In this paper,we discuss the long-time behavior of solutions to the nonclassical diffusion equation with fading memory when the nonlinear term f satisfies critical exponential growth and the external force g(x)∈L^(2)...In this paper,we discuss the long-time behavior of solutions to the nonclassical diffusion equation with fading memory when the nonlinear term f satisfies critical exponential growth and the external force g(x)∈L^(2)(Ω).In the framework of time-dependent spaces,we verify the existence of absorbing sets and the asymptotic compactness of the process,then we obtain the existence of the time-dependent global attractor A={A_t}t∈Rin Mt.Furthermore,we achieve the regularity of A,that is,A_(t) is bounded in M_(t)^(1) with a bound independent of t.展开更多
Android, an open source system exploited by Google, has experienced a rapid development in the past a few years in the field of intelligent mobile because of its advantages-open source and excellent function. The numb...Android, an open source system exploited by Google, has experienced a rapid development in the past a few years in the field of intelligent mobile because of its advantages-open source and excellent function. The number of professionals and enthusiasts who research on Android is growing rapidly in the same time. Android, as an abstraction between software layer and hardware layer based on Linux kernel, can complete the optimization of system by modifying the kernel part. The purpose of this design is to master the processes of kernel-compiling and transplanting, and to learn the methods of memory scheduling algorithm and kernel menaory test. First of all, this thesis introduces the installation of Linux system, and then, it presents the method to build the environment for Android kernel compiling and the process of compiling. The key point of the design is to introduce the SLAB, SLOB, SLUB, SLQB allocators in memory scheduling, and carry on a research on optimization with these memory allocators. HTC Incredible S, as an experimental mobile phone whose Android kernel version is 2.6.35, is employed to deal with all these tests. A comparison of kernel codes before and after optimization has been made. The two kernel codes have been transplanted into the terminal of the experimental mobile phone, which will be respectively tested with its stability, memory performance and overall performance. Finally, it concludes that result of being transplanted the SLQB memory allocator is the optimal one of all.展开更多
Online kernel selection is a fundamental problem of online kernel methods.In this paper,we study online kernel selection with memory constraint in which the memory of kernel selection and online prediction procedures ...Online kernel selection is a fundamental problem of online kernel methods.In this paper,we study online kernel selection with memory constraint in which the memory of kernel selection and online prediction procedures is limited to a fixed budget.An essential question is what is the intrinsic relationship among online learnability,memory constraint,and data complexity.To answer the question,it is necessary to show the trade-offs between regret and memory budget.Previous work gives a worst-case lower bound depending on the data size,and shows learning is impossible within a small memory budget.In contrast,we present distinct results by offering data-dependent upper bounds that rely on two data complexities:kernel alignment and the cumulative losses of competitive hypothesis.We propose an algorithmic framework giving data-dependent upper bounds for two types of loss functions.For the hinge loss function,our algorithm achieves an expected upper bound depending on kernel alignment.For the smooth loss functions,our algorithm achieves a high-probability upper bound depending on the cumulative losses of competitive hypothesis.We also prove a matching lower bound for smooth loss functions.Our results show that if the two data complexities are sub-linear,then learning is possible within a small memory budget.Our algorithmic framework depends on a new buffer maintaining framework and a reduction from online kernel selection to prediction with expert advice.Finally,we empirically verify the prediction performance of our algorithms on benchmark datasets.展开更多
The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle co...The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle costs.To achieve the reliable,rapid,and accurate RUL prognostics,the balance between accuracy and computational burden deserves more attention.In addition,the uncertainty is intrinsically present in RUL prognostic process.Due to the limitation of the uncertainty quantification,the point-wise prognostics strategy is not trustworthy.A Dual Adaptive Sliding-window Hybrid(DASH)RUL probabilistic prognostics strategy is proposed to tackle these deficiencies.The DASH strategy contains two adaptive mechanisms,the adaptive Long Short-Term Memory-Polynomial Regression(LSTM-PR)hybrid prognostics mechanism and the adaptive sliding-window Kernel Density Estimation(KDE)probabilistic prognostics mechanism.Owing to the dual adaptive mechanisms,the DASH strategy can achieve the balance between accuracy and computational burden and obtain the trustworthy probabilistic prognostics.Based on the degradation dataset of aircraft electromagnetic contactors,the superiority of DASH strategy is validated.In terms of probabilistic,point-wise and integrated prognostics performance,the proposed strategy increases by 66.89%,81.73% and 25.84%on average compared with the baseline methods and their variants.展开更多
基金supported by the National Natural Science Foundation of China(No.12171082)the Fundamental Research Funds for the Central Universities(No.2232023G-13).
