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To address the problem of the long convergence time of traditional Lyapunov Vector Field Guidance (LVFG), a fast convergent LVFG designed with guidance function is proposed. The necessary criteria of a fast convergent vector field are first illustrated through an analysis of the contraction and circulation components. Then, two types of guidance functions of the distance between aircraft and target are constructed to replace the original guidance parameter. Considering the saturation constraints from the aircraft performance, the optimal parameter in guidance functions based on offline search is estimated to achieve the best convergence. Meanwhile, standoff tracking for a moving target is solved with analysis of motion correction. Simulation results indicate the proposed vector field guidance converges faster to the standoff circle without traversing the orbit regardless of whether the aircraft’s initial position is inside or outside the circle, even for a moving target. In addition, selection of parameter in the guidance function is proved to make convergence speed approach the limit.

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Acoustic echo cancellation is often applied in communication and video call system to reduce unnecessary echoes generated between speakers and microphones. In these systems, the speech input signal of the adaptive filter is often colored and unstable, which decays the convergence rate of the adaptive filter if the NLMS algorithm is used. In this paper, an improved nonparametric variable step-size subband (NPVSS-NSAF) algorithm is proposed to address the problem. The variable step-size is derived by minimizing the sum of the square Euclidean norm of the difference between the optimal weight vectors to be updated and the past estimated weight vectors. Then the parameters are eliminated by using the power of subband signal noise equal to the power of subband posteriori error. The performance of the proposed algorithm is simulated in the aspects of misalignment and return loss enhancement. Experiment results show a fast convergence rate and low misalignment of the proposed algorithm in system identification. [ABSTRACT FROM AUTHOR]

The underlying models of lithium-ion batteries are spatiotemporal and, therefore, consist of partial differential equations (PDEs), as they have to capture spatiotemporal electrochemical mechanisms. As parametric uncertainty increases, often PDE models become inadequate as modeling errors due to parameter uncertainty are much larger than those due to model reduction to ordinary differential equations (ODEs). In this paper, we focus on a linear ODE model derived from the PDE combined with algebraic nonlinearities to carry out parameter identification. The ODE model captures the diffusion dynamics while the nonlinearities capture the overpotential and open-circuit potential aspects. The parameter identification method consists of a matrix-regressor adaptive observer, whose regressors are composed using both the states of the linear dynamic model and suitably constructed basis functions of the algebraic nonlinearities. Under conditions of persistent excitation, the parameter and state estimation errors are shown to converge to zero if the overpotential is known, and to a compact set if the overpotential is unknown. A PDE-based single-particle model is used as the truth model to evaluate the accuracy of the proposed adaptive observer. It is shown that the adaptive observer is able to carry out fast estimation of internal states for a battery management system, such as the state of charge and state of health, under a range of operating conditions. [ABSTRACT FROM AUTHOR]

Accurate and fast-convergent time synchronization is very important for wireless sensor networks. The flooding time synchronization converges fast, but its transmission delay and by-hop error accumulation seriously reduce the synchronization accuracy. In this article, a rapid-flooding multiple one-way broadcast time-synchronization (RMTS) protocol for large-scale wireless sensor networks is proposed. To minimize the by-hop error accumulation, the RMTS uses maximum likelihood estimations for clock skew estimation and clock offset estimation, and quickly shares the estimations among the networks. As a result, the synchronization error resulting from delays is greatly reduced, while faster convergence and higher-accuracy synchronization is achieved. Extensive experimental results demonstrate that, even over 24-hops networks, the RMTS is able to build accurate synchronization at the third synchronization period, and moreover, the by-hop error accumulation is slower when the network diameter increases. [ABSTRACT FROM AUTHOR]

