基于P4P技术的互联网流量优化系统

P4P技术的提出,主要是因为传统的P2P技术网络传输压力大且运营成本相对较高,因此,运营商在P2P技术的基础上,提出了P4P技术,目的是优化互联网流量系统。     

基于测量的网络生成树拓扑优化

提出一种基于测量的网络生成树拓扑优化算法，可以根据网络上的流量分布动态地调整网络拓扑。仿真结果表明，在网络输入相同时，这种算法可以显著降低网络资源的占用，并可以有效地消除网络上短期流量波动对拓扑稳定性的影响，防止网络拓扑频繁变化。     

基于流量距离因子的P2P缓存选址与容量分配算法

为缓解P2P应用的广泛流行给网络服务提供商(ISP)骨干网络带来的流量压力,进行了P2P缓存部署研究,以避免不合理的缓存部署影响缓存性能发挥和造成缓存投入浪费.首先在综合考虑骨干网络拓扑、内容热度变化和缓存状态更新等信息的基础上建立了基于骨干流量的缓存部署模型,然后定义流量距离因子辅助部署算法设计,并给出了一种基于流量距离因子的P2P缓存选址与容量分配(LSCA)算法,最终通过缓存位置和缓存容量的联合优化设计,充分发挥缓存性能,降低骨干网络P2P流量负载.仿真实验结果表明,针对典型的H&S型、Ladder型骨干网络拓扑,LSCA算法与已有部署算法相比均具有更好的性能.应用LSCA算法后,平均链路使用率比已有算法Degree低14％～21％,比已有算法Centrality低9％ ～ 12％;平均传输跳数比Degree算法低18％ ～29％,比Centrality算法低11％ ～20％.     

基于特征字符串匹配的P2P流量控制

简述P2P的发展,分析P2P流量对主干网络的带宽占用问题及其危害,提出了两种采用Iptables扩展包基于特征字符串匹配限制P2P流量(特别是Bt、eDonkey流量)的有效方法,并比较、分析其应用效果。     

基于拓扑优化的锁紧块优化设计

现有的锁紧块结构单一,且对孔轴的配合精度要求高,这对使用和加工带来诸多不便。通过对现有常规锁紧结构进行优化,以达到宽裕的配合精度和轻量化产品。首先通过拓扑优化,然后根据优化结果和设计经验进行结构的形状优化,再进行尺寸优化,得到最优尺寸参数,最后通过疲劳分析进行校核。结果显示：优化后的模块的重量减轻38%,使用同样锁紧力作用下,检测点P点的变形量是原变形量的2.24倍,达到轻量化设计和宽裕的配合精度,同时5年工况环境可以满足使用。说明使用拓扑优化可以实现产品初期设计和产品的轻量化设计,仿真可完成具体的尺寸参数优化和疲劳强度校核。     

P2P流量的监管研究

随着P2P应用的日益流行,P2P流量消耗了网络的大量带宽,人们意识到越来越有必要对P2P流量和网络行为进行深入的了解、分析,为监控与管理P2P提供技术支持。     

基于双向渐进结构拓扑优化算法的创新设计研究

目的 在数字化设计的背景下,探索基于结构性能化的算法找形方法,运用双向渐进结构拓扑优化算法(Bi-Directional Evolutionary Structural Optimization,BESO)展开创新设计实践研究。方法 在理解双向渐进结构拓扑优化算法的基本内涵、相关理论、历史发展和现状应用的基础上,分析其算法生成的优势及可行性,并以算法的组织模式与生形原理为前提,对其进行几何划分、约束条件、优化技术、结构模拟、材料设定、迭代生形等内容协同一体的生成策略研究,提供了多元选择的设计机会。结果 得到了运用双向渐进结构拓扑优化算法进行的基于初始形态设计、拓扑优化设计和后处理与制造三步骤创新设计实践结果。结论 此设计实践方案验证了该算法生成方法的设计应用可行性,同时也为多领域应用该算法提供了新思路和新方向。     

基于阻尼层拓扑优化的车内噪声控制

首先建立了某车型声振耦合分析模型,并分析计算了车内噪声性能,获知了噪声异常峰值频率。接着对模态贡献量进行分析,确定了对车内噪声贡献量最大的模态阶次及其振型。然后以该阶模态振型最大节点处的频率响应最小化为优化目标,对车身壁板的阻尼层布局进行拓扑优化。最后,经过计算验证结果表明,优化方案使车内前、后排噪声下降10dB(A)以上。上述研究表明,该阻尼层优化方法可作为一种有效的车内噪声控制手段。     

基于物质点描述的双向渐进式拓扑优化方法

:为了克服连续体结构拓扑优化中的数值不稳定现象,定义了表征物质点及其领域有无的物质点拓扑变量,提出基于物质点描述的双向渐进式拓扑优化方法.基于过滤法构造拓扑变量场的插值函数,从而在拓扑优化模型中自然消除了棋盘格现象.为适用于不同单元类型和网格离散形式等,重新定义了灵敏度密度.通过二维数值算例对理论方法进行验证.结果表明:方法在连续体结构拓扑优化设计中具有可行性和有效性.     

