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Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the deep representations in the hyperbolic space provide high fidelity embeddings with few dimensions, especially for data possessing hierarchical structure. Such a hyperbolic neural architecture is quickly extended to different scientific fields, including natural language processing, single-cell RNA-sequence analysis, graph embedding, financial analysis, and computer vision. The promising results demonstrate its superior capability, significant compactness of the model, and a substantially better physical interpretability than its counterpart in the euclidean space. To stimulate future research, this paper presents a comprehensive review of the literature around the neural components in the construction of HDNN, as well as the generalization of the leading deep approaches to the hyperbolic space. It also presents current applications of various tasks, together with insightful observations and identifying open questions and promising future directions. [ABSTRACT FROM AUTHOR]

A new smooth model is proposed in this paper to eliminate the non-differentiability of several types of limits in power flow models. The proposed smooth model accurately represents power system static models with limits as a continuously differentiable function. Analytic results are developed to show that each solution of the proposed smooth model is arbitrarily close to a solution of the corresponding original power flow model with limits. The proposed smooth model includes a wide range of devices with physical limits, such as generators, transformers, HVDCs, phase shifters, and shunts. In addition, it is shown that every generic static bifurcation in the original model is transformed into a saddle-node bifurcation in the smoothed model. Hence, the search for bifurcations in voltage stability assessment can be greatly simplified. Several numerical studies of the proposed smooth model on a 9241-bus power system and on a 13659-bus power system have shown promising results. [ABSTRACT FROM AUTHOR]

This paper presents a passive lumped model for DC cables and overhead lines, taking into account the frequency dependency of the per-unit-length parameters due to the skin and proximity effects. In comparison to distributed parameter line models, such as the Frequency-Dependent Phase model of PSCAD, the proposed model does not require line propagation delay extraction and does not impose any restrictions on the simulation timestep. As a result, for electrically short lines, the introduced model is more computationally efficient than distributed parameter models as it enables simulation using larger timesteps. The performance of the proposed model is evaluated in comparison with the PI model and the Frequency-Dependent Phase model through time-domain simulations of a DC microgrid and a radial DC distribution feeder. The studies performed in PSCAD focus on fault-induced transients. The results indicate that for electrically short lines the developed model is significantly more accurate compared to the PI model and provides similar accuracy with more computational efficiency compared to the Frequency-Dependent Phase model. [ABSTRACT FROM AUTHOR]

This paper introduces a new realization for a phase-domain line model and proposes methodologies to increase its numerical performance. The new realization is based on the management of complex poles that may arise when deriving rational models by means of second-order states. The accuracy and numerical performance of the proposed realization is assessed through its application to representative examples and the comparison against other methodologies. It is demonstrated that the proposed methodology can increase substantially the numerical efficiency of the model. [ABSTRACT FROM PUBLISHER]

Radio-wave propagation in tunnels and mines can be qualitatively understood by representing these environments as overmoded waveguides. Waveguide models are fast and intuitive, but the accurate prediction of the propagation characteristics of realistic geometries is beyond their scope. Such predictions can be derived by computationally intensive techniques, such as ray-tracing and vector parabolic equation (VPE) methods. This paper combines the two approaches, to extract surrogate models of arched tunnels in the form of rectangular waveguides. The parameters of the latter are optimized to match their propagation characteristics to those of the original arched tunnel, as predicted by the VPE method. The accuracy and limitations of these surrogate models are thoroughly evaluated. Their practical usefulness is demonstrated in an application where they are iteratively used, in lieu of the original computationally costly arched tunnel model, to optimally distribute the access points of an in-tunnel wireless communication system. [ABSTRACT FROM PUBLISHER]

Objective: Computational modeling is increasingly used to design charging systems for implanted medical devices. The design of these systems must often satisfy conflicting requirements, such as charging speed, specific absorption rate (SAR) and coil size. Fast electromagnetic solvers are pivotal for enabling multi-criteria optimization. In this paper, we present an analytical model based on the quasi-static approximation as a fast, yet sufficiently accurate tool for optimizing inductive charging systems. Methods: The approximate model was benchmarked against full-wave simulations to validate accuracy and improvement in computation time. The coupling factor of two test coils was measured for lateral and axial displacements and the SAR was measured experimentally in a PAA phantom. Results: The approximate model takes only 11 seconds to compute a single iteration, while the full-wave model takes 5 hours to compute the same case. The maximum difference with full-wave simulations was less than 24% and the mean difference less than 2%. Adding a novel figure of merit into the multi-criterion optimization resulted in a 16% higher charging speed. The measured results of the SAR and coupling factor are within a 5 mm coil offset margin. Conclusion: The proposed approximate model succeeds as a rapid prototyping tool, enabling fast and sufficiently accurate optimization for wireless charging systems. Significance: The approximate model is the first of its kind to compute both the coupling factor and the SAR near conducting structures fast enough to enable optimization of charging speed. [ABSTRACT FROM AUTHOR]

