Determining the number of principal components for best reconstruction

A well-defined variance of reconstruction error (VRE) is proposed to determine the number of principal components in a PCA model for best reconstruction. Unlike most other methods in the literature, this proposed VRE method has a guaranteed minimum over the number of PC's corresponding to the best reconstruction. Therefore, it avoids the arbitrariness of other methods with monotonic indices. The VRE can also be used to remove variables that are little correlated with others and cannot be reliably reconstructed from the correlation-based PCA model. The effectiveness of this method is demonstrated with a simulated process.     

直流绝缘故障检测智能传感器系统

为实现直流供电系统中的绝缘故障检测,研制了一种以AVR单片机为核心的智能传感器系统。该系统利用电磁感应原理检测直流供电系统中电气负载馈电回路的不平衡电流,实现了对电气负载绝缘故障的检测。经过现场测试,该智能传感器系统具有低功耗、高可靠性和易使用的特点,试验结果表明:系统达到了检测准确度要求。     

Distributed routing for signal detection in wireless sensor networks

We study the problem of energy-efficient routing for signal detection in wireless sensor networks. Generic routing protocols use networking-centric measures such as minimum hop or minimum energy to establish routes. These schemes do not take into account the performance of application-specific algorithms that is achievable from the data collected by the nodes along the routes. Routing protocols for signal detection have recently been proposed to facilitate joint optimization of detection performance and energy efficiency by developing metrics that connect detection performance with energy consumption of each link along the routes. In existing routing for signal detection (RSD) schemes, however, the routes are computed centrally requiring complex optimization algorithms and global information such as locations and observation coefficients of all nodes in the network. Clearly, for large-scale networks, or networks with dynamically changing topologies, distributed routing schemes are more practical due to their better flexibility and scalability. We present a distributed RSD protocol where each node, based on locally available information, selects its next-hop with the goal of maximizing the detection performance associated with unit energy expenditure. We show that the proposed protocol is readily implementable in ZigBee networks, and present simulation results that reveal its significant improvements in detection performance and energy efficiency over generic routing protocols.     

Variable reconstruction and sensor fault identification using canonical variate analysis

The detection and identification of faults in dynamic continuous processes has received considerable recent attention from researchers in academia and industry. In this paper, a canonical variate analysis (CVA)-based sensor fault detection and identification method via variable reconstruction is described. Several previous studies have shown that CVA-based monitoring techniques can effectively detect faults in dynamic processes. Here we define two monitoring indices in the state and noise spaces for fault detection and, for sensor fault identification, we propose three variable reconstruction algorithms based on the proposed monitoring indices. The variable reconstruction algorithms are based on the concepts of conditional mean replacement and object function minimization. The proposed approach is applied to a simulated continuous stirred tank reactor and the results are compared to those obtained using the traditional dynamic monitoring technique, dynamic principal component analysis (PCA). The results indicate that the proposed methodology is quite effective for monitoring dynamic processes in terms of sensor fault detection and identification.     

用于神经信号检测的传感器系统的研究

探讨了一种用于神经信号检测的新型传感器系统的设计。提出了一种由二维无源电极和前置信号放大模块微装配而成的三维有源微电极阵列的结构形式,在对电极和电解液接触面间的电学模型进行分析的基础上,完成了二维无源电极和前置放大模块的设计。仿真结果验证了传感器系统用来检测神经信号的可行性和可靠性。     

基于一次投影子空间追踪的压缩感知信号重构

为了降低信号重构算法的复杂度,实现对稀疏度未知信号的重构,提出了一种基于一次投影子空间追踪(OPSP)的信号重构方法。首先根据约束等距性质确定信号稀疏度的上下界,并将最接近上下界中值的整数作为稀疏度的估计值;然后在子空间追踪(SP)算法的框架下,去掉了迭代中观测向量在支撑集上的投影过程,降低了算法的复杂度。为了更准确地衡量算法的重构性能,提出用完整信号的重构概率作为衡量算法重构性能的指标。与传统的SP算法相比,所提算法可以重构稀疏度未知的信号,且重构时间短,重构概率高。仿真结果验证了该算法的有效性。     

