Diagnosis and Localization of Pipeline Leak Based on Fuzzy Decision-making Method

A leak detection plays a key role in the overall integrity monitoring for a oil pipeline system.A fuzzy decision-making approach to pipeline leak localization is proposed in this paper. The two main methods,pressure gradient localization and negative pressure wave localization,are combined with fuzzy logical decision-making method to form a novel fault diagnosis scheme.The combination scheme can improve the precision of localization.An application example,14km long oil pipeline leak detection and localization,is illustrated.This method is compared with others through practical experiments and its validity is confirmed by the results.     

基于Minkowskey距离的模糊入侵检测方法研究

After analyzing the deficiency of traditional intrusion detection system, a new kind of intrusion detection method based on Minkowskey is put forward for reducing false positive rate. The essence of it is to set up a normal behavior fuzzy sub collection A on the basis of watching the normal system transfer of the privilege process, and set up a fuzzy sub collection B with real time transfer array, then detect with the principle of minimum distance in fuzzy discern method.     

A fuzzy logic based supervisory hierarchical control scheme for real time pressure control

This paper describes a supervisory hierarchical fuzzy controller （SHFC） for regulating pressure in a real-time pilot pressure control system. The input scaling factor tuning of a direct expert controller is made using the error and process input parameters in a closed loop system in order to obtain better controller performance for set-point change and load disturbances. This on-line tuning method reduces operator involvement and enhances the controller performance to a wide operating range. The hierarchical control scheme consists of an intelligent upper level supervisory fuzzy controller and a lower level direct fuzzy controller. The upper level controller provides a mechanism to the main goal of the system and the lower level controller delivers the solutions to a particular situation. The control algorithm for the proposed scheme has been developed and tested using an ARM7 microcontroller-based embedded target board for a nonlinear pressure process having dead time. To demonstrate the effectiveness, the results of the proposed hierarchical controller, fuzzy controller and conventional proportional-integral （PI） controller are analyzed. The results prove that the SHFC performance is better in terms of stability and robustness than the conventional control methods.     

Fault Detection under Fuzzy Model Uncertainty

The paper tackles the problem of robust fault detection using Takagi-Sugeno fuzzy models. A model-based strategy is employed to generate residuals in order to make a decision about the state of the process. Unfortunately, such a method is corrupted by model uncertainty due to the fact that in real applications there exists a model-reality mismatch. In order to ensure reliable fault detection the adaptive threshold technique is used to deal with the mentioned problem. The paper focuses also on fuzzy model design procedure. The bounded-error approach is applied to generating the rules for the model using available measurements. The proposed approach is applied to fault detection in the DC laboratory engine.     

An Efficient Method for Cleaning Dirty-Events over Uncertain Data in WSNs

Event detection in wireless sensor networks (WSNs) has attracted much attention due to its importance in many applications. The erroneous abnormal data generated during event detection are prone to lead to false detection results. Therefore, in order to improve the reliability of event detection, we propose a dirty-event cleaning method based on spatio-temporal correlations among sensor data. Unlike traditional fault-tolerant approaches, our method takes into account the inherent uncertainty of sensor measurements and focuses on the type of directional events. A probabilitybased mapping scheme is introduced, which maps uncertain sensor data into binary data. Moreover, we give formulated definitions of transient dirty-event (TDE) and permanent dirty-event (PDE), which are cleaned by a novel fuzzy method and a collaborative cleaning scheme, respectively. Extensive experimental results show the effectiveness of our dirty-event cleaning method.     

A self-recovery fragile watermarking scheme for image authentication with superior localization

To address the problems of the inferior localization and high probability of false rejection in existing self-recovery fragile watermarking algorithms, this paper proposes a new self-recovery fragile watermarking scheme with superior localization, and further discusses the probability of false rejection （PFR） and the probability of false acceptance （PFA） of the proposed scheme. Moreover, four measurements are defined to evaluate the quality of a recovered image. In the proposed algorithm, the original image is divided into 2×2 blocks to improve localization precision and decrease PFR under occurrence of random tampering. The PFR under occurrence of region tampering can be effectively decreased by randomly embedding the watermark of each block in conjunction with a novel method of tamper detection. Compared with the current self-recovery fragile watermarking algorithms, the proposed scheme not only resolves the tamper detection problem of self-embedding watermarking, but also improves the robustness against the random tampering of self-embedding watermarking. In addition, the subjective measurements are provided to evaluate the performance of the self-recovery watermarking schemes for image authentication.     