In this paper, a new sliding mode control (SMC) is applied to a physical nonlinear system. The novelty of this approach is related to the proposed reaching law by overcoming the main limitations of SMC. Unlike existing reaching laws, the suggested one can achieve high performance with significant reducing of a chattering problem and has a very fast convergence time of the system trajectories into the origin. This law benefits from the advantages and overcomes the limitations of both the exponential reaching law (ERL) and the conventional sliding mode control (SMC). Simulation results and comparison study with ERL and SMC are presented and applied on two degrees of freedom robot in order to show the advantage of the proposed adaptive reaching law. Experiments results are performed with electric cylinder (DC Motor) to confirm this proposition in real-time implementation. • The proposed SMC can achieve a high performance with significant reducing of a chattering problem. • A new adaptive reaching law is proposed to achieve very fast reachability convergence. • Experiments and comparative results are presented to prove the superior performance of the proposed controller. [ABSTRACT FROM AUTHOR]

Driven by the need for more efficient and seamless integration of physical models and data, physics-informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains a challenge. In this work, we propose an efficient, gradient-less weighting scheme for PINNs that accelerates the convergence of dynamic or static systems. This simple yet effective attention mechanism is a bounded function of the evolving cumulative residuals and aims to make the optimizer aware of problematic regions at no extra computational cost or adversarial learning. We illustrate that this general method consistently achieves one order of magnitude faster convergence than vanilla PINNs and a minimum relative L 2 error of O (1 0 − 5) , on typical benchmarks of the literature. The method is further tested on the inverse solution of the Navier–Stokes within the brain perivascular spaces, where it considerably improves the prediction accuracy. Furthermore, an ablation study is performed for each case to identify the contribution of the components that enhance the vanilla PINN formulation. Evident from the convergence trajectories is the ability of the optimizer to effectively escape from poor local minima or saddle points while focusing on the challenging domain regions, which consistently have a high residual score. We believe that alongside exact boundary conditions and other model reparameterizations, this type of attention mask could be an essential element for fast training of both PINNs and neural operators. [ABSTRACT FROM AUTHOR]

Multi-image superresolution (SR) techniques produce a high-resolution image from several low-resolution observations. Previous reconstruction-based SR approaches focus more on the optimization models but have not adequately emphasized the mathematic-solving techniques for this typically illconditioned and under-determined large scale problem. Since step size plays an important role in the iterative SR process, and there is a tradeoff between less computation cost and higher accuracy, conventional SR methods either adopt a fixed step size to obtain a higher running speed, or use a computationally expensive line search algorithm to pursue an improvement in accuracy. Taking both cues into consideration, in this paper, we propose an adaptive line search strategy to realize the fast convergence of reconstruction-based SR. The approximate analytical expression of step size is introduced to prevent us from setting it empirically or running iterations to test a proper one. We further modify the proposed strategy to be more adaptive under different SR conditions. Using our strategy, one can accelerate the SR process and obtain the optimal solution with less iteration. Experiments are conducted on both synthetic data sets and realworld scenes. The results have demonstrated the effectiveness and outperformance of our proposed strategy compared with other line search strategies.

Rotated object detection is an extension of object detection that uses an oriented bounding box instead of a general horizontal bounding box to define the object position. It is widely used in remote sensing images, scene text, and license plate recognition. The existing rotated object detection methods usually add an angle prediction channel in the bounding box prediction branch, and smooth L1 loss is used as the regression loss function. However, we argue that smooth L1 loss causes a sudden change in loss and slow convergence due to the angle solving mechanism of open CV (the angle between the horizontal line and the first side of the bounding box in the counter-clockwise direction is defined as the rotation angle), and this problem exists in most existing regression loss functions. To solve the above problems, we propose a decoupling modulation mechanism to overcome the problem of sudden changes in loss. On this basis, we also proposed a constraint mechanism, the purpose of which is to accelerate the convergence of the network and ensure optimization toward the ideal direction. In addition, the proposed decoupling modulation mechanism and constraint mechanism can be integrated into the popular regression loss function individually or together, which further improves the performance of the model and makes the model converge faster. The experimental results show that our method achieves 75.2% performance on the aerial image dataset DOTA (OBB task), and saves more than 30% of computing resources. The method also achieves a state-of-the-art performance in HRSC2016, and saved more than 40% of computing resources, which confirms the applicability of the approach.

