In order to eliminate the subjectivity of wheeze diagnosis and improve the accuracy of objective detecting methods,this paper introduces a wheeze detecting method based on spectrogram entropy analysis.This algorithm m...In order to eliminate the subjectivity of wheeze diagnosis and improve the accuracy of objective detecting methods,this paper introduces a wheeze detecting method based on spectrogram entropy analysis.This algorithm mainly comprises three steps which are preprocessing,features extracting and wheeze detecting based on support vector machine(SVM).Herein,the preprocessing consists of the short-time Fourier transform(STFT) decomposition and detrending.The features are extracted from the entropy of spectrograms.The step of detrending makes the difference of the features between wheeze and normal lung sounds more obvious.Moreover,compared with the method whose decision is based on the empirical threshold,there is no uncertain detecting result any more.Results of two testing experiments show that the detecting accuracy(AC) are 97.1%and 95.7%,respectively,which proves that the proposed method could be an efficient way to detect wheeze.展开更多
We present a hybrid singular spectrum analysis （SSA） and fuzzy entropy method to filter noisy nonlinear time series. With this approach, SSA decomposes the noisy time series into its constituent components including...We present a hybrid singular spectrum analysis （SSA） and fuzzy entropy method to filter noisy nonlinear time series. With this approach, SSA decomposes the noisy time series into its constituent components including both the deterministic behavior and noise, while fuzzy entropy automatically differentiates the optimal dominant components from the noise based on the complexity of each component. We demonstrate the effectiveness of the hybrid approach in reconstructing the Lorenz and Mackey--Class attractors, as well as improving the multi-step prediction quality of these two series in noisy environments.展开更多
Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducte...Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducted a comparative study of KECA with other five dimensionality reduction methods,principal component analysis( PCA),kernel PCA( KPCA),locally linear embedding( LLE),laplacian eigenmaps( LAE) and diffusion maps( DM). Three quality assessment criteria, local continuity meta-criterion( LCMC),trustworthiness and continuity measure(T&C),and mean relative rank error( MRRE) are applied as direct performance indexes to assess those dimensionality reduction methods. Moreover,the clustering accuracy is used as an indirect performance index to evaluate the quality of the representative data gotten by those methods. The comparisons are performed on six datasets and the results are analyzed by Friedman test with the corresponding post-hoc tests. The results indicate that KECA shows an excellent performance in both quality assessment criteria and clustering accuracy assessing.展开更多
A grey model with periodic term for sea-level analysis is presented.The present model keeps some advantages of tea GM (1, 1 )model, which well reflects the trend of sea-level changes and gives out the change rate as ...A grey model with periodic term for sea-level analysis is presented.The present model keeps some advantages of tea GM (1, 1 )model, which well reflects the trend of sea-level changes and gives out the change rate as well as the acceleration of sea level conveniently.level conveniently.In addition, the present model can reproduce the periodic phenomena of sealevel, hence, it overcomes the shortcomings of the GM(1,1) model that is unsuitable for forecasting monthly mean sealevel with apparent periodicity, and its prediction accuracy is improved.The present model is used to analyse Guangxi coast sea level,the results show that the rise rates of relative sea level at Beihai, Weizhou and Bailongwei are 1 .67,2 .51 and 0.89 mm/a respectively, the relative sea level at Shitoubu has a falling trend with a rate of 0. 5- 1 .0 mm/a, the rise rate of eustatic sea level along the Guangxi coast is 2 .0 mm/a. In comparison with the model with a lineartrend term plus a periodic term, the simulation accuracies of both models are about the same.展开更多
The entropy analysis of viscoelastic fluid obeying the simplified Phan-ThienTanner(SPTT)model with variable thermophysical properties are obtained for laminar,steady state and fully developed Couette-Poiseuille flow.T...The entropy analysis of viscoelastic fluid obeying the simplified Phan-ThienTanner(SPTT)model with variable thermophysical properties are obtained for laminar,steady state and fully developed Couette-Poiseuille flow.The homotopy perturbation method(HPM)allows us to solve nonlinear momentum and energy differential equations.The Reynold’s model is used to describe the temperature dependency of thermophysical properties.Results indicate that the increase of the group parameter(Br=U)and the Brinkman number(Br)which show the power of viscous dissipation effect;increases the entropy generation while increasing fluid elasticity(εDe2)decreases the generated entropy.Increasing the Reynolds variational parameter(a)which control the level of temperature dependence of physical properties attenuate entropy generation when moving plate and applied pressure gradient have the opposite direction and decreases entropy generation when moving plate and applied pressure gradient have the same direction or both plates are at rest.Also,increasing elasticity reduces the difference between variable and constant thermophysical properties cases.These results may give guidelines for cost optimization in industrial processes.展开更多
