cells in the X, Y, and Z directions, respectively, with lengths equal to 150 meters. The total number of grids is (5919732) in the geological model. CPI (computer-processed interpretation) for 7 wells contain (facies, porosity, water saturation, and NTG) was imported to Petrel 2016 software. Facies log was upscaled and distributed along the 3D grid. Truncated Gaussian with trend method was used to distribute the facies taking into account the conceptual facies model of the Mishrif formation. The result shows that the trend of sedimentation suggests a retrogradation pattern from NW to SE. Facies1 (Reservoir), dominated by Limestone brown to light brown, with oil shows has good distribution within the area and thinning towards the NW. The petrophysical properties (porosity, water saturation, NTG, and permeability) were distributed using the Sequential Gaussian Simulation (SIS) method and the facies model as a guide for distribution. The results show that petrophysical properties enhanced in the southeast area, representing the reef region compared to the northwest side of the study area. Unit Mishrif B had the highest porosity value and lower water saturation value along the entire field. While the units Mishrif B1, B2, and B3 show a gradual decrease in reservoir properties towards the field's southeast side. The results also show that the conceptual facies model has great benefit in constructing the 3D geological model, reflecting the geological knowledge used to correctly distribute the reservoir properties (porosity and water saturation).
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33

Pfaff, Florian, Kailai Li, and Uwe D. Hanebeck. "The State Space Subdivision Filter for Estimation on SE(2)." Sensors 21, no. 18 (September 21, 2021): 6314. http://dx.doi.org/10.3390/s21186314.

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The SE(2) domain can be used to describe the position and orientation of objects in planar scenarios and is inherently nonlinear due to the periodicity of the angle. We present a novel filter that involves splitting up the joint density into a (marginalized) density for the periodic part and a conditional density for the linear part. We subdivide the state space along the periodic dimension and describe each part of the state space using the parameters of a Gaussian and a grid value, which is the function value of the marginalized density for the periodic part at the center of the respective area. By using the grid values as weighting factors for the Gaussians along the linear dimensions, we can approximate functions on the SE(2) domain with correlated position and orientation. Based on this representation, we interweave a grid filter with a Kalman filter to obtain a filter that can take different numbers of parameters and is in the same complexity class as a grid filter for circular domains. We thoroughly compared the filters with other state-of-the-art filters in a simulated tracking scenario. With only little run time, our filter outperformed an unscented Kalman filter for manifolds and a progressive filter based on dual quaternions. Our filter also yielded more accurate results than a particle filter using one million particles while being faster by over an order of magnitude.
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Zhao, Bin, Qinghua Zeng, Jianye Liu, Chunlei Gao, and Tianyu Zhao. "A new polar alignment algorithm based on the Huber estimation filter with the aid of BeiDou Navigation Satellite System." International Journal of Distributed Sensor Networks 17, no. 3 (March 2021): 155014772110041. http://dx.doi.org/10.1177/15501477211004115.

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For aircrafts equipped with BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated navigation system, BeiDou Navigation Satellite System information can be used to achieve autonomous alignment. However, due to the complex polar environment and multipath effect, BeiDou Navigation Satellite System measurement noise often exhibits a non-Gaussian distribution that will severely degrade the estimation accuracy of standard Kalman filter. To address this problem, a new polar alignment algorithm based on the Huber estimation filter is proposed in this article. Considering the special geographical conditions in the polar regions, the dynamic model and the measurement model of BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated alignment system in the grid frame are derived in this article. The BeiDou Navigation Satellite System measurement noise characteristics in the polar regions are analyzed and heavy-tailed characteristics are simulated, respectively. Since the estimation accuracy of standard Kalman filter can be severely degraded under non-Gaussian noise, a Kalman filter based on the Huber estimation is designed combining grid navigation system and generalized maximum likelihood estimation. The simulation and experiment results demonstrate that the proposed algorithm has better robustness under non-Gaussian noise, and it is effective in the polar regions. By employing the proposed algorithm, the rapidity and accuracy of the alignment process can be improved.
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Wang, Luyang, and Jinhui Lan. "Adaptive Polar-Grid Gaussian-Mixture Model for Foreground Segmentation Using Roadside LiDAR." Remote Sensing 14, no. 11 (May 25, 2022): 2522. http://dx.doi.org/10.3390/rs14112522.

