Emulation Method Research Articles

Effective in-laboratory test method for PV power generation with enhanced PV emulation accuracy

Photovoltaic (PV) systems require outdoor experiments to verify their functionality and reliability. However, outdoor experiments require complicated experimental set-ups including PV panels, power conversion circuits and controllers. In addition, the experimental data obtained from such systems can be easily affected by environmental conditions such as weather, temperature and season. Recently, PV emulation methods have been proposed that can emulate PV characteristics in the laboratory by connecting a DC supply to PV modules in parallel. However, the voltage characteristics of the conventional PV emulator are different from those of real PV panels, which degrades the accuracy of the PV emulator. This paper proposes an effective PV emulation method with accuracy improvements at the maximum power point of the PV panel, which is based on a simple circuit analysis. Using the proposed PV emulation method, the performance verification of a target PV system can be easily and accurately obtained. The proposed emulation method for PV power generation is experimentally verified by comparisons between it, the conventional emulation method and results obtained from outdoor experiments.

Emulation of Multi-Inverter Integrated Weak Grid via Interaction-Preserved Aggregation

Simplified weak grid models are widely used to emulate the nonideal power grid for the design and stability analysis of grid-connected inverters. However, the dynamic behavior of a real grid with multiple inverters is complex. In this article, an emulation method is proposed for multi-inverter integrated weak grids using an interaction-preserved aggregation model associated with grid impedance. First, the interaction among inverters in weak grids is analyzed using the state-space model and participation analysis. Then, a two-inverter aggregation is proposed for a large-scale system. This model can preserve the impact of parameters on the interaction modes (IMs) and predict the IM-related instability of multi-inverter systems. Thus, the two-inverter representation is applied to emulation to preserve interactions and to simplify the testing of multiple inverters. The emulation of the 16-inverter system is implemented on a hardware-in-the-loop (HIL) test bench with one practical inverter controller under test and the remaining parts integrated in the OPAL-RT real-time simulator. The test results show that the instability attributed to the interaction can be identified by the proposed emulation method.

High-Fidelity Router Emulation Technologies Based on Multi-Scale Virtualization

Virtualization has the advantages of strong scalability and high fidelity in host node emulation. It can effectively meet the requirements of network emulation, including large scale, high fidelity, and flexible construction. However, for router emulation, virtual routers built with virtualization and routing software use Linux Traffic Control to emulate bandwidth, delay, and packet loss rates, which results in serious distortions in congestion scenarios. Motivated by this deficiency, we propose a novel router emulation method that consists of virtualization plane, routing plane, and a traffic control method. We designed and implemented our traffic control module in multi-scale virtualization, including the kernel space of a KVM-based virtual router and the user space of a Docker-based virtual router. Experiments show not only that the proposed method achieves high-fidelity router emulation, but also that its performance is consistent with that of a physical router in congestion scenarios. These findings provide good support for network research into congestion scenarios on virtualization-based emulation platforms.

Vision Emulation Methods for Intelligent Vehicle Radiated Immunity Test in Anechoic Chamber

Lane departure warning system (LDWS) application helps enable a safer driving experience for drivers. This application provides additional information about the movement of the vehicle to the driver through front view system, and can warn the driver of dangerous situations through lane departure warning. The warning information is provided through varies sensors such as cameras, analogue and digital interfaces, machine-vision processors. This means that every sensor and every process is safety-relevant and has to be comprehensively tested under the electromagnetic environment. A method to activate and conduct LDWS installed in Vehicle is put forward to test the electromagnetic immunity performance. A virtual vision emulation system which can activate LDWS warning repeatedly and continuously is built and based on the principle of camera imaging. Moreover, it is proved to be feasible for various vehicle by the proper adjustment. With this system, the radiated immunity test for LDWS and other applications based on front camera of intelligent vehicle can be performed in the anechoic chamber.