基于图信号处理的无线传感器网络异常节点检测算法

针对无线传感器网络（WSN）中传感器自身安全性低、检测区域恶劣及资源受限造成节点采集数据异常的问题,提出一种基于图信号处理的WSN异常节点检测算法。首先,依据传感器位置特征建立K-近邻（KNN）图信号模型;然后,基于图信号在低通滤波前后的平滑度之比构建统计检验量;最后,通过统计检验量与判决门限实现异常节点存在性的判断。通过在公开的气温数据集与PM2.5数据集上的仿真验证,实验结果表明,与基于图频域异常检测算法相比,在单个节点异常情况相同条件下,所提算法检测率提升7个百分点;在多个节点异常情况相同条件下,其检测率均达到98%,并且在网络节点异常偏离值较小时仍具有较高的检测率。     

基于磁阻传感器的弱磁信号采集系统设计

针对磁阻传感器输出的微弱磁信号,介绍了一种有效的模拟信号调理方法,设计了一种基于各向异性磁阻传感器的弱磁信号采集系统,通过实验验证和分析,本系统可以成功地应用于弱磁信号的检测与数据的获取,证明了该采集系统的可行性、实用性。     

线性调频信号数字脉压滤波器优化设计

针对线性调频(LFM)信号的高脉压旁瓣问题,提出了一种基于二阶锥规化的数字脉压旁瓣抑制滤波器设计方法.该方法以期望的压缩脉冲波形主瓣为参考,对脉压旁瓣进行控制,将脉压滤波器的设计转化为二阶锥规化问题进行求解.与已有方法相比,所提方法可以兼顾峰值旁瓣、主瓣宽度、处理增益损失等指标,具有设计灵活的优点.仿真结果验证了该方法的有效性,与常用的频域加窗方法相比,该方法的旁瓣抑制效果得到了改善,在归一化多普勒频移为0.005时,脉压主副比仍在44 dB以上,并且脉压信号的相位与参考响应基本一致,较好地满足了现代雷达系统的要求.     

Sliding mode observers for detection and reconstruction of sensor faults

This paper proposes two methods for detecting and reconstructing sensor faults using sliding mode observers. In both methods, fictitious systems are introduced in which the original sensor fault appears as an actuator fault. The original sensor faults are then reconstructed using a ‘secondary’ sliding mode observer. For both methods, there are certain conditions which must be satisfied for successful fault detection and reconstruction. The methods are demonstrated using a chemical process example.     

Sensor selection for received signal strength-based source localization in wireless sensor networks

Generally, localization is a nonlinear problem, while linearization is used to simplify this problem. Reasonable approximations could be achieved when signal-to-noise ratio (SNR) is large enough. Energy is a critical resource in wireless sensor networks, and system lifetime needs to be prolonged through the use of energy efficient strategies during system operation. In this paper, a closed-form solution for received signal strength (RSS)-based source localization in wireless sensor network (WSN) is obtained...     

基于FBG传感器的变压器振动信号检测与分析

为了早期发现电力变压器的振动故障问题,需要选择有效位置对变压器的振动状态进行检测.针对电力变压器铁芯-绕组振动特性,通过有限元分析,仿真得到铁芯-绕组测点位置.以型号为S13-12500/35型油浸式无励磁调压35 kV电力变压器为试验对象,将光纤Bragg光栅(FBG)振动传感器安装于铁芯-绕组的测点位置,对不同负载下变压器振动信号进行检测与频谱分析,结果表明:变压器振动信号频率集中在100 Hz及其倍频处;在80％,90％,100％负载下,幅频信号100 Hz处的振动幅值随着变压器负载的增大而增大.     

