基于多传感器融合技术的隧道运营安全智能巡检机器人

面向公路隧道运营安全全天候、无人化快速巡检的需求,研发了一种悬挂式的隧道运营安全智能巡检机器人,本文重点介绍其功能架构、结构设计、技术难点。机器人硬件系统主要搭载高清相机、红外摄像仪、激光扫描仪及气体传感器,实现结构表观病害、路面异常及交通环境的自动化巡检;软件系统实现多传感器信息的融合、深度学习、智能辨识,自动反馈结构病害与交通安全巡查结果,指导养护与应急工作的开展。智能巡检机器人可大大提升公路隧道交通运维质量与效果,推广价值良好。

煤矿采煤机智能化关键技术探讨

本文探讨煤矿采煤机智能化关键技术的重要性及相关技术。感知与传感技术、智能规划与决策技术、自主导航与定位技术以及通信与协同技术被认为是煤矿采煤机智能化的关键技术。然而,智能化技术在煤矿采煤机中的应用还面临一些挑战,包括传感器选型与布置、工作面地质建模和通信技术。

对虚拟现实若干关键技术的探讨

文章主要探讨了涉及VRML编程的若干关键技术问题,包括:交互下的虚拟感知传感、虚拟人体三维模型、整合Java Applet编程应用等方面。感知传感节点包括VisibilitySensor、ProximitySensor以及Collision节点,主要用以感知场景中的用户是否可见其中的造型或与造型的接近程度以及是否发生碰撞;H-ANIM格式主要构造Web浏览模式下三维人体的分层结构;EAI则主要用于在VRML场景与外部环境之间建立起通信通道,直接扩展VRML的环境交互能力和编程能力。

基于深度学习的电子鼻食品新鲜度检测与识别技术研究

实际生活中,食品储存情况复杂,对食品新鲜度的准确检测十分重要。将常见食品分为水果、蔬菜、肉类三个类别,并根据实际情况,对不同食品储存情况设计相应实验,利用设计的电子鼻系统对食品样本进行检测,将测试得到的样本数据用于建立线性判别分析(Linear Discriminant Analysis, LDA)、支持向量机(Support Vector Machine, SVM)、卷积神经网络(Convolutional Neural Network, CNN)模型,最终利用不同模型对食品样本的状态:新鲜、水果腐败、蔬菜腐败、肉类腐败,作出分类识别。对于实际测试样本,LDA、SVM、CNN识别准确率分别为:45.00%、85.00%、90.00%,结果证明基于深度学习的电子鼻系统可对不同储存情况下的食品新鲜状态作出有效判断,可在一定程度上为受主观因素影响的感官评价提供客观参考,提高食品新鲜度判断准确性。

RFID技术在电力通信设备运维中的研究与应用

将物联网的智能感知与传感技术应用到现场电力通信运维中,利用RFID电子标签对电力通信设备中的"哑资源"进行识别,并通过在电子标签中集成相应传感器实现电力通信设备的状态检测和环境感知,为构建电力通信智能移动运维体系提供准确丰富的数据基础,进而降低运维成本,提高电力通信精益化智能化运维水平。

Effective Context Inconsistency Elimination Algorithm Based on Feedback and Reliability Distribution for loV

In recent years,context aware technology has been widely used in many fields,such as internet of vehicles(IoV).Consistent context information plays a vital role in adapting a system to rapidly changing situations.However,sensor's precision variance,equipment heterogeneity,network delay and the difference of statistical algorithms can lead to inconsistency context and inappropriate services.In this paper,we present an effective algorithm of context inconsistent elimination which is based on feedback and adjusted basic reliability distribution.Through feedback,each sensor's perception precision can be obtained,and with the adjusted basic reliability distribution scheme,we can make full use of all context information by adjusting the influence of every context on whole judgment based on sensor's perception precision and threshold of sensor's perception precision,and then eliminate context inconsistency.In order to evaluate the performance of the proposed context inconsistency elimination algorithm,context aware rate is defined.The simulation results show that the proposed context inconsistency elimination algorithm can obtain the best context aware rate in most cases for the varied error rates of sensors.

