Wyniki wyszukiwania - Biblioteka Nauki

1

A Type Design Method of Large Hydraulic Turbine Based on The Gaussian Mixture Model

100%

Lin L. , Zhong S. , Xie X.

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tom R. 88, nr 9b

153-156

EN

Because of the complex fluid motion in hydraulic turbine and the imperfect design theory, the selection design of Large-scale hydraulic turbine is achieved based on the calculation and analysis on the synthetic characteristic curves, which is subjectivity and low efficiency. To solve this problem, the Gaussian mixture model is used to extract the geometric features from the synthetic characteristic curves so that the retrieval process of the model wheel can be achieved by these geometric features. The search model of the running area from the synthetic characteristic curves is build based on the contour curve similarity transformation method. In the paper, the Monte Carlo method is adopted to obtain the mean values of the synthetic characteristics in the running area so that the evaluation targets can be established by combining the mean values with the hydraulic turbine design experiences. Finally the validity of running area can be evaluated by the evaluation targets. The test results show that the accuracy and efficiency of the selection design of Large-scale hydraulic turbine can be improved by the proposed method.

PL

W artykule przedstawiono problemy projektowania dużych turbin hydraulicznych. Zaproponowano model matematyczny bazujący na mieszanym modelu Gaussa w celu wydobycia parametrów geometrycznych. Zaadaptowano metodę Monte Carlo do określania średnich wartości syntetycznych charakterystyk w obszarze pracy.

2

Fitting a Gaussian mixture model through the Gini index

100%

López-Lobato A. L. , Avendaño-Garrido M. L.

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tom Vol. 31, no. 3

487--500

EN

A linear combination of Gaussian components is known as a Gaussian mixture model. It is widely used in data mining and pattern recognition. In this paper, we propose a method to estimate the parameters of the density function given by a Gaussian mixture model. Our proposal is based on the Gini index, a methodology to measure the inequality degree between two probability distributions, and consists in minimizing the Gini index between an empirical distribution for the data and a Gaussian mixture model. We will show several simulated examples and real data examples, observing some of the properties of the proposed method.

3

Computer-supported process of proteomic data profiling

100%

Plechawska-Wójcik M.

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2014

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tom nr 6

8730--8739

EN

The paper presents the complete and complex process of proteomic data profiling obtained in the process of mass spectrometry. The main described procedure is based on analyzing Gaussian Mixture Model representation of the data. However, the process of mass spectrometry data analysis is more complex and consists of a number of procedures, like data preparation, data pre-processing including baseline correction, detection of outliers and noise removal. The mean spectrum calculated based on received data is modeled with GMM and decomposed using the Expectation-Maximization algorithm. In this process localization of the mean spectrum peaks is found. Those results are applied into each single spectrum in the dataset in the form of Gaussian mask. The result in a set of data ready for further statistical analysis. This procedure enable to perform proteomic data profiling using specific statistical methods.

PL

Artykuł przedstawia kompletny, złożony proces profilowania danych proteomicznych uzyskanych w badaniach spektrometrycznych. Podstawowa opisana procedura analizy jest oparta o analizę modelu reprezentującego dane mieszanin gaussowskich. Jednak proces analizy danych spektrometrycznych jest bardziej złożony i składa się z wielu procedur, takich jak przygotowanie danych, wstępne przetwarzanie danych, detekcji danych odstających oraz usuwania szumu. Widmo średnie obliczone na podstawie otrzymanych danych jest modelowane w oparciu o model mieszanin gaussowskich dekomponowany za pomocą algorytmu maksymalizacji oczekiwanej wartości. W tym procesie wyszukiwane są pozycje pików z widma średniego. Otrzymane wyniki są nakładane na każde pojedyncze widmo w postaci maski gaussowskiej. Rezultatem jest zestaw danych gotowych do dalszej analizy statystycznej. Procedura ta pozwala wykonać profilowanie danych proteomicznych przy użyciu określonych metod statystycznych.

4

Intelligent velocity picking and uncertainty analysis based on the Gaussian mixture model

84%

Wang X. , Gao Y. , Chen C. , Yuan H. , Yuan S.

