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Artykuły w czasopismach na temat „Decomposition-based”

Autor: Grafiati

Data publikacji: 5 czerwca 2025

Data aktualizacji: 2 sierpnia 2025

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1

Uno, Katsuhiro, and Hoang Hoa Tien Dung. "Image Encryption Based on Phase Decomposition Technique." Journal of the Institute of Industrial Applications Engineers 5, no. 2 (2017): 53–58. http://dx.doi.org/10.12792/jiiae.5.53.

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Idate, Sonali R. "AORE Approach Survey: based on Requirement Decomposition Techniques." Journal of Advanced Research in Dynamical and Control Systems 12, SP8 (2020): 883–88. http://dx.doi.org/10.5373/jardcs/v12sp8/20202592.

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3

Luciani, Xavier, and Laurent Albera. "Canonical Polyadic Decomposition based on joint eigenvalue decomposition." Chemometrics and Intelligent Laboratory Systems 132 (March 2014): 152–67. http://dx.doi.org/10.1016/j.chemolab.2013.12.009.

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4

Yang, Zhongyao, Maolin Wu, and Shiguang liu. "Helmholtz decomposition-based SPH." Virtual Reality & Intelligent Hardware 3, no. 2 (2021): 118–28. http://dx.doi.org/10.1016/j.vrih.2021.01.003.

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5

Hooker, J. N., and G. Ottosson. "Logic-based Benders decomposition." Mathematical Programming 96, no. 1 (2003): 33–60. http://dx.doi.org/10.1007/s10107-003-0375-9.

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6

Jiang, Jian-Min, Huibiao Zhu, Qin Li, et al. "Event-based functional decomposition." Information and Computation 271 (April 2020): 104484. http://dx.doi.org/10.1016/j.ic.2019.104484.

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7

Raidl, Günther R. "Decomposition based hybrid metaheuristics." European Journal of Operational Research 244, no. 1 (2015): 66–76. http://dx.doi.org/10.1016/j.ejor.2014.12.005.

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8

Xu, Yao. "An Image Harmonious Fusion Method Based on Two-Scale Decomposition." International Journal of Scientific Engineering and Research 12, no. 3 (2024): 38–42. https://doi.org/10.70729/se24329032432.

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9

Lei Zhang, Lei Zhang, Yi-Yuan Cheng Lei Zhang, Jing Li Yi-Yuan Cheng, Zhi-She Wang Jing Li, Lin-Na Ji Zhi-She Wang, and Wei Liu Lin-Na Ji. "Multimodal Image Fusion Algorithm Based on Two-stage Multiscale Decomposition." 電腦學刊 35, no. 5 (2024): 015–33. http://dx.doi.org/10.53106/199115992024103505002.

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<p>Feature-intensity differences exist between different modal images; hence, complementary features are easily ignored or drowned in most multimodal image fusion algorithms. In this study, a novel two-stage fusion algorithm is proposed to reduce the loss of complementary features. The fusion algorithm is divided into two stages: the first stage adopts the multiscale transform based on the hybrid l0l1 layer decomposition and Gauss filter to decompose the source images into the structure-, large-scale-, and detail-layer images. Further, the previous fusion images were generated using a li

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10

Hongteng Wang, Hongteng Wang, Keming Yu Hongteng Wang, Kexin Huang Keming Yu, and Liyong Ma Kexin Huang. "Degradation Evaluation of Hydropower Equipment Based on Variational Modal Decomposition." 電腦學刊 35, no. 4 (2024): 031–38. http://dx.doi.org/10.53106/199115992024083504003.

