Set Of Realistic Simulations Research Articles

A metaheuristic algorithm for regulating virtual inertia of a standalone microgrid incorporating electric vehicles

AbstractModern electrical networks, particularly microgrids, have seen a sharp rise in acquiring non‐conventional sources. The inertia of the microgrid decreases drastically because RESs have been used in place of traditional synchronous generators. The reduced inertia negatively impacts the dynamics and performance of the microgrid with RESs, which decreases the microgrid's stability, especially in operation on an island. The primary purpose of this research study is to enhance the dynamic security of an island microgrid by merging an electric vehicle with a frequency control technique based on virtual inertia control. A Proportional Integral Derivative Filter Constant (PIDFN) controller optimally created using the Modified Differential Evolution (MDE) method served as the foundation for control in the virtual inertia control loop. The effectiveness of the MDE‐based PIDFN controller was examined considering the diverse operational scenarios are compared and contrasted with those of traditional methods Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO)‐based PIDFN controllers. Real‐time wind and solar power statistics and random load fluctuations were incorporated to provide realistic simulation settings. The outcomes demonstrate that the MDE‐based PIDFN controller performs better in reference frequency tracking and reducing frequency disturbances than the other optimization strategies.

On autoregressive and neural methods for massive-MIMO channel de-noising

In modern wireless communication systems, the Multiple-Input Multiple-Output (MIMO) technology allows to greatly increase power efficiency, the serving area, and the overall cell throughput through the use of the antenna array beamforming. Nevertheless, the MIMO systems require accurate channel state knowledge to apply correct precoding. In 5G Time Division Duplex (TDD) systems, the Channel State Information (CSI) is obtained via Sounding Reference Signals (SRS) transmitted by the User Equipment (UE). UEs have limited power capabilities and thus cannot achieve high Uplink (UL) Signal-to-Noise Ratio (SNR) on gNodeB (gNB) in large bandwidth. There are multiple techniques that can be applied to improve the accuracy of Channel Estimation (CE) in noisy conditions. In this paper, we describe a classical method, namely the Vector Autoregression (VAR) with adaptive model order estimation, as well as a modern Deep Neural Network (DNN) approach for the massive-MIMO channel estimation de-noising problem. The developed methods and signal pre and postprocessing steps are described, followed by their performance evaluation in a set of realistic simulations. The designed algorithms provide results outperforming the baseline spatio-temporal windowing approaches by approximately equal to 2dB effective Downlink (DL) Signal-to-Interference-plus-Noise Ratio (SINR) metric in single and multi-user MIMO scenarios. Extensive simulation results demonstrate the robustness of the developed methods to the dynamic channel conditions.

Proof of Concept Control of a T1DM Model Using Robust Fixed-Point Transformations via Sliding Mode Differentiators

Type 1 Diabetes Mellitus (T1DM) is a disease where insulin production is obstructed in the pancreas, and exogenous administration of the hormone must be utilized. Automatic control of the administration can be achieved using the Artificial Pancreas (AP) concept, whose performance is heavily reliant on the underlying control algorithm. A Robust Fixed-Point Transformations (RFPT)-based control strategy was designed to automate the insulin delivery process, which incorporates a Sliding Mode Differentiator (SMD) to provide higher order derivatives of the blood glucose level. Inter-patient variability, carbohydrate disturbances, and real-life sampling were included in the validation of the method. Results showed that the algorithm could regulate the blood glucose level, with a significant overshoot at the beginning of the control action due to the adaptive nature of the controller. Results indicate that the design requires additional modifications to be feasible in practice, including an extended validation with more virtual patients and realistic simulation settings in the future. Nevertheless, the current control algorithm has several attractive features, which are discussed with respect to PID and Model Predictive Control (MPC).

