本文選題：水泥建模 + 馬爾可夫隨機(jī)場(chǎng)模型�。� 參考：《濟(jì)南大學(xué)》2017年碩士論文

【摘要】：由于水泥作為一種重要的工業(yè)基材料被廣泛應(yīng)用到生產(chǎn)生活的各個(gè)方面,所以促使人們不斷研究探索其水化機(jī)理。但是由于水泥水化內(nèi)部反應(yīng)的極端復(fù)雜性,直到目前科學(xué)家也沒(méi)有完全搞清楚內(nèi)部的反應(yīng)原理和反應(yīng)過(guò)程,而且傳統(tǒng)的分析方法需要消耗很多的時(shí)間也不具有時(shí)間連續(xù)性。隨著計(jì)算機(jī)科學(xué)技術(shù)研究的發(fā)展使得對(duì)水泥水化的研究進(jìn)入計(jì)算材料時(shí)代,雖然之后的研究也取得了一些公認(rèn)的成果,比如Bentz的細(xì)胞自動(dòng)機(jī)模型。但是一般這種建模都是建立在像素與特征的基礎(chǔ)之上的,在建模之前必須進(jìn)行復(fù)雜的水泥圖像配準(zhǔn)等操作,而且一個(gè)極小的像素偏差都可能對(duì)建模結(jié)果產(chǎn)生比較大的影響,不僅對(duì)設(shè)備要求很高,而且工作計(jì)算量都很大。這里我們主要根據(jù)馬爾可夫隨機(jī)場(chǎng)在圖像中的廣泛應(yīng)用為基礎(chǔ),把它應(yīng)用到水泥水化建模中來(lái),由于利用馬爾可夫隨機(jī)場(chǎng)進(jìn)行水泥水化建模只用考慮水泥微觀圖像的鄰域概率分布特征,不用事先進(jìn)行復(fù)雜的圖像配準(zhǔn)和特征選擇,所以提高了準(zhǔn)確度和易用性。本文主要分下面幾個(gè)方面來(lái)進(jìn)行研究分析。(1)適應(yīng)水泥微觀圖像的馬爾可夫模型參數(shù)估計(jì)方法我們提出了一個(gè)新的馬爾可夫模型參數(shù)估計(jì)方法,加權(quán)最小平方差適應(yīng)方法(WLS)。馬爾可夫模型的參數(shù)估計(jì)問(wèn)題對(duì)使用者一直都是非常大的挑戰(zhàn),目前常用的一些方法都存在一定的問(wèn)題,為了使馬爾可夫模型可以順利地用在水泥水化建模中,需要一個(gè)精確度高、時(shí)間復(fù)雜度低又具有噪聲魯棒性的參數(shù)估計(jì)方法。我們提出WLS方法是一個(gè)完整的體系,其中包括主要的WLS參數(shù)估計(jì)方法、零值處理方法和參數(shù)適應(yīng)值評(píng)價(jià)方法。并通過(guò)實(shí)驗(yàn)對(duì)比表明我們的WLS方法比最小平方差方法(LS)最有更高的精確度和噪聲魯棒性。(2)馬爾可夫模型水泥水化建模由于這是第一次把馬爾可夫模型應(yīng)用到水泥水化中來(lái),我們進(jìn)行了可行性分析。提出了一個(gè)基于采樣圖像的水泥水化相似度適應(yīng)值函數(shù),解決了水泥水化馬爾可夫建模的最大難點(diǎn),然后通過(guò)與粒子群算法(PSO)優(yōu)化結(jié)合起來(lái),方便直觀地建立水泥水化圖像的馬爾可夫模型。(3)建立水泥水化馬爾可夫模型之后的后續(xù)利用論文最后給出了建立水泥水化馬爾可夫模型之后廣泛的應(yīng)用前景和研究方法�？梢酝ㄟ^(guò)訓(xùn)練人工神經(jīng)網(wǎng)絡(luò)模擬水泥水化馬爾可夫模型參數(shù)的變化,然后根據(jù)模型參數(shù)的變化可以通過(guò)采樣看出來(lái)水泥微觀結(jié)構(gòu)的變化,進(jìn)而預(yù)測(cè)水泥水化過(guò)程。而且也為探究馬爾可夫模型參數(shù)與水泥各項(xiàng)性能的關(guān)系提供了可能。
[Abstract]:As an important industrial base material, cement has been widely used in various aspects of production and life, so people are constantly studying and exploring its hydration mechanism. However, due to the extreme complexity of the internal reaction of cement hydration, up to now, scientists have not fully understood the internal reaction principle and reaction process, and the traditional analytical methods need to consume a lot of time and do not have time continuity. With the development of computer science and technology, the study of cement hydration has entered the era of computational materials, although the later research has also made some recognized achievements, such as Bentz's cellular automata model. However, this kind of modeling is generally based on pixels and features. Before modeling, complex operations such as cement image registration must be carried out, and a minimal pixel deviation may have a relatively large impact on the modeling results. Not only high requirements for equipment, but also a lot of work calculation. Here we apply Markov random fields to cement hydration modeling, based on the widespread application of Markov random fields in images. Because the cement hydration