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Thematische Bibliographien / Set-Based Methods / Zeitschriftenartikel

Zeitschriftenartikel zum Thema „Set-Based Methods“

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Autor: Grafiati

Veröffentlicht am 1. Juni 2024

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1

Dubois, D., E. Hullermeier und H. Prade. „Fuzzy set-based methods in instance-based reasoning“. IEEE Transactions on Fuzzy Systems 10, Nr. 3 (Juni 2002): 322–32. http://dx.doi.org/10.1109/tfuzz.2002.1006435.

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2

PAN, Qing, und Guo-Liang XU. „Metamorphosis Based on the Level-Set Methods“. Chinese Journal of Computers 32, Nr. 2 (31.07.2009): 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 (Oktober 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 systematic way of setting threshold (determining the arrival time) has been formed and these methods could actually be used in all similar data separation.

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4

De-jun, Wang, Tang Yun, Yu Hong-chuan und Tang Ze-sheng. „Level set methods based on distance function“. Applied Mathematics and Mechanics 24, Nr. 8 (August 2003): 950–60. http://dx.doi.org/10.1007/bf02446501.

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5

Su, Yu-Chen, William James Gauderman, Kiros Berhane und Juan Pablo Lewinger. „Adaptive Set-Based Methods for Association Testing“. Genetic Epidemiology 40, Nr. 2 (28.12.2015): 113–22. http://dx.doi.org/10.1002/gepi.21950.

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6

Dullen, Shawn, Dinesh Verma, Mark Blackburn und Cliff Whitcomb. „Survey on set‐based design (SBD) quantitative methods“. Systems Engineering 24, Nr. 5 (20.05.2021): 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 und Laurent Fribourg. „Control of mechanical systems using set based methods“. International Journal of Dynamics and Control 5, Nr. 3 (02.05.2016): 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 und Stanley Osher. „Level-Set-Based Deformation Methods for Adaptive Grids“. Journal of Computational Physics 159, Nr. 1 (März 2000): 103–22. http://dx.doi.org/10.1006/jcph.2000.6432.

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9

Hussein, A., H. A. Muttlak und E. Al-Sawi. „Group sequential methods based on ranked set samples“. Statistical Papers 54, Nr. 3 (17.04.2012): 547–62. http://dx.doi.org/10.1007/s00362-012-0448-z.

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10

ZHANG, Zhiqian, und 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 the local binary fitting active contour model, we evolve the labeled image to get the final segmentation result. The simulation results show that this method has low computing complexity than the traditional level set method requirements, and can effectively solve the initialization problem of level set.

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12

ABDALLAH, Mohamed, und Samir ASHOUR. „New Parametric Estimation Methods based on Ranked Set Sampling“. GAZI UNIVERSITY JOURNAL OF SCIENCE 32, Nr. 4 (01.12.2019): 1356–68. http://dx.doi.org/10.35378/gujs.477631.

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13

Wang, Jianzhen, und Juanli Li. „An Improved Images Segmentation Methods Based on Level Set“. Journal of Software Engineering 10, Nr. 1 (15.12.2015): 155–62. http://dx.doi.org/10.3923/jse.2016.155.162.

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14

Bechtold, Stephen E., und Michael J. Brusco. „Microcomputer‐based working set generation methods for personnel scheduling“. International Journal of Operations & Production Management 15, Nr. 10 (Oktober 1995): 63–74. http://dx.doi.org/10.1108/01443579510098329.

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15

Nacher, Jose C., und Tatsuya Akutsu. „Minimum dominating set-based methods for analyzing biological networks“. Methods 102 (Juni 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 und Frederic Gibou. „Parallel level-set methods on adaptive tree-based grids“. Journal of Computational Physics 322 (Oktober 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 und Julien Toulouse. „Density-Based Basis-Set Incompleteness Correction for GW Methods“. Journal of Chemical Theory and Computation 16, Nr. 2 (31.12.2019): 1018–28. http://dx.doi.org/10.1021/acs.jctc.9b01067.

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18

Hartmann, Daniel, Matthias Meinke und Wolfgang Schröder. „Differential equation based constrained reinitialization for level set methods“. Journal of Computational Physics 227, Nr. 14 (Juli 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 und Rolf Findeisen. „Model-supported Patient Stratification Using Set-based Estimation Methods“. IFAC-PapersOnLine 51, Nr. 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, Nr. 17 (02.09.2022): 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 to B-series is proposed to merge these techniques in a common formalism—named set-based B-series. We show that the main properties of B-series are preserved. A validated technique, based on Runge–Kutta methods, able to compute such series, is presented. Experiments are provided in order to illustrate the proposed approach.

