基于空间光学传感技术的变电站设备高热故障红外识别方法

无论对技术还是操作人员,变电站设备故障识别过程都有着非常严格的要求,而故障识别精度依旧较低。为此,提出基于空间光学传感技术的变电站设备高热故障红外识别方法。在采集得到变电站设备红外图像后,对其展开阈值分割、图像增强、光照分量估计一系列处理,削弱背景对目标识别的影响,找出目标所在区域;计算目标所在区域的温度概率密度,与温度范围比照,确定高温区域范围,完成对变电站设备高热故障的精准识别。利用所提方法分别识别避雷器、主变套管、电流互感器和隔离开关的故障位置,结果表明,所提方法识别到的目标故障位置非常精准,没有出现误检测或漏识别的情况。

基于空间光传感信息的运动姿态实时跟踪分析与研究

为了排除运动动态性干扰,探讨人体运动下空间光传感信息可作为视觉跟踪特征的可行性,建立起空间三维点与运动姿态对应点的关系,通过计算运动姿态目标点的投影,对摄像机进行标定,根据人体运动动作点的标定,采集到人体运动姿态空间光传感特征,通过提取运动姿态特征,根据人体运动姿态光传感样本点的逆映射原理,重构运动姿态图像,实现运动姿态的实时跟踪。实验结果表明,基于空间光传感信息的运动姿态实时跟踪的横向偏移量还是跟踪准确率方面,都不会受到运动姿态的动态性影响,跟踪方法的准确率可达到1.0,跟踪效率高达0.1,具有更好的稳定性。

基于压缩感知的空间场传感网络优化方法研究

为解决现有空间场传感网络中的传感节点密集、数据冗余和监测响应慢等问题,实现对空间场信息的实时监测,将压缩感知技术引入空间场传感网络优化中。本文提出了对历史数据进行学习的自适应稀疏正交字典的建立方法,通过对稀疏字典的分析改进了传统观测矩阵的建立方法,实现了空间场传感节点布局的最优化;通过聚类方法分析观测数据在字典上的投影系数,获得了空间场正常工作情况下的分布状态曲线,实现了对异常故障点的精准识别。最后,对二源温度场数据进行实验验证,结果表明本文所提出的压缩感知技术可以在完整保留温度场分布特征的前提下,有效降低感知数据的密集程度,并利用稀疏采样的数据,实现对温度场的实时监测和故障识别。该研究对温度场中传感节点高效布置给出了科学指导,避免了空间密集采样的资源浪费,同时也为如何在空间场采样数据欠缺的情况下,精确获取完整的温度场分布提供了新的技术思路。

传感空间在展示空间设计中的表达研究

传感空间指的是在展示空间设计中用来信息交流活动的一种展示手法,是现代社会展示过程中最主要的展示手段和推广方式之一,在展示的过程中可以有效的表达展示的信息,是比较现代的展示形式,主要包括展示空间中的形态、材料等,传感空间的设计在于满意观展者的在展示空间获取信息的感受,最主要的是建立展示者和观展者之间的互动,能够让观展者融入其中,从而感受整个展示空间。

空间传感天线在户外电缆终端局放检测定位中的应用

该文主要研究了空间传感天线在高压电缆户外EB-A终端局放检测及定位的方式方法,验证其有效性。基于多个高频脉冲检测频带的空间传感天线对电缆终端附件的局部放电信号进行不同方向、不同维度的检测及精确定位,并最终实现了局放源电力设备的更换验证、及时消除了电力设备运行中的安全隐患,从而证明了空间传感天线在局放检测定位中的应用是有效的。该文所验证结论对于今后在电缆线路局放测试过程中特别是户外式电缆终端局放测试中发现的疑似放电信号的确切性及定位方法提供了理论依据。

空间无线传感网体系架构及业务实现探讨

对地观测系统已经从单一平台发展成为多星组网、多平台、多传感器的系统。携带传感器的卫星使用星间链路组成了一个空间无线Ad Hoc网络,实现科学数据采集,并把传感器采集的数据传输下载到地面站,这样就形成了一个空间无线传感网系统。文章介绍了空间无线传感网的传感层、通信层、信息层三层体系架构,传感网SWE(Sensor Web Enablement)模型,探讨了信息层业务实现技术流程,阐述了基于SOAP协议架构的信息层业务实现方法,提出了基于Restful Web协议架构的信息层业务实现优化方案,并比较了信息层两种不同方案实现原理和性能,此外提出了通信层网络传输移动管理改进建议。

