Patching Scheme Research Articles

Ground-truth-free Deep Learning for 3-D Seismic Denoising and Reconstruction with Channel Attention Mechanism

Seismic denoising methods using supervised methods rely on a large number of high-quality paired training datasets to reach satisfactory performances. There are two ways to generate labels for network training: one is to simulate the synthetic data using the wave equation, and the other is to utilize denoised data obtained via conventional methods. However, using these labels will limit the networks' noise attenuation performance compared with using large volumes of noise-free data as labels. Here, we propose a ground-truth-free way for three-dimensional (3-D) seismic data processing. First, we use the 3-D patch scheme to divide the noisy seismic data into many fixed-size blocks and then flatten the obtained 3-D patches to expand the training set and capture more higher-order waveform characteristics from the input noisy data. Next, the obtained training dataset is sent into the proposed deep learning (DL) network, where the encoder blocks compress the feature map to extract the waveform features, and the decoder blocks reconstruct the denoised feature map. Notably, the convolutional bottleneck attention module (CBAM) and efficient channel attention (ECA) module are applied to guide the network to focus on signal fluctuation features with fewer network parameters. In addition, the concatenation mechanism is used to enable deep networks to reuse shallow-layer waveform features and mitigate overfitting during training. Finally, the unpatching scheme is used to reconstruct the denoised 3-D seismic data. Numerical experiments demonstrate that the proposed method outperforms benchmark approaches in terms of signal-to-noise ratio (SNR) improvement and useful signal preservation.

Accurate and efficient multiscale simulation of a heterogeneous elastic beam via computation on small sparse patches

Modern `smart' materials have complex microscale structure, often with unknown macroscale closure. The Equation-Free Patch Scheme empowers us to non-intrusively, efficiently, and accurately simulate over large scales through computations on only small well-separated patches of the microscale system. Here the microscale system is a solid beam of random heterogeneous elasticity. The continuing challenge is to compute the given physics on just the microscale patches, and couple the patches across un-simulated macroscale space, in order to establish efficiency, accuracy, consistency, and stability on the macroscale. Dynamical systems theory supports the scheme. This research program is to develop a systematic non-intrusive approach, both computationally and analytically proven, to model and compute accurately macroscale system levels of general complex physical and engineering systems.

Efficient computational homogenisation of 2D beams of heterogeneous elasticity using the patch scheme

Modern ‘smart’ materials have complex heterogeneous microscale structure, often with unknown macroscale closure but one we need to realise for large scale engineering and science. The multiscale Equation-Free Patch Scheme empowers us to non-intrusively, efficiently, and accurately predict large scale, system level, solutions through computations on only small sparse patches of the given detailed microscale system. Here the microscale system is that of a 2D beam of heterogeneous elasticity, with either fixed–fixed, fixed–free, or periodic boundary conditions. We demonstrate that the described multiscale Patch Scheme simply, efficiently, and stably predicts the beam’s macroscale, with a controllable accuracy, at finite scale separation. Dynamical systems theory supports the scheme. This article points the way for others to use this innovative systematic non-intrusive approach, via a developing toolbox of functions, to model and compute accurately macroscale system-levels of general complex physical and engineering systems.

Low-profile dual-polarized differential-fed filtering antenna and its novel array with low sidelobe level

This paper presents a single-layer dual-polarized filtering antenna with good out-of-band suppression. By using inherent characteristic of the patch’s eigenmode and meanwhile merging four filtering strategies, i.e., parasitic patches, slot on the driving patch, and defected groundstructure (DGS), a low-profile filtering antenna design with five radiation nulls has been realized. With the designed filtering antenna, a conventional 1×2 array with a spacing of 0.91λ is first developed. In order to reduce the sidelobe level of the array, a sharing patching scheme is introduced to decrease the interspace between the elements as 0.44λ, with the same physical size as the conventional array. In addition, a concept to measure total isolation between two kinds of the polarized ports in the array, i.e., SddVH, has been derived for the first time. The prototype of the proposed array is fabricated with a profile of 0.038λ. Measured results indicate that the designed novel array has an operational band of 3.53–4.06 GHz, SddVH better than 41 dB, a sidelobe level below −18 dB, and a suppression level above 20 dB with the stopband up to 0.5 GHz and 5 GHz.