Modeling of brain tumor dynamics has the potential to advance therapeutic planning. Current modeling approaches resort to numerical solvers that simulate the tumor progression according to a given differential equation. Using highly-efficient numerical solvers, a single forward simulation takes up to a few minutes of compute. At the same time, clinical applications of tumor modeling often imply solving an inverse problem, requiring up to tens of thousands of forward model evaluations when used for a Bayesian model personalization via sampling. This results in a total inference time prohibitively expensive for clinical translation. While recent data-driven approaches become capable of emulating physics simulation, they tend to fail in generalizing over the variability of the boundary conditions imposed by the patient-specific anatomy. In this paper, we propose a learnable surrogate for simulating tumor growth which maps the biophysical model parameters directly to simulation outputs, i.e. the local tumor cell densities, whilst respecting patient geometry. We test the neural solver in a Bayesian model personalization task for a cohort of glioma patients. Bayesian inference using the proposed surrogate yields estimates analogous to those obtained by solving the forward model with a regular numerical solver. The near real-time computation cost renders the proposed method suitable for clinical settings. The code is available at https://github.com/IvanEz/tumor-surrogate. [ABSTRACT FROM AUTHOR]

This paper presents various synchronous machine models implemented in the computation of electromagnetic transients. This paper proposes new models for achieving better computational efficiency while maintaining precision. In addition to simple infinite bus analysis, the machine models are also compared for a more sophisticated and practical case study. Precision analysis includes the surrounding network accuracy constraints. [ABSTRACT FROM AUTHOR]

This paper describes the design of an efficient parallel implementation of an ice simulator that simulates the behaviour of a ship operating in pack ice. The main idea of the method is to treat ice as a set of discrete objects with very simple properties, and to model the system mechanics mainly as a set of discrete contact and failure events. In this way it becomes possible to parallelize the problem, so that a very large number of ice floes can be modeled. This approach is called the Ice Event Mechanics Modeling (IEMM) method which builds a system solution from a large set of discrete events occurring between a large set of discrete objects. The simulator is developed using the NVIDIA Compute Unified Device Architecture (CUDA). This paper also describes the execution of experiments to evaluate the performance of the simulator and to validate the numerical modeling of ship operations in pack ice. Our results show speed up of 11 times, reducing simulation time for a large ice field (9,801 floes) from over 2 hours to about 12 minutes. [ABSTRACT FROM PUBLISHER]

This paper includes the Neumann boundary conditions in the magnetic charge modeling method for spherical magnetic structures. In the spherical coordinate system, the geometry and the magnetic properties of the imaged magnetic structure are obtained with the method of inversion. This method is applied to a spherical permanent magnet array, and the results are compared with the harmonic modeling and finite-element analysis. In addition to this validation, the modeling methods are compared on their limitations and accuracy. [ABSTRACT FROM AUTHOR]

To handle significant variability in loads, renewable energy generation, as well as various contingencies, (two-stage) robust optimization method has been adopted to construct unit commitment models and to ensure reliable solutions. In this paper, we further explore and extend the modeling capacity of two-stage robust optimization and present two new robust unit commitment variants: the expanded robust unit commitment and the risk constrained robust unit commitment model. We derive structural properties, demonstrate the connection to the popular scenario based stochastic unit commitment models, and present a customized column-and-constraint generation method. Numerical experiments on those models are performed using practical data sets, which illustrate their modeling strength, economic outcomes, and the algorithm performance in solving those models. [ABSTRACT FROM PUBLISHER]

This paper proposes a new dual-band baseband equivalent (BBE) Volterra model devised to predict the behavior of dual-band power amplifiers (PAs) and/or to linearize their response. A series of derivations were applied to the original continuous passband Volterra-series expression to uncover a simple formulation that related the BBE envelopes of the output signals in each band to those at the input in the discrete domain. This yielded an inherently low-complexity dual-band Volterra-series model that does not require the empirical pruning commonly applied to the low-pass equivalent Volterra series. The proposed model was successfully applied to digitally predistort and linearize a dual-band 45-W class-AB GaN PA driven with different dual-band dual-standard test signals. For each band, the model necessitated less than 25 coefficients to reduce the adjacent channel leakage power ratio ACLR by up to 25 dB. [ABSTRACT FROM AUTHOR]

Many critical electronic design automation (EDA) problems suffer from the curse of dimensionality, i.e., the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g., 3-D field solvers discretizations and multirate circuit simulation), nonlinearity of devices and circuits, large number of design or optimization parameters (e.g., full-chip routing/placement and circuit sizing), or extensive process variations (e.g., variability /reliability analysis and design for manufacturability). The computational challenges generated by such high-dimensional problems are generally hard to handle efficiently with traditional EDA core algorithms that are based on matrix and vector computation. This paper presents “tensor computation” as an alternative general framework for the development of efficient EDA algorithms and tools. A tensor is a high-dimensional generalization of a matrix and a vector, and is a natural choice for both storing and solving efficiently high-dimensional EDA problems. This paper gives a basic tutorial on tensors, demonstrates some recent examples of EDA applications (e.g., nonlinear circuit modeling and high-dimensional uncertainty quantification), and suggests further open EDA problems where the use of tensor computation could be of advantage. [ABSTRACT FROM PUBLISHER]

This paper deals with the development of a hardware-in-the-loop (HIL) real-time simulation setup for vehicular systems. In modeling a vehicular drivetrain, the battery model poses the greatest challenge. Mathematical battery models suitable for real-time and transient simulation are often inaccurate due to internal electrochemical reactions in the battery and external factors such as loading conditions. To model these phenomena precisely while maintaining low computational intensity as required for real-time simulation is a prohibitively difficult task. In lieu of a mathematical model, this paper presents a simulation setup where actual batteries are interfaced with a real-time simulator. It provides a platform to test the emerging battery technologies for which dependable models may not be readily accessible. Results are presented for an HIL model of a planetary-gear-system hybrid electric vehicle with actual batteries in the loop of the simulation. [ABSTRACT FROM PUBLISHER]