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<p>Hydropower is the green energy with the most significant comprehensive emission reduction benefits in the whole life cycle. The signals of hydropower equipment include fault information, and it can assist the fault diagnosis of hydropower units. However, most of the existing methods lack the quantitative evaluation of the equipment degradation. A quantitative evaluation method of degradation for hydropower equipment is proposed. Variable modal decomposition (VMD) is employed to obtain decomposed simple signal. The singular values and sample entropy of the intrinsic mode functions are

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11

Zou, Bin, Da Lu, Lamei Zhang, and Wooil M. Moon. "Eigen-Decomposition-Based Four-Component Decomposition for PolSAR Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 3 (2016): 1286–96. http://dx.doi.org/10.1109/jstars.2015.2513161.

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12

Kaleem, Muhammad, Aziz Guergachi, and Sridhar Krishnan. "Hierarchical decomposition based on a variation of empirical mode decomposition." Signal, Image and Video Processing 11, no. 5 (2016): 793–800. http://dx.doi.org/10.1007/s11760-016-1024-0.

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13

Opara, Adam, and Dariusz Kania. "Decomposition-based logic synthesis for PAL-based CPLDs." International Journal of Applied Mathematics and Computer Science 20, no. 2 (2010): 367–84. http://dx.doi.org/10.2478/v10006-010-0027-1.

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Decomposition-based logic synthesis for PAL-based CPLDsThe paper presents one concept of decomposition methods dedicated to PAL-based CPLDs. The proposed approach is an alternative to the classical one, which is based on two-level minimization of separate single-output functions. The key idea of the algorithm is to search for free blocks that could be implemented in PAL-based logic blocks containing a limited number of product terms. In order to better exploit the number of product terms, two-stage decomposition and BDD-based decomposition are to be used. In BDD-based decomposition methods, fu

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14

Li, Xuelong, and Yawei Pang. "Deterministic Column-Based Matrix Decomposition." IEEE Transactions on Knowledge and Data Engineering 22, no. 1 (2010): 145–49. http://dx.doi.org/10.1109/tkde.2009.64.

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15

Klus, Stefan, Patrick Gelß, Sebastian Peitz, and Christof Schütte. "Tensor-based dynamic mode decomposition." Nonlinearity 31, no. 7 (2018): 3359–80. http://dx.doi.org/10.1088/1361-6544/aabc8f.

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16

Wang, Zhiming. "Program Semantics-based Task Decomposition." Journal of Physics: Conference Series 1684 (November 2020): 012051. http://dx.doi.org/10.1088/1742-6596/1684/1/012051.

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17

Nicolay, S. "A wavelet-based mode decomposition." European Physical Journal B 80, no. 2 (2011): 223–32. http://dx.doi.org/10.1140/epjb/e2011-10756-3.

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18

Nazeer, S. Mohamed, Felipe Bordeu, Adrien Leygue, and Francisco Chinesta. "Arlequin based PGD domain decomposition." Computational Mechanics 54, no. 5 (2014): 1175–90. http://dx.doi.org/10.1007/s00466-014-1048-7.

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19

Qu Jian-Ling, Wang Xiao-Fei, Gao Feng, Zhou Yu-Ping, and Zhang Xiang-Yu. "Noise assisted signal decomposition method based on complex empirical mode decomposition." Acta Physica Sinica 63, no. 11 (2014): 110201. http://dx.doi.org/10.7498/aps.63.110201.

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20

Wang, Cheng. "Image Decomposition Based on a Modified Bidimensional Empirical Mode Decomposition Method." Applied Mechanics and Materials 496-500 (January 2014): 1931–36. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1931.

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This study introduces a modified bidimensional empirical mode decomposition method to deal with high resolution images. To avoid solving large linear equations and calculating large matrix, the images are split into several blocks, processed individually, and subsequently joined into one. Thus, the complexity of time and space is lowered efficiently.

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21

Philip, Anu, and M. Devaraju. "Energy Efficient Floating-Point Based Block LU Decomposition on Large Signal Systems." International Journal of Scientific Engineering and Research 5, no. 5 (2017): 8–11. https://doi.org/10.70729/ijser151369.

