一种改进的ZigBee网络Cluster-Tree路由算法

针对ZigBee网络Cluster-Tree算法只按父子关系选择路由可能会带来额外路由开销的问题,提出一种改进的Cluster-Tree路由算法。首先介绍ZigBee网络的地址分配机制,分析Cluster-Tree路由算法,并在此基础上引入邻居表提出改进算法。该算法的基本思想:如果选择邻居节点的路由开销与原算法相比更小,则会选择邻居节点作为下一跳。仿真结果表明,该算法可以减少约30%的路由开销。

Improved Data Stream Clustering Method: Incorporating KD-Tree for Typicality and Eccentricity-Based Approach

Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.

Density Clustering Algorithm Based on KD-Tree and Voting Rules

Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.

A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

Multi-Step Clustering of Smart Meters Time Series:Application to Demand Flexibility Characterization of SME Customers

Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.

ZigBee网络Cluster-Tree优化路由算法研究

通过分析ZigBee协议中Cluster-Tree和AODVjr算法的优缺点,提出一种基于Cluster-Tree+AODVjr的优化路由算法。该算法利用ZigBee协议中的邻居表,通过定义分区来确定目的节点的范围,从而控制广播RREQ分组的跳数,防止无效的RREQ泛洪。此优化算法能够有效地减小路由跳数,缩短传输时延,减少网络中死亡节点的数量,提高数据传送的成功率。

ZigBee传感网络Cluster-Tree改进路由算法研究

ZigBee技术的无线传感器网络是基于分布式地址分配的一种支持拓扑变化、节点移动的新型无线传感网络,拥有强大的自组网能力。针对ZigBee网络的Cluster-Tree算法对簇首能量要求高及节点间非最佳路由的问题,结合节点能量分析提出新的簇首产生办法,并结合AODVjr算法的思路来寻求节点间的最佳路由。仿真结果表明,改进的算法能够有效地提高数据发送成功率,降低网络中的死亡节点数,减小端到端的报文传输时延,提高网络的使用价值。

ZigBee中改进的Cluster-Tree路由算法

针对ZigBee网络的Cluster-Tree算法对簇首能量要求高、选择的路由非最佳路由等问题,结合节点能量分析和节点邻居表,提出一种改进的簇首生成方法,利用AODVjr算法为节点选择最佳路由。仿真结果证明,与原Cluster-Tree算法相比,改进的算法能有效提高数据发送成功率,减少源节点与目标节点间的跳数,降低端到端的报文传输时延,提高网络的使用价值。

基于ZigBee无线网络的Cluster-Tree路由算法研究

针对ZigBee无线网络中Cluster-Tree算法只依靠父子关系路由且ZigBee技术传输带宽的限制,致使网络中负载较重的链路不能及时传递信息,而造成网络拥塞、丢包和较低的吞吐量问题,提出了一种改进算法Z-DMHCTR。该算法针对负载超过一定限度的节点,除了按照原等级树算法路由之外,结合引入的邻居列表信息,寻找节点不与原路径相交的路径同时进行信息传输,从而提高网络带宽利用率,达到提升网络的吞吐量的目的。仿真实验主要从网络吞吐量、端到端数据传输延时等方面入手进行对比。结果表明,改进算法能够有效地提高网络吞吐量,并降低了传输数据的延时。

适于GlobalAllomeTree国际数据平台的标准化中国主要树种树高-胸径方程研建

GlobalAllomeTree作为共享异速方程的国际网络平台,逐渐受到全球高度关注。当前,为促进该项国际合作,针对当前该平台缺乏中国主要树种生长异速方程的现状,系统性更新标准化中国主要树种树高-胸径方程。由于树冠和下部灌木及草丛遮挡,树高相对于胸径测量具有一定的难度,因此需要使用数学工具进行计算。选取了36个树种为材料构建树高-胸径关系方程,以全国主要树种的二元材积模型、各地区一元材积表为基础材料,以取样径阶为1 cm间隔所生成1692组树高-胸径数据作为建立方程样本,1238组外业调查数据为验证样本。建模结果表明:36个主要树种的1692组树高-胸径数据建立的全国通用性树高-胸径方程拟合相关系数(R2)为0.801,方程拟合结果较好,说明可以通过测定胸径,带入树高(H,m)-胸径(D,cm)方程(H=aDb)预估树高;对36个主要树种的树高-胸径方程进行拟合,决定系数R2值均大于0.916,平均误差(ME)、平均绝对误差(MAE)和均方根误差(RMSE)相对较小,方程整体精度较高,可广泛推广;将外业采集的1238组树高-胸径数据,根据36个主要树种树高-胸径方程拟合公式及参数估计值a、b进行方程精度验证,方程预测的平均相对误差为16.86%,在误差允许范围内,并且模型形式规范,可为GlobalAllomeTree平台用户提供科学参考。