Adaptively Detect and Accurately Resolve Macro-scale Shocks in an Efficient Equation-Free Multiscale Simulation

The Equation-Free approach to efficient multiscale numerical computation marries trusted micro-scale simulations to a framework for numerical macro-scale reduction -- the patch dynamics scheme. A recent novel patch scheme empowered the Equation-Free approach to simulate systems containing shocks on the macro-scale. However, the scheme did not predict the formation of shocks accurately, and it could not simulate moving shocks. This article resolves both issues, as a first step in one spatial dimension, by embedding the Equation-Free, shock-resolving patch scheme within a classic framework for adaptive moving meshes. Our canonical micro-scale problems exhibit heterogeneous nonlinear advection and heterogeneous diffusion. We demonstrate many remarkable benefits from the moving patch scheme, including efficient and accurate macro-scale prediction despite the unknown macro-scale closure. Equation-free methods are here extended to simulate moving, forming and merging shocks without a priori knowledge of the existence or closure of the shocks. Whereas adaptive moving mesh equations are typically stiff, typically requiring small time-steps on the macro-scale, the moving macro-scale mesh of patches is typically not stiff given the context of the micro-scale time-steps required for the sub-patch dynamics.

Equation-free patch scheme for efficient computational homogenisation via self-adjoint coupling

Equation-free macroscale modelling is a systematic and rigorous computational methodology for efficiently predicting the dynamics of a microscale complex system at a desired macroscale system level. In this scheme, a given microscale model is computed in small patches spread across the space-time domain, with patch coupling conditions bridging the unsimulated space. For accurate predictions, care must be taken in designing the patch coupling conditions. Here we construct novel coupling conditions which preserve self-adjoint symmetry, thus guaranteeing that the macroscale model maintains some important conservation laws of the original microscale model. Consistency of the patch scheme’s macroscale dynamics with the original microscale model is proved for systems in 1D and 2D space, and these proofs immediately extend to higher dimensions. Expanding from a system with a single configuration to an ensemble of configurations establishes that the proven consistency also holds for cases where the microscale periodicity does not integrally fill the patches. This new self-adjoint patch scheme provides an efficient, flexible, and accurate computational homogenisation, as demonstrated here with canonical examples in 1D and 2D space based on heterogenous diffusion, and is applicable to a wide range of multiscale scenarios of interest to scientists and engineers.

Machine learning techniques to construct patched analog ensembles for data assimilation

Using generative models from the machine learning literature to create artificial ensemble members for use within data assimilation schemes has been introduced in Grooms (2021) [1] as constructed analog ensemble optimal interpolation (cAnEnOI). Specifically, we study general and variational autoencoders for the machine learning component of this method, and combine the ideas of constructed analogs and ensemble optimal interpolation in the data assimilation piece. To extend the scalability of cAnEnOI for use in data assimilation on complex dynamical models, we propose using patching schemes to divide the global spatial domain into digestible chunks. Using patches makes training the generative models possible and has the added benefit of being able to exploit parallelism during the generative step. Testing this new algorithm on a 1D toy model, we find that larger patch sizes make it harder to train an accurate generative model (i.e. a model whose reconstruction error is small), while conversely the data assimilation performance improves at larger patch sizes. There is thus a sweet spot where the patch size is large enough to enable good data assimilation performance, but not so large that it becomes difficult to train an accurate generative model. In our tests the new patched cAnEnOI method outperforms the original (unpatched) cAnEnOI, as well as the ensemble square root filter results from [1].

An Equation Free Algorithm Accurately Simulates Macroscale Shocks Arising From Heterogeneous Microscale Systems

Scientists and engineers often create accurate, trustworthy, computational simulation schemes—but all too often these are too computationally expensive to execute over the time or spatial domain of interest. The equation-free approach is to marry such trusted simulations to a framework for numerical macroscale reduction—the patch dynamics scheme. This article extends the patch scheme to scenarios in which the trusted simulation resolves abrupt state changes on the microscale that appear as shocks on the macroscale. Accurate simulation for problems in these scenarios requires capturing the shock within a novel patch, and also modifying the patch coupling rules in the vicinity in order to maintain accuracy. With these two extensions to the patch scheme, straightforward arguments derive consistency conditions that match the usual order of accuracy for patch schemes. The new scheme is successfully tested to simulate a heterogeneous microscale partial differential equation. This technique will empower scientists and engineers to accurately and efficiently simulate, over large spatial domains, multiscale multiphysics systems that have rapid transition layers on the microscale.