Kijima I and Kijima II models are two important imperfect repair models in literature. These two models use one constant parameter to represent the degree of repair, which is called Repair Effectiveness (RE) in this paper. We developed a more general imperfect repair model by extending the constant RE to a time-dependent function based on the virtual age process, where the Kijima models are special cases of the new model. A simulation method is developed to estimate the cumulative number of failures for the new model, and a Bayesian inference method is proposed to select the best imperfect repair model. Finally, to demonstrate the new model, a numerical example is provided. From this example, the new model shows a more accurate mean and a narrower confidence interval than that of the Non-Homogeneous Poisson Processes, and Kijima I and Kijima II models. [ABSTRACT FROM PUBLISHER]

In this paper, a new equivalent circuit for the leakage inductance of multiwinding (or multisection) transformers is presented. The methods proposed in this paper unify terminal models with models derived from the principle of duality between electric and magnetic equivalent circuits. The new model is identified as the terminal-duality model (TDM). The elements of the circuit in addition to properly representing the transformer behavior at the terminals are physically related to flux paths in the transformer window. The circuit of the TDM consists of a set of mutually coupled inductors available in any circuit simulation program and, in particular, available in all Electromagnetic Transients Program-type programs. The circuit elements of the TDM can be computed in two ways: 1) from the observation of the behavior of the magnetic field in the transformer window (applying the principle of duality) and 2) from measurements on short-circuit tests performed at the transformer terminals. Both methods yield identical results. The 2-D finite-element simulations are used to compute the terminal behavior of a wide variety of winding configurations. Several examples are presented for the illustration of the model capabilities and validation. [ABSTRACT FROM PUBLISHER]

The nonstationary simulation of traveling-wave tubes (TWTs) is an important issue in vacuum electronics as it enables the study of a wide range of spurious phenomena. Software based on general time-domain models generally needs huge computational resources and is often useless for design activities. Various nonstationary reduced models of interaction in a TWT have been developed as an alternative to general ones. Among them, the discrete model of excitation of a periodic waveguide appears to be convenient. However, one of the main obstacles to an accurate representation of the slow-wave structure in this model is that reflections at its terminations are hard to control. In this paper, we present a simple method that overcomes this problem, and we apply it to a coupler of a helix TWT. [ABSTRACT FROM PUBLISHER]

This paper focuses on the optimization of a linear actuator by the “output space mapping (OSM)” method. The underlying objective of this work lies in the minimization of the time required for the achievement of this optimal design. Indeed, in addition to the sole costs of optimization processes strictly speaking, the time needed for the developpement of the models is taken into account. In the context of OSM, two different finite-element models of the same actuator are used. This paper presents these modeling solutions and considers their corresponding accuracy. Results of this multi-objective optimization method are presented and compared with those obtained by the sequential simplex (SS) method based solely on the fine model. Both approaches give similar results. However, the comparison of their performances clearly shows that the OSM algorithm is an effective technique for reducing the computation time of optimization studies, even in the case of relatively simple electromagnetic structures. Hence, this approach leads to an original and effective optimization methodology. [ABSTRACT FROM AUTHOR]

This paper presents a generalized, parallel implementation methodology for real-time simulation of ac machine transients in an FPGA-based real-time simulator. The proposed method adopts nanosecond range simulation time-step and exploits the large response time of a rotating machine to: 1) eliminate the need for predictive-corrective action for the machine electrical and mechanical variables, 2) decouple the solution of the dq0 stator currents, and 3) enable the use of one-time-step delayed interface between the machine and the rest of the system which decouples the machine solution from that of the rest of the system. The proposed method simplifies the solution of the machine model without compromising accuracy or numerical stability of the simulation. This paper also presents a massively parallel, customized hardware architecture tailored to the solution of the mathematical model of ac machines. The proposed method and the developed hardware architecture are tested and verified based on the implementation of a permanent-magnet synchronous machine model and an induction machine-based ac-drive system in a field-programmable gate-array-based simulator. Real-time simulation is achieved with a computation time of 44 ns within the simulation timestep. [ABSTRACT FROM AUTHOR]

This paper proposes a numerical solution of the nonlinear equations that describes the hysteretic behavior of ferromagnetic materials. From the proper definition and solution of these equations, an equivalent circuit model for a ferromagnetic core is developed and proposed. The results obtained from the equivalent circuit are compared with those obtained by a rigorous numerical solution. [ABSTRACT FROM AUTHOR]

Memristors are promising nanoelectronic devices for the implementation of future AI-driven sensor-processor electronic systems, which are essential for the ongoing digitalization of our world. Accurate and computationally cost-effective models for the manufactured memristors are essential for the design of such systems, especially for the simulation of large circuits. In this brief we address the simplification of the JART memristor model, a generic physics-based model of Valence Change Mechanism (VCM) memristors which accurately describes the dynamic behavior of fabricated memristor devices. Furthermore, the proposed model and simplification methodology have the potential to capture the dynamics of a wide range of memristor devices. Importantly, the implicit description of the current through the memristor is replaced by an explicit mathematical relationship. The proper reproduction of memristor dynamics, verified by applying the system-theoretic Dynamic Route Map (DRM) graphical analysis tool, applicable to first-order systems, can be observed through the proposed simplified model and enables the time-efficient simulation of large arrays of VCM devices. [ABSTRACT FROM AUTHOR]