Application of Clustering-based Decision Tree in the Screening of Maize Germplasm

[Objective] This paper aims to construct an improved fuzzy decision tree which is based on clustering,and researches into its application in the screening of maize germplasm.[Method] A new decision tree algorithm based upon clustering is adopted in this paper,which is improved against the defect that traditional decision tree algorithm fails to handle samples of no classes.Meanwhile,the improved algorithm is also applied to the screening of maize varieties.Through the indices as leaf area,plant height,dry weight,potassium（K） utilization and others,maize seeds with strong tolerance of hypokalemic are filtered out.[Result] The algorithm in the screening of maize germplasm has great applicability and good performance.[Conclusion] In the future more efforts should be made to compare improved the performance of fuzzy decision tree based upon clustering with the performance of traditional fuzzy one,and it should be applied into more realistic problems.

A Chinese Web Page Clustering Algorithm Based on the Suffix Tree

In this paper, an improved algorithm, named STC-I, is proposed for Chinese Web page clustering based on Chinese language characteristics, which adopts a new unit choice principle and a novel suffix tree construction policy. The experimental results show that the new algorithm keeps advantages of STC, and is better than STC in precision and speed when they are used to cluster Chinese Web page. Key words clustering - suffix tree - Web mining CLC number TP 311 Foundation item: Supported by the National Information Industry Development Foundation of ChinaBiography: YANG Jian-wu (1973-), male, Ph. D, research direction: information retrieval and text mining.

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.

A Clustering-tree Topology Control Based on the Energy Forecast for Heterogeneous Wireless Sensor Networks

How to design an energy-efficient algorithm to maximize the network lifetime in complicated scenarios is a critical problem for heterogeneous wireless sensor networks (HWSN). In this paper, a clustering-tree topology control algorithm based on the energy forecast (CTEF) is proposed for saving energy and ensuring network load balancing, while considering the link quality, packet loss rate, etc. In CTEF, the average energy of the network is accurately predicted per round (the lifetime of the network is denoted by rounds) in terms of the difference between the ideal and actual average residual energy using central limit theorem and normal distribution mechanism, simultaneously. On this basis, cluster heads are selected by cost function (including the energy, link quality and packet loss rate) and their distance. The non-cluster heads are determined to join the cluster through the energy, distance and link quality. Furthermore, several non-cluster heads in each cluster are chosen as the relay nodes for transmitting data through multi-hop communication to decrease the load of each cluster-head and prolong the lifetime of the network. The simulation results show the efficiency of CTEF. Compared with low-energy adaptive clustering hierarchy (LEACH), energy dissipation forecast and clustering management (EDFCM) and efficient and dynamic clustering scheme (EDCS) protocols, CTEF has longer network lifetime and receives more data packets at base station. © 2014 Chinese Association of Automation.

基于自动终止准则改进的kd-tree粒子近邻搜索研究

对于大规模运动模拟问题而言,近邻点的搜索效率将对整体的运算效率产生显著影响。本文基于关联性分析建立kd-tree的最大深度dmax与粒子总数N的自适应关系式,提出了kd-tree自动终止准则,即ATC-kd-tree,同时还考虑了叶子节点大小阈值n_(0)对近邻搜索效率的影响。试验表明,ATC-kd-tree具有更高的近邻搜索效率,相较于不使用自动终止准则的kd-tree搜索效率最高提升46%,且适用性更强,可求解不同N值的近邻搜索问题,解决了粒子总数N发生改变时需要再次率定最大深度dmax的问题。同时,本文还提出了网格搜索法组合坐标下降法的两步参数优化算法GSCD法。通过2维阿米巴虫形状的参数优化试验发现,GSCD法可更为快速地率定ATC-kd-tree的可变参数,其优化效率比网格搜索法最高提升了205%,相较于改进网格搜索法最高提升了90%。研究结果表明,ATC-kd-tree和GSCD法不仅提高了近邻搜索的效率,也为复杂运动中近邻粒子搜索问题提供了一种更为高效的解决方案,能够显著降低计算资源的消耗,进一步提升模拟的精度和效率。

