基于智能传感器的控制手柄设计

以集成加速度传感器ADXL202为手柄的运动敏感器件,并以PIC18F248 MCU为核心设计了带有CAN接口和RS232接口的控制手柄.该控制手柄可作为工程机械分布式控制系统的一个智能传感器节点.由于ADXL202既能敏感静态加速度,又能敏感动态加速度,从而可获得操作者的加速意图,更具人性化特点. 针对工程机械使用环境中的振动和冲击, 文中又设计了适用于微控制器实现的整系数低通滤波器,实际测试表明, 该控制手柄完全可以满足实际工况的使用要求.     

多传感器信息融合的智能交通控制系统研究

针对城市道路交通网络的交通信号控制,构造了一种基于多传感器信息融合技术的智能交通控制系统.相对于传统的基于单一车辆传感器信号控制系统而言,该系统具有信息的完整性、统一性、多样性和容错性等优点.在多传感器信息融合的基础上,讨论了多种可行的智能控制方案.通过对城市区域交通的多方位控制,达到全面提高控制效果和有效疏导交通的目的.给出的仿真结果表明了其优越性和有效性.     

基于WSN的智能交通灯控制系统设计

针对多路口的交通信号灯控制问题,提出了基于无线传感器网络的两级组织结构,搭建了交通信号灯控制平台.利用传感器节点收集的交通信息,结合模糊控制方法,实现了交通信号灯的无线智能控制.仿真结果表明,该控制器是有效的,其控制效果优于传统的控制方法.     

基于光电传感器的暖通空调温湿度智能控制技术

鉴于暖通空调(Heating, Ventilation and Air Conditioning, HVAC)由于检测精度低,控制过程不完善,导致温、湿度控制效果差,提出基于光电传感器的暖通空调温湿度智能控制技术。分析光电传感器的光电组件,设计光电传感器的投光电路,通过复合悬臂梁的谐振频率实现温度采集,测量传感器的电阻两侧电压变化值获取当前环境湿度;将检测到的数据输入构建的温湿度智能控制系统,通过混合蛙跳算法(SFLA)优化PID控制器的参数,控制暖通空调的温、湿度保持在设定的范围,实现暖通空调温湿度智能控制。实验结果表明：设计的温湿度智能控制技术检测温度的误差低于0.02℃,在外界干扰的情况下,控制温度在26℃,湿度值在80%RH,因此,该技术可有效控制温度和湿度在设定的范围,具备可行性和有效性。     

基于机器学习算法的智能家居远程控制系统

基于机器学习算法的智能家居远程控制系统,由智能控制中心、各类传感器和远程控制App组成。智能控制中心以STM32为主控开发板,连接各种智能设备和传感器。在Windows操作系统下通过MQTT协议与App进行交互,实现设备的智能控制和智能场景切换。同时,支持学习用户的行为习惯和喜好,根据用户习惯实现个性化设置,可以通过大数据分析和智能调节实现对能源的有效利用,达到节能减排的目的。     

基于单片机的智能窗户控制系统的设计

随着科技的进步和社会的发展,人们越来越注重生活质量,智能化家居已逐渐应用到人们的生活中,为人们的生活带来便捷。运用单机片技术、传感器技术设计以湿敏传感器、红外传感器、PM2.5为依据的单机片智能窗户。该技术运用机电一体化,将窗户进行智能控制,结合当前智能控制、机械传动、传感等技术将房内所有窗户进行智能化控制,自动打开、关闭,具有调节室内环境、防盗报警功能。     

基于STM32单片机的无线智能家居控制系统

本文设计了一套基于嵌入式控制的尢线智能家居控制系统,该系统结合ZigBee技术、GPRS通信技术、传感器技术等,实现对家居环境中的温度、空气质量、家电开关、门窗的关闭等进行本地和远程尢线智能控制.     

基于WSN和嵌入式系统的农业温室智能控制器设计

为实现温室系统的智能控制,设计了一个智能温室环境监控系统。以ARM9嵌入式中央处理器为核心,将各类传感器采集来的数据通过无线传感器网络发送到中央处理器,采取层次分析智能控制策略,以作物的干物质含量最高为目标进行自动控制。通过自动、手动、GUI人工干预等多种方式调整执行机构,从而闭环控制温室内小气候环境。经实验表明系统对温室环境控制有效。     

室内智能照明控制装置

介绍一种基于HC6800-MS功能板设计的控制室内照明的装置,该装置主要由电源电路、单片机最小系统、光敏传感器、红外感应传感器,继电器等组成,通过控制人工照明以及窗帘的开闭来实现良好的室内照明环境.尤其是照明控制装置中的智能开关设计,通过与手机巧妙搭配使用可以充分的满足卧室照明的智能控制要求,满足睡眠、夜景、唤醒、起床等多种个性化要求,体现了良好的人性化和节能化.     