Normal factor graph duality offers new possibilities for Monte Carlo algorithms in graphical models. Specifically, we consider the problem of estimating the partition function of the ferromagnetic Ising and Potts models by Monte Carlo methods, which are known to work well at high temperatures but to fail at low temperatures. We propose Monte Carlo methods (uniform sampling and importance sampling) in the dual normal factor graph and demonstrate that they behave differently: they work particularly well at low temperatures. By comparing the relative error in estimating the partition function, we show that the proposed importance sampling algorithm significantly outperforms the state-of-the-art deterministic and Monte Carlo methods. For the ferromagnetic Ising model in an external field, we show the equivalence between the valid configurations in the dual normal factor graph and the terms that appear in the high-temperature series expansion of the partition function. Following this result, we discuss connections with Jerrum–Sinclair’s polynomial randomized approximation scheme (the subgraphs-world process) for evaluating the partition function of ferromagnetic Ising models. [ABSTRACT FROM AUTHOR]

In this paper, we consider the multichannel blind deconvolution problem, where we observe the output of channels $\mathbf {h}_{i}\in \mathbb {R}^n$ ($i=1,...,N$) that all convolve with the same unknown input signal $\mathbf {x}\in \mathbb {R}^n$. We wish to estimate the input signal and blur kernels simultaneously. Existing theoretical results showed that the original inputs are identifiable under subspace assumptions. However, the subspaces discussed before were randomly or generically chosen. Here, we propose deterministic subspace assumption, which is widely used in practice, and give some theoretical results. First of all, we derive tight sufficient condition for identifiability of signal and convolution kernels, which is only violated on a set of Lebesgue measure zero. Then, we present a nonconvex regularization algorithm by a lifting method and approximate the rank-one constraint via the difference of nuclear norm and Frobenius norm. The global minimizer of the proposed nonconvex algorithm is rank-one matrix under mild conditions on parameters and noise level. The stability result is also shown under the assumption that the inputs lie in a compact set. Besides, the computation of our regularization model is carried out and any limit point of iterations converges to a stationary point of our model. Finally, we provide numerical experiments to show that our nonconvex regularization model outperforms convex relaxation models, such as nuclear norm minimization and some nonconvex methods, such as alternating minimization method and spectral method. [ABSTRACT FROM AUTHOR]

In modern communication systems, nonlinearity in power amplifiers (PAs) and in-phase and quadrature-phase (I/Q) imperfections in the transmitter are of enormous concern. With the increase in the importance for highly energy efficient and low complexity models, there is a need to develop low complexity digital predistortion (DPD) methods. In this paper, we present a novel memory polynomial based distributed two block model to alleviate these impairments. Various performance metrics are used to evaluate the design performance and complexity of proposed model as compared to the state of the art predistorter model. Simulation and measurement results indicate the ability of the proposed model to meet the desired design purpose with reduced complexity in terms of number of coefficients, dispersion coefficient, condition number and number of floating points operations required for computing various steps in the inverse modeling algorithm. This is achieved while maintaining reasonable performances in terms of NMSE and ACEPR. The major attribute of the model is the reduction in complexity of the system. The number of complex valued coefficients and the number of floating point operations (FLOPs) are both reduced by 17%–56%, matrix conditioning is improved by 12–33 dB and the dispersion coefficient is reduced by 16–42 dB as compared to the previously proposed joint modulator and power compensation technique. [ABSTRACT FROM AUTHOR]

The waveform relaxation (WR) technique is used to accelerate the time-domain simulation of high-fidelity models of supercapacitors. Because of their high power density, supercapacitors are suitable energy storage options in electrified transportation fleets and renewable energy systems. Given their fast charging/discharging profile, high-fidelity models, such as transmission-line models or multistage ladder structures, are conventionally adopted for design and simulation purposes. High-fidelity models, that include fast dynamic modes, can slow down the simulation process. This is problematic, in particular, since supercapacitors are parts of a larger, more complex system, e.g., a power train, with a wide dynamic range. In this paper, frequency-domain characterization of a supercapacitor is conducted by the electrochemical impedance spectroscopy. Then, the equivalent multistage ladder model of the supercapacitor is extracted and parameterized. The high-fidelity model is verified by considering the measured charging profile of the supercapacitor. The WR technique, including circuit partitioning and time windowing, is considered for the resulting high-fidelity model. WR results in an order-of-magnitude improvement in simulation speed while maintaining an excellent agreement with the hardware measurement and conventional simulations. [ABSTRACT FROM AUTHOR]

