粗糙集在无线传感网络中的智能信息处理研究

针对无线传感器网络中不精确、不确定数据问题,提出了将信息处理和粗糙集技术融为一体的新研究思路,并基于分层簇结构给出了一种层次型智能信息处理方法.无线传感器网络实时森林火灾监测的示例与分析表明,该方法在实际应用中,通过从三个层次进行知识约简等智能数据分析,挖掘实用决策规则,使传感器节点仅自动获取和传送有效的最小数据集信息,实现了智能信息处理、能量消耗和系统性能之间的平衡.     

一种无线传感网络信息处理的层次型智能方法

针对减少无线传感器网络信息获取和处理代价这一关键性问题，提出了将信息处理和粗糙集技术融为一体的新的研究思路，并设计了一种层次型智能信息处理方法。在无线传感器网络实时森林火灾监测的实际应用中，该方法通过从3个层次进行智能数据分析，使传感器节点仅自动获取和传送有用的最小数据集信息，从而有效地使用节点受限资源。     

智能电网WCSN安全体系架构研究

作为下一代电力系统,智能电网应具备向电网控制中心实时可靠传输信息的能力。为了解决智能电网无线传感器网络面临的异构无线网络共存、频谱资源紧张和海量数据处理等问题,我们在智能电网引入了无线认知传感器网络的理念。文章分析了在智能电网中引入认知无线传感器网络（WCSN）的必要性,列举了智能电网、认知无线电网络和无线传感器网络的相关研究成果,描述了智能电网WCSN分层结构包括家域网、邻域网和广域网,提出了智能电网WCSN安全架构。     

基于SIR模型的智能电网WCSN数据伪造攻击研究

为了解决智能电网无线传感器网络面临的异构无线网络共存、频谱资源紧张和海量数据处理等问题,在智能电网引入了认知无线传感器网络（WCSN）。文章对智能电网WCSN中的数据伪造攻击进行了研究。在此类攻击中,恶意认知无线传感器节点（传染节点）通过向其他认知无线传感器节点（易感节点）发送伪造的频谱感知数据和设备能耗信息,导致控制中心做出错误的频谱分配和电力调度决策。采用流行病理论中的SIR模型,对智能电网WCSN中的数据伪造攻击信息传播过程进行了建模,研究了流行病爆发的潜在决定因素。最后,通过仿真验证了智能电网WCSN数据伪造攻击SIR模型,并对系统动态特性进行了分析。     

无线传感器中数据管理技术的探讨

无线传感器网络是一门获取和处理信息的新兴技术,它以数据为中心,提供数据采集、处理和查询功能,其根本任务是准确获取物理世界的有价值信息。数据存储和数据查询是无线传感器网络研究中的重点和热点问题。本文探讨了无线传感器网络的数据存储与查询技术。     

多层Agent数据融合的无线传感器决策网络及其多样性研究

为实现无线传感器网络（WSN）原始感知数据融合中不确定分析的量化决策和智能处理,在 WSN层次化的数据融合机制下,引入粗糙集理论的决策表和决策网络智能技术,设计智能型和规则型2类 Agent,由此构建多层 Agent数据融合的无线传感器决策网络（WSDN）模型,并研究层间智能 Agent型和属性间规则 Agent型 WSDN的多样性。应用结果表明,该模型能较好地适用于典型的 WSN场合,实现 Agent间的融合优化与智能决策。     

无线传感器网络疑误数据节点自动诊断方法

为了提高无线传感器网络疑误数据检测能力,提出基于轮换调度的无线传感器网络疑误数据节点自动诊断方法。通过采用分块区域特征匹配的方法,得到无线传感器网络疑误数据传输的梯度模型,采用资源优化分配方案,进行数据传输信道的均衡调度,得到节点部署分布模型。通过传感信息跟踪采样方法,得到采样信息分布,建立无线传感器网络疑误数据信息特征分析,通过分组特征检测方法进行无线传感器网络疑误数据的信息融合和空间融合调度,提取无线传感器网络疑误数据的关联规则特征集,通过统计信息分析和融合调度的方法,进行无线传感器网络疑误数据的聚类挖掘,采用预算估计算法,得到疑误数据节点定位优化,结合自主学习算法,实现无线传感器网络疑误数据节点的优化定位和诊断检测。仿真结果表明,采用该方法进行无线传感器网络疑误数据节点检测的自适应性较好,特征辨识能力较强。     

