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Numerical approximation of SAV finite difference method for the Allen–Cahn equation Allen-Cahn方程的SAV有限差分法数值逼近
IF 1.2 Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2022-11-19 DOI: 10.1142/s1793962324500016
Han Chen, Langyang Huang, Qingqu Zhuang, Zhifeng Weng
In this paper, the second-order scalar auxiliary variable approach combined with finite difference method is employed for the Allen–Cahn equation that represents a phenomenological model for antiphase domain coarsening in a binary mixture. The second-order backward differentiation formula is used in time. The error estimation of the semi-discrete scheme is derived in the sense of [Formula: see text]-norm. Several numerical simulations in 2D and 3D are demonstrated to verify the accuracy and efficiency of the proposed scheme.
本文采用二阶标量辅助变量法结合有限差分法求解二元混合物中反相域粗化现象模型的Allen-Cahn方程。在时间上采用二阶后向微分公式。在[公式:见文]-范数意义下推导了半离散格式的误差估计。通过二维和三维的数值模拟,验证了该方法的准确性和有效性。
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引用次数: 0
A wavelet-based study on phase and magnitude relationships of the Stockwell transform 基于小波的斯托克韦尔变换相位和幅度关系研究
IF 1.2 Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2022-11-10 DOI: 10.1142/s1793962323500368
K. N. Singh, Sanjeev Kumar
This paper establishes a relationship between the phase and log-magnitude of the Stockwell transform (S-transform). The proposed relationship is derived by defining the S-Transform in terms of wavelet functions. The proposed work is an extension of the study [Holighaus N., Koliander G., Průša Z., Abreu L. D., Characterization of analytic wavelet transforms and a new phaseless reconstruction algorithm, IEEE Trans. Signal Process. 67(15):3894–3908, 2019] carried out to establish a relationship between the phase and magnitude of the continuous wavelet transform. Our methodology exploits the relationship between partial derivatives of the real and imaginary parts of the wavelet and S-transform for a couple of window functions (Gaussian and bi-Gaussian). Apart from the continuous case, these relationships are explicitly shown for the discrete version of the S-transform.
本文建立了斯托克韦尔变换(s变换)的相位与对数幅值之间的关系。该关系是通过定义小波函数的s变换而得到的。提出的工作是研究的延伸[Holighaus N., Koliander G., Průša Z., Abreu L. D.,解析小波变换的表征和一种新的无相重构算法,IEEE。信号处理,67(15):3894-3908,2019],建立了连续小波变换的相位和幅值之间的关系。我们的方法利用小波的实部和虚部偏导数和s变换对一对窗口函数(高斯和双高斯)之间的关系。除了连续的情况外,这些关系在s变换的离散版本中被明确地显示出来。
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引用次数: 0
Optimization of manufacturers based on agent in cloud manufacturing 云制造中基于agent的制造商优化
IF 1.2 Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2022-09-12 DOI: 10.1142/s1793962323410283
Yanjuan Hu, Chao Shi, Wenjun Lv, Yongkui Liu, X. Wang
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引用次数: 2
An Efficient Dual Classification Support Using ISPCE and IRR-GCBANN Techniques for Detection of Thyroid Disease 使用ISPCE和IRR-GCBANN技术检测甲状腺疾病的有效双重分类支持
IF 1.2 Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2022-08-24 DOI: 10.1142/s179396232341026x
L. Shalini, K. Vijayakumar
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引用次数: 0
A Survey on Attribute-Based Encryption for Internet of Things 基于属性的物联网加密研究综述
IF 1.2 Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2022-08-24 DOI: 10.1142/s1793962323410271
Dilip S. V. Kumar, Manoj Kumar, Gaurav Gupta
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引用次数: 0
Analytical modeling and calculation of mechanical characteristics for few piece main-auxiliary parabolic leaf spring with root diagonal segment 根对角段少片主副抛物型钢板弹簧力学特性分析建模与计算
IF 1.2 Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2022-08-10 DOI: 10.1142/s1793962323500320
Yuewei Yu, Leilei Zhao, Lin Yang, Changcheng Zhou, Zhaohui Zhu, Lianshun Tian
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引用次数: 0
Modified Block-Matching Algorithm for Moving Object Tracking in Video Surveillance 视频监控中运动目标跟踪的改进块匹配算法
IF 1.2 Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2022-07-25 DOI: 10.1142/s1793962323500289
S. S. Vasekar, S. Shah
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引用次数: 0
A two-group epidemiological model: Stability analysis and numerical simulation using neural network 两组流行病学模型:稳定性分析及神经网络数值模拟
IF 1.2 Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2022-07-25 DOI: 10.1142/s1793962323500290
M. A. El Yamani, Jaafar El Karkri, S. Lazaar, R. Aboulaich
This work has two principal goals. First, we investigate the asymptotic behavior of a two-group epidemiological model and determine the expression of its basic reproduction number using the dynamical systems approach based on the spectral radius of the relative matrix. Second, we simulate the obtained analytical results using a new deep learning method that associates the ordinary differential equations governing the model to neural networks. A general disease-free equilibrium is considered and sufficient conditions of stability and convergence are formulated. A detailed description of the neural network model used in the simulation is provided. Moreover, the proposed deep learning simulation algorithm is compared to the simulation provided by "odeint", a function from "SciPy" which is a Python library of mathematical routines.
这项工作有两个主要目标。首先,我们研究了两组流行病学模型的渐近行为,并利用基于相对矩阵谱半径的动力系统方法确定了其基本再现数的表达式。其次,我们使用一种新的深度学习方法模拟得到的分析结果,该方法将控制模型的常微分方程与神经网络联系起来。考虑了一般的无病平衡点,给出了稳定性和收敛性的充分条件。对仿真中使用的神经网络模型进行了详细的描述。此外,将所提出的深度学习仿真算法与“odeint”提供的仿真进行了比较,“odeint”是一个来自Python数学例程库“SciPy”的函数。
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引用次数: 0
Mathematical-Based Models for Solution of the Load Flow Problem 基于数学的潮流问题求解模型
IF 1.2 Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2022-07-13 DOI: 10.1142/s1793962323500265
A. Al-Subhi
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引用次数: 0
A new family of decreasing density and hazard Quantile functions for modeling Time-to-event data 一种新的密度递减函数和危害分位数函数,用于时间到事件数据的建模
IF 1.2 Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2022-07-13 DOI: 10.1142/s1793962323500277
K. Shekhawat, V. Sharma
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引用次数: 0
期刊
International Journal of Modeling Simulation and Scientific Computing
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