This paper presents an approximate model of the characteristic currents for rectangular plates. This model provides a broadband description of the characteristic current distributions, which could be used for the far-field or near-field cases. For narrow rectangular plates, a modified cosine function and sine function are adapted for the odd and even modes, respectively. As the narrow case is fundamentally a 1-D problem, the presented solution could be scaled for arbitrary combinations of plate length and operating frequency. For wide rectangular plates, each characteristic current is decomposed into four parts along the plate edges, which decay exponentially toward the opposite edges. Variance of the characteristic currents with plate dimension is observed and briefly discussed. The frequency-dependent but axis-independent mode coefficients are utilized and numerically evaluated to verify the proposed model. The modal solutions of two rectangular plates that are illuminated by a normally incident planar wave are given and compared with the full-wave solutions. The modal solutions composed of the lowest four terms provide an explicit description of the surface currents with reasonable accuracy. Axis dependence of the mode coefficients is briefly discussed, and the first-order expression contributes an improved accuracy when the mode number is larger than 1. [ABSTRACT FROM PUBLISHER]

This paper presents a dimensional-analysis supported scaling procedure applied to a mathematical model of electrochemical batteries. The main objective of this research is to allow for laboratory size-scaled and time-compressed experimental analysis of processes involving large physical magnitudes and evolving over long time spans. These situations are of interest when considering the sizing of battery packs and other components of energy systems, particularly smart grids, and further systems where battery storage is relevant, like hybrid vehicles and other standalone systems, as well as deciding management strategies on them. Voltage-, current- and time-scaled models preserving the dynamic evolution of a group of relevant physical magnitudes are presented. These models have been validated through simulation and physical experiments on a test-bench designed and constructed on purpose. The physical implementation of the scaled models is not possible in the cases where some of the scaled model parameters cannot be met using real batteries. But, as the mathematical construction of the scaled models is always possible, this problem can be circumvented with a Hardware-in-the-loop approach: the scaled battery is numerically emulated on a programmable and controllable power source/sink system, which is run in real-time embedded in the test-bench representing the whole system under study. [ABSTRACT FROM PUBLISHER]

We consider a system subject to shocks that arrive according to a nonhomogeneous pure birth process (NHPBP). As a shock occurs, the system has two types of failures. Type-I failure (minor failure) is rectified by a general repair, whereas type-II failure (catastrophic failure) is removed by an unplanned replacement. The probabilities of these two types of failures depend on the number of shocks since the last replacement. We consider a policy with which the system is replaced at the nth type-I failure, or at any type-II failure. The aim of this paper is to determine the optimal policy n^\ast, the number of minor failures up to replacement that minimizes the expected cost rate of the system subject to NHPBP shocks. The model is a generalization of the existing models, and is more applicable in practice. We present some numerical examples, and show that several classical models are the special cases of our model. [ABSTRACT FROM PUBLISHER]

The modern power electronics era started with the commercialization of the silicon-controlled rectifier in 1957, since when power electronics systems have been installed in a wide range of applications from appliances to traction and utility systems as the technology has advanced. Meanwhile, simulation tools have played an important role in the research and development of power semiconductors and power electronics systems. As power devices and power electronics become increasingly important technologies in today's electric society, it is worth examining the overall integration of power electronics design. This paper reviews the history of simulation technology with reference to power device modeling and discusses the future of power electronics system design. [ABSTRACT FROM PUBLISHER]

This paper presents a kinodynamic motion planner, i.e., Kinodynamic Motion Planning by Interior–Exterior Cell Exploration (\\tt KPIECE), which is specifically designed for systems with complex dynamics, where integration backward in time is not possible, and speed of computation is important. A grid-based discretization is used to estimate the coverage of the state space. The coverage estimates help the planner detect the less-explored areas of the state space. An important characteristic of this discretization is that it keeps track of the boundary of the explored region of the state space and focuses exploration on the less covered parts of this boundary. Extensive experiments show that \\tt KPIECE provides significant computational gain over existing state-of-the-art methods and allows us to solve some harder, previously unsolvable problems. For some problems, \\tt KPIECE is shown to be up to two orders of magnitude faster than existing methods and use up to 40 times less memory. A shared memory parallel implementation is presented as well. This implementation provides better speedup than an embarrassingly parallel implementation by taking advantage of the evolving multicore technology. [ABSTRACT FROM PUBLISHER]

This paper provides the comprehensive development procedures and mathematical model of an adjustable speed drive (ASD) for steady-state solutions, transient trajectories, and local stability based on a Floquet multiplier theory. A complete representation of the ASD is employed, which includes the dynamic equation of the dc link capacitor and the detailed models of the diode rectifier and of the pulse width modulated inverter. The power electronic switches are efficiently represented through smooth functions that allow a larger integration step to be used without the loss of precision in the solution. An efficient technique in the time domain for the computation of the periodic steady-state solution of electric systems, including ASDs based on a discrete-exponential-expansion matrix and the Poincaré map, is used. The obtained results are validated against the solutions obtained with an implementation using the Power System Blockset of Simulink and against the response obtained by the measurements from a 1.5-kVA ASD experimental setup system. [ABSTRACT FROM AUTHOR]

The modified local density approximation (MLDA) was developed to describe quantum corrections due to size quantization near a semiconductor–oxide interface for parabolic ellipsoidal bands. In this paper, we propose an extended MLDA model in order to consider arbitrary band structures. The results are compared with the original MLDA model and to self-consistent Poisson–Schrödinger solutions using both constant effective mass and confined six-band \bf k\cdot \bf p Hamiltonians. The extended MLDA model is verified for three major surface orientations and high stress conditions. [ABSTRACT FROM AUTHOR]

