金属氧化物气体传感器检测混合气体组分浓度的线性方法与原理

金属氧化物气体传感器的交叉敏感特性给混合气体各组分浓度的定量检测带来了困难.基于传感器的灵敏度与被测气体浓度呈对数线性关系,同一传感器对不同被测气体具有不同的灵敏度,不同传感器对同一气体具有不问的敏感特性,描述了采用多个传感器检测混合气体各组分浓度的线性原理与方法,并介绍了识别未知多组分混合气体中某一组分浓度的方法.分析结果表明,在传感器有效测试浓度范围内,有n种组分的混合气体各组分的浓度的对数等于n×n的指数矩阵乘以n×1的指前系数与传感器的输出的函数的对数矩阵.实验结果显示.对混合气体各组分浓度检测的相对误差小于7%.     

热催化气体传感器的特性分析及其设计原则

热催化气体传感器的输出受诸多因素的影响,从理论上分析其输出特性有助于传感器的优化设计和性能评估。从一个热催化气体传感器的典型结构和热催化气体敏感机理出发,应用热平衡方程和热敏电阻的电阻温度关系推导了传感器输出与被测气体浓度、催化反应速度、工作温度以及传感器结构尺寸等之间的定量关系。从推导过程中的诸多假设,得出了热催化气体传感器设计时应遵循的一般原则。     

基于RBF神经网络的单一催化传感器检测混合气体研究

根据催化传感器在不同的工作温度下具有不同气体检测灵敏度的特点,提出基于恒温检测与RBF神经网络的单一热催化传感器检测可燃混合气体的新方法.采用动态学习算法的RBF神经网络建立了多种可燃气体分析的数学模型.通过对甲烷,一氧化碳和氢气三种气体混合的样本进行实验,结果表明所研究的方法可以较好地实现单一催化传感器对多种可燃混合气体的分析.     

基于MEMS技术微双桥催化LPG传感器研究

提出了基于MEMS技术,将电化学生长的Al2O3膜加工成微双桥结构,并成为Al2O3-Pd-Th0-SiO复合催化剂的载体,实现催化元件和补偿元件的单片集成,制作出LPG传感器.单桥结构尺寸为1 mm×1.2 mm,厚度为30-35μm,利用铂薄膜制做加热器,并经反溅刻蚀和激光修调,形成标称阻值(0℃)为10 Ω的热敏感电阻.传感器性能测试结果表明:微双桥催化LPG传感器具有输出信号与气体浓度呈线性特性,可实现体积分数(500～10000)×10-6LPG检测,同时,测试了温度范围为10-50℃和湿度范围为10-90%RH的传感器温湿度特性,结果证明:微双桥催化LPG传感器输出最大偏差为1.2 mV,相当于体积分数600×10-6的LPG浓度.     

基于单一传感器的可燃混合气体RBF网络分析

催化传感器对不同可燃气体或在不同的工作温度下有不同的输出灵敏度,根据这一特点控制单一催化传感器工作在不同的温度,检测可燃混合气体时可以得到不同的输出信号,采用RBF神经网络和动态学习算法,建立了多种可燃气体分析的数学模型。通过对CH4,CO,H23种气体混合的样本进行实验,结果表明:所研究的方法可以较好地实现单一催化传感器对多种可燃混合气体的分析。     

一种混合气体与温度同时检测的传感器阵列

针对主要烟气污染物气体一氧化氮NO/二氧化硫SO₂监测存在的温漂大、温度补偿系统体积大、成本高的问题,采用MEMS工艺,研制了一种三电极碳纳米管传感器阵列。该阵列由三个不同极间距的传感器构成,分别检测NO、SO₂浓度及温度。研究了极间距、气体浓度和温度对传感器的影响,分析了传感器对气体浓度和温度的敏感机理。三个传感器在不同的气体浓度、不同的温度下,具有不同的单值敏感特性。传感器重复性好、灵敏度高;阵列可以不分离混合气体,直接检测NO、SO₂成分及温度,具有温度补偿的功能,能够用于解决烟气污染物检测在工作温度变化时存在的误报问题。     

一种较大驱动电流的锑化铟磁阻式齿轮转速传感器设计

本文介绍一种采用锑化铟-铟共晶体磁敏电阻作为敏感元件的半导体薄膜型齿轮转速传感器，得出了传感器检测距离与磁阻输出信号的关系曲线。传感器驱动级采用推挽输出的形式，输出矩形脉冲负荷能力强，输出拉电流最大可达200mA以上，当温度在-℃30～ 70℃之间变化时，信号处理电路输出依然稳定可靠。     

