Simulation-based Techniques Research Articles

Linear bootstrap methods for vector autoregressive moving-average models

We provide the theoretical justification of bootstrapping stationary invertible echelon vector autoregressive moving-average (VARMA) models using linear methods. The asymptotic validity of the bootstrap is established with strong white noise under parametric and nonparametric assumptions. Our methods are practical and useful for building reliable simulation-based inference and forecasting without implementing nonlinear estimation techniques such as ML which is usually burdensome, time demanding or impractical, particularly in big or highly persistent systems. The relevance of our procedures is more pronounced in the context of dynamic simulation-based techniques such as maximized Monte Carlo (MMC) tests [see Dufour J-M. Monte Carlo tests with nuisance parameters: a general approach to finite-sample inference and nonstandard asymptotics in econometrics. J Econom. 2006;133(2):443–477 and Dufour J-M, Jouini T. Finite-sample simulation-based tests in VAR models with applications to Granger causality testing. J Econom. 2006;135(1–2):229–254 for the VAR case]. Simulation evidence shows that, compared with conventional asymptotics, our bootstrap methods have good finite-sample properties in approximating the actual distribution of the studentized echelon VARMA parameter estimates, and in providing echelon parameter confidence sets with satisfactory coverage.

End-to-end schedulability tests for multiprocessor embedded systems based on networks-on-chip with priority-preemptive arbitration

Simulation-based techniques can be used to evaluate whether a particular NoC-based platform configuration is able to meet the timing constraints of an application, but they can only evaluate a finite set of scenarios. In safety-critical applications with hard real-time constraints, this is clearly not sufficient because there is an expectation that the application should be schedulable on that platform in all possible scenarios. This paper presents a particular NoC-based multiprocessor architecture, as well as a number of analytical methods that can be derived from that architecture, aiming to allow designers to check, for a given platform configuration, whether all application tasks and communication messages always meet their hard real-time constraints in every possible scenario. Experiments are presented, showing the use of the proposed methods when evaluating different task mapping and platform topologies.

Bow and Stern Shape Integrated Optimization for a Full Ship by a Simulation-based Design Technique

Bow and Stern Shape Integrated Optimization for a Full Ship by a Simulation-based Design Technique

Integration of Simulation-Based Energy Management Techniques in Undergraduate Engineering Curriculum to Enhance Students' Learning

The aim of teaching simulation-based energy conservation techniques to the undergraduate engineering students is to involve students with real-life engineering problems and to enhance the students' learning, discipline knowledge and problem-solving skills. A project-based learning course was designed so that the students receive a broad range of introductory exposure to the effective energy management techniques with relevant applications of the theory. This paper highlighted an overview of the course curriculum, simulation principles and assessment procedures for achieving scholarships in teaching, learning and research. It is understood that the students were competent to make use of the advanced knowledge earned in this course in their future career.

SolidWorks®와 LabVIEW®를 연동한 6축 수직 다관절 로봇의 게인 튜닝 연구

. A comparison shows that the results of simulation-based gain tuning and experimental gain tuning are almost the same within a 5% error bound. On the basis ofthe comparison, it can be suggested that the simulation-based technique for gain tuning can be applied instead of experimental gain tuning to a six-axis articulated robot by interlocking SolidWorks

Development of a simulation-based technique to Support the Community-based Tsunami-Evacuation Planning and its Effect

Development of a simulation-based technique to Support the Community-based Tsunami-Evacuation Planning and its Effect

Information Space Receding Horizon Control

In this paper, we present a receding horizon solution to the optimal sensor scheduling problem. The optimal sensor scheduling problem can be posed as a partially observed Markov decision problem whose solution is given by an information space (I-space) dynamic programming (DP) problem. We present a simulation-based stochastic optimization technique that, combined with a receding horizon approach, obviates the need to solve the computationally intractable I-space DP problem. The technique is tested on a sensor scheduling problem, in which a sensor must choose among the measurements of N dynamical systems in a manner that maximizes information regarding the aggregate system over an infinite horizon. While simple, such problems nonetheless lead to very high dimensional DP problems to which the receding horizon approach is well suited.