Minimization of total variation (TV) is a well-known method for image denoising. Recently, the relationship between TV minimization problems and binary MRF models has been much explored. This has resulted in some very efficient combinatorial optimization algorithms for the TV minimization problem in the discrete setting via graph cuts. To overcome limitations, such as staircasing effects, of the relatively simple TV model, variational models based upon higher order derivatives have been proposed. The Euler's elastica model is one such higher order model of central importance, which minimizes the curvature of all level lines in the image. Traditional numerical methods for minimizing the energy in such higher order models are complicated and computationally complex. In this paper, we will present an efficient minimization algorithm based upon graph cuts for minimizing the energy in the Euler's elastica model, by simplifying the problem to that of solving a sequence of easy graph representable problems. This sequence has connections to the gradient flow of the energy function, and converges to a minimum point. The numerical experiments show that our new approach is more effective in maintaining smooth visual results while preserving sharp features better than TV models. [ABSTRACT FROM PUBLISHER]

Design and development process of solar cells can be greatly enhanced by using accurate models that can predict their behavior accurately. Recently, there has been a surge in research efforts in multijunction (MJ) solar cells to improve the conversion efficiency. Modeling of MJ solar cells poses greater challenges because their characteristics depend on the complex photovoltaic phenomena and properties of the materials used. Currently, several commercial complex device modeling software packages, e.g., ATLAS, are available. But these software packages have limitations in predicting the behavior of MJ solar cells because of several assumptions made on the physical properties and complex interactions. Artificial neural networks have the ability to effectively model any nonlinear system with complex mapping between its input and output spaces. In this paper, we proposed a novel Chebyshev neural network (ChNN) to model a dual-junction (DJ) GaInP/GaAs solar cell. Using the ChNN, we have modeled the tunnel junction characteristics and developed models to predict the external quantum efficiency, and I–V characteristics both at one sun and at dark levels. We have shown that the ChNN-based models perform better than the commercial software, ATLAS, in predicting the DJ solar cell characteristics. [ABSTRACT FROM PUBLISHER]

This paper deals with key problems that have been commonly encountered in the implementation of the Preisach model into finite-element (FE) programs. Such problems include the inverse problem imposed by certain FE formulations, the abundance use of experimental data needed for identification, and the complex hysteretic nonlinearity inherited in electromagnetic problems. The aim is to alleviate these problems using new efficient algorithms to facilitate the inclusion of the Preisach model in FE equations. The inversion of the model is evaded by systematically creating an inverted Everett function identified from a few parameters usually provided by the makers of electrical steel. The Everett function and its derivatives are ensured to be smooth and continuous by using cubic spline interpolation, which is important for producing stable iterative solutions in the FE computations. Thorough investigations and FE simulations supported by experiments show that the proposed algorithms are capable of successfully accomplishing good accuracy, fast computation, and numerical convergence. [ABSTRACT FROM AUTHOR]

This paper is focused on structural approaches to study diagnosability properties given a system model taking into account, both simultaneously or separately, integral and differential causal interpretations for differential constraints. We develop a model characterization and corresponding algorithms, for studying system diagnosability using a structural decomposition that avoids generating the full set of system analytical redundancy relations. Simultaneous application of integral and differential causal interpretations for differential constraints results in a mixed causality interpretation for the system. The added power of mixed causality is demonstrated using a Reverse Osmosis Subsystem from the Advanced Water Recovery System developed at the NASA Johnson Space Center. Finally, we summarize our work and provide a discussion of the advantages of mixed causality over just derivative or just integral causality. [ABSTRACT FROM PUBLISHER]

This paper presents for the first time the transient simulation of the 3-D \TE34, 19 mode converter with surface deformation. The simulation results were obtained by solving the Maxwell equations directly on a 3-D domain with a discontinuous Galerkin method. The presented full-wave simulation was possible only through the use of an advanced highly scalable numerical method operating on a large-scale high-performance computing system. Moreover, the properties of the Maxwell solver with respect to accuracy and computation time are discussed for the application to a smooth-wall \TE34, 19 waveguide where an analytical solution is available. [ABSTRACT FROM PUBLISHER]

Speckles (multiplicative noise) in synthetic aperture radar (SAR) make it difficult to interpret the observed image. Due to the edge-preserving feature of total variation (TV), variational models with TV regularization have attracted much interest in reducing speckles. Algorithms based on the augmented Lagrangian function have been proposed to efficiently solve speckle-reduction variational models with TV regularization. However, these algorithms require inner iterations or inverses involving the Laplacian operator at each iteration. In this paper, we adapt Tseng's alternating minimization algorithm with a shifting technique to efficiently remove the speckle without any inner iterations or inverses involving the Laplacian operator. The proposed method is very simple and highly parallelizable; therefore, it is very efficient to despeckle huge-size SAR images. Numerical results show that our proposed method outperforms the state-of-the-art algorithms for speckle-reduction variational models with a TV regularizer in terms of central-processing-unit time. [ABSTRACT FROM AUTHOR]

Multiplicative noise removal based on total variation (TV) regularization has been widely researched in image science. In this paper, inspired by the spatially adapted methods for denoising Gaussian noise, we develop a variational model, which combines the TV regularizer with local constraints. It is also related to a TV model with spatially adapted regularization parameters. The automated selection of the regularization parameters is based on the local statistical characteristics of some random variable. The corresponding subproblem can be efficiently solved by the augmented Lagrangian method. Numerical examples demonstrate that the proposed algorithm is able to preserve small image details, whereas the noise in the homogeneous regions is sufficiently removed. As a consequence, our method yields better denoised results than those of the current state-of-the-art methods with respect to the signal-to-noise-ratio values. [ABSTRACT FROM AUTHOR]

