基于深層信念網(wǎng)絡(luò)的太赫茲光譜識(shí)別研究
發(fā)布時(shí)間：2018-11-24 12:20
【摘要】：太赫茲技術(shù)是近年來(lái)發(fā)展極其迅速的一門(mén)新興交叉學(xué)科,由于其擁有很多獨(dú)特優(yōu)點(diǎn),引起國(guó)內(nèi)外研究人員的廣泛關(guān)注。目前已有一部分太赫茲產(chǎn)品得到了實(shí)際運(yùn)用,展現(xiàn)出極高的使用價(jià)值和廣闊的應(yīng)用前景。由于太赫茲光譜具有的“指紋譜”特性,使得對(duì)太赫茲光譜的識(shí)別成為了鑒別物質(zhì)、特別是大分子物質(zhì)(如毒品、中藥材、農(nóng)作物、食品、爆炸物等)的一種重要手段。當(dāng)前,隨著太赫茲時(shí)域光譜技術(shù)的迅猛發(fā)展,儀器設(shè)備檢測(cè)精度以及使用便捷性的提高,物質(zhì)的太赫茲光譜樣本數(shù)量呈現(xiàn)出急劇增長(zhǎng)的趨勢(shì),如何有效的利用這些太赫茲光譜數(shù)據(jù)對(duì)物質(zhì)進(jìn)行識(shí)別是該領(lǐng)域面臨的一個(gè)較大的問(wèn)題。相比于人工定義、提取特征的方法,深層信念網(wǎng)絡(luò)是從2006年開(kāi)始重新興起的一種自動(dòng)學(xué)習(xí)特征的方法,而且能更有效的處理規(guī)�；瘮�(shù)據(jù),其在語(yǔ)音、圖像以及自然語(yǔ)言處理領(lǐng)域已得到了成功的應(yīng)用。針對(duì)部分物質(zhì)在太赫茲波段內(nèi)沒(méi)有明顯的吸收峰,難以人工定義、提取特征及分類識(shí)別的問(wèn)題。文中結(jié)合深層信念網(wǎng)絡(luò)和KNN分類器的優(yōu)點(diǎn),探討了一種基于深層信念網(wǎng)絡(luò)的太赫茲光譜識(shí)別方法。首先利用S-G濾波和三次樣條插值對(duì)不同物質(zhì)的太赫茲透射光譜進(jìn)行歸一化處理；然后由兩層受限波爾茲曼機(jī)構(gòu)建深層信念網(wǎng)絡(luò)模型,并采用逐層無(wú)監(jiān)督的方法來(lái)訓(xùn)練模型,以自動(dòng)提取太赫茲光譜特征；最后用KNN分類器對(duì)不同物質(zhì)的太赫茲透射光譜進(jìn)行識(shí)別。針對(duì)目前幾乎所有的太赫茲光譜數(shù)據(jù)庫(kù)基本只能提供簡(jiǎn)單的列表查詢、名稱查詢功能,即使用一些關(guān)鍵詞來(lái)檢索對(duì)應(yīng)的光譜,缺少通過(guò)光譜檢索光譜的功能的問(wèn)題。本文探索了一種基于局部敏感哈希算法的太赫茲光譜數(shù)據(jù)庫(kù)的構(gòu)建方法,同時(shí),對(duì)譜檢譜的方法進(jìn)行了研究,并搭建了一個(gè)太赫茲光譜識(shí)別的原型系統(tǒng),對(duì)文中探討方法的有效性進(jìn)行了驗(yàn)證。
[Abstract]:Terahertz technology is a new interdisciplinary subject which has been developing rapidly in recent years. Because of its many unique advantages, terahertz technology has attracted wide attention of researchers at home and abroad. At present, some terahertz products have been used in practice, showing very high use value and broad application prospect. Because of the "fingerprint spectrum" characteristic of terahertz spectrum, the identification of terahertz spectrum has become an important means of identifying substances, especially macromolecules (such as drugs, Chinese medicinal materials, crops, food, explosives, etc.). At present, with the rapid development of terahertz time-domain spectroscopy technology and the improvement of the precision and ease of use of instruments and equipment, the number of terahertz spectrum samples of materials has shown a trend of rapid growth. How to effectively use these terahertz spectral data to identify matter is a big problem in this field. Compared with the manual definition, the deep belief network is an automatic feature learning method that has been emerging since 2006, and it can deal with the large-scale data more effectively. The field of image and natural language processing has been successfully applied. Because there is no obvious absorption peak of some substances in terahertz band, it is difficult to manually define, extract features and identify classification. Combining the advantages of deep belief network and KNN classifier, a terahertz spectral recognition method based on deep belief network is discussed. First, S-G filter and cubic spline interpolation are used to normalize the terahertz transmission spectra of different materials. Then the deep belief network model is built by two-layer constrained Boltzmann mechanism and the unsupervised method is used to train the model to extract terahertz spectrum feature automatically. Finally, the terahertz transmission spectra of different substances are identified by KNN classifier. At present, almost all terahertz spectral databases can only provide simple list query and name query function, even though some keywords are used to retrieve the corresponding spectrum, it lacks the function of spectrum retrieval through spectrum. In this paper, a method of constructing terahertz spectral database based on local sensitive hashing algorithm is explored. At the same time, the method of spectrum detection is studied, and a prototype system of terahertz spectral recognition is built. The validity of the method discussed in this paper is verified.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：O441.4;O433
本文編號(hào)：2353687
[Abstract]:Terahertz technology is a new interdisciplinary subject which has been developing rapidly in recent years. Because of its many unique advantages, terahertz technology has attracted wide attention of researchers at home and abroad. At present, some terahertz products have been used in practice, showing very high use value and broad application prospect. Because of the "fingerprint spectrum" characteristic of terahertz spectrum, the identification of terahertz spectrum has become an important means of identifying substances, especially macromolecules (such as drugs, Chinese medicinal materials, crops, food, explosives, etc.). At present, with the rapid development of terahertz time-domain spectroscopy technology and the improvement of the precision and ease of use of instruments and equipment, the number of terahertz spectrum samples of materials has shown a trend of rapid growth. How to effectively use these terahertz spectral data to identify matter is a big problem in this field. Compared with the manual definition, the deep belief network is an automatic feature learning method that has been emerging since 2006, and it can deal with the large-scale data more effectively. The field of image and natural language processing has been successfully applied. Because there is no obvious absorption peak of some substances in terahertz band, it is difficult to manually define, extract features and identify classification. Combining the advantages of deep belief network and KNN classifier, a terahertz spectral recognition method based on deep belief network is discussed. First, S-G filter and cubic spline interpolation are used to normalize the terahertz transmission spectra of different materials. Then the deep belief network model is built by two-layer constrained Boltzmann mechanism and the unsupervised method is used to train the model to extract terahertz spectrum feature automatically. Finally, the terahertz transmission spectra of different substances are identified by KNN classifier. At present, almost all terahertz spectral databases can only provide simple list query and name query function, even though some keywords are used to retrieve the corresponding spectrum, it lacks the function of spectrum retrieval through spectrum. In this paper, a method of constructing terahertz spectral database based on local sensitive hashing algorithm is explored. At the same time, the method of spectrum detection is studied, and a prototype system of terahertz spectral recognition is built. The validity of the method discussed in this paper is verified.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：O441.4;O433
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