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Roadside LiDAR has become an important sensor for the detection of objects in cities, such as vehicles and pedestrians, which is due to its advantages of all-weather operation and high-ranging accuracy. In order to serve an intelligent transportation system, the efficient and accurate segmentation of vehicles and pedestrians is needed in the coverage area of the LiDAR. In this study, a roadside LiDAR was fixed on brackets on both sides of the road to obtain the point-cloud information on the urban road and the surrounding environment. A segmentation method that is based on a scanning LiDAR sensor is proposed. First, a polar grid that is based on polar coordinates is constructed to count the LiDAR rotations to obtain the original information of the angle and the distance of the point cloud, and the background point-cloud image is dynamically updated over time. By aiming at the complex urban road environment and the interference of trees and light poles in the background, an adaptive polar-grid Gaussian-mixture model (APG-GMM) that uses a point-cloud method is proposed to improve the accuracy of the foreground and background segmentation. A density-adaptive DBSCAN target-clustering algorithm is proposed, as well as a dynamic adaptive neighborhood radius, to solve the problem of the low clustering accuracy that is caused by the uneven density of point clouds that are collected by LiDAR, and to divide the point clouds in the foreground into vehicles and pedestrians. Finally, the method was tested at intersections and urban roads with dense traffic flows. The experimental results show that the proposed algorithm can segment the foreground and background well and can cluster vehicles and pedestrians while reducing the number of calculations and the time complexity.
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36

Cid, M., and N. Stem. "Homogeneous Gaussian Profile P+-Type Emitters: Updated Parameters and Metal-Grid Optimization." Materials Research 5, no. 4 (October 2002): 427–32. http://dx.doi.org/10.1590/s1516-14392002000400006.

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37

?�rsky, Petr, Vojt?ch Hrouda, and Josef Michl. "Cubic-grid Gaussian basis sets for electron scattering calculations. II. Matrix elements." International Journal of Quantum Chemistry 53, no. 4 (February 15, 1995): 431–36. http://dx.doi.org/10.1002/qua.560530409.

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38

Kismiantini, Abelardo Montesinos-López, Bernabe Cano-Páez, J. Cricelio Montesinos-López, Moisés Chavira-Flores, Osval A. Montesinos-López, and José Crossa. "A Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies." Genes 13, no. 12 (December 3, 2022): 2279. http://dx.doi.org/10.3390/genes13122279.

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While genomic selection (GS) began revolutionizing plant breeding when it was proposed around 20 years ago, its practical implementation is still challenging as many factors affect its accuracy. One such factor is the choice of the statistical machine learning method. For this reason, we explore the tuning process under a multi-trait framework using the Gaussian kernel with a multi-trait Bayesian Best Linear Unbiased Predictor (GBLUP) model. We explored three methods of tuning (manual, grid search and Bayesian optimization) using 5 real datasets of breeding programs. We found that using grid search and Bayesian optimization improve between 1.9 and 6.8% the prediction accuracy regarding of using manual tuning. While the improvement in prediction accuracy in some cases can be marginal, it is very important to carry out the tuning process carefully to improve the accuracy of the GS methodology, even though this entails greater computational resources.
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POORTE, R. E. G., and A. BIESHEUVEL. "Experiments on the motion of gas bubbles in turbulence generated by an active grid." Journal of Fluid Mechanics 461 (June 25, 2002): 127–54. http://dx.doi.org/10.1017/s0022112002008273.

