Computational Expenditure Research Articles

Reconfigurable adaptive polarisation‐sensitive array optimisation for multiple interferences elimination in satellite communication

Polarisation-sensitive array (PSA) has earned extensive attention in satellite communication owing to excellent anti-interference performance. Nevertheless, the configuration of traditional PSA where each polarised antenna requires multiple radio frequency (RF) front-ends makes it much more costly in terms of hardware and software for system design. In this paper, resorting to RF switches, a novel reconfigurable PSA optimisation technique is developed that utilises fewer RF front-ends, which can considerably decrease computational expenditure and achieve high anti-jamming performance. An RF switch switching (RFSS) scheme is devised to guide the implementation of PSA reconfiguration. To accurately demonstrate the effect of the array configuration on interference rejection performance, the polarisation-spatial subspace correlation coefficient (PSC) is presented. Then the relationship between the optimal signal to interference plus noise ratio (SINR) and the PSC based on adaptive processing is formulated. Aiming to acquire the optimal reconstructed PSA outputting the maximum SINR, the mathematical model of the PSA reconfiguration problem is established. Subsequently, two optimisation methods are provided to address the problem efficiently, thereby gaining the optimal configuration of the reconstructed PSA and implementing the reconfiguration by RF switches. Numerical and experimental simulations verify the correctness and reliability of the developed scheme and approaches.

Pre-Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding.

The latest biological findings observe that the motionless "lock-and-key" theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory-related studies, thus hindering the possibility of supervised learning. A spatial-temporal pre-training method based on the modified equivariant graph matching networks, dubbed ProtMD which has two specially designed self-supervised learning tasks: atom-level prompt-based denoising generative task and conformation-level snapshot ordering task to seize the flexibility information inside molecular dynamics (MD) trajectories with very fine temporal resolutions is presented. The ProtMD can grant the encoder network the capacity to capture the time-dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen to verify the effectiveness of ProtMD through linear detection and task-specific fine-tuning. A huge improvement from current state-of-the-art methods, with a decrease of 4.3% in root mean square error for the binding affinity problem and an average increase of 13.8% in the area under receiver operating characteristic curve and the area under the precision-recall curve for the ligand efficacy problem is observed. The results demonstrate a strong correlation between the magnitude of conformation's motion in the 3D space and the strength with which the ligand binds with itsreceptor.

A Nonlinear Multigrid Method for the Parameter Identification Problem of Partial Differential Equations with Constraints

In this paper, we consider the parameter identification problem of partial differential equations with constraints. A nonlinear multigrid method is introduced to the process of parameter inversion. By keeping the objective functions on coarse grids consistent with those on fine grids, the proposed method reduces the dimensions of objective functions enormously and mitigates the risk of trapping in local minima effectively. Furthermore, constraints significantly improve the convergence ability of the method. We performed the numerical simulation based on the porosity identification of elastic wave equations in the fluid-saturated porous media, which suggests that the nonlinear multigrid method with constraints decreases the computational expenditure, suppresses the noise, and improves the inversion results.

A method to improve accuracy of definition and promptness of forecasting of spacecraft motion parameters