Retinal Noise Emulation: A Novel Artistic Tool for Cinema That Also Improves Compression Efficiency

In cinema it is standard practice to improve the appearance of images by adding noise that simulates film grain. This is computationally very costly, so it is only done in post-production and not on the set. It is also limiting because the artists are not able to really experiment with the noise nor introduce novel looks. Furthermore, video compression requires a higher bit rate when the source material has film grain or any other type of high frequency texture. In this work, we introduce a method for adding texture to digital cinema that aims to solve these problems. The proposed algorithm is based on modeling retinal noise, with which the images processed by our method have a natural appearance. This “retinal grain” serves a double purpose. One is aesthetic, as it has parameters that allow to vary widely the resulting texture appearance, which make it an artistic tool for cinematographers. Results are validated through psychophysical experiments in which observers, including cinema professionals, prefer our method over film grain synthesis methods from academia and the industry. The other purpose of the retinal noise emulation method is to improve the quality of compressed video by masking compression artifacts, which allows to lower the encoding bit rate while preserving image quality, and to improve image quality while keeping the bit rate fixed. The effectiveness of our approach for improving coding efficiency, with average bit rate savings of 22.5%, has been validated through psychophysical experiments using professional cinema content shot in 4K, color-graded and where the amount of retinal noise was selected by a motion picture specialist based solely on aesthetic preference.

Vision emulation methods for intelligent vehicle radiated immunity test in anechoic chamber

Lane departure warning system (LDWS) application helps enable a safer driving experience for drivers. This application provides additional information about the movement of the vehicle to the driver through front view system, and can warn the driver of dangerous situations through lane departure warning. The warning information is provided through varies sensors such as cameras, analogue and digital interfaces, machine-vision processors. This means that every sensor and every process is safety-relevant and has to be comprehensively tested under the electromagnetic environment. A method to activate and conduct LDWS installed in Vehicle is put forward to test the electromagnetic immunity performance. A virtual vision emulation system which can activate LDWS warning repeatedly and continuously is built and based on the principle of camera imaging. Moreover, it is proved to be feasible for various vehicle by the proper adjustment. With this system, the radiated immunity test for LDWS and other applications based on front camera of intelligent vehicle can be performed in the anechoic chamber.

Gaussian Process Modeling of Heterogeneity and Discontinuities Using Voronoi Tessellations

Many methods for modeling functions over high-dimensional spaces assume global smoothness properties; such assumptions are often violated in practice. We introduce a method for modeling functions that display heterogeneity or contain discontinuities. The heterogeneity is dealt with by using a combination of Voronoi tessellation, to partition the input space, and separate Gaussian processes to model the function over different regions of the partitioned space. The proposed method is highly flexible since it allows the Voronoi cells to combine to form regions, which enables nonconvex and disconnected regions to be considered. In such problems, identifying the borders between regions is often of great importance and we propose an adaptive sampling method to gain extra information along such borders. The method is illustrated by simulated examples and an application to real data, in which we see improvements in prediction error over the commonly used stationary Gaussian process and other nonstationary variations. In our application, a computationally expensive computer model that simulates the formation of clouds is investigated, the proposed method more accurately predicts the underlying process at unobserved locations than existing emulation methods. Supplementary materials for this article are available online.

Cosmology with galaxy–galaxy lensing on non-perturbative scales: emulation method and application to BOSS LOWZ

ABSTRACTWe describe our non-linear emulation (i.e. interpolation) framework that combines the halo occupation distribution (HOD) galaxy bias model with N-body simulations of non-linear structure formation, designed to accurately predict the projected clustering and galaxy–galaxy lensing signals from luminous red galaxies in the redshift range 0.16 < z < 0.36 on comoving scales 0.6 < rp < 30 $h^{-1} \, \text{Mpc}$. The interpolation accuracy is ≲ 1–2 per cent across the entire physically plausible range of parameters for all scales considered. We correctly recover the true value of the cosmological parameter S8 = (σ8/0.8228)(Ωm/0.3107)0.6 from mock measurements produced via subhalo abundance matching (SHAM)-based light-cones designed to approximately match the properties of the SDSS LOWZ galaxy sample. Applying our model to Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 14 (DR14) LOWZ galaxy clustering and galaxy-shear cross-correlation measurements made with Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8) imaging, we perform a prototype cosmological analysis marginalizing over wCDM cosmological parameters and galaxy HOD parameters. We obtain a 4.4 per cent measurement of S8 = 0.847 ± 0.037, in 3.5σ tension with the Planck cosmological results of 1.00 ± 0.02. We discuss the possibility of underestimated systematic uncertainties or astrophysical effects that could explain this discrepancy.

