Self-calibration and algorithm of sample set of piezoresistive pressure sensors

Aiming at piezoresistive pressure sensors, this paper studies simulation of standard pressure by using benchmark current source and self-calibration of the sampling data characteristics. A data fusion algorithm for sample set is presented which transforms a surface problem into a curve fitting and interpolation problem. The simulation result shows that benchmark current source simulating pressure is successful and data fusion algorithm is effective. The maximum measurement error is only 0.098 kPa and maximum relative error is 0.92% at 0-45 kPa and -10-45~C.

A Multi-model Approach for Soft Sensor Development Based on Feature Extraction Using Weighted Kernel Fisher Criterion

Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenomenon in subclasses,so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory.In order to solve these problems,a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy,in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses.Furthermore,the classified data are used to develop a multiple model based on support vector machine.The proposed method is applied to a bisphenol A production process for prediction of the quality index.The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.

Distributed event region fault-tolerance based on weighted distance for wireless sensor networks

Event region detection is the important application for wireless sensor networks（WSNs）, where the existing faulty sensors would lead to drastic deterioration of network quality of service.Considering single-moment nodes fault-tolerance, a novel distributed fault-tolerant detection algorithm named distributed fault-tolerance based on weighted distance（DFWD） is proposed, which exploits the spatial correlation among sensor nodes and their redundant information.In sensor networks, neighborhood sensor nodes will be endowed with different relative weights respectively according to the distances between them and the central node.Having syncretized the weighted information of dual-neighborhood nodes appropriately, it is reasonable to decide the ultimate status of the central sensor node.Simultaneously, readings of faulty sensors would be corrected during this process.Simulation results demonstrate that the DFWD has a higher fault detection accuracy compared with other algorithms, and when the sensor fault probability is 10%, the DFWD can still correct more than 91% faulty sensor nodes, which significantly improves the performance of the whole sensor network.

Research on Data Fusion of Adaptive Weighted Multi-Source Sensor

Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.

Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.

A double weighted LS-SVM model for data estimation in wireless sensor networks

In wireless sensor networks, data missing is a common problem due to sensor faults, time synchronization, malicious attacks, and communication malfunctions, which may degrade the network＇ s performance or lead to inefficient decisions. Therefore, it is necessary to effectively estimate the missing data. A double weighted least squares support vector machines （DWLS-SVM） model for the missing data estimation in wireless sensor networks is proposed in this paper. The algo- rithm first applies the weighted LS-SVM （WLS-SVM） to estimate the missing data on temporal do- main and spatial domain respectively, and then uses the weighted average of these two candidates as the final estimated value. DWLS-SVM considers the possibility of outliers in the dataset and utilizes spatio-temporal dependencies among sensor nodes fully, which makes the estimate more robust and precise. Experimental results on real world dataset demonstrate that the proposed algorithm is outli- er robust and can estimate the missing values accurately.

Cooperative Nodes Localization for Three-Dimensional Underwater Wireless Sensor Network Based on Weighted Centroid Localization Algorithm

The underwater wireless sensor network(UWSN) has the features of mobility by drifting,less beacon nodes,longer time for localization and more energy consumption than the terrestrial sensor networks,which makes it more difficult to locate the nodes in marine environment.Aiming at the characteristics of UWSN,a kind of cooperative range-free localization method based on weighted centroid localization(WCL) algorithm for three-dimensional UWSN is proposed.The algorithm assigns the cooperative weights for the beacon nodes according to the received acoustic signal strength,and uses the located unknown nodes as the new beacon nodes to locate the other unknown nodes,so a fast localization can be achieved for the whole sensor networks.Simulation results indicate this method has higher localization accuracy than the centroid localization algorithm,and it needs less beacon nodes and achieves higher rate of effective localization.