Acta Geophysica

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2022

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tom Vol. 70, no 6

2659--2673

EN

The stacking velocity is often obtained manually. However, manually picking is inefficient and is easily affected by subjective factors such as the priori information and the experience of different processors. To enhance its objectivity, efficiency and consistency, we investigated an unsupervised clustering intelligent velocity picking method based on the Gaussian mixture model (GMM). This method can automatically pick the stacking velocity fast, and provide uncertainty analysis as a quality control. Combined with the geometry feature of energy clusters in velocity spectra, taking advantages of the geometric diversity of energy clusters, GMM can ft the energy clusters with different distributions more appropriately. Then, mean values of the final several submodels are located as the optimal velocity, and the multiples are avoided under the expert knowledge and geological rules. In addition, according to the covariance of submodels, we can derive the uncertainty analysis of the final time-velocity pairs, so as to indicate the reliability of picking velocity at different depths. Moreover, the automated interpreted velocity field is used for both normal moveout (NMO) correction and stacking. The comparison with the manual references is adopted to evaluate the quality of the unsupervised clustering intelligent velocity picking method. Both synthetic data and 3D field data have shown that the proposed unsupervised intelligent velocity picking method can not only achieve similar accuracy with manual results, but also get rid of multiples. Furthermore, compared with manual picking, it can significantly improve the efficiency and accuracy in identifying pore and cave structures, as well as indicating the uncertainty of time-velocity pairs by variance.

5

Speaker identification based on Gaussian mixture model : experiments with Polish language utterances

84%

Dąbrowska A. , Drgas S. , Cetnarowicz D. , Chmielewska I.

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2008

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tom Vol. 49, nr 4

29-33

EN

In this paper results of experiments with the prototype speaker recognition system based on Gaussian mixture model (GMM) and mel-cepstral coefficients (MFCCs) are presented for Polish Corpora database [4]. The minimum amount of data to train a reliable model and the minimum length of a signal to recognize speakers have been determined. Furthermore, the speaker discriminative properties of Polish phonemes have been investigated. The phonemes with the best speaker discriminative properties have been determined.

PL

Przedstawiono eksperymenty identyfikacji mówcy za pomocą prototypowego systemu rozpoznawania mowy na podstawie sumy rozkładów normalnych (GMM) i współczynników mel-cepstralnych, (MFCC), uzyskanych z wykorzystaniem polskojęzycznej bazy Corpora [4]. W eksperymentach zbadano minimalną ilość danych potrzebnych do wytrenowania wiarygodnego modelu oraz długość sygnału wymaganą do poprawnej klasyfikacji. Ponadto przebadano dyskryminacyjne właściwości polskich fonemów do identyfikacji mówcy. Wyodrębniono fonemy, które w największym stopniu przyczyniają się do poprawnego rozpoznawania.

6

Ocena funkcjonalności systemu rozpoznawania mówcy dla zdegradowanej jakości sygnału głosowego

84%

Kamiński K. , Dobrowolski A. P. , Majda E.

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tom R. 90, nr 8

164-167

PL

W artykule przedstawiono wyniki badań automatycznego systemu rozpoznawania mówcy (ASR – ang. Automatic Speaker Recognition), przeprowadzonych na podstawie komercyjnej bazy głosów TIMIT. Badania prowadzone były pod kątem zastosowania ASR jako systemu automatycznego rozpoznawania rozmówcy telefonicznego. Przedstawiono również wpływ liczebności bazy głosów oraz stopień oddziaływania kompresji stratnej MP3 na skuteczność rozpoznawania mówcy.

EN

The article presents the results of tests of an automatic speaker recognition system (ASR) conducted on the basis of the TIMIT commercial voice database. The research was conducted with the aim of using ASR as a system for automatic recognition of telephone callers. The impact of the number of voices in the database and the effect of lossy MP3 compression on the effectiveness of speaker recognition has also been shown.

7

Implementacja systemu identyfikacji słów izolowanych przy zastosowaniu Modeli Mikstur Gaussowskich

84%

Kamiński K.

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2012

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tom T. 20

111--119

PL

W referacie przedstawiony został sposób działania systemu identyfikacji słów izolowanych, który w etapie klasyfikacji wykorzystuje Modele Mikstur Gaussowskich. Referat zawiera również wyniki testów skuteczności omawianego systemu w rozpoznawaniu cyfr. System został zaimplementowany w środowisku Matlab. Kolejnym etapem pracy autora będzie implementacja powyższego systemu na zestawie uruchomieniowym DSK 6713.

