后验Monte Carlo-Gaussian采样粒子滤波WSN网络定位*

环境因素导致无线传感器网络定位存在噪声影响,实质上是非平滑的非线性问题,针对传统粒子滤波算法在处理该问题时精度不高的缺点,提出一种基于后验泊松分布的Monte Carlo-Gaussian重采样粒子滤波算法的无线传感器网络定位算法。首先,基于粒子滤波算法,借鉴扩展卡尔曼滤波算法采用近似后验高斯分布思想,设计了后验泊松分布Monte Carlo-Gaussian重采样粒子滤波器。其次,采用该滤波器设计实现了无线传感器网络定位算法,解决了非平滑非线性的噪声干扰定位问题。最后,分别对滤波器和定位算法的性能进行了对比仿真实验,结果验证了所提算法的有效性。     

基于代码结构信息的软件缺陷定位方法研究

大型软件系统的缺陷定位通常采用源代码与缺陷报告的文本相似性的排序方法,但是这种基于文本相似性的方法没能充分利用源代码结构信息,缺陷定位的准确率低。为了解决这个问题,本文提出了一种基于源代码结构（如类名、方法名、变量名、注释等）信息和历史缺陷报告信息进行缺陷定位的方法,对于源代码结构的不同部分,在与缺陷报告进行词语匹配时,赋予它们不同的权重,同时充分利用历史缺陷报告信息,提高缺陷定位的精度。本文使用了Eclipse作为测试项目进行实验,通过与BugLocator和BugScout等方法的结果进行对比,结果表明本文方法能显著的提高缺陷定位的精度,验证了本文方法的可行性和有效性。     

Development of a stable localized visual inspection system for underwater structures

To avert potential crisis from Japan’s aging infrastructure and declining birth rate, the Japanese Government is planning to introduce robotic technology for the inspection of social infrastructure (such as pipes, dams, and bridges). Recording underwater positions is a difficult task for human divers who undertake conventional dam inspections. This study presents the Anchor Diver 5.2 system for efficient and effectual dam inspection. Anchor Diver 5.2 is based on an extended-tether-maneuvered remotely operated vehicle (ROV) equipped with cameras. The ROV is lowered into water by a hoist system from a boat and implements a visual survey of the concrete underwater structure. To improve the visibility of the ROV in murky and cloudy water, a novel concept named Water Loupe is proposed. In addition, a simple boat-fixing method is proposed to provide a stable base on the water surface, and the underwater position of the ROV, which cannot be accessed by global positioning systems, is recorded using a feasible localization method. Finally, the developed system was evaluated in field experiments conducted in the Amagase Dam, Japan, and its merits and problems are discussed.     

Time–frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification

In this paper, we design time–frequency localized three-band biorthogonal linear phase wavelet filter bank for epileptic seizure electroencephalograph (EEG) signal classification. Time–frequency localized analysis and synthesis low-pass filters (LPF) are designed using convex semidefinite programming (SDP) by transforming a nonconvex problem into a convex SDP using semidefinite relaxation technique. Three-band parameterized lattice biorthogonal linear phase perfect reconstruction filter bank (BOLPPRFB) is chosen and nonlinear least squares algorithm is used to determine its parameters values that generate the designed analysis and synthesis LPF such that the band-pass and high-pass filters are also well localized in time and frequency domain. The designed analysis and synthesis three-band wavelet filter banks are compared with the standard two-band filter banks like Daubechies maximally regular filter banks, Cohen–Daubechies–Feauveau (CDF) biorthogonal filter banks and orthogonal time–frequency localized filter banks. Kruskal–Wallis statistical test is employed to measure the statistical significance of the subband features obtained from the various two and three-band filter banks for epileptic seizure EEG signal classification. The results show that the designed three-band analysis and synthesis filter banks both outperform two-band filter banks in the classification of seizure and seizure-free EEG signals. The designed three-band filter banks and multi-layer perceptron neural network (MLPNN) are further used together to implement a signal classifier that provides classification accuracy better than the recently reported results for epileptic seizure EEG signal classification.     

基于morlet小波与模态分析的突发型声发射源定位研究

在建立基于声发射技术的裂纹产生与拓展检测系统的基础上.以薄钢板为实验对象,研究了Lamb波在薄板中传播的频散及多模态特性,并结合morlet小波时频联合分析方法,提出了一种针对声发射信号在薄板中传播时产生的不同模态进行识别的方法.通过提取不同模态波到达同一传感器的时间差并依据不同模态波对应的波速实现对出现裂纹的位置进行定位.经过实验研究表明,运用该方法不仅能准确地识别A0、S0模态波并实现对声发射源进行精确定位.     

基于弹簧粒子模型的大规模WSN定位算法的衍生算法

为了提高基于弹簧粒子模型的大规模无线传感器网络定位算法(LASPM定位算法)的鲁棒性,将对LASPM基本定位算法进行优化及改进,并提出一系列的改进衍生算法.针对弱节点将设计简单的迭代定位方法,提出了3个补丁算法,分别用于处理局部极值、剔除坏节点和处理节点动态变化等问题.仿真实验结果表明,新算法的节点计算复杂度、通信复杂度在网络规模增大时仍然保持常量,节点计算步数不随网络规模变化而变化,时间复杂度也保持常量.实验研究结果表明,本文的定位算法具有良好的鲁棒性.     

基于蒙特卡洛方法的移动传感网节点定位优化算法

无线传感器网络正在被应用到各种各样的监测环境中,在这些应用场景中,传感器节点的位置信息大都是至关重要的.目前对传感器节点定位方面的研究大都只针对静态WSN的情况,对于移动WSN节点定位的研究仍然十分有限.该文提出了移动WSN中节点间互相优化定位的新思路,通过判断式筛选出定位精度高的节点,并协助其他节点进行定位条件的优化.所提出的算法TSBMCL通过更精确的裁剪待定位节点的蒙特卡洛盒,并增加节点的粒子滤波条件来实现节点的精确定位.大规模的仿真结果表明,该算法可精确的锁定节点位置区域,高效的采样得到节点的位置样本,相比于传统的移动WSN蒙特卡洛定位方法,大大提高了节点的定位精度.     

Path-planning and navigation of a mobile robot as discrete optimization problems

There is huge diversity among navigation and path-planning problems in the real world because of the enormous number and great variety of assumptions about the environments, constraints, and tasks imposed on a robot. To deal with this diversity, we propose a new solution to the path-planning and navigation of a mobile robot. In our approach, we formulated the following two problems at each time-step as discrete optimization problems: (1) estimation of a robot's location, and (2) action decision. For the first problem, we minimize an objective function that includes a data term, a constraint term, and a prediction term. This approach is an approximation of Markov localization. For the second problem, we define and minimize another objective function that includes a goal term, a smoothness term, and a collision term. Simulation results show the effectiveness of our approach. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

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.