SUMMARYThe smooth and nonsmooth approaches to the discrete element method (DEM) are examined from a computational perspective. The main difference can be understood as using explicit versus implicit time integration. A formula is obtained for estimating the computational effort depending on error tolerance, system geometric shape and size, and on the dynamic state. For the nonsmooth DEM (NDEM), a regularized version mapping to the Hertz contact law is presented. This method has the conventional nonsmooth and smooth DEM as special cases depending on size of time step and value of regularization. The use of the projected Gauss-Seidel solver for NDEM simulation is studied on a range of test systems. The following characteristics are found. First, the smooth DEM is computationally more efficient for soft materials, wide and tall systems, and with increasing flow rate. Secondly, the NDEM is more beneficial for stiff materials, shallow systems, static or slow flow, and with increasing error tolerance. Furthermore, it is found that just as pressure saturates with depth in a granular column, due to force arching, also the required number of iterations saturates and become independent of system size. This effect make the projected Gauss-Seidel solver scale much better than previously thought.

Based on two fractional-order chaotic complex drive systems and one fractional-order chaotic complex response system with different dimensions, we propose generalized combination complex synchronization. In this new synchronization scheme, there are two complex scaling matrices that are non-square matrices. On the basis of the stability theory of fractional-order linear systems, we design a general controller via active control. Additionally, by virtue of two complex scaling matrices, generalized combination complex synchronization between fractional-order chaotic complex systems and real systems is investigated. Finally, three typical examples are given to demonstrate the effectiveness and feasibility of the schemes.

The system capacity for future mobile communication needs to be increased to fulfill the emerging requirements of mobile services and innumerable applications. The cellular topology has for long been regarded as the most promising way to provide the required increase in capacity. However with the emerging densification of cell deployments, the traditional cellular structure limits the efficiency of the resource, and the coordination between different types of base stations is more complicated and entails heavy cost. Consequently, this study proposes frameless network architecture (FNA) to release the cell boundaries, enabling the topology needed to implement the FNA resource allocation strategy. This strategy is based on resource pooling incorporating a new resource dimension-antenna/antenna array. Within this architecture, an adaptive resource allocation method based on genetic algorithm is proposed to find the optimal solution for the multi-dimensional resource allocation problem. Maximum throughput and proportional fair resource allocation criteria are considered. The simulation results show that the proposed architecture and resource allocation method can achieve performance gains for both criteria with a relatively low complexity compared to existing schemes.

Network traffic classification based on machine learning is an important branch of pattern recognition in computer science. It is a key technology for dynamic intelligent network management and enhanced network controllability. However, the traffic classification methods still facing severe challenges: The optimal set of features is difficult to determine. The classification method is highly dependent on the effective characteristic combination. Meanwhile, it is also important to balance the experience risk and generalization ability of the classifier. In this paper, an improved network traffic classification model based on a support vector machine is proposed. First, a filter-wrapper hybrid feature selection method is proposed to solve the false deletion of combined features caused by a traditional feature selection method. Second, to balance the empirical risk and generalization ability of support vector machine (SVM) traffic classification model, an improved parameter optimization algorithm is proposed. The algorithm can dynamically adjust the quadratic search area, reduce the density of quadratic mesh generation, improve the search efficiency of the algorithm, and prevent the over-fitting while optimizing the parameters. The experiments show that the improved traffic classification model achieves higher classification accuracy, lower dimension and shorter elapsed time and performs significantly better than traditional SVM and the other three typical supervised ML algorithms.

We study the performance of multiuser document prefetching in a two-tier heterogeneous wireless system. Mobility-aware prefetching was previously introduced to enhance the experience of a mobile user roaming between heterogeneous wireless access networks. However, an undesirable effect of multiple prefetching users is the potential for system instability due to the racing behavior between the document access delay and the user prefetching quantity. This phenomenon is particularly acute in the heterogeneous environment. We investigate into alleviating the system traffic load through prefetch thresholding, accounting for server queuing prioritization. We propose a novel analysis framework to evaluate the performance of the thresholding approach. Numerical and simulation results show that the proposed analysis is accurate for a wide variety of access, service, and mobility patterns. We further demonstrate that stability can be maintained even under heavy usage, providing both the same scalability as a non-prefetching system and the performance gain associated with prefetching.