Data-driven model based design and analysis of antenna structures

Data-driven models, or metamodels, offer an efficient way to mimic the behaviour of computation-intensive simulators. Subsequently, the usage of such computationally cheap metamodels is indispensable in the design of contemporary antenna structures where computation-intensive simulations are often performed in a large scale. Although metamodels offer sufficient flexibility and speed, they often suffer from an exponential growth of required training samples as the dimensionality of the problem increases. In order to alleviate this issue, a Gaussian process based approach, known as Gradient-Enhanced Kriging (GEK), is proposed in this work to achieve cost-efficient modelling of antenna structures. The GEK approach incorporates adjoint-based sensitivity data in addition to function data obtained from electromagnetic simulations. The approach is illustrated using a dielectric resonator and an ultra-wideband antenna structures. The method demonstrates significant accuracy improvement with the less number of training samples over the Ordinary Kriging (OK) approach which utilises function data only. The discussed technique has been favourably compared with OK in terms of computational cost

Bayesian model predictive control: Efficient model exploration and regret bounds using posterior sampling

Tight performance specifications in combination with operational constraints make model predictive control (MPC) the method of choice in various industries. As the performance of an MPC controller depends on a sufficiently accurate objective and prediction model of the process, a significant effort in the MPC design procedure is dedicated to modeling and identification. Driven by the increasing amount of available system data and advances in the field of machine learning, data-driven MPC techniques have been developed to facilitate the MPC controller design. While these methods are able to leverage available data, they typically do not provide principled mechanisms to automatically trade off exploitation of available data and exploration to improve and update the objective and prediction model. To this end, we present a learning-based MPC formulation using posterior sampling techniques, which provides finite-time regret bounds on the learning performance while being simple to implement using off-the-shelf MPC software and algorithms. The performance analysis of the method is based on posterior sampling theory and its practical efficiency is illustrated using a numerical example of a highly nonlinear dynamical car-trailer system

Adapting to Unknown Smoothness by Aggregation of Thresholded Wavelet Estimators

We study the performances of an adaptive procedure based on a convex combination, with data-driven weights, of term-by-term thresholded wavelet estimators. For the bounded regression model, with random uniform design, and the nonparametric density model, we show that the resulting estimator is optimal in the minimax sense over all Besov balls under the $L^2$ risk, without any logarithm factor

Data-driven adaptive model-based predictive control with application in wastewater systems

This study is concerned with the development of a new data-driven adaptive model-based predictive controller (MBPC) with input constraints. The proposed methods employ subspace identification technique and a singular value decomposition (SVD)-based optimisation strategy to formulate the control algorithm and incorporate the input constraints. Both direct adaptive model-based predictive controller (DAMBPC) and indirect adaptive model-based predictive controller (IAMBPC) are considered. In DAMBPC, the direct identification of controller parameters is desired to reduce the design effort and computational load while the IAMBPC involves a two-stage process of model identification and controller design. The former method only requires a single QR decomposition for obtaining the controller parameters and uses a receding horizon approach to process input/output data for the identification. A suboptimal SVD-based optimisation technique is proposed to incorporate the input constraints. The proposed techniques are implemented and tested on a fourth order non-linear model of a wastewater system. Simulation results are presented to compare the direct and indirect adaptive methods and to demonstrate the performance of the proposed algorithms

Data driven approaches for smart city planning and design: a case scenario on urban data management

Purpose Because of the use of digital technologies in smart cities, municipalities are increasingly facing issues related to urban data management and are seeking ways to exploit these huge amounts of data for the actualization of data driven services. However, only few studies discuss challenges related to data driven strategies in smart cities. Accordingly, the purpose of this study is to present data driven approaches (architecture and model), for urban data management needed to improve smart city planning and design. The developed approaches depict how data can underpin sustainable urban development. Design/methodology/approach Design science research is adopted following a qualitative method to evaluate the architecture developed based on top-level design using a case data from workshops and interviews with experts involved in a smart city project. Findings The findings of this study from the evaluations indicate that the identified enablers are useful to support data driven services in smart cities and the developed architecture can be used to promote urban data management. More importantly, findings from this study provide guidelines to municipalities to improve data driven services for smart city planning and design. Research limitations/implications Feedback as qualitative data from practitioners provided evidence on how data driven strategies can be achieved in smart cities. However, the model is not validated. Hence, quantitative data is needed to further validate the enablers that influence data driven services in smart city planning and design. Practical implications Findings from this study offer practical insights and real-life evidence to define data driven enablers in smart cities and suggest research propositions for future studies. Additionally, this study develops a real conceptualization of data driven method for municipalities to foster open data and digital service innovation for smart city development. Social implications The main findings of this study suggest that data governance, interoperability, data security and risk assessment influence data driven services in smart cities. This study derives propositions based on the developed model that identifies enablers for actualization of data driven services for smart cities planning and design. Originality/value This study explores the enablers of data driven strategies in smart city and further developed an architecture and model that can be adopted by municipalities to structure their urban data initiatives for improving data driven services to make cities smarter. The developed model supports municipalities to manage data used from different sources to support the design of data driven services provided by different enterprises that collaborate in urban environment.acceptedVersio

Variable-fidelity optimization of microwave filters using co-kriging and trust regions

In this paper, a variable-fidelity optimization methodology for simulation-driven design optimization of filters is presented. We exploit electromagnetic (EM) simulations of different accuracy. Densely sampled but cheap low-fidelity EM data is utilized to create a fast kriging interpolation model (the surrogate), subsequently used to find an optimum design of the high-fidelity EM model of the filter under consideration. The high-fidelity data accumulated during the optimization process is combined with the existing surrogate using the co-kriging technique. This allows us to improve the surrogate model accuracy while approaching the optimum. The convergence of the algorithm is ensured by embedding it into the trust region framework that adaptively adjusts the search radius based on the quality of the predictions made by the co-kriging model. Three filter design cases are given for demonstration and verification purposes