Earth Velocity and Rigid-Body Attitude Estimation on SO(3) Using Biased Measurements

This article addresses the problem of simultaneously estimating: 1) the attitude of a rigid body on the special orthogonal group of order 3 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {SO}(3)$</tex-math></inline-formula> ); 2) the Earth’s spin vector expressed in body-fixed coordinates; and 3) a pair of time-varying sensor measurement biases. The available sensor readings include biased angular velocity measurements collected from a set of high-grade rate gyros sensitive to the rotation of the planet and biased vector observations of some known inertial reference field. The estimation solution consists of a Lagrangian-based pseudo Kalman filtering routine, which is partially constrained to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {SO}(3)$</tex-math></inline-formula> vis-á-vis the attitude. Most noticeably, the geometric relationship between the Earth’s angular velocity and the inertial field is leveraged to relax common practical, as well as theoretical, demands closely linked to conventional attitude estimation solutions, namely, the need for explicit body-fixed measurements of two inertial vector fields. The proposed algorithm is validated through a set of realisticsimulations. Experimental results are also included to further demonstrate the performance of this strategy in a real-world application.

A wheel slip control scheme for aeronautical braking applications based on neural network estimation

Due to safety reasons, anti-skid braking control strategies in aircraft can rely only on sensors that are integral to the landing gear, and these currently are limited to wheel rotational speed and braking pressure. Thus, up to now anti-lock-braking systems (ABS) are developed as threshold-based algorithms that regulate the wheel deceleration. However, as it is known from the Automotive field, slip control offers superior closed-loop properties and robustness, paired with a much easier and time-saving tuning phase. This work aims at investigating first of all if slip control does indeed offer the same performance advancement also in the aeronautical braking context and, secondly, whether the wheel slip can be effectively estimated and employed in a closed-loop braking controller without the need of additional sensors on the landing gear. To do this, we propose a wheel slip control scheme where a data-driven approach using a neural network architecture is used to solve the problem of wheel slip estimation. The proposed control scheme is tested within a very realistic simulation setting, capturing all the non-linear effects peculiar of the aeronautical context, and compared with a standard deceleration-based ABS in multiple braking maneuvers carried out within a large operational envelope. The results show that superior performance and robustness can indeed be achieved with the proposed control approach, paving the way for the adoption of wheel slip abs strategies also for aircraft braking.

Performance analysis of sparse array based massive MIMO via joint convex optimization

Massive multiple-input multiple-output (MIMO) technology enables higher data rate transmission in the future mobile communications. However, exploiting a large number of antenna elements at base station (BS) makes effective implementation of massive MIMO challenging, due to the size and weight limits of the masssive MIMO that are located on each BS. Therefore, in order to miniaturize the massive MIMO, it is crucial to reduce the number of antenna elements via effective methods such as sparse array synthesis. In this paper, a multiple-pattern synthesis is considered towards convex optimization (CO). The joint convex optimization (JCO) based synthesis is proposed to construct a codebook for beamforming. Then, a criterion containing multiple constraints is developed, in which the sparse array is required to full-fill all constraints. Finally, extensive evaluations are performed under realistic simulation settings. The results show that with the same number of antenna elements, sparse array using the proposed JCO-based synthesis outperforms not only the uniform array, but also the sparse array with the existing CO-based synthesis method. Furthermore, with a half of the number of antenna elements that on the uniform array, the performance of the JCO-based sparse array approaches to that of the uniform array.

Voxel-wise and spatial modelling of binary lesion masks: Comparison of methods with a realistic simulation framework

ObjectivesWhite matter lesions are a very common finding on MRI in older adults and their presence increases the risk of stroke and dementia. Accurate and computationally efficient modelling methods are necessary to map the association of lesion incidence with risk factors, such as hypertension. However, there is no consensus in the brain mapping literature whether a voxel-wise modelling approach is better for binary lesion data than a more computationally intensive spatial modelling approach that accounts for voxel dependence. MethodsWe review three regression approaches for modelling binary lesion masks including mass-univariate probit regression modelling with either maximum likelihood estimates, or mean bias-reduced estimates, and spatial Bayesian modelling, where the regression coefficients have a conditional autoregressive model prior to account for local spatial dependence. We design a novel simulation framework of artificial lesion maps to compare the three alternative lesion mapping methods. The age effect on lesion probability estimated from a reference data set (13,680 individuals from the UK Biobank) is used to simulate a realistic voxel-wise distribution of lesions across age. To mimic the real features of lesion masks, we propose matching brain lesion summaries (total lesion volume, average lesion size and lesion count) across the reference data set and the simulated data sets. Thus, we allow for a fair comparison between the modelling approaches, under a realistic simulation setting. ResultsOur findings suggest that bias-reduced estimates for voxel-wise binary-response generalized linear models (GLMs) overcome the drawbacks of infinite and biased maximum likelihood estimates and scale well for large data sets because voxel-wise estimation can be performed in parallel across voxels. Contrary to the assumption of spatial dependence being key in lesion mapping, our results show that voxel-wise bias-reduction and spatial modelling result in largely similar estimates. ConclusionsBias-reduced estimates for voxel-wise GLMs are not only accurate but also computationally efficient, which will become increasingly important as more biobank-scale neuroimaging data sets become available.

