The correlation-regression method, as one of the indirect sampling methods, is only sporadically used in geological and mining activities. Theoretically, it should be particularly useful for predicting the content of some chemical components in limestone and marl deposits due to the correlation between them. The results of simple and multiple correlation and regression analysis for 5 selected components (CaO, SiO2, Al2O3, MgO, and SO3), determined in samples from exploratory boreholes and blast holes carried out in the Barcin-Piechcin-Pakość deposit, are presented in the article. The determination coefficients were used as a measure of the correlation power and the quality of the regression models. A very strong linear correlation between CaO and SiO2 content and strong linear correlations between CaO and Al2O3 and SiO2 with Al2O3 have been found. The correlation relationships of the remaining pairs of oxides are weak or very weak and do not provide a basis for prediction of their content based on regression models binding them with the content of other components. The use of nonlinear models for these pairs of oxides results in only a slight improvement in the quality of regression, insignificant from a practical point of view. The application of multiple regression models, linking the content of the mentioned components (with the exception of CaO), leads to similar conclusions. Compared to the determination coefficients of a simple linear correlation, a strong increase in determination coefficients obtained in two cases was found to be artificial and caused by a correlation between the content of the selected components acting as independent variables. From the geological and mining point of view, the results of the analysis indicate the possibility of a fully reliable prediction of SiO2 content and the limited reliability of the Al2O3 content prediction when the CaO content is determined using simple linear regression models.
Applying the commonly accepted definitions of identity to landscape as our field of research, in particular landscape in protected areas, we assume that identity is the deepest relationship with the landscape (surroundings) perceived by man, with its historical layers of content (the culture and tradition of a place) and form (the canon of a place). An evaluation of change in time should be the keynote of deliberations on place identity. Basing on the current status of research, a review of specialist literature and the author’s experience to date, the above definitions and terms may be referenced to talk about “former” and “new” place identity, especially if we acquiesce to what is termed “the culture of a place” that originates in love for it and willingness to participate in the act of creation that has been launched upon the site. Author tries to explain this fenomenom on example of revitalization, on scale of conntry or even the Europe – the cultural – strategic landscape od Zamość Fortress.
Two-dimensional (2D) positive systems are 2D state-space models whose state, input and output variables take only nonnegative values. In the paper we explore how linear matrix inequalities (LMIs) can be used to address the stability problem for 2D positive systems. Necessary and sufficient conditions for the stability of positive systems have been provided. The results have been obtained for most popular models of 2D positive systems, that is: Roesser model, both Fornasini-Marchesini models (FF-MM and SF-MM) and for the general model.
Extraction of the foetal electrocardiogram from single-channel maternal abdominal signals without disturbing its morphology is difficult. We propose to solve the problem by application of projective filtering of time-aligned ECG beats. The method performs synchronization of the beats and then employs the rules of principal component analysis to the desired ECG reconstruction. In the first stage, the method is applied to the composite abdominal signals, containing maternal ECG, foetal ECG, and various types of noise. The operation leads to maternal ECG enhancement and to suppression of the other components. In the next stage, the enhanced maternal ECG is subtracted from the composite signal, and this way the foetal ECG is extracted. Finally, the extracted signal is also enhanced by application of projective filtering. The influence of the developed method parameters on its operation is presented.