Prediction-Based Distance Weighted Algorithm for Target Tracking in Binary Sensor Network

Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algorithms and particle filtering algorithm.The weighted algorithms have good realtime property,however have poor estimation property and some of them does not suit for target’s variable velocity model.The particle filtering algorithm can estimate target's position more accurately with poor realtime property and is not suitable for target’s constant velocity model.In this paper distance weight is adopted to estimate the target’s position,which is different from the existing distance weight in other papers.On the analysis of principle of distance weight (DW),prediction-based distance weighted(PDW) algorithm for target tracking in BSN is proposed.Simulation results proved PDW fits for target's constant and variable velocity models with accurate estimation and good realtime property.

基于组合加权k近邻分类的无线传感网络节点复制攻击检测方法

无线传感网络节点体积小,隐蔽性强,节点复制攻击检测的难度较大,为此提出一种基于组合加权k近邻分类的无线传感网络节点复制攻击检测方法。通过信标节点的空间位置数据与相距跳数得出各节点之间的相似程度,结合高斯径向基核函数求解未知节点的横轴、纵轴的空间坐标,确定各网络节点的空间位置;根据网络节点的属性特征与投票机制建立节点复制攻击模型,凭借组合加权k近邻分类法划分节点类型,并将结果传送至簇头节点,由簇头节点做出最后的仲裁,识别出节点复制攻击行为。仿真结果表明,所提方法的节点复制攻击检测率最大值为99.5%,最小值为97.9%,对节点复制攻击检测的耗时为5.41 s,通信开销数据包数量最大值为209个,最小值为81个。

无线传感网络拥塞控制中节点选择强制博弈方法

无线传感网络中节点数据流量较大,分布范围较广,节点选择寻优计算复杂,导致浪费大量算力在节点选择上,拥塞控制效果不佳。提出一种无线传感网络拥塞控制中节点选择强制博弈方法,计算单条网络链路传输成本,依据传输数据耗费能量,计算传输节点可用能量比值,在传输变量权重值基础上,求得传输节点实际拥堵指数,完成节点选择。计算网络连接层的数据包丢弃概率,确定网络拥塞程度,汇聚节点数据流建立节点选择强制博弈模型,明确传感网络稳态传输条件,利用流量正态分布算法,在节点选择博弈中做出强制选择。经仿真分析证明:所提方法分组递交率保持在83%以上,丢包数量保持在100 pkt/s之内,网络传输节点端到端的延迟在0.3 s以内。

基于椭球计算的集员滤波传感器故障检测方法

为了解决未知故障信号诊断问题,提出了一种基于椭球计算的集员滤波传感器故障检测方法。为了防止数据冲突和降低能量消耗,通过加权一次丢弃调度来控制传感器单元和滤波器之间的信息分发。然后有效将分布式集员滤波方法应用于处理WTOD协议下未知但有界的故障检测问题。进一步通过处理具有一定不等式约束的优化问题,对两个最小迹意义下的椭球进行了优化。最后仿真案例结果证明了提出方法能够有效实现传感器网络的未知故障信号检测,且不需要了解所有状态,极大地扩展了适用范围。

基于Hall和GMR的多传感器融合方法及实现

目前霍尔传感器(Hall)和巨磁阻(GMR)传感器均广泛地应用于电力系统电流测量。为同时发挥二者的优势、降低各自的局限性,在分析Hall和GMR的温度特性、噪声特性和被测电流范围的基础上,提出了一种基于Hall和GMR的多传感器融合方案。在定义GMR和Hall的灵敏度差值ΔS基础上,将被测电流i和灵敏度差值ΔS构成的融合域划分为四个域,在域Ⅱ采用多传感加权观测融合Kalman滤波算法,将Hall和GMR的观测量和观测噪声融合后与状态方程联立进行Kalman滤波;在域Ⅰ采用数据加权融合最优权值分配的方法,给Hall的测量数据赋予较大权值,GMR的测量数据赋予较小的权值;在域Ⅲ,权值分配情况相反,各域之间可实现数据融合的平滑过渡。基于多传感器融合方法,设计了一种组合式闭环电流传感器,包括磁芯、电路部分设计及仿真。仿真和样机实验结果表明,在域Ⅱ时多传感器融合值与真实值的均方根误差低至0.004;在域Ⅰ、Ⅲ时电流测量的相对误差E_(i)均在0.255%以下。与单一传感器相比,多传感器融合的方法使组合式传感器测量电流范围增大,适用于温度变化范围较大的场景,电流测量精度及可信度更高。

