AN IDENTITY-BASED KEY AGREEMENT SCHEME FOR LARGE SCALE SENSOR NETWORKS

To solve the problems of high memory occupation, low connectivity and poor resiliency against node capture, which existing in the random key pre-distribution techniques while applying to the large scale Wireless Sensor Networks （WSNs）, an Identity-Based Key Agreement Scheme （IBKAS） is proposed based on identity-based encryption and Elliptic Curve Diffie-Hellman （ECDH）. IBKAS can resist man-in-the-middle attacks and node-capture attacks through encrypting the key agreement parameters using identity-based encryption. Theoretical analysis indicates that comparing to the random key pre-distribution techniques, IBKAS achieves significant improvement in key connectivity, communication overhead, memory occupation, and security strength, and also enables efficient secure rekcying and network expansion. Furthermore, we implement IBKAS for TinyOS-2.1.2 based on the MICA2 motes, and the experiment results demonstrate that IBKAS is feasible for infrequent key distribution and rekeying for large scale sensor networks.

Cascading failure in the wireless sensor scale-free networks

In the practical wireless sensor networks （WSNs）, the cascading failure caused by a failure node has serious impact on the network performance. In this paper, we deeply research the cascading failure of scale-free topology in WSNs. Firstly, a cascading failure model for scale-free topology in WSNs is studied. Through analyzing the influence of the node load on cascading failure, the critical load triggering large-scale cascading failure is obtained. Then based on the critical load, a control method for cascading failure is presented. In addition, the simulation experiments are performed to validate the effectiveness of the control method. The results show that the control method can effectively prevent cascading failure.

L3SN: A Level-Based, Large-Scale, Longevous Sensor Network System for Agriculture Information Monitoring

We developed L3SN, a scalable, longevous, adaptive, and internet accessible wireless sensor network system for agriculture information monitoring, which is meticulously designed to meet the requirement of thousands hectares coverage, years of time monitoring and the adverse environment. The system architecture, the agriculture sensor device, the mesh protocol, and the web-based information processing platform are introduced. We also presented some implementation experience. The mesh protocol (LayerMesh) is highlighted, in which “stair scheduling” and “distributed dynamic load-balancing” are proposed to response the scalability, longevity and adaptivity requirements. We believe the design of L3SN is useful to many other large-scale, longevous applications such as hydrologic monitoring, geological monitoring etc.

MDS and Trilateration Based Localization in Wireless Sensor Network

Localization of sensor nodes is crucial in Wireless Sensor Network because of applications like surveillance, tracking, navigation etc. Various optimization techniques for localization have been proposed in literature by different researchers. In this paper, we propose a two phase hybrid approach for localization using Multidi- mensional Scaling and trilateration, namely, MDS with refinement using trilateration. Trilateration refines the estimated locations obtained by the MDS algorithm and hence acts as a post optimizer which improves the accuracy of the estimated positions of sensor nodes. Through extensive simulations, we have shown that the proposed algorithm is more robust to noise than previous approaches and provides higher accuracy for estimating the positions of sensor nodes.

Monte Carlo Analysis of Nodes Deployment in Wireless Sensor Network Using Range-Free Location Methods

A Monte Carlo Analysis of nodes deployment for large-scale and non-homogeneous wireless sensor networks, has been done. Through simulations of random deployments of nodes over a square area using different densities, assuming that our network is composed by Anchor nodes （special sensors with known position） and simple Sensor nodes, the latter are supposed to estimate their own position after being placed within the coverage area with the minimum Anchor nodes needed to ＇feed＇ them with the necessary information. The goal is then to assist decision-makers in selecting among different alternatives to deploy the networks, according to resources features and availability, hence this method provides an estimate value of how many Anchor nodes should be deployed in a given area to trigger the location algorithm in the greatest possible number of Sensor nodes in the network.

