支持向量机参数优化方法在木质粉尘火花探测中的应用
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摘要
为提高木质粉尘火花检测的准确性,利用基于支持向量机(SVM)的物质分类方法检测木质粉尘火花。选取马尾松和杨木粉尘为研究对象,将两种粉尘分组点燃试验,获取火花和灰分的高光谱图像,提取感兴趣区域(region of interest,ROI)内的发光度数据进行预处理。利用感兴趣区域内的数据建立SVM分类模型,分别利用网格搜索法(GS)、遗传算法(GA)以及粒子群算法(PSO)对两类树种的SVM分类模型进行参数优化,并将三种参数优选方法的分类预测准确率进行对比。结果表明,三种优化方法均能够很好地检测两种树种的木粉火花,其中网格搜索法检测准确率明显高于其余两种,更适于木质粉尘火花探测,这为人造板生产过程中能够高效检测木质粉尘火花提供了一定的理论依据。
In order to improve the accuracy of wood dust spark detection, a material classification method based on Support Vector Machine(SVM) was used to detect wood dust sparks. The Pinus massoniana and poplar dust were selected as the research object. The two kinds of dusts were grouped into ignition test to obtain the hyperspectral image of spark and ash, and the luminosity data in the region of interest(ROI) was extracted for pretreatment. The SVM classification model is established by using the data in the region of interest, and the SVM classification model of the two types of trees is optimized by Grid Search method(GS), Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) respectively. The classification prediction accuracy of the parameter optimization method is compared. The results show that the three optimization methods can detect the wood powder sparks of the two tree species well. The detection accuracy of the grid search method is significantly higher than the other two, which is more suitable for wood dust spark detection. It provides a theoretical basis for the efficient detection of wood dust sparks.
In order to improve the accuracy of wood dust spark detection, a material classification method based on Support Vector Machine(SVM) was used to detect wood dust sparks. The Pinus massoniana and poplar dust were selected as the research object. The two kinds of dusts were grouped into ignition test to obtain the hyperspectral image of spark and ash, and the luminosity data in the region of interest(ROI) was extracted for pretreatment. The SVM classification model is established by using the data in the region of interest, and the SVM classification model of the two types of trees is optimized by Grid Search method(GS), Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) respectively. The classification prediction accuracy of the parameter optimization method is compared. The results show that the three optimization methods can detect the wood powder sparks of the two tree species well. The detection accuracy of the grid search method is significantly higher than the other two, which is more suitable for wood dust spark detection. It provides a theoretical basis for the efficient detection of wood dust sparks.
引文
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[23]沈国峰,陶涛,黄琼涛,等.遗传算法在家具生产调度中的应用[J].林产工业, 2018, 45(12):57-60.
[2]Abbasi T, Abbasi S A. Dust explosions-cases,causes,consequences,and control[J]. Journal of Hazardous Materials,2007,140(1-2):7.
[3]栾婷婷,李蔚然,赵文统,等.木制品粉尘爆炸事故概率分析与对策研究[J].安全, 2017, 38(5):4-6.
[4]牛京萍.我国新建刨花板项目安全预评价[J].林产工业, 2017,44(10):57-61, 63.
[5]周曦禾,周捍东,丁涛,等.木材加工企业木粉尘燃爆成因剖析及其防控[J].林产工业, 2015, 42(11):49-52.
[6]Onoyama H, Ryu C,Suguri M, et al. Nitrogen prediction model of rice plant at panicle initiation stage using ground-based hyperspectral imaging:growing degree-days integrated model[J]. Precision Agriculture,2015,16(5):558-570.
[7]Chu X, Guo Y J, He J Y, et al.Comparison of Different Hyperspectral Vegetation Indices for Estimating Canopy Leaf Nitrogen Accumulation in Rice[J].Plant&Soil,2013,376(1-2):193-209.
[8]乔红波,师越,司海平,等.基于近地成像光谱的小麦全蚀病等级监测[J].农业工程学报, 2014, 30(20):172-178.
[9]王丽凤,张长利,赵越,等.高光谱成像技术的玉米叶片氮含量检测模型[J].农机化研究, 2017, 39(11):140-147.
[10]李丹,何建国,刘贵珊,等.基于高光谱成像技术的小黄瓜水分无损检测[J].红外与激光工程, 2014, 43(7):2393-2397.
[11]Cortes C, Vapnik V. Support Vector Network[J]. 1995, 20(3):273-297.
[12]张公让,万飞.基于网格搜索的SVM在入侵检测中的应用[J].计算机技术与发展, 2016(1):97-100.
[13]Chapelle O, Vapnik V,Bousquet O, et al.Choosing Multiple Parameters for Support Vector Machines[J]. Machine Learning, 2002, 46(1-3):131-159.
[14]王学顺,孙一丹,黄安民.木材红外光谱的树种识别研究[J].森林工程, 2015, 31(6):65-70.
[15]李松,伏云发,陈健,等.五种SVM参数寻优识别运动想象脑电比较研究[J].传感器与微系统, 2017, 36(4):42-45.
[16]王健峰.基于改进网格搜索法SVM参数优化的说话人识别研究[D].哈尔滨:哈尔滨工程大学, 2012.
[17]王兴玲,李占斌.基于网格搜索的支持向量机核函数参数的确定[J].中国海洋大学学报(自然科学版)自然科学版, 2005, 35(5):859-862.
[18]周瑛,陈基漓.遗传算法综述[J].轻工科技, 2008, 24(1):84-85.
[19]蒋华荣,郁雪.应用遗传算法优化子空间的SVM分类算法[J].计算机科学, 2013, 40(11):255-260.
[20]于仕兴,李学春,黄安民,等.粒子群支持向量机结合NIR测定桉木木质素[J].东北林业大学学报, 2013, 41(2):123-126.
[21]莫卫林,杨浩,熊智新,等.改进遗传算法PID参数优化在流浆箱总压控制中的应用[J].中国造纸, 2017, 36(8):35-40.
[22]陈帅帅,赵倩梅,熊智新,等.基于IPSO的PID参数自整定在流浆箱总压控制中的应用[J].中国造纸, 2015, 34(11):37-41.
[23]沈国峰,陶涛,黄琼涛,等.遗传算法在家具生产调度中的应用[J].林产工业, 2018, 45(12):57-60.