High-Accuracy Dynamic Load Emulation Method for Electrical Drives

This article addresses the problem of dynamic load emulation (DLE) with high accuracy of any mechanical load for which the mathematical model is known. The control strategy of a load machine is used to design tasks of control algorithms of variable speed and torque drives for educational purposes and several engineering applications, as transportation and renewable energy. The structure uses a set-point feed-forward compensator with speed-tracking controller, which provides robustness under parametric variation and nonmodeled dynamics. Nonlinear effects and constraints of the experimental setup shaft can be included in the compensator, which makes the strategy an alternative method for systems operating under different degrees of uncertainty. Moreover, different control approaches can be applied to both compensator and speed controller. The method can be applied with any rotary machines, where two induction motors (IMs) with directly coupling are adopted. The method was compared to two other DLE methods and was validated through nonlinear models whose inertia and friction are not constant. The simulated and experimental results are very similar, even when high uncertainty is assumed.

A Novel Random Angular Sampling Method for Spatial and Temporal Channel Emulation

In this letter, a novel angular sampling method–random offset sampling (ROS) is proposed for the simulation of ray-based physical reference channel models. This new method employs an asymmetrical random angular offset to the equal power sampling method, which makes the simulated channel match the spatial–temporal characteristics of the reference channel model more exactly. It is shown that this new method can make better utilization of the symmetrical property of power angular spectrum distributions with very limited sampling sinusoids, especially performing pretty well in case of small angular spread. With small modifications, ROS can be directly embedded in the state-of-the-art standardized channel simulation models.

A Statistical Method for Emulation of Computer Models With Invariance-Preserving Properties, With Application to Structural Energy Prediction

Statistical design and analysis of computer experiments is a growing area in statistics. Computer models with structural invariance properties now appear frequently in materials science, physics, biology, and other fields. These properties are consequences of dependency on structural geometry, and cannot be accommodated by standard statistical emulation methods. In this article, we propose a statistical framework for building emulators to preserve invariance. The framework uses a weighted complete graph to represent the geometry and introduces a new class of function, called the relabeling symmetric functions, associated with the graph. We establish a characterization theorem of the relabeling symmetric functions and propose a nonparametric kernel method for estimating such functions. The effectiveness of the proposed method is illustrated by examples from materials science. Supplemental material for this article can be found online.

Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation.

SummaryA central problem in biomechanical studies of personalized human left ventricular modelling is estimating the material properties and biophysical parameters from in vivo clinical measurements in a timeframe that is suitable for use within a clinic. Understanding these properties can provide insight into heart function or dysfunction and help to inform personalized medicine. However, finding a solution to the differential equations which mathematically describe the kinematics and dynamics of the myocardium through numerical integration can be computationally expensive. To circumvent this issue, we use the concept of emulation to infer the myocardial properties of a healthy volunteer in a viable clinical timeframe by using in vivo magnetic resonance image data. Emulation methods avoid computationally expensive simulations from the left ventricular model by replacing the biomechanical model, which is defined in terms of explicit partial differential equations, with a surrogate model inferred from simulations generated before the arrival of a patient, vastly improving computational efficiency at the clinic. We compare and contrast two emulation strategies: emulation of the computational model outputs and emulation of the loss between the observed patient data and the computational model outputs. These strategies are tested with two interpolation methods, as well as two loss functions. The best combination of methods is found by comparing the accuracy of parameter inference on simulated data for each combination. This combination, using the output emulation method, with local Gaussian process interpolation and the Euclidean loss function, provides accurate parameter inference in both simulated and clinical data, with a reduction in the computational cost of about three orders of magnitude compared with numerical integration of the differential equations by using finite element discretization techniques.

