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This paper aims to investigate the strategy for low-cost yield optimization of miniaturized microstrip couplers using variable-fidelity electromagnetic (EM) simulations.

Usefulness of data-driven models constructed from structure frequency responses formulated in the form of suitably defined characteristic points for statistical analysis is investigated. Reformulation of the characteristics leads to a less nonlinear functional landscape and reduces the number of training samples required for accurate modeling. Further reduction of the cost associated with construction of the data-driven model, is achieved using variable-fidelity methods. Numerical case study is provided demonstrating feasibility of the feature-based modeling for low cost statistical analysis and yield optimization.

It is possible, through reformulation of the structure frequency responses in the form of suitably defined feature points, to reduce the number of training samples required for its data-driven modeling. The approximation model can be used as an accurate evaluation engine for a low-cost Monte Carlo analysis. Yield optimization can be realized through minimization of yield within the data-driven model bounds and subsequent model re-set around the optimized design.

The investigated technique exceeds capabilities of conventional Monte Carlo-based approaches for statistical analysis in terms of computational cost without compromising its accuracy with respect to the conventional EM-based Monte Carlo.

The proposed tolerance-aware design approach proved useful for rapid yield optimization of compact microstrip couplers represented using EM-simulation models, which is extremely challenging when using conventional approaches due to tremendous number of EM evaluations required for statistical analysis.

The purpose of this paper is to investigate the strategies and algorithms for expedited design optimization of microwave and antenna structures in multi-objective setup.

Formulation of the multi-objective design problem-oriented toward execution of the population-based metaheuristic algorithm within the segmented search space is investigated. Described algorithmic framework exploits variable fidelity modeling, physics- and approximation-based representation of the structure and model correction techniques. The considered approach is suitable for handling various problems pertinent to the design of microwave and antenna structures. Numerical case studies are provided demonstrating the feasibility of the segmentation-based framework for the design of real-world structures in setups with two and three objectives.

Formulation of appropriate design problem enables identification of the search space region containing Pareto front, which can be further divided into a set of compartments characterized by small combined volume. Approximation model of each segment can be constructed using a small number of training samples and then optimized, at a negligible computational cost, using population-based metaheuristics. Introduction of segmentation mechanism to multi-objective design framework is important to facilitate low-cost optimization of many-parameter structures represented by numerically expensive computational models. Further reduction of the design cost can be achieved by enforcing equal-volumes of the search space segments.

The study summarizes recent advances in low-cost multi-objective design of microwave and antenna structures. The investigated techniques exceed capabilities of conventional design approaches involving direct evaluation of physics-based models for determination of trade-offs between the design objectives, particularly in terms of reliability and reduction of the computational cost. Studies on the scalability of segmentation mechanism indicate that computational benefits of the approach decrease with the number of search space segments.

The proposed design framework proved useful for the rapid multi-objective design of microwave and antenna structures characterized by complex and multi-parameter topologies, which is extremely challenging when using conventional methods driven by population-based metaheuristics algorithms. To the authors knowledge, this is the first work that summarizes segmentation-based approaches to multi-objective optimization of microwave and antenna components.

This study aims to focus on the grid connected inverter.

The grid connected inverter for harmonic suppression was designed, the topological structure of the inverter and the design of LCL filter were analyzed, then a PIR controller was proposed and finally simulation and experiment were carried out.

The simulation results showed that the distortion rates of the 5th, 7th and 11th harmonics under PIR control were 0.14%, 0.13% and 0.06%, respectively, which were significantly lower than that under PI control. The system test results also showed that the current waveform under PI control was rough and total harmonic distortion (THD) content was 3.8%; under PIR control, the grid connected current waveform was relatively smooth, with fewer spikes and burrs, and the THD content was 1.9%, indicating that the harmonics were effectively suppressed.

The experimental results verify that the inverter and PIR controller designed in this study are effective for harmonic suppression. This work makes some contributions to the improvement of the effect of harmonic suppression and promotion of the better application of grid connected inverter.