Maximum power point tracking (MPPT) techniques play an important role in efficiency improvement of photovoltaic systems. But, the fast variation of solar irradiation affects the work of these techniques, which degrades the efficiency of these systems. To treat this problem, the present paper proposes a simple MPPT algorithm based on developing a formula for calculating the duty cycle of the converter. This method is based on the verity that the power slope of the PV is null at MPP. This technique consists on an improved INC algorithm, which is used to avoid the poor tracking of the MPP under fast changing irradiance and the high-power oscillation in steady state. The proposed method is verified and simulated. The simulation showed that the response of the proposed algorithm only needs 22% of the time taken by the conventional incremental conductance algorithm to track the maximum power point (MPP) during solar irradiation variation. In addition, the proposed technique shows a negligent oscillation in the power generated after tracking the maximum power point (MPP). [ABSTRACT FROM AUTHOR]

Here, an implementation of three‐phase grid‐tied single‐stage solar photovoltaic (PV) system is done at adverse grid situations of voltage unbalances. The proposed convex combination of least mean square (CCLMS) control approach is adaptive in nature, which extracts the fundamental component of load current (FCI) with fast convergence and provides good performance than conventional controls. The CCLMS algorithm overcomes the compromise of LMS filters between precision and tracking capability due to fixed stepping by providing a combination of two filters for precision and fast tracking. The perturb and observe (P&O)‐based maximum power point tracking (MPPT) is employed for peak power extraction from a PV array. The system is tested in the laboratory on a developed prototype at several adverse conditions like voltages unbalance, load currents unbalances, and irradiation changes. The system performance is acceptable and harmonics distortion is in the limits according to the IEEE‐519 standard while supplying energy to the utility and to the loads. [ABSTRACT FROM AUTHOR]

Model reference adaptive control is one of the popular methods that simultaneously deals with uncertainties and reduces conservatism. However, it usually suffers from slow convergence and poor tracking at the beginning of the adaptation. On the other hand, attaining fast convergence by increasing the learning rate could cause oscillation in the control response, which results in system instability. Some of the solutions that have been presented so far use prediction error, low-pass filter, or normalizing the control signal. In this article, a novel robust normalized Lyapunov design is proposed for model reference adaptive control to achieve fast convergence and to avoid oscillatory response. In contrast to the other solutions, it uses a new Lyapunov function to guarantee the global asymptotic stability and to prove the robustness of the closed-loop system against bounded uncertainties. The performance of the proposed method is compared with two other model reference adaptive controls using simulations. In addition, an industrial selective compliance assembly robot arm is used for further verification. Results indicate that that the proposed normalized Lyapunov design reduces the tracking error by 62.4% on average; it also has faster convergence and shows robustness against uncertainties such as payload changes. [ABSTRACT FROM AUTHOR]

In this work, a dynamic computational model is developed for a single chamber microbial fuel cell (MFC), consisting of a bio-catalyzed anode and an air-cathode. Electron transfer from the biomass to the anode is assumed to take place via intracellular mediators as they undergo transformation between reduced and oxidized forms. A two-population model is used to describe the biofilm at the anode and the MFC current is calculated based on charge transfer and Ohm's law, while assuming a non-limiting cathode reaction rate. The open circuit voltage and the internal resistance of the cell are expressed as a function of substrate concentration. The effect of operating parameters such as the initial substrate (COD) concentration and external resistance, on the Coulombic efficiency, COD removal rate and power density of the MFC system is studied. Even with the simple formulation, model predictions were found to be in agreement with observed trends in experimental studies. This model can be used as a convenient tool for performing detailed parametric analysis of a range of parameters and assist in process optimization. • A fast converging mathematical model is developed to describe a microbial fuel cell. • Model based on bioanode kinetics assuming a non-limiting cathode. • Effect of operating parameters on system performance is studied. • Proposed model can serve as a good starting point for MFC system optimization studies. [ABSTRACT FROM AUTHOR]