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21

Sadamitsu, Kugatsu, Kuniko Saito, Kenji Imamura, Yoshihiro Matsuo und Genichiro Kikui. „Entity Set Expansion based on Bootstrapping Methods using Topic Information“. Journal of Natural Language Processing 19, Nr. 2 (2012): 89–106. http://dx.doi.org/10.5715/jnlp.19.89.

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22

Amini, Amin, und Navid Nikraz. „Proposing Two Defuzzification Methods based on Output Fuzzy Set Weights“. International Journal of Intelligent Systems and Applications 8, Nr. 2 (08.02.2016): 1–12. http://dx.doi.org/10.5815/ijisa.2016.02.01.

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23

ZHANG, LIHE, und ZHENZHEN LIU. „IMAGE COSEGMENTATION BASED ON LOCAL AND GLOBAL LEVEL SET METHODS“. International Journal of Image and Graphics 12, Nr. 03 (Juli 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 enlarge the driven force of some points and introduce the edge indicator function to avoid the curve over-shrinking or over-expanding on the salient boundaries. In addition, we introduce global image statistics to better the curve evolution and try to avoid the given energy functional converging to a local minimum. Practical experiments show that our algorithm can obtain better segmentation results.

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24

Chen, Ying, und 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 play an important role, which should not make evolution curve to terminate at local minima or pass through the boundary. This study can provide certain referential significance in how to select image segmentation algorithm based on level set.

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25

Wang, Bei, Yi Xie und 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 ignition technology

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26

Dubois, Didier, und Henri Prade. „Fuzzy set and possibility theory-based methods in artificial intelligence“. Artificial Intelligence 148, Nr. 1-2 (August 2003): 1–9. http://dx.doi.org/10.1016/s0004-3702(03)00118-8.

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27

Burger, Martin, Christina Stöcker und Axel Voigt. „Finite Element-Based Level Set Methods for Higher Order Flows“. Journal of Scientific Computing 35, Nr. 2-3 (Juni 2008): 77–98. http://dx.doi.org/10.1007/s10915-008-9204-x.

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28

He, Lin, Chiu-Yen Kao und Stanley Osher. „Incorporating topological derivatives into shape derivatives based level set methods“. Journal of Computational Physics 225, Nr. 1 (Juli 2007): 891–909. http://dx.doi.org/10.1016/j.jcp.2007.01.003.

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29

Potiomkin, M. M., und I. Y. Svida. „Multicriteria Decision Making Based on a Set of Optimization Methods“. Cybernetics and Systems Analysis 54, Nr. 4 (Juli 2018): 594–99. http://dx.doi.org/10.1007/s10559-018-0060-x.

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30

Alavi-Majd, Hamid, Soheila Khodakarim, Farid Zayeri, Mostafa Rezaei-Tavirani, Seyyed Mohammad Tabatabaei und Maryam Heydarpour-Meymeh. „Assessment of gene set analysis methods based on microarray data“. Gene 534, Nr. 2 (Januar 2014): 383–89. http://dx.doi.org/10.1016/j.gene.2013.08.063.

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31

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

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32

Ubukata, Seiki, Hiroki Kato, Akira Notsu und Katsuhiro Honda. „Rough Set-Based Clustering Utilizing Probabilistic Memberships“. Journal of Advanced Computational Intelligence and Intelligent Informatics 22, Nr. 6 (20.10.2018): 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 Laplace’s principle of indifference, whereas RMCM assigns the probabilities according to rough memberships, which represent conditional probabilities based on the object’s neighborhood derived from a binary relation. In addition, we discuss the theoretical validity of our RMCM approach and compare it with other methods considered in this study. Furthermore, we conducted numerical experiments for evaluating the classification performances of the abovementioned methods. Based on our experimental results, the methods were found to be effective.

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33

Vasantha, M. „Comparison of Tree Based Supervised Classification Methods with Mammogram Data Set“. International Journal of Computer Sciences and Engineering 7, Nr. 4 (30.04.2019): 504–6. http://dx.doi.org/10.26438/ijcse/v7i4.504506.

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34

Rajeswari, M., K. S. Gurumurthy, L. Pratap Reddy, S. N. Omkar und Senthilnath J. „Automatic Road Extraction based on Normalized Cuts and Level set Methods“. International Journal of Computer Applications 18, Nr. 7 (31.03.2011): 10–16. http://dx.doi.org/10.5120/2298-2988.

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35

Wolff, F., und V. Krebs. „Nonlinear Set Observation for Consistency-Based Diagnosis Using Implicit Interval Methods“. IFAC Proceedings Volumes 42, Nr. 8 (2009): 1204–9. http://dx.doi.org/10.3182/20090630-4-es-2003.00197.

