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The purpose of this study is to propose a framework for expedited antenna optimization with numerical derivatives involving gradient variation monitoring throughout the optimization run and demonstrate it using a benchmark set of real-world wideband antennas. A comprehensive analysis of the algorithm performance involving multiple starting points is provided. The optimization results are compared with a conventional trust-region (TR) procedure, as well as the state-of-the-art accelerated TR algorithms.

The proposed algorithm is a modification of the TR gradient-based algorithm with numerical derivatives in which a monitoring of changes of the system response gradients is performed throughout the algorithm run. The gradient variations between consecutive iterations are quantified by an appropriately developed metric. Upon detecting stable patterns for particular parameter sensitivities, the costly finite differentiation (FD)-based gradient updates are suppressed; hence, the overall number of full-wave electromagnetic (EM) simulations is significantly reduced. This leads to considerable computational savings without compromising the design quality.

Monitoring of the antenna response sensitivity variations during the optimization process enables to detect the parameters for which updating the gradient information is not necessary at every iteration. When incorporated into the TR gradient-search procedures, the approach permits reduction of the computational cost of the optimization process. The proposed technique is dedicated to expedite direct optimization of antenna structures, but it can also be applied to speed up surrogate-assisted tasks, especially solving sub-problems that involve performing numerous evaluations of coarse-discretization models.

The introduced methodology opens up new possibilities for future developments of accelerated antenna optimization procedures. In particular, the presented routine can be combined with the previously reported techniques that involve replacing FD with the Broyden formula for directions that are satisfactorily well aligned with the most recent design relocation and/or performing FD in a sparse manner based on relative design relocation (with respect to the current search region) in consecutive algorithm iterations.

Benchmarking against a conventional TR procedure, as well as previously reported methods, confirms improved efficiency and reliability of the proposed approach. The applications of the framework include direct EM-driven design closure, along with surrogate-based optimization within variable-fidelity surrogate-assisted procedures. To the best of the authors’ knowledge, no comparable approach to antenna optimization has been reported elsewhere. Particularly, it surmounts established methodology by carrying out constant supervision of the antenna response gradient throughout successive algorithm iterations and using gathered observations to properly guide the optimization routine.

A framework for reliable modeling of high-frequency structures by nested kriging with an improved sampling procedure is developed and extensively validated. A comprehensive benchmarking including conventional kriging and previously reported design of experiments technique is provided. The proposed technique is also demonstrated in solving parameter optimization task.

The keystone of the proposed approach is to focus the modeling process on a small region of the parameter space (constrained domain containing high-quality designs with respect to the selected performance figures) instead of adopting traditional, hyper-cube-like domain defined by the lower and upper parameter bounds. A specific geometry of the domain is explored to improve a uniformity of the training data set. In consequence, the predictive power of the model is improved.

Building the model in a constrained domain allows for a considerable reduction of a training data set size without a necessity to either narrow down the parameter ranges or to reduce the parameter space dimensionality. Improving uniformity of training data set allocation permits further reduction of the computational cost of setting up the model. The proposed technique can be used to expedite the parameter optimization and enables locating good initial designs in a straightforward manner.

The developed framework opens new possibilities inaccurate surrogate modeling of high-frequency structures described by a large number of geometry and/or material parameters. Further extensions can be investigated such as the inclusion of the sensitivity data into the model or exploration of the particular geometry of the model domain to further reduce the computational overhead of training data acquisition.

The efficiency of the proposed method has been demonstrated for modeling and parameter optimization of high-frequency structures. It has also been shown to outperform conventional kriging and previous constrained modeling approaches. To the authors’ knowledge, this approach to formulate and handle the modeling process is novel and permits the establishment of accurate surrogates in highly dimensional spaces and covering wide ranges of parameters.

This study aims to propose a computationally efficient framework for multi-objective optimization (MO) of antennas involving nested kriging modeling technology. The technique is demonstrated through a two-objective optimization of a planar Yagi antenna and three-objective design of a compact wideband antenna.

The keystone of the proposed approach is the usage of recently introduced nested kriging modeling for identifying the design space region containing the Pareto front and constructing fast surrogate model for the MO algorithm. Surrogate-assisted design refinement is applied to improve the accuracy of Pareto set determination. Consequently, the Pareto set is obtained cost-efficiently, even though the optimization process uses solely high-fidelity electromagnetic (EM) analysis.

The optimization cost is dramatically reduced for the proposed framework as compared to other state-of-the-art frameworks. The initial Pareto set is identified more precisely (its span is wider and of better quality), which is a result of a considerably smaller domain of the nested kriging model and better predictive power of the surrogate.

