Signal attenuation caused by the propagation path between the compromising emanation source (the location of secured IT equipment) and the location of the antenna of the potential infiltrating system has a direct influence on the electromagnetic safety of IT equipment. The article presents original analytical relationships necessary to estimate the attenuation values introduced by the propagation path of the potential compromising emanation signal, which correspond to the most probable locations of IT equipment in relation to the location of the potential infiltrating system. The author of the article analyzes various location scenarios for IT equipment – a potential source of compromising emanations – with a potential infiltrating system located either within or outside the boundaries of a building, in which said IT equipment is located. The aforementioned scenarios are characterized by the lowest propagation path attenuation of potential compromising emanation generated by the secured IT equipment and provide for location masking of the potential infiltrating system. Example design of protective solutions for IT equipment elaborated by article author in the form of a shielding enclosure is presented in the article as well.
Finite mixture and Markov-switching models generalize and, therefore, nest specifications featuring only one component. While specifying priors in the general (mixture) model and its special (single-component) case, it may be desirable to ensure that the prior assumptions introduced into both structures are compatible in the sense that the prior distribution in the nested model amounts to the conditional prior in the mixture model under relevant parametric restriction. The study provides the rudiments of setting compatible priors in Bayesian univariate finite mixture and Markov-switching models. Once some primary results are delivered, we derive specific conditions for compatibility in the case of three types of continuous priors commonly engaged in Bayesian modeling: the normal, inverse gamma, and gamma distributions. Further, we study the consequences of introducing additional constraints into the mixture model’s prior on the conditions. Finally, the methodology is illustrated through a discussion of setting compatible priors for Markov-switching AR(2) models.
Machine learning (ML) methods facilitate automated data mining. The authors compare the effectiveness of selected ML methods (RBF networks, Kohonen networks, and random forest) as modelling tools supporting the selection of materials in ecodesign. Applied in the design process, ML methods help benefit from the knowledge, experience and creativity of designers stored in historical data in databases. Implemented into a decision support system, the knowledge can be utilized – in the case under analysis – in the process of design of environmentally friendly products. The study was initiated with an analysis of input data for the selection of materials. The input data, specified in cooperation with designers, include both technological and environmental parameters which guarantee the desired compatibility of materials. Next, models were developed using selected ML methods. The models were assessed and implemented into an expert system. The authors show which models best fit their purpose and why. Models supporting the selection of materials, connections and disassembly methods help boost the recycling properties of designed products.
This paper presents that the effect of single aperture size of metallic enclosure on electrical shielding effectiveness (ESE) at 0 – 1 GHz frequency range has been investigated by using both Robinson’s analytical formulation and artificial neural networks (ANN) methods that are multilayer perceptron (MLP) networks and a radial basis function neural network (RBFNN). All results including measurement have been compared each other in terms of aperture geometry of metallic enclosure. The geometry of single aperture varies from square to rectangular shape while the open area of aperture is fixed. It has been observed that network structure of MLP 3-40-1 in modeling with ANN modeled with fewer neurons in the sense of overlapping of faults and data and modeled accordingly. In contrast, the RBFNN 3-150-1 is the other detection that the network structure is modeled with more neurons and more. It can be seen from the same network-structured MLP and RBFNN that the MLP modeled better. In this paper, the impact of dimension of rectangular aperture on shielding performance by using RBFNN and MLP network model with ANN has been studied, as a novelty.
Studies on food preference of herbivores include no-choice test and test with choice or multiple choice. Conclusions from statistic analyses of these tests are compared descriptively. The definition of compatibility index and consumption growth index has enabled us to use nonparametric test for verification of hypotheses about homogeneity of the consumption growths of selected plant species under no-choice and multiple choice conditions. The studies were conducted on food preference of the slug Deroceras reticulatum. It has been found that Chamaenerion angustifolium, Geraniumpusillum and Potentilla anserina can be used to reduce this slug feeding on cultivated plants. It has been also found that seedlings of Polygonum aviculare can be used as alternative food for slugs.
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