本文選題：煤泥浮選 + 泡沫圖像　； 參考：《中國礦業(yè)大學》2017年碩士論文

【摘要】：浮選是利用礦物顆粒表面潤濕性的不同來實現(xiàn)礦物分選的一門技術,其泡沫的表面視覺特征直接反映浮選生產工況。目前,國內大多數(shù)選廠對浮選狀態(tài)的判別仍然停留在人工觀測的基礎上,極大地限制了浮選技術的推廣和發(fā)展。將圖像識別技術引入浮選不但可以有助于浮選作業(yè)的穩(wěn)定優(yōu)化運行,而且可以解放出大量的人力和物力,對其的后續(xù)發(fā)展具有十分重要的意義。本文以“趙樓煤礦浮選加藥自動控制系統(tǒng)”項目為背景,對煤泥浮選泡沫圖像識別系統(tǒng)進行了深入的研究,有效地克服了傳統(tǒng)浮選工藝所固有的弊病,其研究內容主要可概括為如下幾個方面:1)針對煤泥浮選泡沫圖像易受到混合噪聲,特別是高斯-椒鹽混合噪聲,干擾的情況,提出了一種新型濾波算法,其不僅在去除泡沫圖像的混合噪聲上要明顯優(yōu)于傳統(tǒng)濾波算法,而且可以滿足一定的實時性需求。2)對煤泥浮選泡沫圖像的特征提取算法進行了研究,并選擇紋理、尺寸和速度特征參數(shù)作為系統(tǒng)的輸入。其中,本文分析和總結了傳統(tǒng)分水嶺算法的不足之處,并對其進行了相關改進,基本實現(xiàn)了煤泥浮選泡沫圖像的精準分割。3)通過經驗公式和相關實驗,確定了BP神經網絡隱含層神經元的最優(yōu)數(shù)目,并在相同網絡參數(shù)下,對比了BP神經網絡和遺傳BP神經網絡的性能。進一步確定了遺傳BP神經網絡作為本系統(tǒng)的識別算法。4)出于對浮選車間工作環(huán)境等方面的考慮,對工業(yè)相機進行了合理的選型,并根據(jù)系統(tǒng)需求,對其后臺數(shù)據(jù)庫和前臺界面進行了設計和介紹。
[Abstract]:Flotation is a technique to realize mineral separation by using the different wettability of mineral particle surface. The surface visual characteristics of the foam directly reflect the flotation production conditions. At present, the discrimination of flotation state in most domestic concentrators still stays on the basis of manual observation, which greatly limits the popularization and development of flotation technology. The introduction of image recognition technology into flotation can not only contribute to the stable and optimal operation of flotation operations, but also liberate a large number of manpower and material resources, which is of great significance to the subsequent development of flotation operations. In this paper, based on the project of "automatic control system for flotation of Zhaolou Coal Mine", the image recognition system of coal slime flotation foam is deeply studied, which effectively overcomes the inherent disadvantages of traditional flotation technology. The main research contents can be summarized as follows: (1) A new filtering algorithm is proposed to solve the problem of mixed noise, especially Gao Si salt and pepper mixed noise, in the slime flotation foam image. It is not only superior to the traditional filtering algorithm in removing the mixed noise of foam image, but also can meet the real-time requirement. 2) the feature extraction algorithm of coal slime floatation foam image is studied, and the texture is selected. The dimension and velocity characteristic parameters are used as the input of the system. Among them, this paper analyzes and summarizes the shortcomings of the traditional watershed algorithm, and improves it, basically realizes the accurate segmentation of slime floatation foam image. 3) through empirical formula and related experiments, The optimal number of hidden layer neurons in BP neural network is determined, and the performance of BP neural network and genetic BP neural network are compared under the same network parameters. Furthermore, it is determined that genetic BP neural network is the recognition algorithm of the system. 4) considering the working environment of floatation workshop, the industrial camera is selected reasonably, and according to the system requirement, The background database and front interface are designed and introduced.
【學位授予單位】：中國礦業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TD923;TD94;TP391.41

【參考文獻】

相關期刊論文 前10條

1 汪祖輝;孫劉杰;邵雪;姜中敏;;一種結合小波變換和維納濾波的圖像去噪算法[J];包裝工程;2016年13期

2 楊建國;熊經緯;徐蘭;項前;;基于遺傳神經網絡算法的紡織生產過程參數(shù)反演[J];東華大學學報(自然科學版);2016年02期

3 王平均;王偉;;礦井視頻圖像小波域改進非局部均值濾波[J];金屬礦山;2016年03期

4 郭文俊;喬世東;;基于三幀間差分的障礙物檢測算法[J];山西大同大學學報(自然科學版);2015年06期

5 周萌萌;任子暉;周旋旋;;基于窗口的自適應中值濾波算法[J];電視技術;2015年22期

6 潘濤;吳曉波;張偉偉;劉瑋;;改進自適應中值濾波算法在圖像去噪中的應用[J];后勤工程學院學報;2015年05期

7 李嘉浪;李華君;徐慶;;基于小波閾值的非局部均值去噪[J];計算機工程與科學;2015年08期

8 蔡利平;史文中;張華;苗則朗;何鵬飛;;邊緣走向自適應的多尺度分水嶺分割算法[J];中國礦業(yè)大學學報;2015年04期

9 尹遜越;廖一鵬;;一種浮選泡沫視頻圖像自適應篩選方法[J];微型機與應用;2015年12期

10 張燕紅;李瑛;王鳳芹;張正霞;;基于形態(tài)學重建和極大值標記的分水嶺分割算法[J];計算技術與自動化;2015年02期

相關博士學位論文 前1條

1 許燦輝;礦物浮選氣泡速度和尺寸分布特征提取方法與應用[D];中南大學;2011年

，

本文編號：2005903