基于鲁棒马氏距离统计量的多源融合抗差估计方法

为了有效抵御复杂多变城市环境下的全球卫星导航系统(GNSS)信号干扰、增强多源融合定位可靠性,提出一种基于鲁棒马氏距离的多源融合抗差估计方法。该方法在分析观测值故障传播特点以及典型方差膨胀抗差估计模型基础上,基于相邻新息序列构造鲁棒马氏距离检验统计量。历史新息的引入能够提高系统观测冗余,同时不同观测量间的新息交互增强了异常检验统计量的鲁棒性。根据鲁棒马氏距离的统计特性,给出抗差关键门限取值规则并分别结合两种典型加权策略自适应调节观测值噪声矩阵。利用典型城市峡谷环境下惯性导航系统(INS)/GNSS/激光雷达(LiDAR)/VINS多源融合车载数据进行相关实验,与现有方法相较,所提方法能够将三维均方根定位误差最低限制在3.37 m。通过对比不同组显著性水平下的定位结果,进一步说明所提方法在城市峡谷环境下定位的优越性。

基于特征融合与域自适应的刀具磨损在线监测

机床状态监测对于机床健康管理以及保证工件加工质量具有重要意义。针对现有刀具磨损预测模型存在训练时间长、收敛速度慢以及泛化能力弱等问题,提出了一种分布式一维卷积神经网络对刀具磨损进行预测。采用残差连接与通道注意力模块顺序堆叠的方式作为特征提取模块,并通过交叉验证以选择合适的网络层数。由于不同传感器所提取到的特征信息可能存在冗余,使用权重差异策略以提高特征提取的有效性以及全面性。此外,考虑到训练集与测试集分布可能存在差异从而影响模型的泛化性能,引入了域自适应方法提高模型在未知数据集中的表现。为验证模型效果,使用PHM 2010铣刀磨损数据集进行实验。实验结果表明,该模型在C1、C4、C6三把刀具上的平均RMSE和平均MAE分别为6.97和6.29,与TCN、TDConvLSTM等模型相比有12%以上的提升。

多维力传感器静态标定装置的研究进展

为保证多维力传感器测量准确度,必须对该传感器进行静态标定,因此研究多维力传感器静态标定装置具有重要意义。首先,从多维力传感器静态标定需求出发,按工作原理将目前国内外主流使用的多维力传感器静态标定装置划分为静重式和叠加式这两类。然后,介绍了砝码式、杠杆式、自动式这三种静重式多维力传感器静态标定装置和液压式、龙门式、试验机式、移动平台式、电机式这五种叠加式多维力传感器静态标定装置的工作原理,对各种装置的国内外研究现状和存在不足分别进行了评述,并分析对比了各种装置的关键技术特点及优缺点。最后,从通用性、重复性、多维力模拟能力、成本效益等方面对多维力传感器静态标定装置进行了展望,指出了装置的设计和实施面临的挑战。该研究为后续多维力传感器静态标定装置的研创提供了参考。

基于分类平均跳距的无线传感器网络节点CADV-Hop定位方法

针对原型DV-Hop定位算法中平均跳距的计算产生较大定位误差的问题,提出了一种分类平均跳距的CADV-Hop算法。首先,揭示了无线传感网络中不同跳数路径的单跳距离分布不一致的现象,并分析了这种差异出现的原因与规律。其次,按照跳数对信标间路径分类再计算各类路径平均跳距的分类平均跳距。最后,在分类平均跳距所提供未知节点与最近信标节点之间更精确距离估计的基础上,结合加权最小二乘法最终实现节点坐标解算。仿真实验表明,CADV-Hop算法在不增加算法复杂度以及额外硬件的情况下有效地降低了定位误差,随着信标节点数量增加,CADV-Hop算法比原型DV-Hop算法和两种改进的DV-Hop算法具有更高的定位精度。