Fault detection is beneficial for chiller routine operation management in building automation systems.Considering the limitations of traditional principal component analysis(PCA)algorithm for chiller fault detection,a...Fault detection is beneficial for chiller routine operation management in building automation systems.Considering the limitations of traditional principal component analysis(PCA)algorithm for chiller fault detection,a so-called kernel entropy component analysis(KECA)method has been developed and the development results are reported in this paper.Unlike traditional PCA,in KECA,the feature extraction or dimensionality reduction is implemented in a new space,called kernel feature space.The new space is nonlinearly related to the input space.The data set in the kernel feature space is projected onto a principal component subspace constructed by the feature space principal axes determined by the maximum Rényi entropy rather than the top eigenvalues.The proposed KECA is more suitable to deal with nonlinear process without Gaussian assumption.Using the available experimental data from ASHRAE RP-1043,seven typical chiller faults were tested by the proposed KECA method,and the results were compared to that of PCA.Two statistics,i.e.T2 and squared prediction error(SPE),were employed for fault detection monitoring.The fault detection results showed that the proposed KECA method had a better performance in terms of a higher detection accuracy in comparison to the traditional PCA.For the seven typical faults,the fault detection ratios were over 55%,even at their corresponding least severity level when using the proposed KECA based chiller fault detection method.展开更多
Influences of large-scale climatic phenomena, such as the E1Nifio/La Nifia-Southem Oscillation （ENSO） and the Pacific Decadal Oscillation （PDO）, on the temporal variations of the annual water discharge at the Liji...Influences of large-scale climatic phenomena, such as the E1Nifio/La Nifia-Southem Oscillation （ENSO） and the Pacific Decadal Oscillation （PDO）, on the temporal variations of the annual water discharge at the Lijin station in the Huanghe （Yellow） River and at the Datong station in the Changjiang （Yangtze） River were examined. Using the empirical mode decomposition-maximum entropy spectral analysis （EMD- MESA） method, the 2- to 3-year, 8- to 14-year, and 23-year cyclical variations of the annual water discharge at the two stations were discovered. Based on the analysis results, the hydrological time series on the inter- annual to interdecadal scales were constructed. The results indicate that from 1950 to 2011, a significant downward trend occurred in the natural annual water discharge in Huanghe River. However, the changes in water discharge in Changjiang River basin exhibited a slightly upward trend. It indicated that the changes in the river discharge in the Huanghe basin were driven primarily by precipitation. Other factors, such as the precipitation over the Changjiang River tributaries, ice melt and evaporation contributed much more to the increase in the Changjiang River basin. Especially, the impacts of the inter-annual and inter-decadal climate oscillations such as ENSO and PDO could change the long-term patterns of precipitation over the basins of the two major rivers. Generally, low amounts of basin-wide precipitation on interannual to interdecadal scales over the two rivers corresponded to most of the warm ENSO events and the warm phases of the PDO, and vice versa. The positive phases of the PDO and ENSO could lead to reduced precipitation and consequently affect the long-term scale water discharges at the two rivers.展开更多
It is theoretically demonstrated that singular spectrum analysis (SSA) is an equivalent form of Maximum Entropy Spectrum Analysis (MESA) which is essential1y a nonlinear estimation of the classical power spectrum. Bo...It is theoretically demonstrated that singular spectrum analysis (SSA) is an equivalent form of Maximum Entropy Spectrum Analysis (MESA) which is essential1y a nonlinear estimation of the classical power spectrum. Both methods have respectively some different features in application as a result of the difference of description of manners. The numerical experiments show that SSA possesses some special advantage in climatic diagnosis and prediction, e.g., to steadily and accurately identify periods and investigate on domain of time in combination with frequency,which cannot be replaced by MESA. Thus SSA has extensive application in the near future.展开更多
