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Journal articles on the topic 'Set-Based Methods'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

Dubois, D., E. Hullermeier, and H. Prade. "Fuzzy set-based methods in instance-based reasoning." IEEE Transactions on Fuzzy Systems 10, no. 3 (2002): 322–32. http://dx.doi.org/10.1109/tfuzz.2002.1006435.

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2

PAN, Qing, and Guo-Liang XU. "Metamorphosis Based on the Level-Set Methods." Chinese Journal of Computers 32, no. 2 (2009): 213–20. http://dx.doi.org/10.3724/sp.j.1016.2009.00213.

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3

Wang, Xu. "Methods to Set Threshold Based on MATLAB." Applied Mechanics and Materials 121-126 (October 2011): 954–59. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.954.

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In setting threshold to determine the arrival time in mechanical vibration signals which are collected by sensors and contain noise, five methods based on MATLAB have been proposed. Direct observation of the figure, powering operation to data, variance theory, reverse search and MATLAB programming has been used. Each method’s merits and demerits will be discussed so that it can be varied according to different situations. Consistent results have been achieved by those methods. MATLAB code has also been presented for better understanding of the algorithm and reproduction. After this analysis, a

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4

De-jun, Wang, Tang Yun, Yu Hong-chuan, and Tang Ze-sheng. "Level set methods based on distance function." Applied Mathematics and Mechanics 24, no. 8 (2003): 950–60. http://dx.doi.org/10.1007/bf02446501.

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5

Su, Yu-Chen, William James Gauderman, Kiros Berhane, and Juan Pablo Lewinger. "Adaptive Set-Based Methods for Association Testing." Genetic Epidemiology 40, no. 2 (2015): 113–22. http://dx.doi.org/10.1002/gepi.21950.

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6

Dullen, Shawn, Dinesh Verma, Mark Blackburn, and Cliff Whitcomb. "Survey on set‐based design (SBD) quantitative methods." Systems Engineering 24, no. 5 (2021): 269–92. http://dx.doi.org/10.1002/sys.21580.

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7

Le Coënt, Adrien, Florian De Vuyst, Christian Rey, Ludovic Chamoin, and Laurent Fribourg. "Control of mechanical systems using set based methods." International Journal of Dynamics and Control 5, no. 3 (2016): 496–512. http://dx.doi.org/10.1007/s40435-016-0245-y.

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8

Liao, Guojun, Feng Liu, Gary C. de la Pena, Danping Peng, and Stanley Osher. "Level-Set-Based Deformation Methods for Adaptive Grids." Journal of Computational Physics 159, no. 1 (2000): 103–22. http://dx.doi.org/10.1006/jcph.2000.6432.

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9

Hussein, A., H. A. Muttlak, and E. Al-Sawi. "Group sequential methods based on ranked set samples." Statistical Papers 54, no. 3 (2012): 547–62. http://dx.doi.org/10.1007/s00362-012-0448-z.

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10

ZHANG, Zhiqian, and Hirohisa NOGUCHI. "1108 Level set-based topology optimization with material heterogeneity using meshfree methods and dual discretizations." Proceedings of The Computational Mechanics Conference 2007.20 (2007): 687–88. http://dx.doi.org/10.1299/jsmecmd.2007.20.687.

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11

Han, Hong Wei. "Improved Algorithm Based on Level Set Image Segmentation Methods." Applied Mechanics and Materials 687-691 (November 2014): 4128–31. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4128.

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Image segmentation is one of the most fundamental and important areas in the field of image processing and computer vision. The traditional level set methods need initialize the level set function as a distance function. If the initial contour is selected inappropriate, we may not get the desired ideal segmentation result. In order to solve the problem of level set automation initial, we proposed a new image segmentation algorithm based on level set and marker extraction. First, we extract the internal mark as level set initial curve by using Extended-minima transform. And then, through using

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12

ABDALLAH, Mohamed, and Samir ASHOUR. "New Parametric Estimation Methods based on Ranked Set Sampling." GAZI UNIVERSITY JOURNAL OF SCIENCE 32, no. 4 (2019): 1356–68. http://dx.doi.org/10.35378/gujs.477631.

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13

Wang, Jianzhen, and Juanli Li. "An Improved Images Segmentation Methods Based on Level Set." Journal of Software Engineering 10, no. 1 (2015): 155–62. http://dx.doi.org/10.3923/jse.2016.155.162.

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14

Bechtold, Stephen E., and Michael J. Brusco. "Microcomputer‐based working set generation methods for personnel scheduling." International Journal of Operations & Production Management 15, no. 10 (1995): 63–74. http://dx.doi.org/10.1108/01443579510098329.