基于语义单元的足球视频事件检测

A semantic unit based event detection scheme in soccer videos is proposed in this paper. The scheme can be characterized as a three-layer framework. At the lowest layer, low-level features including color, texture, edge, shape, and motion are extracted. High-level semantic events are defined at the highest layer. In order to connect low-level features and high-level semantics, we design and define some semantic units at the intermediate layer. A semantic unit is composed of a sequence of consecutives frames with the same cue that is deduced from low-level features. Based on semantic units, a Bayesian network is used to reason the probabilities of events. The experiments for shoot and card event detection in soccer videos show that the proposed method has an encouraging performance.     

Level Set method for license plate localization technology

Localization of license plate is an important factor in license plate recognition system. Currently although there are some methods for the localization, some limits such as low accuracy exist. So a better method should be found to solve this problem. Level Set, which has been proved efficient currently, gives new prospect to license plate localization. In this paper, based on the original thought of Level Set method, the Mumford-Shah model with Level Set method is obtained, further the finite difference and a third order TVD （Total Variation Diminishing） Runge-Kutta time discretization scheme is analyzed, and applied in license plate image localization. Computation results show that better edge detection results from level set method are obtained compared to other edge detection methods such as Roberts, Sobel and Canny. Level Set method drops much edge of non-target area which has a lot of value to target edge detection and target position tracking.     

Research on an improved wireless network intrusion detection algorithm

Data mining technology is applied to the network intrusion detection, and precision of the detection will be improved by the superiority of data mining. This paper, first analyzes the method of wireless network intrusion detection, presents a wireless network intrusion detection algorithm based on association rule mining. The application of fuzzy association rules in the wireless network intrusion detection is mainly discussed; a comparative analysis with the classical algorithm Apriori is made by experiment. The result shows that the fuzzy rule mining algorithm is more convenient than Apriori algorithm.     

Cyclic autocorrelation based blind OFDM detection and identification for cognitive radio

Cognitive radio is considered as a promising technique to increase the utilization of limited spectral resource. The key issue in cognitive radio is to design a reliable spectrum sensing method that is able to detect the signal in the target channel as well as to recognize different signals. In this paper, focusing on classifying different OFDM signals, we propose a two-step detection and identification approach. The key parameters to separate different OFDM signals are the subcarrier spacing and guard interval. A simple but reliable peak detection method is adopted in the first step, while a peak searching method is used to determine the length of guard interval. Simulations are carried out in AWGN to verify the validation of the proposed method. It is shown that our method can satisfy the detection and identification requirement with a low false alarm probability.     

基于负压波法的输油管道泄漏检测定位系统

为实现对石油管道的泄漏进行检测并定位,简要分析了国内外输油管道泄漏检测定位方法及其应用现状,重点研究了负压波泄漏检测与定位算法在实际中的应用.尤其对负压波定位公式中需要的首、末站压力拐点的时间差,即各站压力下降的拐点寻找进行了比较深入的研究与分析.在此基础上设计了一套基于ADAM5510的泄漏检测系统,给出了系统的总体组成结构、各部分功能及软件设计思想.现场试验表明设计的泄漏检测与定位方法具有实用价值.     