Emulation of CPU-demanding reactive transport models: a comparison of Gaussian processes, polynomial chaos expansion, and deep neural networks

This paper presents a detailed comparison between 3 methods for emulating CPU-intensive reactive transport models (RTMs): Gaussian processes (GPs), polynomial chaos expansion (PCE), and deep neural networks (DNNs). State-of-the-art open source libraries are used for each emulation method while the CPU-time incurred by one forward run of the considered RTMs varies from 1 h to between 1 h and 30 min and 5 days. Besides direct emulation of the simulated uranium concentration time series, replacing the original RTM by its emulator is also investigated for global sensitivity analysis (GSA), uncertainty propagation, and probabilistic calibration using Markov chain Monte Carlo (MCMC) sampling. The selected DNN is found to be superior to both GPs and PCE in reproducing the input–output behavior of the considered 8-dimensional and 13-dimensional CPU-intensive RTMs. This even though the used training sets are small, from 75 to 500 samples. Furthermore, the two used PCE variants, standard PCE and sparse PCE (sPCE), appear to always provide the least accuracy while not differing much in performance. As a consequence of its better emulation capabilities, the DNN method outperforms the two other methods for uncertainty propagation. For the GSA application, the DNN and GP methods offer equally good approximations to the true first-order and total-order Sobol’ sensitivity indices while PCE does less well. Most surprisingly, despite its superior emulation skills, the DNN approach leads to the worst solution of the considered synthetic inverse problem which involves 1224 measurement data with low noise. This apparently contradicting behavior of the used DNN is at least partially due to the small but complicated deterministic noise that affects the DNN-based predictions. Indeed, this complex error structure can drive the emulated solutions far away from the true posterior distribution in case of high-quality measurement data. Among the considered 3 methods, only the GP method allows for retrieving emulated posterior solutions that jointly (1) fit the high-quality measurement data to the appropriate noise level (log-likelihood value) and (2) most closely fit the true model parameter values. Overall, our findings indicate that when the available training set is relatively small (75 to 500 input-output examples) and fixed beforehand, PCE is not the best choice for emulating CPU-intensive RTMs. Instead, GPs or DNNs should be preferred. However, a DNN can deliver overly biased model calibration results. In contrast, the GP method performs fairly well across all considered tasks: direct emulation, global sensitivity analysis, uncertainty propagation, and calibration.

Emulating a Novel Clinical Trial Using Existing Observational Data. Predicting Results of the PreVent Study.

Rationale: "Target trial emulation" has been proposed as an observational method to answer comparative effectiveness questions, but it has rarely been attempted concurrently with a randomized clinical trial (RCT).Objectives: We tested the hypothesis that blinded analysts applying target trial emulation to existing observational data could predict the results of an RCT.Methods: PreVent (Preventing Hypoxemia with Manual Ventilation during Endotracheal Intubation) was a multicenter RCT examining the effects of positive-pressure ventilation during tracheal intubation on oxygen saturation and severe hypoxemia. Analysts unaware of PreVent's results used patient-level data from three previous trials evaluating airway management interventions to emulate PreVent's eligibility criteria, randomization procedure, and statistical analysis. After PreVent's release, results of this blinded observational analysis were compared with those of the RCT. Difference-in-differences estimates for comparison of treatment effects between the observational analysis and the PreVent trial are reported on the absolute scale.Results: Using observational data, we were able to emulate PreVent's randomization procedure to produce balanced groups for comparison. The lowest oxygen saturation during intubation was higher in the positive-pressure ventilation group than the no positive-pressure ventilation group in the observational analysis (n = 360; mean difference = 1.8%; 95% confidence interval [CI] = -1.0 to 4.6) and in the PreVent trial (n = 401; mean difference = 3.9%; 95% CI = 1.4 to 6.4), though the observational analysis could not exclude no difference. Difference-in-differences estimates comparing treatment effects showed reasonable agreement for lowest oxygen saturation between the observational analysis and the PreVent trial (mean difference = -2.1%; 95% CI = -5.9 to 1.7). Positive-pressure ventilation resulted in lower rates of severe hypoxemia in both the observational analysis (risk ratio = 0.60; 95% CI = 0.38 to 0.93) and in the PreVent trial (risk ratio = 0.48; 95% CI = 0.30 to 0.77). The absolute reduction in the incidence of severe hypoxemia with positive-pressure ventilation was similar in the observational analysis (9.4%) and the PreVent trial (12.0%), though the difference between these estimates had wide CIs (mean difference = 2.5%; 95% CI = -8.0 to 13.6%).Conclusions: Applying target trial emulation methods to existing observational data for the evaluation of a novel intervention produced results similar to those of a randomized trial. These findings support the use of target trial emulation for comparative effectiveness research.

Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction

The accurate prediction of global solar radiation (GSR) with remote sensing in metropolitan, regional and remote, yet solar-rich sites, is a core requisite for cleaner energy utilization, monitoring and conversion of renewable energy into usable power. Data-driven models that investigate the feasibility of solar-fueled energies, face challenges in respect to identifying their appropriate input data as such variables may not be available at all sites due to a lack of environmental monitoring system. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-derived predictors are employed to train three-phase hybrid SVR model for monthly GSR prediction. Firstly, to acquire relevant model input features, MODIS variables are screened with the Particle Swarm Optimization (PSO) algorithm, and secondly, a Gaussian emulation method of sensitivity analysis is incorporated on all screened variables to ascertain their relative role in predicting GSR. To address pertinent issues of non-stationarities, PSO selected variables are decomposed with Maximum Overlap Discrete Wavelet Transformation prior to its incorporation in Support Vector Regression (SVR), constructing a three-phase PSO-W-SVR hybrid model where the hyper-parameters are acquired by evolutionary (i.e., PSO & Genetic Algorithm) and Grid Search methods. Three-phase PSO-W-SVR hybrid model is benchmarked with alternative machine learning models. Thirty-nine model scenarios are formulated: 13 without feature selection (e.g., SVR), 13 with feature selection (e.g., PSO-SVR for two-phase models) and the remainder 13 with feature selection strategy coupled with data decomposition algorithm (e.g., PSO-W-SVR leading to a three-phase model). Metrics such as skill score (RMSESS), root mean square error (RMSE), mean absolute error (MAE), Willmott’s (WI), Legates & McCabe’s (E1) and Nash–Sutcliffe coefficients (ENS) are applied to comprehensively evaluate prescribed models. Empirical results register high performance of three-phase hybrid PSO-W-SVR models, exceeding the prescribed alternative models. High predictive ability evidenced by a low RRMSE and high E1 ascertains PSO-W-SVR hybrid model as considerably favorable in its capability to be enriched by MODIS satellite-derived variables. Maximum Overlap Discrete Wavelet Transform algorithm is also seen to provide resolved patterns in satellite variables, leading to a superior performance compared to the other data-driven model. The research avers that a three-phase hybrid PSO-W-SVR model can be a viable tool to predict GSR using satellite derived data as predictors, and is particularly useful for exploration of renewable energies where satellite footprint are present but regular environmental monitoring systems may be absent.

Toward Model Checking-Driven Fair Comparison of Dynamic Thermal Management Techniques Under Multithreaded Workloads

Dynamic thermal management (DTM) techniques are being widely used for attenuation of thermal hot spots in many-core systems. Conventionally, DTM techniques are analyzed using simulation and emulation methods, which are in-exhaustive due to their inherent limitations and cannot provide for a comprehensive comparison between DTM techniques owing to the wide range of corresponding design parameters. In order to handle the above discrepancies, we propose to use model checking, a state-space-based formal method, to model, evaluate, and compare DTM techniques across various functional and performance parameters. The suggested framework includes a modeling flow and a set of generic modules that realistically model many-core and DTM parameters like temperature, power, application, intercore communication and task migration, etc. For analysis purpose, the framework provides a common ground for comparing DTM techniques by formalizing DTM principles and performance parameters as a set of logical properties. These properties are verified for different task load configurations, e.g., multithreaded, malleable, and the applications which do not support migration. We analyze state-of-the-art central (c-) and distributed (d-) DTM techniques to demonstrate the generality and efficacy of our approach. Our formal analysis shows that the state-of-the-art cDTM technique performs better than dDTM in terms of achieving thermal stability, task migration, and communication overhead. We believe that conventional analysis methods do not facilitate such an exhaustive comparison among the DTM techniques.

