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Wyszukujesz frazę "84.35.+I" wg kryterium: Temat


Wyświetlanie 1-11 z 11
Tytuł:
Automatic Classification of LFM Signals for Radar Emitter Recognition Using Wavelet Decomposition and LVQ Classifier
Autorzy:
Świercz, E.
Powiązania:
https://bibliotekanauki.pl/articles/1505216.pdf
Data publikacji:
2011-04
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
84.40.Ua
84.35.+i
Opis:
The paper presents a novel approach, based on the wavelet decomposition and the learning vector quantisation algorithm, to automatic classification of signals with linear frequency modulation, generated by radar emitters. The goal of radar transmitter classification is to determine the particular transmitter, from which a signal originated, using only the just received waveform. To categorise a current linear frequency modulation signal to the particular transmitter, the discrete wavelet decomposition of the received signal is accomplished in order to get a representative set of features with good classification properties. The learning vector quantisation algorithm with a previously defined set of features as an input of the learning vector quantisation neural net is proposed as the intelligent classification algorithm, which combines competitive learning with supervision. After the learning process, the learning vector quantisation algorithm is ready to perform the classification process for different data than data used in the learning stage. Simulation results show the high classification accuracy for experimentally chosen wavelets and suggested architecture of the learning vector quantisation classifier.
Źródło:
Acta Physica Polonica A; 2011, 119, 4; 488-494
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Signal Processing and Analysis of Pathological Speech Using Artificial Intelligence and Learning Systems Methods
Autorzy:
Wszołek, W.
Izworski, A.
Izworski, G.
Powiązania:
https://bibliotekanauki.pl/articles/1400072.pdf
Data publikacji:
2013-06
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
43.70.+I
84.35.+i
Opis:
In this paper, selected results are presented of research which is carried on for over a decade and covers valuation of chosen signal processing methods suitable to analyze and valuate pathological speech. This valuation is necessary during solving many medical diagnostics problems and when planning therapy and rehabilitation of certain types of diseases. All presented examples are used in clinical practice in the area of dentistry, dental surgery, otolaryngology and most of all, in phoniatrics and speech correction.
Źródło:
Acta Physica Polonica A; 2013, 123, 6; 995-1000
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prediction Primary Radiation Shielding Wall Thickness with Artificial Neural Networks
Autorzy:
Akkaş, A.
Başyiğit, C.
Necip Kurtarici, M.
Powiązania:
https://bibliotekanauki.pl/articles/1399661.pdf
Data publikacji:
2013-02
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
28.41.Qb
84.35.+i
Opis:
In this study, wall thickness for using in primary radiation shielding was determined in different energy ranges using tenth value layer by artificial neural networks. Radiation energy values, tenth value layers and negative logarithm of transmission factor (n) were selected as input parameters and wall shielding thickness values selected as output parameters. Consequently, developed artificial neural networks model outputs were compared with experimental results and it was seen that the results were harmonious.
Źródło:
Acta Physica Polonica A; 2013, 123, 2; 171-172
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modelling of Power Loss in Electrical Steels
Autorzy:
Kucuk, I.
Erturk, K.
Haciismailoglu, M.
Derebasi, N.
Powiązania:
https://bibliotekanauki.pl/articles/1813590.pdf
Data publikacji:
2008-01
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
75.50.Bb
84.35.+i
Opis:
This paper presents a new artificial neural network approach based on loss separation model to compute power loss on different types of electrical steels. The network was trained by a Levenberg-Marquardt algorithm. The results obtained by using the proposed model were compared with a commonly used conventional model. The comparison has shown that the neural network model is in good agreement with experimental data with respect to the conventional model.
Źródło:
Acta Physica Polonica A; 2008, 113, 1; 147-150
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prediction of Two-Element Cylindrical Electrostatic Lens Parameters Using Dynamic Artificial Neural Network
Autorzy:
Isik, A.
Powiązania:
https://bibliotekanauki.pl/articles/1189865.pdf
Data publikacji:
2015-06
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
84.35.+i
07.05.Mh
41.85.Ne
Opis:
Two-element cylindrical electrostatic lens systems allowed to control low energy electron or charged particle beam have great importance. In this context, dynamic artificial neural network using nonlinear autoregressive exogenous model has been utilized to predict optimum linear magnification and overall voltage values for these lens designs. The focusing characteristics of electron beam in two-element cylindrical lens systems are investigated with two different nonlinear autoregressive exogenous based artificial neural network models. First artificial neural network model is employed for predicting of voltage ratios of lenses and magnification values. This model interpolates among the object (P) and image positions (Q) and finally finds optimum voltage ratios and magnification values through training dataset. Due to the deviations of electron trajectories in a real lens system, the spherical aberration effects are also taken into account to determine the optimal lens parameters. Therefore, the second artificial neural network model is constructed for predicting spherical aberration coefficients in image point. For each of artificial neural network models, training, test and validation data set are obtained from SIMION 8.1 ion and electron optics software. Artificial neural network model outputs are compared with the SIMION data and very good agreements are found. While artificial neural network is frequently applied in different fields, this is the first study that uses dynamic artificial neural network to predict the parameters of electrostatic lens. It is believed that this pioneering work will be a guide for the future investigations in lens design systems.