无线传感器网络路由协议研究进展

融合了传感器、微机电系统和网络通信等技术而形成的无线传感器网络(WSN)是一种全新的信息获取和处理技术.路由协议是无线传感器网络研究中富有挑战性的问题,现有无线自组网络和互联网络路由协议不适用于无线传感器网络.无线传感器网络路由协议主要考虑如何针对不同的应用和网络构成使用有效的路由算法,在不损害数据传输的前提下实现整个网络的生命周期最大化.根据网络中节点功能是否一致,无线传感器网络包括平面和分层路由协议两大类.本文根据各种协议的工作原理,对比分析了各种路由协议在路由方式、冲突避免、数据融合和网络生命周期最大化等方面的优劣,并指出了下一步的研究方向.     

无线传感器网络中分布式数据挖掘算法研究

研究无线传感器网络中数据挖掘问题.针对大量高维冗余且不确定的无线传感器网络监测数据传送到中央服务器上使用传统的数据挖掘方法进行挖掘的缺点相当明显,导致耗费了珍贵的网络能量.为解决上述问题,提出在每个传感器节点上进行基于粗糙集与遗传BP网络相结合的分布式数据挖掘算法,利用GA来训练BP网络的阈值和权值来构成遗传BP网络(GABP),克服BP网络的某些不足;利用粗糙集RS在消除冗余信息、处理不确定性数据等方面的优势,缩减训练数据输入BP网络的维度.仿真结果表明,改进算法能有效地实现对无线传感器网络中的数据进行分类,取得了较理想的效果.     

无线传感器网络在智能交通中的应用

针对目前中国的交叉路口多、车流量大、交通混乱等现象,阐述了基于无线传感器网络的智能交通系统的基本原理。结合国内外将无线传感器网络应用于智能交通网系统的已有研究应用情况,指出了无线传感器网络技术应用于智能交通中的交通信息采集、交通信息传输、交通控制和诱导等方面的几个关键技术,以便为进一步研究并拓展无线传感器网络在智能交通中的应用提供参考。     

A fault tolerant single-chip intelligent agent with feature extraction capability

Autonomy and adaptability are key features of intelligent agents. Many applications of intelligent agents, such as the control of ambient intelligence environments and autonomous intelligent robotic systems, require the processing of information coming in from many available sensors to produce adequate output responses in changing scenarios. Autonomy, in these cases, applies not only to the ability of the agent to produce correct outputs without human guidance, but also to its ubiquity and/or portability, low-power consumption and integrability. In this sense, an embedded electronic system implementation paradigm can be applied to the design of autonomous intelligent agents in order to satisfy the above mentioned characteristics. However, processing complex computational intelligence algorithms with tight delay constraints in resource-constrained and low power embedded systems is a challenging engineering problem. In this paper a single-chip intelligent agent based on a computationally efficient neuro-fuzzy information processing core is described. The system has been endowed with an information preprocessing module based on Principal Component Analysis (PCA) that permits a substantial reduction of the input space dimensionality with little loss of modeling capability. Moreover, the PCA module has been tested as a means to achieve deep adaptability in changing environment dynamics and to endow the agent with fault tolerance in the presence of sensor failures. For data driven trials and research, a data set obtained from an experimental intelligent inhabited environment has been used as a benchmark system.     

无线传感器网络智能信息处理研究

由大量微小传感器节点组成的无线传感器网络主要用于从目标对象收集信息,但由于节点资源受限,给无线传感器网络的信息处理带来了严峻挑战,为此,必须采取简单、高效的处理策略.本文综述了无线传感器网路环境下智能信息处理的最新研究进展,包括网内聚合、数据压缩和分布式存储和查询等方法,对各种算法的优缺点进行评述,并指出了其关键问题.孙优贤(1940-),男,教授,博士生导师,中国工程院院土,研究方向为复杂系统理论、分布控制系统以及企业综合自动化等.     

