无线传感器网络中异常读数检测算法研究*

针对无线传感器网络的离群点检测算法由于没有充分考虑数据的时空关联性和网络的分布特性,导致检测精度低、通信量大和计算复杂度高等局限,提出了基于时空关联的分布计算与过滤的在线离群点检测算法。该算法在各传感器节点上利用传感器读数的时间关联性生成候选离群点,并利用空间关联性对候选离群点进行过滤得到局部离群点,最终将所有传感器节点上的局部离群点集中到sink节点上获得全局离群点。利用时空关联性提高了检测精度,利用分布计算与过滤减少了通信量和计算量,理论分析和实验结果均表明该算法优于现有算法。     

Evolutionary learning of dynamic probabilistic models with large time lags

In this paper, we explore the automatic explanation of multivariate time series (MTS) through learning dynamic Bayesian networks (DBNs). We have developed an evolutionary algorithm which exploits certain characteristics of MTS in order to generate good networks as quickly as possible. We compare this algorithm to other standard learning algorithms that have traditionally been used for static Bayesian networks but are adapted for DBNs in this paper. These are extensively tested on both synthetic and real-world MTS for various aspects of efficiency and accuracy. By proposing a simple representation scheme, an efficient learning methodology, and several useful heuristics, we have found that the proposed method is more efficient for learning DBNs from MTS with large time lags, especially in time-demanding situations. © 2001 John Wiley & Sons, Inc.     

Incremental computation of time-varying query expressions

We present and analyze algorithms for the incremental computation of time-varying queries in which selection predicates refer to the state of a clock. Such queries occur naturally in many situations where temporal data are processed. Incremental techniques for query computation have proven to be more efficient than other techniques in many situations. However, all existing incremental techniques for query computation assume that old query results remain valid if no intermediate changes are made to the underlying database. Unfortunately, this assumption does not hold for time-varying queries whose results may change just because time passes. In order to solve this problem, we introduce the notion of a superview which contains all current tuples that will eventually satisfy the selection predicate of a time-varying selection. Based on the notion of superview, we develop efficient algorithms for the incremental computation of time-varying selections. Our algorithms, combined with existing incremental algorithms, allow complex time-varying queries to benefit from the proven efficiency of incremental techniques. It is important to notice that without our algorithms, the existing algorithms for incremental computation would be useless for any time-varying query expression     

A statistical complement to deterministic algorithms for the retrieval of aerosol optical thickness from radiance data

As a complement to the conventional deterministic geophysical algorithms, we consider a faster, but less accurate approach: training regression models to predict aerosol optical thickness (AOT) from radiance data. In our study, neural networks trained on a global data set are employed as a global retrieval method. Inverse distance spatial interpolation and region-specific neural networks trained on restricted, localized areas provide local models. We then develop two integrated statistical methods: local error correction of global retrievals and an optimal weighted average of global and local components. The algorithms are evaluated on the problem of deriving AOT from raw radiances observed by the Multi-angle Imaging SpectroRadiometer (MISR) instrument onboard NASA's Terra satellite. Integrated statistical approaches were clearly superior to global and local models alone. The best compromise between speed and accuracy was obtained through the weighted averaging of global neural networks and spatial interpolation. The results show that, while much faster, statistical retrievals can be quite comparable in accuracy to the far more computationally demanding deterministic methods. Differences in quality vary with season and model complexity.     

Objective functions for training new hidden units in constructiveneural networks

In this paper, we study a number of objective functions for training new hidden units in constructive algorithms for multilayer feedforward networks. The aim is to derive a class of objective functions the computation of which and the corresponding weight updates can be done in O(N) time, where N is the number of training patterns. Moreover, even though input weight freezing is applied during the process for computational efficiency, the convergence property of the constructive algorithms using these objective functions is still preserved. We also propose a few computational tricks that can be used to improve the optimization of the objective functions under practical situations. Their relative performance in a set of two-dimensional regression problems is also discussed.     