An interpretation of radial basis function networks as zero-mean Gaussian process emulators in cluster space

Emulators provide approximations to computationally expensive functions and are widely used in diverse domains, despite the ever increasing speed of computational devices. In this paper we establish a connection between two independently developed emulation methods: radial basis function networks and Gaussian process emulation. The methodological relationship is established by starting from the observation that the concept of correlation between random variables in Gaussian process emulation can be interpreted as a correlation function applied to points in input space. This correlation function is then extended to apply to clusters, i.e. to sets of points. It is then shown that the extended Gaussian process emulation method is equivalent to radial basis function networks, provided that the prior mean in Gaussian process emulation is chosen zero. This elegant connection might increase understanding of the principles of both types of emulation, and might act as a catalyst for mutually beneficial research in emulation domains that were hitherto considered independent.

Statistical approximation of high-dimensional climate models

We propose a general emulation method for constructing low-dimensional approximations of complex dynamic climate models. Our method uses artificially designed uncorrelated CO2 emissions scenarios, which are much better suited for the construction of an emulator than are conventional emissions scenarios. We apply our method to the climate model MAGICC to approximate the impact of emissions on global temperature. Comparing the temperature forecasts of MAGICC and our emulator, we show that the average relative out-of-sample forecast errors in the low-dimensional emulation models are below 2%. Our emulator offers an avenue to merge modern macroeconomic models with complex dynamic climate models.

A Virtual Over-the-Air Method for 5G Massive MIMO Base Station Testing With Flexible Virtual Probes

Massive multiple-input-multiple-output (MIMO) is known as a large-scale antenna technology and has been enabled for the fifth generation (5G) mobile communication systems. The over-the-air (OTA) testing of massive MIMO antenna system has attracted a lot of attention in terms of evaluating the radio performance of a 5G base station (BS). As a promising OTA test methodology, the sectored multi-probe anechoic chamber (MPAC) setup is employed for the performance testing of massive MIMO BS. However, the system cost and installation are still an issue to be considered in practice (e.g., the anechoic chamber and multiple probes), though it can accurately reconstruct the flexible propagation channels by channel emulation methods. This paper proposes a virtual OTA test framework for the performance testing of massive MIMO BS on the basis of MPAC setups. As an alternative, the proposed virtual OTA test system adopting the concept of virtual probes solves the inconveniences and limitations of the actual probe installation in MPAC setups. The BS array responses to the actual probes of MPAC setups are generated in a phase matrix for the virtual probes. First, in this paper, the system description of virtual OTA testing is given. Then, the emulation method of spatial clusters is presented for the virtual OTA testing. Finally, simulations are performed with good channel emulation accuracy.

Implementation and Analysis of 3D Channel Emulation Method in Multi-Probe Anechoic Chamber Setups

The multi-probe anechoic chamber (MPAC) based method is a candidate to assess the performance of multiple input multiple output (MIMO) devices over the air. This paper investigates the core problem, channel emulation method (CEM), in MPAC setups. The CEM is utilized to map the desired channel onto the limited number of probes to synthesize the target channel environment within a certain test area. In addition, the channel in realistic propagation environment is a three-dimensional (3D) channel, which can achieve higher data transmission rate by containing both azimuth and elevation dimension. Hence, it is urgent to investigate the CEM for 3D channels in MPAC setups. In this paper, the implementation of a novel CEM for 3D channels is presented, which allocates the subpaths in a simple way and complements the drawbacks of prefaded synthesis method and spatial fading emulator. Furthermore, the impact of the target channel and probe configuration on the emulation accuracy in terms of temporal correlation function (TCF) and spatial correlation function (SCF) is discussed. Simulation results demonstrate that the emulation accuracy of TCF depends more on the motion direction of MIMO devices while that of SCF depends more on the probes distribution. Moreover, probes number decides the emulation accuracy first. If the same number of probes are applied, the coincidence of the probe configuration and the target channel determines the emulation accuracy.