Performance Of Emulator Research Articles

Emulations of Oncology Trials Using Real-World Data: A Systematic Literature Review.

By evaluating published emulations of oncology RCTs studies in which both the active and comparator groups are sourced from RWD and target trial results are available for benchmarking, this systematic review aims to gain insight into factors related to emulation performance. Thirteen oncology emulation studies using various types of RWD were identified through an online database search of PubMed through 2022. Based on the ROBINS-I tool, most studies (N=8) had a serious risk of overall bias driven by risk of bias from confounding. Approximately half of the studies (N=6) fully proxied the RCT entry criteria. Of 11 RWD studies that provided sufficient detail to quantify emulation performance, the emulation HR estimate fell within the 95% CI of the trial estimate in 9 of the studies. There were no clear trends between risk of bias or degree to which the entry criteria were proxied and emulation performance. Findings may have been influenced by publication bias and researcher degrees of freedom, as only one emulation study pre-registered its protocol. Tools for comprehensively characterizing factors that affect emulation performance, including the real-world clinical context as it relates to the RCT research question, are needed to evaluate the feasibility of a RCT emulation.

A High-Quality and Space-Efficient Design for Memristor Emulation

The paper presents a new design for a compact memristor emulator that uses a single active component and a grounded capacitor. This design incorporates a current backward transconductance amplifier as the active element, enabling the emulation of both grounded and floating memristors in incremental and decremental modes. The paper provides an in-depth analysis of the circuit, covering ideal, non-ideal, and parasitic factors. The theoretical performance of the memristor emulator is confirmed through post-layout simulations with 180 nm generic process design kit (gpdk) technology, demonstrating its capability to operate at low voltages (±1 V) with minimal power consumption. Additionally, the emulator shows strong performance under variations in process, voltage, and temperature (PVT) and functions effectively at a frequency of 2 MHz. Experimental validation using commercially available integrated circuits further supports the proposed design.

Model based data-driven optimisation of automated fibre placement set up

The composites manufacturing industry relies heavily on manual hand layup, unsuitable for increasing demands. Automated Fiber Placement (AFP) offers a solution, but setup complexity and extensive trials inflate costs. Digital tools promise to expedite development, but CPU-intensive simulations limit large-scale parameter optimisation. This paper introduces Gaussian Process (GP) models for understanding AFP parameters’ relationship with nip-point temperature. The GP emulator streamlines optimisation, offering accurate power curves with minimal simulations. An efficient method to enhance emulator performance through sequential design is also proposed, providing a cost-effective decision tool for AFP optimisation.

Machine Learning Emulation of Subgrid‐Scale Orographic Gravity Wave Drag in a General Circulation Model With Middle Atmosphere Extension

AbstractGravity wave parameterizations contribute to uncertainties in middle atmosphere modeling. To investigate the potential for using machine learning to represent atmospheric gravity waves and the impact of implementing such schemes in a general circulation model (GCM), we train a random forest (RF) emulator on outputs from an existing complex parameterization scheme for orographic gravity wave drag (GWD). The performance of the RF emulator is then evaluated with a focus on stratospheric climatology and variability in climate simulations from the middle atmosphere resolving Beijing Climate Center Atmospheric Circulation Model. In offline tests, the predicted orographic GWD by the RF agrees remarkably well with the target GWD throughout the troposphere and the middle atmosphere. The RF emulator can reproduce the observed climatology of zonal‐mean wind and air temperature in the GCM simulation, as well as its target scheme. Compared to the target orographic GWD parameterization scheme, the RF emulator can reproduce the breakdown of the polar vortex in the Southern Hemisphere stratosphere. This study demonstrates the feasibility of using machine learning to emulate parameterized orographic GWD for modeling the stratosphere with a GCM.