Optical Convolutional Neural Network With WDM-Based Optical Patching and Microring Weighting Banks

We propose an optical convolutional neural network (OCNN) architecture for high-speed and energy-efficient deep learning accelerators. The WDM-based optical patching scheme (WDM-OPS) is adopted as the data-feeding structure for its superior energy efficiency and the microring banks are used for the large-scale weighting and summing (the computing core). We thoroughly investigate the performance (including prediction accuracy, speed, and energy efficiency) of this architecture in different system defects. The results indicate that, the prediction accuracy of OCNN can reach 97% in the MNIST dataset with a computing speed of over 100 TMAC/s on condition of achievable low insertion loss. It is also observed that the WDM-OPS notably reduces the energy consumption of the electro-optic modulation and thus the OCNN becomes an exceptionally energy-efficient architecture among several well-known optical architectures. In the evaluations, instead of merely considering the computing core, we take the holistic optical system including lasers, electro-optic modulators, data preprocessing, photodetection and transimpedance amplification into consideration. Therefore, this work provides a potential guide for the systematic implementation of the OCNN architecture.

Large‐scale simulation of shallow water waves via computation only on small staggered patches

AbstractA multiscale computational scheme is developed to use given small microscale simulations of complicated physical wave processes to empower macroscale system‐level predictions. By coupling small patches of simulations over unsimulated space, large savings in computational time are realizable. Here, we generalize the patch scheme to the case of wave systems on staggered grids in two‐dimensional (2D) space. Classic macroscale interpolation provides a generic coupling between patches that achieves consistency between the emergent macroscale simulation and the underlying microscale dynamics. Spectral analysis indicates that the resultant scheme empowers feasible computation of large macroscale simulations of wave systems even with complicated underlying physics. As an example of the scheme's application, we use it to simulate some simple scenarios of a given turbulent shallow water model.

Optical patching scheme for optical convolutional neural networks based on wavelength-division multiplexing and optical delay lines.

Recent progress on optical neural networks (ONNs) heralds a new future for efficient deep learning accelerators, and novel, to the best of our knowledge, architectures of optical convolutional neural networks (CNNs) provide potential solutions to the widely adopted convolutional models. So far in optical CNNs, the data patching (a necessary process in the convolutional layer) is mostly executed with electronics, resulting in a demand for large input modulator arrays. Here we experimentally demonstrate an optical patching scheme to release the burden of electronic data processing and to cut down the scale of the input modulator array for optical CNNs. Optical delay lines replace electronics to execute data processing, which can reduce the scale of the input modulator array. The adoption of wavelength-division multiplexing enables a single group of optical delay lines to simultaneously process multiple input data, reducing the system complexity. The optical patching scheme provides a new solution to the problem of data input, which is challenging and concerned with the field of ONNs.

Formal Analysis and Improvement on Ultralightweight Mutual Authentication Protocols of RFID

Ultralightweight mutual authentication protocols (UMAP) of Radio frequency identification (RFID) systems have attracted much attention from researchers. Many studies reveal that most of UMAP suffer malicious attack. To improve security of UMAP, formal analysis is performed with Simple promela interpreter (SPIN). Two typical UMAPs, which are RCIA and RAPP, are selected as our case study. A protocol abstract modeling method is presented to make UMAP can be formalized simply. Using SPIN, verification results show that RCIA and RAPP are both vulnerable against desynchronization attack. A Generalized model of UMAP (G-UMAP) and a general patching scheme are presented for resisting the attack. To validate the patching scheme, formal verification is then performed for the improved protocol. SPIN verification shows that the improved RCIA and RAPP both gain higher security. The above proposed modeling method has great significance for similar UMAP analyzing, and the proposed patching scheme is proved to be practical and reliable.