Recovering 6D object pose has gained much focus, because of its application in robotic intelligent manipulation to name but a few. This paper presents an approach for 6D object pose refinement from noisy depth images obtained from a consumer depth sensor. Compared to the state of the art aimed at the same goal, the proposed method has high precision, high robustness to partial occlusions and noise, low computation cost and fast convergence. This is achieved by using an iterative scheme that only employs Random Forest to minimize a cost function of object pose which can quantify the misalignment between the ground truth and the estimated one. The random forest in our algorithm is learnt only using synthetic depth images rendered from 3D model of the object. Several experimental results show the superior performance of the proposed approach compared to ICP-based algorithm and optimization-based algorithm, which are generally used for 6D pose refinement in depth images. Moreover, the iterative process of our algorithm can be much faster than the state of the art by only using one CPU core. [ABSTRACT FROM AUTHOR]

Abstract Compared to G k continuity, C k continuity simplifies the construction of functions on surfaces and their refinement, e.g. to solve differential equations on the surface. The new class of free-form surfaces introduced in this paper is almost everywhere parametrically C 2. In particular, a new bi-6 construction combines a fast-contracting C 2 guided subdivision surface with a tiny multi-sided G 1 cap. The cap is chosen to be smaller than any refinement anticipated for geometric modeling or computing on surfaces. Fast contraction means that one subdivision step shrinks the remaining hole more than three steps of Catmull–Clark subdivision. This yields smooth surfaces consisting of a finite number of pieces that are suitable for engineering practice. Both the subdivision construction and the cap are guided by a reference surface that has a different structure and lower smoothness, but conveys the basic shape of a fair surface. [ABSTRACT FROM AUTHOR]

Abstract The mass flow rate of Poiseuille flow of rarefied gas through long ducts of two-dimensional cross-sections with arbitrary shape is critical in the pore-network modeling of gas transport in porous media. Here, for the first time, the high-order hybridizable discontinuous Galerkin (HDG) method is used to find the steady-state solution of the linearized Bhatnagar–Gross–Krook equation on two-dimensional triangular meshes. The velocity distribution function and its traces are approximated in piecewise polynomial spaces (of degree up to 4) on the triangular meshes and mesh skeletons, respectively. By employing a numerical flux that is derived from the first-order upwind scheme and imposing its continuity weakly on the mesh skeletons, global systems for unknown traces are obtained with fewer coupled degrees of freedom when compared to the original discontinuous Galerkin formulation. To achieve fast convergence to the steady-state solution, a diffusion-like equation for flow velocity, which is asymptotic-preserving into the fluid dynamic limit, is solved by the HDG simultaneously on the same meshes. The proposed HDG-synthetic iterative scheme is proved to be accurate and efficient. Specifically, for flows in the near-continuum regime, numerical simulations have shown that, to achieve the same level of accuracy, our scheme could be faster than the conventional iterative scheme by two orders of magnitude, also it is faster than the synthetic iterative scheme based on the finite difference discretization in the spatial space by one order of magnitude. In addition, the implicit HDG method is more efficient than an explicit discontinuous Galerkin gas kinetic solver, as well as the implicit discontinuous Galerkin scheme when the degree of approximating polynomial is larger than 2. The HDG-synthetic iterative scheme is ready to be extended to simulate rarefied gas mixtures and the Boltzmann collision operator. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of two-dimensional (2D) direction of arrival (DOA) estimation of multiple signals for generalized coprime planar arrays consisting of two rectangular uniform planar subarrays. We propose a fast-convergence trilinear decomposition approach, which uses propagator method (PM) as the initialization of the angle estimation to speed the convergence of trilinear decomposition. The received signal of each subarray can be fitted into a trilinear model or parallel factor (PARAFAC) model so that the trilinear alternating least square algorithm can be used to estimate the angle information. Meanwhile, the necessary initialization of DOA estimates can be achieved via PM, which endows the proposed approach a fast convergence and subsequently results in a low complexity. Specifically, we eliminate the ambiguous estimates by utilizing the coprime property and the true DOA estimates can be achieved by selecting the nearest ones of all DOA estimates. The proposed approach can obtain the same estimation performance as the conventional PARAFAC algorithm, but with a low computational cost. Numerical simulation results are provided to validate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Adaptive thresholding methods have proved to yield a high signal‐to‐noise ratio (SNR) and fast convergence in sparse signal recovery. The robustness of a class of iterative sparse recovery algorithms, such as the iterative method with adaptive thresholding, has been found to outperform the state‐of‐art methods in respect of reconstruction quality, convergence speed, and sensitivity to noise. In this study, the authors introduce a new method for compressed sensing, using the sensing matrix and measurements. In our method, they iteratively threshold the signal and project the thresholded signal onto the translated null space of the sensing matrix. The threshold level is assigned adaptively. The results of the simulations reveal that the authors' proposed method outperforms other methods in the signal reconstruction (in terms of the SNR). This performance advantage is noticeable when the number of available measurements approaches twice the sparsity number. [ABSTRACT FROM AUTHOR]