The proposed technique can be generalized to accommodate low- and high-fidelity EM simulations in a straightforward manner. The future work will incorporate variable-fidelity simulations to further reduce the cost of the training data acquisition.

The fast MO optimization procedure with the use of the nested kriging modeling technology for approximation of the Pareto set has been proposed and its superiority over state-of-the-art surrogate-assisted procedures has been proved. To the best of the authors’ knowledge, this approach to multi-objective antenna optimization is novel and enables obtaining optimal designs cost-effectively even in relatively high-dimensional spaces (considering typical antenna design setups) within wide parameter ranges.

A technique for accelerated design optimization of antenna input characteristics is developed and comprehensively validated using real-world wideband antenna structures. Comparative study using a conventional trust-region algorithm is provided. Investigations of the effects of the algorithm control parameters are also carried out.

An optimization methodology is introduced that replaces finite differentiation (FD) by a combination of FD and selectively used Broyden updating formula for antenna response Jacobian estimations. The updating formula is used for directions that are sufficiently well aligned with the design relocation that occurred in the most recent algorithm iteration. This allows for a significant reduction of the number of full-wave electromagnetic simulations necessary for the algorithm to converge; hence, it leads to the reduction of the overall design cost.

Incorporation of the updating formulas into the Jacobian estimation process in a selective manner considerably reduces the computational cost of the optimization process without compromising the design quality. The algorithm proposed in the study can be used to speed up direct optimization of the antenna structures as well as surrogate-assisted procedures involving variable-fidelity models.

This study sets a direction for further studies on accelerating procedures for the local optimization of antenna structures. Further investigations on the effects of the control parameters on the algorithm performance are necessary along with the development of means to automate the algorithm setup for a particular antenna structure, especially from the point of view of the search space dimensionality.

The proposed algorithm proved useful for a reduced-cost optimization of antennas and has been demonstrated to outperform conventional algorithms. To the authors’ knowledge, this is one of the first attempts to address the problem in this manner. In particular, it goes beyond traditional approaches, especially by combining various sensitivity estimation update measures in an adaptive fashion.

A novel framework for expedited antenna optimization with an iterative prediction-correction scheme is proposed. The methodology is comprehensively validated using three real-world antenna structures: narrow-band, dual-band and wideband, optimized under various design scenarios.

The keystone of the proposed approach is to reuse designs pre-optimized for various sets of performance specifications and to encode them into metamodels that render good initial designs, as well as an initial estimate of the antenna response sensitivities. Subsequent design refinement is realized using an iterative prediction-correction loop accommodating the discrepancies between the actual and target design specifications.

The presented framework is capable of yielding optimized antenna designs at the cost of just a few full-wave electromagnetic simulations. The practical importance of the iterative correction procedure has been corroborated by benchmarking against gradient-only refinement. It has been found that the incorporation of problem-specific knowledge into the optimization framework greatly facilitates parameter adjustment and improves its reliability.

The proposed approach can be a viable tool for antenna optimization whenever a certain number of previously obtained designs are available or the designer finds the initial effort of their gathering justifiable by intended re-use of the procedure. The future work will incorporate response features technology for improving the accuracy of the initial approximation of antenna response sensitivities.

The proposed optimization framework has been proved to be a viable tool for cost-efficient and reliable antenna optimization. To the knowledge, this approach to antenna optimization goes beyond the capabilities of available methods, especially in terms of efficient utilization of the existing knowledge, thus enabling reliable parameter tuning over broad ranges of both operating conditions and material parameters of the structure of interest.

The purpose of this paper is to develop artificial neural networks (ANNs) allowing us to simulate the local thermal insulation of clothing protecting against cold on a basis of the characteristics of materials and design solutions used.

For this purpose, laboratory tests of thermal insulation of clothing protecting against cold as well as thermal resistance of textile systems used in the clothing were performed. These tests were conducted with a use of thermal manikin and so-called skin model, respectively. On a basis of results gathered, 12 ANNs were developed that correspond to each thermal manikin’s segment besides hands and feet which are not covered by protective clothing.

In order to obtain high level of simulations, optimization measures for the developed ANNs were introduced. Finally, conducted validation indicated a very high correlation (above 0.95) between theoretical and experimental results, as well as a low error of the simulations (max 8 percent).

The literature reports addressing the problem of modeling thermal insulation of clothing focus mainly on the impact of the degree of fit and the velocity of air movement on thermal insulation properties, whereas reports dedicated to modeling the impact of the construction of clothing protecting against cold as well as of diverse material systems used within one design of clothing on its thermal insulation are scarce.