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The random motion of nearly spherical bubbles in the turbulent flow behind a grid is studied experimentally. In quiescent water these bubbles rise at high Reynolds number. The turbulence is generated by an active grid of the design of Makita (1991), and can have turbulence Reynolds number Rλ of up to 200. Minor changes in the geometry of the grid and in its mode of operation improves the isotropy of the turbulence, compared with that reported by Makita (1991) and Mydlarski & Warhaft (1996). The trajectory of each bubble is measured with high spatial and temporal resolution with a specially developed technique that makes use of a position-sensitive detector. Bubble statistics such as the mean rise velocity and the root-mean-square velocity fluctuations are obtained by ensemble averaging over many identical bubbles. The resulting bubble mean rise velocity is significantly reduced (up to 35%) compared with the quiescent conditions. The vertical bubble velocity fluctuations are found to be non-Gaussian, whereas the horizontal displacements are Gaussian for all times. The diffusivity of bubbles is considerably less than that of fluid particles. These findings are qualitatively consistent with results obtained through theoretical analysis and numerical simulations by Spelt & Biesheuvel (1997).
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40

Bodini, Olivier. "On the strange kinetic aesthetic of rectangular shape partitions." Pure Mathematics and Applications 30, no. 1 (June 1, 2022): 37–44. http://dx.doi.org/10.2478/puma-2022-0007.

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Abstract In this paper, we focus on shape partitions. We show that for any fixed k, one can symbolically characterize the shape partition on a k × n rectangular grid by a context-free grammar. We explicitly give this grammar for k = 2 and k = 3 (for k = 1, this corresponds to compositions of integers). From these grammars, we deduce the number of shape partitions for the k × n rectangular grids for k ∈ {1, 2, 3} and every n, as well as the limiting Gaussian distribution of the number of connected components. This also enables us to randomly and uniformly generate shape partitions of large size.
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41

Li, Tao, Anthony F. Desmond, and Thanasis Stengos. "Dimension Reduction via Penalized GLMs for Non-Gaussian Response: Application to Stock Market Volatility." Journal of Risk and Financial Management 14, no. 12 (December 4, 2021): 583. http://dx.doi.org/10.3390/jrfm14120583.

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We fit U.S. stock market volatilities on macroeconomic and financial market indicators and some industry level financial ratios. Stock market volatility is non-Gaussian distributed. It can be approximated by an inverse Gaussian (IG) distribution or it can be transformed by Box–Cox transformation to a Gaussian distribution. Hence, we used a Box–Cox transformed Gaussian LASSO model and an IG GLM LASSO model as dimension reduction techniques and we attempted to identify some common indicators to help us forecast stock market volatility. Via simulation, we validated the use of four models, i.e., a univariate Box–Cox transformation Gaussian LASSO model, a three-phase iterative grid search Box–Cox transformation Gaussian LASSO model, and both canonical link and optimal link IG GLM LASSO models. The latter two models assume an approximately IG distributed response. Using these four models in an empirical study, we identified three macroeconomic indicators that could help us forecast stock market volatility. These are the credit spread between the U.S. Aaa corporate bond yield and the 10-year treasury yield, the total outstanding non-revolving consumer credit, and the total outstanding non-financial corporate bonds.
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42

Chow, James C. L., Runqing Jiang, and Daniel Markel. "Dosimetric variations in calculation grid size in prostate VMAT: a dose-volume histogram analysis using the Gaussian error function." Journal of Radiotherapy in Practice 17, no. 2 (November 23, 2017): 162–70. http://dx.doi.org/10.1017/s1460396917000619.

