摘要
随着社会的飞速发展,气体检测技术也显得越来越重要。如对矿井中易燃易爆气体进行检测,可以有效预防瓦斯爆炸等矿难事故的发生;对工业中有毒气体储存罐周围的气体进行检测,可以及时发现并报告有毒气体的泄漏情况,最大程度地降低有毒气体泄漏造成的危害;对蔬菜食品的挥发性气体进行检测,不仅可以评判蔬菜食品的新鲜程度,还可以区别产品的真假与产品的等级;对病人呼出的气体进行检测,可以快速地实现诊断,既节省诊断时间,又节省了大量的人力物力,简便快捷;……。这些都说明电子鼻检测技术实用性和重要性。
电子技术的发展历程表明电子鼻技术很难在短期内实现硬件上的突破,而软件算法理论的飞速发展更新则为电子鼻技术的进步提供了可能,因而现在绝大多数研究者将电子鼻技术的研究中心转移到软件实现上来。高性能的软件算法能够提供更高的识别正确率、更短的识别时间,从而在一定程度上弥补了硬件以及环境不确定性所带来的误差。电子鼻技术软件算法的核心技术在于模式识别算法,而在进行算法实现之前还必须对数据进行一定的处理——特征选择和提取。本论文也是针对电子鼻软件算法理论方面的研究,将数据截断技术应用于传感器相应数据的截取,提出了一种新的产生特征向量的方法——特征重组法,再将特征重组法得到的特征向量输入到设计的模式识别系统中,最终实现分类。
本论文的主要研究工作如下:
(1)文章的第二章十分详尽地介绍了当前几乎所有已应用于电子鼻系统的模式识别算法的应用情况,十分有利于刚刚涉足本领域的研究者全面了解本领域研究已取得的成果和最新研究进展。
(2)研究了数据截断技术对传感器响应数据的影响。以滤波器逼近算法为研究背景,研究了最优Hankel逼近算法与直接截断逼近算法之间的关系,并对其进行了仿真实验,说明了结合函数逼近的数据截断技术应用于传感器响应数据采集的可行性。
(3)提出了基于特征值重组的模式识别算法。不同模式识别算法中所采用的特征提取手段产生的特征向量不能最大限度地发掘数据的有用信息,而将不同特征提取算法的特征提取结果按照一定的比进行重组,所得到的有用信息将大大提高,更有利于识别分类。
With the social developing rapidly, gas detection technology is becoming more and more important. For example, gas detection in flammable and explosive for mine, can effectively prevent gas explosion accidents. Gas detection around the industrial toxic gas tanks, can discover and report toxic gas leakage timely, so that can be the greatest degree to reduce the harm caused by leakage of toxic gases. The gas detection in volatility of vegetable and food, not only can judge how fresh vegetable and food are, but also can distinction which level the product is. For patients with the gas exhaled testing, can quickly realize diagnosis, which saving time and a lot of manpower, and it is very convenient, ... . All these examples show the practicality and importance of electronic nose detection technology, thus further development and promotion of electronic nose technology becomes an trend of gas detection technology.
It is well known that, the hardware of electronic nose can not make a breakthrough in the short time. But software can achieve updates or improvement in short term, thus, most researchers began to focus on software algorithm of the electronic nose technology. The software algorithm can provide high recognition accuracy and low recognition time, which can compensate the error caused by hardware and environmental uncertainty. Pattern recognition algorithm is the key technology of electronic nose software. Data for algorithm must be prepared, the process is called feature extraction and selection. This paper also focus on the study of signal processing and pattern recognition algorithm about electronic nose. We disign a new style PID controller to improve the dynamic performance of data acquisition system, and put forward a new method to produce feature vectors, then put the feature vector into the designed pattern recognition system and realizes the classification finally.
Main research work we have done as follow:
Firstly, we analysed the current research progress of this field, and given the most comprehensive review about algorithm and application, it is very useful for some researchers who begin focus on this field.
Secondly, we introduced several common signal processing and pattern recognition algorithm in the research of this field, and analysed the advantages and disadvantages of them.
Thirdly, we researched how to use the truncated technology to get the data of sensor response. Our research based on two filter approximation algorithms, the optimal Hankel approximation with direct truncation algorithm approximate algorithm were studied. Simulation results show the feasibility of data truncation technology applied to data collection.
Finally, we designed a new signal processing and pattern recognition algorithm, and the classification result show the superiority of the new method.
引文
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