發(fā)布時(shí)間：2018-07-05 13:17

  本文選題：次聲信號(hào) + 特征提取　； 參考：《中國(guó)地質(zhì)大學(xué)(北京)》2015年碩士論文

【摘要】：次聲是一種人耳聽(tīng)不到的低頻信號(hào),它的頻率范圍在0.01~20Hz之間。自然界中海嘯、火山噴發(fā)、極光、地震、泥石流,人類(lèi)活動(dòng)中的核爆炸、火箭發(fā)射、炮兵射擊等都會(huì)產(chǎn)生次聲信號(hào)。由于各種事件激發(fā)次聲的機(jī)理不盡相同,各類(lèi)型事件產(chǎn)生的次聲信號(hào)在頻率軸上的能量分布不同。從監(jiān)測(cè)到的次聲信號(hào)本身的特點(diǎn),可以反推出產(chǎn)生次聲信號(hào)的事件類(lèi)型,從而達(dá)到次聲信號(hào)分類(lèi)識(shí)別的目的。對(duì)次聲信號(hào)的特征提取和分類(lèi)識(shí)別研究一直是次聲信號(hào)處理領(lǐng)域中的熱點(diǎn)內(nèi)容。總結(jié)前人的研究成果發(fā)現(xiàn),次聲信號(hào)的分類(lèi)識(shí)別算法主要是沿著兩條主線路不斷的取得突破和發(fā)展。一方面是次聲信號(hào)特征提取技術(shù)的研究;另一方面是模式識(shí)別算法的設(shè)計(jì)研究。次聲事件識(shí)別的關(guān)鍵環(huán)節(jié)是前者,即如何從信號(hào)中提取有效的特征作為識(shí)別依據(jù)。而識(shí)別效果的好壞本質(zhì)上也是由所選用的模式特征決定的。對(duì)特征提取這一方向的研究重點(diǎn)主要在于挖掘能夠表現(xiàn)信號(hào)類(lèi)別的特征,以及如何有效的提取這些特征的信號(hào)處理技術(shù)。分類(lèi)識(shí)別這一部分的研究重點(diǎn)在于合適的匹配提取到的特征向量,研究各種分類(lèi)模型的算法和結(jié)構(gòu),設(shè)計(jì)準(zhǔn)確高效的分類(lèi)器,完成準(zhǔn)確劃分信號(hào)類(lèi)別的最終目的。本文將研究通過(guò)次聲信號(hào)對(duì)自然災(zāi)害進(jìn)行事件分類(lèi),研究的重點(diǎn)是次聲信號(hào)特征提取的技術(shù)方法和模式識(shí)別分類(lèi)算法的設(shè)計(jì)。本論文的主要內(nèi)容如下:論文首先詳細(xì)地介紹了自然災(zāi)害中次聲信號(hào)分類(lèi)識(shí)別的研究背景和重要意義,著重對(duì)比分析了各種次聲信號(hào)特征提取算法的優(yōu)點(diǎn)和不足;研究了三種技術(shù)方法用于次聲信號(hào)的特征提取和兩種分類(lèi)算法用于分類(lèi)識(shí)別;在此基礎(chǔ)上,使用采集的次聲數(shù)據(jù)對(duì)完整的分類(lèi)模型進(jìn)行試驗(yàn)驗(yàn)證。分析試驗(yàn)結(jié)果,對(duì)比各個(gè)方法的有效性。本論文以自然災(zāi)害中各種事件產(chǎn)生的次聲信號(hào)為研究對(duì)象,以降低實(shí)際次聲監(jiān)測(cè)中的誤報(bào)率為目的,研究了地震、海嘯、火山、泥石流等所產(chǎn)生的次聲信號(hào)的特征提取技術(shù)和分類(lèi)識(shí)別算法。期望本研究的成果可以對(duì)次聲信號(hào)處理方法和實(shí)際應(yīng)用產(chǎn)生一定的參考作用。
[Abstract]:Infrasound is a kind of low frequency signal which can not be heard by human ear. Its frequency range is between 0.01 Hz and 20 Hz. In nature, tsunamis, volcanic eruptions, auroras, earthquakes, mudslides, nuclear explosions in human activities, rocket launches and artillery fire all produce infrasound signals. The infrasound signals produced by different events have different energy distribution on the frequency axis because of the different mechanism of infrasound excitation. From the characteristics of the monitored infrasound signal, the event type of the infrasound signal can be inferred and the classification and recognition of the infrasound signal can be achieved. The feature extraction and classification recognition of infrasound signal has been a hot topic in the field of infrasound signal processing. It is found that the classification and recognition algorithm of infrasound signal is mainly a breakthrough and development along two main lines. On the one hand, the feature extraction of infrasound signal is studied; on the other hand, the design of pattern recognition algorithm is studied. The key link of infrasound event recognition is the former, that is, how to extract effective features from the signal as the basis for recognition. The recognition effect is essentially determined by the selected pattern features. The research focus of feature extraction is mainly on mining the features that can represent signal categories and how to extract these features effectively. The research focus of this part is on matching the extracted feature vectors, studying the algorithms and structures of various classification models, designing accurate and efficient classifiers, and accomplishing the final goal of accurately classifying the signals. In this paper, we will study the event classification of natural disasters by infrasound signals, focusing on the feature extraction of infrasound signals and the design of pattern recognition classification algorithm. The main contents of this paper are as follows: firstly, the research background and significance of infrasound signal classification and recognition in natural disasters are introduced in detail, and the advantages and disadvantages of various infrasound signal feature extraction algorithms are compared and analyzed. Three technical methods are studied for feature extraction of infrasound signals and two classification algorithms for classification and recognition. Based on this, the complete classification model is verified by using the collected infrasound data. The experimental results are analyzed and the effectiveness of each method is compared. In this paper, the infrasonic signals produced by various events in natural disasters are taken as the research object, with the aim of reducing the false alarm rate in the actual infrasound monitoring, the earthquake, tsunami and volcano are studied. Feature extraction technology and classification recognition algorithm of infrasound signal produced by debris flow. It is expected that the results of this study can be used as a reference for infrasound signal processing methods and practical applications.
【學(xué)位授予單位】：中國(guó)地質(zhì)大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TN912.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 加玉濤;羅志增;;肌電信號(hào)特征提取方法綜述[J];電子器件;2007年01期

相關(guān)博士學(xué)位論文 前1條

1 李新欣;船舶及鯨類(lèi)聲信號(hào)特征提取和分類(lèi)識(shí)別研究[D];哈爾濱工程大學(xué);2012年

相關(guān)碩士學(xué)位論文 前2條

1 任際周;基于次聲波技術(shù)的滑坡監(jiān)測(cè)預(yù)警系統(tǒng)研究[D];成都理工大學(xué);2012年

2 尚媛媛;次聲波信號(hào)分析方法研究[D];昆明理工大學(xué);2013年

，

本文編號(hào)：2100328