The subject of the presented paper is the methods used to define spacecraft motion parameters. The goal of the paper is to develop approaches to improve accuracy of definition and promptness of forecasting of motion parameters. The tasks are 1) to determine the aspects influencing the result accuracy at defining initial conditions of the spacecraft motion, 2) to reveal the drawbacks of traditional methods and 3) to suggest some possible ways that can be used to improve accuracy and promptness at defining the spacecraft motion parameters. During the research it is revealed that the result accuracy at defining the initial conditions of the spacecraft motion is influenced by three aspects – a random component caused by presence of random errors in trajectory measurements, a dynamic component due to dynamic errors of the used model of the spacecraft motion and the error defined by the size of convergence region of the minimization method that is used to solve the multi-point boundary problem. To receive the least inaccuracy at defining the spacecraft motion parameters it is necessary to process some optimal quantity of measuring information which should ensure sufficient compensation of random errors and at the same time prevent the dynamic error from influencing considerably. At it it should be taken into account that the amount of the measured information should not be less than it is necessary to ensure convergence of the algorithm used to define those motion parameters. The main results are as follows. For solving the task of increasing accuracy of spacecraft motion parameters definition the paper suggests a new method based on minimization with definition of initial approximation region. The suggested method implements a non-local approach to minimization of the goal function, which ensures better as compared to traditional methods convergence. To increase the promptness of spacecraft motion forecasting it is suggested to use the mathematical apparatus of differential transformations. It allows decreasing the computational expenditures 3 to 4 times as compared to the regular ballistic and navigational algorithms while the given accuracy is preserved. On the basis of the performed research the following conclusions can be made. To increase the accuracy of the spacecraft motion parameters definition it is necessary to optimize the required number of measurement orbit passes, for that it is appropriate to use the Nelder-Mead search method. At implementing the advanced coordinate methods of the spacecraft control, to increase the promptness of the spacecraft motion forecasting, it is necessary to considerably decrease the computational expenditures for the motion parameters definition. To achieve that it is reasonable to use the mathematical apparatus of the differential transformations.

Expedited Gradient-Based Design Closure of Antennas Using Variable-Resolution Simulations and Sparse Sensitivity Updates

Numerical optimization has been playing an increasingly important role in the design of contemporary antenna systems. Due to the shortage of design-ready theoretical models, optimization is mainly based on electromagnetic (EM) analysis, which tends to be costly. Numerous techniques have evolved to abate this cost, including surrogate-assisted frameworks for global optimization, or sparse sensitivity updates for speeding up local search. In the latter, CPU-heavy updates of the system response sensitivity through finite differentiation are suppressed based on, e.g., the magnitude of design variability during the optimization run. Another approach is to incorporate variable-resolution simulations. Recently, a technique exploiting a continuous spectrum of admissible model fidelity levels has been reported, thereby allowing for a considerable reduction of computational expenditures. Seeking further savings, this work introduces an accelerated gradient-based algorithm with sparse sensitivity updates and variable-resolution EM simulations. Our technique is validated using four broadband antennas, and demonstrated to offer substantial (around 80%) savings over the benchmark while maintaining acceptable design quality.

Performance-Driven Yield Optimization of High-Frequency Structures by Kriging Surrogates

Uncertainty quantification is an important aspect of engineering design, as manufacturing tolerances may affect the characteristics of the structure. Therefore, the quantification of these effects is indispensable for an adequate assessment of design quality. Toward this end, statistical analysis is performed, for reliability reasons, using full-wave electromagnetic (EM) simulations. Still, the computational expenditures associated with EM-driven statistical analysis often turn out to be unendurable. Recently, a performance-driven modeling technique has been proposed that may be employed for uncertainty quantification purposes and can enable circumventing the aforementioned difficulties. Capitalizing on this idea, this paper discusses a procedure for fast and simple surrogate-based yield optimization of high-frequency structures. The main concept of the approach is a tailored definition of the surrogate domain, which is based on a couple of pre-optimized designs that reflect the directions featuring maximum variability of the circuit responses with respect to its dimensions. A compact size of such a domain allows for the construction of an accurate metamodel therein using moderate numbers of training samples, and subsequently, it is employyed to enhance the yield. The implementation details are dedicated to a particular type of device. Results obtained for a ring-slot antenna and a miniaturized rat-race coupler imply that the cost of yield optimization process can be reduced to few dozens of EM analyses.

E-COMMERCE AND ECONOMIC GROWTH IN INDONESIA: ANALYSIS OF PANEL DATA REGRESSION

Digital economy has a major contribution to Indonesia's Gross Domestic Product (GDP). Digital economy sector that has the highest growth is e-commerce. The development of e-commerce and rapid improvements in internet infrastructure are expected to encourage public consumption, so as to increase GDP growth. The purpose of the research examines the general overview of economic growth and several e-commerce indicators in Indonesia and analyzes the effect of several e-commerce indicators on economic growth in Indonesia from 2016 to 2018. Economic growth and several e-commerce indicators in Indonesia have a positive trend. DKI Jakarta has some of the highest e-commerce indicators compared to other provinces, while East Nusa Tenggara has the biggest number of additional points internet access locations from 2016 to 2018. The results of panel data regression analysis show that the variable computer users, e-commerce users, internet access, and expenditure on information technology communication have a positive and significant effect on economic growth in Indonesia and the e-commerce indicators that have the greatest influence is computer users. Thus, the results of this study indicate the importance of utilizing the role of e-commerce in promoting economic growth in Indonesia. Keywords: economic growth; e-commerce; panel