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AbstractBackgroundVarying the calculation grid size can change the results of dose-volume and radiobiological parameters in a treatment plan, and therefore has an impact on the treatment planning quality assurance.PurposeThis study investigated the dosimetric influence of the calculation grid size variation in the prostate volumetric modulated arc therapy (VMAT) plan.Methods and materialsDose distributions of 10 prostate VMAT plans were acquired using calculation grid sizes of 1–5 mm. Dose-volume histogram (DVH) analysis was carried out to determine the dose-volume variation corresponding to the grid size change using the Gaussian error function (GEF). At the same time, dose-volume points, dose-volume parameters and radiobiological parameters were calculated based on DVHs of targets and organs at risk (OARs) for each grid size.ResultsComparing percentage variations of GEF parameters between the planning target volume (PTV) and clinical target volume (CTV), GEF parameters of the PTV were found varied more significantly than the CTV. This resulted in larger variations of dose-volume (%ΔCI=40·02 versus 13·55%, %ΔHI=12·45 versus 2·93% and %ΔGI=0·22 versus 0·06%) and radiobiological parameters (%ΔTCP=0·61 versus 0·25% and %ΔEUD=2·11 versus 0·26%) of the PTV compared with CTV. For OARs, the rectal wall showed a larger dose-volume variation than the rectum. However, similar dose-volume variation due to grid size change was not found in the bladder, bladder wall and femur.ConclusionsKnowing the dosimetric variation in this study is important to the radiotherapy staff in the quality assurance for the prostate VMAT planning.
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Fan, Song Hai, and Shu Hong Yang. "Detection and Identification of Error in On-Line Monitoring of Transmission Line." Applied Mechanics and Materials 687-691 (November 2014): 869–73. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.869.

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Systematic approach for the transmission line positive sequence parameters, temperature, and sag based on wavelet analysis to detect error is developed in this work. Unbiased (random/Gaussian) error such as, transient meter failures, transient meter malfunction, and measurements captured during system transients, are inherently in the form of large abrupt change of short duration in a measurement-sequence. These should be detected before the data is used because their presence will lead to insecure and unstable of power grid. The test results of the proposed method based on data of Sichuan power grid are presented.
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44

Lubbe, Foster, Jacques Maritz, and Thomas Harms. "Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study." Energies 13, no. 20 (October 21, 2020): 5509. http://dx.doi.org/10.3390/en13205509.

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The proliferation of solar power systems could cause instability within existing power grids due to the variable nature of solar power. A well-defined statistical model is important for managing the supply-and-demand dynamics of a power system that contains a significant variable renewable energy component. It is furthermore important to consider the inherent uncertainty in the data when modeling such a complex power system. Gaussian process regression has the potential to address both of these concerns: the probabilistic modeling of solar radiation data could assist in managing the variability of solar power, as well as provide a mechanism to deal with uncertainty. In this paper, solar radiation data was obtained from the Southern African Universities Radiometric Network and used to train a Gaussian process regression model which was developed especially for this purpose. Attention was given to constructing an appropriate Gaussian process kernel. It was found that a carefully constructed kernel allowed for the successful interpolation of global horizontal irradiance data, with a root-mean-squared error of 82.2W/m2. Gaps in the data, due to possible meter failure, were also bridged by the Gaussian process with a root-mean-squared error of 94.1 W/m2 and accompanying confidence intervals. A root-mean-squared error of 151.1 W/m2 was found when forecasting the global horizontal irradiance with a forecasting horizon of five days. These results, achieved in modeling solar radiation data using Gaussian process regression, could open new avenues in the development of probabilistic renewable energy management systems. Such systems could aid smart grid operators and support energy trading platforms, by allowing for better-informed decisions that incorporate the inherent uncertainty of stochastic power systems.
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45

Cheng, Qiangqiang, Yiqi Yan, Shichao Liu, Chunsheng Yang, Hicham Chaoui, and Mohamad Alzayed. "Particle Filter-Based Electricity Load Prediction for Grid-Connected Microgrid Day-Ahead Scheduling." Energies 13, no. 24 (December 8, 2020): 6489. http://dx.doi.org/10.3390/en13246489.