Community mining using three closely joint techniques based on community mutual membership and refinement strategy

Community structure has become one of the central studies of the topological structure of complex networks in the past decades. Although many advanced approaches have been proposed to identify community structure, those state-of-the-art methods still lack efficiency in terms of a balance between stability, accuracy and computation time. Here, we propose an algorithm with different stages, called TJA-net, to efficiently identify communities in a large network with a good balance between accuracy, stability and computation time. First, we propose an initial labeling algorithm, called ILPA, combining K-nearest neighbor (KNN) and label propagation algorithm (LPA). To produce a number of sub-communities automatically, ILPA iteratively labels a node in a network using the labels of its adjacent nodes and their index of closeness. Next, we merge sub-communities using the mutual membership of two communities. Finally, a refinement strategy is designed for modifying the label of the wrongly clustered nodes at boundaries. In our approach, we propose and use modularity density as the objective function rather than the commonly used modularity. This can deal with the issue of the resolution limit for different network structures enhancing the result precision. We present a series of experiments with artificial and real data set and compare the results obtained by our proposed algorithm with the ones obtained by the state-of-the-art algorithms, which shows the effectiveness of our proposed approach. The experimental results on large-scale artificial networks and real networks illustrate the superiority of our algorithm.     

A local adaptive weighted interpolation for deinterlacing

In this paper, a local weighted interpolation method for intra-field deinterlacing is proposed as an improved version of the DCS (deinterlacing with awareness of closeness and similarity) algorithm. The original DCS method is derived from bilateral filter which takes the local spatial closeness and pixel similarity into account when calculating the weight of interpolation. The proposed algorithm achieves three improvements: 1) instead of the line average, a more accurate interpolation filter is used to estimate the center missing pixel; 2) the center-independent interpolation method is proposed to replace the center-dependent interpolation strategy; 3) the adaptive weighted interpolation method is used to improve the accuracy of interpolation. Experimental results show that the proposed algorithm provides superior performance in terms of both objective and subjective image qualities when compared with other conventional benchmarks, including DCS algorithms with low complexity.     

空间投影在K-means算法中的研究与应用

为了加快[K]-means计算速度和寻找最优聚类子空间,使用特定的变换矩阵对数据进行投影,将特征空间划分为聚类空间和噪声空间,前者包含全部空间结构信息,后者不包含任何信息。将噪声空间舍弃,在聚类空间下进行[K]-means每一次迭代。算法不同于PCA [K]-means先降维再聚类,而是在迭代过程中达到筛选维度的效果,并将保留的维度反馈给下一次迭代,同时聚类空间的维度信息是自动发现的,没有引入额外的参数。实验证明AC [K]-means算法相较于已有同类型算法在准确度和计算时间方面都得到了大幅提升。     

天气雷达网资料拼图方法研究

天气雷达组网拼图,是克服单部雷达探测范围有限,发挥多部雷达相互辅助大范围监测灾害性天气的有效手段。本文在分析现有天气雷达组网拼图方法和存在问题的基础上,对天气雷达组网拼图的资料网格化、重叠区域处理、资料投影等方面作了进一步研究。对雷达反射率因子网格化问题,对比分析了Barnes和双线性插值算法的特性,说明Barnes插值算法网格化资料平滑且能较好地保存雷达资料特征;在重叠区域的资料订正方面,采用概率分布方法,实现了对雷达资料作用距离和天线指北等系统误差的订正,结果表明订正后的资料在重叠区域中各雷达资料的吻合度得到了加强;最后采用兰勃特投影法将天气雷达组网拼图投影到统一底图中。     

Spatial interpolation of McArthur's Forest Fire Danger Index across Australia: Observational study

Fire danger indices are used by fire management agencies to assess fire weather conditions and issue public warnings. The most widely used fire danger indices in Australia are the McArthur Fire Forest Danger Index and the Grassland Fire Danger Index. These indices are calculated at weather stations using measurements of weather variables and fuel information. For a vast country like Australia when assessing the risk of severe fire weather events, it is also important to calculate the spatial distribution of these indices considering the extreme tail of the distribution. The spatial distribution of one of the fire weather danger indices regularly used in Australia is presented in this paper. In particular, we present the spatial distribution of the long-term tendency of extreme values of the McArthur Forest Fire Danger Index (FFDI). This indicator of fire weather conditions was assessed by calculating the return period of its extreme values by fitting extreme value distributions to data sets of FFDI at 78 recording stations around Australia. The spatial distribution of these return periods was obtained by using spatial interpolation algorithms with the recording stations measurements. Two conventional and two new algorithms based on machine-learning techniques were tested. This study shows that the best interpolation results for the FFDI can be obtained by using a combination of random forest and inverse distance weighting interpolation algorithms. The spatial distribution of the seasonal FFDI return period shows that the highest FFDI over large parts of southern Australia occurs during the summer months whilst in northern Australia it occurs in spring. The results also show that the FFDI in eastern Australia, the most populated region of the country, is higher inland than in the coastal areas particularly during spring and summer.