The performance of filtered leapfrog schemes in benchmark simulations

AbstractThe stabilisation of the leapfrog time‐stepping scheme provided by the Robert–Asselin filter has enabled decades of atmospheric and oceanic research and weather and climate predictions. The unfortunate concomitant reduction from second‐order accuracy to first‐order accuracy inflicted by the filter has recently ushered in a new generation of leapfrog time filters that preserve the second‐order accuracy, including the Robert–Asselin–Williams (RAW) filter and its variants. These modern filtered leapfrog schemes have previously been shown to improve numerical simulations made using both simple conceptual models and comprehensive general circulation models. However, their performance in standard benchmark experiments has not previously been assessed. Here we evaluate these filtered leapfrog schemes in four classic benchmark experiments: linear scalar advection; a nonlinear density current in the quasi‐compressible equations; a nonlinear rising warm bubble in the fully compressible equations; and the linked behaviour of nonlinear twin tropical cyclones in the rotating shallow‐water equations. For a given time‐step size, the filtered leapfrog schemes are found to compare favourably with the third‐order Runge–Kutta (RK3) scheme. They are also less computationally expensive than RK3, at roughly one‐third to one‐half the cost per time step. For a given computational expenditure, the filtered leapfrog schemes are found to produce smaller errors with respect to the analytical solution (where available) than RK3. Furthermore, the filtered leapfrog schemes are found to be numerically stable, even when the discretisation method splits the slow advection and diffusion modes from the fast acoustic and gravity‐wave modes. Given that implementing filter upgrades requires only minimally invasive changes to an existing computer code, our results provide support for the continued use of filtered leapfrog schemes in atmosphere and ocean models.

Low-Cost Modeling of Microwave Components by Means of Two-Stage Inverse/Forward Surrogates and Domain Confinement

Full-wave electromagnetic (EM) analysis is one of the most important tools in the design of modern microwave components and systems. EM simulation permits reliable evaluation of circuits at the presence of cross-coupling effects or substrate anisotropy, as well as for accounting for interactions with the immediate environment. However, repetitive analyses required by EM-driven procedures, such as parametric optimization or statistical analysis, may entail considerable computational expenditures, often prohibitive. Tackling the high-cost issue fostered the shift toward the incorporation of fast replacement models, including both physics-based surrogates and data-driven ones. While the latter is more popular and versatile, the construction of reliable approximation metamodels for microwave components is hindered by the curse of dimensionality and nonlinearity of system responses. The recent performance-driven modeling methodologies are capable of alleviating these difficulties by confining the surrogate domain to a vicinity of the optimum design manifold (i.e., the region that contains high-quality designs rather than the entire parameter space). Although setting up the model in a constrained domain requires small amounts of training data, domain definition itself requires a set of preoptimized reference designs, acquisition of which is an expensive endeavor. This work proposes a novel approach, which replaces the reference designs with a small set of random observables, thereby considerably reducing the overall cost of the model setup. Comprehensive verification involving several miniaturized microstrip structures demonstrates that our methodology is competitive to performance-driven frameworks both in terms of modeling accuracy and computational efficiency with an average savings of around 80%.