This paper aims to investigate deterministic strategies for low-cost multi-objective design optimization of compact microwave structures, specifically, impedance matching transformers. The considered methods involve surrogate modeling techniques and variable-fidelity electromagnetic (EM) simulations. In contrary to majority of conventional approaches, they do not rely on population-based metaheuristics, which permit lowering the design cost and improve reliability.

There are two algorithmic frameworks presented, both fully deterministic. The first algorithm involves creating a path covering the Pareto front and arranged as a sequence of patches relocated in the course of optimization. Response correction techniques are used to find the Pareto front representation at the high-fidelity EM simulation level. The second algorithm exploits Pareto front exploration where subsequent Pareto-optimal designs are obtained by moving along the front by means of solving appropriately defined local constrained optimization problems. Numerical case studies are provided demonstrating feasibility of solving real-world problems involving expensive EM-simulation models of impedance transformer structures.

It is possible, by means of combining surrogate modeling techniques and constrained local optimization, to identify the set of alternative designs representing Pareto-optimal solutions, in a realistic time frame corresponding to a few dozen of high-fidelity EM simulations of the respective structures. Multi-objective optimization for the considered class of structures can be realized using deterministic approaches without defaulting to evolutionary methods.

The present study can be considered a step toward further studies on expedited optimization of computationally expensive simulation models for miniaturized microwave components.

The proposed algorithmic solutions proved useful for expedited multi-objective design optimization of miniaturized microwave structures. The problem is extremely challenging when using conventional methods, in particular evolutionary algorithms. To the authors’ knowledge, this is one of the first attempts to investigate deterministic surrogate-assisted multi-objective optimization of compact components at the EM-simulation level.

Strategies for accelerated multi-objective optimization of compact microwave and RF structures are investigated, including the possibility of exploiting surrogate modeling techniques for electromagnetic (EM)-driven design speedup for such components. The paper aims to discuss these issues.

Two algorithmic frameworks are described that are based on fast response surface approximation models, structure decomposition, and Pareto front refinement. Numerical case studies are provided demonstrating feasibility of solving real-world problems involving multi-objective optimization of miniaturized microwave passives and expensive EM-simulation models of such structures.

It is possible, through appropriate combination of the surrogate modeling techniques and response correction methods, to identify the set of alternative designs representing the best possible trade-offs between conflicting design objectives in a realistic time frame corresponding to a few dozen of high-fidelity EM simulations of the respective structures.

The present study sets a direction for further studied on expedited optimization of computationally expensive simulation models for miniaturized microwave components.

The proposed algorithmic framework proved useful for fast design of microwave structures, which is extremely challenging when using conventional methods. To the authors’ knowledge, this is one of the first attempts to surrogate-assisted multi-objective optimization of compact components at the EM-simulation level.

The purpose of this paper is to investigate strategies for expedited dimension scaling of electromagnetic (EM)-simulated microwave and antenna structures, exploiting the concept of variable-fidelity inverse surrogate modeling.

A fast inverse surrogate modeling technique is described for dimension scaling of microwave and antenna structures. The model is established using reference designs obtained for cheap underlying low-fidelity model and corrected to allow structure scaling at high accuracy level. Numerical and experimental case studies are provided demonstrating feasibility of the proposed approach.

It is possible, by appropriate combination of surrogate modeling techniques, to establish an inverse model for explicit determination of geometry dimensions of the structure at hand so as to re-design it for various operating frequencies. The scaling process can be concluded at a low computational cost corresponding to just a few evaluations of the high-fidelity computational model of the structure.

The present study is a step toward development of procedures for rapid dimension scaling of microwave and antenna structures at high-fidelity EM-simulation accuracy.

The proposed modeling framework proved useful for fast geometry scaling of microwave and antenna structures, which is very laborious when using conventional methods. To the authors’ knowledge, this is one of the first attempts to surrogate-assisted dimension scaling of microwave components at the EM-simulation level.