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36

Zhai, Junhai, Mengyao Zhai und Xiaomeng Kang. „Condensed fuzzy nearest neighbor methods based on fuzzy rough set technique“. Intelligent Data Analysis 18, Nr. 3 (30.04.2014): 429–47. http://dx.doi.org/10.3233/ida-140649.

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37

Hauser, J., M. Sambridge und N. Rawlinson. „Multiarrival wavefronts in phase space: Level Set and ray based methods“. ASEG Extended Abstracts 2006, Nr. 1 (Dezember 2006): 1–4. http://dx.doi.org/10.1071/aseg2006ab062.

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38

de León-Cantón, P., und J. Lunze. „Dependable control of uncertain linear systems based on set-theoretic methods“. International Journal of Control 83, Nr. 6 (19.05.2010): 1248–64. http://dx.doi.org/10.1080/00207171003682663.

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39

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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40

Szczuka, Marcin, und Piotr Wojdyłło. „Neuro-wavelet classifiers for EEG signals based on rough set methods“. Neurocomputing 36, Nr. 1-4 (Februar 2001): 103–22. http://dx.doi.org/10.1016/s0925-2312(00)00338-6.

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41

Ekel, P., J. Queiroz, R. Parreiras und R. Palhares. „Fuzzy set based models and methods of multicriteria group decision making“. Nonlinear Analysis: Theory, Methods & Applications 71, Nr. 12 (Dezember 2009): e409-e419. http://dx.doi.org/10.1016/j.na.2008.11.087.

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42

Zhu, Benliang, Xianmin Zhang und Sergej Fatikow. „Filter the shape sensitivity in level set-based topology optimization methods“. Structural and Multidisciplinary Optimization 51, Nr. 5 (13.12.2014): 1035–49. http://dx.doi.org/10.1007/s00158-014-1194-8.

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43

Krause, Rolf, und Christina Mohr. „Level set based multi-scale methods for large deformation contact problems“. Applied Numerical Mathematics 61, Nr. 4 (April 2011): 428–42. http://dx.doi.org/10.1016/j.apnum.2010.11.007.

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44

Hu, Zhiyao, Xiaoqiang Teng, Deke Guo, Bangbang Ren, Pin Lv und Zhong Liu. „Comparing set reconciliation methods based on bloom filters and their variants“. Tsinghua Science and Technology 21, Nr. 2 (April 2016): 157–67. http://dx.doi.org/10.1109/tst.2016.7442499.

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45

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

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46

Wijesinghe, Janith, und Pengwen Chen. „Matrix Balancing Based Interior Point Methods for Point Set Matching Problems“. SIAM Journal on Imaging Sciences 16, Nr. 3 (13.07.2023): 1068–105. http://dx.doi.org/10.1137/22m1479476.

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47

William-West, Tamunokuro, Armand Florentin Donfack Kana und 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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48

Maglaras, G., E. Nikolaidis, R. T. Haftka und H. H. Cudney. „Analytical-experimental comparison of probabilistic methods and fuzzy set based methods for designing under uncertainty“. Structural Optimization 13, Nr. 2-3 (April 1997): 69–80. http://dx.doi.org/10.1007/bf01199225.

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49

Wanting Zhou, Wanting Zhou, Man Li Wanting Zhou, Lei Li Man Li und Kuo-Hui Yeh Lei Li. „Prediction of SET on SRAM Based on WOA-BP Neural Network“. 網際網路技術學刊 24, Nr. 2 (März 2023): 267–73. http://dx.doi.org/10.53106/160792642023032402005.

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<p>The incidence of high-energy particles into the semiconductor device would induce single event transients (SETs), which is a main threaten to MOS device. And the incidence distances and Linear Energy Transfers (LETs) have important effects on the SET current. A machine learning method based on Whale Optimization Algorithm-Back Propagation neural network (WOA-BPNN) model considering injection distances and LETs has been proposed to predict SET current in this paper. And this method could effectively reduce the simulation time from hours to seconds compared to device model. The current data that predicted by this method has been compared with the Technology Computer Aided Design (TCAD) simulation result which obtained in the background of the 40 nm process technology, the regression coefficient between the predicted value based on the proposed method and the TCAD simulation result was 99.76%, and the maximum integral relative error was 0.287% while the minimum integral relative error is 0.04%. Besides, the proposed method is also compared with PSO-BPNN (Particle Swarm Optimization, PSO) and GA-BPNN (Genetic Algorithm, GA), and the results demonstrated that the WOA-BPNN has prediction accuracy and timing saving advantages over the other two methods.</p> <p> </p>

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50

Liang, Weibin. „A Set of Point-Targets-Based Polarimetric Calibration Methods Based on General Polarimetric System Model“. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020): 2502–19. http://dx.doi.org/10.1109/jstars.2020.2991591.

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