Vector Single-Source Surface Integral Equation for TE Scattering From Cylindrical Multilayered Objects

A single-source surface integral equation (SS-SIE) for transverse electric (TE) scattering from cylindrical multilayered objects is proposed in this paper. By incorporating the differential surface admittance operator (DSAO) and recursively applying the surface equivalence theorem from innermost to outermost boundaries, an equivalent model with only electric current density on the outermost boundary can be obtained. In addition, an integration approach is proposed, where the small argument expansion of the Hankel function is used to evaluate the singular and nearly singular integrals. Compared with other SIEs, such as the Poggio-Miller-Chang-Harrington-Wu-Tsai (PMCHWT) formulation, the computational expenditure is reduced for multilayered structures because only a single source is needed on the outermost boundary. As shown in the numerical results, the proposed method generates only 19% of unknowns, uses 26% of memory, and requires 29% of the CPU time of the PMCHWT formulation.

Dispersion and Stability Analysis for TLM Unstructured Block Meshing

The insatiable demand to optimize and engineer new functionalities in electromagnetic devices has risen their geometrical complexity to unprecedented levels. This usually results in computationally multiscale problems with some tiny components of great importance to the overall behavior of these devices. Block meshing is a powerful technique for treating such problems. The usage of variable mesh size is adaptable for adequately representing the geometrical details without exhausting the computational resources with a uniform fine grid in the entire computational domain. In addition, block meshing allows the use of cubic cells only for which time step in maximum and velocity error is minimal in each subregion. In this article, we present a mathematical formulation for stability and dispersion analysis when using block meshing in the transmission-line matrix (TLM) method. These relations permit us to compute the maximum mesh size and time step that guarantee a tolerable level of numerical dispersion, hence minimizing the computational expenditures. Moreover, we study the case of adopting the local time step and demonstrate the origins of the instability that may appear in this case. Finally, some numerical experiments are presented to show the advantages of the proposed approach when using TLM block meshing. A similar procedure can be used for FDTD or FIT with block meshing.

Is It Possible to Obtain Benefits by Reducing the Contribution of the Digital Signal Processing Techniques to the Control of the Active Power Filter?

This paper presents a simple yet efficient control method for active power filters that can be used to improve power quality. Applying this method can open the way towards limiting the hardware and computational expenditure, which are needed for control of the active filter, while maintaining its required performance. The method is based on the indirect approach of obtaining reference signals combined with the closed-loop current control technique. Monitoring of changes of energy stored in reactance elements of the active filter is the base for obtaining reference signals for compensation. The active filter can perform classical compensation and, additionally, can perform some extra functionality for managing of active power in the system. In particular, it can stabilize the supplying source power, enable energy exchange between loads connected on DC and AC sides of the active filter, and—in a case of generating loads—enable their energy storage and redistribution amongst consuming loads. The presented method can be useful for voltage-source as current-source inverter based active filters, and for DC systems as well as for AC single- or three-phase ones.

Reliability-based design optimization of a spar-type floating offshore wind turbine support structure

The application of reliability-based design optimization (RBDO) methods to offshore wind turbine systems is highly relevant regarding economic efficiency and for considering prevailing uncertainties within the design process. Furthermore, RBDO is a very promising approach in optimizing systems when classification and standardization are not fully available. The level of difficulty of design optimization already increases when including the reliability aspect, but becomes even more challenging when dealing with the highly complex system of floating wind turbines (FWTs), which has not yet been applied. Thus, this paper presents for the first time an integrated framework for RBDO of FWTs, combining concepts of optimization with reliability-based design and advanced modeling, requiring reasonable computational effort and time expenditure. In preprocessing, environmental conditions, limit states, and uncertainties are specified, an appropriate reliability assessment approach is elaborated, and response surfaces for various system geometries in the optimization design space are generated ahead of the RBDO execution. These are finally used by means of an interpolation approach for the reliability calculation integrated in the iterative design optimization. On the example of a spar-buoy FWT system, the application of the presented methodology and the feasibility of coupling FWT design optimization with reliability assessment are shown.