Surface-potential-based compact charge models for symmetric double-gate metal–oxide–semiconductor field-effect transistors (SDG-MOSFETs) are based on the fundamental assumption of having equal oxide thicknesses for both gates. However, for practical devices, there will always be some amount of asymmetry between the gate oxide thicknesses due to process variations and uncertainties, which can affect device performance significantly. In this paper, we propose a simple surface-potential-based charge model, which is applicable for tied double-gate MOSFETs having same gate work function but could have any difference in gate oxide thickness. The proposed model utilizes the unique so-far-unexplored quasi-linear relationship between the surface potentials along the channel. In this model, the terminal charges could be computed by basic arithmetic operations from the surface potentials and applied biases, and thus, it could be implemented in any circuit simulator very easily and extendable to short-channel devices. We also propose a simple physics-based perturbation technique by which the surface potentials of an asymmetric device could be obtained just by solving the input voltage equation of SDG devices for small asymmetry cases. The proposed model, which shows excellent agreement with numerical and TCAD simulations, is implemented in a professional circuit simulator through the Verilog-A interface and demonstrated for a 101-stage ring oscillator simulation. It is also shown that the proposed model preserves the source/drain symmetry, which is essential for RF circuit design. [ABSTRACT FROM AUTHOR]

An efficient time-domain discrete model of the interaction of an electron beam with an electromagnetic wave propagating in a slow-wave structure has been described by Kuznetsov. Using a projection of Maxwell's equations onto the eigenmodes of the structure, the evolution of the entire electromagnetic field can be reduced to the evolution of its amplitude alone in each period of the waveguide. The number of degrees of freedom of the system is thus greatly reduced. This model has been successfully applied in the case of coupled-cavity traveling-wave tubes (TWTs) and can be applied to klystrons, where the circuit field is localized between the gaps of the cavities. In this paper, we present the results of numerical simulations performed by applying this model to a helix TWT. We show that the results obtained are in very good agreement with theory. We also compare our results with those from frequency-domain TWT simulation software. [ABSTRACT FROM PUBLISHER]

In this paper, we present a framework for the simulation of electronic devices based on a multiscale and multiphysics approach. A formal description is provided that includes both multiscale and multiphysics problems and which can be linked to already established multiscale methods. We present a set of simulations of an AlGaN/GaN nanocolumn based on a multiscale coupling between atomistic descriptions and continuous media models, illustrating the application of such a multiscale approach to electronic device simulation. [ABSTRACT FROM PUBLISHER]

Soil volumetric structure is an important parameter for tillage operation. The aim of this paper is to assess whether volume characteristics can be inferred from radar measurements. A 2-D numerical model (the 2DSCAT model) was developed based on a numerical solver using a time-domain finite-element method to solve Maxwell's equations. Perfectly matched layers were implemented as well as a near- to far-field transformation. A focused incident beam was generated by adapting the boundary conditions. To represent the soil structure, a simulator was developed describing the soil as biphasic media (fine earth and clods). Clods were represented by randomly deformed ellipses, with randomly determined dimensions, locations, and orientations. The model performed successfully, as evaluated against exact analytical solutions available for an infinite perfectly conducting cylinder and the reflection of flat semi-infinite media. The model was then evaluated against measurements made by an X-band FM continuous-wave radar on a box filled with dry clods of different sizes. The effect of the clod size on the backscattering power was very well reproduced, showing the potential of using a 2-D numerical model to understand microwave-backscattering patterns from cloddy soils. Analysis of the volume scattering shows that this phenomenon can be mostly hidden in the scatter diagram by surface scattering when the latter occurred. However, the volume scattering gives a stronger residual signal in time because of propagation through the medium. Thus, time studies of the scattering signal provide further information about volume heterogeneities. [ABSTRACT FROM AUTHOR]

A waveform channel is considered where the transmitted signal is corrupted by Wiener phase noise and additive white Gaussian noise. A discrete-time channel model that considers the effect of filtering on the phase noise is developed. The model is based on a multi-sample receiver, i.e., an integrate-and-dump filter whose output is sampled at a rate higher than the signaling rate. It is shown that, at high Signal-to-Noise Ratio (SNR), the multi-sample receiver achieves a rate that grows logarithmically with the SNR if the number of samples per symbol (oversampling factor) grows with the cubic root of the SNR. Moreover, the pre-log factor is at least 1/2 and can be achieved by amplitude modulation. For an approximate discrete-time model of the multi-sample receiver, the capacity pre-log at high SNR is shown to be at least 3/4 if the number of samples per symbol grows with the square root of the SNR. The analysis shows that phase modulation achieves a pre-log of at least 1/4, while amplitude modulation still achieves a pre-log of 1/2. This is strictly greater than the capacity pre-log of the (approximate) discrete-time Wiener phase noise channel with only one sample per symbol, which is 1/2. Numerical simulations are used to compute lower bounds on the information rates achieved by the multi-sample receiver. The simulations show that oversampling is beneficial for both strong and weak phase noise at high SNR. In fact, the information rates are sometimes substantially larger than when using commonly-used approximate discrete-time models. [ABSTRACT FROM PUBLISHER]

