A New Algorithm of Self Organization in Wireless Sensor Network

Self organization is one of the most important characteristics in an Ad-hoc Sensor Network. Thousands of Sensors are deployed in a geographical area randomly without considering the location factor. After deployment, sensors are to self organize themselves to form a network of their own. How well the network is formed determines the life of the whole network as well as the quality of data transmission. Self organization based on clustering has proven to be very useful in this regard. Since hierarchical clustering reduces energy consumption by routing data from one node to another. In this paper, we discuss a new algorithm for self organization of sensors deployed in a geographical area. The algorithm forms clusters of sensors by ordering them using a unique triangulation method. This algorithm not only considers all sensors but also groups them so that their inherent clustering property is preserved.

Development of seam tracking system with ultrasonic sensor using self-tuning fuzzy control

One kind of the SAW seam tracking system with contactless ultrasonic sensor is presented in this paper. The new contactless ultrasonic sensor for seam tracking and the working principle of the seam tracking with the sensor are introduced. Based on the experiments, the optimal values of the fuzzy control parameters α and k 3 are defined by means of the off line adjusting method. Because the self tuning fuzzy control is adopted in the seam tracking system, the overshoot of the system is restrained, the steady state error is reduced, and the system's response speed is improved effectively. The results of the SAW seam tracking experiments show that this system's tracking accuracy is up to ±0.5 mm and the system can satisfy the requirements of the engineering application.

An Average Distance Based Self-Relocation and Self-Healing Algorithm for Mobile Sensor Networks

The sensing coverage of a wireless sensor network is an important measure of the quality of service. It is desirable to develop energy efficient methods for relocating mobile sensors in order to achieve optimum sensing coverage. This paper introduces an average distance based self-relocation and self-healing algorithm for randomly deployed mobile sensor networks. No geo-location or relative location information is needed by this algorithm thereby no hardware such as GPS is required. The tradeoff is that sensors need to move longer distance in order to achieve certain coverage. Simulations are conducted in order to evaluate the proposed relocation and self-healing algorithms. An average of 94% coverage is achieved in the cases that we are examined with or without obstacles.

Laser self-mixing interference fibre sensor

Fibre sensors exhibit a number of advantages over other sensors such as high sensitivity, electric insulation, corrosion resistance, interference rejection and so on. And laser self-mixing interference can accurately detect the phase difference of feedback light. In this paper, a novel laser self-mixing interference fibre sensor that combines the advantages of fibre sensors with those of laser self-mixing interference is presented. Experimental configurations are set up to study the relationship between laser power output and phase of laser feedback light when the fibre trembles or when the fibre is stretched or pressed. The theoretical analysis of pressure sensors based on laser self-mixing interference is indicated to accord with the experimental results.

Fiber Optic Humidity Sensor Based on Self-Assembled Polyelectrolyte Multilayers

Polyelectrolyte multilayers were self-assembled onto planar glass substrates and multimode optic fibers. The multilayer thin films deposited on glass substrates were characterized by using UV-vis spectroscopy and X-ray photoelectron spectroscope. The multilayer thin films containing hydrophilic side-groups possessed are affinity for uwer molecules. The adsorption and desorption of free water vapor gave rise to the changes in the refractive index and in the reflectance of the thin films. A multilayer thin film based fiber optic humidity sensor with an LED light source of 0.85 mum was designed. Under certain conditions, the rejected light intensity of the thin film sensor was a function of the humidity of air. About 30 bilayers was optimal for the multilayer thin film sensor working at wavelength of 0.85 mum. This sensor can work over almost the whole relative humidity range with very good sensitivity.

