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Journal articles on the topic 'High-order modeling'

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Author: Grafiati

Published: 14 March 2023

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1

Chang, Yuan Lung. "Inferring Markov Chain for Modeling Order Book Dynamics in High Frequency Environment." International Journal of Machine Learning and Computing 5, no. 3 (June 2015): 247–51. http://dx.doi.org/10.7763/ijmlc.2015.v5.515.

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2

Newman, Christopher, Geoff Womeldorff, Luis Chacón, and Dana A. Knoll. "High-Order/Low-Order Methods for Ocean Modeling." Procedia Computer Science 51 (2015): 2086–96. http://dx.doi.org/10.1016/j.procs.2015.05.477.

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3

Qing Yang, Qing Yang, Ning Li Qing Yang, Shiyan Hu Ning Li, Heyong Li Shiyan Hu, and Jingwei Zhang Heyong Li. "Click-Through Rate Prediction Algorithm Based on Modeling of Implicit High-Order Feature Importance." 網際網路技術學刊 23, no. 5 (September 2022): 1077–86. http://dx.doi.org/10.53106/160792642022092305016.

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<p>Click-through rate (CTR) prediction plays a central role in online advertising and recommendation systems. In recent years, with the successful application of deep neural networks (DNNs) in many fields, researchers have integrated deep learning into CTR prediction algorithms to model implicit high-order features. However, most of these existing methods unify the weights of implicit higher-order features to predict user behaviors. The importance of such features of different dimensions for predicting user click behaviors are different. Base on this, we propose a prediction method that dynamically learns the importance of implicit high-order features. Specifically, we integrate the output features of deep and shallow components, and adaptively learn the weights of implicit high-order features from among all features through the designed attention network, which effectively capturing the deep interests of users. In addition, this framework has strong versatility and can be combined with shallow models such as Logistic Regression (LR) and Factorization Machines (FMs) to form different models and achieve optimal performance. The extended experiment is conducted on two large-scale datasets, AVAZU and SafeDrive, and the experimental results show that the performance of the proposed model is superior to that of existing baseline models.</p> <p> </p>

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4

Chen, Jing-Bo. "High-order time discretizations in seismic modeling." GEOPHYSICS 72, no. 5 (September 2007): SM115—SM122. http://dx.doi.org/10.1190/1.2750424.

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Seismic modeling plays an important role in explor-ation geophysics. High-order modeling schemes are in demand for practical reasons. In this context, I present three kinds of high-order time discretizations: Lax-Wendroff methods, Nyström methods, and splitting methods. Lax-Wendroff methods are based on the Taylor expansion and the replacement of high-order temporal derivatives by spatial derivatives, Nyström methods are simplified Runge-Kutta algorithms, and splitting methods comprise substeps for one-step computation. Based on these methods, three schemes with third-order and fourth-order accuracy in time and pseudospectral discretizations in space are presented. I also compare their accuracy, stability, and computational complexity, and discuss advantages and shortcomings of these algorithms. Numerical experiments show that the fourth-order Lax-Wendroff scheme is more efficient for short-time simulations while the fourth-order Nyström scheme and the third-order splitting scheme are more efficient for long-term computations.

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5

Gavva, S. P. "Modeling of High-Order Overtone Molecular Vibrations." Russian Physics Journal 48, no. 3 (March 2005): 275–79. http://dx.doi.org/10.1007/s11182-005-0119-9.

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6

Takeuchi, Ichiro, Kazuya Nakagawa, and Koji Tsuda. "Machine Learning Algorithm for High-Order Interaction Modeling." Journal of the Robotics Society of Japan 35, no. 3 (2017): 215–20. http://dx.doi.org/10.7210/jrsj.35.215.

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7

Dorf, M., M. Dorr, J. Hittinger, W. Lee, and D. Ghosh. "High-order finite-volume modeling of drift waves." Journal of Computational Physics 373 (November 2018): 446–54. http://dx.doi.org/10.1016/j.jcp.2018.07.009.

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8

Hestholm, Stig. "Acoustic VTI modeling using high-order finite differences." GEOPHYSICS 74, no. 5 (September 2009): T67—T73. http://dx.doi.org/10.1190/1.3157242.

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Two second-order wave equations for acoustic vertical transversely isotropic (VTI) media are transformed to six first-order coupled partial differential equations for a more straighforward numerical implementation of the derivatives. The resulting first-order equations have a more natural form for discretization by any finite-difference, pseudospectral, or finite-element method. I discretized the new equations by high-order finite differences and used synthetic seismograms and snapshots for anisotropic and isotropic cases. The relative merits of placing the source deep and close to a free surface are assessed, illustrating advantages of exciting the source inside or outside of a near-surface, thin, isotropic layer. Results show that traveltimes from deep seismic reflectors can remain virtually unaffected when near-surface isotropic layers are included in acoustic VTI media.