EN

The paper presents a method of identifying isolated words, which uses at the classification stage Gaussian Mixture Models. The paper also concerns test results of effectiveness of the discussed system in numbers recognition. The system was implemented in Matlab environment. The next stage of developer work is to implement the aforementioned system on a runtime set DSK 6713.

8

Preprocessing large datasets using Gaussian mixture modelling to improve prediction accuracy of truck productivity at mine sites

84%

Fan C. , Zhang N. , Jiang B. , Liu W. V.

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tom Vol. 67, no. 4

661--680

EN

The historical datasets at operating mine sites are usually large. Directly applying large datasets to build prediction models may lead to inaccurate results. To overcome the real-world challenges, this study aimed to handle these large datasets using Gaussian mixture modelling (GMM) for developing a novel and accurate prediction model of truck productivity. A large dataset of truck haulage collected at operating mine sites was clustered by GMM into three latent classes before the prediction model was built. The labels of these latent classes generated a latent variable. Two multiple linear regression (MLR) models were then constructed, including the ordinary-MLR (O-MLR) and the hybrid GMM-MLR models. The GMM-MLR model incorporated the observed input variables and a latent variable in the form of interaction terms. The O-MLR model was the baseline model and did not involve the latent variable. The GMM-MLR model performed considerably better than the O-MLR model in predicting truck productivity. The interaction terms quantitatively measured the differences in how the observed input variables affected truck productivity in three classes (high, medium, and low truck productivity). The haul distance was the most crucial input variable in the GMM-MLR model. This study provides new insights into handling massive amounts of data in truck haulage datasets and a more accurate prediction model for truck productivity.

9

Voice Conversion Based on Hybrid SVR and GMM

83%

Song P. , Jin Y. , Zhao L. , Zou C.

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10

Hybrid texture and gradient modeling for dynamic background subtraction identification systemin tobacco plant using 5G data service

67%

Gowda Thirthe M. T. , Chandrika J.

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tom Vol. 30, no. 1

41--54

EN

Background: Detecting the plants as objects of interest in any vision-based input sequence is highly complex due to nonlinear background objects such as rocks, shadows,etc. Therefore, it is a difficult task and an emerging one with the development of precision agriculture systems. The nonlinear variations of pixel intensity with illuminationand other causes such as blurs and poor video quality also make the object detection taskchallenging. To detect the object of interest, background subtraction (BS) is widely usedin many plant disease identification systems, and its detection rate largely depends on thenumber of features used to suppress and isolate the foreground region and its sensitivitytoward image nonlinearity. Methodology: A hybrid invariant texture and color gradient-based approach is proposed to model the background for dynamic BS, and its performance is validated byvarious real-time video captures covering different kinds of complex backgrounds and various illumination changes. Based on the experimental results, a simple multimodal featureattribute, which includes several invariant texture measures and color attributes, yieldsfinite precision accuracy compared with other state-of-art detection methods. Experimental evaluation of two datasets shows that the new model achieves superior performanceover existing results in spectral-domain disease identification model. 5G assistance: After successful identification of tobacco plant and its analysis, the finalresults are stored in a cloud-assisted server as a database that allows all kinds of 5G servicessuch as IoT and edge computing terminals for data access with valid authentication fordetailed analysis and references.

11

The Role of Climatic Variables for Estimating Probability of Abundance of Tree Species

67%

Antúnez P. , Wehenkel C. , López-Sánchez C. A. , Hernández-Díaz J. C.

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tom Vol. 65, nr 3

324--338

EN

We analyzed the influence of climatic variables on the abundance of native tree species in 1,490 sampling plots systematically distributed in the Sierra Madre Occidental (state of Durango, Northwestern Mexico, 26°50′ and 22°17′N and 107°09′ and 102°30′W). We used the Weibull distribution and the finite Gaussian mixture model to study the climatic limits of 15 tree species in relation to seven variables thought to affect species abundance. We found that although they may occur in the same geographical region, some species display a wider range of ecological tolerance than others. Of the 15 species under study, only two (Quercus magnoliifolia and Q. arizonica) can be considered generalists in relation to some climatic variables, while the other 13 species behaved as specialists, implying a narrower range of distribution. The analytical techniques used enabled us to demarcate the zones in which the probability of abundance of each species is highest in relation to the climate variables considered. The findings could be used to help define climate for the 15 studied tree species of economic and ecological interest.