三维地震数据的可视化

传统的地震数据解释方法是利用地震剖面与水平切片的二维图象显示三维数据,它具有很大的局限性。科学可视化技术的出现使得三维数据三维解释成为可能。将科学可视化技术应用于三维地震数据场,则产生了三维地震数据可视化技术,它主要包括面可视化与直接体绘制两种技术。本文主要研究三维地震数据场的直接体绘制方法——光线投射算法。     

二进制传感器网络基于预测的距离加权目标跟踪算法

Binary sensor network(BSN) are becoming more attractive due to the low cost deployment, small size, low energy consumption and simple operation.There are two different ways for target tracking in BSN, the weighted algorithms and particle filtering algorithm.The weighted algorithms have good realtime property, however have poor estimation property and some of them does not suit for target’s variable velocity model. The particle filtering algorithm can estimate target's position more accurately with poor realtime property and is not suitable for target’s constant velocity model. In this paper distance weight is adopted to estimate the target’s position, which is different from the existing distance weight in other papers. On the analysis of principle of distance weight (DW), prediction-based distance weighted(PDW) algorithm for target tracking in BSN is proposed. Simulation results proved PDW fits for target's constant and variable velocity models with accurate estimation and good realtime property.     

基于遗传算法和LSSVM的网络安全事件发生频率预测

Since the frequency of network security incidents is nonlinear, traditional prediction methods such as ARMA, Gray systems are difficult to deal with the problem. When the size of sample is small, methods based on artificial neural network may not reach a high degree of preciseness. Least Squares Support Vector Machines (LSSVM) is a kind of machine learning methods based on the statistics learning theory, it can be applied to solve small sample and non-linear problems very well. This paper applied LSSVM to predict the occur frequency of network security incidents. To improve the accuracy, it used an improved genetic algorithm to optimize the parameters of LSSVM. Verified by real data sets, the improved genetic algorithm (IGA) converges faster than the simple genetic algorithm (SGA), and has a higher efficiency in the optimization procedure. Specially, the optimized LSSVM model worked very well on the prediction of frequency of network security incidents.     

一种改进的人工鱼群算法

本文介绍了基本人工鱼群算法,并在步长及觅食行为这两方面对基本人工鱼群算法做出了改进,提出一种改进的人工鱼群算法（IAFSA）。实验结果证明,改进的人工鱼群算法具有较好的收敛性。     

量子算法与量子计算实验

从量子体系的基本特性出发 ,介绍了量子计算的基本概念和物理背景 ,系统阐述了几种主要的量子算法以及量子计算在实验方面的发展现状。对比经典计算机 ,讨论了量子计算机的优越性、实现量子计算的困难和以期克服的途径。     

Detecting communities in clustered networks based on group action on set

In this paper, we propose a well targeted algorithm (GAS algorithm) for detecting communities in high clustered networks by presenting group action technology on community division. During the processing of this algorithm, the underlying community structure of a clustered network emerges simultaneously as the corresponding partition of orbits by the permutation groups acting on the node set are achieved. As the derivation of the orbit partition, an algebraic structure r-cycle can be considered as the origin of the community. To be a priori estimation for the community structure of the algorithm, the community separability is introduced to indicate whether a network has distinct community structure. By executing the algorithm on several typical networks and the LFR benchmark, it shows that this GAS algorithm can detect communities accurately and effectively in high clustered networks. Furthermore, we compare the GAS algorithm and the clique percolation algorithm on the LFR benchmark. It is shown that the GAS algorithm is more accurate at detecting non-overlapping communities in clustered networks. It is suggested that algebraic techniques can uncover fresh light on detecting communities in complex networks.     

激光诱导击穿光谱数据特征自动提取方法研究

根据激光诱导击穿光谱(LIBS)展宽主要为非线性Lorentz函数模型,利用Levenberg-Marquardt(L-M)算法对该理论模型中的待定参数进行优化估值,扣除了光谱信号中的连续背景,校正和还原了峰位和峰强,并对拟合数据点自动提取方法进行了分析;以MatrixVB与VB混合编程相结合,实现了基于L-M算法的L...     

Scheduling with few changes

In this work we consider scheduling problems where a sequence of assignments from products to machines – or from tasks to operators, or from workers to resources – has to be determined, with the goal of minimizing the costs (=money, manpower, and/or time) that are incurred by the interplay between those assignments. To account for the different practical requirements (e.g. few changes between different products/tasks on the same machine/operator, few production disruptions, or few changes of the same worker between different resources), we employ different objective functions that are all based on elementary combinatorial properties of the schedule matrix. We propose simple and efficient algorithms to solve the corresponding optimization problems, and provide hardness results where such algorithms most likely do not exist.     

Efficient exponential-time algorithms for edit distance between unordered trees

The edit distance problem for rooted unordered trees is known to be NP-hard. Based on this fact, this paper studies exponential-time algorithms for the problem. For a general case, an $O(\min ({1.26}^{n_1+n_2},2^{b_1+b_2}\cdot \text{<mi mathvariant="italic" is="true">poly</mi>}(n_1,n_2)))$ time algorithm is presented, where $n_1$ and $n_2$ are the numbers of nodes and $b_1$ and $b_2$ are the numbers of branching nodes in two input trees. This algorithm is obtained by a combination of dynamic programming, exhaustive search, and maximum weighted bipartite matching. For bounded degree trees over a fixed alphabet, it is shown that the problem can be solved in $O({(1+ϵ)}^{n_1+n_2})$ time for any fixed $ϵ>0$. This result is achieved by avoiding duplicate calculations for identical subsets of small subtrees.