倒立摆系统稳定控制之研究

多级倒立摆系统作为一个典型的非线性、多变量、高阶次、强耦合和自然不稳定的复杂被控系统,一直是人们检验、比较各种控制理论和方法的理想实验平台,半个多世纪以来,国内外许多机构对它进行了广泛的研究,积累了丰富的资料。这里对多级倒立摆稳定控制的研究现状进行总结,探讨了它的发展趋势。目前主要的控制方法有线性控制、预测控制和智能控制三类,智能控制是当前研究的主流,它包括模糊控制、拟人控制、计算智能控制、云模型控制等。继续深入研究各种智能控制方法及其组合应用是今后的发展方向。     

基于ZigBee技术的人体健康监测平台

基于ZigBee技术,采用脉搏、ECG和SpO₂等微型传感器,感知人体生理信号;利用多变量自回归模型进行算法处理和数据融合,提高信号的可靠性和准确度;通过ZigBee无线网络,将信号传送到终端设备,开发一种对人体的心率、脉搏和血压进行同时、实时、随时监测系统,由此诊断人体的健康状况。实验表明,该平台性能稳定,在数据采集和传输等方面均达到了设计要求。     

A new feature extraction method for odour classification

A new method for real time classification of volatile chemical substance traces is presented. The method is based on electrochemical signals of an array of semiconductor gas sensors. In these sensor signals characteristic patterns of different substances are hidden. There are non-linear correlative relationships between the measured sensor signals and the chemical substances which are treated using two methods derived from statistical learning theory (Support Vector Machine - SVM, Maximum Likelihood Estimation - MLE) for the detection of the substance characteristics in the sensor signals. A key criterion for the presented pattern recognition is a newly developed type of features, which is specially adapted to the low frequency signals of semiconductor sensors. The presented features are based on the evaluation of the range of the transient response in the sensor signals in the frequency domain.To derive the new features, both real measurement data and synthetic generated signals were used. In the experiments the focus was set on the creation of reproducible sensor signals to get characteristic signal patterns. Synthetic signals were derived from a Gaussian Plume Model. With the new features, training data sets were calculated using the classification methods SVM and MLE. With these training data sets new sensor measurements may be assigned to the substances which are to be sought. The advantage of the presented method is that no feature reduction is needed and no loss of information occurs in the learning process.The classification results based on the new features have been compared with the classification based on a conventional method for feature extraction. It was proved that the recognition rate of the substances used with the new feature type is higher.The substance classification is primarily limited by the sensitivity of the semiconductor sensors, because sufficiently large sensor signals must have been provided to obtain appropriate substance patterns. At the present stage of development the method presented is suitable for the classification of substance groups, such as nitro aromatics or alcohols, but not for specific substances.     

Heuristic approaches for the optimal wiring in large scale robotic skin design

This paper faces the problem of optimizing the wiring and the connections in a tactile skin for robots. The robotic skin is a device composed of a network of tactile sensors, whose wiring can be very complex: the control of this complexity is a key problem. In the considered robotic skin, skin elements are grouped into skin patches, which output tactile data that have to be read by a micro-controller. The logical connections between the sensors must be defined in order to route signals through the network. A finite set of micro-controllers is given and a set of constraints is imposed on the given assignment and routing. The considered problem has a combinatorial nature and it can be formulated as a Minimum Constrained Spanning Forest problem with costs on arcs that cannot be a priori defined as they are solution-dependent. The problem is NP-hard. The paper introduces a mathematical formulation and then proposes a Multi-Start Heuristic algorithm and an Ant Colony Optimization approach whose effectiveness is evaluated through experimental tests performed on both real and synthetically generated instances.     

A preliminary study of combining mass spectrometric data with audio and video signals for real-time monitoring of controlled lab-scale fires

The different aspects of sensors integration, and specifically that of a Mass Spectrometer (MS) with audio and video signals, are investigated for detecting and monitoring indoor fire events. The present study focuses on comparing the capabilities of a variety of chemical sensors, on answering technical challenges in regard to the integration of chemical, audio and video signals and on discussing integration issues for potential field applications. Controlled, small scale fire experiments were carried out in the laboratory. A commercial MS coupled with an in-house developed Pulsed Sampling System (PSS), was used for on-line sampling and near real-time monitoring of the evolved volatiles. The detection limit of PSS-MS was found to be 150 ppbv and its linearity was confirmed up to 10 ppmv using benzene gas standards. The profiles of ions with m/z 57, 78, 91 and 106, corresponding to indicative Volatile Organic Compounds (VOCs) of the fire event, were recorded and compared with the concentration profiles of CO₂, CO, O₂, NO and H/C (C₃H₈), acquired by the gas sensors of a commercial exhaust gas analyzer. Audio and video signals were recorded by a microphone and a visual camera, simultaneously, with PSS-MS data. Two types of fire experiments were performed in order to simulate field conditions: (a) direct fire monitoring, in case of unobstructed direct fire view and (b) indirect fire monitoring through reflection of audio and video signals on metallic surfaces, for simulating obstacles preventing direct fire view. The information derived by audio and video signals reaffirmed the chemical detection inferences for both types of fire experiments, thus increasing the credibility of each individual method. Occasionally, video, audio and chemical information were complementary, thus counterbalancing the detection limitations of the individual methods. The integrated approach of combining MS data with audio and video signals appears to be a promising method in safety and security applications, where reliable, early detection and real-time monitoring is necessary.     