考虑多尺度纹理特征的红外传感图像频域增强

红外传感图像质量容易受探测器和传输距离影响,导致图像亮度和对比度较低、轮廓细节模糊等问题。为此,提出了考虑多尺度纹理特征的红外传感图像频域增强方法。引入残差学习策略,基于多尺度纹理特征搭建多尺度卷积神经网络模型,进行图像去噪。对去噪后图像进行傅里叶变换,获取红外传感图像的低频图像和高频图像。针对低频图像部分,调节图像灰度和对比度以增强低频分量。针对高频图像部分,利用Log算子和Laplace算子增强图像细节及边缘。加权融合两者处理结果,选取Gamma校正调节对比度,增强高频分量。融合两种增强后图像,实现红外传感图像频域增强。实验结果表明,该方法峰值信噪比高于43,信息熵大于8,边缘强度超过82,对比度熵大于8.1,平均梯度大于8。

基于改进遗传算法的大型体育场馆传感器优化布置研究

为高效、准确地对大型体育场馆结构进行健康监测,根据其结构特点,提出了一种基于多种群非支配重组遗传算法(MP-RGA)的传感器优化布置方法。引入二重编码解决离散优化问题,通过多种群策略提高算法的搜索能力,通过非支配重组解决以往遗传算法优化后期的多样性丧失问题,最后采用自适应化交叉、变异策略改善算法前期的收敛速度和后期的寻优能力。以西安国际足球中心屋盖为例,通过数值算例验证不同传感器布置数量下利用该算法确定传感器布置方案时的有效性。结果表明:与量子遗传算法(QGA)和粒子群算法(PSO)相比,多种群非支配重组遗传算法收敛速度更快,以模态置信度准则为目标的优化效果分别提升了41.88%和91.27%;依据所提算法优化得到的传感器布置方案实际应用效果良好,表明该算法适用于大型体育场馆的传感器优化布置问题。

A spatiotemporal signal processing technique for wafer-scale IC thermomechanical stress monitoring by an infrared camera

In this paper, we describe a new silicon-die thermal monitoring approach using spatiotemporal signal processing technique for Wafer-Scale IC thermome- chanical stress monitoring. It is proposed in the context of a wafer-scale-based (WaferICTM) rapid prototyping platform for electronic systems. This technique will be embedded into the structure of the WaferIC, and will be used as a preventive measure to protect the wafer from possible damages that can be caused by excessive thermomechanical stress. The paper also presents spatial and spatiotemporal algorithms and the experimental results from an IR images collection campaign conducted using an IR camera.

A Quaternion Scaled Unscented Kalman Estimator for Inertial Navigation States Determination Using INS/GPS/Magnetometer Fusion

This Inertial Navigation System (INS), Global Positioning System (GPS) and fluxgate magnetometer technologies have been widely used in a variety of positioning and navigation applications. In this paper, a low cost solid state INS/GPS/Magnetometer integrated navigation system has been developed that incorporates measurements from an Inertial Navigation System (INS), Global Positioning System (GPS) and fluxgate magnetometer (Mag.) to provide a reliable complete navigation solution at a high output rate. The body attitude estimates, especially the heading angle, are fundamental challenges in a navigation system. Therefore targeting accurate attitude estimation is considered a significant contribution to the overall navigation error. A better estimation of the body attitude estimates leads to more accurate position and velocity estimation. For that end, the aim of this research is to exploit the magnetometer and accelerometer data in the attitude estimation technique. In this paper, a Scaled Unscented Kalman Filter (SUKF) based on the quaternion concept is designed for the INS/GPS/Mag integrated navigation system under large attitude error conditions. Simulation and experimental results indicate a satisfactory performance of the newly developed model.

基于红外传感器的复杂场景目标自动识别方法

由于复杂场景中目标求解区域隶属度不同,导致目标识别精准度低,提出基于红外传感器的目标自动识别方法。将复杂场景中的目标特征形式转换为红外形式,输入至红外传感器中,通过目标的光线特征获取红外图像。以复杂场景目标变换特性为基础,结合采用尺度不变特征变换方法,计算场景中不同类型目标的特征权重,求解目标在不同区域范围的隶属度,将权重值与隶属度值作为对比参照,建立对比序列。计算图像质心值与区域内所有像素点的奇异值向量,按照奇异值向量大小组成序列,与预设目标参数实行比对,提取符合特征和奇异值变化表达的像素点,完成目标的高效识别。实验结果表明,所提方法的损失函数最高值仅为1.3,识别精准度最高值达到了97.8%以上,说明所提方法的识别结果与目标之间存在高度一致性,并且识别结果分辨率高、画面清晰直观。

