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Electronics
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Advanced Macromodeling and Optimization Techniques in Electrical Engineering
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Advanced Macromodeling and Optimization Techniques in Electrical Engineering

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 7696

Special Issue Editors

Dr. Luciano De Tommasi

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Guest Editor
International Energy Research Centre, Tyndall National Institute, University College Cork, T12 E138 Cork‎, Ireland
Interests: renewable energies; microgrids; energy management systems; power electronics; energy efficiency; heating ventilation air conditioning systems; system identification; reduced order modelling; control systems; electronic circuits
Special Issues, Collections and Topics in MDPI journals
Dr. Ivo Couckuyt

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Guest Editor
Department of Information Technology (INTEC), Ghent University - imec, iGent, Technologiepark-Zwijnaarde 126, B-9052 Gent, Belgium
Interests: data-efficient machine learning; surrogate modeling; Bayesian optimization; active learning; generative model-based design
Dr. Domenico Spina

E-Mail Website
Guest Editor
Department of Information Technology (INTEC), Ghent University - imec, iGent, Technologiepark-Zwijnaarde 126, B-9052 Gent, Belgium
Interests: surrogate modeling; machine learning; uncertainty quantification; RF and microwave engineering

Special Issue Information

Dear Colleagues,

This Special Issue deals with scalable design and optimization methodologies for electronic system and control, including applications to integrated circuit design, signal integrity simulations, power electronic systems’ design and control, and communication networks’ design and automation. Scalable methods include surrogate modeling, machine learning, or other efficient methodologies to expedite the design and optimization process, using analytical models. Surrogate-based optimization involves the utilization of surrogate models in multicriteria optimization. This issue welcomes novel contributions to improve screening of important design parameters, balance between wide design space exploration and local search, and generate truly novel designs (free-form optimization). Suitable contributions will highlight novel algorithmic ideas in the form of pseudo-code or flow charts and their comparison against state-of-the-art reference algorithms. Scalability of proposed formulations are evaluated, taking into account the specific solver used: deterministic, stochastic, exploiting derivatives or derivative-free. Analysis of the proposed methodologies takes into account the computational effort, types of nonlinearity (including multimodality and discontinuities), discrete and continuous parameters, and the noise as induced by the design specifications, objectives and constraints.

Topics of interest for this Special Issue include, but are not limited to:

  • Surrogate modelling;
  • Metamodeling;
  • Machine learning for engineering design;
  • Surrogate-assisted optimization;
  • Electronic circuits design;
  • Control algorithm design;
  • Sensitivity analysis;
  • Uncertainty Quantification;
  • Evolutionary optimization algorithms.

Dr. Luciano De Tommasi
Dr. Ivo Couckuyt
Dr. Domenico Spina
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Surrogate-assisted optimization
  • Black-box optimization
  • Surrogate modelling
  • Metamodeling
  • Electronic circuits design
  • Control algorithm design
  • Sensitivity analysis
  • Ensemble surrogate models
  • Multiobjective optimization
  • Evolutionary optimization algorithms.

Published Papers (4 papers)