Investigations on entropy generation and thermal irreversibility analysis are conducted for liquid lead-bismuth eutectic(LBE)in an annular pipe.To find better performance in convective heat transfer,the computational ...Investigations on entropy generation and thermal irreversibility analysis are conducted for liquid lead-bismuth eutectic(LBE)in an annular pipe.To find better performance in convective heat transfer,the computational fluid dynamics(CFD)code based on the finite volume method(FVM)is adopted to solve this problem.The elevated temperature LBE flows in the annular pipe,and four types of heat flux,including constant,linear increase and decrease,and parabolic distributions are imposed at the inside wall of the annular pipe.The investigations are conducted for the specific average heat input of 200 kW/m^(2),and the different Peclet number Pe is set from 1200 to 3200.The SST k-ωturbulent model and Cheng-Tak Prt model are adopted.The mesh independence validation and models verification are also conducted and the maximum Nu error is 5.43%compared with previous experimental correlations.The results from the local and system scales,respectively,including volumetric dimensionless entropy generation,Ns,Be,and Ep,are discussed.The results indicate that the viscous friction and heat transfer caused by entropy generation can be found in the viscous sub-layer and buffer layer respectively.Heat transfer is the primary factor that leads to irreversible losses.Besides,the results show that the best thermodynamic performance occurs under parabolic distributed heat flux in the research scope.展开更多
Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between moni...Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study,we present a straightforward and fast computational method, the multivariable linkage coarse graining(MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.展开更多
Estimating the Probability Density Function（PDF） of the performance function is a direct way for structural reliability analysis,and the failure probability can be easily obtained by integration in the failure domai...Estimating the Probability Density Function（PDF） of the performance function is a direct way for structural reliability analysis,and the failure probability can be easily obtained by integration in the failure domain.However,efficiently estimating the PDF is still an urgent problem to be solved.The existing fractional moment based maximum entropy has provided a very advanced method for the PDF estimation,whereas the main shortcoming is that it limits the application of the reliability analysis method only to structures with independent inputs.While in fact,structures with correlated inputs always exist in engineering,thus this paper improves the maximum entropy method,and applies the Unscented Transformation（UT） technique to compute the fractional moments of the performance function for structures with correlations,which is a very efficient moment estimation method for models with any inputs.The proposed method can precisely estimate the probability distributions of performance functions for structures with correlations.Besides,the number of function evaluations of the proposed method in reliability analysis,which is determined by UT,is really small.Several examples are employed to illustrate the accuracy and advantages of the proposed method.展开更多
This study is devoted to investigate the inherent irreversibility and thermal stability in a reactive electrically conducting fluid flowing steadily through a channel with isothermal walls under the influence of a tra...This study is devoted to investigate the inherent irreversibility and thermal stability in a reactive electrically conducting fluid flowing steadily through a channel with isothermal walls under the influence of a transversely imposed magnetic field.Using a perturbation method coupled with a special type of Hermite-Pade' approximation technique,the simplified governing non-linear equation is solved and the important properties of overall flow structure including velocity field,temperature field and thermal criticality conditions are derived which essentially expedite to obtain expressions for volumetric entropy generation numbers,irreversibility distribution ratio and the Bejan number in the flow field.展开更多
文摘In order to eliminate the subjectivity of wheeze diagnosis and improve the accuracy of objective detecting methods,this paper introduces a wheeze detecting method based on spectrogram entropy analysis.This algorithm mainly comprises three steps which are preprocessing,features extracting and wheeze detecting based on support vector machine(SVM).Herein,the preprocessing consists of the short-time Fourier transform(STFT) decomposition and detrending.The features are extracted from the entropy of spectrograms.The step of detrending makes the difference of the features between wheeze and normal lung sounds more obvious.Moreover,compared with the method whose decision is based on the empirical threshold,there is no uncertain detecting result any more.Results of two testing experiments show that the detecting accuracy(AC) are 97.1%and 95.7%,respectively,which proves that the proposed method could be an efficient way to detect wheeze.
文摘We present a hybrid singular spectrum analysis （SSA） and fuzzy entropy method to filter noisy nonlinear time series. With this approach, SSA decomposes the noisy time series into its constituent components including both the deterministic behavior and noise, while fuzzy entropy automatically differentiates the optimal dominant components from the noise based on the complexity of each component. We demonstrate the effectiveness of the hybrid approach in reconstructing the Lorenz and Mackey--Class attractors, as well as improving the multi-step prediction quality of these two series in noisy environments.
