The authors announce a newly-proved theorem of theirs. This theorem is of principal significance to numerical computation of operator equations of the first kind.
In this paper,the Galerkin finite element method(FEM)together with the characteristic-based split(CBS)scheme are applied to study the case of the non-linear Boussinesq approximation within sinusoidal heating inclined ...In this paper,the Galerkin finite element method(FEM)together with the characteristic-based split(CBS)scheme are applied to study the case of the non-linear Boussinesq approximation within sinusoidal heating inclined enclosures filled with a non-Darcy porous media and nanofluids.The enclosure has an inclination angle and its side-walls have varying sinusoidal temperature distributions.The working fluid is a nanofluid that is consisting of water as a based nanofluid and Al2O3 as nanoparticles.The porous medium is modeled using the Brinkman Forchheimer extended Darcy model.The obtained results are analyzed over wide ranges of the non-linear Boussinesq parameter 0≤ζ≤1,the phase deviation 00≤Φ≤1800,the inclination angle 00≤γ≤900,the nanoparticles volume fraction 0%≤φ≤4%,the amplitude ratio 0≤a≤1 and the Rayleigh number 104≤Ra≤106.The results revealed that the average Nusselt number is enhanced by 0.73%,26.46%and 35.42%at Ra=104,105 and 106,respectively,when the non-linearBoussinesq parameter is varied from 0 to 1.In addition,rate of heat transfer in the case of a non-uniformly heating is higher than that of a uniformly heating.Non-linear Boussinesq parameter rises the flow speed and heat transfer in an enclosure.Phase deviation makes clear changes on the isotherms and heat transfer rate on the right wall of an enclosure.An inclination angle varies the flow speed and it has a slight effect on heat transfer in an enclosure.展开更多
This research is performed based on the modeling of biological signals. We can produce Heart Rate (HR) and Heart Rate Variability (HRV) signals synthetically using the mathematical relationships which are used as inpu...This research is performed based on the modeling of biological signals. We can produce Heart Rate (HR) and Heart Rate Variability (HRV) signals synthetically using the mathematical relationships which are used as input for the Integral Pulse Frequency Modulation (IPFM) model. Previous researches were proposed same methods such as one model of ECG signal synthetically based on RBF neural network, a model based on IPFM with random threshold, method was based on the estimation of produced signals which are dependent on autonomic nervous system using IPFM model with fixed threshold, a new method based on the theory of vector space that based on time-varying uses of IPMF model (TVTIPMF) and special functions, and two different methods for producing HRV signals with controlled characteristics and structure of time-frequency (TF) for using non-stationary HRV analysis. In this paper, several chaotic maps such as Logistic Map, Henon Map, Lorenz and Tent Map have been used. Also, effects of sympathetic and parasympathetic nervous system and an internal input to the SA node and their effects in HRV signals were evaluated. In the proposed method, output amount of integrator in IPFM model was compared with chaotic threshold level. Then, final output of IPFM model was characterized as the HR and HRV signal. So, from HR and HRV signals obtaining from this model, linear features such as Mean, Median, Variance, Standard Deviation, Maximum Range, Minimum Range, Mode, Amplitude Range and frequency spectrum, and non-linear features such as Lyapunov Exponent, Shanon Entropy, log Entropy, Threshold Entropy, sure Entropy and mode Entropy were extracted from artificial HRV and compared them with characteristics as extracted from natural HRV signal. Also, in this paper two patients that called high sympathetic Balance and Cardiovascular Autonomy Neuropathy (CAN) which is detected and evaluated by HRV signals were simulated. These signals by changing the values of the some coefficients of the normal simulated signal and with extracted frequency feature from these signals were simulated. For final generation of these abnormal signals, frequency features such as energy of low frequency band (EL), energy of high frequency band (HL), ratio of energy in low frequency band to the energy in high frequency band (EL/EH), ratio of energy in low frequency band to the energy in all frequency band (EL/ET) and ratio of energy in high frequency band to the energy in all frequency band (EH/ET) from abnormal signals were extracted and compared with these extracted values from normal signals. The results were closely correlated with the real data which confirm the effectiveness of the proposed model. Various signals derived from the output of this model can be used for final analysis of the HRV signals, such as arrhythmia detection and classification of ECG and HRV signals. One of the applications of the proposed model is the easy evaluation of diagnostic ECG signal processing devices. Such a model can also be used in signal compression and telemedicine application.展开更多
文摘The authors announce a newly-proved theorem of theirs. This theorem is of principal significance to numerical computation of operator equations of the first kind.