A process distribution approach for multisensor data fusion systems based on geographical dataspace partitioning

In this work, we present a new approach to distributed sensor data fusion (SDF) systems in multitarget tracking, called TSDF (Tessellated SDF), centered around a geographical partitioning (tessellation) of the data. A functional decomposition divides SDF into components that can be assigned to processing units, parallelizing the processing. The tessellation implicitly defines the set of tracks potentially yielding correlations with the sensor plots (observations) in a tile. Some tracks may occur as correlation candidates for multiple tiles. Conflicts caused by correlations of such tracks with plots in different tiles, are resolved by combining all involved tracks and plots into independent data association problems. The benefit of the TSDF approach to a clustering-based process distribution is independence of the problem space, which yields better scalability and manageability characteristics. The TSDF approach allows scaling in more than one way. It allows SDF for single sensor, multiple sensors on a single platform, and even for multiple sensors on multiple platforms. It also provides the flexibility to scale the processing to the size of the problem. This enables a better control of the throughput, to meet various timing constraints.     

基于粗糙集方法的不完备信息系统的知识推理

含空值决策表的分析一直是智能信息处理领域的一个难点,论文讨论基于赋值容差关系的数据不完全的信息系统中的数据处理方法,并用实例说明不完全决策表中属性约简和决策规则的发现方法。     

CREATING AND USING CAUSAL MODELS

I describe an approach to the problem of forming hypotheses about hidden mechanisms within devices—the “black box” problem for physical systems. The approach involves enumerating different causal structures for devices, placing an ordering on these hypothesis types, and carefully controlling the construction of hypotheses, as well as enumerating a set of physical and causal constraints to prune hypotheses. I relate in detail the performance of an implemented causal modeling system on the surprisingly puzzling pocket tire gauge. Results from several examples indicate that the ideas presented support capabilities for maintaining manageably sized hypothesis sets and for making fine distinctions among hypotheses.

I also describe the use of causal models in the problem of monitoring physical systems. Specifically, I address two issues that arise in the task of detecting anomalous behavior in complex systems with numerous sensor channels: how to adjust alarm thresholds dynamically, within the changing operating context of the system, and how to use sensors selectively, so that nominal operation can be verified reliably without processing a prohibitive amount of sensor data. My approach involves simulation of a causal model of the system, which provides information on expected sensor values, and on dependencies between predicted events, that is useful in assessing the relative importance of events so that sensor resources can be allocated effectively.     

Forest fire analysis with remote sensing data

Forest fires cause major damage to the environment, human health and property, and endanger life. Fires can be monitored and analysed over large areas in a timely and cost-effective manner by using satellite sensor imagery in combination with spatial analysis as provided by Geographical Information Systems (GIS). In this study, the forest area damage caused by a large fire which occurred in the Marmaris, province of Mugla in July 1996 was analysed using satellite sensor images. Digital image processing methods, such as spectral profile analysis, vegetation indices and multispectral classification, were applied to the satellite sensor images acquired before and after the forest fire. Besides the conventional maximum likelihood classification algorithm, a multilayer feed-forward neural network architecture was also used for comparison and evaluation of its effectiveness. A GIS database was constructed from the raster (satellite sensor data), vector (the forest type and topographical maps) and ancillary data (meteorological data). The GIS is being used to develop an information and decision support system to monitor and predict forest fire activity, and to enhance fire management efficiency. This study highlights the deficiencies in the current approach to fire management and emphasizes the need for an improved method along the lines outlined.     

基于神经网络的智能传感器的数据处理

为提高智能传感器的测量准确度,利用神经网络良好的非线性映射能力,对传感器的标定数据进行输入-输出特性的反非线性逼近,同时,利用传感器实验数据进行神经网络的训练。结果表明:与传统的数据处理方法相比较,利用神经网络进行的传感器数据处理,能使传感器的准确度由±6.67提高到到±0.98。     

基于模糊控制的智能感控视力保护仪

针对青少年长时间使用电子产品导致视力下降的问题,提出一种基于模糊控制的智能感控视力保护仪;该保护仪的控制系统综合运用了传感器技术、数字信息处理技术、模拟数据处理技术以及智能算法,实现了软硬件的结合;针对检测过程中距离和光照强度难调节的问题,引入了模糊控制算法,通过模糊控制规则来调节距离和光照,并进行了仿真实验,将采集到的光照强度和检测距离作为输入量,定时时间作为输出量,达到提醒学生的目的 ;对比实验结果表明,模糊控制比PID控制的定时时间增加了10.6 min,相对时间增大了18.563％,能够为学生创造更良好的学习环境.