基于免疫赦免机制的智能分布式入侵检测系统

入侵检测系统是在网络系统遭受攻击之前进行拦截和响应的一种积极主动的安全防护系统。分布式智能入侵检测系统DIIDS(Distributed Intelligent Intrusion Detection System)就是在分布式环境中将人工智能技术应用于入侵检测中,从而可以提高IDS(Intrusion Detection System)的检测的实时性、准确性和容错性。本文设计了一种基于免疫赦免机制的DIIDS。该方法以降低入侵检测系统的时延性与提高入侵检测系统的智能性、恢复能力为核心,改善入侵检测系统的实时性。为实现用户信息的安全转移和检测系统自身的免疫进化,构建了一种免疫赦免智能Agent植入机制(IPAT),从而提高了系统的可用性。     

Efficient collision detection using a dual OBB-sphere bounding volume hierarchy

We present an efficient algorithm for collision detection between static rigid objects using a dual bounding volume hierarchy which consists of an oriented bounding box (OBB) tree enhanced with bounding spheres. This approach combines the compactness of OBBs and the simplicity of spheres. The majority of distant objects are separated using the simpler sphere tests. The remaining objects are in close proximity, where some separation axes are significantly more effective than others. We select 5 from among the 15 potential separating axes for OBBs. Experimental results show that our algorithm achieves considerable speedup in most cases with respect to the existing OBB algorithms.     

A probabilistic reputation model based on transaction ratings

This work introduces a probabilistic model allowing to compute reputation scores as close as possible to their intrinsic value, according to the model. It is based on the following, natural, consumer-provider interaction model. Consumers are assumed to order items from providers, who each has some intrinsic, latent, “quality of service” score. In the basic model, the providers supply the items with a quality following a normal law, centered on their intrinsic “quality of service”. The consumers, after the reception and the inspection of the item, rate it according to a linear function of its quality - a standard regression model. This regression model accounts for the bias of the consumer in providing ratings as well as his reactivity towards changes in item quality. Moreover, the constancy of the provider in supplying an equal quality level when delivering the items is estimated by the standard deviation of his normal law of item quality generation. Symmetrically, the consistency of the consumer in providing similar ratings for a given quality is quantified by the standard deviation of his normal law of ratings generation. Two extensions of this basic model are considered as well: a model accounting for truncation of the ratings and a Bayesian model assuming a prior distribution on the parameters. Expectation-maximization algorithms, allowing to estimate the parameters based on the ratings, are developed for all the models. The experiments suggest that these models are able to extract useful information from the ratings, are robust towards adverse behaviors such as cheating, and are competitive in comparison with standard methods. Even if the suggested models do not show considerable improvements over other competing models (such as Brockhoff and Skovgaard’s model [12]), they, however, also permit to estimate interesting features over the raters - such as their reactivity, bias, consistency, reliability, or expectation.     

Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada

To effectively manage forested ecosystems an accurate characterization of species distribution is required. In this study we assess the utility of hyperspectral Airborne Imaging Spectrometer for Applications (AISA) imagery and small footprint discrete return Light Detection and Ranging (LiDAR) data for mapping 11 tree species in and around the Gulf Islands National Park Reserve, in coastal South-western Canada. Using hyperspectral imagery yielded producer's and user's accuracies for most species ranging from > 52-95.4 and > 63-87.8%, respectively. For species dominated by definable growth stages, pixel-level fusion of hyperspectral imagery with LiDAR-derived height and volumetric canopy profile data increased both producer's (+ 5.1-11.6%) and user's (+ 8.4-18.8%) accuracies. McNemar's tests confirmed that improvements in overall accuracies associated with the inclusion of LiDAR-derived structural information were statistically significant (p < 0.05). This methodology establishes a specific framework for mapping key species with greater detail and accuracy then is possible using conventional approaches (i.e., aerial photograph interpretation), or either technology on its own. Furthermore, in the study area, acquisition and processing costs were lower than a conventional aerial photograph interpretation campaign, making hyperspectral/LiDAR fusion a viable replacement technology.     

无线传感器网络中基于RSSI的节点距离预测

无线传感器网络大量应用在环境监测、目标跟踪、安全监控等领域,因此网络的自身定位是大多数应用的基础。常用的定位方法必须测量节点间的距离。为了预测距离值,根据实验获取的RSSI值与对应的距离值,先对实验数据进行滤波处理,建立面向Matlab神经网络工具箱的神经网络预测模型,利用神经网络的特性和Matlab工具箱的强大功能,通过实测数据对网络进行训练。预测结果表明,距离精度达到1 m之内。     

海洋食品微生物生长智能通用预测平台

针对海洋食品对虾生产加工的特点,设计与开发了对虾生产加工中微生物生长智能通用预测平台,为海洋食品的安全生产与加工提供分析手段。     

A class of high-resolution algorithms for incompressible flows

We present a class of a high-resolution Godunov-type algorithms for solving flow problems governed by the incompressible Navier-Stokes equations. The algorithms use high-resolution finite volume methods developed in LeVeque (SIAM J Numer Anal 1996;33:627-665) for the advective terms and finite difference methods for the diffusion and the Poisson pressure equation. The high-resolution algorithm advects the cell-centered velocities using the divergence-free cell-edge velocities. The resulting cell-centered velocity is then updated by the solution of the Poisson equation. The algorithms are proven to be robust for constant-density flows at high Reynolds numbers via an example of lid-driven cavity flow. With a slight modification for the projection operator in the constant-density solvers, the algorithms also solve incompressible flows with finite-amplitude density variation. The strength of such algorithms is illustrated through problems like Rayleigh-Taylor instability and the Boussinesq equations for Rayleigh-Bénard convection. Numerical studies of the convergence and order of accuracy for the velocity field are provided. While simulations for two-dimensional regular-geometry problems are presented in this study, in principle, extension of the algorithms to three dimensions with complex geometry is feasible.     

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。     

基于RBF预估神经网络控制器的无线传感器网络拥塞算法

无线传感器网络中,汇聚节点是网络的瓶颈.由于传感器网络自身的特点,传统有线网络中的拥塞控制策略不再适用.已有的大多数拥塞控制策略和算法都没有充分考虑往返时延(RTT)对算法性能的影响.同时由于实际传感器网络运行中存在非线性、时间延迟和参数时变等干扰因素,若设计的控制器参数固定,不具有学习能力,则实际运行中收敛性差,收敛速度慢,无法达到控制队列长度的目标.针对以上问题,提出一种基于灰色预估神经网络控制队列的控制器,利用RBF神经网络的自学习能力解决网络实时变化时算法参数的在线整定问题,并利用灰色GM(1,1)预测器有效地解决了大时滞对网络性能的影响,最后通过仿真验证了这一算法的有效性.