Coastal morphodynamic emulator for early warning short-term forecasts

The use of numerical models to anticipate the effects of floods and storms in coastal regions is essential to mitigate the damages of these natural disasters. However, local studies require high spatial and temporal resolution numerical models, limiting their use due to the involved high computational costs. This constraint becomes even more critical when these models are used for real-time monitoring and warning systems. Therefore, the objective of this paper was to reduce the computational time of coastal morphodynamic models simulations by implementing a deep learning emulator. The emulator performance was evaluated using different scenarios run with the XBeach software, which considered different grid resolutions and the effects of a storm event in the morphodynamic patterns around a breakwater and a groin. The morphodynamic simulation time was reduced by 23%, and it was identified that the major restriction to reducing the computational cost was the hydrodynamic numerical model simulation.

A novel neural emulator identification of nonlinear dynamical systems using Lyapunov stability theory

This paper deals with a new weight-updating algorithm using Lyapunov stability theory (LST) for the training of a neural emulator (NE), of nonlinear systems, connected by an autonomous algorithm inspired from the real-time recurrent learning (RTRL). The proposed method is formulated by an inequality-constraint optimization problem where the Lagrange multiplier theory is used as the optimization tool. The contribution of this paper is the integration of the LST into the Lagrange constraint function to synthesize a new analytical adaptation gain rate satisfying the asymptotic stability of the NE and providing good emulation performances. To confirm the good performances and the convergence ability of the proposed adaptation algorithm, a numerical example and an experimental validation on a chemical reactor are proposed.

Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to the carbon dioxide benchmark

Surrogate models are widely used to improve the computational efficiency in various geophysical simulation problems by reducing the number of model runs. Conventional one-layer surrogate representations are based on global (e.g. polynomial chaos expansion, PCE) or on local kernels (e.g., Gaussian process emulator, GPE). Global representations omit some details, while local kernels require more model runs. The existing multi-resolution PCE is a promising hybrid: it is a global representation with local refinement. However, it can not (yet) estimate the uncertainty of the resulting surrogate, which techniques like the GPE can do. We propose to join multi-resolution PCE and GPE s into a joint surrogate framework to get the best out of both worlds. By doing so, we correct the surrogate bias and assess the remaining uncertainty of the surrogate itself. The resulting multi-resolution emulator offers a pathway for several active learning strategies to improve the surrogate at acceptable computational costs, compared to the existing PCE-kriging approach it adds the multi-resolution aspect. We analyze the performance of a multi-resolution emulator and a plain GPE using didactic test cases and a CO2 benchmark, that is representative of many alike problems in the geosciences. Both approaches show similar improvements during the active learning, but our multi-resolution emulator leads to much more stable results than the GPE. Overall, our suggested emulator can be seen as a generalization of multi-resolution PCE and GPE concepts that offers the possibility for active learning.

Reliability-based Multi-objective Optimization with Uncertain Wind Power

The unpredictability for wind power interprets the critical factor limiting its large-scale penetration in microgrids. Gaussian and Cauchy distributions describe both the arbitrary and vague characteristics of wind power to evaluate wind power unpredictability. Afterward, the wind power is presumed an undefined arbitrary variable, and a multi-objective optimization model concerning the credibility density function is developed to balance the economics and the reliability of microgrids. For the solution of the model, the NSGA-II algorithm is applied to seize the Pareto front. By fully deliberating the cognitive uncertainty of the dispatchers, the evidence-theory-based decision-making is employed to drive the eventual scheduling from the Pareto set. The emulation performances substantiate the appropriateness and efficacy of the proffered model with unpredictable wind power by providing a compromise scheme between economics and reliability.

Energy-Efficient UAV-Based IoT Communications With WiFi Suppression in 5 GHz ISM Bands