Direct data-driven LPV control for active braking in aircraft

Abstract Designing effective braking controllers for aircrafts is a challenging task, due to the highly time-varying dynamical features and the very different working conditions of interest. Traditional controllers rely on threshold-based acceleration approaches, but it has been recently proved that slip-based solutions can enhance performance and allow direct tire monitoring. However, also slip-based solutions need adaptation. This paper shows how this can be achieved via LPV data-driven design, with an approach that has its major strength in easing the controller design phase, as it learns both controller parameters and scheduling functions from data. The proposed slip controller ensures both safety and performance, and it is tested within a very realistic simulation setting, endowed with measured noises due to slip estimation errors.

HOLISMOKES

We present a systematic search for wide-separation (with Einstein radiusθE ≳ 1.5″), galaxy-scale strong lenses in the 30 000 deg2of the Pan-STARRS 3πsurvey on the Northern sky. With long time delays of a few days to weeks, these types of systems are particularly well-suited for catching strongly lensed supernovae with spatially-resolved multiple images and offer new insights on early-phase supernova spectroscopy and cosmography. We produced a set of realistic simulations by painting lensed COSMOS sources on Pan-STARRS image cutouts of lens luminous red galaxies (LRGs) with redshift and velocity dispersion known from the sloan digital sky survey (SDSS). First, we computed the photometry of mock lenses ingribands and applied a simple catalog-level neural network to identify a sample of 1 050 207 galaxies with similar colors and magnitudes as the mocks. Second, we trained a convolutional neural network (CNN) on Pan-STARRSgriimage cutouts to classify this sample and obtain sets of 105 760 and 12 382 lens candidates with scores ofpCNN &gt; 0.5 and &gt; 0.9, respectively. Extensive tests showed that CNN performances rely heavily on the design of lens simulations and the choice of negative examples for training, but little on the network architecture. The CNN correctly classified 14 out of 16 test lenses, which are previously confirmed lens systems above the detection limit of Pan-STARRS. Finally, we visually inspected all galaxies withpCNN &gt; 0.9 to assemble a final set of 330 high-quality newly-discovered lens candidates while recovering 23 published systems. For a subset, SDSS spectroscopy on the lens central regions proves that our method correctly identifies lens LRGs atz ∼ 0.1–0.7. Five spectra also show robust signatures of high-redshift background sources, and Pan-STARRS imaging confirms one of them as a quadruply-imaged red source atzs = 1.185, which is likely a recently quenched galaxy strongly lensed by a foreground LRG atzd = 0.3155. In the future, high-resolution imaging and spectroscopic follow-up will be required to validate Pan-STARRS lens candidates and derive strong lensing models. We also expect that the efficient and automated two-step classification method presented in this paper will be applicable to the ∼4 mag deepergristacks from theRubinObservatory Legacy Survey of Space and Time (LSST) with minor adjustments.

Design and experimental validation of a nonlinear controller for underactuated surface vessels

This paper addresses the problem of trajectory tracking control of an underactuated surface vessel moving in a two-dimensional space in the presence of unknown disturbances. In a preliminary stage, a couple of nonlinear observers is derived to obtain an estimate of the perturbations, which are assumed to originate from unmodeled time-varying dynamics and/or exogenous disturbances. Secondly, we resort to the Lyapunov-based backstepping technique to design two stabilizing control laws, governing the thrust force and torque actuations, that are proved to render the overall control system error globally uniformly bounded. Each control law yields an actuator signal which is implicitly bounded with respect to the position error, and the resulting estimation and tracking errors can be made arbitrarily small by tweaking the control parameters. A set of realistic simulations results is presented to validate our strategy. Experimental trials using an autonomous surface vehicle are also showcased to further demonstrate the efficacy and robustness of the proposed controller.