Hierarchical self-localization of underwater wireless sensor network nodes

The follow-up application of underwater wireless sensor network is influenced by accuracy of self-localization of nodes. The self-localization of nodes is discussed in this paper. First of all, nodes of underwater wireless sensor network are classified into several levels according to the accuracy of position of nodes and the levels are from the first to the fifth in accordance with accuracy of nodes from high to low respectively. Secondly, the level of anchor nodes can be known by those unknown nodes from the information given by the anchor nodes themselves, At the same time the unknown nodes are able to be located in the area controlled by the first level of anchor nodes that are as the aggregation. Then the positioning algorithm is designed correspondingly in accordance with the accuracy level of nodes. Finally, the positioning algorithm is simulated and analyzed. The result shows that the unknown nodes can be located effectively by hierarchical control.

Study on the Surface Acoustic Wave Sensor with Self-Assembly Imprinted Film of Calixarene Derivatives to Detect Organophosphorus Compounds

The molecularly imprinted technology and the self-assembly technique were used together on the calixarene surface acoustic wave (SAW) chemical sensors to detect organophosphorus compounds. 25-(thioalkyl-alkoxy)-p-tertbutylcalix[4] arene with self-assembled monolayer character was the sensitive coating of the sensors. The sensors had a special response to organophosphorus compounds and the response frequency shift of this sensor to organophosphorus compounds in 0.1 mg/m3 was 350 Hz. The response frequency increased linearly with the increase of the concentration of DMMP in the range from 0.1 to 0.6 mg/m3. The possible explanation of the interaction between the coatings and organophosphorus compounds was discussed.

基于电磁感应自驱动传感器的呼吸率监测系统研究

呼吸率是呼吸功能的一个重要参数,实时监测呼吸率对发现和预防呼吸疾病有着重要意义。设计了一种基于电磁感应自驱动呼吸气流传感器的呼吸率监测系统,该系统将传感器置于口腔/鼻下通过监测呼吸气流的变化来监测人体的呼吸率。实验测得传感器在呼吸缓慢、呼吸正常、呼吸急促时周期分别为3.8,2.3,1.6 s,小于人呼吸周期时间(3~6 s),系统采用蓝牙通信技术传输数据,方便测试者移动。该系统实现了对人体呼吸率稳定、实时、准确的采集,为后续呼吸疾病的预防和诊断提供了一定的研究价值。

Self-diagnosis method for faulty modules on wireless sensor node

In order to diagnose the working status of each module on sensor node and make sure the wireless sensor networks （WSN） work properly, the components of sensor node and their working characteristics are studied. An on-line fault self-diagnosis method for sensor node is proposed. First, a flexible fault sensing circuit is designed as a state detection module on sensor node. Second, a self- diagnosis algorithm is proposed based on the hardware design and the failure analysis on sensor node. Finally, in order to ensure the WSN reliability, the voltage changes of each module working statuses can be observed using the state detection module and the faulty module will be found out timely. The experimental results show that this self-diagnosis method is suitable to sensor nodes in WSN.

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.

十字滑台式多维不倒杆自主纠偏实验装置设计

为了让学生理解海洋风力发机不倒杆结构,该文设计了基于十字滑台的多维不倒杆自主纠偏实验装置,上位机测量并绘制不倒杆在三维空间中的角度、角速度和角加速度的变化,探究抵御外部干扰的相关物理量。通过研究不同受风力面积、不同材料的杆的平衡性,得出选用密度较大、迎风面积小的杆可较好抵御外界干扰。该装置的灵敏度较高,能够作为应用物理和新能源等专业的实验教学装备,有助于深入理解新能源领域的控制理论,提高学生动手能力。

A LOCAL DYNAMIC CLUSTER SELF-ORGANIZATION ALGORITHM IN WIRELESS SENSOR NETWORKS FOR RAINFALL MONITORING

Wireless Sensor Networks for Rainfall Monitoring (RM-WSNs) is a sensor network for the large-scale regional and moving rainfall monitoring,which could be controlled deployment. Delivery delay and cross-cluster calculation leads to information inaccuracy by the existing dynamic collabo-rative self-organization algorithm in WSNs. In this letter,a Local Dynamic Cluster Self-organization algorithm (LDCS) is proposed for the large-scale regional and moving target monitoring in RM-WSNs. The algorithm utilizes the resource-rich node in WSNs as the cluster head,which processes target information obtained by sensor nodes in cluster. The cluster head shifts with the target moving in chance and re-groups a new cluster. The target information acquisition is limited in the dynamic cluster,which can reduce information across-clusters transfer delay and improve the real-time of information acquisition. The simulation results show that,LDCS can not only relieve the problem of "too frequent leader switches" in IDSQ,also make full use of the history monitoring information of target and con-tinuous monitoring of sensor nodes that failed in DCS.