文摘In this paper,we discuss the long-time behavior of solutions to the nonclassical diffusion equation with fading memory when the nonlinear term f satisfies critical exponential growth and the external force g(x)∈L^(2)(Ω).In the framework of time-dependent spaces,we verify the existence of absorbing sets and the asymptotic compactness of the process,then we obtain the existence of the time-dependent global attractor A={A_t}t∈Rin Mt.Furthermore,we achieve the regularity of A,that is,A_(t) is bounded in M_(t)^(1) with a bound independent of t.
文摘Android, an open source system exploited by Google, has experienced a rapid development in the past a few years in the field of intelligent mobile because of its advantages-open source and excellent function. The number of professionals and enthusiasts who research on Android is growing rapidly in the same time. Android, as an abstraction between software layer and hardware layer based on Linux kernel, can complete the optimization of system by modifying the kernel part. The purpose of this design is to master the processes of kernel-compiling and transplanting, and to learn the methods of memory scheduling algorithm and kernel menaory test. First of all, this thesis introduces the installation of Linux system, and then, it presents the method to build the environment for Android kernel compiling and the process of compiling. The key point of the design is to introduce the SLAB, SLOB, SLUB, SLQB allocators in memory scheduling, and carry on a research on optimization with these memory allocators. HTC Incredible S, as an experimental mobile phone whose Android kernel version is 2.6.35, is employed to deal with all these tests. A comparison of kernel codes before and after optimization has been made. The two kernel codes have been transplanted into the terminal of the experimental mobile phone, which will be respectively tested with its stability, memory performance and overall performance. Finally, it concludes that result of being transplanted the SLQB memory allocator is the optimal one of all.
基金supported by the National Natural Science Foundation of China under Grant No.62076181.
文摘Online kernel selection is a fundamental problem of online kernel methods.In this paper,we study online kernel selection with memory constraint in which the memory of kernel selection and online prediction procedures is limited to a fixed budget.An essential question is what is the intrinsic relationship among online learnability,memory constraint,and data complexity.To answer the question,it is necessary to show the trade-offs between regret and memory budget.Previous work gives a worst-case lower bound depending on the data size,and shows learning is impossible within a small memory budget.In contrast,we present distinct results by offering data-dependent upper bounds that rely on two data complexities:kernel alignment and the cumulative losses of competitive hypothesis.We propose an algorithmic framework giving data-dependent upper bounds for two types of loss functions.For the hinge loss function,our algorithm achieves an expected upper bound depending on kernel alignment.For the smooth loss functions,our algorithm achieves a high-probability upper bound depending on the cumulative losses of competitive hypothesis.We also prove a matching lower bound for smooth loss functions.Our results show that if the two data complexities are sub-linear,then learning is possible within a small memory budget.Our algorithmic framework depends on a new buffer maintaining framework and a reduction from online kernel selection to prediction with expert advice.Finally,we empirically verify the prediction performance of our algorithms on benchmark datasets.
基金co-supported by the National Natural Science Foundation of China(Nos.52272403,52402506)Natural Science Basic Research Program of Shaanxi,China(Nos.2022JC-27,2023-JC-QN-0599)。
文摘The reliable,rapid,and accurate Remaining Useful Life(RUL)prognostics of aircraft power supply and distribution system are essential for enhancing the reliability and stability of system and reducing the life-cycle costs.To achieve the reliable,rapid,and accurate RUL prognostics,the balance between accuracy and computational burden deserves more attention.In addition,the uncertainty is intrinsically present in RUL prognostic process.Due to the limitation of the uncertainty quantification,the point-wise prognostics strategy is not trustworthy.A Dual Adaptive Sliding-window Hybrid(DASH)RUL probabilistic prognostics strategy is proposed to tackle these deficiencies.The DASH strategy contains two adaptive mechanisms,the adaptive Long Short-Term Memory-Polynomial Regression(LSTM-PR)hybrid prognostics mechanism and the adaptive sliding-window Kernel Density Estimation(KDE)probabilistic prognostics mechanism.Owing to the dual adaptive mechanisms,the DASH strategy can achieve the balance between accuracy and computational burden and obtain the trustworthy probabilistic prognostics.Based on the degradation dataset of aircraft electromagnetic contactors,the superiority of DASH strategy is validated.In terms of probabilistic,point-wise and integrated prognostics performance,the proposed strategy increases by 66.89%,81.73% and 25.84%on average compared with the baseline methods and their variants.