基于HyperMesh的立式加工中心结构拓扑优化

应用CAD/CAE系统建立XH786A立式加工中心的整机模型,并进行动态分析.找出机床薄弱环节,利用优化软件HyperMesh对薄弱环节进行优化设计,得到优化结果和优化前相比较,表明拓扑优化结果具有理论指导意义,为后续详细的设计提供理论依据.     

基于自相似流的无线Mesh网络信道分配算法

根据业务流的自相似性提出了一种新颖的信道分配算法。该算法首先根据流的自相似性,估计网络中的节点的业务量。然后,根据业务量多少确定占用信道的优先级。业务量大的节点可以优先占用信道。这样,解决了“热点”信道资源紧张的问题。同时,算法对“非热点”的吞吐量影响很小。理论分析证明,这种算法可以有效提高网络吞吐量。仿真结果表明,运用这种算法,网络的吞吐量比最小干扰固定信道分配算法和最小干扰混合信道分配算法可分别提高70％和40％。     

应用业务预测的动态带宽分配算法

当前以太无源光网络中动态带宽分配算法主要是基于轮询机制.其中限制带宽分配算法根据各个ONU发送的队列长度能够动态分配带宽,但是这种算法不能实时获得各个ONU的队列长度.因此,利用维纳滤波的方法对自相似业务流量进行预测,预测的结果应用于限制动态带宽分配,得出一种改进的动态带宽分配算法.这种改进算法通过预测ONU的队列长度,更确切地反映了其状态信息,有利于OLT进行准确地带宽分配.对这种算法进行了仿真分析,在业务负载介于0.1～0.5的情况下,平均包时延比较接近0.1ms,达到了改进目的.     

Ad hoc网络中一种跨层流量分配算法——CLATA

针对ad hoc网络各协议层的功能都相互关联的特点,提出了一种在ad hoc网络中基于网络平均时延最小的跨层自适应流量分配算法（CLATA）。该算法将网络层自适应流量分配信息传递给媒体接入控制（MAC）层,以改进MAC层中的冲突退避算法,实现网络平均时延最小化,提高网络的利用率。仿真实验结果表明,该算法可以动态调整链路之间的流量,并具有快速的自适应性,优化网络资源的利用。     

Two-dimensional correlation optimized warping algorithm for aligning GC x GC-MS data

A two-dimensional (2-D) correlation optimized warping (COW) algorithm has been developed to align 2-D gas chromatography coupled with time-of-flight mass spectrometry (GC x GC/TOF-MS) data. By partitioning raw chromatographic profiles and warping the grid points simultaneously along the first and second dimensions on the basis of applying a one-dimensional COW algorithm to characteristic vectors, nongrid points can be interpolatively warped. This 2-D algorithm was directly applied to total ion counts (TIC) chromatographic profiles of homogeneous chemical samples, i.e., samples including mostly identical compounds. For heterogeneous chemical samples, the 2-D algorithm is first applied to certain selected ion counts chromatographic profiles, and the resultant warping parameters are then used to warp the corresponding TIC chromatographic profiles. The developed 2-D COW algorithm can also be applied to align other 2-D separation images, e.g., LC x LC data, LC x GC data, GC x GC data, LC x CE data, and CE x CE data.     

基于预期容量损失的OFDMA子载波分配算法

针对正交频分多址接入(OFDMA)系统的子载波分配问题,提出了一种基于预期容量损失的分配算法,该算法将子载波的联合分配分解为逐次分配以降低复杂度.每次分配时,该算法利用多用户频率选择性信道的特性,将用户在不同子载波上的速率差值作为每次分配中预期容量损失的度量,优先对预期容量损失最大的用户分配子载波,以期避免由逐次分配的非最优性导致的系统容量损失.理论分析和仿真结果表明,此算法能够在低复杂度的条件下得到近似最优解,在保证用户服务质量的同时,有效地提高了系统容量.     