地理空间传感网信息公共服务平台的设计与实现

随着对地观测技术的发展,传感器类型与数量不断增加,为了高效管理传感网中相关的信息与服务资源,便于用户按需、全面和准确地获取并应用这些资源,基于传感网信息模型和服务接口规范,设计并实现了地理空间传感网信息公共服务平台GeoSensor。该平台通过调用和组合传感器注册服务、传感器观测服务、实时制图服务和卫星位置服务等多种服务,实时获取传感器、观测数据和数据产品等信息资源,并在天地图上以图形、文字、表格和视频等方式形象生动地展示这些信息资源。GeoSensor系统实现了对遥感、移动和原位等类型的对地观测传感器及其观测数据等资源的在线搜索、即时获取、网络控制和实时制图,可满足应急处理时对传感器检索、观测数据获取、传感器控制和数据产品生成等功能的需求。

地理空间传感网融合服务技术与应用

随着遥感数据网、传感网、物联网和人工智能的发展,逐渐形成空天地海立体化、集成化和一体化的地理空间传感网。地理空间传感网感知资源呈现出多源、异构和分散的特征,面向多层次用户个性化、即时化和智能化应用需求,存在异构资源共享管理、多协议实时接入、时空无缝感知、自动化感知和精准预测等技术挑战。静态地理信息服务由于无法提供鲜活的地理信息,难以满足地理事件的综合监测、决策预警和聚焦应用需求,急需发展地理空间传感网融合服务技术和实时动态地理信息服务平台。本文围绕信息物理网环境下空天地海观测平台的观测高效共享和融合服务问题,提出了传感网观测共享信息模型和点面观测协同无缝重建模型,突破了观测在线接入、集成管理、星地融合、时空预测和聚焦服务等地理空间传感网融合服务关键技术,研制了包含"感-联-知-控"等功能的传感网时空信息网络感知服务系统GeoSensor,介绍了GeoSensor在流域、海洋和城市等典型应用。未来将进一步发展"人-水-城"智能感知认知理论,突破"空天地海人"群智感知、空间智能和认知服务技术,开展长江经济带应用。

基于地理本体的空间传感网数据处理

传感网的研究和应用已经成为推动新信息时代发展的重要动力,而传感网络的运行无法避免结构复杂的海量传感数据的处理.尽管目前已有大量的数据融合算法和模型的出现,但无法避免数据语义、网络语义异构等等问题.结合传感网的特征和地理本体的研究,本文首先提出了空间传感网的定义,随后提出了基于空间传感网的地理本体库公理化模型,最后给出了应用于湖泊领域的实践,结果显示采用文中所提方法既能够节约传感网有限的动力资源,又能够有效消除传感网中的异构.

Space-Time Correlation Based Fast Regional Spectrum Sensing in Cognitive Radio

In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into small meshes,and all meshes are clustered into highly related groups using the spatial correlation among them. In each group,some representative meshes are selected as detecting meshes(DMs)using a multi-center mesh(MCM)clustering algorithm,while other meshes(EMs)are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle. Thus,detecting fewer meshes saves the sensing consumption. Since two independent estimation processes may provide contradictory results,minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing,our scheme is capable to reduce the consumption of traditional sensing method with acceptable sensing performance.

矿用提升机钢丝绳损伤检测装置的设计

为避免矿用提升机钢丝绳断绳引发煤矿安全事故,设计钢丝绳损伤检测装置,以及时评估钢丝绳损伤情况,杜绝"带病"工作。基于霍尔漏磁检测原理,设计并实现钢丝绳损伤检测总体方案、硬件以及软件设计方案并完成试验分析。结果表明:该检测装置能够准确、及时检测钢丝绳内外断丝损伤,可保障矿井提升机连续、安全、稳定运行。

Assessment of wind products obtained from multiple microwave scatterometers over the China Seas