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22

Zhao, Jiajia, Guohui Chen, Xuan Bi, Wangyang Cai, Lei Yue, and Ming Tang. "Fast mode decomposition for few-mode fiber based on lightweight neural network." Chinese Optics Letters 22, no. 2 (2024): 020604. http://dx.doi.org/10.3788/col202422.020604.

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23

Yetis, F. A., and K. Saitou. "Decomposition-Based Assembly Synthesis Based on Structural Considerations." Journal of Mechanical Design 124, no. 4 (2002): 593–601. http://dx.doi.org/10.1115/1.1519276.

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This paper presents a method for decomposition of structural products in order to provide the product designer with choices for feasible assemblies. The synthesis of assemblies is done by decomposing a complex structure obtained via structural topology optimization into an assembly of multiple structural members with simpler geometries. The aim is at providing a systematic approach to explore a large number of decompositions prior to the detailed component design phase. Initially, the structure, which is represented as a bitmap image, is transformed to a graph with equivalent topology through

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24

Arcaini, Paolo, Angelo Gargantini, and Elvinia Riccobene. "Decomposition-Based Approach for Model-Based Test Generation." IEEE Transactions on Software Engineering 45, no. 5 (2019): 507–20. http://dx.doi.org/10.1109/tse.2017.2781231.

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25

Kalbhag, Abhijna R., Prakash Hegade, and Ashok Shettar. "Decomposition as Design-Based Intervention for Problem Based Learning." Journal of Engineering Education Transformations 38, IS2 (2025): 268–75. https://doi.org/10.16920/jeet/2025/v38is2/25032.

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Combining Problem-Based and Computational Thinking in teaching can benefit both teachers and students. The decomposition from computational thinking skill can help computer science students break down complex system design problems into smaller, manageable parts, while considering social factors and different roles of each part. This study proposes a research question to use decomposition as an intervention to observe its effectiveness in problem-solving scenarios. And also observe how structured instructions can impact students' ability to build system design models within problem solving env

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26

Perez-Meana, Hector Manuel, Mariko Nakano Miyatake, and Luis Nino-de-Rivera. "Adaptive Filtering Based on Subband Decomposition." Telecommunications and Radio Engineering 56, no. 4-5 (2001): 14. http://dx.doi.org/10.1615/telecomradeng.v56.i4-5.140.

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27

Sun, Zhoubao, Xiaodong Zhang, Haoyuan Li Yan Xiao, and Haifeng Guo. "Recommender Systems Based on Tensor Decomposition." Computers, Materials & Continua 66, no. 1 (2020): 621–30. http://dx.doi.org/10.32604/cmc.2020.012593.

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28

Sigtermans, David. "A Path-Based Partial Information Decomposition." Entropy 22, no. 9 (2020): 952. http://dx.doi.org/10.3390/e22090952.

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Based on the conceptual basis of information theory, we propose a novel mutual information measure—‘path-based mutual information’. This information measure results from the representation of a set of random variables as a probabilistic graphical model. The edges in this graph are modeled as discrete memoryless communication channels, that is, the underlying data is ergodic, stationary, and the Markov condition is assumed to be applicable. The associated multilinear stochastic maps, tensors, transform source probability mass functions into destination probability mass functions. This allows fo

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29

Wang, Wen Jun, Ju Bo Zhu, and Xiao Jun Duan. "Eigenvalue Decomposition Based Modified Newton Algorithm." Applied Mechanics and Materials 347-350 (August 2013): 2586–89. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2586.

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When the Hessian matrix is not positive, the Newton direction maybe not the descending direction. A new method named eigenvalue decomposition based modified Newton algorithm is presented, which first takes eigenvalue decomposition on the Hessian matrix, then replaces the negative eigenvalues with their absolutely values, finally reconstruct Hessian matrix and modify searching direction. The new searching direction is always the descending direction, and the convergence of the algorithm is proved and conclusion on convergence rate is presented qualitatively. At last, a numerical experiment is g

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30

Hong, Hong, Xin-long Wang, Zhi-yong Tao, and Shuan-ping Du. "Centroid-based sifting for empiricalmode decomposition." Journal of Zhejiang University SCIENCE C 12, no. 2 (2011): 88–95. http://dx.doi.org/10.1631/jzus.c1000037.

