Improving Network Availability through Optimized Multipath Routing and Incremental Deployment Strategies

Currently,distributed routing protocols are constrained by offering a single path between any pair of nodes,thereby limiting the potential throughput and overall network performance.This approach not only restricts the flow of data but also makes the network susceptible to failures in case the primary path is disrupted.In contrast,routing protocols that leverage multiple paths within the network offer a more resilient and efficient solution.Multipath routing,as a fundamental concept,surpasses the limitations of traditional shortest path first protocols.It not only redirects traffic to unused resources,effectively mitigating network congestion,but also ensures load balancing across the network.This optimization significantly improves network utilization and boosts the overall performance,making it a widely recognized efficient method for enhancing network reliability.To further strengthen network resilience against failures,we introduce a routing scheme known as Multiple Nodes with at least Two Choices(MNTC).This innovative approach aims to significantly enhance network availability by providing each node with at least two routing choices.By doing so,it not only reduces the dependency on a single path but also creates redundant paths that can be utilized in case of failures,thereby enhancing the overall resilience of the network.To ensure the optimal placement of nodes,we propose three incremental deployment algorithms.These algorithms carefully select the most suitable set of nodes for deployment,taking into account various factors such as node connectivity,traffic patterns,and network topology.By deployingMNTCon a carefully chosen set of nodes,we can significantly enhance network reliability without the need for a complete overhaul of the existing infrastructure.We have conducted extensive evaluations of MNTC in diverse topological spaces,demonstrating its effectiveness in maintaining high network availability with minimal path stretch.The results are impressive,showing that even when implemented on just 60%of nodes,our incremental deployment method significantly boosts network availability.This underscores the potential of MNTC in enhancing network resilience and performance,making it a viable solution for modern networks facing increasing demands and complexities.The algorithms OSPF,TBFH,DC and LFC perform fast rerouting based on strict conditions,while MNTC is not restricted by these conditions.In five real network topologies,the average network availability ofMNTCis improved by 14.68%,6.28%,4.76%and 2.84%,respectively,compared with OSPF,TBFH,DC and LFC.

INCREMENTAL AUGMENT ALGORITHM BASED ON REDUCED Q-MATRIX

Reduced Q-matrix （Qr matrix） plays an important role in the rule space model （RSM） and the attribute hierarchy method （AHM）. Based on the attribute hierarchy, a valid/invalid item is defined. The judgment method of the valid/invalid item is developed on the relation between reachability matrix and valid items. And valid items are explained from the perspective of graph theory. An incremental augment algorithm for constructing Qr matrix is proposed based on the idea of incremental forward regression, and its validity is theoretically considered. Results of empirical tests are given in order to compare the performance of the incremental augment algo-rithm and the Tatsuoka algorithm upon the running time. Empirical evidence shows that the algorithm outper-forms the Tatsuoka algorithm, and the analysis of the two algorithms also show linear growth with respect to the number of valid items. Mathematical models with 10 attributes are built for the two algorithms by the linear regression analysis.

A Novel Incremental Mining Algorithm of Frequent Patterns for Web Usage Mining

Because data warehouse is frequently changing, incremental data leads to old knowledge which is mined formerly unavailable. In order to maintain the discovered knowledge and patterns dynamically, this study presents a novel algorithm updating for global frequent patterns-IPARUC. A rapid clustering method is introduced to divide database into n parts in IPARUC firstly, where the data are similar in the same part. Then, the nodes in the tree are adjusted dynamically in inserting process by ＂pruning and laying back＂ to keep the frequency descending order so that they can be shared to approaching optimization. Finally local frequent itemsets mined from each local dataset are merged into global frequent itemsets. The results of experimental study are very encouraging. It is obvious from experiment that IPARUC is more effective and efficient than other two contrastive methods. Furthermore, there is significant application potential to a prototype of Web log Analyzer in web usage mining that can help us to discover useful knowledge effectively, even help managers making decision.

Maximum Power Point Tracking Using the Incremental Conductance Algorithm for PV Systems Operating in Rapidly Changing Environmental Conditions

Maximum Power Point Tracking (MPPT) is an important process in Photovoltaic (PV) systems because of the need to extract maximum power from PV panels used in these systems. Without the ability to track and have PV panels operate at its maximum power point (MPP) entails power losses;resulting in high cost since more panels will be required to provide specified energy needs. To achieve high efficiency and low cost, MPPT has therefore become an imperative in PV systems. In this study, an MPP tracker is modeled using the IC algorithm and its behavior under rapidly changing environmental conditions of temperature and irradiation levels is investigated. This algorithm, based on knowledge of the variation of the conductance of PV cells and the operating point with respect to the voltage and current of the panel calculates the slope of the power characteristics to determine the MPP as the peak of the curve. A simple circuit model of the DC-DC boost converter connected to a PV panel is used in the simulation;and the output of the boost converter is fed through a 3-phase inverter to an electricity grid. The model was simulated and tested using MATLAB/Simulink. Simulation results show the effectiveness of the IC algorithm for tracking the MPP in PV systems operating under rapidly changing temperatures and irradiations with a settling time of 2 seconds.