Parameter Estimation Methods for Chaotic Intercellular Networks

We have investigated simulation-based techniques for parameter estimation in chaotic intercellular networks. The proposed methodology combines a synchronization–based framework for parameter estimation in coupled chaotic systems with some state–of–the–art computational inference methods borrowed from the field of computational statistics. The first method is a stochastic optimization algorithm, known as accelerated random search method, and the other two techniques are based on approximate Bayesian computation. The latter is a general methodology for non–parametric inference that can be applied to practically any system of interest. The first method based on approximate Bayesian computation is a Markov Chain Monte Carlo scheme that generates a series of random parameter realizations for which a low synchronization error is guaranteed. We show that accurate parameter estimates can be obtained by averaging over these realizations. The second ABC–based technique is a Sequential Monte Carlo scheme. The algorithm generates a sequence of “populations”, i.e., sets of randomly generated parameter values, where the members of a certain population attain a synchronization error that is lesser than the error attained by members of the previous population. Again, we show that accurate estimates can be obtained by averaging over the parameter values in the last population of the sequence. We have analysed how effective these methods are from a computational perspective. For the numerical simulations we have considered a network that consists of two modified repressilators with identical parameters, coupled by the fast diffusion of the autoinducer across the cell membranes.

Perioperative simulation learning and post-registration development

Competence to practise in the perioperative environment requires specialist knowledge (Gillespie and Hamlin, 2009). Newly qualified staff in this environment can experience difficulty in making the transition into practice (Stratton, 2011) and often feel overwhelmed by the skills required (Callaghan, 2010). Simulation-based learning techniques are increasingly used by practice educators specifically within these environments (Cato and Murray, 2010) to aid with acquisition of skills, emergency care delivery, general post-registration development and also as a standardised indicator of 'competence' (Bullock et al, 2008; Cato and Murray, 2010). This article will consider the impact of this educational strategy on the learner's lifelong development following registration, and its position in relation to the widely accepted learning paradigms of Benner's 'Novice to Expert' and Maslow's 'Hierarchy of Needs'. Through discussion of the nature of education in the practice setting, the reader will be prompted to reconsider the actual value of simulation-based learning in the post-registration arena and how this may be used to redefine simulation in the clinical setting.

A Novel Hybrid Artificial Intelligence Technique for Colpitts Oscillator Design

In the design of the common base Colpitts oscillator, the resistance values of Thevenin’s resistors significantly influenced the transient time and steady state response of the resulting circuit. Various traditional approaches such as intuitive reasoning, mathematical calculation, and simulation-based techniques have been proposed in the literature for this purpose. Some of the aforementioned techniques involve rigorous mathematics, intuition, and experimentation in determining appropriate component values for optimal performance, stable steady state performance, and short transient response time from the resulting oscillator. In this paper, a new method of designing Colpitts oscillator using hybrid artificial intelligence comprising evolutionary-based Genetic Algorithm (GA) and artificial neural network (ANN) has been proposed. GA has been used in selecting various optimum resistance values of Thevenin’s resistors for maximizing long-term stability of the output waveform thus ensuring stable steady response of the designed circuit. ANN has been utilized in learning the nonlinear relationship between Thevenin’s resistors and transient time response of the Colpitts oscillator. Upon ANN convergence, optimum resistance values of obtained from GA process are fed into the trained ANN in predicting transient response time of each circuit. Optimized values with the shortest transient response time are finally selected for the Colpitts oscillator. The designed circuit successfully achieved optimization between its transient time response and steady state response. Hence, successfully reducing computation associated with existing traditional techniques in designing similar optimum Colpitts oscillator and achieving stable steady state output. Furthermore, this work has also demonstrated that ANN is capable of predicting the transient time of circuit with reasonable accuracy.

Asymptotics Beats Monte Carlo: The Case of Correlated Local Vol Baskets

We consider a basket of options with both positive and negative weights in the case where each asset has a smile, i.e., evolves according to its own local volatility and the driving Brownian motions are correlated. In the case of positive weights, the model has been considered in a previous work by Avellaneda, Boyer‐Olson, Busca, and Friz. We derive highly accurate analytic formulas for the prices and the implied volatilities of such baskets. The relative errors are of order 10−4 (or better) for T=½, 10−3 for T=2, and 10−2 for T=10 (years). The computational time required to implement these formulas is under two seconds even in the case of a basket on 100 assets. The combination of accuracy and speed makes these formulas potentially attractive both for calibration and for pricing. In comparison, simulation‐based techniques are prohibitively slow in achieving a comparable degree of accuracy. Thus the present work opens up a new paradigm in which asymptotics may arguably be used for pricing as well as for calibration. © 2014 Wiley Periodicals, Inc.