Unmanned Aerial Vehicle (UAV) based communication is a promising technology for offering wireless communication services. UAVs can typically be adopted as aerial base stations to collect data from ground Internet of Things (IoT) devices. However, the Ground-to-Air link of UAV-based IoT communications is prone to be interfered by surrounding WiFi devices in the Industrial, Scientific, Medical (ISM) band. This paper aims to enable reliable uplink communications for IoT devices in 5 GHz ISM bands, thereby reducing the hover time and energy consumption of the UAV. To this end, we first study and design suppression frames suitable for WiFi devices. Then a suppression method is proposed, which is shown to be effective for all the tested devices, based on emulation performance verification of the frames for multiple commercial off-the-shelf (COTS) WiFi devices. Moreover, by taking the energy consumption of the UAV and multiple transmission parameters of the suppression method into account, we formulate an optimization problem for the desired uplink, which is designed to minimize the energy consumption of the UAV, under the constraints of satisfying the link channel capacity. Given that the optimization problem is non-convex mixed-integer nonlinear programming, this paper proposes an efficient algorithm that takes advantage of the suppression performance of the utilized frames. Numerical simulations demonstrate the performance of the proposed algorithm in terms of the WiFi device density, UAV weight, channel capacity requirement and parameters of the suppression method. The communication quality of the desired Ground-to-Air link is shown to be significantly improved, while the consumed energy is demonstrated to be obviously reduced.

Errors in Simple Climate Model Emulations of Past and Future Global Temperature Change

AbstractClimate model emulators are widely used to generate temperature projections for climate scenarios, including in the recent Intergovernmental Panel on Climate Change Sixth Assessment Report. Here we evaluate the performance of a two‐layer energy balance model in emulating historical and future temperature projections from Coupled Model Intercomparison Project Phase 6 models. We find that emulation errors can be large (>0.5°C for SSP2‐4.5) and differ markedly between climate models, forcing scenarios and time periods. Errors arise in emulating the near‐surface temperature response to both greenhouse gas and aerosol forcing; in some periods the errors due to these forcings oppose one another, giving the spurious impression of better emulator performance. Climate feedbacks are assumed constant in the emulator, introducing time‐varying or state dependent feedbacks may reduce prediction errors. Close emulations can be produced for a given period but, crucially, this does not guarantee reliable emulations of other scenarios and periods. Therefore, rigorous out‐of‐sample evaluation is necessary to characterize emulator performance.

Flooding and Overflow Mitigation Using Deep Reinforcement Learning Based on Koopman Operator of Urban Drainage Systems

AbstractIn recent studies, deep reinforcement learning (RL) methods have been used for the real‐time control of urban drainage systems (UDSs). However, the training process of an RL agent is computationally expensive since collecting the training data requires multiple simulations of a UDS model. An effective solution to this issue is to replace the original UDS model with an emulator, a simpler model which emulates the specified output of the system. However, the adaptability and interpretability of the existing UDS emulators still require further improvements. To address these challenges, a new emulator with increased adaptability and interpretability, called Koopman emulator, was developed by using the Koopman operator and a deep learning technique in this study. Then, the Koopman emulator was used to replace the UDS model in RL to establish an emulator‐based RL framework for fast and efficient training. This framework was used to train emulator‐based RL agents for flooding and overflow mitigation in a UDS located in Eastern China. Baseline emulators were also employed for comparison. All the methods were evaluated through simulations. The following results were obtained: (a) The Koopman emulator demonstrated a better emulation performance than the baseline emulators. (b) The RL agents based on the Koopman emulator achieved a similar control effect with a faster training process and more efficient data usage compared with the RL agents based on the storm water management model. (c) The uncertainty analysis showed that the RL agents based on the Koopman emulator exhibited acceptable robustness when facing different rainfall events and input perturbations.

The importance of uncertainty quantification in model reproducibility.

Many computer models possess high-dimensional input spaces and substantial computational time to produce a single model evaluation. Although such models are often ‘deterministic’, these models suffer from a wide range of uncertainties. We argue that uncertainty quantification is crucial for computer model validation and reproducibility. We present a statistical framework, termed history matching, for performing global parameter search by comparing model output to the observed data. We employ Gaussian process (GP) emulators to produce fast predictions about model behaviour at the arbitrary input parameter settings allowing output uncertainty distributions to be calculated. History matching identifies sets of input parameters that give rise to acceptable matches between observed data and model output given our representation of uncertainties. Modellers could proceed by simulating computer models’ outputs of interest at these identified parameter settings and producing a range of predictions. The variability in model results is crucial for inter-model comparison as well as model development. We illustrate the performance of emulation and history matching on a simple one-dimensional toy model and in application to a climate model.This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico’.