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31

Lendzion-Bielun, Zofia, Urszula Narkiewicz, and Walerian Arabczyk. "Cobalt-based Catalysts for Ammonia Decomposition." Materials 6, no. 6 (2013): 2400–2409. http://dx.doi.org/10.3390/ma6062400.

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32

Kamalov, Firuz. "Orthogonal variance decomposition based feature selection." Expert Systems with Applications 182 (November 2021): 115191. http://dx.doi.org/10.1016/j.eswa.2021.115191.

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33

KUMAR DHAR, Pranab, and Tetsuya SHIMAMURA. "Audio Watermarking Based on Eigenvalue Decomposition." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E97.A, no. 12 (2014): 2658–61. http://dx.doi.org/10.1587/transfun.e97.a.2658.

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34

Bhatia, Nikhil, Laksha Sharma, Shreya Srivastava, Nidhish Katyal, and Roshan Srivastav. "Streamflow Decomposition Based Integrated ANN Model." Open Journal of Modern Hydrology 03, no. 01 (2013): 15–19. http://dx.doi.org/10.4236/ojmh.2013.31003.

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35

Valova, I., and Y. Kosugi. "Hadamard-based image decomposition and compression." IEEE Transactions on Information Technology in Biomedicine 4, no. 4 (2000): 306–19. http://dx.doi.org/10.1109/4233.897063.

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36

Zheltkov, Dmitry, and Eugene Tyrtyshnikov. "Global optimization based on TT-decomposition." Russian Journal of Numerical Analysis and Mathematical Modelling 35, no. 4 (2020): 247–61. http://dx.doi.org/10.1515/rnam-2020-0021.

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AbstractIn contrast to many other heuristic and stochastic methods, the global optimization based on TT-decomposition uses the structure of the optimized functional and hence allows one to obtain the global optimum in some problem faster and more reliable. The method is based on the TT-cross method of interpolation of tensors. In this case, the global optimum can be found in practice even in the case when the approximation of the tensor does not possess a high accuracy. We present a detailed description of the method and its justification for the matrix case and rank-1 approximation.

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37

Kalyanpur, A., S. Patwardhan, B. K. Boguraev, A. Lally, and J. Chu-Carroll. "Fact-based question decomposition in DeepQA." IBM Journal of Research and Development 56, no. 3.4 (2012): 13:1–13:11. http://dx.doi.org/10.1147/jrd.2012.2188934.

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38

Liu, Chaoqun. "Rortex based velocity gradient tensor decomposition." Physics of Fluids 31, no. 1 (2019): 011704. http://dx.doi.org/10.1063/1.5084739.

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39

Yamazaki, Wataru, Kisa Matsushima, and Kazuhiro Nakahashi. "Drag Decomposition-Based Adaptive Mesh Refinement." Journal of Aircraft 44, no. 6 (2007): 1896–905. http://dx.doi.org/10.2514/1.31064.

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40

Wheeler, K. R., M. H. Chang, and K. H. Knuth. "Gesture-based control and EMG decomposition." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 36, no. 4 (2006): 503–14. http://dx.doi.org/10.1109/tsmcc.2006.875418.

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41

Saxena, N., and K. K. Sharma. "Hilbert vibration decomposition based image fusion." Electronics Letters 52, no. 19 (2016): 1605–7. http://dx.doi.org/10.1049/el.2016.2270.

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42

Ghafuri, Jehan, and Sabah Jassim. "Singular-Value-Decomposition-Based Matrix Surgery." Entropy 26, no. 8 (2024): 701. http://dx.doi.org/10.3390/e26080701.