Anaphylaxis is an increasing problem in public health. Th e food allergens (mainly milk, eggs, and peanuts) are the most frequent cause of anaphylaxis in children and youth. In order to defi ne the cause of anaphylaxis, skin tests, the determination of the concentration of specifi c IgE in the blood and basophil activation test are conducted. In vitro tests are preferred due to the risk of allergic response during in vivo tests. Component-resolved diagnosis (CRD) is an additional tool in allergology, recommended in the third level of diagnostics when there are diagnostic doubts aft er the above mentioned tests have been carried out. The paper presents 3 cases of patients with anaphylactic response, and the application of CRD in these patients helped in planning the treatment. Patient 1 is a 4-year-old boy with diagnosed atopic dermatitis and bronchial asthma reported an anaphylactic shock at the age of seven months caused by cow’s milk and the exacerbation of bronchial asthma aft er eating some fruit. Patient 2 is a 35-year-old woman who has had anaphylactic shock three times: in June 2015, 2016, and 2017 and associates these episodes with the consumption of dumplings with a caramel, bun, and the last episode took place during physical exertion few hours aft er eating waffl e. Patient 3 is a 26-year-old man with one-time loss of consciousness after eating mixed nuts and drinking beer. CRD off ers the possibility to conduct a detailed diagnostic evaluation of patients with a history of anaphylactic reaction.
The petrographic composition of coal has a significant impact on its technological and sorption properties. That composition is most frequently determined by means of microscope quantitative analyses. Thus, aside from the purely scientific aspect, such measurements have an important practical application in the industrial usage of coal, as well as in issues related to the safety in underground mining facilities. The article discusses research aiming at analyzing the usefulness of selected parameters of a digital image description in the process of automatic identification of macerals of the inertinite group using neural networks. The description of the investigated images was based on statistical parameters determined on the basis of a histogram and co-occurrence matrix (Haralick parameters). Each of the studied macerals was described by means of a 20-element feature vector. An analysis of its principal components (PCA) was conducted, along with establishing the relationship between the number of the applied components and the effectiveness of the MLP network. Based on that, the optimum number of input variables for the investigated classification task was chosen, which resulted in reduction of the size of the network’s hidden layer. As part of the discussed research, the authors also analyzed the process of classification of macerals of the inertinite group using an algorithm based on a group of MLP networks, where each network possessed one output. As a result, average recognition effectiveness of 80.9% was obtained for a single MLP network, and of 93.6% for a group of neural networks. The obtained results indicate that it is possible to use the proposed methodology as a tool supporting microscopic analyses of coal.
The paper presents results of the field tests on membrane biogas enrichment performed with the application of mobile membrane installation (MMI) with the feed stream up to 10 Nm3/h. The mobile installation equipped with four hollow fibre modules with polyimide type membranes was tested at four different biogas plants. Two of them were using agricultural substrates. The third one was constructed at a municipal wastewater plant and sludge was fermented in a digester and finally in the fourth case biogas was extracted from municipal waste landfill site. Differences in the concentration of bio-methane in feed in all cases were observed and trace compounds were detected as well. High selectivity polyimide membranes, in proper module arrangements, can provide a product of high methane content in all cases. The content of other trace compounds, such as hydrogen sulphide, water vapour and oxygen on the product did not exceed the values stated by standard for a biogas as a vehicle fuel. The traces of hydrogen sulphide and water vapour penetrated faster to the waste stream enriched in carbon dioxide, which could lead to further purification of the product – methane being hold in the retentate (H2O > H2S > CO2 > O2 > CH4 > N2). In the investigated cases, when concentration of N2 was low and concentration of CH4 higher than 50%, it was possible to upgrade methane to concentration above 90% in a two-stage cascade. To performsimulation ofCH4 andCO2 permeation through polyimide membrane,MATLABwas used. Simulation program has included permeation gaseous mixture with methane contents as observed at field tests in the range of 50 and 60% vol. The mass transport process was estimated for a concurrent hollow fibre membrane module for given pressure and temperature conditions and different values of stage cut. The obtained results show good agreement with the experimental data. The highest degree of methane recovery was obtained with gas concentrating in a cascade with recycling of the retentate.
To overcome the detrimental influence of α impulse noise in power line communication and the trap of scarce prior information in traditional noise suppression schemes , a power iteration based fast independent component analysis (PowerICA) based noise suppression scheme is designed in this paper. Firstly, the pseudo-observation signal is constructed by weighted processing so that single-channel blind separation model is transformed into the multi-channel observed model. Then the proposed blind separation algorithm is used to separate noise and source signals. Finally, the effectiveness of the proposed algorithm is verified by experiment simulation. Experiment results show that the proposed algorithm has better separation effect, more stable separation and less implementation time than that of FastICA algorithm, which also improves the real-time performance of communication signal processing.