Probabilistic forecast of wind speed based on Bayesian emulator using monitoring data

Wind speed forecasting can serve a wide spectrum of purposes, including scheduling of a power system and dynamic control of structures. A lot of models are widely used to forecast wind speed, consisting of deterministic models (e.g., physical models, statistical models, and artificial intelligence models) and probabilistic models (e.g., Bayesian model). The wind speed has the characteristics of random, nonlinear, and uncertainty, which highlights the importance of using Bayesian model to predict the wind speed. In this study, a Bayesian emulator with Gaussian process prior is adopted for probabilistic forecast of wind speed. The present Bayesian emulator approach not only maintains the data-driven property which guarantees its high flexibility in modeling the complexity of the target system but also allows for the efficient, probabilistic evaluation of the wind speed in terms of the predictive mean and variance. Nevertheless, the modeling performance of the Bayesian emulator directly depends on the selected covariance function. The influence of different types of covariance functions, which include squared-exponential (SE) covariance function, Matern (MA) covariance function, periodic (PE) covariance function, and composite covariance function, on forecasting performance of the wind speed is studied. One-month wind monitoring data collected by structural health monitoring (SHM) system installed on Jiubao bridge are employed to demonstrate the effectiveness of Bayesian emulator for forecasting the wind speed.

High-Performance Routing Emulation Technologies Based on a Cloud Platform

Currently, the emergence of edge computing provides low-latency and high-efficiency computing for the Internet of Things (IoT). However, new architectures, protocols, and security technologies of edge computing need to be verified and evaluated before use. Since network emulation based on a cloud platform has advantages in scalability and fidelity, it can provide an effective network environment for verifying and evaluating new edge computing technologies. Therefore, we propose a high-performance emulation technology supporting the routing protocol based on a cloud platform. First, we take OpenStack as a basic network environment. To improve the performance and scalability of routing emulation, we then design the routing emulation architecture according to the software-defined network (SDN) and design the cluster scheduling mechanism. Finally, the design of the Open Shortest Path First (OSPF) protocol can support communication with physical routers. Through extensive experiments, we demonstrate that this technology not only can provide a realistic OSPF protocol but also has obvious advantages in the overhead and performance of routing nodes compared with those of other network emulation technologies. Furthermore, the realization of the controller cluster improves the scalability in the emulation scale.

Viable System Verilog Assertions(SVA) Praxis in AMBA AHB-Lite Protocol Design

Digital integrated circuit(ic design entanglement has been far reaching since the demonstration by kil by in 1958.in this day and age system on chip(soc) design verification accommodate billion , more specifically trillion transistors designs came into existence due to artificial intelligence(ai) designs. The expertise designers tried to ramp up design process by using effective eda tools , still the time wheel move in recursive process. In order to accelerate time wheel of design process specified design methodology needed for every design. The overview of various design methodologies followed in the market now a days. Emulation performance by using veloce platform in bfm mode on ahb lite transmissions. Simulation by using software eda tools is slow going on wheel of design when we go for higher abstraction. Accelerated simulation and emulation using hardware is costly in contrast with software simulation. Prototyping is expedient. Formal verification and intelligent software simulations are frail. The possibility of selection between various hardware engines become ravelled. It develop into perspicuous only amalgamation of engines will assist design verification teams to be triumphant. In this design combination of hardware accelerated simulator as a combination of emulator used to accelerate time wheel by using arm amba ahb lite protocol as a design.