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15

Nacher, Jose C., and Tatsuya Akutsu. "Minimum dominating set-based methods for analyzing biological networks." Methods 102 (June 2016): 57–63. http://dx.doi.org/10.1016/j.ymeth.2015.12.017.

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16

Mirzadeh, Mohammad, Arthur Guittet, Carsten Burstedde, and Frederic Gibou. "Parallel level-set methods on adaptive tree-based grids." Journal of Computational Physics 322 (October 2016): 345–64. http://dx.doi.org/10.1016/j.jcp.2016.06.017.

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17

Loos, Pierre-François, Barthélémy Pradines, Anthony Scemama, Emmanuel Giner, and Julien Toulouse. "Density-Based Basis-Set Incompleteness Correction for GW Methods." Journal of Chemical Theory and Computation 16, no. 2 (2019): 1018–28. http://dx.doi.org/10.1021/acs.jctc.9b01067.

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18

Hartmann, Daniel, Matthias Meinke, and Wolfgang Schröder. "Differential equation based constrained reinitialization for level set methods." Journal of Computational Physics 227, no. 14 (2008): 6821–45. http://dx.doi.org/10.1016/j.jcp.2008.03.040.

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19

Rudolph, Nadine, Petar Andonov, Heinrich J. Huber, and Rolf Findeisen. "Model-supported Patient Stratification Using Set-based Estimation Methods." IFAC-PapersOnLine 51, no. 18 (2018): 892–97. http://dx.doi.org/10.1016/j.ifacol.2018.09.233.

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20

Alexandre dit Sandretto, Julien. "Set-Based B-Series." Mathematics 10, no. 17 (2022): 3165. http://dx.doi.org/10.3390/math10173165.

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B-series were defined to unify the formalism of solutions for ordinary differential equations defined by series. Runge–Kutta schemes can be seen as truncated B-series, similar to Taylor series. In the prolific domain of reachability analysis, i.e., the process of computing the set of reachable states for a system, many techniques have been proposed without obvious links. In the particular case of uncertain initial conditions and/or parameters in the definition of differential equations, set-based approaches are a natural and elegant method to compute reachable sets. In this paper, an extension

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21

Sadamitsu, Kugatsu, Kuniko Saito, Kenji Imamura, Yoshihiro Matsuo, and Genichiro Kikui. "Entity Set Expansion based on Bootstrapping Methods using Topic Information." Journal of Natural Language Processing 19, no. 2 (2012): 89–106. http://dx.doi.org/10.5715/jnlp.19.89.

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22

Amini, Amin, and Navid Nikraz. "Proposing Two Defuzzification Methods based on Output Fuzzy Set Weights." International Journal of Intelligent Systems and Applications 8, no. 2 (2016): 1–12. http://dx.doi.org/10.5815/ijisa.2016.02.01.

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23

ZHANG, LIHE, and ZHENZHEN LIU. "IMAGE COSEGMENTATION BASED ON LOCAL AND GLOBAL LEVEL SET METHODS." International Journal of Image and Graphics 12, no. 03 (2012): 1250019. http://dx.doi.org/10.1142/s0219467812500192.

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In this paper, we propose a novel cosegmentation algorithm based on active contour model which utilizes local and global image statistics. Many localized region-based active contour models have been proposed to solve a challenging problem of the property (such as intensity, color, texture, etc.) inhomogeneities that often occurs in real images, but these models usually cannot reasonably evolve the curve in this situation that some center points along the curve are in homogeneous regions and their local regions are far away from the object. In order to overcome the difficulties we selectively e

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24

Chen, Ying, and Feng Yu Yang. "Analysis of Iris Images Segmentation Methods Based on Level Set." Advanced Materials Research 753-755 (August 2013): 2985–89. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2985.

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Iris recognition plays an important role in personal identification. In this study, we utilized CREASEG experimental platform to analyze the performance of some state-of-the-art image segmentation algorithms based on level set. Performance evaluation criteria include segmentation accuracy and computation time of pupil and iris localization. Four iris images were taken as experimental samples. The experimental results on those image samples demonstrate that Chan-Vese model achieve the best performance among all six algorithms. Furthermore, experimental results also show that energy functions pl

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25

Wang, Bei, Yi Xie, and Jian Guo Xiong. "Gasify Micro-Oil Ignition Technology Based on Level Set Methods." Advanced Materials Research 497 (April 2012): 387–91. http://dx.doi.org/10.4028/www.scientific.net/amr.497.387.