基于模糊神经网络味觉信号识别的研究

文中提出了一种基于模糊神经网络方法的味觉信号识别模型,利用小波变换实现了对传感器所采集的味觉信号进行数据压缩及特征抽取,以模糊神经网络作为味觉信号的识别工具。     

无线MEMS电容式加速度传感器在港口吊机动挠度测试中的应用

港口吊机抓斗运行时,横梁将产生动挠度.动挠度仪或涡流传感器可以直接测量物理位移,但需要固定的安装点,其使用环境受限.动挠度信号主要以低频为主,普通的直接安装在吊机梁上的传感器因不能测得低频信号而无法测量动挠度.为此,开发了种基于电容式低频振动传感器的无线动挠度测试系统,相对目前其他动挠度测试仪器及测试方法,系统使用方便...     

Remote observation of the sea surface and atmosphere The oceanic skin effect

Abstract

A comparison is made between the measurements of sea surface temperature (SST), obtained using an infrared radiometer mounted on a vessel of the British Antarctic Survey, and from a conventional Meteorological Office rubber bucket with mercury thermometer. These measurements are used to investigate the size and variability of the oceanic skin effect from the tropical Atlantic to the waters of Antarctica. The implication of the skin effect on the validation of satellite-borne infrared sensors of the sea surface temperature is also investigated. In terms of the overall average for the complete Atlantic Ocean data set, the skin of the sea is about 0-30 deg K cooler than the bulk at about 10cm below the surface. There are only a few cases of the skin being warmer than the bulk temperature, on the other hand there are some occasions when the skin can be of the order of 1 -0 deg K cooler than the bulk. There is a suggestion that the skin effect at night-time is smaller than it is during the day-time, and a possible explanation of this is given in terms of the complication of the diurnal thermocline. The skin effect can be an important source of error in the validation of space-borne sensors of SST, particularly with the requirement for high accuracy of SST measurement for climate studies. In terms of the retrieval of SST from satellite infrared sensors the skin effect is only one of several physical effects that create uncertainty in the value of SST.     

基于LabVIEW的智能交流接触器动态性能测试系统

建立了一种智能交流接触器同步信号测试系统。设计了一种带隔离互感器的强弱电接入电路,实现了智能交流接触器强电输入信号和弱电传感器信号的同步采集。测试平台可以准确地对各路信号进行采集,同时可以将各路信号进行同步,实时反映智能交流接触器的各项动态指标。     

Signal processing evaluation of myoelectric sensor placement in low‐level gestures: sensitivity analysis using independent component analysis

Surface electromyogram (sEMG) is a technique in which electrodes are placed on the skin overlying a muscle to detect the electrical activity. Multiple electrical sensors are essential for extracting intrinsic physiological and contextual information from the corresponding sEMG signals. The reason, why more than just one sEMG signal capture has to be used, is as follows: Due to signal propagation inside the human body in terms of an electrical conductor, there cannot be a one‐to‐one mapping of activities between muscle fibre groups and corresponding sEMG sensing electrodes. Each of such electrodes rather records a composition of many, and widely activity‐independent signals, and such kind of raw signal capture cannot be efficiently used for pattern matching due to its linear dependency. On the other hand, Independent component analysis (ICA) provides the perfect answer of separating skin surface recordings into a set of independent muscle actions. Hence, there is a need for a method that indicates the quality of the sensor placements in sEMG. The purpose of this paper is to describe the use of source separation for sEMG using ICA. The actual use in practical sEMG experiments is demonstrated, when the number of recording channels for electrical muscle activities is varied.     

基于准分布式FBG传感器的光纤入侵报警系统

根据FBG传感器的应变、振动和冲击特征,报道了一种基于准分布式FBG传感器的入侵报警系统.首先根据小波降噪原理和小波多尺度边缘检测的方法分别进行降噪和报警信号分割,同时对分割信号分类和特征分析以进行报警判断和识别;其次分析了入侵监测系统的五种报警模式;最后根据LabVIEW开发了入侵报警软件.经过实验证明入侵报警系统能够完成自动、实时报警和报警定位.