Fast and accurate optical flow estimation is a challenging problem in computer vision. In this paper, we present a novel model to solve the optical flow problem by combining the strengths of the Split Bregman method with the advantages of an efficient variational framework. It allows us to employ different regularization tensors for the Split Bregman regularizer to preserve motion discontinuities. Simultaneously, a novel nonlocal Split Bregman method with adaptive support weights is also developed for the smoothness regularization to preserve motion details, repel outliers, enhance contrast, and reduce motion blurring and staircase effect. The proposed algorithm shows significant improvements in preserving sharp flow edges and important motion details. Most of all, it requires only a few iterations to achieve fast convergence and runs faster than the classic TV-L1 flow method. Our method significantly outperforms the current state-of-the-art methods on the challenging MPI-Sintel dataset and shows good performance on the Kitti flow 2015 and Middlebury datasets. [ABSTRACT FROM PUBLISHER]

This paper discusses issues encountered by the hyperbolic method for diffusion (Nishikawa, 2007) [1] in dimensional heat conduction problems, and proposes a practical resolution. It is shown that the relaxation length must be scaled by a reference length of a domain of interest for solving dimensional equations, and the corresponding non-dimensionalized length should be given an optimal value for fast iterative convergence. To achieve both, a practical formula is proposed for computing a reference length for a given computational grid, such that ( 2 π ) − 1 gives an optimal value for rectangular domains and also serves as an effective approximation for general domains. Numerical results confirm that the proposed scaling is critically important for rendering hyperbolic diffusion schemes independent of the grid unit and for achieving optimal performance of a hyperbolic diffusion solver. [ABSTRACT FROM AUTHOR]

We consider small-influence aggregative games with a large number of players n. For this class of games we present a best-reply dynamic with the following two properties. First, the dynamic reaches Nash approximate equilibria in quasi-linear (in n) number of steps, and the quasi-linear bound is tight. Second, Nash approximate equilibria are played by the dynamic with a limit frequency that is exponentially (in n) close to 1. [ABSTRACT FROM AUTHOR]

