Pejman Tahmasebi · Water Resources Research · 2017
Abstract Dealing with complex and geologically realistic modeling of subsurface systems requires detailed spatial data sets. Such a big data can be usually provided through an image. Despite various developments, the training image‐based techniques are still not well‐designed for modeling multiscale and complex structures. Pixel‐based methods honor the conditioning point data with poor reproduction of large‐scale features, while some other techniques, termed pattern‐based, represent a superior reproduction of long‐range and complex structures and being biased around the conditioning data. In this paper, a new look at the geostatistical modeling using a hybrid pattern‐pixel‐based simulation (HYPPS) is proposed, wherein the pixel and pattern‐based techniques are used simultaneously. A perfect reproduction of the conditioning point data is achieved using the proposed HYPPS method. The algorithm is developed for single and multivariate simulations. This method is applied on different 2‐D/3‐D categorical data and the results show significant improvement over the previous techniques.
Cited by 59
Euntai Kim, Heejin Lee, Changhoon Lee et al. · Journal of Advanced Computational Intelligence and Intelligent Informatics · 1999
We propose an approach to Takagi-Sugeno fuzzy modeling via a genetic algorithm consisting of 2 tuning steps - coarse and fine. A moving genetic algorithm (MGA) is proposed and used for fine tuning to obtain robust modeling results. Simulation results demonstrate the algorithm’s validity.
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Puneet Gupta, Phalguni Gupta, Phalguni Gupta et al. · Applied Mathematical Modelling · 2016
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John Hourdos, Michael Janson, David Levinson et al. · University of Minnesota Digital Conservancy (University of Minnesota) · 2015
While High Occupancy Vehicle (HOV) lanes have been used for decades as a strategy for mitigating congestion, research has shown that they are not always effective. A 2001 study of the I-394 and I-35W HOV lanes in Minnesota found that the HOV lanes were on average underutilized, moving fewer people than the General-Purpose Lanes (GPL) even with the increased number of passengers per vehicle. To address the issue of underuse, in 2003 the Minnesota Legislature authorized the conversion of the I-394 HOV lanes into High-Occupancy Toll (HOT) lanes, named the MnPASS Express Lanes. The MnPASS lanes operate using a fully dynamic pricing schedule, where pricing is dictated by the level of congestion in the HOT lane. To better understand the nature of HOT lanes and the decisions of their users, this study explored the possibilities for a microscopic traffic simulation-based model of HOT lanes. Based on a series of field studies where the price of the toll was changed while observing changes in demand in the HOT lane, models describing the lane choice behavior of MnPASS users were developed and calibrated. These models interfaced with the traffic simulation software Aimsun through a number of extension modules and tested on the two MnPASS corridors of I-394 and I35W corridors in the west and south suburbs of Minneapolis, Minnesota. The integrated HOT simulation tool was also used to develop and test a number of alternative pricing strategies including a more efficient version of the current strategy.
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Abdel-Razzak Merheb, Hassan Noura, François Bateman · Lebanese Science Journal · 2022
In this paper, the mathematical modeling of a new bio-inspired evolutionary search algorithm called Ecological Systems Algorithm (ESA) is presented. ESA imitates ecological rules to find iteratively the optimum of a given function through interaction between predator and prey search species. ESA is then compared to the well-known Genetic Algorithm which is a powerful bio-inspired stochastic search/optimization algorithm used for decades. Simulation results of the two algorithms optimizing ten different benchmark functions are used to investigate and compare both algorithms based on their speed, performance, reliability, and efficiency.