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Research

19 pages, 1488 KiB  
Article
Run-Time Hierarchical Management of Mapping, Per-Cluster DVFS and Per-Core DPM for Energy Optimization
by Weiming Qiu, Yonghao Chen, Dihu Chen, Tao Su and Simei Yang
Electronics 2022, 11(7), 1094; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11071094 - 30 Mar 2022
Cited by 2 | Viewed by 1285
Abstract
Heterogeneous cluster-based multi/many-core systems (e.g., ARM big.LITTLE, supporting dynamic voltage and frequency scaling (DVFS) at cluster level and dynamic power management (DPM) at core level) have attracted much attention to optimize energy on modern embedded systems. For concurrently executing applications on such a [...] Read more.
Heterogeneous cluster-based multi/many-core systems (e.g., ARM big.LITTLE, supporting dynamic voltage and frequency scaling (DVFS) at cluster level and dynamic power management (DPM) at core level) have attracted much attention to optimize energy on modern embedded systems. For concurrently executing applications on such a platform, this paper aims to study how to appropriately apply the three system configurations (mapping, DVFS, and DPM) to reduce both dynamic and static energy. To this end, this paper first formulates the dependence of the three system configurations on heterogeneous cluster-based systems as a 0–1 integrated linear programming (ILP) model, taking into account run-time configuration overheads (e.g., costs of DPM mode switching and task migration). Then, with the 0–1 ILP model, different run-time strategies (e.g., considering the three configurations in fully separate, partially separate, and holistic manners) are compared based on a hierarchical management structure and design-time prepared data. Experimental case studies offer insights into the effectiveness of different management strategies on different platform sizes (e.g., #cluster × #core, 2 × 4, 2 × 8, 4 × 4, 4 × 8), in terms of application migration, energy efficiency, resource efficiency, and complexity. Full article
(This article belongs to the Special Issue Advanced Macromodeling and Optimization Techniques in Electrical Engineering)
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16 pages, 2538 KiB  
Article
Optimization-Based Antenna Miniaturization Using Adaptively Adjusted Penalty Factors
by Marzieh Mahrokh and Slawomir Koziel
Electronics 2021, 10(15), 1751; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10151751 - 21 Jul 2021
Cited by 3 | Viewed by 1872
Abstract
The continuing trend for miniaturization of electronic devices necessitates size reduction of the comprising components and circuitry. Specifically, integrated circuit-antenna modules therein require compact radiators in applications such as 5G communications, implantable and on-body devices, or internet of things (IoT). The conflict between [...] Read more.
The continuing trend for miniaturization of electronic devices necessitates size reduction of the comprising components and circuitry. Specifically, integrated circuit-antenna modules therein require compact radiators in applications such as 5G communications, implantable and on-body devices, or internet of things (IoT). The conflict between the demands for compact size and electrical and field performance can be mitigated by means of constrained numerical optimization. Evaluation of performance-related constraints requires expensive electromagnetic (EM) analysis of the system at hand; therefore, their explicit handling is inconvenient. A workaround is the penalty function approach where the primary objective (typically, antenna size) is complemented by additional terms quantifying possible constraint violations. The penalty coefficients that determine contributions of these terms are normally adjusted manually, which hinders precise control over antenna performance figures and often leads to inferior results in terms of achieved miniaturization rates. This paper proposes a novel algorithm featuring an automated adjustment of the penalty factors throughout the optimization process. Our methodology is validated using three broadband antenna structures. The obtained results demonstrate that the presented adaptive adjustment permits a precise control over the constraint violations while leading to better miniaturization rates as compared to manual penalty term setup. Full article
(This article belongs to the Special Issue Advanced Macromodeling and Optimization Techniques in Electrical Engineering)
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11 pages, 3602 KiB  
Article
Normalized Partial Scattering Cross Section for Performance Evaluation of Low-Observability Scattering Structures
by Muhammad Abdullah, Slawomir Koziel and Stanislaw Szczepanski
Electronics 2021, 10(14), 1731; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10141731 - 19 Jul 2021
Cited by 1 | Viewed by 1735
Abstract
The development of diffusion metasurfaces created new opportunities to elevate the stealthiness of combat aircraft. Despite the potential significance of metasurfaces, their rigorous design methodologies are still lacking, especially in the context of meticulous control over the scattering of electromagnetic (EM) waves through [...] Read more.
The development of diffusion metasurfaces created new opportunities to elevate the stealthiness of combat aircraft. Despite the potential significance of metasurfaces, their rigorous design methodologies are still lacking, especially in the context of meticulous control over the scattering of electromagnetic (EM) waves through geometry parameter tuning. Another practical issue is insufficiency of the existing performance metrics, specifically, monostatic and bistatic evaluation of the reflectivity, especially at the design stage of metasurfaces. Both provide limited insight into the RCS reduction properties, with the latter being dependent on the selection of the planes over which the evaluation takes place. This paper introduces a novel performance metric for evaluating scattering characteristics of a metasurface, referred to as Normalized Partial Scattering Cross Section (NPSCS). The metric involves integration of the scattered energy over a specific solid angle, which allows for a comprehensive assessment of the structure performance in a format largely independent of the particular arrangement of the scattering lobes. We demonstrate the utility of the introduced metric using two specific metasurface architectures. In particular, we show that the integral-based metric can be used to discriminate between the various surface configurations (e.g., checkerboard versus random), which cannot be conclusively compared using traditional methods. Consequently, the proposed approach can be a useful tool in benchmarking radar cross section reduction performance of metamaterial-based, and other types of scattering structures. Full article
(This article belongs to the Special Issue Advanced Macromodeling and Optimization Techniques in Electrical Engineering)
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18 pages, 3277 KiB  
Article
Cost-Efficient EM-Driven Size Reduction of Antenna Structures by Multi-Fidelity Simulation Models
by Anna Pietrenko-Dabrowska and Slawomir Koziel
Electronics 2021, 10(13), 1536; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10131536 - 24 Jun 2021
Cited by 7 | Viewed by 1743
Abstract
Design of antenna systems for emerging application areas such as the Internet of Things (IoT), fifth generation wireless communications (5G), or remote sensing, is a challenging endeavor. In addition to meeting stringent performance specifications concerning electrical and field properties, the structure has to [...] Read more.
Design of antenna systems for emerging application areas such as the Internet of Things (IoT), fifth generation wireless communications (5G), or remote sensing, is a challenging endeavor. In addition to meeting stringent performance specifications concerning electrical and field properties, the structure has to maintain small physical dimensions. The latter normally requires searching for trade-off solutions because miniaturization has detrimental effects on antenna characteristics, including the impedance matching, gain, efficiency, or axial ratio bandwidth. Furthermore, explicit size reduction is more demanding than optimization with respect to other figures of merit. On the one hand, it is a constrained task with acceptance thresholds set on the bandwidth, gain, etc. On the other hand, optimum solutions are normally located at the boundary of the feasible region, traversing of which is a difficult problem by itself. The necessity of using full-wave electromagnetic (EM) analysis for antenna evaluation only aggravates the problem due to high computational costs associated with numerical optimization algorithms. This paper proposes a procedure for expedited optimization-based miniaturization of antenna structures involving trust-region gradient search and multi-fidelity EM simulations, as well as implicit handling of design constraints using a penalty function approach. The assumed model management scheme is associated with the convergence status of the optimization process with the lowest fidelity model employed at the early stages of the algorithm run and the discretization density of the structure gradually increased to reach the high-fidelity level towards the end of the run. This allows us to achieve a considerable computational speedup without compromising the reliability. Our methodology is demonstrated using two broadband microstrip antennas. The obtained CPU savings exceed seventy percent as compared to the reference procedure involving high-fidelity model only. Full article
(This article belongs to the Special Issue Advanced Macromodeling and Optimization Techniques in Electrical Engineering)
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