基金Climbing Peak Discipline Project of Shanghai Dianji University,China(No.15DFXK02)Hi-Tech Research and Development Programs of China(No.2007AA041600)
文摘Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducted a comparative study of KECA with other five dimensionality reduction methods,principal component analysis( PCA),kernel PCA( KPCA),locally linear embedding( LLE),laplacian eigenmaps( LAE) and diffusion maps( DM). Three quality assessment criteria, local continuity meta-criterion( LCMC),trustworthiness and continuity measure(T&C),and mean relative rank error( MRRE) are applied as direct performance indexes to assess those dimensionality reduction methods. Moreover,the clustering accuracy is used as an indirect performance index to evaluate the quality of the representative data gotten by those methods. The comparisons are performed on six datasets and the results are analyzed by Friedman test with the corresponding post-hoc tests. The results indicate that KECA shows an excellent performance in both quality assessment criteria and clustering accuracy assessing.
文摘A grey model with periodic term for sea-level analysis is presented.The present model keeps some advantages of tea GM (1, 1 )model, which well reflects the trend of sea-level changes and gives out the change rate as well as the acceleration of sea level conveniently.level conveniently.In addition, the present model can reproduce the periodic phenomena of sealevel, hence, it overcomes the shortcomings of the GM(1,1) model that is unsuitable for forecasting monthly mean sealevel with apparent periodicity, and its prediction accuracy is improved.The present model is used to analyse Guangxi coast sea level,the results show that the rise rates of relative sea level at Beihai, Weizhou and Bailongwei are 1 .67,2 .51 and 0.89 mm/a respectively, the relative sea level at Shitoubu has a falling trend with a rate of 0. 5- 1 .0 mm/a, the rise rate of eustatic sea level along the Guangxi coast is 2 .0 mm/a. In comparison with the model with a lineartrend term plus a periodic term, the simulation accuracies of both models are about the same.
文摘The entropy analysis of viscoelastic fluid obeying the simplified Phan-ThienTanner(SPTT)model with variable thermophysical properties are obtained for laminar,steady state and fully developed Couette-Poiseuille flow.The homotopy perturbation method(HPM)allows us to solve nonlinear momentum and energy differential equations.The Reynold’s model is used to describe the temperature dependency of thermophysical properties.Results indicate that the increase of the group parameter(Br=U)and the Brinkman number(Br)which show the power of viscous dissipation effect;increases the entropy generation while increasing fluid elasticity(εDe2)decreases the generated entropy.Increasing the Reynolds variational parameter(a)which control the level of temperature dependence of physical properties attenuate entropy generation when moving plate and applied pressure gradient have the opposite direction and decreases entropy generation when moving plate and applied pressure gradient have the same direction or both plates are at rest.Also,increasing elasticity reduces the difference between variable and constant thermophysical properties cases.These results may give guidelines for cost optimization in industrial processes.
基金The financial supports for the Natural Science Foundation of Zhejiang Province(Project No.LQ19E060007)are gratefully acknowledged.
文摘Fault detection is beneficial for chiller routine operation management in building automation systems.Considering the limitations of traditional principal component analysis(PCA)algorithm for chiller fault detection,a so-called kernel entropy component analysis(KECA)method has been developed and the development results are reported in this paper.Unlike traditional PCA,in KECA,the feature extraction or dimensionality reduction is implemented in a new space,called kernel feature space.The new space is nonlinearly related to the input space.The data set in the kernel feature space is projected onto a principal component subspace constructed by the feature space principal axes determined by the maximum Rényi entropy rather than the top eigenvalues.The proposed KECA is more suitable to deal with nonlinear process without Gaussian assumption.Using the available experimental data from ASHRAE RP-1043,seven typical chiller faults were tested by the proposed KECA method,and the results were compared to that of PCA.Two statistics,i.e.T2 and squared prediction error(SPE),were employed for fault detection monitoring.The fault detection results showed that the proposed KECA method had a better performance in terms of a higher detection accuracy in comparison to the traditional PCA.For the seven typical faults,the fault detection ratios were over 55%,even at their corresponding least severity level when using the proposed KECA based chiller fault detection method.