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Li Yiyu · University of Southern California Digital Library · 2013
Amyloid fibrils are associated with over 40 human diseases. For example, fibrils of human islet amyloid polypeptide (hIAPP), ?-synuclein, and amyloid-? are pathological hallmarks of type 2 diabetes, Parkinson?s disease and Alzheimer?s disease, respectively. Understanding of fibril structure and that of the protofilaments that constitute the fibrils will help to reveal the mechanism of fibril formation in human diseases and to facilitate therapeutic intervention. However, the fibril structures of full-length amyloid proteins/peptides are difficult to determine by direct experimental approaches since they are neither soluble nor crystallizable. ? The goals of this thesis are to develop algorithms, programs and protocols for the modeling and determination of amyloid fibril structures based on experimental data mainly obtained from election paramagnetic resonance (EPR) spectroscopy and electron microscopy (EM), and then to use the resulting models for explaining previously unclear aspects of the experimental data and for aiding experimental design to obtain more structural details of the amyloid fibrils. ? In the thesis work, a simulation protocol for modeling amyloid protofilaments mainly based on EPR data was developed. This protocol was demonstrated to be able to produce structures of hIAPP protofilaments consistent with the experimental data. Then, to investigate the properties of the protofilament structures obtained and to generate fibril models consisting of more than one protofilament, a new program, MFIBRIL, was developed for flexible construction of fibrils from a monomeric unit. Several potential models of the hIAPP fibril were constructed using MFIBRIL and then refined by equilibration using molecular dynamics simulations in the NAMD software package. The refined models were evaluated by predicting the ring-to-ring distances using the MFIBRIL analysis tool and the inter-spin label distances and residue mobilities using PRONOX (another program developed in our laboratory) and comparing the predictions to the experimental data. This work led to identification of a favorable hIAPP fibril model consisting of two protofilaments with strong hydrophobic interactions between I26 and V32 surrounding hydrophilic interactions between S28 and T30 in the second ?-strand (C-terminal) regions. The programs and protocols developed in this work are applicable to structural determination of other amyloid fibrils. ? In addition to the modeling of amyloid fibrils, two other projects are described in the thesis. The first is addition to the solvation function of WATGEN (a program developed in our laboratory for modeling the water network at protein-peptide interfaces) to protein-RNA interfaces and investigation of the features of solvated protein-RNA interfaces. The second project involves modeling of the transport mechanism of the human dipeptide transporter (hPEPT1).
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Tan Ren-chun, Lan Yao · Geo-spatial Information Science · 2007
3D reconstruction of terrain model based on digital line graphics (DLG) is discussed. An auto-coupling triangles algorithm based on triangle topological relationship is put forward, and the topological data model of complicated terrain is developed. Based on this data model, automatic 3D topological reconstruction of terrain is realized.
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Sassan Ahmadi And, Sassan Ahmadi, Andreas S. Spanias · 2007
A new phase modeling algorithm for sinusoidal analysis and synthesis of speech signals is presented. Short-time sinusoidal phases are efficiently approximated by incorporating linear prediction, spectral sampling, delay compensation, and phase correction techniques. The algorithm is different than phase compensation methods proposed for multi-pulse LPC in that it has been tailored to sinusoidal transform coding of speech signals. Performance analysis on a large speech database indicates considerable improvement in temporal and spectral matching between the original and reconstructed signals as compared to other sinusoidal phase models as well as improved subjective quality of the reproduced speech. 1. INTRODUCTION The sinusoidal model represents speech by a linear combination of sinusoids with time-varying amplitudes, frequencies, and phases [4]-[10]. Although successful techniques have been developed for quantization of the sinusoidal amplitudes and frequencies [5],[9], there is sti...
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Салахлы, C. А., Осипов, А. Н., Гавриленко, В. С. et al. · Репозиторий БГУИР (BSUIR Repository) · 2024
В статье предложен подход к моделированию помехоустойчивого алгоритма для измерения концентрации глюкозы в крови с использованием кодов Баркера и псевдослучайных последовательностей. Основное внимание уделено повышению точности измерений в условиях высокого уровня шумов, что особенно важно для энергоэффективных подкожных глюкозных сенсоров, работающих на низкой интенсивности флуоресценции. Разработанный метод направлен на улучшение соотношения сигнал/шум и предназначен для применения в системах непрерывного мониторинга глюкозы (CGM).
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James Ledoux · HAL (Le Centre pour la Communication Scientifique Directe) · 2006
International audience
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Irina Nikolayevna Chubarova · 2015
We study engineered genetic algorithm (EGA), which is a self-optimizing, robust and stable optimization tool that operates under complicated modeling settings. The self-optimization feature of EGA improves the efficiency of the algorithm performance by implementing simulated annealing principles to genetic algorithm (GA) itself. An application has been considered using several compartmental models for modeling cholera epidemics in Haiti in 2010.
