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1
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Pattern Recognition Applied to Analysis of Gas Sensors' Array Data

100%
Marczyński P., Szpakowski A., Tyszkiewicz C., Pustelny T.
Acta Physica Polonica A
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2012
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vol. 122
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issue 5
847-849
EN
For an array of eight chemoresistive gas sensors a computer pattern recognition system was built. Multivariate data analysis was performed for measurements of three gases' dilutions: hydrogen (H_2), methane (CH_4), and carbon monoxide (CO). The pattern recognition system included a feature subset selection algorithm involving PCA and objective function. Dimensionality reduction was applied to two kinds of patterns: three aforementioned gases and six different concentrations of hydrogen. For patterns of the three gases, classification tests were performed using k-NN algorithm and N-fold based validation method.
2
Content available remote

Multivariate Analysis in Gas Sensing Applications

100%
Szpakowski A., Tyszkiewicz C., Pustelny T.
Acta Physica Polonica A
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2008
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vol. 114
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issue 6A
A-239-A-242
EN
In gas sensing applications we often use sensors which have poor selectivity. Such sensors are mature, cheap devices with simple application circuits. Unfortunately, their cross sensitivity greatly restricts their usefulness when used in systems which employs univariate analysis. One method which allows to overcome this difficulty is utilization of multivariate analysis methods applied to the response from the group of such non-selective sensors. This work presents the multivariate, pattern recognition system utilized to process the response of an array of non-selective semiconductor sensors to obtain the qualitative information.
3
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Gas Sensing Supported by Pattern Recognition

80%
Tyszkiewicz C., Szpakowski A., Pustelny T.
Acta Physica Polonica A
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2009
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vol. 116
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issue 3
419-421
EN
The system composed of the array of eight semiconductor, chemoresistive gas sensors was used for the classification of hydrogen, methane and carbon oxide gaseous samples. The classification task was performed by pattern recognition methods applied to the multivariate response of the array. The pattern recognition scheme used for classification uses a feature subset selection algorithm coupled with an objective function designed for clustering and a multilayer perceptron classifier.
4
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Generalized Linear Models for European Union Countries Energy Data

80%
İyıt N., Yonar H., Genç A.
Acta Physica Polonica A
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2016
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vol. 130
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issue 1
397-400
EN
The class of generalized linear models is an extension of traditional linear models that allows the mean of the response variable to be linearly dependent on the explanatory variables through a link function. Generalized linear models allow the probability distribution of the response variable to be a member of an exponential family of distributions. The exponential family of distributions include many common discrete and continuous distributions such as normal, binomial, multinomial, negative binomial, Poisson, gamma, inverse Gaussian, etc. Also link functions can be built as identity, logit, probit, power, log, and complementary log-log link functions. In this study, supply, transformation and consumption, imports and exports of solid fuels, oil, gas, electricity, and renewable energy annual data of European Union countries between 2005 and 2013 years are investigated by using generalized linear models. In this case, the response variable is taken as annual complete energy balances of European Union countries as a continuous variable having positive values, and the distribution of the response variable comes from the gamma distribution with log-link function.
5
Content available remote

Chaos in the Brain

80%
Kříž J.
Acta Physica Polonica A
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2011
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vol. 120
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issue 6A
A-127-A-131
EN
We describe several links between EEG data processing and quantum mechanics. Then we show examples of exploitation of methods commonly used in quantum chaos for EEG data analysis.
6
Content available remote

Determination of the Protein-Binding Properties of Camptothecins by Means of Optical Spectroscopy Methods

80%
Ziomkowska B., Cyrankiewicz M., Wybranowski T., Kruszewski S.
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vol. 125
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issue 4A
A-61-A-65
EN
Optical spectroscopy methods are widely used in studies of drugs. The affinity of camptothecins - anticancer agents - to human serum albumin (HSA) was determined in this work. Camptothecins (CPTs) exist in two forms: active lactone and open ring inactive carboxylate. In blood, the hydrolysis process of lactone form occurs which leads to deactivation of CPTs. Research is being done on biophysical properties of synthesized CPT compounds, in particular on binding to albumin. The affinity to plasma proteins is an important determinant of stability of CPTs in blood. The following analogues of CPT were tested in this paper: irinotecan, SN-38, topotecan, and 9-amino camptothecin. Using the method of fluorescence anisotropy measurement, the association constants of the studied compounds to HSA were determined. The authors attempted to determine the deactivation rate of topotecan in HSA solution using Principal Component Analysis and Factor Analysis of absorption spectra recorded during hydrolysis process of lactone form.
7
Content available remote

A Random Matrix Approach to Dynamic Factors in Macroeconomic Data

80%
Snarska M.
Acta Physica Polonica A
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2012
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vol. 121
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issue 2B
B-110-B-120
EN
We show how random matrix theory can be applied to develop new algorithms to extract dynamic factors from macroeconomic time series. In particular, we consider a limit where the number of random variables N and the number of consecutive time measurements T are large but the ratio N/T is fixed. In this regime the underlying random matrices are asymptotically equivalent to Free Random Variables (FRV).Application of these methods for macroeconomic indicators for Poland economy is also presented.
8
Content available remote

Bayesian Value-at-Risk and Expected Shortfall for a Large Portfolio (Multi- and Univariate Approaches)

80%
Pajor A., Osiewalski J.
Acta Physica Polonica A
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2012
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vol. 121
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issue 2B
B-101-B-109
EN
Bayesian assessments of value-at-risk and expected shortfall for a given portfolio of dimension n can be based either on the n-variate predictive distribution of future returns of individual assets, or on the univariate model for portfolio volatility. In both cases, the Bayesian VaR and ES fully take into account parameter uncertainty and non-linear relationship between ordinary and logarithmic returns. We use the n-variate type I MSF-SBEKK(1,1) volatility model proposed specially to cope with large n. We compare empirical results obtained using this (more demanding) multivariate approach and the much simpler univariate approach based on modelling volatility of the whole portfolio (of a given structure).
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