Specific requirements are designed and implemented in electronic and telecommunication systems for received signals, especially high-frequency ones, to examine and control the signal radiation. However, as a serious drawback, no special requirements are considered for the transmitted signals from a subsystem. Different industries have always been struggling with electromagnetic interferences affecting their electronic and telecommunication systems and imposing significant costs. It is thus necessary to specifically investigate this problem as every device is continuously exposed to interferences. Signal processing allows for the decomposition of a signal to its different components to simulate each component. Radiation control has its specific complexities in systems, requiring necessary measures from the very beginning of the design. This study attempted to determine the highest radiation from a subsystem by estimating the radiation fields. The study goal was to investigate the level of radiations received and transmitted from the adjacent systems, respectively, and present methods for control and eliminate the existing radiations. The proposed approach employs an algorithm which is based on multi-component signals, defect, and the radiation shield used in the subsystem. The algorithm flowchart focuses on the separation and of signal components and electromagnetic interference reduction. In this algorithm, the detection process is carried out at the bounds of each component, after which the separation process is performed in the vicinity of the different bounds. The proposed method works based on the Fourier transform of impulse functions for signal components decomposition that was employed to develop an algorithm for separation of the components of the signals input to the subsystem.
To investigate the effect of different proximate index on minimum ignition temperature(MIT) of coal dust cloud, 30 types of coal specimens with different characteristics were chosen. A two-furnace automatic coal proximate analyzer was employed to determine the indexes for moisture content, ash content, volatile matter, fixed carbon and MIT of different types of coal specimens. As the calculated results showed that these indexes exhibited high correlation, a principal component analysis (PCA) was adopted to extract principal components for multiple factors affecting MIT of coal dust, and then, the effect of the indexes for each type of coal on MIT of coal dust was analyzed. Based on experimental data, support vector machine (SVM) regression model was constructed to predicate the MIT of coal dust, having a predicating error below 10%. This method can be applied in the predication of the MIT for coal dust, which is beneficial to the assessment of the risk induced by coal dust explosion (CDE).
This work presents a simulation of the response of packets of microbubbles in an ultrasonic pulse-echo scan line. Rayleigh-Plesset equation has been used to predict the echo from numerically obtained radial dynamics of microbubbles. Varying the number of scattering microbubbles on the pulse wave form has been discussed. To improve microbubble-specific imaging at high frequencies, the subharmonic and second harmonic signals from individual microbubbles as well as microbubbles packets were simulated as a function of size and pressure. Two different modes of harmonic generation have been distinguished. The strength and bandwidth of the subharmonic component in the scattering spectrum of microbubbles is greater than that of the second harmonic. The pressure spectra provide quantitative and detailed information on the dynamic behaviour of ultrasound contrast agent microbubbles packet.
The Gaussian mixture model (GMM) method is popular and efficient for voice conversion (VC), but it is often subject to overfitting. In this paper, the principal component regression (PCR) method is adopted for the spectral mapping between source speech and target speech, and the numbers of principal components are adjusted properly to prevent the overfitting. Then, in order to better model the nonlinear relationships between the source speech and target speech, the kernel principal component regression (KPCR) method is also proposed. Moreover, a KPCR combined with GMM method is further proposed to improve the accuracy of conversion. In addition, the discontinuity and oversmoothing problems of the traditional GMM method are also addressed. On the one hand, in order to solve the discontinuity problem, the adaptive median filter is adopted to smooth the posterior probabilities. On the other hand, the two mixture components with higher posterior probabilities for each frame are chosen for VC to reduce the oversmoothing problem. Finally, the objective and subjective experiments are carried out, and the results demonstrate that the proposed approach shows greatly better performance than the GMM method. In the objective tests, the proposed method shows lower cepstral distances and higher identification rates than the GMM method. While in the subjective tests, the proposed method obtains higher scores of preference and perceptual quality.