基金Supported by the National Basic Research Program of China(973 Program)(No.2010CB951202)the National Natural Science Foundation of China(Nos.41376055,41030856)
文摘Influences of large-scale climatic phenomena, such as the E1Nifio/La Nifia-Southem Oscillation （ENSO） and the Pacific Decadal Oscillation （PDO）, on the temporal variations of the annual water discharge at the Lijin station in the Huanghe （Yellow） River and at the Datong station in the Changjiang （Yangtze） River were examined. Using the empirical mode decomposition-maximum entropy spectral analysis （EMD- MESA） method, the 2- to 3-year, 8- to 14-year, and 23-year cyclical variations of the annual water discharge at the two stations were discovered. Based on the analysis results, the hydrological time series on the inter- annual to interdecadal scales were constructed. The results indicate that from 1950 to 2011, a significant downward trend occurred in the natural annual water discharge in Huanghe River. However, the changes in water discharge in Changjiang River basin exhibited a slightly upward trend. It indicated that the changes in the river discharge in the Huanghe basin were driven primarily by precipitation. Other factors, such as the precipitation over the Changjiang River tributaries, ice melt and evaporation contributed much more to the increase in the Changjiang River basin. Especially, the impacts of the inter-annual and inter-decadal climate oscillations such as ENSO and PDO could change the long-term patterns of precipitation over the basins of the two major rivers. Generally, low amounts of basin-wide precipitation on interannual to interdecadal scales over the two rivers corresponded to most of the warm ENSO events and the warm phases of the PDO, and vice versa. The positive phases of the PDO and ENSO could lead to reduced precipitation and consequently affect the long-term scale water discharges at the two rivers.
文摘It is theoretically demonstrated that singular spectrum analysis (SSA) is an equivalent form of Maximum Entropy Spectrum Analysis (MESA) which is essential1y a nonlinear estimation of the classical power spectrum. Both methods have respectively some different features in application as a result of the difference of description of manners. The numerical experiments show that SSA possesses some special advantage in climatic diagnosis and prediction, e.g., to steadily and accurately identify periods and investigate on domain of time in combination with frequency,which cannot be replaced by MESA. Thus SSA has extensive application in the near future.
基金supported by the National Key R&D Program of China(2020YFB1901900)。
文摘Investigations on entropy generation and thermal irreversibility analysis are conducted for liquid lead-bismuth eutectic(LBE)in an annular pipe.To find better performance in convective heat transfer,the computational fluid dynamics(CFD)code based on the finite volume method(FVM)is adopted to solve this problem.The elevated temperature LBE flows in the annular pipe,and four types of heat flux,including constant,linear increase and decrease,and parabolic distributions are imposed at the inside wall of the annular pipe.The investigations are conducted for the specific average heat input of 200 kW/m^(2),and the different Peclet number Pe is set from 1200 to 3200.The SST k-ωturbulent model and Cheng-Tak Prt model are adopted.The mesh independence validation and models verification are also conducted and the maximum Nu error is 5.43%compared with previous experimental correlations.The results from the local and system scales,respectively,including volumetric dimensionless entropy generation,Ns,Be,and Ep,are discussed.The results indicate that the viscous friction and heat transfer caused by entropy generation can be found in the viscous sub-layer and buffer layer respectively.Heat transfer is the primary factor that leads to irreversible losses.Besides,the results show that the best thermodynamic performance occurs under parabolic distributed heat flux in the research scope.
基金supported by the National Natural Science Foundation of China(Grant No.51375375)
文摘Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study,we present a straightforward and fast computational method, the multivariable linkage coarse graining(MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.
基金supported by the Equipment Development Department ‘‘13th Five-year” Equipment Research Field Foundation of China Central Military Commission(No.6140244010216HT15001)
文摘Estimating the Probability Density Function（PDF） of the performance function is a direct way for structural reliability analysis,and the failure probability can be easily obtained by integration in the failure domain.However,efficiently estimating the PDF is still an urgent problem to be solved.The existing fractional moment based maximum entropy has provided a very advanced method for the PDF estimation,whereas the main shortcoming is that it limits the application of the reliability analysis method only to structures with independent inputs.While in fact,structures with correlated inputs always exist in engineering,thus this paper improves the maximum entropy method,and applies the Unscented Transformation（UT） technique to compute the fractional moments of the performance function for structures with correlations,which is a very efficient moment estimation method for models with any inputs.The proposed method can precisely estimate the probability distributions of performance functions for structures with correlations.Besides,the number of function evaluations of the proposed method in reliability analysis,which is determined by UT,is really small.Several examples are employed to illustrate the accuracy and advantages of the proposed method.
文摘This study is devoted to investigate the inherent irreversibility and thermal stability in a reactive electrically conducting fluid flowing steadily through a channel with isothermal walls under the influence of a transversely imposed magnetic field.Using a perturbation method coupled with a special type of Hermite-Pade' approximation technique,the simplified governing non-linear equation is solved and the important properties of overall flow structure including velocity field,temperature field and thermal criticality conditions are derived which essentially expedite to obtain expressions for volumetric entropy generation numbers,irreversibility distribution ratio and the Bejan number in the flow field.
