A simple decision tree-based disturbance monitoring system for VSC-based HVDC transmission link integrating a DFIG wind farm

Fault detection and classification is a key challenge for the protection of High Voltage DC (HVDC) transmission lines. In this paper, the Teager–Kaiser Energy Operator (TKEO) algorithm associated with a decision tree-based fault classi f ier is proposed to detect and classify various DC faults. The Change Identification Filter is applied to the average and differential current components, to detect the first instant of fault occurrence (above threshold) and register a Change Identified Point (CIP). Further, if a CIP is registered for a positive or negative line, only three samples of currents (i.e., CIP and each side of CIP) are sent to the proposed TKEO algorithm, which produces their respective 8 indices through which the, fault can be detected along with its classification. The new approach enables quicker detection allowing utility grids to be restored as soon as possible. This novel approach also reduces computing complexity and the time required to identify faults with classification. The importance and accuracy of the proposed scheme are also thor oughly tested and compared with other methods for various faults on HVDC transmission lines.     

室内场景下应用拓扑结构的高效路径规划算法

针对基于随机采样的路径规划算法效率低且采样具有随机性的问题,提出一种应用拓扑结构的高效路径规划算法ATIRRT^*。通过引入拓扑节点代替STIRRT*算法中Harris角点检测算法得到的特征点进行采样,给出基于阈值的自适应选择方法来消除路径骨架上提取的冗余特征点,利用该阈值得到的拓扑节点可以使随机树的扩展更具方向性,从而减少寻找初始路径的时间和代价。根据非单一父节点的连接方式加强交叉支路上的拓扑节点间的联系,通过节点扩充策略增加相邻拓扑节点间的节点数量以加快优化算法的收敛。在此基础上定义相关约束条件将初始路径分段并进行逐段优化,以提高优化算法的效率。在常规环境、狭长空间和仿真的室内环境3种类型地图上的仿真结果表明,相较于STIRRT^*算法,改进算法在规划路径长度上平均减少8%,在规划时间上平均降低10%,可快速地找到更优的初始路径,同时在优化过程中减少了无用的探索空间,提高了搜索效率。     

高性能众核处理器芯片时钟网络设计

随着芯片工艺演进与设计规模增加,高性能众核处理器芯片时钟网络设计面临时序和功耗的全方位挑战。为降低芯片时钟网络功耗并缓解时钟网络分布受片上偏差影响导致的时钟偏斜,在H-Tree+MESH混合时钟网络结构的基础上,结合新一代众核处理器芯片面积大及核心时钟网络分布广的特点,基于标准多源时钟树设计策略构建多源时钟树综合（MRCTS）结构,通过全局H-Tree时钟树保证芯片不同区域间时钟偏斜的稳定可控,利用局部时钟树综合进行关键路径的时序优化以实现时序收敛。实验结果表明,MRCTS能在保证时钟延时、时钟偏斜等性能参数可控的基础上,有效降低时钟网络的负载和功耗,大幅压缩综合子模块的布线资源,加速关键路径的时序收敛,并且在相同电源电压和时钟频率的实测条件下,可获得约22.15%的时钟网络功耗优化。     

基于贝叶斯树的主机异常检测

主要研究Windows平台下异常检测方法,提出了一种利用Windows Native API调用序列和基于贝叶斯树算法的主机服务进程规则和对应概率分布的生成算法,并建立正常模型.根据长为N-1的Windows Native APIs调用序列预测第N个调用的概率分布,对生成的概率序列用U检验方法作为异常检测算法.实验结果...     

Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones

In New Caledonia (21°S, 165°E), shade-grown coffee plantations were abandoned for economic reasons in the middle of the 20th century. Coffee species (Coffea arabica, C. canephora and C. liberica) were introduced from Africa in the late 19th century, they survived in the wild and spontaneously cross-hybridized. Coffee species were originally planted in native forest in association with leguminous trees (mostly introduced species) to improve their growth. Thus the canopy cover over rustic shade coffee plantations is heterogeneous with a majority of large crowns, attributed to leguminous trees. The aim of this study was to identify suitable areas for coffee inter-specific hybridization in New Caledonia using field based environmental parameters and remotely sensed predictors. Due to the complex structure of tropical vegetation, remote sensing imagery needs to be spatially accurate and to have the appropriate bands for monitoring vegetation cover. Quickbird panchromatic (black and white) imagery at 0.6 to 0.7 m spatial resolutions and multispectral imagery at 2.4 m spatial resolution were pansharpened and used for this study. The two most suitable remotely sensed indicators, canopy heterogeneity and tree crown size, were acquired by the sequential use of tree crown detection (neural network), image processing (such as textural analysis) and classification. All models were supervised and trained on learning data determined by human expertise. The final model has two remotely sensed indicators and three physical parameters based on the Digital Elevation Model: elevation, slope and water flow accumulation. Using these five predictive variables as inputs, two modelling methods, a decision tree and a neural network, were implemented. The decision tree, which showed 96.9% accuracy on the test set, revealed the involvement of ecological parameters in the hybridization of Coffea species. We showed that hybrid zones could be characterized by combinations of modalities, underlining the complexity of the environment concerned. For instance, forest heterogeneity and large crown size, steep slopes (> 53.5%) and elevation between 194 and 429 m asl, are favourable factors for Coffea inter-specific hybridization. The application of the neural network on the whole area gave a predictive map that distinguished the most suitable areas by means of a nonlinear continuous indicator. The map provides a confidence level for each area. The most favourable areas were geographically localized, providing a clue for the detection and conservation of favourable areas for Coffea species neo-diversity.     

基于故障树和模糊推理的故障诊断研究

针对地空导弹武器装备结构复杂,用传统专家系统进行故障诊断时存在知识获取困难和推理效率低的问题,提出了一种基于故障树分析和模糊推理相结合的智能故障诊断方法。对某型地空导弹武器装备的故障分析诊断表明,该方法具有诊断速度快、诊断结果准确率高的特点,有效实现了装备故障的智能分析诊断。     

基于抗体选择策略的克隆挖掘算法

针对如何有效地利用大量的原始数据分析现状来预测未来的问题,基于抗体选择策略提出一种克隆选择挖掘算法。通过评估抗体的支持度、可信度和亲和度,求得有效的关联规则。实验结果表明,该算法能较快地获得可理解的规则,并且具有较高的准确率。     

P2P存储系统副本一致性维护策略

副本策略是提高P2P存储系统的可扩展性、容错性、可用性的有效手段。随着新的应用发展,P2P中副本应用从只读静态文件共享转变为读写动态文件交互,副本的一致性维护变得越来越重要。提出一种副本信息传播树的维护策略,该策略把文件副本的相关信息保存在二叉树上,通过订阅和取消订阅算法维护副本的一致。实验证明该策略能有效减少维护开销,避免相关问题的出现。     

Tree pattern mining with tree automata constraints

Most work on pattern mining focuses on simple data structures such as itemsets and sequences of itemsets. However, a lot of recent applications dealing with complex data like chemical compounds, protein structures, XML and Web log databases and social networks, require much more sophisticated data structures such as trees and graphs. In these contexts, interesting patterns involve not only frequent object values (labels) appearing in the graphs (or trees) but also frequent specific topologies found in these structures. Recently, several techniques for tree and graph mining have been proposed in the literature. In this paper, we focus on constraint-based tree pattern mining. We propose to use tree automata as a mechanism to specify user constraints over tree patterns. We present the algorithm CoBMiner which allows user constraints specified by a tree automata to be incorporated in the mining process. An extensive set of experiments executed over synthetic and real data (XML documents and Web usage logs) allows us to conclude that incorporating constraints during the mining process is far more effective than filtering the interesting patterns after the mining process.     

基于结构标记树的XML可查询压缩方法

针对支持查询的XML数据压缩方法存在的路径和数据重复等问题,通过去除XML数据中的重复路径,简化XML数据结构,提出结构标记树的概念及其生成算法,设计一种基于结构标记树的可查询XML数据压缩方法SSTQC,对XML数据进行压缩和组织查询。SSTQC一次扫描XML文档,具有较好的的压缩性能和查询效率。