Unconventional superconductivity in the extended Hubbard model: Weak-coupling renormalization group

We employ the weak-coupling renormalization group approach to study unconventional superconducting phases emerging in the extended, repulsive Hubbard model on paradigmatic two-dimensional lattices. Repulsive interactions usually lead to higher-angular momentum Cooper pairing. By considering not only longer-ranged hoppings, but also non-local electron-electron interactions, we are able to find superconducting solutions for all irreducible representations on the square and hexagonal lattices, including extended regions of chiral topological superconductivity. For the square, triangular and honeycomb lattices, we provide detailed superconducting phase diagrams as well as the coupling strengths which quantify the corresponding critical temperatures depending on the bandstructure parameters, band filling, and interaction parameters. We discuss the sensitivity of the method with respect to the numerical resolution of the integration grid and the patching scheme. Eventually we show how to efficiently reach a high numerical accuracy.

Bond-ordered states and f -wave pairing of spinless fermions on the honeycomb lattice

Spinless fermions on the honeycomb lattice with repulsive nearest-neighbor interactions are known to harbour a quantum critical point at half-filling, with critical behaviour in the Gross-Neveu (chiral Ising) universality class. The critical interaction strength separates a weak-coupling semimetallic regime from a commensurate charge-density-wave phase. The phase diagram of this basic model of correlated fermions on the honeycomb lattice beyond half-filling is, however, less well established. Here, we perform an analysis of its many-body instabilities using the functional renormalization group method with a basic Fermi surface patching scheme, which allows us to treat instabilities in competing channels on equal footing also away from half-filling. Between half-filling and the van-Hove filling, the free Fermi surface is hole-like and we again find a charge-density wave instability to be dominant at large interactions. Moreover, its characteristics are those of the half-filled case. Directly at the van-Hove filling the nesting property of the free Fermi surface stabilizes a dimerized bond-order phase. At lower filling the free Fermi surface becomes electron-like and a superconducting instability with $f$-wave symmetry is found to emerge from the interplay of intra-unitcell repulsion and collective fluctuations in the proximity to the charge-density wave instability. We estimate the extent of the various phases and extract the corresponding order parameters from the effective low-energy Hamiltonians.

A mobile assisted coverage hole patching scheme based on particle swarm optimization for WSNs

Wireless sensor networks (WSNs) have drawn much research attention in recent years due to the superior performance in multiple applications, such as military and industrial monitoring, smart home, disaster restoration etc. In such applications, massive sensor nodes are randomly deployed and they remain static after the deployment, to fully cover the target sensing area. This will usually cause coverage redundancy or coverage hole problem. In order to effectively deploy sensors to cover whole area, we present a novel node deployment algorithm based on mobile sensors. First, sensor nodes are randomly deployed in target area, and they remain static or switch to the sleep mode after deployment. Second, we partition the network into grids and calculate the coverage rate of each grid. We select grids with lower coverage rate as candidate grids. Finally, we awake mobile sensors from sleep mode to fix coverage hole, particle swarm optimization (PSO) algorithm is used to calculate moving position of mobile sensors. Simulation results show that our algorithm can effectively improve the coverage rate of WSNs.

Stokes flow singularity at a corner joining solid and porous walls at arbitrary angle

Motivated by the internal flow geometry of spiral-wound membrane modules with ladder-type spacers, we consider the Stokes flow singularity at a corner that joins porous and solid walls at arbitrary wedge angle varTheta . Seepage flux through the porous wall is coupled to the pressure field by Darcy’s law; slip is described by a variant of the Beavers–Joseph boundary condition. On a macroscopic, outer length scale, the singularity appears like a jump discontinuity in the normal velocity, characterized by a non-integrable 1 / r divergence of the pressure. For arbitrary varTheta , we develop an algebraic criterion to determine the admissible radial exponent(s) in a leading, inner similarity solution—which represents a weaker, integrable singularity in the pressure. A complete map of exponent versus varTheta is provided for 0< varTheta < 2 pi : this has an intricate structure with infinitely many solution branches clustering around varTheta = pi and varTheta = 2 pi . By generalizing the similarity form with a (ln r) term and iterating on the slip and seepage conditions, we can carry the outer and inner power series to arbitrarily high order. Nevertheless, a numerical splice is required in between. For this purpose, we apply an iterative, numerical-asymptotic patching scheme described by Nitsche and Parthasarathi (J Fluid Mech 713:183–215, 2012). Detailed velocity and pressure profiles are calculated for three wedge angles (varTheta = 3pi /4, pi /2, pi /4) and two dimensionless slip lengths (sigma = 20, 40). The general trends for decreasing wedge angle are (i) weakening of the pressure singularity, (ii) increasing magnitude of the radial component of velocity, and (iii) movement of the inner–outer transition farther from the corner. Wedges with varTheta < pi are seen to differ fundamentally from the flat wedge (varTheta = pi ) previously considered by Nitsche and Parthasarathi (2012).