光学气体检测中的插值-数据查表浓度计算与补偿方法

文中从红外光学双波长检测技术的基本原理出发,重点分析了光学气体传感器输出信号的处理计算方法。提出了一种利用输出信号线性比值与传感器相对吸收率之间的关系所对应的气体浓度计算模型。通过对模型进行数学分析,提出了一种适合多场合快速求解应用的插值-数据查表浓度计算与补偿方法。采用该方法,结合微处理器软件控制技术,快速、方便地实现了气体浓度的检测,软件具有两点法自动校准功能,最终能达到300×10-6的分辨能力,满足了大多预警、报警场合的应用需求。     

用于室内有毒气体快速检测的便携式CC/SAW电子鼻

开发了一种用于室内空气质量快速检测的便携式电子鼻气体分析仪.该仪器利用毛细管分离柱(CC)将混合气体选择性分离,实现对气体的分类识别,然后借助声表面波(SAW)传感器频率响应测量每种组分对应的浓度,实现对气体的量化.实验针对17种有毒有害气体进行测试,结果表明该系统能对低浓度复杂混合气体进行快速检测,且具有较高的灵敏度,良好的选择性和重复性.这为不同应用场合下的痕量气体检测提供了一种可行的技术.     

基于微纳电离式传感器的低浓度甲烷阈值气敏机理研究

在CH_4浓度较低时,目前煤矿井下CH_4的检测方法存在灵敏度较低、反应较慢等问题。现有微纳电离式气体传感器研究主要针对高浓度气体的检测,且使用的仿真模型为一维简化放电模型,忽略了N_2、CH_4分子与电离产生离子的横向漂移与扩散,对低浓度CH_4气体的准确检测还有待进一步验证。针对上述问题,在现有研究的基础上考虑了离子的横向漂移与扩散,并加入了等离子体模块,采用流体-化学动力学混合方法建立了常温常压下CH_4-N_2混合气体在微纳场域下的二维放电模型,分析了在常温常压下CH_4-N_2混合气体的安全放电电压、气敏性以及放电电流密度与CH_4浓度之间的变化关系。分析结果表明:电离式传感器的安全放电电压为200V,且信噪比较高;在CH_4-N_2混合气体中,CH_4对N_2的电离过程起抑制作用;电离式传感器的输出电流密度随CH_4体积分数的增加(0.25%～1.5%)而线性递减,体现了电离式传感器对低浓度杂质气体的敏感特性,利用CH_4浓度与电流密度之间单调递减的线性关系,可以实现对低浓度CH_4的检测。     

以太网中网络监听的检测

网络监听给以太网安全带来了极大的威胁。一直以来,缺乏很好的网络监听检测方法。本文论述了以太网中网络监听的原理,在此基础上针对不同的网络环境分别提出了基于广播式原理和交换式原理的监听检测有效方法。     

Detecting Objectionable Videos

This paper addresses the problem of detecting objectionable videos, which has never been carefully studied before. Our method can be efficiently used to filter objectionable videos on Internet. One tensor based key-frame selection algorithm, one cube based color model and one objectionable video estimation algorithm are presented. The key frame selection is based on motion analysis using the three-dimensional structure tensor. Then the cube based color model is employed to detect skin color in each key frame. Finally, the video estimation algorithm is applied to estimate objectionable degree in videos. Experimental results on a variety of real-world videos downloaded from Internet show that this method is promising.     

Detecting pattern-based outliers

Outlier detection targets those exceptional data that deviate from the general pattern. Besides high density clustering, there is another pattern called low density regularity. Thus, there are two types of outliers w.r.t. them. We propose two techniques: one to identify the two patterns and the other to detect the corresponding outliers.     

敏感视频检测

This paper addresses the problem of detecting objectionable videos, which has never been carefully studied before. Our method can be efficiently used to filter objectionable videos on Internet. One tensor based key-frame selection algorithm, one cube based color model and one objectionable video estimation algorithm are presented. The key frame selection is based on motion analysis using the three-dimensional structure tensor. Then the cube based color model is employed to detect skin color in each key frame. Finally, the video estimation algorithm is applied to estimate objectionable degree in videos. Experimental results on a variety of real-world videos downloaded from Internet show that this method is promising.     

