{"currentpage":1,"firstResult":0,"maxresult":10,"pagecode":5,"pageindex":{"endPagecode":5,"startPagecode":1},"records":[{"abstractinfo":"采用电子背散射衍射技术测定50SW1300冷轧无取向硅钢中不同尺寸范围晶粒的含量,利用<span style=\"color:red\">主</span><span style=\"color:red\">成分</span><span style=\"color:red\">回归</span><span style=\"color:red\">分析</span>法,综合研究不同尺寸范围晶粒的含量对无取向硅钢磁性能的影响.结果表明:通过<span style=\"color:red\">主</span><span style=\"color:red\">成分</span><span style=\"color:red\">回归</span><span style=\"color:red\">分析</span>法能够从不同尺寸范围晶粒的含量的多个影响因素中获取主要的因素,定量研究它们对无取向硅钢磁性能的影响规律.<span style=\"color:red\">分析</span>表明,无取向硅钢的铁损与不同尺寸范围晶粒的含量之间存在着可靠的多元线性关系,在一定范围内,较大尺寸晶粒的含量越多,其对铁损优化的作用越明显;而无取向硅钢的磁感与不同尺寸范围晶粒的含量之间并无线性关系.","authors":[{"authorName":"陈凌峰","id":"99f0d59a-b18a-4b8d-a16a-f9543b7a474c","originalAuthorName":"陈凌峰"},{"authorName":"赵志毅","id":"fec09e87-4e09-4289-b869-3fde9b8bc858","originalAuthorName":"赵志毅"},{"authorName":"王宝明","id":"d9fd9623-b67b-44a6-8883-9af1607e4b20","originalAuthorName":"王宝明"},{"authorName":"黄赛","id":"db9ec7a5-d5a4-447b-90b8-5241b7b5b62a","originalAuthorName":"黄赛"},{"authorName":"薛润东","id":"e593392f-76db-4bac-967c-714d27d1f91a","originalAuthorName":"薛润东"},{"authorName":"郑攀峰","id":"bde3eca1-7233-4753-a76c-f3cb91db2ddb","originalAuthorName":"郑攀峰"}],"doi":"","fpage":"215","id":"b8bd5fac-7220-4ce0-94a4-57ffcb9bd482","issue":"11","journal":{"abbrevTitle":"CLRCLXB","coverImgSrc":"journal/img/cover/CLRCLXB.jpg","id":"15","issnPpub":"1009-6264","publisherId":"CLRCLXB","title":"材料热处理学报"},"keywords":[{"id":"0ec90ac2-9969-4abd-aa07-fda8aee29fec","keyword":"无取向硅钢","originalKeyword":"无取向硅钢"},{"id":"d973838a-a843-4ada-b98b-647590f35302","keyword":"<span style=\"color:red\">主</span><span style=\"color:red\">成分</span><span style=\"color:red\">回归</span><span style=\"color:red\">分析</span>","originalKeyword":"主成分回归分析"},{"id":"94bd725f-da33-41d5-8a60-5a2c684c611d","keyword":"晶粒尺寸","originalKeyword":"晶粒尺寸"},{"id":"b21178da-2963-4a61-8cf9-a412b15a7588","keyword":"铁损","originalKeyword":"铁损"},{"id":"ddf339fd-6b90-4922-b4f3-a800cf1dd1c7","keyword":"磁感","originalKeyword":"磁感"}],"language":"zh","publisherId":"jsrclxb201411040","title":"晶粒尺寸对无取向硅钢磁性能影响的<span style=\"color:red\">主</span><span style=\"color:red\">成分</span><span style=\"color:red\">回归</span><span style=\"color:red\">分析</span>","volume":"35","year":"2014"},{"abstractinfo":"采用扫描电镜、场发射扫描电镜、能谱仪等对50SW1300冷轧无取向硅钢中的夹杂物分不同尺寸区间进行数量统计,利用<span style=\"color:red\">主</span><span style=\"color:red\">成分</span><span style=\"color:red\">回归</span><span style=\"color:red\">分析</span>法,即数据的标准化处理 <span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span><span style=\"color:red\">回归</span><span style=\"color:red\">分析</span> 标准化的变量还原成原始变量 确定显著影响因素,综合<span style=\"color:red\">分析</span>夹杂物总量及各尺寸区间的夹杂物数量对无取向硅钢磁性能的影响.结果表明:<span style=\"color:red\">主</span><span style=\"color:red\">成分</span><span style=\"color:red\">回归</span><span style=\"color:red\">分析</span>能够从夹杂物尺寸区间及数量的多个影响因素中提取主要的因素,定量研究其对磁性能的影响.