Because the heat release of plutonium material, the composite structure is heated and the stress and strain of the composite structure will increase, which will affect the thermodynamic properties of the structure. The thermodynamic analysis of complex structures, which are composed of concentric structures of plutonium, beryllium, tungsten, explosives, and steel, was carried out. The results showed that when the structure is spherical, the temperature is higher than that of the ellipsoid structure. Stress of the elliptical structure is greater than the spherical structure. This study showed that the more flat the shell is, the greater the stress concentration point occurs at the long axis, and the maximum stress occurs at the beryllium layer. These conclusions provide theoretical support for the plutonium composite component testing.
An equiatomic multi-component alloy Ni20Ti20Ta20Co20Cu20 (at. %) was obtained using vacuum arc melting. In order to characterize such an alloy, microstructure analysis has been performed using Scanning and Transmission Electron Microscopy, Electron Backscattered Diffraction, X-ray Diffraction and Energy Dispersive X-ray Spectroscopy techniques. Microstructure analysis revealed the presence of one rhombohedral and two cubic phases. Energy Dispersive X-ray Spectroscopy measurements revealed that both observed phases include five chemical elements in the structure. Using Rietveld refinement approach the lattice parameters were refined for the observed phases.
A new NiTi-based multi-component Ni35Ti35Ta10Co10Cu10 (at.%) alloy was obtained by vacuum arc melting. The microstructure of the alloy has been studied using scanning and transmission electron microscopy, backscatter electron diffraction and X-ray diffraction techniques. The performed measurements showed presence of two cubic and one tetragonal phases. Energy dispersive X-ray spectroscopy analysis confirmed that all the observed phases contained all five principal elements.
The results of statistical analysis applied in order to evaluate the effect of the high melting point elements to pressure die cast silumin on its tensile strength Rm, unit elongation A and HB were discussed. The base alloy was silumin with the chemical composition similar to ENAC 46000. To this silumin, high melting point elements such as Cr, Mo, V and W were added. All possible combinations of the additives were used. The content of individual high melting point additives ranged from 0.05 to 0.50%. The tests were carried out on silumin with and without above mentioned elements. The values of Rm, A and HB were determined for all the examined chemical compositions of the silumin. The conducted statistical analysis showed that each of the examined high melting point additives added to the silumin in an appropriate amount could raise the values of Rm, A and HB. To obtain the high tensile strength of Rm = 291 MPa in the tested silumin, the best content of each of the additives should be in the range of 0.05-0.10%. To obtain the highest possible elongation A of about 6.0%, the best content of the additives should be as follows: chromium in the range of 0.05-0.15%, molybdenum 0.05% or 0.15%, vanadium 0.05% and tungsten 0.15%. To obtain the silumin with hardness of 117 HB, chromium, molybdenum and vanadium content should be equal to about 0.05%, and tungsten to about 0.5%.
The article describes the detection of a defect in a cast iron casting. It analyzes the cause of the crack in the Turbine Component casting. In this article, we are focusing on a particular turbine casting that is commonly used in automobiles as one of the components for turbochargers. The turbine is a casting made of ductile cast iron with a visible crack on the naked eye. The formation of cracks in castings is a common but undesirable phenomenon in the foundry practice. It is important to identify the errors, but also to know the cause of defects in castings. The solution is a detailed error analysis. In this paper I used metallographic analysis and magnetic powder method. The crack formation is due to tension in the casting, which results in tensile, shear, or shear forces. The crack formation kinetics is difficult because it is still very low during hardening and shortly after the casting is overloaded. The crack is most often due to core resistance or shrinkage molds that begin after the surface layer is tightened when the strength of the material is negligible to the end of the crystallisation.