The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: measurement of the BAO and growth rate of structure of the luminous red galaxy sample from the anisotropic correlation function between redshifts 0.6 and 1

ABSTRACT We present the cosmological analysis of the configuration-space anisotropic clustering in the completed Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey (eBOSS) Data Release 16 galaxy sample. This sample consists of luminous red galaxies (LRGs) spanning the redshift range 0.6 &amp;lt; $z$ &amp;lt; 1, at an effective redshift of $z$eff = 0.698. It combines 174 816 eBOSS and 202 642 BOSS LRGs. We extract and model the baryon acoustic oscillation (BAO) and redshift-space distortion (RSD) features from the galaxy two-point correlation function to infer geometrical and dynamical cosmological constraints. The adopted methodology is extensively tested on a set of realistic simulations. The correlations between the inferred parameters from the BAO and full-shape correlation function analyses are estimated. This allows us to derive joint constraints on the three cosmological parameter combinations: DM($z$)/rd, DH($z$)/rd, and fσ8($z$), where DM is the comoving angular diameter distance, DH is the Hubble distance, rd is the comoving BAO scale, f is the linear growth rate of structure, and σ8 is the amplitude of linear matter perturbations. After combining the results with those from the parallel power spectrum analysis of Gil-Marin et al., we obtain the constraints: DM/rd = 17.65 ± 0.30, DH/rd = 19.77 ± 0.47, and fσ8 = 0.473 ± 0.044. These measurements are consistent with a flat Lambda cold dark matter model with standard gravity.

Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles

The latest manifestation of “all connected world" is the Internet of Things (IoT), and Internet of Vehicles (IoV) is one of the key examples of IoT these days. In Social IoV (SIoV), each vehicle is treated as a social object where it establishes and manages its own Social Network (SN). Incidentally, most of the SIoV research in the literature is related to proximity-based connectivity and interactions. In this paper, we bring people in the loop by incorporating their SNs. While emphasizing a recommendation scenario, in which vehicles may require recommendations from SNs of their owners (in addition to their own SIoV), we proposed an agent-based model of information sharing (for context-based recommendations) on a hypothetical population of smart vehicles. Some important hypotheses were tested using a realistic simulation setting. The simulation results reveal that a recommendation using weak ties is more valuable than a recommendation using strong ties in pure SIoV. The simulation results also demonstrate that recommendations using the most-connected person in the social network are not more valuable than recommendation using a random person in the social network. The model presented in this paper can be used to design a multi-scale recommendation system, which uses SIoV and a typical SN in combination.

A simple two-component description of energy equipartition and mass segregation for anisotropic globular clusters

In weakly-collisional stellar systems such as some globular clusters, partial energy equipartition and mass segregation are expected to develop as a result of the cumulative effect of stellar encounters, even in systems initially characterized by star-mass independent density and energy distributions. In parallel, numerical simulations have demonstrated that radially-biased pressure anisotropy slowly builds up in realistic models of globular clusters from initial isotropic conditions, leading to anisotropy profiles that, to some extent, mimic those resulting from incomplete violent relaxation known to be relevant to elliptical galaxies. In this paper, we consider a set of realistic simulations realized by means of Monte Carlo methods and analyze them by means of self-consistent, two-component models. For this purpose, we refer to an underlying distribution function originally conceived to describe elliptical galaxies, which has recently been truncated and adapted to the context of globular clusters. The two components are supposed to represent light stars (combining all main sequence stars) and heavy stars (giants, dark remnants, and binaries). We show that this conceptually simple family of two-component truncated models provides a reasonable description of simulated density, velocity dispersion, and anisotropy profiles, especially for the most relaxed systems, with the ability to quantitatively express the attained levels of energy equipartition and mass segregation. In contrast, two-component isotropic models based on the King distribution function do not offer a comparably satisfactory representation of the simulated globular clusters. With this work, we provide a new reliable diagnostic tool applicable to nonrotating globular clusters that are characterized by significant gradients in the local value of the mass-to-light ratio, beyond the commonly used one-component dynamical models. In particular, these models are supposed to be an optimal tool for the clusters that underfill the volume associated with the boundary surface determined by the tidal interaction with the host galaxy.