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9

Tsai, Hsing-Chih. "Modeling concrete strength with high-order neural networks." Neural Computing and Applications 27, no. 8 (August 26, 2015): 2465–73. http://dx.doi.org/10.1007/s00521-015-2017-6.

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10

Huang, Kai, Vadim Backman, and Igal Szleifer. "Modeling High-Order Chromatin Structure in Single Cells." Biophysical Journal 118, no. 3 (February 2020): 550a—551a. http://dx.doi.org/10.1016/j.bpj.2019.11.3010.

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11

Pasquetti, Richard. "High-order LES modeling of turbulent incompressible flow." Comptes Rendus Mécanique 333, no. 1 (January 2005): 39–49. http://dx.doi.org/10.1016/j.crme.2004.09.018.

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12

Parise, Mauro, and Saverio Cristina. "HIGH-ORDER ELECTROMAGNETIC MODELING OF SHORTWAVE INDUCTIVE DIATHERMY EFFECTS." Progress In Electromagnetics Research 92 (2009): 235–53. http://dx.doi.org/10.2528/pier09022608.

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13

Loewenthal, D., C. J. Wang, O. G. Johnson, and C. Juhlin. "High Order Finite Difference Modeling and Reverse Time Migration." Exploration Geophysics 22, no. 3 (September 1991): 533–45. http://dx.doi.org/10.1071/eg991533.

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14

Davoudi, A., P. L. Chapman, J. Jatskevich, and A. Khaligh. "Reduced-Order Modeling of High-Fidelity Magnetic Equivalent Circuits." IEEE Transactions on Power Electronics 24, no. 12 (December 2009): 2847–55. http://dx.doi.org/10.1109/tpel.2009.2031436.

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15

Lynett, Patrick J. "Nearshore Wave Modeling with High-Order Boussinesq-Type Equations." Journal of Waterway, Port, Coastal, and Ocean Engineering 132, no. 5 (September 2006): 348–57. http://dx.doi.org/10.1061/(asce)0733-950x(2006)132:5(348).

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16

Brandner, Marek, Jiří Egermaier, and Hana Kopincová. "High order well-balanced scheme for river flow modeling." Mathematics and Computers in Simulation 82, no. 10 (June 2012): 1773–87. http://dx.doi.org/10.1016/j.matcom.2012.05.013.

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17

Nguyen, Ngoc Anh Thi, Hyung-Jeong Yang, and Sunhee Kim. "HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling." Biosystems 158 (August 2017): 57–67. http://dx.doi.org/10.1016/j.biosystems.2017.02.004.

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18

Ha, Sang-Gyu, Jeahoon Cho, Eun-Ki Kim, Yong Bae Park, and Kyung-Young Jung. "FDTD Dispersive Modeling With High-Order Rational Constitutive Parameters." IEEE Transactions on Antennas and Propagation 63, no. 9 (September 2015): 4233–38. http://dx.doi.org/10.1109/tap.2015.2452961.

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19

Raman, Vijay, Osama Mohammed, Kang Yen, and Kurt Ramdin. "An improved modeling technique for high‐order acoustic spectra." Journal of the Acoustical Society of America 86, S1 (November 1989): S40. http://dx.doi.org/10.1121/1.2027497.

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20

Waggy, Scott B., Alan Hsieh, and Sedat Biringen. "Modeling high-order statistics in the turbulent Ekman layer." Geophysical & Astrophysical Fluid Dynamics 110, no. 5 (June 13, 2016): 391–408. http://dx.doi.org/10.1080/03091929.2016.1196202.

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21

Grivet-Talocia, S., and F. Canavero. "Wavelet-based high-order adaptive modeling of lossy interconnects." IEEE Transactions on Electromagnetic Compatibility 43, no. 4 (2001): 471–84. http://dx.doi.org/10.1109/15.974626.

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22

Alauzet, F., and A. Loseille. "High-order sonic boom modeling based on adaptive methods." Journal of Computational Physics 229, no. 3 (February 2010): 561–93. http://dx.doi.org/10.1016/j.jcp.2009.09.020.

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23

Tong, P., D. Yang, B. Hua, and M. Wang. "A High-Order Stereo-Modeling Method for Solving Wave Equations." Bulletin of the Seismological Society of America 103, no. 2A (March 21, 2013): 811–33. http://dx.doi.org/10.1785/0120120144.