基金the Deanship of Scientific Research at King Khalid University for funding this work through research groups program under Grant Number(R.G.P2/72/41).
文摘In this paper,the Galerkin finite element method(FEM)together with the characteristic-based split(CBS)scheme are applied to study the case of the non-linear Boussinesq approximation within sinusoidal heating inclined enclosures filled with a non-Darcy porous media and nanofluids.The enclosure has an inclination angle and its side-walls have varying sinusoidal temperature distributions.The working fluid is a nanofluid that is consisting of water as a based nanofluid and Al2O3 as nanoparticles.The porous medium is modeled using the Brinkman Forchheimer extended Darcy model.The obtained results are analyzed over wide ranges of the non-linear Boussinesq parameter 0≤ζ≤1,the phase deviation 00≤Φ≤1800,the inclination angle 00≤γ≤900,the nanoparticles volume fraction 0%≤φ≤4%,the amplitude ratio 0≤a≤1 and the Rayleigh number 104≤Ra≤106.The results revealed that the average Nusselt number is enhanced by 0.73%,26.46%and 35.42%at Ra=104,105 and 106,respectively,when the non-linearBoussinesq parameter is varied from 0 to 1.In addition,rate of heat transfer in the case of a non-uniformly heating is higher than that of a uniformly heating.Non-linear Boussinesq parameter rises the flow speed and heat transfer in an enclosure.Phase deviation makes clear changes on the isotherms and heat transfer rate on the right wall of an enclosure.An inclination angle varies the flow speed and it has a slight effect on heat transfer in an enclosure.
文摘This research is performed based on the modeling of biological signals. We can produce Heart Rate (HR) and Heart Rate Variability (HRV) signals synthetically using the mathematical relationships which are used as input for the Integral Pulse Frequency Modulation (IPFM) model. Previous researches were proposed same methods such as one model of ECG signal synthetically based on RBF neural network, a model based on IPFM with random threshold, method was based on the estimation of produced signals which are dependent on autonomic nervous system using IPFM model with fixed threshold, a new method based on the theory of vector space that based on time-varying uses of IPMF model (TVTIPMF) and special functions, and two different methods for producing HRV signals with controlled characteristics and structure of time-frequency (TF) for using non-stationary HRV analysis. In this paper, several chaotic maps such as Logistic Map, Henon Map, Lorenz and Tent Map have been used. Also, effects of sympathetic and parasympathetic nervous system and an internal input to the SA node and their effects in HRV signals were evaluated. In the proposed method, output amount of integrator in IPFM model was compared with chaotic threshold level. Then, final output of IPFM model was characterized as the HR and HRV signal. So, from HR and HRV signals obtaining from this model, linear features such as Mean, Median, Variance, Standard Deviation, Maximum Range, Minimum Range, Mode, Amplitude Range and frequency spectrum, and non-linear features such as Lyapunov Exponent, Shanon Entropy, log Entropy, Threshold Entropy, sure Entropy and mode Entropy were extracted from artificial HRV and compared them with characteristics as extracted from natural HRV signal. Also, in this paper two patients that called high sympathetic Balance and Cardiovascular Autonomy Neuropathy (CAN) which is detected and evaluated by HRV signals were simulated. These signals by changing the values of the some coefficients of the normal simulated signal and with extracted frequency feature from these signals were simulated. For final generation of these abnormal signals, frequency features such as energy of low frequency band (EL), energy of high frequency band (HL), ratio of energy in low frequency band to the energy in high frequency band (EL/EH), ratio of energy in low frequency band to the energy in all frequency band (EL/ET) and ratio of energy in high frequency band to the energy in all frequency band (EH/ET) from abnormal signals were extracted and compared with these extracted values from normal signals. The results were closely correlated with the real data which confirm the effectiveness of the proposed model. Various signals derived from the output of this model can be used for final analysis of the HRV signals, such as arrhythmia detection and classification of ECG and HRV signals. One of the applications of the proposed model is the easy evaluation of diagnostic ECG signal processing devices. Such a model can also be used in signal compression and telemedicine application.