modeling based on Markov random field only takes into account the neighborhood probability distribution features of cement microscopic images and does not need to carry out complicated image registration and feature selection in advance the accuracy and ease of use are improved. In this paper, we study and analyze the parameter estimation method of Markov model which adapts to cement microscopic image. We propose a new Markov model parameter estimation method, weighted least square difference adaptation method. The problem of parameter estimation of Markov model is always a great challenge to the user. Some commonly used methods have some problems. In order to make Markov model can be used in cement hydration modeling smoothly. A parameter estimation method with high accuracy, low time complexity and noise robustness is needed. We propose that the WLS method is a complete system, including the main WLS parameter estimation method, zero value processing method and parameter fitness evaluation method. The experimental results show that our WLS method has higher accuracy and noise robustness than the least square difference method (LSs) for cement hydration modeling based on Markov model because it is the first time to apply Markov model to cement hydration. We carried out a feasibility analysis. A fitness function of cement hydration similarity based on sampling image is proposed, which solves the biggest difficulty of cement hydration Markov modeling, and then combines with particle swarm optimization (PSO) optimization. It is convenient and intuitionistic to establish the Markov model of cement hydration image. (3) the subsequent utilization after the establishment of cement hydration Markov model. Finally, the paper gives the extensive application prospect and research method after the establishment of cement hydration Markov model. The parameters of cement hydration Markov model can be simulated by training artificial neural network. According to the change of model parameters, the change of cement microstructure can be seen by sampling, and the cement hydration process can be predicted. It is also possible to explore the relationship between the parameters of Markov model and the properties of cement.
【學(xué)位授予單位】：濟(jì)南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TQ172.1;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 王琳;楊波;趙秀陽(yáng);陳月輝;常鈞;;由硅酸鹽水泥的觀測(cè)數(shù)據(jù)反向萃取水化早期動(dòng)力學(xué)方程[J];中國(guó)科學(xué):技術(shù)科學(xué);2010年05期

2 閻培渝;鄭峰;;水泥基材料的水化動(dòng)力學(xué)模型[J];硅酸鹽學(xué)報(bào);2006年05期

相關(guān)博士學(xué)位論文 前1條

1 王琳;基于計(jì)算智能的水泥水化過(guò)程建模方法與關(guān)鍵技術(shù)研究[D];山東大學(xué);2011年

相關(guān)碩士學(xué)位論文 前2條

1 梁志鋒;基于反向建模的細(xì)胞自動(dòng)機(jī)水泥水化仿真方法研究[D];濟(jì)南大學(xué);2015年

2 王燕文;元胞自動(dòng)機(jī)環(huán)境下水泥水化過(guò)程模擬及算法[D];武漢理工大學(xué);2011年

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本文編號(hào)：1791236