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24

Lee, Kyunghoon, Taewoo Nam, Christopher Perullo, and Dimitri N. Mavris. "Reduced-Order Modeling of a High-Fidelity Propulsion System Simulation." AIAA Journal 49, no. 8 (August 2011): 1665–82. http://dx.doi.org/10.2514/1.j050887.

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25

Kaiqi Huang, Chong Wang, and Dacheng Tao. "High-Order Topology Modeling of Visual Words for Image Classification." IEEE Transactions on Image Processing 24, no. 11 (November 2015): 3598–608. http://dx.doi.org/10.1109/tip.2015.2449081.

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26

Liu, Yinsheng, Zhenhui Tan, and Xia Chen. "Modeling the Channel Time Variation Using High-Order-Motion Model." IEEE Communications Letters 15, no. 3 (March 2011): 275–77. http://dx.doi.org/10.1109/lcomm.2011.011011.102026.

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27

Wang, Jian, Wen-Yan Yin, Pei-Guo Liu, and Qing-Huo Liu. "High-Order Interface Treatment Techniques for Modeling Curved Dielectric Objects." IEEE Transactions on Antennas and Propagation 58, no. 9 (September 2010): 2946–53. http://dx.doi.org/10.1109/tap.2010.2052562.

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28

Lin, Zhirong, and Xin Yuan. "Numerical modeling and high-order scheme for wet steam flow." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 11 (April 22, 2015): 1846–60. http://dx.doi.org/10.1177/0954406215584394.

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A mathematical model of three-dimensional nonequilibrium condensing wet-steam flow is established in Eulerian form, based on conservation laws for a mixture of steam and water droplets. The method of moments is introduced in modeling the droplet spectrum. To describe the nonequilibrium condensing process, models for classical nucleation and enhanced droplet growth are applied. A special high-order implicit scheme is constructed for this condensing flow model. Tables based on IAPWS-IF97 formulae are used in solving the thermal properties of wet steam. The numerical results for a two-dimensional supersonic nozzle and a low-pressure steam turbine stage are compared with experimental data. The good agreement indicates the effectiveness of the condensation model and numerical scheme.

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29

Song, Wei, Zhun Yang, Xin-Qing Sheng, and Yang Hao. "Accurate modeling of high order spatial dispersion of wire medium." Optics Express 21, no. 24 (November 25, 2013): 29836. http://dx.doi.org/10.1364/oe.21.029836.

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30

Janković, I., and R. Barnes. "High-order line elements in modeling two-dimensional groundwater flow." Journal of Hydrology 226, no. 3-4 (December 1999): 211–23. http://dx.doi.org/10.1016/s0022-1694(99)00140-7.

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31

Wininger, Clinton W., and Jeffrey J. Heys. "Particle transport modeling in pulmonary airways with high-order elements." Mathematical Biosciences 232, no. 1 (July 2011): 11–19. http://dx.doi.org/10.1016/j.mbs.2011.03.011.

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32

Bingbing Ni, Shuicheng Yan, Meng Wang, A. A. Kassim, and Qi Tian. "High-Order Local Spatial Context Modeling by Spatialized Random Forest." IEEE Transactions on Image Processing 22, no. 2 (February 2013): 739–51. http://dx.doi.org/10.1109/tip.2012.2222895.

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33

Wu, Jinglai, Zhen Luo, Jing Zheng, and Chao Jiang. "Incremental modeling of a new high-order polynomial surrogate model." Applied Mathematical Modelling 40, no. 7-8 (April 2016): 4681–99. http://dx.doi.org/10.1016/j.apm.2015.12.002.

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34

Zhou, Dawei, Si Zhang, Mehmet Yigit Yildirim, Scott Alcorn, Hanghang Tong, Hasan Davulcu, and Jingrui He. "High-Order Structure Exploration on Massive Graphs." ACM Transactions on Knowledge Discovery from Data 15, no. 2 (April 2021): 1–26. http://dx.doi.org/10.1145/3425637.