The electrical power produced from the photovoltaic panel depends mainly on weather conditions. Hence, the implementation of Maximum Power Point Tracking is mandatory to extract as much power as possible from the photovoltaic panel. In this paper, we propose a new MPPT technique to achieve a better tradeoff between the dynamic response and the oscillations. The new method is more practical under very fast variations of solar irradiation, compared to the traditional ones. The proposed MPPT technique is a combination of an algorithm that generates an initial value of the duty cycle which correspond to the MPP for any irradiation level and another existing MPPT method which generates the step size and compute the duty cycle. This combination guarantee an accurate tracking direction, fast convergence and negligible oscillations around the MPP. For comparison purposes, the proposed method, Fuzzy logic controller and Perturb and Observe have been simulated in Matlab/Simulink using a sinusoidal and ramp profiles of irradiation. The simulation reveals that the new MPPT method performs well under these dynamic profiles. In other words, the MPP was tracked with high accuracy and the efficiency was improved up to 99%. [ABSTRACT FROM AUTHOR]

In this paper, a self-organizing fuzzy neural network with adaptive gradient algorithm (SOFNN-AGA) is proposed for nonlinear systems modeling. First, a potentiality of fuzzy rules (PFR) method is introduced by using the output of normalized layer and the error reduction ratio (ERR) in the training process. And a structure learning approach is developed to determine the network size based on PFR. Second, a novel adaptive gradient algorithm (AGA) with adaptive learning rate is designed to adjust the parameters of SOFNN-AGA. Moreover, a theoretical analysis on the convergence of SOFNN-AGA is given to show the efficiency in both fixed structure and self-organizing structure cases. Finally, to demonstrate the merits of SOFNN-AGA, simulation and experimental results of several benchmark problems and a real world application are examined for nonlinear systems modeling with comparisons against other existing methods. Some promising results are reported in this study, indicating that the proposed SOFNN-AGA performs better favorably in terms of both convergence speed and modeling accuracy. [ABSTRACT FROM AUTHOR]

This paper develops a mathematical approach to calculate the ground state energies of μ p d and μ d t muonic ions. The calculations have been performed by combination of Nikiforov–Uvarov method and linear variational approach. Basis kets have been introduced to expand terms of wave function using a well definition of vector space in the hyper-spherical coordinates. The full calculations have been performed and compared with other variational calculations in the hyper-spherical harmonic formalism. The results of the variational energies show a fast convergence with a relatively small number of candidate basis functions. [ABSTRACT FROM AUTHOR]

Self-organization has the potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. The convergence of self-organizing control, however, is slow in some practical applications in comparison with control by conventional deterministic systems using global information. It is therefore important to facilitate the convergence of self-organizing controls. In controlled self-organization, which introduces an external controller into self-organizing systems, the network is controlled to guide systems to a desired state. Although existing controlled self-organization schemes could achieve the same state, it is difficult for an external controller to collect information about the network and to provide control inputs to the network, especially when the network size is large. This is because the computational cost for designing the external controller and for calculating the control inputs increases rapidly as the number of nodes in the network becomes large. Therefore, we partition a network into several subnetworks and introduce two types of controllers, a central controller and several sub-controllers that control the network in a hierarchical manner. In this study, we propose a hierarchical optimal feedback mechanism for self-organizing systems and apply this mechanism to potential-based self-organizing routing. Simulation results show that the proposed mechanism improves the convergence speed of potential-field construction (i.e., route construction) up to 10.6-fold with low computational and communication costs. [ABSTRACT FROM AUTHOR]

In the smart grid environment, demand side management makes it possible to encourage consumers participating more actively via proper pricing schemes. In this paper, we analyze previous real-time pricing models and then provide a comprehensive model. A new criterion for designing real-time pricing models is proposed, which can guarantee that the optimal solution obtained from centralized algorithms is the same as that from the distributed algorithms. Furthermore, a fast distributed dual gradient algorithm is proposed to achieve the optimal solution. Compared with the widely used distributed dual sub-gradient algorithm, theoretically, the proposed one does not only accelerate the convergence rate, but also overcome the possible non-convergence during iteration process, which is a demerit in the traditional method. This new solver is of great importance in the application of dynamic pricing mechanism due to real-time requirement. More specifically, this new algorithm can largely reduce the information exchange between the energy provider and its customers, making the proposed method more practical. The simulations also validate its effectiveness and efficiency in solving real-time pricing problems. [ABSTRACT FROM PUBLISHER]