矿用移动式真空泵阻垢装置的设计与应用

煤矿的瓦斯抽放泵利用泵壳和叶轮间不同心的安装结构,在叶轮作旋转时,构成与叶轮成偏心的水环,充满在叶轮间的容积,在固定的吸气口和排气口的相应配合下,完成吸气、压缩和排气作用。循环水作为瓦斯抽放泵的重要组成部分,长时间运行的状态下不可避免的会产生大量的水垢附着在泵体内壁和叶轮上。传统的除垢方法是利用药剂和水垢的化学反应分解水垢,并不能从根本上解决结垢问题。根据以上背景进行瓦斯抽采泵阻垢装置的设计与应用,通过系统对循环水水质的实时监测,可以动态往循环水管投放阻垢剂,保证设备整体系统无新垢产生,能阻止95%以上的水垢产生,减少黏泥及菌类对抽采泵系统的影响。装置智能化程度高,在线监测水质指标,自动添加阻垢剂。

CK6E系列电子皮带秤秤台故障自动切换方法的研究

对电子皮带秤压力传感器故障、两个应急秤秤台故障产生的原因进行研究,通过在电控PLC程序编写中增加秤台切换功能,解决秤台在压力传感器故障、秤体水平和垂直故障时的无缝切换,确保电子秤计量的准确性和物料运行的流畅性。试验结果表明,在使用秤台故障自动切换方法后,车间生产过程中再没有出现因电子秤故障引起的物料中断、停机现象。

叠层笼养蛋鸡无线体温监测试验

现代化蛋鸡养殖中,体温是判断蛋鸡是否健康的一项重要指标。为了了解笼养蛋鸡的体温变化规律,利用蛋鸡可穿戴无线体温传感监测系统,对笼养蛋鸡开展全天全时体温监控试验。试验结果表明,正常笼养蛋鸡体温在38.41~41.85℃范围内变化,呈现出一定的昼夜节律特性,全天体温最低点出现在21:00—22:00;发生发热性疫病时蛋鸡平均体温比正常情况升高0.21~1.30℃;早间时段和晚间时段,患病蛋鸡体温异常升高的均值更大,相对于午间时段更易区分正常蛋鸡和异常蛋鸡。叠层笼养蛋鸡体温监测可为建立鸡体温健康判断模型提供理论参考,对实现精准掌握蛋鸡规模化养殖群体健康状态、提高生产效益具有重要的现实意义。

High-Velocity Impact Studies on Scaled Leading Edges of Horizontal Tail with Smart Composite Layers

Bird strike studies on typical aluminium leading edges of the Horizontal Tail (HT) with and without Glass Fibre Shape Memory Polymer (GF-SMP) layers are carried out. A one-fifth scaled model of HT is designed and fabricated. The parameters like bird dimension and energy requirements are accordingly scaled to conduct the bird strike tests. Two leading-edge components have been prepared, namely one with AL 2024-T3 aluminium alloy and the other specimen of the same dimension and material, additionally having GF-SMP composite layers inside the metallic leading edge, in order to enhance its impact resistance. Bird strike experiments are performed on both the specimens, impacting at the centre of the leading edge in the nose tip region with an impact velocity of 115 m/s. The test component is instrumented with linear post-yield strain gauges on the top side and the PZT sensors on the bottom. Furthermore, the impact scenario is monitored using a high-speed camera at 7000 fps. The bird strike event is simulated by an equation of state model, in which the mass of the bird is idealized using smooth particle hydrodynamics element in PAMCRASH? explicit solver. The strain magnitude and its pattern including time duration are found to be in a good correlation between test and simulation. Key metrics are evaluated to devise an SHM scheme for the load and impact event monitoring using strain gauges and PZT sensors. GF-SMP layers have improved the impact resistance of the aluminium leading edge which is certainly encouraging towards finding a novel solution for the high-velocity impact.