A Gaussian-Process-Based Global Sensitivity Analysis of Cultivar Trait Parameters in APSIM-Sugar Model: Special Reference to Environmental and Management Conditions in Thailand

Process-based crop models are advantageous for the identification of management strategies to cope with both temporal and spatial variability of sugarcane yield. However, global optimization of such models is often computationally expensive. Therefore, we performed global sensitivity analysis based on Gaussian process emulation to evaluate the sensitivity of cane dry weight to trait parameters implemented in the Agricultural Productions System Simulator (APSIM)-Sugar model under selected environmental and management conditions in Khon Kaen (KK), Thailand. Emulators modeled 30 years, three soil types and irrigated or rainfed conditions, and emulator performance was investigated. rue, green_leaf_no, transp_eff_cf, tt_emerg_to_begcane and cane_fraction were identified as the most influential parameters and together they explained more than 90% of total variance on the simulator output. Moreover, results indicate that the sensitivity of sugarcane yield to the most influential parameters is affected by water stress conditions and nitrogen stress. Our findings can be used to improve the efficiency and accuracy of modeling and to identify appropriate management strategies to address temporal and spatial variability of sugarcane yield in KK.

Emulation of Wind Turbine System Using Vector Controlled Induction Motor Drive

Depleting fossil fuels and concerns about global warming have forced a paradigm shift toward renewable energy resources in meeting our energy requirements. Wind energy utilization has been growing at a rapid rate fuelling research and development in high power wind turbines (WTs). To advance research and education in wind energy conversion systems, a controlled test bed is necessary that does not depend on wind availability. Majority of the existing WT emulation schemes employ a torque compensation loop to account for the inertia difference between the WT and motor used for emulation. However, the loop suffers from noise and instability issues. Also, the torque compensation term is sensitive to parameter estimates. This article proposes one degree of freedom (1-DOF) and 2-DOF control structures for WT emulation. It first illustrates the design and performance of 1-DOF control structure. It further proposes two controllers along with their prefilters in 2-DOF structure, which has superior performance than 1-DOF structure. The design of the controllers and prefilters is explained with the help of root locus. The performance of the WT emulator with the proposed control structures is demonstrated through simulations and experiments on a 7.5 kW doubly fed induction generator driven by a 5.5 kW squirrel cage induction motor, where the dynamics and steady-state operation of a WT–generator system are emulated.

FCS Based Memcapacitor Emulator Circuit

In this paper we have designed a grounded memcapacitor emulator which is a part of memristive system providing a nonlinear relationship between Charge and Voltage. The Floating Current Source (FCS) possesses a low power consumption feature, has been utilized for the memcapacitor emulation. In this study, a memcapacitor emulator is designed utilizing discrete circuit elements. The proposed emulator circuit exhibits a simple design consisting of MOSFETs and capacitors. The emulator performance is verified theoretically, and computer simulations and results are discussed here.

Analysis of designed PV Emulator using FOPID Controller and Fuzzy logic controller by load variation

Necessity and popularity of renewable sources like solar power have widened the application of power converters. One such application is in PV Emulator, which is used for testing of proposed Solar PV panel before its installation. Research using resistive load variation for PV Emulator is done previously but the method using switch with resistor for load variation is not covered .This aspect is covered in this work. The aim of this paper is to analyse designed PV Emulator for obtaining VI characteristics by varying load parameters using FOPID Controller and fuzzy logic controller. The selection of converter is done considering its low cost, easy control and simple circuit. Controllers are implemented to get comparative analysis of load variation on the performance of PV Emulator.

Machine Learning Building Blocks for Real-Time Emulation of Advanced Transport Power Systems

The revolution of artificial intelligence (AI) is transforming major industries worldwide. With accurate inferencing, AI has caught the attention of many engineers and scientists. Promisingly, hardware-in-the-loop (HIL) emulation can adopt this type of modeling method as one of the alternatives after comprehensive investigation. This paper proposes an approach for emulating power electronic motor drive transients for advanced transportation applications (ATAs) using machine learning building blocks (MLBBs) without any traditional circuit-oriented transient solver. The more electric aircraft (MEA) power system is chosen as a case study to validate the real-time emulation performance of MLBBs. Inside MLBBs, neural networks (NNs) have been applied to build component-level, device-level, and system-level models for various equipment. These models are well trained in a cluster and transplanted into the field-programmable gate array (FPGA) based hardware platform. Finally, MLBB emulation results are compared with PSCAD/EMTDC for system-level and SaberRD for device-level, which showed high consistency for model accuracy and high speed-up for hardware execution.