The paper carries out an experimental investigation of a new Maximum Power Point Tracking (MPPT) method for standalone Photovoltaic (PV) systems. The new method combines a novel mechanism of global maximum power point identifying loop when the system undergoes multiple maximum power points and the use of adaptive variable step Hill Climbing (HC) MPPT technique to track the identified Global Maximum Power Point (GMPP). To figure out the advantages of the proposed method, it is implemented together with three local MPPTs (e.g., two conventional MPPTs and fuzzy logic-based one) and compared with six recently developed Global MPPT methods. Obtained experimental results as well as comparison outcomes show that the proposed MPPT technique is fast while tracking GMPP (around 2.4 s) and resilient against perturbations that may occur during the operation of the PV system. Moreover, the proposed MPPT method boasts other advantages such as ease of implementation, no dependence on the PV system and requires only two conventional sensors of voltage and current respectively. [ABSTRACT FROM AUTHOR]

Numerical modeling of periodically patterned graphene sheets (PPGS) embedded in planar multilayered media using Fourier-based methods suffers from very slow convergence because of the fact that the conductivity is zero in unfilled areas of the patterned surface and, thus, the so-called Li’s inverse rule is not applicable. In this paper, a simple and efficient approach is proposed to overcome this problem such that the exact boundary condition can be applied and the surface current density on PPGS can be obtained accurately. Here, the PPGS is modeled as a conductive surface and only its conductivity representation by the Fourier series is modified. The proposed method can be used easily for 1-D and 2-D periodic structures without the need to change the basic formulations of Fourier-based methods. Fast convergence and accuracy of the method will be demonstrated by computing the absorption of 1-D and 2-D PPGS. Moreover, the proposed method is utilized to design a wideband tunable wide-angle polarizer consisting of two-parallel PPGS separated by a 0.5- \mu \textm layer of SiO2. The transmittance of the structure exceeds 95% from microwaves up to 2 THz. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid, multiobjective optimization algorithm enabling global optimum tracking in permanent-magnet (PM) traction motor design. The methodology developed is based on the Artificial Bee Colony technique, strength Pareto evolutionary algorithm, and differential evolution strategy ensuring fast and reliable convergence to the optimal Pareto front. The effectiveness of the derived methodology is compared with other well-established and powerful algorithms from the literature through both appropriate test functions and an application example concerning an unequal teeth surface-mounted PM wheel motor design. [ABSTRACT FROM AUTHOR]

In this article, we propose a new evolutionary algorithm, referred as h omologous G ene R eplacement G enetic A lgorithm (hGRGA) that includes a novel and generic operator called h omologous G ene R eplacement (hGR). The hGR operator improves the chromosomes in gene level to promote their overall functionality. The hGRGA effectively encodes the key idea of the natural evolutionary process that locates and utilizes good local schema present in the genes of a chromosome through hGR operator. The proposed hGRGA is evaluated and compared with two variants of GA and two other state-of-the-art evolutionary computing algorithms based on widely-used benchmark functions with a motivation to apply to wider varieties of optimization problems. The simulation results show that the new algorithm can offer faster convergence and better precision while finding optima. Our analysis shows that hGR is effectively a scalable operator that makes hGRGA well suited for real world problems with increasing size and complexity. [ABSTRACT FROM AUTHOR]

Aiming at improving the convergence speed of irregular LDPC codes, this paper proposed a construction algorithm of LDPC codes with fast convergence. By reordering the column of parity check matrix of the original irregular LDPC codes，it could enhance the reliability of the information bits and reduce the average iteration times, and thus obtained an improvement of the convergence speed. Simulation results show that，LDPC codes which was designed using the proposed algorithm have faster convergence speed for variable-node-based layered belief-propagation (VL-BP) decoding algorithm. In addition, for both belief-propagation (BP) decoding algorithm and VL-BP decoding algorithm, the designed LDPC codes possess a better error performance. [ABSTRACT FROM AUTHOR]

