本文選題：液滴分析技術 + 近紅外光譜�。� 參考：《南京信息工程大學》2017年碩士論文

【摘要】：隨著當前國際環(huán)境和形勢的變化,公民在公共場合的人身安全問題變得日益突出。目前各交通領域的安檢設備主要都是基于X射線成像原理來獲取乘客攜帶物的信息,而大部分安檢通道使用開蓋試喝和氣味檢測的方法檢測液態(tài)攜帶物,因此對液態(tài)危險物的檢測是安檢領域所重點關心的技術。本文將光纖液滴分析方法、電容液滴分析方法和近紅外光譜分析方法相結合,研究了各方法鑒別危險液體實施方案,并通過實驗結果分析判定各方案的可行性。本文首先構建了光譜液滴分析系統(tǒng),設計了光纖信號檢測電路和電容信號檢測電路,通過硬件電路和采集軟件的結合,完成了液滴檢測實驗平臺的搭建。同時使用光譜儀對液體樣本的吸光度信息進行采集。使用實驗平臺做初步實驗,對一些典型的液體樣本進行檢測分析,驗證該系統(tǒng)的可用性。運用仿真技術對樣本的光纖、電容、光譜信號進行融合,得到樣本的三維指紋圖,從中提取特征參數(shù)并檢驗這些參數(shù)的識別能力�；诓杉降墓庾V數(shù)據(jù),研究了近紅外光譜技術對易燃液體快速鑒別的方法。采集了典型易燃液體和非易燃液體樣品的近紅外吸收光譜圖,對數(shù)據(jù)進行平滑處理和基線校正。在建立判別預測模型時,對液體樣品的光譜數(shù)據(jù)進行主成分分析,選取了特征波長點,從而提取樣本的特征參數(shù)。利用距離判別、貝葉斯判別、Fisher判別三種方法建立了判別預測模型,結果表明Fisher判別法既準確又快速。同時,針對這些樣本的光纖、電容液滴指紋圖,進行去噪處理和歸一化,選用波形分析法提取特征參數(shù),再使用極限學習機算法構建分類判別模型。驗證了光纖電容液滴系統(tǒng)對危險液體鑒別的可行性。
[Abstract]:With the change of the current international environment and situation, the personal safety of citizens in public has become increasingly prominent. At present, security equipment in various traffic fields is mainly based on the principle of X-ray imaging to obtain information on passengers' carriers, and most security channels use open lid testing and odor detection methods to detect liquid carriers. Therefore, the detection of liquid hazardous substances is a key technology in the field of security inspection. In this paper, the optical fiber droplet analysis method, capacitive droplet analysis method and near infrared spectrum analysis method are combined to study the scheme of identifying dangerous liquid by each method, and the feasibility of each scheme is determined by the analysis of experimental results. In this paper, the spectral droplet analysis system is constructed, and the optical fiber signal detection circuit and the capacitance signal detection circuit are designed. Through the combination of the hardware circuit and the acquisition software, the experimental platform of the liquid drop detection is built. At the same time, the absorbance information of liquid sample was collected by spectrometer. Some typical liquid samples are tested and analyzed by using the experimental platform to verify the usability of the system. Using the simulation technology, the fiber, capacitance and spectral signals of the sample are fused, and the three-dimensional fingerprint of the sample is obtained, from which the characteristic parameters are extracted and the recognition ability of these parameters is tested. Based on the collected spectral data, the method of fast identification of flammable liquids by near infrared spectroscopy (NIR) was studied. The near infrared absorption spectra of typical flammable and non-flammable liquid samples were collected, and the data were smoothed and baseline corrected. When the discriminant prediction model is established, the spectral data of the liquid sample are analyzed by principal component analysis, and the characteristic wavelength points are selected to extract the characteristic parameters of the sample. The discriminant prediction model is established by using the three methods of distance discrimination and Bayesian discriminant / Fisher discriminant. The results show that Fisher discriminant is accurate and fast. At the same time, according to the fiber and capacitance drop fingerprint of these samples, the denoising and normalization are carried out, and the characteristic parameters are extracted by waveform analysis method, and then the classification and discrimination model is constructed by using the extreme learning machine algorithm. The feasibility of identifying dangerous liquids with fiber capacitor droplet system is verified.
【學位授予單位】：南京信息工程大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;D035.3

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本文編號：2015010