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This paper is motivated by the need to stabilise the impact of deep learning (DL) training for medical image analysis on the conditioning of convolution filters in relation to model overfitting and robustness. We present a simple strategy to reduce square matrix condition numbers and investigate its effect on the spatial distributions of point clouds of well- and ill-conditioned matrices. For a square matrix, the SVD surgery strategy works by: (1) computing its singular value decomposition (SVD), (2) changing a few of the smaller singular values relative to the largest one, and (3) reconstruct

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43

Kania, Dariusz. "Logic Decomposition for PAL-Based CPLDs." Journal of Circuits, Systems and Computers 24, no. 03 (2015): 1550042. http://dx.doi.org/10.1142/s0218126615500425.

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The Programmable Array Logic (PAL)-based logic block is the core of the great majority of Complex Programmable Logic Devices (CPLDs). The purpose of this paper is to compare two models of decomposition dedicated to PAL-based devices. Non-standard usage of decomposition, which leads to the reduction of used PAL-based logic blocks in a programmable structure, is the aim of the presented methods. Each decomposition step is optimized for implementation in a PAL-based structure that is characterized by a PAL-based logic block. The essence of decomposition models is oriented towards minimizing the n

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44

Hui, K. C., and Yiu-Bun Wu. "Feature-based Decomposition of Trimmed Surfaces." Computer-Aided Design and Applications 1, no. 1-4 (2004): 485–93. http://dx.doi.org/10.1080/16864360.2004.10738291.

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Besson, Olivier, Nicolas Dobigeon, and Jean-Yves Tourneret. "CS Decomposition Based Bayesian Subspace Estimation." IEEE Transactions on Signal Processing 60, no. 8 (2012): 4210–18. http://dx.doi.org/10.1109/tsp.2012.2197619.

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46

Festa, Adriano. "Domain decomposition based parallel Howard’s algorithm." Mathematics and Computers in Simulation 147 (May 2018): 121–39. http://dx.doi.org/10.1016/j.matcom.2017.04.008.

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47

Zhang, Hongying, and Shimei Dai. "Image Inpainting Based on Wavelet Decomposition." Procedia Engineering 29 (2012): 3674–78. http://dx.doi.org/10.1016/j.proeng.2012.01.551.

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48

Ma, Ming, Rui Zhang, Yong Liu, Haoyang Gao, and Yu Guo. "Nonconvex optimization-based inverse spectral decomposition." Journal of Geophysics and Engineering 16, no. 4 (2019): 764–72. http://dx.doi.org/10.1093/jge/gxz046.

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Abstract As a time–frequency analysis tool, inverse spectral decomposition (ISD) could be utilized to obtain a high-resolution time–frequency map via the inversion strategy. In the established inversion function, an analytic signal is disintegrated as a coefficient matrix whose elements represent the weights of the wavelet components with the different dominant frequencies and the time location in the complex wavelet library. By using a sparse constraint, a high-quality inverse decomposition result could be generated. In this paper, a modified ISD technique based on a nonconvex optimization al

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49

Renaud, Olivier, Jean-Luc Starck, and Fionn Murtagh. "Prediction Based on a Multiscale Decomposition." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 02 (2003): 217–32. http://dx.doi.org/10.1142/s0219691303000153.

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A wavelet-based forecasting method for time series is introduced. It is based on a multiple resolution decomposition of the signal, using the redundant "à trous" wavelet transform which has the advantage of being shift-invariant. The result is a decomposition of the signal into a range of frequency scales. The prediction is based on a small number of coefficients on each of these scales. In its simplest form it is a linear prediction based on a wavelet transform of the signal. This method uses sparse modelling, but can be based on coefficients that are summaries or characteristics of large par

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50

Cohen, Jeremy Emile, and Nicolas Gillis. "Dictionary-Based Tensor Canonical Polyadic Decomposition." IEEE Transactions on Signal Processing 66, no. 7 (2018): 1876–89. http://dx.doi.org/10.1109/tsp.2017.2777393.

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