基于ZigBee网络的Cluster-Tree能量优化算法

在ZigBee网络的Cluster-Tree算法中,簇首节点容易过早耗尽自身能量,减少网络的整体寿命。针对此问题,给出了更改簇首节点的方法,避免剩余能量低的簇首节点转发大数据,减少节点到协调器的跳数,提高网络的应用价值。

CLUSTER OF WORKSTATIONS BASED ON DYNAMIC LOAD BALANCING FOR PARALLEL TREE COMPUTATION DEPTH-FIRST-SEARCH

The real problem in cluster of workstations is the changes in workstation power or number of workstations or dynmaic changes in the run time behavior of the application hamper the efficient use of resources. Dynamic load balancing is a technique for the parallel implementation of problems, which generate unpredictable workloads by migration work units from heavily loaded processor to lightly loaded processors at run time. This paper proposed an efficient load balancing method in which parallel tree computations depth first search (DFS) generates unpredictable, highly imbalance workloads and moves through different phases detectable at run time, where dynamic load balancing strategy is applicable in each phase running under the MPI(message passing interface) and Unix operating system on cluster of workstations parallel platform computing.

Street-Level IP Geolocation Algorithm Based on Landmarks Clustering

Existing IP geolocation algorithms based on delay similarity often rely on the principle that geographically adjacent IPs have similar delays.However,this principle is often invalid in real Internet environment,which leads to unreliable geolocation results.To improve the accuracy and reliability of locating IP in real Internet,a street-level IP geolocation algorithm based on landmarks clustering is proposed.Firstly,we use the probes to measure the known landmarks to obtain their delay vectors,and cluster landmarks using them.Secondly,the landmarks are clustered again by their latitude and longitude,and the intersection of these two clustering results is taken to form training sets.Thirdly,we train multiple neural networks to get the mapping relationship between delay and location in each training set.Finally,we determine one of the neural networks for the target by the delay similarity and relative hop counts,and then geolocate the target by this network.As it brings together the delay and geographical coordinates clustering,the proposed algorithm largely improves the inconsistency between them and enhances the mapping relationship between them.We evaluate the algorithm by a series of experiments in Hong Kong,Shanghai,Zhengzhou and New York.The experimental results show that the proposed algorithm achieves street-level IP geolocation,and comparing with existing typical streetlevel geolocation algorithms,the proposed algorithm improves the geolocation reliability significantly.

基于Blending-Clustering集成学习的大坝变形预测模型

【目的】变形是反映大坝结构性态最直观的效应量,构建科学合理的变形预测模型是保障大坝安全健康运行的重要手段。针对传统大坝变形预测模型预测精度低、误报率高等问题导致的错误报警现象,【方法】选取不同预测模型和聚类算法集成,构建了一种Blending-Clustering集成学习的大坝变形预测模型,该模型以Blending对单一预测模型集成提升预测精度为核心,并通过Clustering聚类优选预测值改善模型稳定性。以新疆某面板堆石坝变形监测数据为实例分析,通过多模型预测性能比较,对所提出模型的预测精度和稳定性进行全面评估。【结果】结果显示:Blending-Clustering模型将预测模型和聚类算法集成,均方根误差(RMSE)和归一化平均百分比误差(nMAPE)明显降低,模型的预测精度得到显著提高;回归相关系数(R~2)得到提升,模型具备更强的拟合能力;在面板堆石坝上22个测点变形数据集上的预测评价指标波动范围更小,模型的泛化性和稳定性得到有效增强。【结论】结果表明:Blending-Clustering集成预测模型对于预测精度、泛化性和稳定性均有明显提升,在实际工程具有一定的应用价值。

ZigBee网络中Cluster-Tree拓扑的改进与优化

无线传感器网络(WSN)是一种具有感测、计算和传输能力的小型传感器节点的集合。由于单个传感器节点的功能有限,特别是能量的存储和数据的存储,所以需要制定良好的网络拓扑结构和路由协议。文中重点研究基于IEEE 802.15.4标准的Zig Bee无线传感器网络,提出基于最小生成树(MST)的高效聚类拓扑结构MSCT,最终的目的在于以最小的成本构建一个网络拓扑结构。文中的拓扑结构还考虑了室内环境,提出了处理墙壁和障碍物导致的路径损耗和信号衰减的度量,并计算节点之间的链接的权重。最后,通过能量的损耗和网络的寿命来论证文中提出的MSCT拓扑结构比Cluster-Tree拓扑结构更具有优势性。