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In order to improve the Micro-oil ignition and low fuel load stable combustion in the thermal power system, the standard k-ε model is used in the analysis of flow field for studying the character of the mixture of coal particles, oil and gas mixture. The trajectories of two-phase flow are simulated by the Level Set Methods (LSM). The results describe the velocity distribution of the X-axis of the two-phase flow, concentration and track of coal particles, the thermal distribution of mixed fluid in the combustion chamber. It provides a theoretical basis for the further study of Micro-oil ignitio

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26

Cheng, Gong, Mathieu Morlighem, and G. Hilmar Gudmundsson. "Numerical stabilization methods for level-set-based ice front migration." Geoscientific Model Development 17, no. 16 (2024): 6227–47. http://dx.doi.org/10.5194/gmd-17-6227-2024.

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Abstract. Numerical modeling of ice sheet dynamics is a critical tool for projecting future sea level rise. Among all the processes responsible for the loss of mass of the ice sheets, enhanced ice discharge triggered by the retreat of marine-terminating glaciers is one of the key drivers. Numerical models of ice sheet flow are therefore required to include ice front migration in order to reproduce today's mass loss and to be able to predict their future. However, the discontinuous nature of calving poses a significant numerical challenge for accurately capturing the motion of the ice front. In

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27

Dubois, Didier, and Henri Prade. "Fuzzy set and possibility theory-based methods in artificial intelligence." Artificial Intelligence 148, no. 1-2 (2003): 1–9. http://dx.doi.org/10.1016/s0004-3702(03)00118-8.

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28

Burger, Martin, Christina Stöcker, and Axel Voigt. "Finite Element-Based Level Set Methods for Higher Order Flows." Journal of Scientific Computing 35, no. 2-3 (2008): 77–98. http://dx.doi.org/10.1007/s10915-008-9204-x.

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29

He, Lin, Chiu-Yen Kao, and Stanley Osher. "Incorporating topological derivatives into shape derivatives based level set methods." Journal of Computational Physics 225, no. 1 (2007): 891–909. http://dx.doi.org/10.1016/j.jcp.2007.01.003.

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30

Potiomkin, M. M., and I. Y. Svida. "Multicriteria Decision Making Based on a Set of Optimization Methods." Cybernetics and Systems Analysis 54, no. 4 (2018): 594–99. http://dx.doi.org/10.1007/s10559-018-0060-x.

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31

Alavi-Majd, Hamid, Soheila Khodakarim, Farid Zayeri, Mostafa Rezaei-Tavirani, Seyyed Mohammad Tabatabaei, and Maryam Heydarpour-Meymeh. "Assessment of gene set analysis methods based on microarray data." Gene 534, no. 2 (2014): 383–89. http://dx.doi.org/10.1016/j.gene.2013.08.063.

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32

Rohlfing, Ingo, and Carsten Schneider. "Clarifying misunderstandings, moving forward: Towards standards and tooks for set-theoretic methods." Qualitative & Multi-Method Research 12, no. 2 (2014): 27–34. https://doi.org/10.5281/zenodo.894398.

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Critical debates can push boundaries if they are open-ended in nature. In this spirit, we appreciate David Collier’s efforts in putting together a set of articles about set theory and QCA in a symposium published in the previous issue of this newslet-ter. Many important issues about the principles and current practices of set-theoretic methods are raised and at least some contributors seem open to the possibility that set theory and QCA are worthy of being pursued and improved. We are grate-ful to the editor of the newsletter, Robert Adcock, for inviting us to respond to these arguments—an inv

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33

Xu, Feng, Junbo Tan, Xueqian Wang, and Bin Liang. "Conservatism comparison of set-based robust fault detection methods: Set-theoretic UIO and interval observer cases." Automatica 105 (July 2019): 307–13. http://dx.doi.org/10.1016/j.automatica.2019.04.005.

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34

Ubukata, Seiki, Hiroki Kato, Akira Notsu, and Katsuhiro Honda. "Rough Set-Based Clustering Utilizing Probabilistic Memberships." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 6 (2018): 956–64. http://dx.doi.org/10.20965/jaciii.2018.p0956.

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Representing the positive, possible, and boundary regions of clusters, rough set-based C-means clustering methods, such as generalized rough C-means (GRCM) and rough set C-means (RSCM), are promising for analyzing vague cluster shapes and realizing reliable classification. In this study, we consider rough set-based clustering approaches that utilize probabilistic memberships as variants of GRCM and RSCM, including π generalized rough C-means (πGRCM), π rough set C-means (πRSCM), and rough membership C-means (RMCM). πGRCM and πRSCM assign equal probabilities of cluster belonging according to La

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35

Vasantha, M. "Comparison of Tree Based Supervised Classification Methods with Mammogram Data Set." International Journal of Computer Sciences and Engineering 7, no. 4 (2019): 504–6. http://dx.doi.org/10.26438/ijcse/v7i4.504506.