基于冗余关系分析的传感器自诊断设计方法研究

工业过程中传感器数量众多且可靠性要求高,而传统定期检测评估其健康状况的方式不但费时费力且不能满足传感器智能化的发展需求。针对这一问题,提出了一种基于测量数据统计相关性的传感器自诊断设计方法。利用传感器测量数据建立其统计关系模型,借助自编码器提取传感器数据特征并将其编码为二进制形式。在同时考虑传感器测量数据统计独立和统计相关两种情况下,在有参考值时,通过引入故障检测概率和误检概率建立了独立统计模型实现传感器的故障自诊断;在无参考值情况下,借助高斯Copula函数建立多元统计依赖模型评估参数之间的相关性,并利用贝叶斯理论在不依赖参考值的情况下自学习获取传感器的健康状况。本研究以镍闪速炉系统为例,两种模式下测量系统中健康传感器的故障检测后验概率达到了0.92,即故障统计模型的参数与建模期望相符。实验结果表明,所提方法在两种模式下均能准确识别出测量系统中的故障传感器,验证了所提方法的有效性与可行性。

基于自激振荡原理的双线圈磨粒检测技术研究

为满足实际工业应用的需要,基于LC自激振荡,提出双线圈式磨粒检测技术。首先建立了等效电路模型,其次引入磁场均匀度系数的概念并利用COMSOL Multiphysics仿真软件研究了管道域中的磁场分布及金属颗粒通过线圈时的输出响应,最后制作传感器进行试验。结果显示,该传感器管道域的磁场均匀度系数达0.91以上,可通过检测电压、频率的变化获取磨粒信息。研究结果为电感式磨损传感器研发提供一种新思路。

基于CNN-BiLSTM-SA网络的人类活动识别

针对传统的神经网络对人类活动行为识别精度不高的问题,本文提出了一种基于双通道机制的卷积神经网络叠加双向长短期记忆网络和自注意力的混合网络模型(convolutional neural network-bi-directional long short-term memory-self-attention,CNN-BiLSTM-SA)。首先将数据集中的加速度和角速度数据作为网络的两个输入,然后使用卷积神经网络叠加双向长短期记忆网络的模式搭建系统,最后引入自注意力机制增强系统的分类能力。实验结果表明,在UCI-HAR数据集中,本网络的平均F 1分数达到98.6%,平均准确率达到98.4%,比卷积神经网络叠加长短期记忆神经网络模型(convolutional neural network-long short-term memory,CNN-LSTM)收敛速度更快并且准确率提高了4.2%,在人类活动识别方面,具有更广阔的应用前景。

里德堡原子电场传感器的自校准性解析及测量不确定度评定

传统电场传感器需要标准场或更高准确度等级的传感器进行校准,而基于原子固有能级结构和光谱特征的里德堡原子电场测量可溯源至基本物理常数,量子传感自带校准功能。该文阐述了里德堡原子与外场相互作用的量子相干效应,说明了里德堡原子传感器的电场测量原理,并解析了其自校准性。基于所构建的针对低频电场的里德堡原子传感测量系统,分析了误差来源,定义和计算了各种不确定度分量,得到合成相对标准不确定度为3.885%;保守考虑,在大于或等于95.45%的包含概率下得到扩展不确定度为7.77%。在此过程中,明确了不确定度的主要影响因素并提出了减小其影响的建议。最后给出传感系统实测电磁诱导透明(EIT)光谱频移量与电场强度之间的拟合函数,拟合确定系数达到0.9969,验证了所构建的低频电场量子测量系统的准确性。