基于虚拟仪器的输油管道泄漏监测定位系统的实现

为了解决目前的输油管道泄漏监测和定位问题,提出了一个基于虚拟仪器的输油管道泄漏监测定位系统的实现方法。其采用了因特网来传输数据,提出了基于负压波和流量平衡法的四段法判断泄漏,分析了负压力波传到首末两站的时间差对泄漏定位的影响以及使用小波变换检测时间差的方法。系统在实际应用中取得了良好的效果。     

输油管网泄漏检测与定位方法的研究

提出了一种新颖的基于模式识别的输油管网泄漏检测与定位的方法．该方法采集管网每个端点的压力信号，并基于模式识别的原理对管网进行泄漏检测与定位．仿真实验结果表明了该方法的有效性．     

基于动态压力变送器的输油管道泄漏检测与定位系统

设计了一套基于动态压力变送器的输油管道泄漏检测与定位系统,阐述了其系统组成和检测原理.通过动态压力变送器获取管道的动态压力信号,采用基于经验模态分解的方法提取信号的特征向量,再利用支持向量机实现对管道泄漏的识别.最后采用相关时延估计算法获得管道泄漏点的位置.通过现场应用实例表明,动态压力变送器具有更高的检测灵敏度和泄漏分辨力.该系统能够对管道泄漏进行正确识别,可以有效地降低误报警率,并提高了泄漏检测的灵敏度和定位精度.     

基于信息融合的管道泄漏检测与定位技术应用研究

基于目前已经在石油输送管道中应用的SCADA系统将信息融合技术应用于管道泄漏检测与定位系统中;该方法应用管道泄漏时的多种特征信号,采用最优加权融合算法将多元信息综合处理,扩展了时间和空间上的检测范围,提高了泄漏检测系统的灵敏度和可靠性;在实验室用水泵和不锈钢管道组成的水循环系统进行了模拟实验,通过对比验证了信息融合检漏方法比单一检漏方法具有明显的优越性。     

基于大维数据驱动的油气管网泄漏监控模糊决策方法

输油管网状态量多及工艺复杂,难以建立精确的管网数学模型,为了能够实时监控管网的安全运行情况,本文提出一种基于大维数据驱动的管网泄漏监控模糊决策方法.首先利用管网现有的数据信息,在不对数据进行降维处理的情况下,从信息物理系统的角度出发,将油气管网的拓扑结构、阀门开度等管道物理数据以及压力、流量等运行信息数据结合起来对复杂管网系统建立数据驱动模型.然后基于大维随机矩阵谱理论,将得到的信息物理数据协方差矩阵谱分布及圆环率作为模糊决策的条件对管网运行情况进行判断.当管网拓扑发生动态变化时,提出的方法可以有效地解决误报率高的问题.最后通过仿真及实例的分析,可以证明所提出方法的有效性.     

基于信息融合算法的管道泄漏检测技术研究

基于目前已经在石油输送管道中应用的SCADA系统，将信息融合技术应用于管道泄漏检测系统中；该方法应用管道泄漏时的多种特征信号，采用最优加权融合算法将多元信息综合处理，扩展了时间和空间上的检测范围，提高了泄漏检测系统的灵敏度和可靠性。在实验室用水泵和不锈钢管道组成的水循环系统进行了模拟实验，通过对比验证了信息融合检漏方法比单一检漏方法具有明显的优越性。     

管道泄漏计算机在线检测系统及其算法实现

设计了管道泄满计算机在线检测系统的总体结构，提出了一种基于工况分析的逻辑判决故障检测算法，当输油管道工作状况不断发生改变时，这种方法能够动态估计管道运行数据的正常值范围，很好地解决输油管道泄漏的故障检测问题，结合GPS技术和负压波定位算法，大大提高了报警准确性和定位精度，现场测试结果和实际工业应用证明了该方法的有效性。     

Bootstrap与模糊聚类在管道泄漏检测的研究

管道泄漏信息缺失对可靠提取小泄漏与工况变化所产生的负压波模式的特征一直是一瓶颈，也是降低系统误报警率的关键因素之一。本文利用小样本统计的Bootstrap方法，对观测样本进行模拟采样并进行统计分析，在此基础上，对管道不同工况提取特征．并用模糊聚类分析的方法分类管道小泄漏与工况变化的运行模式。仿真实验表明该方法应用在管道泄漏诊断中是简单实用和可行的。