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36

Rajeswari, M., K. S. Gurumurthy, L. Pratap Reddy, S. N. Omkar, and Senthilnath J. "Automatic Road Extraction based on Normalized Cuts and Level set Methods." International Journal of Computer Applications 18, no. 7 (2011): 10–16. http://dx.doi.org/10.5120/2298-2988.

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37

Wolff, F., and V. Krebs. "Nonlinear Set Observation for Consistency-Based Diagnosis Using Implicit Interval Methods." IFAC Proceedings Volumes 42, no. 8 (2009): 1204–9. http://dx.doi.org/10.3182/20090630-4-es-2003.00197.

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38

Zhai, Junhai, Mengyao Zhai, and Xiaomeng Kang. "Condensed fuzzy nearest neighbor methods based on fuzzy rough set technique." Intelligent Data Analysis 18, no. 3 (2014): 429–47. http://dx.doi.org/10.3233/ida-140649.

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39

Hauser, J., M. Sambridge, and N. Rawlinson. "Multiarrival wavefronts in phase space: Level Set and ray based methods." ASEG Extended Abstracts 2006, no. 1 (2006): 1–4. http://dx.doi.org/10.1071/aseg2006ab062.

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40

de León-Cantón, P., and J. Lunze. "Dependable control of uncertain linear systems based on set-theoretic methods." International Journal of Control 83, no. 6 (2010): 1248–64. http://dx.doi.org/10.1080/00207171003682663.

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41

Bilski, Piotr. "Data set preprocessing methods for the artificial intelligence-based diagnostic module." Measurement 54 (August 2014): 180–90. http://dx.doi.org/10.1016/j.measurement.2014.03.023.

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42

Szczuka, Marcin, and Piotr Wojdyłło. "Neuro-wavelet classifiers for EEG signals based on rough set methods." Neurocomputing 36, no. 1-4 (2001): 103–22. http://dx.doi.org/10.1016/s0925-2312(00)00338-6.

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43

Ekel, P., J. Queiroz, R. Parreiras, and R. Palhares. "Fuzzy set based models and methods of multicriteria group decision making." Nonlinear Analysis: Theory, Methods & Applications 71, no. 12 (2009): e409-e419. http://dx.doi.org/10.1016/j.na.2008.11.087.

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44

Zhu, Benliang, Xianmin Zhang, and Sergej Fatikow. "Filter the shape sensitivity in level set-based topology optimization methods." Structural and Multidisciplinary Optimization 51, no. 5 (2014): 1035–49. http://dx.doi.org/10.1007/s00158-014-1194-8.

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45

Krause, Rolf, and Christina Mohr. "Level set based multi-scale methods for large deformation contact problems." Applied Numerical Mathematics 61, no. 4 (2011): 428–42. http://dx.doi.org/10.1016/j.apnum.2010.11.007.

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46

Hu, Zhiyao, Xiaoqiang Teng, Deke Guo, Bangbang Ren, Pin Lv, and Zhong Liu. "Comparing set reconciliation methods based on bloom filters and their variants." Tsinghua Science and Technology 21, no. 2 (2016): 157–67. http://dx.doi.org/10.1109/tst.2016.7442499.

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47

Ullah, B., J. Trevelyan, and P. C. Matthews. "Structural optimisation based on the boundary element and level set methods." Computers & Structures 137 (June 2014): 14–30. http://dx.doi.org/10.1016/j.compstruc.2014.01.004.

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48

Wijesinghe, Janith, and Pengwen Chen. "Matrix Balancing Based Interior Point Methods for Point Set Matching Problems." SIAM Journal on Imaging Sciences 16, no. 3 (2023): 1068–105. http://dx.doi.org/10.1137/22m1479476.

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49

William-West, Tamunokuro, Armand Florentin Donfack Kana, and Musa Adeku Ibrahim. "Shadowed-set-based three-way clustering methods: An investigation of new optimization-based principles." Information Sciences 591 (April 2022): 1–24. http://dx.doi.org/10.1016/j.ins.2022.01.018.

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50

Maglaras, G., E. Nikolaidis, R. T. Haftka, and H. H. Cudney. "Analytical-experimental comparison of probabilistic methods and fuzzy set based methods for designing under uncertainty." Structural Optimization 13, no. 2-3 (1997): 69–80. http://dx.doi.org/10.1007/bf01199225.

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