Sea surface winds （SSWs） are vital to many meteorological and oceanographic applications, especially for regional study of short-range forecasting and Numerical Weather Prediction （NWP） assimilation. Spaceborne seatterometers can provide global ocean surface vector wind products at high spatial resolution. However, given the limited spatial coverage and revisit time for an individual sensor, it is valuable to study improvements of multiple microwave scatterometer observations, including the advanced scatterometer onboard parallel satellites MetOp-A （ASCAT-A） and MetOp-B （ASCAT-B） and microwave scatterometers aboard Oceansat-2 （OSCAT） and HY-2A （HY2-SCAT）. These four scatterometer-derived wind products over the China Seas （0°-40°N, 105°-135°E） were evaluated in terms of spatial coverage, revisit time, bias of wind speed and direction, after comparison with ERA-Interim forecast winds from the European Centre for Medium-Range Weather Forecasts （ECMWF） and spectral analysis of wind components along the satellite track. The results show that spatial coverage of wind data observed by combination of the four sensors over the China Seas is about 92.8% for a 12-h interval at 12：00 and 90.7% at 24：00, respectively. The analysis of revisit time shows that two periods, from 5：30-8：30 UTC and 17：00-21：00 UTC each day, had no observations in the study area. Wind data observed by the four sensors along satellite orbits in one month were compared with ERA-Interim data, indicating that bias of both wind speed and direction varies with wind speed, especially for speeds less than 7 m/s. The bias depends on characteristics of each satellite sensor and its retrieval algorithm for wind vector data. All these results will be important as guidance in choosing the most suitable wind product for applications and for constructing blended SSW products.

A new measure for evaluating spatially related properties of traffic information credibility

With the wide applications of sensor network technology in traffic information acquisition systems,a new measure will be quite necessary to evaluate spatially related properties of traffic information credibility.The heterogeneity of spatial distribution of information credibility from sensor networks is analyzed and a new measure,information credibility function(ICF),is proposed to describe this heterogeneity.Three possible functional forms of sensor ICF and their corresponding expressions are presented.Then,two feasible operations of spatial superposition of sensor ICFs are discussed.Finally,a numerical example is introduced to show the calibration method of sensor ICF and obtain the spatially related properties of expressway in Beijing.The results show that the sensor ICF of expressway in Beijing possesses a negative exponent property.The traffic information is more abundant at or near the locations of sensor,while with the distance away from the sensor increasing,the traffic information credibility will be declined by an exponential trend.The new measure provides theoretical bases for the optimal locations of traffic sensor networks and the mechanism research of spatial distribution of traffic information credibility.

Design of capacitance sensor system for void fraction measurement

Simulation and optimization were applied to a capacitive sensor system based on electrical tomography technology. Sensors, consisting of Morgantown Energy Technology Center (METC) axial synchro driving guard electrodes and two sets of detecting electrodes, make it possible to obtain simultaneously two groups of signals of the void fraction in oil-gas two-phase flow. The computational and experimental results showed that available sensors, charactered by high resolution and fast real-time response can be used for real-time liquid-gas two-phase flow pattern determination.

Optimal Control of a Beam with Discontinuously Distributed Piezoelectric Sensors and Actuators

Because of its light weight, broadband, and adaptable properties, smart material has been widely applied in the active vibration control （AVC） of flexible structures. Based on a firstorder shear deformation theory, by coupling the electrical and mechanical operation, a 4-node quadrilateral piezoelectric composite element with 24 degrees of freedom for generalized displacements and one electrical potential degree of freedom per piezoelectric layer was derived. Dynamic characteristics of a beam with discontinuously distributed piezoelectric sensors and actuators were presented. A linear quadratic regulator （LQR） feedback controller was designed to suppress the vibration of the beam in the state space using the high precise direct （HPD） integration method.

Wavelet Transform-Based Distributed Compressed Sensing in Wireless Sensor Networks

Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distributed Wavelet Basis Generation(DWBG) algorithm performing at the sink to obtain the distributed wavelet basis in WSN.And on this basis,a Wavelet Transform-based Distributed Compressed Sensing(WTDCS) algorithm is proposed to compress and reconstruct the sensed data with spatial correlation.Finally,we make a detailed analysis of relationship between reconstruction performance and WTDCS algorithm parameters such as the compression ratio,the channel Signal-to-Noise Ratio(SNR),the observation noise power and the correlation decay parameter by simulation.The simulation results show that WTDCS can achieve high performance in terms of energy and reconstruction accuracy,as compared to the conventional distributed wavelet transform algorithm.

