The degradation coefficient is proposed to evaluate the degradation degree of organic coatings by directly anaIyzing the Bode plots of the electrochemical impedance spectroscopy （EIS） data. This paper investigated t...The degradation coefficient is proposed to evaluate the degradation degree of organic coatings by directly anaIyzing the Bode plots of the electrochemical impedance spectroscopy （EIS） data. This paper investigated the degradation of phenolic epoxy coating/tinplate system by EIS and the degradation coefficient value, which correlates well with the results of breakpoint frequency and variation of phase angle at 10 Hz. Furthermore, the degradation process was confirmed by scanning electron microscope （SEM） and scanning probe microscopy （SPM）. It is concluded that degradation coefficient can be used for the fast evaluation of degradation degree of organic coatings in practical appli- cations.展开更多
In this paper we consider the numerical solution of the one-dimensional heat equation on unbounded domains. First an exact semi-discrete artificial boundary condition is derived by discretizing the time variable with ...In this paper we consider the numerical solution of the one-dimensional heat equation on unbounded domains. First an exact semi-discrete artificial boundary condition is derived by discretizing the time variable with the Crank-Nicolson method. The semi-discretized heat equation equipped with this boundary condition is then proved to be unconditionally stable, and its solution is shown to have second-order accuracy. In order to reduce the computational cost, we develop a new fast evaluation method for the convolution operation involved in the exact semi-discrete artificial boundary condition. A great advantage of this method is that the unconditional stability held by the semi-discretized heat equation is preserved. An error estimate is also given to show the dependence of numerical errors on the time step and the approximation accuracy of the convolution kernel. Finally, a simple numerical example is presented to validate the theoretical results.展开更多
Hardware transient faults are proven to have a significant impact on deep neural networks (DNNs), whose safety-critical misclassification (SCM) in autonomous vehicles, healthcare, and space applications is increased u...Hardware transient faults are proven to have a significant impact on deep neural networks (DNNs), whose safety-critical misclassification (SCM) in autonomous vehicles, healthcare, and space applications is increased up to four times. However, the inaccuracy evaluation using accurate fault injection is time-consuming and requires several hours and even a couple of days on a complete simulation platform. To accelerate the evaluation of hardware transient faults on DNNs, we design a unified and end-to-end automatic methodology, A-Mean, using the silent data corruption (SDC) rate of basic operations (such as convolution, addition, multiply, ReLU, and max-pooling) and a static two-level mean calculation mechanism to rapidly compute the overall SDC rate, for estimating the general classification metric accuracy and application-specific metric SCM. More importantly, a max-policy is used to determine the SDC boundary of non-sequential structures in DNNs. Then, the worst-case scheme is used to further calculate the enlarged SCM and halved accuracy under transient faults, via merging the static results of SDC with the original data from one-time dynamic fault-free execution. Furthermore, all of the steps mentioned above have been implemented automatically, so that this easy-to-use automatic tool can be employed for prompt evaluation of transient faults on diverse DNNs. Meanwhile, a novel metric “fault sensitivity” is defined to characterize the variation of transient fault-induced higher SCM and lower accuracy. The comparative results with a state-of-the-art fault injection method TensorFI+ on five DNN models and four datasets show that our proposed estimation method A-Mean achieves up to 922.80 times speedup, with just 4.20% SCM loss and 0.77% accuracy loss on average. The artifact of A-Mean is publicly available at https://github.com/breatrice321/A-Mean.展开更多
The prevention of fatigue damages is a crucial issue for NPPs （nuclear power plants）. The AFC （AR.EVA fatigue concept） provides for a multi-step and mnlti-disciplinary process against fatigue during the design and...The prevention of fatigue damages is a crucial issue for NPPs （nuclear power plants）. The AFC （AR.EVA fatigue concept） provides for a multi-step and mnlti-disciplinary process against fatigue during the design and operating phase of NPPs. The entire process of fatigne design is based on an installed FAMOS （fatigue monitoring system）. In this way, realistic load data are available to manage the component ageing and enable the optimization of operating modes. The measured temperatures are processed via a FFE （fast fatigue evaluation）. Thus, an online fatigue evaluation of the cumulative usage factor is performed after every operational cycle. This procedure gives a first fatigue status of the power plant. Furthermore, a DFC （detailed fatigue calculation） conforming to the code rules is carried out in order to determine the state of the plant at the highest loaded positions. These finite element analyses include determination of thermal transient and subsequent stresses and strains. Fatigue environmental factors are taken into account in these studies.展开更多
In this work,we develop a finite difference/finite element method for the two-dimensional distributed-order time-space fractional reaction-diffusion equation(2D-DOTSFRDE)with low regularity solution at the initial tim...In this work,we develop a finite difference/finite element method for the two-dimensional distributed-order time-space fractional reaction-diffusion equation(2D-DOTSFRDE)with low regularity solution at the initial time.A fast evaluation of the distributedorder time fractional derivative based on graded time mesh is obtained by substituting the weak singular kernel for the sum-of-exponentials.The stability and convergence of the developed semi-discrete scheme to 2D-DOTSFRDE are discussed.For the spatial approximation,the finite element method is employed.The convergence of the corresponding fully discrete scheme is investigated.Finally,some numerical tests are given to verify the obtained theoretical results and to demonstrate the effectiveness of the method.展开更多
基金Supported by Major State Basic Research Program of China ("973"Program,No. 2011CB610500)
文摘The degradation coefficient is proposed to evaluate the degradation degree of organic coatings by directly anaIyzing the Bode plots of the electrochemical impedance spectroscopy （EIS） data. This paper investigated the degradation of phenolic epoxy coating/tinplate system by EIS and the degradation coefficient value, which correlates well with the results of breakpoint frequency and variation of phase angle at 10 Hz. Furthermore, the degradation process was confirmed by scanning electron microscope （SEM） and scanning probe microscopy （SPM）. It is concluded that degradation coefficient can be used for the fast evaluation of degradation degree of organic coatings in practical appli- cations.