旋变二次谐波测角误差自校正

旋变幅值误差和正交误差在角速度频谱上表现为旋变转频的二次谐波,是旋变测角误差的主要来源,影响伺服系统角速度控制精度和稳定度。本文提出了一种基于特征频率参考的二次谐波误差自校正方法。首先,分析旋变测角误差产生的机理,获知其幅值误差和正交误差的互不相关性,并证明对旋变输出信号进行幅值调整和相位差调整可实现二次谐波误差校正。然后,在旋变和旋变数字信号转换器(Resolver-to-Digital Converter,RDC)之间设计基于比例放大的幅值校正器和基于交叉调节的相角校正器。最后,根据误差信号在线性控制系统中特征频率不变的特性,对伺服系统进行匀速控制,以角速度频谱中二次谐波频率的幅值为参考基准,分别调整幅值校正器和相角校正器,校正二次谐波误差。实验结果表明:本方法可将旋变二次谐波测角误差幅值降低78.5%,伺服系统速率波动量降低40.5%。本方法实现了旋变二次谐波测角误差的自校正,大幅提升了旋变的测角精度和伺服系统的转速控制稳定度。

PB840呼吸机气路分析及故障维修

呼吸机是一种能替代患者自主通气的医疗仪器,广泛应用于呼吸衰竭患者、呼吸支持治疗以及急救复苏过程,是医院急诊、重症加强护理病房最重要的抢救仪器。本文介绍了2例PB840呼吸机故障,故障现象均为呼吸机报警、停止送气,报警代码相似。第1例故障经过呼吸机扩展自检(Extenden Self Test,EST)后分析自检数据,发现为呼出端流量传感器Q3故障,经过柠檬酸浸泡处理Q3后故障解除;第2例故障为氧气调零流量检测失败,具体原因为氧比例电磁阀故障,更换配件后故障解除。通过两例故障提示维修人员需熟悉仪器的气路图,熟练掌握呼吸机EST方法,提高维修准确度。

用于应变传感器的自愈合抗冻离子水凝胶

近年来,导电水凝胶在电子驱动器、医疗监测传感器及可穿戴设备等领域的应用备受关注。然而,多数凝胶机械强度低、寿命短、抗冻性能差,从而导致低温下无法使用。为了解决该问题,以聚乙烯醇、离子液体1-丁基-3-甲基咪唑四氟硼酸盐和植酸为原料,在水与二甲基亚砜(DMSO)二元溶液中借助多重氢键及离子基团间的静电作用实现了超分子自组装,制备出一种可用于应变传感的自愈合、耐低温多功能离子水凝胶。研究发现在凝胶中引入DMSO,凝胶表现出优异的抗冻能力;同时,调节DMSO的含量可优化凝胶的强度和韧性,当DMSO体积分数为40%时,最大拉伸强度和断裂伸长率分别可达4.43 MPa和869.1%。该离子水凝胶在低温应变传感器、智能可穿戴响应元件等领域展现出较好的应用潜力。

面向WSN异常节点检测的融合重构机制与对比学习方法

针对无线传感器网络(WSN)异常检测中的自监督学习异常检测方法需要解决负例样本信息表示单一缺乏多样性和提取WSN节点采集到的多模态数据时空特征不够充分影响异常检测性能的问题。对此提出了一种结合对比学习和重构机制的无线传感器网络异常节点检测方法。首先,通过设计一种对比学习策略为重构机制模型提供足够充足的正负例样本,并结合生成对抗网络(GAN)生成具有多样性特性的负例样本;其次,设计了一种基于多头注意力机制和图神经网络的双层时空特征提取模块。通过在实际公开数据集上的系列对比实验及其实验结果表明,所提方法相比于传统异常检查方法和最近的图神经网络方法具有更好的精确率和召回率。