From Traditional to Automated Mobility on Demand: A Comprehensive Framework for Modeling On-Demand Services in SimMobility

Mobility on demand (MoD) systems have recently emerged as a promising paradigm for sustainable personal urban mobility in cities. In the context of multi-agent simulation technology, the state-of-the-art lacks a platform that captures the dynamics between decentralized driver decision-making and the centralized coordinated decision-making. This work aims to fill this gap by introducing a comprehensive framework that models various facets of MoD, namely heterogeneous MoD driver decision-making and coordinated fleet management within SimMobility, an agent- and activity-based demand model integrated with a dynamic multi-modal network assignment model. To facilitate such a study, we propose an event-based modeling framework. Behavioral models were estimated to characterize the decision-making of drivers using a GPS dataset from a major MoD fleet operator in Singapore. The proposed framework was designed to accommodate behaviors of multiple on-demand services such as traditional MoD, Lyft-like services, and automated MoD (AMoD) services which interact with traffic simulators and a multi-modal transportation network. We demonstrate the benefits of the proposed framework through a large-scale case study in Singapore comparing the fully decentralized traditional MoD with the future AMoD services in a realistic simulation setting. We found that AMoD results in a more efficient service even with increased demand. Parking strategies and fleet sizes will also have an effect on user satisfaction and network performance.

Spatial crowdsourcing with mobile agents in vehicular networks

In the last years, the automotive industry has shown interest in the addition of computing and communication devices to cars, thanks to technological advances in these fields, in order to meet the increasing demand of “connected” applications and services. Although vehicular ad hoc networks (VANETs) have not been fully developed yet, they could be used in a near future as a means to provide a number of interesting applications and services that need the exchange of data among vehicles and other data sources.In this paper, we propose a spatial crowdsourcing schema for the opportunistic collection of information within an interest area in a city or region (e.g., measures about the environment, such as the concentration of certain gases in the atmosphere, or information such as the availability of parking spaces in an area), using vehicular ad hoc communications. We present a method that exploits mobile agent technology to accomplish the distributed collection and querying of data among vehicles in such a scenario. Our proposal is supported by an extensive set of realistic simulations that prove the feasibility of the approach.

Associating host galaxy candidates to massive black hole binaries resolved by pulsar timing arrays

We propose a novel methodology to select host galaxy candidates of future pulsar timing array (PTA) detections of resolved gravitational waves (GWs) from massive black hole binaries (MBHBs). The method exploits the physical dependence of the GW amplitude on the MBHB chirp mass and distance to the observer, together with empirical MBH mass-host galaxy correlations, to rank potential host galaxies in the mass-redshift plane. This is coupled to a null-stream based likelihood evaluation of the GW amplitude and sky position in a Bayesian framework that assigns to each galaxy a probability of hosting the MBHB generating the GW signal. We test our algorithm on a set of realistic simulations coupling the likely properties of the first PTA resolved GW signal to synthetic all-sky galaxy maps. For a foreseeable PTA sky-localization precision of 100 squared degrees, we find that the GW source is hosted with 50% (90%) probability within a restricted number of <50 (<500) potential hosts. These figures are orders of magnitude smaller than the total number of galaxies within the PTA sky error-box, enabling extensive electromagnetic follow-up campaigns on a limited number of targets.

Recurrent network model for learning goal-directed sequences through reverse replay.

Reverse replay of hippocampal place cells occurs frequently at rewarded locations, suggesting its contribution to goal-directed path learning. Symmetric spike-timing dependent plasticity (STDP) in CA3 likely potentiates recurrent synapses for both forward (start to goal) and reverse (goal to start) replays during sequential activation of place cells. However, how reverse replay selectively strengthens forward synaptic pathway is unclear. Here, we show computationally that firing sequences bias synaptic transmissions to the opposite direction of propagation under symmetric STDP in the co-presence of short-term synaptic depression or afterdepolarization. We demonstrate that significant biases are created in biologically realistic simulation settings, and this bias enables reverse replay to enhance goal-directed spatial memory on a W-maze. Further, we show that essentially the same mechanism works in a two-dimensional open field. Our model for the first time provides the mechanistic account for the way reverse replay contributes to hippocampal sequence learning for reward-seeking spatial navigation.