Structural Stress Identification Using Fuzzy Pattern Recognition and Information Fusion Technique

In order to ensure the service security of space structures under wind load, the stress identification method based on the combination of fuzzy pattern recognition and information fusion technique is proposed, in which the measurements of limited strain sensors arranged on the structure are used. Firstly, the structure is divided into several regions according to the similarity and the most unfavorable region is selected to be the key region for stress identification, while the different numbers of the strain sensors are located on the key region and the normal regions; secondly, the different stress distributions of the key region are obtained based on the measurements of the strain sensors located on the key region and the normal regions separately, in which the fuzzy pattern recognition is used to identify the different stress distributions; thirdly, the stress distributions obtained by the measurements of sensors in normal regions are selected to calculate the synthesized stress distribution of the key region by D-S evidence theory; fourthly, the weighted fusion algorithm is used to assign the different fusion coefficients to the selected stress distributions obtained by the measurements of the normal regions and the key region, while the synthesized stress distribution of the key region can be obtained. Numerical study on a lattice shell model is carried out to validate the reliability of the proposed stress identification method. The simulated results indicate that the method can improve identification accuracy and be effective by different noise disturbing.

A Ring Signature Suitable for Wireless Sensor Networks

Wireless Sensor Network （WSN） nodes are severely limited by their power, communication bandwidth, and storage space, and the traditional signature algorithm is not suitable for WSN environments. In this paper, we present a ring signature scheme designed for WSNs. In this scheme, all of the wireless sensor nodes are divided into several sub-groups and the sub-group nodes are used to generate the signature instead of the WSN cluster nodes. This scheme can effectively avoid the single node failure problem, and it also has a high availability. All nodes are flee to sign their own message, and the nodes that generate signatures can simultaneously calculate their own part of the signature, meeting the distributed parallel computing requirements. Compared with the traditional ring signature, this scheme reduces the energy consumption, and therefore is very suitable for WSNs.

Characterizing Landscape Spatial Heterogeneity in Multisensor Images with Variogram Models

Most evaluation of the consistency of multisensor images have focused on Normalized Difference Vegetation Index （NDVI） products for natural landscapes, often neglecting less vegetated urban landscapes. This gap has been filled through quantifying and evaluating spatial heterogeneity of urban and natural landscapes from QuickBird, Satellite pour l＇observation de la Terre （SPOT）, Ad- vanced Spacebome Thermal Emission and Reflection Radiometer （ASTER） and Landsat Thematic Mapper （TM） images with variogram analysis. Instead of a logarithmic relationship with pixel size observed in the corresponding aggregated images, the spatial variability decayed and the spatial structures decomposed more slowly and complexly with spatial resolution for real multisensor im- ages. As the spatial resolution increased, the proportion of spatial variability of the smaller spatial structure decreased quickly and only a larger spatial structure was observed at very coarse scales. Compared with visible band, greater spatial variability was observed in near infrared band for both densely and less densely vegetated landscapes. The influence of image size on spatial heterogeneity was highly dependent on whether the empirical sernivariogram reached its sill within the original image size. When the empirical semivariogram did not reach its sill at the original observation scale, spatial variability and mean characteristic length scale would increase with image size; otherwise they might decrease. This study could provide new insights into the knowledge of spatial heterogeneity in real multisen- sor images with consideration of their nominal spatial resolution, image size and spectral bands.

Heterogeneous sensors data fusion method based on peak picking in probability density space

The multi-sensor multi-target localization and data fusion problem is discussed, and a new data fusion method called joint probability density matrix （JPDM） has been proposed, which can associate with and fuse measurements from spatially distributed heterogeneous sensors to produce good estimates of the targets. Based on probabilistic grids representation, the uncertainty regions of all the measurements are numerically combined in a general framework. The NP-hard multi-sensor data fusion problem has been converted to a peak picking problem in the grids map. Unlike most of the existing data fusion methods, the JPDM method does not need association processing, and will not lead to combinatorial explosion. Its convergence to the CRB with a diminishing grid size has been proved. Simulation results are presented to illustrate the effectiveness of the proposed technique.