Novel shape detection systems based on FBG sensor net for intelligent endoscope

This dissertation aims at providing steady sensing for the shape detection of colonoscopes. The research especially deals with the key techniques of fiber bragg grating (FBG) large curvature sensor and sensor net, integrates the techniques of mechatronics and computer graphics, and develops real time FBG shape sensing system and incremental shape sensing system for colonoscopies.

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.

Minimum sensing cell measurement in network domain based on PIR sensor array

To reasearch on the infrared target perception by pyroelectric infrared (PIR) sensor in network domain measurement,a closed sensing network domain composed of eight-PIR-sensor array is proposed for the minimum sensing cell measurement in network domain and to realize the moving target perception and trajectory prediction. Moreover,the feasibility and accuracy of the proposed method are verified through experiments. The experimental results demonstrate that the maximum error between the real trajectory and the predicted trajectory of the minimum sensing cell measurement method is 0.64 m,which can achieve infrared target perception and moving trajectory prediction.

Iontronic pressure sensor with high sensitivity and linear response over a wide pressure range based on soft micropillared electrodes

Electronic skins and flexible pressure sensors are important devices for advanced healthcare and intelligent robotics.Sensitivity is a key parameter of flexible pressure sensors.Whereas introducing surface microstructures in a capacitive-type sensor can significantly improve its sensitivity,the signal becomes nonlinear and the pressure response range gets much narrower,significantly limiting the applications of flexible pressure sensors.Here,we designed a pressure sensor that utilizes a nanoscale iontronic interface of an ionic gel layer and a micropillared electrode,for highly linear capacitance-to-pressure response and high sensitivity over a wide pressure range.The micropillars undergo three stages of deformation upon loading:initial contact(0-6 k Pa)and structure buckling(6-12 k Pa)that exhibit a low and nonlinear response,as well as a post-buckling stage that has a high signal linearity with high sensitivity(33.16 k Pa-1)over a broad pressure range of 12-176 k Pa.The high linearity lies in the subtle balance between the structure compression and mechanical matching of the two materials at the gel-electrode interface.Our sensor has been applied in pulse detection,plantar pressure mapping,and grasp task of an artificial limb.This work provides a physical insight in achieving linear response through the design of appropriate microstructures and selection of materials with suitable modulus in flexible pressure sensors,which are potentially useful in intelligent robots and health monitoring.

Bioinspired wet-resistant organogel for highly sensitive mechanical perception

Smart flexible electronics with underwater motion detection have become a promising research aspect in intelligent perception.Inspired by the strong adaptability of marine creatures to complex underwater environments,conventional biocompatible hydrogels are worth developing into organogels with preferred underwater adhesive properties,hydrophobic and antiswelling performance,and motion perception ability.Herein,a highly sensitive organogel sensor exhibiting good hydrophobicity,electromechanical properties,and adhesion properties was prepared for underwater utilization by regulating the chemical components and internal interactions.The synergistic effect of massive reversible noncovalent bonds ensures the organogel’s excellent underwater adhesion to multifarious substrates.Meanwhile,the interactions of hydrophobic conductive fillers and the dynamic hydrophobic associations in the organogel endow it with satisfactory hydrophobic performance(contact angle of111.8°)and antiswelling property(equilibrium swelling ratio of-31%after 15-day immersion).The fabricated flexible organogel strain sensor exhibits high sensitivity(gauge factor of1.96),ultrafast response rate(79.1 ms),low limit of detection(0.45 Pa),and excellent cyclic stability(1044 tensile cycles followed by 3981 compressive cycles).Results demonstrate the proposed organogel’s precise perception of sophisticated human motions in air and underwater,which expands its application scenarios.