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This paper proposes a particle filter (PF)-based electricity load prediction method to improve the accuracy of the microgrid day-ahead scheduling. While most of the existing prediction methods assume electricity loads follow normal distributions, we consider it is a nonlinear and non-Gaussian process which is closer to the reality. To handle the nonlinear and non-Gaussian characteristics of electricity load profile, the PF-based method is implemented to improve the prediction accuracy. These load predictions are used to provide the microgrid day-ahead scheduling. The impact of load prediction error on the scheduling decision is analyzed based on actual data. Comparison results on a distribution system show that the estimation precision of electricity load based on the PF method is the highest among several conventional intelligent methods such as the Elman neural network (ENN) and support vector machine (SVM). Furthermore, the impact of the different parameter settings are analyzed for the proposed PF based load prediction. The management efficiency of microgrid is significantly improved by using the PF method.
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46

Perron, Maxime, and Philip Sura. "Climatology of Non-Gaussian Atmospheric Statistics." Journal of Climate 26, no. 3 (February 1, 2013): 1063–83. http://dx.doi.org/10.1175/jcli-d-11-00504.1.

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Abstract A common assumption in the earth sciences is the Gaussianity of data over time. However, several independent studies in the past few decades have shown this assumption to be mostly false. To be able to study non-Gaussian climate statistics, one must first compile a systematic climatology of the higher statistical moments (skewness and kurtosis; the third and fourth central statistical moments, respectively). Sixty-two years of daily data from the NCEP–NCAR Reanalysis I project are analyzed. The skewness and kurtosis of the data are found at each spatial grid point for the entire time domain. Nine atmospheric variables were chosen for their physical and dynamical relevance in the climate system: geopotential height, relative vorticity, quasigeostrophic potential vorticity, zonal wind, meridional wind, horizontal wind speed, vertical velocity in pressure coordinates, air temperature, and specific humidity. For each variable, plots of significant global skewness and kurtosis are shown for December–February and June–August at a specified pressure level. Additionally, the statistical moments are then zonally averaged to show the vertical dependence of the non-Gaussian statistics. This is a more comprehensive look at non-Gaussian atmospheric statistics than has been taken in previous studies on this topic.
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47

Wang, Ella Xi, Thomas Nordlander, Martin Asplund, Anish M. Amarsi, Karin Lind, and Yixiao Zhou. "3D NLTE spectral line formation of lithium in late-type stars." Monthly Notices of the Royal Astronomical Society 500, no. 2 (October 31, 2020): 2159–76. http://dx.doi.org/10.1093/mnras/staa3381.

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ABSTRACT Accurately known stellar lithium abundances may be used to shed light on a variety of astrophysical phenomena such as big bang nucleosynthesis, radial migration, ages of stars and stellar clusters, and planet engulfment events. We present a grid of synthetic lithium spectra that are computed in non-local thermodynamic equilibrium (NLTE) across the stagger grid of three-dimensional (3D) hydrodynamic stellar atmosphere models. This grid covers three Li lines at 610.4, 670.8, and 812.6 nm for stellar parameters representative of FGK-type dwarfs and giants, spanning Teff = 4000–7000 K, log g = 1.5–5.0, $[\rm {Fe}/\rm {H}]= -4.0$–0.5, and A(Li) = −0.5–4.0. We find that our abundance corrections are up to 0.15 dex more negative than in previous work, due to a previously overlooked NLTE effect of blocking of UV lithium lines by background opacities, which has important implications for a wide range of science cases. We derive a new 3D NLTE solar abundance of A(Li) = 0.96 ± 0.05, which is 0.09 dex lower than the commonly used value. We make our grids of synthetic spectra and abundance corrections publicly available through the breidablik package. This package includes methods for accurately interpolating our grid to arbitrary stellar parameters through methods based on Kriging (Gaussian process regression) for line profiles, and multilayer perceptrons (a class of fully connected feedforward neural networks) for NLTE corrections and 3D NLTE abundances from equivalent widths, achieving interpolation errors of the order of 0.01 dex.
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Alamaniotis, Miltiadis, and Georgios Karagiannis. "Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short Term Wind Speed Forecasting in Smart Power." International Journal of Monitoring and Surveillance Technologies Research 5, no. 3 (July 2017): 1–14. http://dx.doi.org/10.4018/ijmstr.2017070101.