A NON－INCREMENTAL TIME－SPACE ALGORITHM FOR NUMERICAL SIMULATION OF FORMING PROCESS

A non-incremental time-space algorithm is proposed for numerical. analysis of forming process with the inclusion of geometrical, material, contact-frictional nonlinearities. Unlike the widely used Newton-Raphson counterpart, the present scheme features an iterative solution procedure on entire time and space domain. Validity and feasibility of foe present scheme are further justiced by the numerical investigation herewith presented.

Springback prediction for incremental sheet forming based on FEM-PSONN technology

In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.

Reliability-based multidisciplinary design optimization using incremental shifting vector strategy and its application in electronic product design

Use of multidisciplinary analysis in reliabilitybased design optimization(RBDO) results in the emergence of the important method of reliability-based multidisciplinary design optimization(RBMDO). To enhance the efficiency and convergence of the overall solution process,a decoupling algorithm for RBMDO is proposed herein.Firstly, to decouple the multidisciplinary analysis using the individual disciplinary feasible(IDF) approach, the RBMDO is converted into a conventional form of RBDO. Secondly,the incremental shifting vector(ISV) strategy is adopted to decouple the nested optimization of RBDO into a sequential iteration process composed of design optimization and reliability analysis, thereby improving the efficiency significantly. Finally, the proposed RBMDO method is applied to the design of two actual electronic products: an aerial camera and a car pad. For these two applications, two RBMDO models are created, each containing several finite element models(FEMs) and relatively strong coupling between the involved disciplines. The computational results demonstrate the effectiveness of the proposed method.

A new insertion sequence for incremental Delaunay triangulation

Incremental algorithm is one of the most popular procedures for constructing Delaunay triangulations （DTs）. However, the point insertion sequence has a great impact on the amount of work needed for the construction of DTs. It affects the time for both point location and structure update, and hence the overall computational time of the triangulation algorithm. In this paper, a simple deterministic insertion sequence is proposed based on the breadth-first-search on a Kd-tree with some minor modifications for better performance. Using parent nodes as search-hints, the proposed insertion sequence proves to be faster and more stable than the Hilbert curve order and biased randomized insertion order （BRIO）, especially for non-uniform point distributions over a wide range of benchmark examples.

Analysis and optimization of variable depth increments in sheet metal incremental forming

A method utilizing variable depth increments during incremental forming was proposed and then optimized based on numerical simulation and intelligent algorithm.Initially,a finite element method(FEM) model was set up and then experimentally verified.And the relation between depth increment and the minimum thickness tmin as well as its location was analyzed through the FEM model.Afterwards,the variation of depth increments was defined.The designed part was divided into three areas according to the main deformation mechanism,with Di(i=1,2) representing the two dividing locations.And three different values of depth increment,Δzi(i=1,2,3) were utilized for the three areas,respectively.Additionally,an orthogonal test was established to research the relation between the five process parameters(D and Δz) and tmin as well as its location.The result shows that Δz2 has the most significant influence on the thickness distribution for the corresponding area is the largest one.Finally,a single evaluating indicator,taking into account of both tmin and its location,was formatted with a linear weighted model.And the process parameters were optimized through a genetic algorithm integrated with an artificial neural network based on the evaluating index.The result shows that the proposed algorithm is satisfactory for the optimization of variable depth increment.

Incremental Network Programming for Wireless Sensors

We present an incremental network programming mechanism which reprograms wireless sensors quickly by transmitting the incremental changes using the Rsync algorithm;we generate the difference of the two program images allowing us to distribute only the key changes. Unlike previous approaches, our design does not assume any prior knowledge of the program code structure and can be applied to any hardware platform. To meet the resource constraints of wireless sensors, we tuned the Rsync algorithm which was originally made for updating binary files among powerful host machines. The sensor node processes the delivery and the decoding of the difference script separately making it easy to extend for multi-hop network programming. We are able to get a speed-up of 9.1 for changing a constant and 2.1 to 2.5 for changing a few lines in the source code.