Profiling wind and greenhouse gases by infrared-laser occultation: results from end-to-end simulations in windy air

Abstract. The new mission concept of microwave and infrared-laser occultation between low-Earth-orbit satellites (LMIO) is designed to provide accurate and long-term stable profiles of atmospheric thermodynamic variables, greenhouse gases (GHGs), and line-of-sight (l.o.s.) wind speed with focus on the upper troposphere and lower stratosphere (UTLS). While the unique quality of GHG retrievals enabled by LMIO over the UTLS has been recently demonstrated based on end-to-end simulations, the promise of l.o.s. wind retrieval, and of joint GHG and wind retrieval, has not yet been analyzed in any realistic simulation setting. Here we use a newly developed l.o.s. wind retrieval algorithm, which we embedded in an end-to-end simulation framework that also includes the retrieval of thermodynamic variables and GHGs, and analyze the performance of both stand-alone wind retrieval and joint wind and GHG retrieval. The wind algorithm utilizes LMIO laser signals placed on the inflection points at the wings of the highly symmetric C18OO absorption line near 4767 cm−1 and exploits transmission differences from a wind-induced Doppler shift. Based on realistic example cases for a diversity of atmospheric conditions, ranging from tropical to high-latitude winter, we find that the retrieved l.o.s. wind profiles are of high quality over the lower stratosphere under all conditions, i.e., unbiased and accurate to within about 2 m s−1 over about 15 to 35 km. The wind accuracy degrades into the upper troposphere due to the decreasing signal-to-noise ratio of the wind-induced differential transmission signals. The GHG retrieval in windy air is not vulnerable to wind speed uncertainties up to about 10 m s−1 but is found to benefit in the case of higher speeds from the integrated wind retrieval that enables correction of wind-induced Doppler shift of GHG signals. Overall both the l.o.s. wind and GHG retrieval results are strongly encouraging towards further development and implementation of a LMIO mission.

Characterizing the hydrodynamic response to the initial conditions

In hydrodynamics, the momentum distribution of particles at the end of the evolution is completely determined by initial conditions. We study quantitatively to what extent anisotropic flow is determined by predictors such as the initial eccentricity in a set of realistic simulations, and we also show the importance of nonlinear terms in order to correctly predict the quadrangular flow. This knowledge will be important for making a more direct link between experimental observables and hydrodynamic initial conditions, the latter being poorly constrained at present.

Mapping the hydrodynamic response to the initial geometry in heavy-ion collisions

We investigate how the initial geometry of a heavy-ion collision is transformed into final flow observables by solving event-by-event ideal hydrodynamics with realistic fluctuating initial conditions. We study quantitatively to what extent anisotropic flow (${v}_{n}$) is determined by the initial eccentricity ${\ensuremath{\varepsilon}}_{n}$ for a set of realistic simulations, and we discuss which definition of ${\ensuremath{\varepsilon}}_{n}$ gives the best estimator of ${v}_{n}$. We find that the common practice of using an ${r}^{2}$ weight in the definition of ${\ensuremath{\varepsilon}}_{n}$ in general results in a poorer predictor of ${v}_{n}$ than when using ${r}^{n}$ weight, for $n&gt;2$. We similarly study the importance of additional properties of the initial state. For example, we show that in order to correctly predict ${v}_{4}$ and ${v}_{5}$ for noncentral collisions, one must take into account nonlinear terms proportional to ${\ensuremath{\varepsilon}}_{2}^{2}$ and ${\ensuremath{\varepsilon}}_{2}{\ensuremath{\varepsilon}}_{3}$, respectively. We find that it makes no difference whether one calculates the eccentricities over a range of rapidity or in a single slice at $z=0$, nor is it important whether one uses an energy or entropy density weight. This knowledge will be important for making a more direct link between experimental observables and hydrodynamic initial conditions, the latter being poorly constrained at present.