A methodology to create and develop electrical compact models for memristive devices is presented. It comprises a six-step algorithm that uses experimental data of memristive devices in order to obtain a compact model suitable for circuit-level simulations. The model methodology makes use of the definition of the intrinsic variables of memristive systems, to obtain the state map that describes the system, from which the mathematical expressions of the model are obtained. In order to illustrate the usage of the algorithm, a compact memristive model is developed by using experimental data from a TiO2-based resistive switching (RS) device. The resulting mathematical model is compared to the experimental data by computing the mean absolute percentage error (MAPE) between both, which results in 11%, and reduces to 6% after a piecewise approach. The circuit-level representation is implemented using a SPICE macromodel and a Verilog-A module, from which the memristive behavior is also properly reproduced. [ABSTRACT FROM AUTHOR]

A novel embedded domain decomposition method (EDDM) is proposed to simulate electromagnetic scattering from structures in an inhomogeneous medium. In this method, the inhomogeneous medium (e.g., layered substrate) is set as the background subdomain, and the integrated structures (e.g., metallic/dielectric components) with properly defined buffer regions (BRs) are set as the embedded subdomains, where the meshes of two can be completely independent and arbitrarily overlapping. The major contribution of this work lies in the following three aspects. First, to allow for arbitrary inclusion of the embedded subdomains in an inhomogeneous background subdomain, an adaptive BR method is developed. The self-coupling equation is rederived to ensure the consistency of the BR and the inhomogeneous background, and a new mutual-coupling equation is introduced to account for the material and/or conductor differences. Second, the boundary element method (BEM) accelerated by the multilevel fast multipole algorithm (MLFMA) is utilized to truncate the finite-element region so that the computation capability can be enhanced and the extra air box setting of the background subdomain in the original EDDM can be avoided. Third, for structure adjustments such as translations and rotations, we further propose an inherited calculation to avoid mesh regeneration and repeated computation of the self-coupling and preconditioning matrices and also reduce the iteration counts and solution time. Numerical examples have shown the accuracy, robustness, and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Many complex nonlinear optimization or control issues can be transformed into the solving of time-varying nonlinear equations (TVNEs), playing a fundamental role in the control and management of complex systems. As a result, a robust and high-precision online solution method is significant for TVNE. However, there are three main challenges for handling TVNE via the existing methods: First, short-time invariance assumption frequently leveraged in the existing methods leads to lagging errors that are difficult to eliminate. Second, it is difficult in dealing with unknown noise disturbance during the solution process, which causes low solution accuracy or solution failure. Third, existing continuous-time methods are hard to be implemented on digital equipments. In this article, an anti-noise discrete-time neural dynamics (DTND) is designed and studied to overcome the above issues systematically. The theoretical analysis and numerical simulations demonstrate that the proposed model effectively eliminates the lagging errors and achieves the accurate solution of the TVNE in a noisy environment. Moreover, to verify the superior numerical computational property of the DTND model, the intention recognition of lower limbs is explored from the artificial systems, computational experiments, and parallel execution (ACP) framework. Specifically, a nonlinear artificial dynamic system (NADS) concerning the human surface electromyogram (sEMG) signals and joint information is established, which performs in parallel with the actual human lower limb physical experiments. Simulation results illustrate that, within the acceptable range of the digital computer, the controller designed by the DTND model can well guide the NADS to accurately recognize the motion intention of the human lower limb. [ABSTRACT FROM AUTHOR]

Recent missions reveal that rubble-pile asteroids usually have a wide distribution of bare rocks in various scales. The dynamical evolution of a lander interacting with such rough surfaces is still an open problem. This article investigates the influence of the explicitly refined rocky terrains on the natural landing motion of a cuboid asteroid lander. The methodology for constructing the local terrains with centimeter-sized to meter-sized rocks is developed based on manipulating the original polyhedron shape model. Local rocky terrains refined with different-sized rocks (20 cm, 40 cm, 80c m, and 1 m) are generated. Moreover, hypothetical local terrains in different numbers of 1-m-sized rocks are presented to assess their influence on the landing motion. Numerical simulations are performed to characterize the dynamical behavior of the lander in rocky terrains. The distributions of the first and second touchdown positions, the transfer time, and the locomotion distance are taken as the three key indicators for examining the motion difference in these rocky terrains. The mechanism for the landing motion is analyzed by considering the collision slope. The results are expected to provide guidelines for the lander deployment and the path planning of a rover on rocky asteroids. [ABSTRACT FROM AUTHOR]

A discretized magnetic media model is developed to improve our ability to describe the high-frequency recording performance of perpendicular magnetic media. The new model differs from the traditional macrospin model in that the magnetic grains are subdivided into cells, which have varying magnetic properties and independent magnetic moments. This discretized media model has two realizations: one with a discretized magnetic boundary and one without the magnetic boundary. We focus on the resolution and the signal-to-noise ratio obtained from a high-density bit pattern as the bit length approaches the grain pitch. Simulation results demonstrating the impact of a discretized cap in this recording regime will be discussed. The increased computational effort needed to perform a simulation using this discretized media model, and has been addressed by porting this model to a cluster of graphics processing units. [ABSTRACT FROM AUTHOR]