Tb(Ⅲ)functionalized MOF based self-calibrating sensor integrated with logic gate operation for efficient epinephrine detection in serum

By anchoring Tb^(3+)ions on its free carboxyl groups of the nanocaged NiMOF,a dual-emission self-calibrating sensor of Tb^(3+)@NiMOF was fabricated through coordination post-synthetic modification(PSM)strategy.With Tb^(3+)ions as the secondary fluorescent signal and sensing active sites,Tb^(3+)@NiMOF presents great potentials in visually and efficiently monitoring EPI in serum,with high sensitivity and selectivity,fast response,excellent recyclable,and the low detection limit(LOD,3.06 ng/mL).Furthermore,a tandem combinational logic gate based intelligent detection system was constructed to improve the practicability and convenience of epinephrine(EPI)detection in serum by comparing the light emitted colour with the series standard colour cards preset in the smartphone.This work provides a promising approach of developing metal-organic frameworks(MOFs)based self-calibrating sensors for intelligent detection of bioactive molecules.

基于高斯滤波与均值聚类的异质多源传感器数据加权融合

异质多源传感器之间工作频率存在差异,导致数据之间的一致性较差,加权融合后的观测误差较大,因此提出基于高斯滤波与均值聚类的异质多源传感器数据加权融合方法。采用高斯滤波对异质多源传感器数据空间单元格进行划分,建立基于单元格的最佳连通域,保留传感器内部数据,完成传感器数据的高斯滤波平滑处理。引入均值聚类对异质多源传感器数据进行一致性处理。通过免疫粒子群搜索最优权重和参数,利用最优权重和参数完成异质多源传感器数据加权融合。仿真结果表明,所提方法能够降低融合后传感器数据的观测误差与均方误差,观测误差与均方误差最小值均为0.002。因此,说明所提方法提高了融合后异质多源传感器数据的可利用性。

两级融合的多传感器数据融合算法研究

针对智慧工厂监测环境中多源数据融合精度问题,提出了一种两级融合的多传感器数据融合方法,旨在提高多源数据融合的准确性和可靠性。该方法分为一级数据融合和二级决策融合,首先采用卡尔曼滤波结合自适应加权平均对同类型传感器进行数据降噪融合处理,其次利用人工兔优化算法(ARO)优化ELM神经网络进行决策融合。实验结果表明,基于ARO优化ELM神经网络的多传感器数据融合算法在融合精度方面优于其他先进算法。经验证,所提出的两级融合多传感器数据融合方法具有更好的融合性能,有效提升感知系统的可靠性和鲁棒性,实现更加准确和可靠的监测和预测。

基于多应用场景的改进DV-Hop定位模型

针对距离矢量跳(DV-Hop)定位模型定位精度低、优化策略场景依赖性强的问题,提出一种基于函数分析和模拟定参的改进DV-Hop模型——函数修正距离矢量跳(FuncDV-Hop)定位模型。首先,分析DV-Hop模型的平均跳距、距离估计和最小二乘法中的误差原因,引入待定系数优化、阶跃函数分段实验、带等效点的权重函数策略和极大似然估计修正;其次,考虑多应用场景,用控制变量法,分别将总节点数、信标节点比例、通信半径、信标节点数和待测节点数作为变量,设计对照实验;最后,进行仿真定参和整合优化测试两阶段实验,最终的改进策略较原DV-Hop模型的定位精度提高了23.70%~75.76%,平均优化率57.23%。实验结果表明,FuncDV-Hop模型的优化率最高达到了50.73%,与基于遗传算法和神经动力学改进的DV-Hop模型相比,FuncDV-Hop模型的优化率提升了0.55%~18.77%。所提模型不引入其他参量,不增加无线传感器网络(WSN)的协议开销,且有效提高定位精度。