The primary goal of this work is to extend the work done in, Tamer (2009), to provide high accuracy satellite attitude and orbit estimates needed for imaging purposes and also before execution of spacecraft orbital maneuvers for the next Egyptian scientific satellite. The problem of coarse satellite attitude and orbit estimation based on magnetometer measurements has been treated in the literature. The current research expands the field of application from coarse and slow converging estimates to accurate and fast converging attitude and orbit estimates within 0.1°, and 10 m for attitude angles and spacecraft location respectively (1-σ). The magnetometer is used for both spacecraft attitude and orbit estimation, aided with gyro to provide angular velocity measurements, star sensor to provide attitude quaternion, and GPS receiver to provide spacecraft location. The spacecraft under consideration is subject to solar radiation pressure forces and moments, aerodynamics forces and moments, earth’s oblateness till the fourth order (i.e. J4), gravity gradient moments, and residual magnetic dipole moments. The estimation algorithm developed is powerful enough to converge quickly (actually within 10 s) despite very large initial estimation errors with sufficiently high accuracy estimates.

• To calculate speckle fields from extended rough surfaces rigorously, a simulator using surface integral equation method accelerated via a fast algorithm MLFMM was implemented. When a proper formulation is chosen, the computation complexity of the solver is reduced from O(N 3) to O(N 1.3), which makes electrical large problems calculable. We have investigated four accurate formulations for different materials. We find that two non-preconditioned formulations are fast and stable for large dielectric surfaces and a preconditioned one for metallic surfaces. Using ICTF for silicon surfaces we have calculated and compared the speckle fields from extended Si surfaces with a size of 60 λ × 60 λ and with different surface roughness. Thanks to relatively large surface calculation, we can observe the evolution of speckles from near field to far field regime. A full randomization of speckle fields is manifested through investigating the correlation of surface roughness with angular correlation coefficient, speckle contrast and transversal distribution of correlation coefficient of speckle fields in free-space geometry. Our results demonstrate that when the proper formulation is chosen, SIE-MLFMM is a realistic simulation tool for dielectric extended rough surfaces even on a computer with moderate power. This capability would enable more sophisticated investigations concerning speckles. This report presents a speckle simulator which calculates rigorously speckle fields with full randomization from rough surfaces. To achieve this, surface integral equation (SIE) method accelerated via multilevel fast multipole method (MLFMM) is implemented. Among several linear formulations to combine the electric and magnetic field integral equations, we demonstrate that one is most suitable for dielectric rough surfaces and one for metallic rough surfaces. As examples, silver and silicon rough surfaces are simulated at a wavelength of 500 nm. To investigate speckles from extended areas, silicon rough surfaces with a size of 30 × 30 µm2 and two million unknowns are calculated. Based on the resultant speckle fields, speckle contrast, angular speckle-correlation and transverse autocorrelation function of intensity in the van Cittert-Zernike zone are further calculated and known relations versus surface roughness are manifested, indicating that sufficient statistic surface information is encoded in the speckle fields. The SIE-MLFMM method with the suitable formulation proves to be an efficient rigorous simulation tool for relatively large rough surface problems and would enable more profound numerical studies for speckle involved optical metrologies. [ABSTRACT FROM AUTHOR]

In this work, we study the convergence of an efficient iterative method, the fast sweeping method (FSM), for numerically solving static convex Hamilton-Jacobi equations. First, we show the convergence of the FSM on arbitrary meshes. Then we illustrate that the combination of a contraction property of monotone upwind schemes with proper orderings can provide fast convergence for iterative methods. We show that this mechanism produces different behavior from that for elliptic problems as the mesh is refined. An equivalence between the local solver of the FSM and the Hopf formula under linear approximation is also proved. Numerical examples are presented to verify our analysis. [ABSTRACT FROM AUTHOR]