改进萤火虫算法优化粒子滤波的信号源定位

为提高无线电信号源的定位精度,运用粒子滤波方法对其进行定位估计。针对粒子滤波存在的粒子退化问题,提出改进的萤火虫算法优化粒子滤波。首先对萤火虫算法的吸引度公式进行改进,并利用迭代时刻粒子最优值指导个体的移动过程。然后运用改进的萤火虫算法与粒子滤波机制相结合,使粒子趋向于高似然区域,提高粒子的有效性,避免粒子退化,提高粒子滤波算法的滤波精度。最后,将改进后的算法用于无线电信号源定位算法中并进行仿真试验。实验结果表明:该文提出的算法定位结果最大定位误差为0.23%,该算法相比粒子滤波算法的定位精度有很大的提高,是一种有效的、实用性较强的定位估计算法。     

基于子载波权衡的多用户OFDMA资源分配算法

在多用户OFDMA资源分配中提出一种基于子载波权衡的资源分配算法.资源分配过程中,在对公平性最差的用户进行子载波分配时,进行子载波权衡.根据权衡后子载波信道增益的变化情况,进行子载波分配.通过子载波权衡,将待分配的子载波分配给权衡用户;同时对于公平性最差的用户,从已分配给权衡用户的子载波中获得一个权衡子载波;使得权衡后分配的子载波较权衡之前获得更高的信道增益.仿真结果表明,算法在保证用户公平性的同时,提升了系统的频谱利用率.     

Optimal buffer allocation strategy for minimizing work-in-process inventory in unpaced production lines

Several researchers have previously studied the problem of allocating buffer storage to maximize the throughput of a production line for a given total amount of buffer space. In this paper we study the optimal buffer allocation problem of minimizing the average work-in-process subject to a minimum required throughput and a constraint on the total buffer space. Although these two buffer allocation problems are closely related, our results show, surprisingly, that their optimal buffer allocations have very different patterns. Specifically, we show that the optimal buffer allocations for the problem considered here generally exhibit a monotonically increasing property where an increasing amount of buffer space is assigned toward the end of the line. This monotonically increasing property generally holds for both the balanced and unbalanced lines. On the basis of our empirical results, we develop a good heuristic for selecting the optimal buffer allocations.     

Automatic breast cancer detection based on optimized neural network using whale optimization algorithm

Breast cancer is the second deadliest type of cancer. Early detection of breast cancer can considerably improve the effectiveness of treatment. A significant early sign of breast cancer is the mass. However, separating the cancerous masses from the normal portions of the breast tissue is usually a challenge for radiologists. Recently, because of the availability of high‐accuracy computing, computer‐aided detection systems based on image processing have become capable of accurately diagnosing the various types of cancers. The main purpose of this study is to utilize a powerful image segmentation method for the diagnosis of cancerous regions through mammography, based on a new configuration of the multilayer perceptron (MLP) neural network. The most popular method for minimizing the errors in an MLP neural network is backpropagation. However, this method has certain drawbacks, such as a low convergence speed and becoming trapped at the local minimum. In this study, a new training algorithm based on the whale optimization algorithm is proposed for the MLP network. This algorithm is capable of solving various problems toward the current algorithms for the analyzed systems. The proposed method is validated on the Mammographic Image Analysis Society database, which contains 322 digitized mammography images, and the Digital Database for Screening Mammography, which contains approximately 2500 digitized mammography images. To assess the detection performance of the proposed system, the correct detection rate, percentage of identification with false acceptance, and percentage of identification with false rejection were evaluated and compared using various methods. The results indicate that the proposed method is highly efficient and yields significantly better accuracy compared with other methods.     

Genetic algorithm for minimizing assembly variation in selective assembly

Selective assembly is a method of obtaining high-precision assemblies from relatively low-precision components. In selective assembly, the mating parts are manufactured with wide tolerances. The mating part population is partitioned to form selective groups, and corresponding selective groups are then assembled interchangeably. If the mating parts are manufactured in different processes and in different machines, their standard deviations will be different. It is impossible that the number of parts in the selective group will be the same. A large number of surplus parts are expected according to the difference in the standard deviations of the mating parts. A method is proposed to find the selective groups to minimize the assembly variation and surplus parts when the parts are assembled linearly. A genetic algorithm is used to find the best combination of the selective groups to minimize the assembly variation. Selective assembly is successfully applied using a genetic algorithm to achieve high-precision assemblies without sacrificing the benefit of wider tolerance in manufacturing.