Fast Discovering Frequent Patterns for Incremental XML Queries

It is nontrivial to maintain such discovered frequent query patterns in real XML-DBMS because the transaction database of queries may allow frequent updates and such updates may not only invalidate some existing frequent query patterns but also generate some new frequent query patterns. In this paper, two incremental updating algorithms, FUX-QMiner and FUXQMiner, are proposed for efficient maintenance of discovered frequent query patterns and generation the new frequent query patterns when new XMI, queries are added into the database. Experimental results from our implementation show that the proposed algorithms have good performance. Key words XML - frequent query pattern - incremental algorithm - data mining CLC number TP 311 Foudation item: Supported by the Youthful Foundation for Scientific Research of University of Shanghai for Science and TechnologyBiography: PENG Dun-lu (1974-), male, Associate professor, Ph.D, research direction: data mining, Web service and its application, peerto-peer computing.

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

The existing Maximum Power Point Tracking(MPPT)method has low tracking efficiency and poor stability.It is easy to fall into the Local Maximum Power Point(LMPP)in Partial Shading Condition(PSC),resulting in the degradation of output power quality and efficiency.It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms,and their performance in tracking the Global Maximum Power Point(GMPP)varies.Thus,a Cuckoo search algorithm(CSA)combined with the Incremental conductance Algorithm(INC)is proposed(CSA-INC)is put forward for the MPPT method of photovoltaic power generation.The method can improve the tracking speed by more than 52%compared with the traditional Cuckoo Search Algorithm(CSA),and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization(PSO)and the Gravitational Search Algorithm(GSA).CSA-INC has an average tracking efficiency of 99.99%and an average tracking time of 0.19 s when tracking the GMPP,which improves PV power generation’s efficiency and power quality.

基于增量式PID算法的香精施加系统设计

为解决加热不燃烧烟草制品生产过程中香精施加量无法根据薄片质量变化进行实时调控的问题,设计了一种基于增量式PID算法的香精施加系统。该系统由称重单元、螺杆泵、喷嘴、流量传感器等组成,通过称重单元实时获取薄片质量数据,根据理论计算模型确定香精施加量,基于增量式PID算法驱动螺杆泵定量输出香精至喷嘴中,利用流量传感器对香精施加量实时反馈,实现香精精准施加。验证结果表明,当薄片质量发生变化时,香精施加量可快速调整,最大超调量为1%,最长调整时间为1.8 s,系统具有较好的跟踪性能;施加量检测值与理论值的偏移率为0.23%~0.52%,施加量组内波动为3.11%~4.17%,系统可实现香精的准确、均匀、稳定施加。

丘陵山地柑橘果园机器人自主导航与精确控制系统设计与试验

为实现丘陵山地柑橘果园机器人的自主导航,设计了一种基于激光雷达和惯性传感器的果园自主导航系统.使用激光SLAM融合多传感器所获数据,并利用误差状态卡尔曼滤波器进行位姿优化,从而构建果园的三维全局点云地图.对全局地图进行分析和处理,确定机器人的安全行驶区域,并应用基于机器人运动学多约束条件的三次非均匀B样条曲线轨迹优化算法实现路径规划,使用NDT-ICP算法实现机器人在全局地图中的定位.为适应丘陵山地果园地形复杂性,提出了一种基于预瞄跟踪的自校正增量PID控制策略,利用递归最小二乘法实时调整PID参数,并在机器人履带式行走机构上进行了系统整合测试.试验结果表明,误差状态卡尔曼滤波器优化后的地图精度与优化前相比有明显提升.机器人以1.2 m/s速度行驶时,导航控制系统的直线行驶平均位置偏差和航向角偏差分别为0.18 m和4.2°,转弯行驶平均位置偏差和航向角偏差分别为0.38 m和16.7°,可满足丘陵山地柑橘果园智能农机自主导航作业需求.

基于组合相似度动态聚类和词熵的网络话题在线检测

[研究目的]为实现网络热点话题的在线检测,提升增量式聚类算法的聚类效果,提出了基于组合相似度的动态聚类算法,同时通过计算词熵实现主题词提取和演化跟踪。[研究方法]通过CIFG-BiLSTM-CRF模型实现文本的命名实体识别,计算文本与话题的实体相似度,再取文本词向量与话题中心余弦相似度的最大值作为词向量相似度,二者结合判断文本所属话题。在聚类过程中利用时间窗口策略实现话题中心和成员文本的动态更新。同时,计算文本词熵,生成话题的词熵和列表,实现话题主题词提取和演化跟踪。实验以新冠疫情新闻为数据实现话题在线检测,并展示了话题主题词的演化和跟踪过程。[研究结论]实验表明,与传统相似度计算方法相比,组合相似度能够获得更好的聚类效果,聚类过程中提取出的话题主题词也正确地反映了原始数据的热点话题内容。