Detecting digital chameleons

Conversations are characterized by an interactional synchrony between verbal and nonverbal behaviors [Kendon, A. (1970). Movement coordination in social interaction: some examples described. Acta Psychologica, 32(2), 101–125]. A subset of these contingent conversational behaviors is direct mimicry. During face to face interaction, people who mimic the verbal [Giles, H., Coupland, J., & Coupland, N. (1991). Accommodation theory: Communication, context, and consequence. In Giles, H., Coupland, J., & Coupland, N. Contexts of accommodation. Developments in applied sociolinguistics. Cambridge: Cambridge University Press] and nonverbal behaviors [Chartrand, T. L., & Bargh, J. A. (1999). The chameleon effect: the perception-behavior link and social interaction. Journal of Personality and Social Psychology, 76, 893–910] gain social advantage. Most research examining mimicry behavior in interaction examines ‘implicit mimicry’ in which the mimicked individual is unaware of the behavior of the mimicker. In this paper, we examined how effective people were at explicitly detecting mimicking computer agents and the consequences of mimic detection in terms of social influence and interactional synchrony. In Experiment 1, participant pairs engaged in a “one-degree of freedom” Turing Test. When the computer agent mimicked them, users were significantly worse than chance at identifying the other human. In Experiment 2, participants were more likely to detect mimicry in an agent that mirror-mimicked their head movements (three degrees of freedom) than agents that either congruently mimicked their behaviors or mimicked those movements on another rotational axis. We discuss implications for theories of interactivity.     

Detecting moving objects

The detection of moving objects is important in many tasks. This paper examines moving object detection based primarily on optical flow. We conclude that in realistic situations, detection using visual information alone is quite difficult, particularly when the camera may also be moving. The availability of additional information about camera motion and/or scene structure greatly simplifies the problem. Two general classes of techniques are examined. The first is based upon the motion epipolar constraint—translational motion produces a flow field radially expanding from a focus of expansion (FOE). Epipolar methods depend on knowing at least partial information about camera translation and/or rotation. The second class of methods is based on comparison of observed optical flow with other information about depth, for example from stereo vision. Examples of several of these techniques are presented.A preliminary version of this article appeared in The Proceedings of the First International Conference on Computer Vision, London, June 1987.     

Detecting stable matrices

Stable matrices are related to the values of their entries by the concept of an all negative quasi-dominant diagonal. Stable matrices are related to the signs of their entries by the signs of cycles in their corresponding signed directed graphs. This paper establishes relationships between stable matrices and conditions on the values of cycles. For some cases the conditions on the values of cycles are satisfied if and only if the matrix has a quasi-dominant diagonal.     

Detecting quantum entanglement

We review the criteria for separability and quantum entanglement, both in a bipartite as well as a multipartite setting. We discuss Bell inequalities, entanglement witnesses, entropic inequalities, bound entanglement and several features of multipartite entanglement. We indicate how these criteria bear on the experimental detection of quantum entanglement.     

Detecting value-based scalar dependence

Precise value-based data dependence analysis for scalars is useful for advanced compiler optimizations. The new method presented here for flow and output dependence uses Factored Use and Def chains (FUD chains), our interpretation and extension of Static Single Assignment. It is precise with respect to conditional control flow and dependence vectors. Our method detects dependences which are independent with respect to arbitrary loop nesting, as well as loop-carried dependences. A loop-carried dependence is further classified as being carried from the previous iteration, with distance 1, or from any previous iteration, with direction <. This precision cannot be achieved by traditional analysis, such as dominator information or reaching definitions. To compute anti- and input dependence, we use Factored Redef-Use chains, which are related to FUD chains. We are not aware of any prior work which explicitly deals with scalar data dependence utilizing a sparse graph representation. A preliminary version of this paper appeared in theSeventh Anual Workshop on Languages and Compilers for Parallel Computing, August 1994. Supported in part by NSF Grant CCR-9113885 and a grant from Intel Corporation and the Oregon Advanced Computing Institute.     

On Detecting Spatial Outliers

The ever-increasing volume of spatial data has greatly challenged our ability to extract useful but implicit knowledge from them. As an important branch of spatial data mining, spatial outlier detection aims to discover the objects whose non-spatial attribute values are significantly different from the values of their spatial neighbors. These objects, called spatial outliers, may reveal important phenomena in a number of applications including traffic control, satellite image analysis, weather forecast, and medical diagnosis. Most of the existing spatial outlier detection algorithms mainly focus on identifying single attribute outliers and could potentially misclassify normal objects as outliers when their neighborhoods contain real spatial outliers with very large or small attribute values. In addition, many spatial applications contain multiple non-spatial attributes which should be processed altogether to identify outliers. To address these two issues, we formulate the spatial outlier detection problem in a general way, design two robust detection algorithms, one for single attribute and the other for multiple attributes, and analyze their computational complexities. Experiments were conducted on a real-world data set, West Nile virus data, to validate the effectiveness of the proposed algorithms.