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김병주 · Open Access System for Information Sharing (Pohang University of Science and Technology) · 2021
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Michael O. McCracken, Allan Snavely, Allen D. Malony · Lecture notes in computer science · 2003
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T. Thurgate, B. Pack · 2005
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Zhengji Li, Xiaohai He, Qizhi Teng et al. · Physical review. E · 2020
Computed tomography (CT) images of large core samples acquired by imaging equipment are insufficiently clear and ineffectively describe the tiny pore structure; conversely, images of small core samples are insufficiently globally representative. To alleviate these challenges, the idea of a super-resolution reconstruction algorithm is combined with that of a three-dimensional core reconstruction algorithm, and a multiscale core CT image fusion reconstruction algorithm is proposed. To obtain sufficient image quality with high resolution, a large-scale core image is used to provide global feature information as well as information regarding the basic morphological structure of a large-scale pore and particle. Then the texture pattern and the tiny pore distribution information of a small-scale core image is used to refine the coarse large-scale core image. A blind image quality assessment is utilized to estimate the degradation model of core images at different scales. A multilevel pattern mapping dictionary containing local binary patterns is designed to speed up the pattern matching procedure, and an adaptive weighted reconstruction algorithm is designed to reduce the blockiness. With our method, images of the same core at different scales were successfully fused. The proposed algorithm is extensively tested on microstructures of different rock samples; all cases of the reconstructed results and those of the actual sample were found to be in good agreement with each other. The final reconstructed image contains both large-scale and small-scale information that can provide a better understanding of the core samples and inform the accurate calculation of parameters.
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Elise Dusseldorp, Claudio Conversano · 2013
Regression trunk model estimation proposed by Dusseldorp and Meulman (2004) <<a href="https://doi.org/10.1007%2Fbf02295641" target="_top">doi:10.1007/bf02295641</a>> and Dusseldorp, Conversano, Van Os (2010) <<a href="https://doi.org/10.1198%2Fjcgs.2010.06089" target="_top">doi:10.1198/jcgs.2010.06089</a>>, integrating a regression tree and a multiple regression model.
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Pi‐Sheng Deng · IGI Global eBooks · 2011
Genetic algorithms (GAs) are stochastic search techniques based on the concepts of natural population genetics for exploring a huge solution space in identifying optimal or near optimal solutions (Davis, 1991)(Holland, 1992)(Reeves & Rowe, 2003), and are more likely able to avoid the local optima problem than traditional gradient based hill-climbing optimization techniques when solving complex problems. In essence, GAs are a type of reinforcement learning technique (Grefenstette, 1993), which are able to improve solutions gradually on the basis of the previous solutions. GAs are characterized by their abilities to combine candidate solutions to exploit efficiently a promising area in the solution space while stochastically exploring new search regions with expected improved performance. Many successful applications of this technique are frequently reported across various kinds of industries and businesses, including function optimization (Ballester & Carter, 2004)(Richter & Paxton, 2005), financial risk and portfolio management (Shin & Han, 1999), market trading (Kean, 1995), machine vision and pattern recognition (Vafaie & De Jong, 1998), document retrieval (Gordon, 1988), network topological design (Pierre & Legault, 1998)(Arabas & Kozdrowski, 2001), job shop scheduling (Özdamar, 1999), and optimization for operating system’s dynamic memory configuration (Del Rosso, 2006), among others. In this research we introduce the concept and components of GAs, and then apply the GA technique to the modeling of the batch selection problem of flexible manufacturing systems (FMSs). The model developed in this paper serves as the basis for the experiment in Deng (2007). Request access from your librarian to read this chapter's full text.
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Xin Ma, Jianhuang Wu, Renhui Ma · Journal of Computer-Aided Design & Computer Graphics · 2010
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Sergey V. Ten, Kohei Otsuyama · Lecture notes in computer science · 1997
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Matthew Aguirre (9558032), Jeffrey P. Spence (15317543), Guy Sella (230321) et al. · Figshare · 2025
<b>(A)</b> Overview of network generating algorithm, based on a growth process with preferential attachment. At each step, randomly add either a node or an edge, with the source and target determined by the out- and in-degree distributions, and node membership in groups. <b>(B)</b> Key graph properties can be tuned by changing the parameters of the generating algorithm. We validate this in 1,000 synthetic graphs with 500 nodes each, produced with various generating parameters. The same networks are plotted in all four panels, indicating robustness across different background distributions of parameters. Note that in the left two panels, CV is the coefficient of variation of the in- or out-degree distribution, i.e. the mean degree over its standard deviation.
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