微尺度3D打印柔性传感器创新实验设计与实践

介绍了一种利用微尺度3D打印柔性应变传感器的实验设计及实践,旨在培养学生的创新意识和实践能力,提高教学质量和水平。该实验借助微尺度3D打印技术,实现了传感器微型化和高精度制备,有效地提升了传感器的灵敏度和稳定性。在实践过程中,学生需要独立进行实验设计、制备和测试,注重实验数据的统计和分析,培养了学生的创新思维和实践操作能力。

基于自适应多尺度注意力机制的CNN-GRU矿用电动机健康状态评估

利用多传感器信息融合技术进行电动机健康状态评估时,矿用电动机监测数据中存在异常值和缺失值,而卷积神经网络和循环神经网络等深度学习模型在数据质量下降严重的情况下难以有效提取数据特征和更新网络权重,导致梯度消失或爆炸等问题。针对上述问题,提出了一种基于自适应多尺度注意力机制的CNN-GRU(CNN-GRU-AMSA)模型,用于评估矿用电动机健康状态。首先,对传感器采集的电动机运行数据进行填补、剔除和标准化处理,并以环境温度变化作为依据对矿用电动机运行数据进行工况划分。然后,根据马氏距离计算出电动机电流、电动机三相绕组温度、电动机前端轴承温度和电动机后端轴承温度等健康评估指标的健康指数(HI),采用Savitzky–Golay滤波器对指标HI进行降噪、平滑、归一化处理,并结合主成分分析法计算的不同指标对矿用电动机的贡献度,对指标HI进行加权融合得到矿用电动机HI。最后,将矿用电动机HI输入CNN-GRU-AMSA模型中,该模型通过动态调整注意力权重,实现对不同尺度特征的信息融合,从而准确输出电动机健康状态评估结果。实验结果表明,与其他常见的深度学习模型CNN,CNN-GRU,CNN-LSTM,CNN-LSTM-Attention相比,CNN-GRU-AMSA模型在均方根误差、平均绝对误差、准确率、Macro F1及Micro F1等评价指标上更优,且预测残差的波动范围更小,稳定性更优。

基于新搜索策略的改进法医调查算法

为解决法医搜索算法的搜索方程存在振荡等问题,构建一种改进的法医调查算法。引入均值机制和莱维飞行策略对分析调查结果进行改进,提高算法的勘探能力;调查方向充分使用当前个体的有效信息,引入自适应动态调整缩放因子及最优个体引导机制,增强算法的探索活力;利用趋优避劣方法对算法的追捕行动进行改进,改善种群个体质量;追捕行动扩展采用单优学习策略解决振荡问题。求解11个标准函数和无线传感网络覆盖问题的结果显示,与对比算法比较,改进算法的优化能力具有显著优势。

烟草专用电子皮带秤结构优化

为解决烟草制丝行业中粉尘大、维护保养不便问题,提高烟草制丝的生产效率和设备保养效率,构建了一种针对烟草用电子皮带秤侧板开合的连杆机构模型,并在此基础上对秤台其他部分进行结构优化。首先分析现有烟草用电子皮带秤的结构和实际生产情况,模拟护板闭合与开启状态建立了2种模型,并结合电子皮带秤内部实际尺寸进行计算校核;然后优化称重传感器连接部件和秤体支撑部件,建立优化后的传感器支撑与U型支撑组件模型,并与原有模型对比,阐述其优点;最后进行有限元静力学分析,比较新旧2种结构的静力学计算结果。结果表明,优化后的传感器支撑结构最大应力与应变远小于现有结构,进一步验证了烟草用电子皮带秤结构优化的重要性与新传感器支撑的可用性。开合用连杆机构模型与相应部件的结构优化为烟草行业电子皮带秤的后续发展提供了一种新的方案。

新型平板式动态汽车衡结构设计及校准研究

基于整车式动态汽车衡制造成本高、施工周期长等诸多问题,设计一款分离式秤面的新型平板式动态汽车衡,该秤体由秤台、秤台连接板、传感器、底板和承载框架组成。传感器作为秤体中最核心部件,设计一种梁式称重传感器结构,阐述测量机制,通过数值模拟分析,确定传感器中电阻应变片粘贴方案,并说明秤体中传感器的布局方案。使用ANSYS软件对秤体整体结构进行有限元分析,得到秤体结构的总变形和等效应力。对新秤的静态标定进行校准研究,提出一种使用权重影响因子计算载荷数值的校准优化方法,通过数值模拟48种不同工况,求得秤体中48枚应变片节点处的剪应变权重影响因子数值,为新秤的校准提供了数据支持。