Towards Quantitative Image Reconstruction Using Monte-Carlo Simulations in Multi-Wire Proportional Chamber-Based Small Animal PET

The quadHIDAC is a dedicated high resolution small animal PET scanner based on multi-wire proportional chamber detectors. It provides very high spatial resolution, good sensitivity, and a large field-of-view allowing the examination of more than one animal in a single scan or a whole body scan of larger species in a single bed position. Excellent image quality has been obtained through list-mode-based EM reconstruction using resolution recovery. However, quantitative results on local uptake are still missing due to both (1) lack of energy discrimination and (2) inaccurate characterization of random events recorded by the scanner. In this work we describe how quantification can be achieved through the use of an appropriate Monte Carlo simulation of the quadHIDAC. The complex structure of the quadHIDAC has been completely implemented in a Geant4 simulation. Point sources, line sources, cylindrical phantoms, and voxelized image data have been successfully simulated and compared to real measurements. The magnitude and spatial distribution of scattered and random events could be quantitatively characterized for the different phantom geometries. The simulation provides access to information on single hit distributions resulting in appropriate randoms corrections in image reconstruction. Both randoms and scatter correction is demonstrated to improve quantification. Their accuracy and performance was tested on simulated and measured data. Using simulation-based correction techniques for quantitative image reconstruction, multi-wire proportional chamber-based PET technology may be attractive for future high resolution PET scanners.

Synthetical Dynamic Balancing of Mechanism Based on Optimization Simulation

The dynamic balancing of crank-slider mechanism was achieved with the guidance of the computer aided simulation-based optimization technique. Taking the reaction force of the rotary pair of connecting links and the input torque as optimization objective functions, the design optimization process was carried out with random-direction optimization method after determining the sensitive independent variables and their variation ranges based on the synthetical planning. The modal damping parameter of simulation model was optimized with the guidance of dynamic balancing experiment and the accuracy of simulation was also improved. Finally, by comparing the results from experiment and from optimization simulation, it is shown that optimization simulation could facilitate to instruct and implement the synthetical dynamic balancing for four-bar mechanism.

Simulation of Reactive Diffusion in Clays By a Continuous-Time Markovian Particle-Tracking Scheme

Clay minerals and clay rocks are considered as efficient components of engineered and natural barriers in many high-level radioactive waste disposal programs worldwide because of their low permeabilities and high sorption capabilities. In this paper we present an approach for modeling solute diffusive transport in saturated clay minerals at the structure map scale based on an extension of the Kolmogorov–Dmitriev theory of stochastic branching processes. The proposed modeling framework allows a simple description of (i) the dual-porosity behavior typical of some compacted clay structures, where diffusion occurs both in the interlayers and in the pore space between clay particles, and (ii) the chemical interactions between the dissolved ions and the solid matrix surfaces (both at nonequilibrium and equilibrium conditions). Furthermore, the Markovian nature of the modeling approach lends itself to a continuous-time particle-tracking scheme of resolution of the model equations, which, in general, easily allows us to deal with the complex geometrical structures of the porous media. Finally, in order to account for the uncertainty in the geometrical properties of the clay samples within the proposed modeling framework, the structure map is represented as a stochastic network of interconnected, one-dimensional interlayers with different lengths and orientations. The combined methods are demonstrated on a two-dimensional literature case study of diffusion through a compacted clay sample, and the resulting average diffusion coefficients are in general agreement with those obtained by a different simulation-based technique found in the literature. Then, the methods are applied to model the diffusion of the weakly sorbing cation Na+, providing results in accordance with physical intuition.

Exospheric O2 densities at Europa during different orbital phases

Europa's exosphere is a mixture of different species among which sputtered H2O and H2 dominate in the highest altitudes and O2, formed mainly by radiolysis of ice and subsequent release of the produced molecules, prevails at lower altitudes. Europa's O2 exosphere has been demonstrated through both observation and simulation-based techniques to be spatially non-uniform. In the present study we investigate Europa's exospheric O2 characteristics under the external conditions that are likely in the Jupiter's magnetospheric environment, applying the Europa Global model of Exospheric Outgoing Neutrals (EGEON, Plainaki et al., 2012) for different configurations between the positions of Europa, Jupiter and the Sun. We demonstrate for the first time that the spatial distribution of Europa's exosphere is explicitly time-variable due to the time-varying relative orientations of solar illumination and the incident plasma direction. We show that the O2 release efficiency depends both on solar illumination and plasma impact direction. The modeled densities at different orbital phases of Europa are compared, a posteriori, with the analysis results from two observations in order to validate the model. Using the outputs of EGEON we also calculate the supply of neutral oxygen atoms of exospheric origin to Europa's neutral cloud.

