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
In this article, we consider the existence of trajectory and global attractors for nonclassical diffusion equations with linear fading memory. For this purpose, we will apply the method presented by Chepyzhov and Mira...In this article, we consider the existence of trajectory and global attractors for nonclassical diffusion equations with linear fading memory. For this purpose, we will apply the method presented by Chepyzhov and Miranville [7, 8], in which the authors provide some new ideas in describing the trajectory attractors for evolution equations with memory.展开更多
The diffusion behavior driven by bounded noise under the influence of a coupled harmonic potential is investigated in a two-dimensional coupled-damped model. With the help of the Laplace analysis we obtain exact descr...The diffusion behavior driven by bounded noise under the influence of a coupled harmonic potential is investigated in a two-dimensional coupled-damped model. With the help of the Laplace analysis we obtain exact descriptions for a particle’s two-time dynamics which is subjected to a coupled harmonic potential and a coupled damping. The time lag is used to describe the velocity autocorrelation function and mean square displacement of the diffusing particle. The diffusion behavior for the time lag is also discussed with respect to the coupled items and the amplitude of bounded noise.展开更多
Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any time.With the use of mobile devices,communication services generate numerous data for every moment....Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any time.With the use of mobile devices,communication services generate numerous data for every moment.Given the increasing dense population of data,traffic learning and prediction are the main components to substantially enhance the effectiveness of demand-aware resource allocation.A novel deep learning technique called radial kernelized LSTM-based connectionist Tversky multilayer deep structure learning(RKLSTM-CTMDSL)model is introduced for traffic prediction with superior accuracy and minimal time consumption.The RKLSTM-CTMDSL model performs attribute selection and classification processes for cellular traffic prediction.In this model,the connectionist Tversky multilayer deep structure learning includes multiple layers for traffic prediction.A large volume of spatial-temporal data are considered as an input-to-input layer.Thereafter,input data are transmitted to hidden layer 1,where a radial kernelized long short-term memory architecture is designed for the relevant attribute selection using activation function results.After obtaining the relevant attributes,the selected attributes are given to the next layer.Tversky index function is used in this layer to compute similarities among the training and testing traffic patterns.Tversky similarity index outcomes are given to the output layer.Similarity value is used as basis to classify data as heavy network or normal traffic.Thus,cellular network traffic prediction is presented with minimal error rate using the RKLSTM-CTMDSL model.Comparative evaluation proved that the RKLSTM-CTMDSL model outperforms conventional methods.展开更多
Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularl...Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.展开更多
This paper is devoted to study the long-time dynamics for a nonlinear viscoelastic Kirchhoff plate equation.Under some growth conditions of g and f,the existence of a global attractor is granted.Furthermore,in the sub...This paper is devoted to study the long-time dynamics for a nonlinear viscoelastic Kirchhoff plate equation.Under some growth conditions of g and f,the existence of a global attractor is granted.Furthermore,in the subcritical case,this global attractor has finite Hausdorff and fractal dimensions.展开更多
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 NSFC Grant (11031003)the Fundamental Research Funds for the Central Universities+1 种基金support by Fund of excellent young teachers in Shanghai (shgcjs008)Initial Fund of SUES (A-0501-11-016)
文摘In this article, we consider the existence of trajectory and global attractors for nonclassical diffusion equations with linear fading memory. For this purpose, we will apply the method presented by Chepyzhov and Miranville [7, 8], in which the authors provide some new ideas in describing the trajectory attractors for evolution equations with memory.
基金supported by the National Natural Science Foundation of China(11101333 and 11302172)the Natural Science Foundation of Shaanxi(2011GQ1018)the Northwestern Polytechnical University Foundation for Fundamental Research(JC201152)
文摘The diffusion behavior driven by bounded noise under the influence of a coupled harmonic potential is investigated in a two-dimensional coupled-damped model. With the help of the Laplace analysis we obtain exact descriptions for a particle’s two-time dynamics which is subjected to a coupled harmonic potential and a coupled damping. The time lag is used to describe the velocity autocorrelation function and mean square displacement of the diffusing particle. The diffusion behavior for the time lag is also discussed with respect to the coupled items and the amplitude of bounded noise.
文摘Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any time.With the use of mobile devices,communication services generate numerous data for every moment.Given the increasing dense population of data,traffic learning and prediction are the main components to substantially enhance the effectiveness of demand-aware resource allocation.A novel deep learning technique called radial kernelized LSTM-based connectionist Tversky multilayer deep structure learning(RKLSTM-CTMDSL)model is introduced for traffic prediction with superior accuracy and minimal time consumption.The RKLSTM-CTMDSL model performs attribute selection and classification processes for cellular traffic prediction.In this model,the connectionist Tversky multilayer deep structure learning includes multiple layers for traffic prediction.A large volume of spatial-temporal data are considered as an input-to-input layer.Thereafter,input data are transmitted to hidden layer 1,where a radial kernelized long short-term memory architecture is designed for the relevant attribute selection using activation function results.After obtaining the relevant attributes,the selected attributes are given to the next layer.Tversky index function is used in this layer to compute similarities among the training and testing traffic patterns.Tversky similarity index outcomes are given to the output layer.Similarity value is used as basis to classify data as heavy network or normal traffic.Thus,cellular network traffic prediction is presented with minimal error rate using the RKLSTM-CTMDSL model.Comparative evaluation proved that the RKLSTM-CTMDSL model outperforms conventional methods.
文摘Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.
基金the Natural Science Foundation of Guangdong Province(No.2016A030310262)。
文摘This paper is devoted to study the long-time dynamics for a nonlinear viscoelastic Kirchhoff plate equation.Under some growth conditions of g and f,the existence of a global attractor is granted.Furthermore,in the subcritical case,this global attractor has finite Hausdorff and fractal dimensions.