The purpose of this paper is to compare morphological and physical features of three kinds of materials intended for the insulating layer in the clothing protecting against cold – high-bulk non-woven, goose down (GD) and duck down (DD).

Comparison of thermal performance of developed textile systems with the non-woven, GD and DD content was based on basic biophysical properties related to comfort sensations of the user such as thermal resistance, water vapor resistance and air permeability. In this study, light microscopy and scanning electron microscopy methods were employed to visualize the surface and internal structure of non-woven, GD and DD samples.

The paper indicates the advantages and disadvantages of each of selected insulating material. For the down samples, significantly higher thermal resistance in a dry state than for the non-woven samples can be achieved. Meanwhile, textile systems with the non-woven provide lower value of water vapor resistance. The selected textile systems for the research were characterized by a comparable air permeability.

This paper allows for an evaluation of high-bulk non-woven, DD and GD samples in terms of providing optimal thermal performance in clothing protecting against cold.

The purpose of this paper is to analyze the impact of design solutions used in clothing on the thermal resistance of the material systems from which the clothing is made, design solutions used in clothing on its thermal insulation and clothing size on its thermal insulation properties.

This study involved laboratory tests of clothing protecting against cold and textile systems used in this type of garment using a “skin model” test stand and a thermal manikin.

Analysis of the results obtained from tests carried out showed that the design solutions used in a garment can model its local and overall insulation. It was found that using a bib in trousers has a dominant influence on the thermal properties of clothing. An important parameter is also the use of a hood, as well as the length of the jacket. No significant effect of other structural solutions, such as jacket fastening, pockets and reflective tapes, on the thermal performance of the clothing set was noted.

Although the reports available in the literature pay a lot of attention to the impact of the design of clothing protecting against cold on its thermal performance, most of the presented research results relate to the aspects of fit, whereas the analyses of the effects of other aspects of garment construction on thermal properties are lacking. Therefore, the analysis of the impact of design solutions used in clothing on its thermal insulation properties is a key original factor of this paper.

The purpose of this chapter is to provide a viewpoint on the importance of liberalization for the development of service economy. This study of the authors results from the conducted research and experience obtained so far in the field of liberalization of the services market and its effects on Polish service enterprises. In addition, it contains practical insights on the liberalization, taking into consideration the benefits and barriers perceived by Polish service enterprises entering the markets of Central and Eastern Europe countries (CEEC) and for the economy. This chapter provides insight for the researchers and practitioners who are interested in the problematic of the liberalization of the services market in the context of managing a company operating in the CEEC area.

New member countries of the European Union such as Lithuania, Latvia and Poland are an interesting subject of study due to the fact that processes of changes in consumption and consumer behaviour in these countries are characterized by large dynamics. The purpose of this paper is to present consumer behaviour in the market of catering services in all the above‐mentioned countries as well as in Ukraine. Catering services are an important market on account of their functions and the needs they satisfy. Moreover, on the one hand, under favourable conditions the market of catering services develops dynamically, and, on the other, in time of economic crisis we can feel its consequences fast because of decreasing demand. The possibility of observing consumer behaviour and its sensitivity to factors influencing food purchase decisions is interesting to both researchers and business practice.

This paper is of an empirical character. The presented consumer research findings, which refer to gastronomic services are a part of a study aimed at examining the situation of consumers and consumer behaviours in four countries: Lithuania, Latvia, Poland and Ukraine. Consumer research like Omnibus, which has been conducted by GfK Research Company for a research project: “The Position of a Consumer and Changes in Consumption in the Central‐Eastern Europe following the EU Enlargement” (the author has been the manager of the project), is representative with regard to: age, sex and the size of location. The research method was a direct (face‐to‐face) interview, based on a standardized questionnaire, conducted in a respondent's house.

Analysis of the obtained qualitative and quantitative data allows us to conclude that consumers react to factors creating demand in various ways; consumers‐respondents are differentiated with regard to frequency of using catering outlets; social and demographic features also influence consumer behaviour; and the greatest number of respondents use the services of cafés.

The paper examines consumer behaviour in the gastronomic services market. The findings may be interesting for entrepreneurs whose activity is connected with the gastronomic sector or who are interested in diversifying onto the markets analysed in the research. What is worth emphasizing is that the majority of respondents perceive a positive influence of Europeanisation on quality, competition, the level of service or observing consumer rights. These evaluations also concern the changes which occur in domestic markets of catering services.