Wyniki wyszukiwania - BazTech

1

A generalized equivalent temperature model in a time-varying environment

Sun L., Gu X.-H., Song P., Di Y.

Eksploatacja i Niezawodność

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2017

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Vol. 19, no. 3

432--440

EN

Accelerated degradation test at high temperature level is a common method to accelerate the degradation of products by elevating temperature, and the obtained degradation data are then used to obtain the estimate of the performance at normal temperature after extrapolating the degradation through accelerating model. However, the normal temperature is ever-changing rather than constant. Therefore, a generalized equivalent temperature model based on power law degradation path is proposed to establish a connection between accelerated degradation data and degradation data at normal temperature. The model takes the equal degradation measure as a principle and the conclusion is demonstrated that the increments of the degradation under the same magnitude, same time and different orders of temperature stresses are same. The result shows that the empirical equivalent temperature model is a special case of the proposed model. The accuracy of the proposed model is finally demonstrated by a case study of nitrile rubber O-rings.

PL

Przyspieszone badania degradacji (badania starzeniowe) prowadzone w warunkach wysokiej temperatury stanowią powszechnie stosowaną metodę przyspieszania starzenia produktów poprzez podwyższanie temperatury. Otrzymane w takich badaniach dane degradacyjne wykorzystuje się do szacowania wydajności produktu w temperaturze normalnej na zasadzie ekstrapolacji. Głównym ograniczeniem tej metody jest fakt, że normalna temperatura nie jest stała lecz zmienia się w czasie. Dlatego też, aby skorelować dane z przyspieszonej degradacji z danymi dotyczącymi starzenia w normalnej temperaturze, zaproponowaliśmy uogólniony model temperatury równoważnej oparty na krzywej degradacji opisanej prawem potęgowym. W modelu przyjęto zasadę równego stopnia degradacji i wykazano, że przyrosty degradacji przy tej samej wartości i czasie działania naprężeń termicznych różnego rzędu są takie same. Wyniki pokazują, że empiryczny model temperatury równoważnej jest szczególnym przypadkiem proponowanego przez nas modelu. Trafność opisanego w pracy modelu wykazano na podstawie studium przypadku dotyczącego uszczelek nitrylowych, tzw. oringów.

2

Spectral Mapping Using Kernel Principal Components Regression for Voice Conversion

Song P., Zhao L., Bao Y.

Archives of Acoustics

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2013

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Vol. 38, No. 1

39--45

EN

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.

3

Voice Conversion Based on Hybrid SVR and GMM

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

Archives of Acoustics

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2012

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Vol. 37, No. 2

143-149

EN

A novel VC (voice conversion) method based on hybrid SVR (support vector regression) and GMM (Gaussian mixture model) is presented in the paper, the mapping abilities of SVR and GMM are exploited to map the spectral features of the source speaker to those of target ones. A new strategy of F0 transfor- mation is also presented, the F0s are modeled with spectral features in a joint GMM and predicted from the converted spectral features using the SVR method. Subjective and objective tests are carried out to evaluate the VC performance; experimental results show that the converted speech using the proposed method can obtain a better quality than that using the state-of-the-art GMM method. Meanwhile, a VC method based on non-parallel data is also proposed, the speaker-specific information is investigated us- ing the SVR method and preliminary subjective experiments demonstrate that the proposed method is feasible when a parallel corpus is not available.