Good coupling for the multiscale patch scheme on systems with microscale heterogeneity

Computational simulation of microscale detailed systems is frequently only feasible over spatial domains much smaller than the macroscale of interest. The ‘equation-free’ methodology couples many small patches of microscale computations across space to empower efficient computational simulation over macroscale domains of interest. Motivated by molecular or agent simulations, we analyse the performance of various coupling schemes for patches when the microscale is inherently ‘rough’. As a canonical problem in this universality class, we systematically analyse the case of heterogeneous diffusion on a lattice. Computer algebra explores how the dynamics of coupled patches predict the large scale emergent macroscale dynamics of the computational scheme. We determine good design for the coupling of patches by comparing the macroscale predictions from patch dynamics with the emergent macroscale on the entire domain, thus minimising the computational error of the multiscale modelling. The minimal error on the macroscale is obtained when the coupling utilises averaging regions which are between a third and a half of the patch. Moreover, when the symmetry of the inter-patch coupling matches that of the underlying microscale structure, patch dynamics predicts the desired macroscale dynamics to any specified order of error. The results confirm that the patch scheme is useful for macroscale computational simulation of a range of systems with microscale heterogeneity.

Traffic-Aware Patching for Cyber Security in Mobile IoT

The various types of communication technologies and mobility features in Internet of Things (IoT) on the one hand enable fruitful and attractive applications, but on the other hand facilitates malware propagation, thereby raising new challenges on handling IoT-empowered malware for cyber security. Comparing with the malware propagation control scheme in traditional wireless networks where nodes can be directly repaired and secured, in IoT, compromised end devices are difficult to be patched. Alternatively, blocking malware via patching intermediate nodes turns out to be a more feasible and practical solution. Specifically, patching intermediate nodes can effectively prevent the proliferation of malware propagation by securing infrastructure links and limiting malware propagation to local device-to-device dissemination. This article proposes a novel traffic-aware patching scheme to select important intermediate nodes to patch, which applies to the IoT system with limited patching resources and response time constraint. Experiments on real-world trace datasets in IoT networks are conducted to demonstrate the advantage of the proposed traffic-aware patching scheme in alleviating malware propagation.

A mixed decomposition-spline approach for the numerical solution of a class of singular boundary value problems

In this paper, we formulate a patching scheme to solve numerically a class of boundary value problems. The strategy is being founded on a merger of two methods, a modified decomposition technique applied on a small interval near the singularity and a fourth order spline collocation technique which is used on the rest of the domain of the problem. The convergence of the proposed method is analyzed and a fourth-order rate of convergence is proved. Some examples are given to test the efficiency of the scheme and to verify the order of the rate of convergence. The numerical results are compared with exact solutions and the outcomes of other existing numerical methods.

Delay guaranteed VoD services over group-based integrated fiber-wireless (FiWi) access networks with energy efficiency

In group-based fiber-wireless (FiWi) access networks, where wireless clients are divided into several groups according to their interested Internet services, VoD services will meet new challenges considering the diversity of video requesting probability for each wireless group. In this paper, we categorize wireless groups into “video-hot” group with great interest in VoD services and “video-cold” group with infrequent video requesting. For video requests from “video-hot” groups, we firstly propose an energy-efficient optical network unit (ONU) selection approach to reduce ONU power consumption by selecting a subset of ONUs to support such requests, then our previously proposed rebroadcast and patching scheme can be effectively applied. For video requests from “video-cold” groups, a batched multicasting scheme is presented for energy and bandwidth saving. Both of the VoD schemes mentioned in this study are with the aim to minimize worst-case playback delay (WPD) which is particularly important as Internet users. In view of that some ONUs may need to handle video requests from two kinds of group, optimal ONU channel assignment aims to minimize weighted overall WPD is also studied. The efficiency of the work is finally demonstrated by the simulations.