本文關(guān)鍵詞：基于云計(jì)算的海量高鐵噪聲數(shù)據(jù)并行處理方法研究　出處：《西南交通大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 并行濾波 并行預(yù)處理 MapReduce 高速鐵路 噪聲

【摘要】：隨著高速鐵路的飛速發(fā)展,高速鐵路的安全與舒適成為當(dāng)前研究的一個(gè)熱點(diǎn)問(wèn)題。安裝在列車上的傳感器采集的噪聲數(shù)據(jù)反映了列車的運(yùn)行狀況,并與列車的安全息息相關(guān)。然而在噪聲數(shù)據(jù)采集的過(guò)程中由于種種因素的影響,采集的列車噪聲數(shù)據(jù)中含有不同頻率和特點(diǎn)的干擾數(shù)據(jù),干擾數(shù)據(jù)直接影響了數(shù)據(jù)的分析與處理。研究表明預(yù)處理和濾波處理可以有效地去除數(shù)據(jù)中的干擾數(shù)據(jù)。然而,隨著采集的數(shù)據(jù)量越來(lái)越大,而傳統(tǒng)的預(yù)處理與濾波方法均采用的是單機(jī)處理的方式,效率低下,無(wú)法滿足實(shí)際需求。云計(jì)算技術(shù)是解決上述難題的一項(xiàng)關(guān)鍵技術(shù),其中的MapReduce模型可用于大規(guī)模數(shù)據(jù)的并行運(yùn)算,由于其良好的并行效果且不用了解其底層架構(gòu),目前已有很多學(xué)者利用MapReduce進(jìn)行算法設(shè)計(jì),且取得了良好的成果。因此本文擬將云計(jì)算技術(shù)應(yīng)用到預(yù)處理與濾波方法中以提高列車噪聲數(shù)據(jù)處理的效率,具有重要的實(shí)際應(yīng)用價(jià)值。 本文首先對(duì)預(yù)處理、濾波和云計(jì)算的國(guó)內(nèi)外研究現(xiàn)狀進(jìn)行介紹。然后概述云計(jì)算技術(shù)與預(yù)處理方法,研究了預(yù)處理方法的并行化,提出了基于MapReduce的海量高鐵噪聲數(shù)據(jù)并行預(yù)處理算法,用Speedup和Sizeup并行化指標(biāo)來(lái)評(píng)價(jià)算法的性能,實(shí)驗(yàn)結(jié)果表明并行預(yù)處理算法性能提升顯著。緊接著,討論了高通濾波、低通濾波、動(dòng)窗濾波和中位值濾波等傳統(tǒng)濾波技術(shù),并對(duì)濾波方法進(jìn)行并行化改進(jìn),提出基于MapReduce的海量高鐵噪聲數(shù)據(jù)并行濾波算法。波形展示和濾波正確性實(shí)驗(yàn)分析表明濾波效果明顯。信噪比和均方差實(shí)驗(yàn)給出了高通濾波和低通濾波的最佳濾波參數(shù)。采用Speedup、 Sizeup和Scaleup這三個(gè)并行化參數(shù)評(píng)價(jià)并行濾波算法的性能,結(jié)果表明本文所提出的并行動(dòng)窗濾波和并行中位值濾波算法性能提升顯著；并行高通濾波和并行低通濾波算法由于使用了公共變量和受算法自身時(shí)間復(fù)雜度影響,并行效果受到一定影響。
[Abstract]:With the rapid development of high-speed railway, high-speed railway safety and comfort has become a hot issue in current research. The noise data collected by sensors installed on the train to reflect the running status of the trains, and is closely related to the safety of the train. However, due to various factors in the process of noise data acquisition, the data of different frequency interference and the characteristics of the noise data collected in the train with the interference data directly affects the data processing and analysis. The results indicate that the preprocessing and filtering can effectively remove the interference data. However, with the increasing of the data, while pretreatment with the traditional filtering methods are used is single the way, efficiency is low, can not meet the actual demand. Cloud computing is a key technology to solve the above problems, the MapReduce model can be used for large scale Parallel computing data, due to its good parallel efficiency and do not know the underlying architecture, many researchers have applied MapReduce to design algorithm, and achieved good results. So this thesis intends to apply cloud computing technology to pretreatment and filtering to improve the noise data processing efficiency, has important practical application value.
Firstly, preprocessing, research actuality of filtering and cloud computing. Then an overview of cloud computing technology and pretreatment methods, the study of parallel preprocessing methods, proposes a parallel preprocessing algorithm for high-speed railway noise data based on MapReduce, to evaluate the performance of the algorithm by using Speedup and Sizeup parallel index and the experiment results show that the parallel preprocessing algorithm performance significantly. Then, the high pass filter, low-pass filter, dynamic filtering technology of traditional filtering median filtering and window, and the filtering method is improved by parallel computing, parallel filtering algorithm proposed high-speed railway noise data based on MapReduce waveform display and filtering the correct. Experimental analysis shows that the filtering effect is obvious. The signal-to-noise ratio and the mean variance experiment provides the optimum filtering parameters of high pass filter and low-pass filter. By using Speedup, Sizeup and Scaleup three The performance evaluation parameters of parallel parallel filtering algorithm, the results show that the proposed median filtering algorithm and performance improvement action window filtering and parallel in parallel; parallel high pass filter and low pass filtering algorithm due to the use of public variables and time complexity of the algorithm itself, parallel effect affected.

【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TN911.4;TP338.6

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