基金Acknowledgments. This work is supported partially by the National Natural Science Foundation of China under Grant No. 10401020, the Alexander von Humboldt Foundation, and the Key Project of China High Performance Scientific Computation Research 2005CB321701.
文摘In this paper we consider the numerical solution of the one-dimensional heat equation on unbounded domains. First an exact semi-discrete artificial boundary condition is derived by discretizing the time variable with the Crank-Nicolson method. The semi-discretized heat equation equipped with this boundary condition is then proved to be unconditionally stable, and its solution is shown to have second-order accuracy. In order to reduce the computational cost, we develop a new fast evaluation method for the convolution operation involved in the exact semi-discrete artificial boundary condition. A great advantage of this method is that the unconditional stability held by the semi-discretized heat equation is preserved. An error estimate is also given to show the dependence of numerical errors on the time step and the approximation accuracy of the convolution kernel. Finally, a simple numerical example is presented to validate the theoretical results.
基金Project supported by the Shanghai Pujiang Talent Program,China(No.21PJD026)。
文摘Hardware transient faults are proven to have a significant impact on deep neural networks (DNNs), whose safety-critical misclassification (SCM) in autonomous vehicles, healthcare, and space applications is increased up to four times. However, the inaccuracy evaluation using accurate fault injection is time-consuming and requires several hours and even a couple of days on a complete simulation platform. To accelerate the evaluation of hardware transient faults on DNNs, we design a unified and end-to-end automatic methodology, A-Mean, using the silent data corruption (SDC) rate of basic operations (such as convolution, addition, multiply, ReLU, and max-pooling) and a static two-level mean calculation mechanism to rapidly compute the overall SDC rate, for estimating the general classification metric accuracy and application-specific metric SCM. More importantly, a max-policy is used to determine the SDC boundary of non-sequential structures in DNNs. Then, the worst-case scheme is used to further calculate the enlarged SCM and halved accuracy under transient faults, via merging the static results of SDC with the original data from one-time dynamic fault-free execution. Furthermore, all of the steps mentioned above have been implemented automatically, so that this easy-to-use automatic tool can be employed for prompt evaluation of transient faults on diverse DNNs. Meanwhile, a novel metric “fault sensitivity” is defined to characterize the variation of transient fault-induced higher SCM and lower accuracy. The comparative results with a state-of-the-art fault injection method TensorFI+ on five DNN models and four datasets show that our proposed estimation method A-Mean achieves up to 922.80 times speedup, with just 4.20% SCM loss and 0.77% accuracy loss on average. The artifact of A-Mean is publicly available at https://github.com/breatrice321/A-Mean.
文摘The prevention of fatigue damages is a crucial issue for NPPs （nuclear power plants）. The AFC （AR.EVA fatigue concept） provides for a multi-step and mnlti-disciplinary process against fatigue during the design and operating phase of NPPs. The entire process of fatigne design is based on an installed FAMOS （fatigue monitoring system）. In this way, realistic load data are available to manage the component ageing and enable the optimization of operating modes. The measured temperatures are processed via a FFE （fast fatigue evaluation）. Thus, an online fatigue evaluation of the cumulative usage factor is performed after every operational cycle. This procedure gives a first fatigue status of the power plant. Furthermore, a DFC （detailed fatigue calculation） conforming to the code rules is carried out in order to determine the state of the plant at the highest loaded positions. These finite element analyses include determination of thermal transient and subsequent stresses and strains. Fatigue environmental factors are taken into account in these studies.
基金supported by the National Natural Science Foundation of China(Nos.11671343,11601460)the Natural Science Foundation of Hunan Province of China(No.2018JJ3491)the Project of Scientific Research Fund of Hunan Provincial Science and Technology Department,China(No.2018WK4006).
文摘In this work,we develop a finite difference/finite element method for the two-dimensional distributed-order time-space fractional reaction-diffusion equation(2D-DOTSFRDE)with low regularity solution at the initial time.A fast evaluation of the distributedorder time fractional derivative based on graded time mesh is obtained by substituting the weak singular kernel for the sum-of-exponentials.The stability and convergence of the developed semi-discrete scheme to 2D-DOTSFRDE are discussed.For the spatial approximation,the finite element method is employed.The convergence of the corresponding fully discrete scheme is investigated.Finally,some numerical tests are given to verify the obtained theoretical results and to demonstrate the effectiveness of the method.