<span style=\"color:red\">分析</span>表明,显著影响无取向硅钢铁损的夹杂物为100～500 nm的AlN、AlN+MnS、MnS、Al2O3、AlN+Al2O3,而劣化磁感最明显的夹杂物尺寸区间为100～200 nm.","authors":[{"authorName":"王宝明","id":"32b1a683-040f-47fd-bee0-7384edfa05b7","originalAuthorName":"王宝明"},{"authorName":"赵志毅","id":"7a21c5fe-1ac4-47b5-a6bb-60d88def4a32","originalAuthorName":"赵志毅"},{"authorName":"陈凌峰","id":"0ccc8c36-e15f-4c59-bd7e-0322d5063a34","originalAuthorName":"陈凌峰"},{"authorName":"黄赛","id":"1dcf7f92-c3bc-43b2-bce1-c1ebc0ab5859","originalAuthorName":"黄赛"},{"authorName":"罗文彬","id":"222fd372-9c3d-465d-93aa-0e8bcee5c8d8","originalAuthorName":"罗文彬"},{"authorName":"郑攀峰","id":"dde5eeb5-530c-4c71-8db8-5ace33323708","originalAuthorName":"郑攀峰"}],"doi":"10.13228/j.issn.1000-7571.2014.10.001","fpage":"1","id":"de63b1a1-68dd-4068-82ba-0d7b9ee8b582","issue":"10","journal":{"abbrevTitle":"YJFX","coverImgSrc":"journal/img/cover/YJFX.jpg","id":"71","issnPpub":"1000-7571","publisherId":"YJFX","title":"冶金分析   "},"keywords":[{"id":"69e0b209-64c0-41d5-8149-b0f0321df1b4","keyword":"无取向硅钢","originalKeyword":"无取向硅钢"},{"id":"7171b266-88b5-40b1-ad1b-cd26e7c93ec4","keyword":"<span style=\"color:red\">主</span><span style=\"color:red\">成分</span><span style=\"color:red\">回归</span><span style=\"color:red\">分析</span>","originalKeyword":"主成分回归分析"},{"id":"85f7ad97-7a11-4656-a63b-c227b3233294","keyword":"夹杂物","originalKeyword":"夹杂物"},{"id":"94fa7f89-f380-47b0-9b72-b8852937ef27","keyword":"铁损","originalKeyword":"铁损"},{"id":"e12397fc-6516-4504-890f-519b4535078d","keyword":"磁感","originalKeyword":"磁感"}],"language":"zh","publisherId":"yjfx201410001","title":"夹杂物尺寸及数量对无取向硅钢磁性能影响的<span style=\"color:red\">主</span><span style=\"color:red\">成分</span><span style=\"color:red\">回归</span><span style=\"color:red\">分析</span>","volume":"34","year":"2014"},{"abstractinfo":"用<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>的方法,对LF精炼炉合金加料的实际数据进行处理,建立合金加料多元线性<span style=\"color:red\">回归</span>模型,用另外的30组实际样本数据对模型进行预测验证,将预测结果同未进行数据处理所建立的合金加料多元线性<span style=\"color:red\">回归</span>模型预测结果进行比较,显示<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>后所建模型具有较好的预测效果.","authors":[{"authorName":"王希娟","id":"817473df-2f93-4ce9-aecc-324e71b761da","originalAuthorName":"王希娟"},{"authorName":"冯京晓","id":"9f510da3-517b-491b-9bef-efe9d01a1403","originalAuthorName":"冯京晓"}],"doi":"10.3969/j.issn.1004-244X.2008.03.011","fpage":"42","id":"d8551401-9a4b-4567-8d93-f716afea80eb","issue":"3","journal":{"abbrevTitle":"BQCLKXYGC","coverImgSrc":"journal/img/cover/BQCLKXYGC.jpg","id":"4","issnPpub":"1004-244X","publisherId":"BQCLKXYGC","title":"兵器材料科学与工程   "},"keywords":[{"id":"2c899a8c-5e28-4e46-976b-a4bc47046524","keyword":"<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>","originalKeyword":"主成分分析"},{"id":"e48af6cb-32c2-45ba-98c4-2a4f29572323","keyword":"LF精炼炉","originalKeyword":"LF精炼炉"},{"id":"9390be07-3505-4305-b475-5a0a7b6128f7","keyword":"合金配料","originalKeyword":"合金配料"}],"language":"zh","publisherId":"bqclkxygc200803011","title":"基于<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>的合金加料<span