The purpose of this paper is to familiarize the reader with the capabilities of EFAB technology, a unique additive manufacturing process which yields fully assembled, functional mechanisms from metal on the micro to millimeter scale, and applications in medical devices.

The process is based on multi‐layer electrodeposition and planarization of at least two metals: one structural and one sacrificial. After a period of initial commercial development, it was scaled up from a prototyping‐only to a production process, and biocompatible metals were developed for medical applications.

The process yields complex, functional metal micro‐components and mechanisms with tight tolerances from biocompatible metals, in low‐high production volume.

The process described has multiple commercial applications, including minimally invasive medical instruments and implants, probes for semiconductor testing, military fuzing and inertial sensing devices, millimeter wave components, and microfluidic devices.

The process described in this paper is unusual among additive fabrication processes in being able to manufacture in high volume, and in its ability to produce devices with microscale features. It is one of only a few additive manufacturing processes that can produce metal parts or multi‐component mechanisms.

The paper aims to discuss the design, fabrication, communication, testing, and simulation of a new tactile probe called Elastirob used to measure the modulus of elasticity of biological soft tissues and soft materials.

Both finite element modeling and experimental approaches were used in this analysis. Elastirob, with the ability to apply different rates of strain on testing specimens, is accompanied by a tactile display called TacPlay. This display is a custom‐designed user‐friendly interface and is able to evaluate the elasticity in each part of the stress‐strain curve.

A new device is being constructed that can measure the modulus of elasticity of a sensed object. The results of Elastirob applied on two specimens are reported and compared by the results of experiments obtained by an industrial testing machine. Acceptable validations of Elastirob were achieved from the comparisons.

The designed system can be miniaturized to be used in minimally invasive surgeries in the future.

Elastirob determines the elasticity by drawing the stress‐strain curve and then calculating its slope. The combination of the force sensing resistor, microcontroller and stepper motor provides Elastirob with the ability to apply different rates of strain on testing specimens.

It can be employed in both in vivo and in vitro tests for measuring stiffness of touch objects. For the first time, a device has been designed and tested which is a few orders of magnitude smaller than its industrial counterparts and has considerably lower weight.

The purpose of this paper is to develop new knowledge in experimental characterization of contaminants in engine lubricants, using surface plasmon resonance (SPR) sensing that can be applicable for on‐line condition monitoring of lubricant quality and engine component performance.

The effect of change in optical properties (e.g. transparency, absorption, and refractive index) of engine lubricants caused by the introduction of contaminants, such as gasoline, coolant, and water, on the surface plasmon resonance characteristics is analyzed experimentally. In SPR measurement, variations in both the refractive index and absorption cause changes in the SPR curve, which is the dependence of reflectivity vs incidence angle. The SPR characteristics (e.g. refractivity) of engine lubricant contaminated by gasoline, water and coolant at different concentration are measured as a function of resonance angle and analyzed with respect to different concentration (1%‐10%) of contaminants. Also, pattern recognition analysis between fresh and used engine lubricants is performed, to show applicability of Bayesian classification methodology for on‐line monitoring and predicting engine lubricant condition.

It was shown experimentally that attenuation of surface plasmons due to introduction of contaminants to the engine lubricant leads to a noticeable change in resonance angle and reflectivity minimum of the SPR curve due to an increase in the dielectric permittivity. In addition, the changes in the SPR characteristics were observed between fresh and used engine lubricant, causing resonance angle and reflectivity minimum of the SPR curve to shift.

The knowledge generated in this study lays the informational basis to further develop an on‐line system for engine lubricant condition monitoring using miniaturized SPR sensors fully suitable for on board applications.

SPR characterization is originally applied for analysis of optical properties of engine lubricants caused by the introduction of contaminants, such as gasoline, coolant, and water.

Montford's modular, walk‐in environmental test chamber offers a new, cost‐effective approach to large enclosure construction. It minimises site work and provides an adaptable design for economical large batch thermal cycling of a wide variety of products. Significantly, all of the unit's thermal conditioning and control devices are supplied ready tested to provide the advantages of a factory‐built system and to speed commissioning.