Źródło:
Acta Physica Polonica A; 2015, 127, 6; 1717-1721
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection and Classification of Selected Noise Sources in Long-Term Acoustic Climate Monitoring
Autorzy:
Kłaczyński, M.
Wszołek, T.
Powiązania:
https://bibliotekanauki.pl/articles/1490331.pdf
Data publikacji:
2012-01
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
43.60.Lq
84.35.+i
43.55.Ka
Opis:
Continuous acoustical climate monitoring of the environment raises several problems related to large quantities of the recorded data, which often represents information unrelated to the studied noise source. Manual verification of such data is time-consuming and costly. Therefore, developing effective methods for automatic identification of transport noise sources becomes an important task for the proper determination of noise levels. This paper presents a concept of such method of automatic detection and classification of the noise sources from the air and railway transportation in the acoustic environmental monitoring.
Źródło:
Acta Physica Polonica A; 2012, 121, 1A; A-179-A-182
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Gas Sensing Supported by Pattern Recognition
Autorzy:
Tyszkiewicz, C.
Szpakowski, A.
Pustelny, T.
Powiązania:
https://bibliotekanauki.pl/articles/1807758.pdf
Data publikacji:
2009-09
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
02.50.Sk
07.07.Df
84.35.+i
Opis:
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.
Źródło:
Acta Physica Polonica A; 2009, 116, 3; 419-421
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Neural Network Model of an Ising Spin Glass
Autorzy:
Wilson, K. F.
Goossens, D. J.
Powiązania:
https://bibliotekanauki.pl/articles/2013998.pdf
Data publikacji:
2000-05
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
75.10.-b
75.10.Nr
84.35.+i
Opis:
The behaviour of an Ising spin glass (S=1/2) with infinite range interactions is modelled using a numerical simulation based on a neural network. Thermodynamic variables are defined on the network, and are found to obey the Thouless-Anderson-Palmer theory when the applied magnetic field is zero. When a magnetic field is applied along the spin direction, complex field-dependent behaviour appears, including a state in which the Edwards-Anderson order parameter is independent of temperature below the critical temperature.
Źródło:
Acta Physica Polonica A; 2000, 97, 5; 983-986
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optical Biopsy Method for Breast Cancer Diagnosis Based on Artificial Neural Network Classification οf Fluorescence Landscape Data
Autorzy:
Dramićanin, T.
Zeković, I.
Dimitrijević, B.
Ribar, S.
Dramićanin, M.
Powiązania:
https://bibliotekanauki.pl/articles/1795706.pdf
Data publikacji:
2009-10
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
87.64.kv
84.35.+i
87.19.xj
33.50.-j
Opis:
Supervised self-organizing map, a type of artificial neural network, is applied for classification of human breast tissue samples utilizing data obtained from fluorescence landscape measurements. Female breast tissue samples were taken soon after the surgical resection, identified and stored at -80°C until fluorescence measurements. From fluorescence landscapes obtained in UV-VIS region spectral features showing statistically significant differences between malignant and normal samples are identified and further quantified to serve as a training input to neural network. Additional set of samples was used as a test group input to trained network in order to evaluate performance of proposed optical biopsy method. Classification sensitivity of 83.9% and specificity of 88.9% are found.
Źródło:
Acta Physica Polonica A; 2009, 116, 4; 690-692
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Computational Modeling of Magnetic Properties and Glass Forming Ability of Bulk Amorphous Materials
Autorzy:
Kabaer, M.
Ovalioglu, H.
Kucuk, I.
Powiązania:
https://bibliotekanauki.pl/articles/1534071.pdf
Data publikacji:
2010-11
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
64.70.pe
84.35.+i
75.50.Vv
75.50.Kj
Opis:
A model based on artificial neural network was designed for the simulation and estimation of glass forming ability parameters and saturation magnetization and coercivity of bulk glassy alloys. Its performance is evaluated by the influences of different kinds of alloys and elements on the glass forming ability and magnetic properties. The values of glass forming ability parameters and saturation magnetization and coercivity values estimated by artificial neural network agree well with the experimental values, indicating that the model is reliable and adequate.
Źródło:
Acta Physica Polonica A; 2010, 118, 5; 827-828
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural Approximation of Empirical Functions
Autorzy:
Roj, J.
Powiązania:
https://bibliotekanauki.pl/articles/1399400.pdf
Data publikacji:
2013-09
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
84.35.+i
07.05.Mh
06.20.-f
06.20.F-
06.20.fb
02.60.Ed
Opis:
The paper presents the results of simulation studies of selected neural network structures used for non-linear function approximation based on a limited accuracy data. There was performed the analysis of the interdependence of the network structure and the size of the set of learning patterns. The approximation inaccuracy was expressed by the uncertainty interval width. The approximation properties of the neural method were compared with those of the piece-wise linear and polynomial: "cubic" and "spline" methods.
Źródło:
Acta Physica Polonica A; 2013, 124, 3; 554-557
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-11 z 11

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