Robustness of geometric networks

Kamran, Khashayar

doi:10.17760/d20316373

Cited by 8 publications

(18 citation statements)

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“…To assess our feature dimensionality reduction method, we designed comparative experiments with down-sampling [26] and random projection [27] algorithms under SRC in the threshold-filtering MSAR1.0 dataset. The comparative experimental results are shown in Table 3.…”

Section: Recognition Resultsmentioning

confidence: 99%

Oil Tank Detection and Recognition via Monogenic Signal

Fan,

Yin,

Yang

2024

Remote Sensing

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With the rapid development of synthetic aperture radar (SAR) techniques, satellite systems’ capabilities to acquire information are continually improving. The PAZ satellite, with its high resolution and wide scanning swath, can provide high-quality data support for SAR applications. Oil tanks serve as energy storage devices, and their identification holds significant value in both military and civilian fields. Challenges in the detection and recognition of oil tanks using classical methods include poor detection, slow computation speed, and multiple windows of correct recognition. This paper centers on the analysis of oil tanks using PAZ data. We employ a sliding-window approach to acquire candidate target windows, process the windows through Weibull distribution modeling and hole filling, and extract target features using the monogenic signal based on regional L2 norm. The results demonstrate that the proposed method effectively improves the accuracy, and the model exhibits strong generalization ability and robustness.

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Section: Recognition Resultsmentioning

confidence: 99%

Oil Tank Detection and Recognition via Monogenic Signal

Fan,

Yin,

Yang

2024

Remote Sensing

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“…More details can be found in [1,[3][4][5][6]. Fractional differential equations have become very useful to model numerous phenomena in different areas such as economics, chemistry, physics, acoustics, biology, viscoelasticity, engineering, and electromagnetics [6][7][8][9][10][11][12][13]. It is worth to mention that the main aspect of arbitrary order derivative is the the memory effect, i.e., the future state of a physical system depends on the present as well as past states.…”

Section: Introductionmentioning

confidence: 99%

“…Another group of meshless methods, which are based on radial basis functions (RBFs), are one of the best tools for approximating the solution of different real-world problems [12,28]. The main features of RBFs are their smoothness, spectral convergence, and ease of implementation.…”

Section: Introductionmentioning

confidence: 99%

“…A finite difference time stepping scheme is stable if the errors remain constant or decay during computations. Also, in time-stepping techniques, the accuracy is achieved at a very small step size, and hence this method encounter an exponential increase of computing costs with advancing time and thus have low efficiency in the simulation of long time history fractional problems [39][40][41]. In order to circumvent the drawback of the finite difference time stepping method an alternative way is to use LT in time.…”

Section: Introductionmentioning

confidence: 99%

“…In [43] the author coupled LT with finite element method for parabolic type differential equations. The coupling of LT with other methods can be found in the references [44][45][46]. In this article we propose a local RBF-based method coupled with LT for the numerical solution of solute transport models.…”

Section: Introductionmentioning

confidence: 99%

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On the Numerical Approximation of Mobile-Immobile Advection-Dispersion Model of Fractional Order Arising from Solute Transport in Porous Media

et al. 2022

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The fractional mobile/immobile solute transport model has applications in a wide range of phenomena such as ocean acoustic propagation and heat diffusion. The local radial basis functions (RBFs) method have been applied to many physical and engineering problems because of its simplicity in implementation and its superiority in solving different real-world problems easily. In this article, we propose an efficient local RBFs method coupled with Laplace transform (LT) for approximating the solution of fractional mobile/immobile solute transport model in the sense of Caputo derivative. In our method, first, we employ the LT which reduces the problem to an equivalent time-independent problem. The solution of the transformed problem is then approximated via the local RBF method based on multiquadric kernels. Afterward, the desired solution is represented as a contour integral in the left half complex along a smooth curve. The contour integral is then approximated via the midpoint rule. The main advantage of the LT-RBFs method is the avoiding of time discretization technique due which overcomes the time instability issues, second is its local nature which overcomes the ill-conditioning of the differentiation matrices and the sensitivity of the shape parameter, since the local RBFs method only considers the discretization points in each local domain around the collocation point. Due to this, sparse and well-conditioned differentiation matrices are produced, and third is the low computational cost. The convergence and stability of the numerical scheme are discussed. Some test problems are performed in one and two dimensions to validate our numerical scheme. To check the efficiency, accuracy, and efficacy of the scheme the 2D problems are solved in complex domains. The numerical results confirm the stability and efficiency of the method.

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