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Modeling and exploring high-order connectivity patterns, also called network motifs, are essential for understanding the fundamental structures that control and mediate the behavior of many complex systems. For example, in social networks, triangles have been proven to play the fundamental role in understanding social network communities; in online transaction networks, detecting directed looped transactions helps identify money laundering activities; in personally identifiable information networks, the star-shaped structures may correspond to a set of synthetic identities. Despite the ubiquity of such high-order structures, many existing graph clustering methods are either not designed for the high-order connectivity patterns, or suffer from the prohibitive computational cost when modeling high-order structures in the large-scale networks. This article generalizes the challenges in multiple dimensions. First ( Model ), we introduce the notion of high-order conductance, and define the high-order diffusion core, which is based on a high-order random walk induced by the user-specified high-order network structure. Second ( Algorithm ), we propose a novel high-order structure-preserving graph clustering framework named HOSGRAP , which partitions the graph into structure-rich clusters in polylogarithmic time with respect to the number of edges in the graph. Third ( Generalization ), we generalize our proposed algorithm to solve the real-world problems on various types of graphs, such as signed graphs, bipartite graphs, and multi-partite graphs. Experimental results on both synthetic and real graphs demonstrate the effectiveness and efficiency of the proposed algorithms.

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35

de Oliveira, Pedro M., Michael P. Sitte, Marco Zedda, Andrea Giusti, and Epaminondas Mastorakos. "Low-order modeling of high-altitude relight of jet engine combustors." International Journal of Spray and Combustion Dynamics 13, no. 1-2 (June 2021): 20–34. http://dx.doi.org/10.1177/17568277211021322.

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A physics-based, low-order ignition model is used to assess the ignition performance of a kerosene-fueled gas-turbine combustor under high-altitude relight conditions. The ignition model used in this study is based on the motion of virtual flame particles and their extinction according to a Karlovitz number criterion, and a stochastic procedure is used to account for the effects of spray polydispersity on the flame’s extinction behavior. The effects of large droplets arising from poor fuel atomization at sub-idle conditions are then investigated in the context of the model parameters and the combustor’s ignition behavior. For that, a Reynolds-averaged Navier-Stokes simulation of the cold flow in the combustor was performed and used as an input for the ignition model. Ignition was possible with a Sauter mean diameter (SMD) of 50 μm, and was enhanced by increasing the spark volume. Although doubling the spark volume at larger SMDs (75 and 100 μm) resulted in the suppression of short-mode failure events, ignition was not achieved due to a reduction of the effective flammable volume in the combustor. Overall, a lower ignition probability is obtained when using the stochastic procedure for the spray, which is to be expected due to the additional detrimental effects associated with poor spray atomisation and high polydispersity.

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36

Antelmi, Alessia, Gennaro Cordasco, Vittorio Scarano, and Carmine Spagnuolo. "Modeling and Evaluating Epidemic Control Strategies With High-Order Temporal Networks." IEEE Access 9 (2021): 140938–64. http://dx.doi.org/10.1109/access.2021.3119459.

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37

Ekblad, Mark. "Reduced-order modeling and controller design for a high-performance helicopter." Journal of Guidance, Control, and Dynamics 13, no. 3 (May 1990): 439–49. http://dx.doi.org/10.2514/3.25356.

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38

Barbarossa, S., A. Scaglione, and G. B. Giannakis. "Product high-order ambiguity function for multicomponent polynomial-phase signal modeling." IEEE Transactions on Signal Processing 46, no. 3 (March 1998): 691–708. http://dx.doi.org/10.1109/78.661336.

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39

Seriani, Geza, Jose M. Carcione, and Enrico Priolo. "Numerical simulation of interface waves by high‐order spectral modeling techniques." Journal of the Acoustical Society of America 92, no. 4 (October 1992): 2456. http://dx.doi.org/10.1121/1.404528.

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40

PADOVANI, E., E. PRIOLO, and G. SERIANI. "LOW AND HIGH ORDER FINITE ELEMENT METHOD: EXPERIENCE IN SEISMIC MODELING." Journal of Computational Acoustics 02, no. 04 (December 1994): 371–422. http://dx.doi.org/10.1142/s0218396x94000233.

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The finite element method (FEM) is a numerical technique well suited to solving problems of elastic wave propagation in complex geometries and heterogeneous media. The main advantages are that very irregular grids can be used, free surface boundary conditions can be easily taken into account, a good reconstruction is possible of irregular surface topography, and complex geometries, such as curved, dipping and rough interfaces, intrusions, cusps, and holes can be defined. The main drawbacks of the classical approach are the need for a large amount of memory, low computational efficiency, and the possible appearance of spurious effects. In this paper we describe some experience in improving the computational efficiency of a finite element code based on a global approach, and used for seismic modeling in geophysical oil exploration. Results from the use of different methods and models run on a mini-superworkstation APOLLO DN10000 are reported and compared. With Chebyshev spectral elements, great accuracy can be reached with almost no numerical artifacts. Static condensation of the spectral element's internal nodes dramatically reduces memory requirements and CPU time. Time integration performed with the classical implicit Newmark scheme is very accurate but not very efficient. Due to the high sparsity of the matrices, the use of compressed storage is shown to greatly reduce not only memory requirements but also computing time. The operation which most affects the performance is the matrix-by-vector product; an effective programming of this subroutine for the storage technique used is decisive. The conjugate gradient method preconditioned by incomplete Cholesky factorization provides, in general, a good compromise between efficiency and memory requirements. Spectral elements greatly increase its efficiency, since the number of iterations is reduced. The most efficient and accurate method is a hybrid iterative-direct solution of the linear system arising from the static condensation of high order elements. The size of 2D models that can be handled in a reasonable time on this kind of computer is nowadays hardly sufficient, and significant 3D modeling is completely unfeasible. However the introduction of new FEM algorithms coupled with the use of new computer architectures is encouraging for the future.