QoS-Guaranteed Adaptive Modulation and Coding for Wireless Scalable Video Multicast

Multicast is a promising solution to address spectrum scarcity in wireless video streaming, while facing tough challenge in provisioning quality of service (QoS) to heterogeneous users. This article presents an adaptive modulation and coding (AMC) scheme to accommodate both spatial diversity and temporal variation on users’ channel conditions for QoS provisioning in wireless scalable video multicast. The QoS-guaranteed AMC policy optimization is formulated as constrained stochastic optimization based on Markov decision processes. An adaptive policy iteration algorithm is developed to find the optimal AMC policy online without any prior knowledge of users’ channel statistics or intensive calculations. This algorithm is attractive due to adaptability to unknown environments and less run-time computation expenditure. Simulations results demonstrate the effectiveness of the proposed scheme.

Обобщенный алгоритм определения параметров орбиты ИСЗ на основе квадратичных функционалов.

The questions of development a generalized algorithm for determining the parameters of the low circular orbit (LCO) of an Earth satellite (ES) based on the use of quadratic functionals are in the focus of this paper. The functionals represent the square of the differences between the measured values of the ES sighting angles and the frequency of the received signal with the values of the same parameters obtained for the assumed values of the Keplerian orbital elements in accordance with the adopted model of the ES motion. Estimates of the orbit parameters are formed from the condition of the minimum of the proposed quality functionals. The proposed algorithm is aimed at the developing two equations for the relationship between the measured values of the azimuth and elevation angles, as well as the frequency of the received satellite signal and the parameters of the satellite orbit. The use of the indicated constraint equations makes it possible to pass from the six-dimensional space of the Keplerian orbital elements to the four-dimensional space of the Keplerian orbital elements when constructing the algorithm and choosing the initial approximations of the orbit parameters. Such a reduction in the dimension of space makes it possible to significantly reduce the amount of computational expenditure, which ensures the stability of the algorithm and expands the possibilities of its practical use with limited resources (computing power and restrictions on the permissible processing time). The following Keplerian orbital elements are proposed as four basic parameters: eccentricity, ascending node longitude, orbital inclination, and perigee argument. The other two elements, the semi-major axis of the orbit and the mean anomaly, are expressed as functions of four basic parameters. This choice is determined by the fact that, in the case of LCO, the pivoting of the initial values of the eccentricity and the argument of perigee is quite simple, which makes it possible to ensure convergence to the exact values of the orbit parameters in a wide value of the initial approximations. Within the Keplerian approximation of the satellite's orbital motion, mathematical relations are presented that determine the operations performed within the framework of the considered algorithm. However, a more complete consideration of the factors influencing the motion of the satellite only leads to more volumetric relations, but does not fundamentally affect the construction of the algorithm itself.

Accurate Modeling of Frequency Selective Surfaces Using Fully-Connected Regression Model With Automated Architecture Determination and Parameter Selection Based on Bayesian Optimization

Surrogate modeling has become an important tool in the design of high-frequency structures. Although full-wave electromagnetic (EM) simulation tools provide an accurate account for the circuit characteristics and performance, they entail considerable computational expenditures. Replacing EM analysis by fast surrogates provides a way to accelerate the design procedures. Unfortunately, modeling of microwave passives is a challenging task due to their highly-nonlinear outputs. Frequency selective surfaces (FSSs) constitute a representative example with their multi-resonant reflection and transmission responses that need to be represented over broad frequency ranges. Deep neural networks (DNNs) seem to be the promising techniques for handling such cases. However, a serious practical issue associated with their employment is an appropriate selection of the model parameters, including its architecture. A common practice is experience-driven setup, heavily based on trial and error, which does not guarantee the optimum model determination and may lead to multiple problems such as poor generalization or high variance of the model predictive power with respect to the training data set selection. This paper proposes a novel modeling framework, referred to as a fully-connected regression model (FCRM), where the crucial role is played by Bayesian Optimization (BO), incorporated to determine the DNN-based model setup, including both its architecture and the hyperparameter values, in a fully automated manner. For validation, FCRM is applied to construct the model of a Minkowski Fractal-Based FSS. The efficacy of the methodology is demonstrated through comparisons with several benchmark techniques, including the DNN surrogates established using the traditional methods as well as conventional regression models. The numerical results indicate that FCRM exhibits considerably improved prediction power and reduced sensitivity to the training sample assignment.