Problems associated with designing silencers are presented. Results of direct tests of silencers for cooperation with systems of axial fans, as well as results of numerical tests of a two stage acoustic silencer, are given. The numerical tests enabled determining the distribution of acoustic field inside the silencer and in the surrounding area. In those tests A sound insertion losses for different variants of installation inside the silencer, as well as for two different types of absorbing material used to fill the silencer walls, were determined. Impact of design features of silencers on effectiveness of noise reduction is described. Also, a technical sketch of a universal silencer with significant noise reduction (DipS = 39:1 dB) which can be successfully used in many ventilation systems is presented
The Bulletin of the Polish Academy of Sciences: Technical Sciences (Bull.Pol. Ac.: Tech.) is published bimonthly by the Division IV Engineering Sciences of the Polish Academy of Sciences, since the beginning of the existence of the PAS in 1952. The journal is peer‐reviewed and is published both in printed and electronic form. It is established for the publication of original high quality papers from multidisciplinary Engineering sciences with the following topics preferred: Artificial and Computational Intelligence, Biomedical Engineering and Biotechnology, Civil Engineering, Control, Informatics and Robotics, Electronics, Telecommunication and Optoelectronics, Mechanical and Aeronautical Engineering, Thermodynamics, Material Science and Nanotechnology, Power Systems and Power Electronics. Journal Metrics: JCR Impact Factor 2018: 1.361, 5 Year Impact Factor: 1.323, SCImago Journal Rank (SJR) 2017: 0.319, Source Normalized Impact per Paper (SNIP) 2017: 1.005, CiteScore 2017: 1.27, The Polish Ministry of Science and Higher Education 2017: 25 points. Abbreviations/Acronym: Journal citation: Bull. Pol. Ac.: Tech., ISO: Bull. Pol. Acad. Sci.-Tech. Sci., JCR Abbrev: B POL ACAD SCI-TECH Acronym in the Editorial System: BPASTS.
Independent Component Analysis (ICA) can be used for single channel audio separation, if a mixed signal is transformed into time-frequency domain and the resulting matrix of magnitude coefficients is processed by ICA. Previous works used only frequency (spectral) vectors and Kullback-Leibler distance measure for this task. New decomposition bases are proposed: time vectors and time-frequency components. The applicability of several different measures of distance of components are analysed. An algorithm for clustering of components is presented. It was tested on mixes of two and three sounds. The perceptual quality of separation obtained with the measures of distance proposed was evaluated by listening tests, indicating "beta" and "correlation" measures as the most appropriate. The "Euclidean" distance is shown to be appropriate for sounds with varying amplitudes. The perceptual effect of the amount of variance used was also evaluated.
Forecasting yield curves with regime switches is important in academia and financial industry. As the number of interest rate maturities increases, it poses difficulties in estimating parameters due to the curse of dimensionality. To deal with such a feature, factor models have been developed. However, the existing approaches are restrictive and largely based on the stationarity assumption of the factors. This inaccuracy creates non-ignorable financial risks, especially when the market is volatile. In this paper, a new methodology is proposed to adaptively forecast yield curves. Specifically, functional principal component analysis (FPCA) is used to extract factors capable of representing the features of yield curves. The local AR(1) model with time-dependent parameters is used to forecast each factor. Simulation and empirical studies reveal the superiority of this method over its natural competitor, the dynamic Nelson-Siegel (DNS) model. For the yield curves of the U.S. and China, the adaptive method provides more accurate 6- and 12-month ahead forecasts.
In order to understand commands given through voice by an operator, user or any human, a robot needs to focus on a single source, to acquire a clear speech sample and to recognize it. A two-step approach to the deconvolution of speech and sound mixtures in the time-domain is proposed. At first, we apply a deconvolution procedure, constrained in the sense, that the de-mixing matrix has fixed diagonal values without non-zero delay parameters. We derive an adaptive rule for the modification of the de-convolution matrix. Hence, the individual outputs extracted in the first step are eventually still self-convolved. This corruption we try to eliminate by a de-correlation process independently for every individual output channel.