Multi-Modal and Multi-Temporal Data Fusion: Outcome of the 2012 GRSS Data Fusion Contest

The 2012 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society (GRSS) aimed at investigating the potential use of very high spatial resolution (VHR) multi-modal/multi-temporal image fusion. Three different types of data sets, including spaceborne multi-spectral, spaceborne synthetic aperture radar (SAR), and airborne light detection and ranging (LiDAR) data collected over the downtown San Francisco area were distributed during the Contest. This paper highlights the three awarded research contributions which investigate (i) a new metric to assess urban density (UD) from multi-spectral and LiDAR data, (ii) simulation-based techniques to jointly use SAR and LiDAR data for image interpretation and change detection, and (iii) radiosity methods to improve surface reflectance retrievals of optical data in complex illumination environments. In particular, they demonstrate the usefulness of LiDAR data when fused with optical or SAR data. We believe these interesting investigations will stimulate further research in the related areas.

The long-term impact of family difficulties during childhood on labor market outcomes

The literature on child development shows that the promotion of cognitive and non-cognitive skills is essential to prevent inequalities in adult socioeconomic outcomes. In this context, the family environment plays a strategic role, as during childhood, it represents the most important institution for child development. This paper evaluates the long-term impact of various family difficulties during childhood on adult labor market outcomes. Evidence of negative impacts on employment probability and wages emerges from applying propensity score matching to the UK National Child Development Study. Simulation-based sensitivity analysis and standard parametric techniques support our findings. We also find that the intensity of the negative impact appears to increase with the number of recorded family difficulties, while the negative effect does not decline over the cohort’s working life. Moreover, we find that housing and economic (financial and unemployment) problems are responsible for the more serious disadvantages, while disabilities of family members and familial disharmony do not produce statistically negative impacts per se but tend to do so only if associated with other family difficulties, including economic and housing difficulties.

Simulation optimization for allocation of epidemic-control resources

We consider the problem of resource allocation (RA) in the control of epidemics where a fixed budget is allocated among competing healthcare interventions to achieve the best health benefits, and propose a simulation-optimization framework to address a general form of the problem. While traditional approaches to the epidemic RA problem suffer from restrictive assumptions to facilitate exact analytical solutions, a simulation-based technique relaxes such assumptions and provides a more realistic representation of the epidemic. Coupling the simulation model with optimization techniques enables us to analyze the behavior of RA outcomes with regard to different investment strategies and seek optimal allocations. We discuss implementation steps and illustrate our approach for an RA problem in the control of influenza pandemic with several interacting healthcare interventions.

Estimation of the workload correlation in a Markov fluid queue

This paper considers a Markov fluid queue, focusing on the correlation function of the stationary workload process. A simulation-based computation technique is proposed, which relies on a coupling idea. Then an upper bound on the variance of the resulting estimator is given, which reveals how the coupling time and the busy period of the Markov fluid queue affect the performance of the computation procedure. A numerical assessment, in which we compare the proposed technique with naive simulation, gives an indication of the achievable efficiency gain in various scenarios.

Outage minimization for parallel fading channels with limited feedback

We address an optimal power allocation problem for minimizing the outage probability for M parallel block-Nakagami-fading channels under a long-term average sum transmit power constraint with finite rate feedback of channel state information (CSI). A simulation-based optimization technique called simultaneous perturbation stochastic approximation algorithm (SPSA) is employed first to numerically derive a locally optimal power codebook. Due to the high computational complexity and long convergence time of SPSA, we make an ordering assumption on the power codebook entries and derive effective hyperplane based approximations to the channel quantization regions and present a number of low-complexity suboptimal quantized power codebook design algorithms. Unlike existing work on outage minimization for multiple-input multiple-output (MIMO) channels with limited feedback, we do not assume that identical transmission power is used for all channels within each channel quantization region. We also do not resort to a Gaussian approximation for the instantaneous mutual information in general as used in many existing work. Based on our power ordering assumption and hyperplane based approximations, we show that allocating identical power to all channels within a given channel quantization region in the limited feedback scenario is asymptotically optimal only at high average power (or average signal-to-noise ratio (SNR)) for the Rayleigh fading case, whereas for the general Nakagami case, the transmit power allocation for an individual channel within each quantized region is asymptotically proportional to the corresponding Nakagami fading parameter (severity of fading). We also present a novel diversity order result for the outage probability for the Nakagami fading case. Finally, we derive a suitable Gaussian approximation based low-complexity power allocation scheme for a large number of parallel channels, which has important applications in wideband slow-fading orthogonal frequency-division multiplexing (OFDM) systems. Extensive numerical results illustrate that only a few bits of feedback close the gap substantially in outage performance between the limited feedback case and the full instantaneous CSI at the transmitter case.