基于卡尔曼滤波的时变稀疏信号快速收敛重构算法

为了提高时变稀疏信号的重构性能,提出了融合压缩感知和卡尔曼滤波的快速收敛重构算法。通过伪测量（ PM）技术以及扩展卡尔曼滤波形式的线性化将近似l0范数约束有效地融合到卡尔曼滤波架构中,求解常规的卡尔曼滤波方程,重构出稀疏信号。此外,针对PM阶段的扩展卡尔曼滤波形式,提出了快速收敛算法,有效地保证了算法的收敛和精度。仿真结果表明,相比原有基于卡尔曼滤波的恢复算法,所提算法的重构精度更高、收敛更快。     

嵌入式彩色图像传感器重构系统的设计与实现

为了获取高品质的彩色图像,实现特殊作业现场的图像采集,提出了一种嵌入式彩色CCD图像传感器重构系统的设计方法.通过对图像边界方向的有效判断来完善图像高频区和低频区边界的质量,同时增加三原色之间的耦合性来提高图像清晰度,并且完成了系统的嵌入式底层实现.测试证明:该系统色彩还原效果良好,重构处理时间为34ms,稳定性好,实...     

材料力学微应变信号检测系统设计与实现

材料力学应变测量实验中,试件在小负荷作用下所产生的应变信号十分微弱。根据微弱应变信号的特征,采用微弱信号检测原理设计出信号调理方案并对应变信号进行处理,在此基础上设计出微应变数据采集与处理系统。     

基于振动信号的传感器节点性能测试系统设计

传感器节点用于采集轮轨作用下钢轨的振动信号以分析钢轨振动特性。为了测试传感器节点的工作性能,设计并实现了能提供稳定振动信号的测试系统。测试系统基于MSP430单片机,由控制单元、激振器、简支梁结构以及监控软件组成。实验表明：在给定加载条件下测试系统可稳定提供10N的激振力,产生峰值为93×10－6的动态应变和97 gn 的振动加速度。通过分析采集数据的时频特性,可以有效发现故障节点。     

震动信号识别传感器系统设计

设计了一种具有信号识别功能的震动传感器,用于监测地面运动引起的信号,实现安防区域24h无人监控.系统利用处理器实现信号识别算法,通过无线芯片将识别结果发送给数据控制系统中心,实现传感器信号识别和信息传输功能.实验测试结果表明:该系统能够在3s内有效地对25m距离内的人和车辆信号进行识别,正确识别率达96％;在10s内对100 m距离内的轮式车和履带式车识别,正确识别率达90％.     

基于TMS系列DSP实现LFM信号的实时脉冲压缩

研究了以定点通用型DSP芯片TMS320VC5509为硬件核心的LFM信号实时脉冲压缩技术。在CCS 3.3软件开发平台上实现,采用C语言编程,结合Matlab仿真得到LFM信号和匹配滤波器的系数,在时域实现脉冲压缩算法。为有效抑制副瓣的影响,采用海明窗加权函数对滤波器进行调制。采用单位阶数耗时衡量滤波算法,通过增加滤波模块接口的方法对数据存储更新方式进行优化,并结合FIR滤波器的特性简化了卷积算法。测试表明,系统工作稳定,处理精度和实时性均达到了较高标准,可以满足工程应用的要求。     

Two empirical methods for improving the performance of statistical multirate high-resolution signal reconstruction

The problem of reconstructing a known high-resolution signal from a set of its low-resolution parts exposed to additive white Gaussian noise is addressed in this paper from the perspective of statistical multirate signal processing. To enhance the performance of the existing high-resolution signal reconstruction procedure that is based on using a set of linear periodically time-varying (LPTV) Wiener filter structures, we propose two empirical methods combining empirical mode decomposition- and least squares support vector machine regression-based noise reduction schemes with these filter structures. The methods originate from the idea of reducing the effects of white Gaussian noise present in the low-resolution observations before applying them directly to the LPTV Wiener filters. Performances of the proposed methods are evaluated over one-dimensional simulated signals and two-dimensional images. Simulation results show that, under certain conditions, considerable improvements have been achieved by the proposed methods when compared with the previous study that only uses a set of LPTV Wiener filter structures for the signal reconstruction process.     

基于SKPCA的卫星整流罩空调系统传感器故障检测研究

针对卫星整流罩空调系统对传感器故障检测的高可靠性的要求,在分析常见故障模式的基础上,采用了基于统计量核主元分析（ SKPCA）的故障检测方法,建立整流罩空调系统传感器故障检测模型,对整流罩空调系统传感器容易出现的偏置故障进行了验证实验。实验结果验证了SKPCA方法在整流罩空调系统传感器故障检测中的正确性和有效性。