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This article describes how the integration of renewable energy in the power grid is a critical issue in order to realize a smart grid infrastructure. To that end, intelligent methods that monitor and currently predict the values of critical variables of renewable energy are essential. With respect to wind power, such variable is the wind speed given that it is of great interest to efficient schedule operation of a wind farm. In this article, a new methodology for predicting wind speed is presented for very short-term prediction horizons. The methodology integrates multiple Gaussian process regressors (GPR) via the adoption of an optimization problem whose solution is given by the particle swarm optimization algorithm. The optimized framework is utilized for the average hourly wind speed prediction for a prediction horizon of six hours ahead. Results demonstrate the ability of the methodology in accurately forecasting the wind speed. Furthermore, obtained forecasts are compared with those taken from single Gaussian process regressors as well from the integration of the same multiple GPR using a genetic algorithm.
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Nesti, T., J. Moriarty, A. Zocca, and B. Zwart. "Large fluctuations in locational marginal prices." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2202 (June 7, 2021): 20190438. http://dx.doi.org/10.1098/rsta.2019.0438.

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This paper investigates large fluctuations of locational marginal prices (LMPs) in wholesale energy markets caused by volatile renewable generation profiles. Specifically, we study events of the form P ( LMP ∉ ∏ i = 1 n [ α i − , α i + ] ) , where LMP is the vector of LMPs at the n power grid nodes, and α − , α + ∈ R n are vectors of price thresholds specifying undesirable price occurrences. By exploiting the structure of the supply–demand matching mechanism in power grids, we look at LMPs as deterministic piecewise affine, possibly discontinuous functions of the stochastic input process, modelling uncontrollable renewable generation. We use techniques from large deviations theory to identify the most likely ways for extreme price spikes to happen, and to rank the nodes of the power grid in terms of their likelihood of experiencing a price spike. Our results are derived in the case of Gaussian fluctuations, and are validated numerically on the IEEE 14-bus test case. This article is part of the theme issue ‘The mathematics of energy systems’.
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Badeke, Ronny, Volker Matthias, Matthias Karl, and David Grawe. "Effects of vertical ship exhaust plume distributions on urban pollutant concentration – a sensitivity study with MITRAS v2.0 and EPISODE-CityChem v1.4." Geoscientific Model Development 15, no. 10 (May 25, 2022): 4077–103. http://dx.doi.org/10.5194/gmd-15-4077-2022.

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Abstract. The modeling of ship emissions in port areas involves several uncertainties and approximations. In Eulerian grid models, the vertical distribution of emissions plays a decisive role for the ground-level pollutant concentration. In this study, model results of a microscale model, which takes thermal plume rise and turbulence into account, are derived for the parameterization of vertical ship exhaust plume distributions. This is done considering various meteorological and ship-technical conditions. The influence of three different approximated parameterizations (Gaussian distribution, single-cell emission and exponential Gaussian distribution) on the ground-level concentration are then evaluated in a city-scale model. Choosing a Gaussian distribution is particularly suitable for high wind speeds (>5 m s−1) and a stable atmosphere, while at low wind speeds or unstable atmospheric conditions the plume rise can be more closely approximated by an exponential Gaussian distribution. While Gaussian and exponential Gaussian distributions lead to ground-level concentration maxima close to the source, with single-cell emission assumptions the maxima ground-level concentration occurs at a distance of about 1500 m from the source. Particularly high-resolution city-scale studies should therefore consider ship emissions with a suitable Gaussian or exponential Gaussian distribution. From a distance of around 4 km, the selected initial distribution no longer shows significant differences for the pollutant concentration near the ground; therefore, model studies with lower resolution can reasonably approximate ship plumes with a single-cell emission.
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