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41

Moore, Jason H., and Lance W. Hahn. "Petri net modeling of high-order genetic systems using grammatical evolution." Biosystems 72, no. 1-2 (November 2003): 177–86. http://dx.doi.org/10.1016/s0303-2647(03)00142-4.

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42

Nicolet, Christophe, Bob Greiveldinger, Jean Jacques Herou, Basile Kawkabani, Philippe Allenbach, Jean-Jacques Simond, and Francois Avellan. "High-Order Modeling of Hydraulic Power Plant in Islanded Power Network." IEEE Transactions on Power Systems 22, no. 4 (November 2007): 1870–80. http://dx.doi.org/10.1109/tpwrs.2007.907348.

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43

Imai, Jun. "Modeling uncertainty of elastic vibrating systems with unknown high order eigenstructures." IFAC Proceedings Volumes 32, no. 2 (July 1999): 4365–70. http://dx.doi.org/10.1016/s1474-6670(17)56744-0.

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44

Chertok, A. V., V. Yu Korolev, and A. Yu Korchagin. "Modeling High-Frequency Non-Homogeneous Order Flows by Compound Cox Processes*." Journal of Mathematical Sciences 214, no. 1 (February 22, 2016): 44–68. http://dx.doi.org/10.1007/s10958-016-2757-6.

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45

Priolo, Enrico, José M. Carcione, and Géza Seriani. "Numerical simulation of interface waves by high‐order spectral modeling techniques." Journal of the Acoustical Society of America 95, no. 2 (February 1994): 681–93. http://dx.doi.org/10.1121/1.408428.

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46

Fan, Na, Lian-Feng Zhao, Ying-Jie Gao, and Zhen-Xing Yao. "A discontinuous collocated-grid implementation for high-order finite-difference modeling." GEOPHYSICS 80, no. 4 (July 2015): T175—T181. http://dx.doi.org/10.1190/geo2015-0001.1.

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47

Gao, Yan, Zhiyong Dai, and Wenfen Liu. "Modeling Dynamic Trust and Risk Evaluation Based on High-Order Moments." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/789820.

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This paper proposes a dynamic trust and risk evaluation model based on high-order moments. The credibility of an entity is measured with trust degree and risk value comprehensively. Firstly, considering the dynamic and time decay characters of trust, a time attenuation function is defined, and direct trust is further expressed. Subsequently, in order to improve the accuracy of feedback trust, a filter mechanism is constructed to eliminate the false feedback, combining coefficient of skewness with hypothesis test. More importantly, the weights of direct trust and feedback trust are derived subjectively and adaptively with the moments and frequency of direct interactions. Furthermore, risk is evaluated with direct risk and feedback risk, which are obtained by mainly using coefficient of variation and coefficient of kurtosis. Risk value can be used to measure the stability of providing services. Simulation results show that the proposed model not only has high accuracy, but also resists effectively collusive attacks and strategic malicious behaviors.

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48

Chen, Lei, Yan Yan, and Changyin Sun. "A novel modeling and controlling approach for high‐order nonlinear systems." Asian Journal of Control 22, no. 3 (February 18, 2019): 1295–305. http://dx.doi.org/10.1002/asjc.2000.

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49

Pais, Matthew J., Felipe A. C. Viana, and Nam H. Kim. "Enabling high-order integration of fatigue crack growth with surrogate modeling." International Journal of Fatigue 43 (October 2012): 150–59. http://dx.doi.org/10.1016/j.ijfatigue.2012.03.010.

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50

Li, Jizhou, and Béatrice Rivière. "Numerical Modeling of Miscible Viscous Fingering Instabilities by High-Order Methods." Transport in Porous Media 113, no. 3 (May 24, 2016): 607–28. http://dx.doi.org/10.1007/s11242-016-0715-x.

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