自动确定边角混合网独立闭合差的增量算法

为评定外业观测数据质量和发现粗差,提出了一种闭合差搜索的通用算法。首先,针对高速铁路隧道中的各类边角混合网,基于平差理论,计算由边角混合网中测站数、非测站数和观测数确定其独立闭合差的数目,给出最简闭合差构成原则,求出余弦和正弦闭合差,推导闭合差限差计算模型,建立由闭合差探测观测值粗差的方法;进而导出闭合差增量与测站递增产生的重叠观测点数之间的数学关系式,设计数据结构实现测站与结点间双向多对多的拓扑映射关系,并基于分治思想,提出队列-测站双循环的增量算法来自动搜索最简且独立的闭合差;最后,以西南某高速铁路隧道构建的边角混合网为例进行算法验证。结果表明:增量算法能够在混合网中自动确定100%数量的最简独立闭合差,对观测值中存在的粗差100%予以探测,验证了采用该算法搜索闭合差及检核粗差的有效性。

基于SLAM技术的矿区巷道巡检机器人路径规划优化

针对矿区巷道环境复杂、道路狭窄等特点,提出了一种基于SLAM(SimultaneousLocalizationandMapping)技术的矿区巷道巡检机器人路径规划优化方法,实现机器人的自主定位和地图构建。采用激光雷达、RGB-D相机等多种传感器相融合,获取矿区三维点云数据,并使用SLAM算法实时构建矿区三维地图。同时,通过配准当前获取的点云数据与已构建的地图,实现机器人在矿区内的自主定位。针对矿山中存在的狭窄、弯曲、分支等复杂环境,提出了一种增量式A~*优化算法用于路径规划。该算法在传统A~*算法的基础上,引入了路径平滑、走廊宽度约束等优化策略,能生成满足矿区复杂环境约束的平滑可行路径。算法采用增量式方式更新,只需对改变的局部区域重新进行路径搜索,大大减少了整体路径规划的计算耗时。通过试验验证该算法性能,结果表明:与传统路线规划方法相比,该算法能够快速、精准地完成巡检任务,为矿区巷道巡检机器人推广应用提供了参考。

三阶段自适应采样和增量克里金辅助的昂贵高维优化算法

代理辅助进化算法已广泛应用于求解代价高昂的多目标优化问题,但大多数由于代理模型的局限性而仅限于解决决策变量低维的问题。为了解决高维的昂贵多目标优化问题,提出了一种基于三阶段自适应采样策略的改进增量克里金辅助的进化算法。该算法使用改进的增量克里金模型来近似每个目标函数,此模型的超参数根据预测的不确定性进行自适应更新,降低计算复杂度的同时保证模型在高维上的准确性;此外,在模型管理方面提出一种三阶段自适应采样的策略,将采样过程分为不同的优化阶段以更有针对性的选择个体,能够首先保证收敛性,提高算法的收敛速度。为了验证算法的有效性,在包含各种特征的两组测试问题DTLZ(deb-thiele-laumanns-zitzler)、MaF(many-objective function)和路径规划实际工程问题上与最新的同类型算法进行实验对比,结果表明该算法在解决决策变量高维的昂贵多目标优化问题上具有较强的竞争力。

考虑复杂扰动的轻型商用车路径跟踪混合控制方法

外界扰动、模型不确定性和参数摄动等复杂扰动直接影响智能车辆路径跟踪控制的精度和行驶安全性。商用车的载重特性使其在行驶过程中受到的复杂扰动更为明显,为提升自动驾驶商用车路径跟踪精度和平顺性,提出一种路径跟踪混合控制方法。首先,建立鲁棒性强的基于扩张观测器的滑模控制器和变化平稳的增量式LQR控制器,其中增量式LQR的参数使用粒子群算法整定。然后,使用模糊控制器将两者相结合,根据车速和横向误差调整权重系数,在提升系统鲁棒性的同时削弱抖振。最后,进行了仿真分析和实车试验。试验数据表明,SMC+LQR具备较好的控制效果,能应对外界的复杂扰动。

基于改进LGWO-INC算法的MPPT控制研究

光伏发电阵列板在局部遮阴下会产生多个功率峰值,传统算法难以准确快速追踪光伏最大功率点(maximum power point,MPP),该文提出一种基于莱维飞行灰狼算法(Levy grey wolf optimization,LGWO)与电导增量法(incremental conductance,INC)结合的复合算法追寻MPP,莱维飞行帮助灰狼算法跳出局部最优,搜寻MPP附近时,切换电导增量算法减少系统振荡,在静态与动态局部遮阴下通过Simulink进行光伏并网仿真验证。研究结果显示,所提复合算法收敛效果快速精确,并且符合并网谐波(total harmonic distortion,THD)含量要求,可保证系统的稳定运行。