Study of an ESP's performance handling liquid-liquid flow and unstable O-W emulsions part II: Coupled CFD-PBM modelling

The internal flow behavior of an ESP handling dispersed oil-water and emulsion flows remains quite unexplored. Previous studies have focused either on the individual droplet dynamics or on a simplified and limited modelling of the ESP's global performance. Hence, this study seeks to implement and analyze the applicability of a more robust and far-reaching CFD-PBM model which maintains a reasonable computational expenditure. Good agreement with experimental data was attained by the Eulerian-PBM framework (<10% error) and acceptable mean flow predictions were achieved with a less robust and computationally demanding VOF model (15% error). The internal flow behavior was noted to be governed by droplet size and effective viscosity variations, which induce the formation adverse pressure gradients leading to the emergence of backflow currents and eddies, particularly at higher oil fractions and lower flowrates. At higher flowrates and latter stages, well adapted flow is always attained, regardless of the mixture handled.

Reduction of Hydrodynamic Mixing Models on the Basis of the DMD Algorithm

With the use of decomposition into dynamic modes, reduced models of hydrodynamic mixing have been constructed and rather exact space–time pictures of impurity distribution for various periodic regimes of mixing have been obtained with substantial reduction of computational expenditures. It is shown that after processing the results of solution of the problem on mixing in a rectangular cavern with mobile bottom and lid by the DMD method, the gain in the information storage amounts to more than 80%. The proposed approach can also be applied for processing experimental data.

Issues of Multi-Motor Electric Drive Dynamics through the Example of Converter Tilting Mechanism

Many heavy machines and mechanisms of mining and metallurgical industry, for example, basic-oxygen converters (BOCs), are equipped with multi-motor electric drive. Reducing the influence of the disadvantages characteristic of a branched multi-connected system is possible by creating control systems based on mathematical models of the considered electromechanical systems (EMS). The full mathematical model takes into account the number of EMS motors, gear backlashes, elasticity of shafting, the effect of dissipative forces, etc. Disadvantage of this approach is complexity of such models, which leads to high computational costs and time expenditure for their implementation. To analyze dynamic processes arising under acceleration and deceleration conditions of the electromechanical system of the converter tilting mechanism, it is proposed to use a simplified equivalent simulation model, which would take into account changing the EMS natural oscillation frequency for any operation conditions based on process and design features. Based on the simulation model analysis, it was concluded that it is necessary to assess mechanical loads in the system by comparing their current and basis values, as well as taking into account the damping properties of the electric drive. To reduce dynamic loads, it is proposed to form a law of changing the control voltage using an intensity generator, which reduces the risks of occurrence of elastic moments, significantly exceeding those permissible for this class of mechanisms. The results of thermal and vibration diagnostics for assessing malfunctions of key units of the converter tilting mechanism, as well as assessing the system behavior based on the Matlab Simulink model with varying shafting elasticity and normalized gear backlashes, showed significant effect of the latters on the dynamic loads.

PI-Net: A Deep Learning Approach to Extract Topological Persistence Images.

Topological features such as persistence diagrams and their functional approximations like persistence images (PIs) have been showing substantial promise for machine learning and computer vision applications. This is greatly attributed to the robustness topological representations provide against different types of physical nuisance variables seen in real-world data, such as view-point, illumination, and more. However, key bottlenecks to their large scale adoption are computational expenditure and difficulty incorporating them in a differentiable architecture. We take an important step in this paper to mitigate these bottlenecks by proposing a novel one-step approach to generate PIs directly from the input data. We design two separate convolutional neural network architectures, one designed to take in multi-variate time series signals as input and another that accepts multi-channel images as input. We call these networks Signal PI-Net and Image PI-Net respectively. To the best of our knowledge, we are the first to propose the use of deep learning for computing topological features directly from data. We explore the use of the proposed PI-Net architectures on two applications: human activity recognition using tri-axial accelerometer sensor data and image classification. We demonstrate the ease of fusion of PIs in supervised deep learning architectures and speed up of several orders of magnitude for extracting PIs from data. Our code is available at https://github.com/anirudhsom/PI-Net.