style=\"color:red\">回归</span>模型的研究","volume":"31","year":"2008"},{"abstractinfo":"以 DBC-偶氮氯膦为显色剂,根据钇(Y3+)与其它稀土组分的显色络合物吸收光谱差别较大的特点,提出应用<span style=\"color:red\">主</span><span style=\"color:red\">成分</span><span style=\"color:red\">回归</span>法选择性测定混合稀土中钇的计算光度<span style=\"color:red\">分析</span>法.通过对不同组成的人工模拟样品的<span style=\"color:red\">分析</span>可知,本法在∑RE/Y3+=10 时仍能准确地测定钇的含量.该法用于测定龙南稀土氧化物、GSD-4、GSD-6、GSD-7 等一系列标准样品的钇含量,取得了与标准值一致的结果.","authors":[{"authorName":"刘德龙","id":"6f438a31-1b03-4e8b-853f-83b287e88d2b","originalAuthorName":"刘德龙"}],"doi":"10.3969/j.issn.0258-7076.2000.05.018","fpage":"391","id":"05f0cebe-40be-4684-b2cb-40ffd3b7a074","issue":"5","journal":{"abbrevTitle":"XYJS","coverImgSrc":"journal/img/cover/XYJS.jpg","id":"67","issnPpub":"0258-7076","publisherId":"XYJS","title":"稀有金属"},"keywords":[{"id":"f1c60f54-3360-465d-8c45-dabe7f0ce6a5","keyword":"钇","originalKeyword":"钇"},{"id":"55309882-b68a-44e3-9aac-dab405871a4b","keyword":"稀土<span style=\"color:red\">分析</span>","originalKeyword":"稀土分析"},{"id":"6669eb10-390f-4b24-957c-65bac7a24522","keyword":"<span style=\"color:red\">主</span><span style=\"color:red\">成分</span><span style=\"color:red\">回归</span>法","originalKeyword":"主成分回归法"},{"id":"59e361af-1ea0-4a5c-babd-72cb0bfff71d","keyword":"分光光度法","originalKeyword":"分光光度法"},{"id":"bec5170f-d323-4706-b378-1329e2130441","keyword":"化学计量学","originalKeyword":"化学计量学"}],"language":"zh","publisherId":"xyjs200005018","title":"<span style=\"color:red\">主</span><span style=\"color:red\">成分</span><span style=\"color:red\">回归</span>光度法测定混合稀土中钇","volume":"24","year":"2000"},{"abstractinfo":"为了实现扫描仪在不同光源、不同观察者条件下准确获取颜色信息,最大程度的避免同色异谱现象,本文采用光谱的方法对扫描仪进行特性化处理,通过多项式<span style=\"color:red\">回归</span>和BP神经网络分别与<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>法结合,首先对检测样本的光谱反射率进行<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>,提取<span style=\"color:red\">主</span><span style=\"color:red\">成分</span>与<span style=\"color:red\">主</span><span style=\"color:red\">成分</span>系数,通过实验得到<span style=\"color:red\">主</span><span style=\"color:red\">成分</span>系数与多项式<span style=\"color:red\">回归</span>、BP神经网络结构之间的转换模型,实现了扫描仪低维RGB信号对原始光谱反射率信息的重构,进而实现扫描仪的光谱特性化.实验结果表明,多项式项数为19项时,达到训练样本的均方根误差为1.7％,检测样本的均方根误差为1.9％.而包含15个隐层节点的单隐层BP神经网络结构为比较合理的网络结构,达到训练样本的均方根误差为1.3％,检测样本的均方根误差为1.5％.对彩色扫描仪的特征化处理,采用多项式<span style=\"color:red\">回归</span>法得到光谱特性化精度较低,采用BP神经网络模型能够实现更高的光谱特性化精度.","authors":[{"authorName":"于海琦","id":"db34c9ce-55f2-426a-8237-1d1ead4ffaff","originalAuthorName":"于海琦"},{"authorName":"刘真","id":"0a2506eb-52b7-4d0a-96bd-7a530279f164","originalAuthorName":"刘真"},{"authorName":"田全慧","id":"f6766354-0b5f-439d-9526-d78fbf37b98d","originalAuthorName":"田全慧"}],"doi":"10.7517/j.issn.1674-0475.2015.02.161","fpage":"161","id":"d9c479a1-0703-44e0-835f-1a69dee40fe3","issue":"2","journal":{"abbrevTitle":"YXKXYGHX","coverImgSrc":"journal/img/cover/YXKXYGHX.jpg","id":"74","issnPpub":"1674-0475","publisherId":"YXKXYGHX","title":"影像科学与光化学   "},"keywords":[{"id":"6dcaebe0-1336-4704-965e-acc6d925462d","keyword":"彩色扫描仪","originalKeyword":"彩色扫描仪"},{"id":"6cf54572-a432-46af-99b3-aaba4439ddaf","keyword":"光谱特征化","originalKeyword":"光谱特征化"},{"id":"b1dfe1ec-155f-403e-b049-dc031bcb0d26","keyword":"多项式<span style=\"color:red\">回归</span>","originalKeyword":"多项式回归"},{"id":"d81283b4-5f94-40b1-bbb2-4404ab735d17","keyword":"BP神经网络","originalKeyword":"BP神经网络"},{"id":"b1f0157a-eb95-4bd7-b7ed-c81da535bb08","keyword":"<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>","originalKeyword":"主成分分析"}],"language":"zh","publisherId":"ggkxyghx201502008","title":"基于<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>的彩色扫描仪光谱特性化","volume":"33","year":"2015"},{"abstractinfo":"针对梅山炼钢厂冶炼中磷铁水的生产实践,本文利用统计<span style=\"color:red\">回归</span>的方法对现场的生产数据进行了<span style=\"color:red\">回归</span><span style=\"color:red\">分析</span>,得出了冶炼终点钢水磷含量、碳含量以及终点钢水温度的预测模型,通过<span style=\"color:red\">回归</span><span style=\"color:red\">分析</span>找出影响冶炼终点钢水<span style=\"color:red\">成分</span>及温度的主要因素,并提出了改进措施.","authors":[{"authorName":"刘冬梅","id":"a8d099d4-bab5-4c68-af3d-f53844dafb67","originalAuthorName":"刘冬梅"},{"authorName":"吴伟","id":"67845275-4964-4591-b4e7-9ac4462b0b4b","originalAuthorName":"吴伟"},{"authorName":"黄伟青","id":"16391c97-9045-4c34-aaee-09fedcdc5c9a","originalAuthorName":"黄伟青"},{"authorName":"邹宗树","id":"c83df539-3802-4baf-8059-3c258ef4d48d","originalAuthorName":"邹宗树"}],"doi":"10.3969/j.issn.1671-6620.2004.01.001","fpage":"3","id":"9f4639ff-5c70-42c8-b2f0-9088dc9f8b35","issue":"1","journal":{"abbrevTitle":"CLYYJXB","coverImgSrc":"journal/img/cover/CLYYJXB.jpg","id":"17","issnPpub":"1671-6620","publisherId":"CLYYJXB","title":"材料与冶金学报"},"keywords":[{"id":"8835abfb-02f9-4195-87f9-70d7ffe1e64a","keyword":"复吹转炉","originalKeyword":"复吹转炉"},{"id":"2d9d529f-49be-4c94-a0e2-27a61cb4cd58","keyword":"冶炼终点","originalKeyword":"冶炼终点"},{"id":"2fbb8539-3b8e-4734-ba80-b481073f1e6b","keyword":"钢水<span style=\"color:red\">成分</span>","originalKeyword":"钢水成分"},{"id":"a55451e5-33a7-4da7-a567-c4bb4b2c69c1","keyword":"<span style=\"color:red\">回归</span><span style=\"color:red\">分析</span>","originalKeyword":"回归分析"}],"language":"zh","publisherId":"clyyjxb200401001","title":"顶底复吹转炉冶炼终点钢水<span style=\"color:red\">成分</span>及温度<span style=\"color:red\">回归</span>预测<span style=\"color:red\">分析</span>","volume":"3","year":"2004"},{"abstractinfo":"为了识别电镀光亮剂中的未知<span style=\"color:red\">成分</span>及其作用,建立了高效液相色谱(HPLC)-质谱(MS)定性<span style=\"color:red\">分析</span>电镀镍<span style=\"color:red\">主</span>光亮剂<span style=\"color:red\">成分</span>的方法.以水:甲醇=98:2(体积比)为流动相,用Hypersil BDS C18色谱柱(5 μm250.0 mm×4.6 mm)进行分离,然后用MS/MS对分离峰进行一级和二级质谱<span style=\"color:red\">分析</span>.结果表明:本法能够定性<span style=\"color:red\">分析</span>镀镍<span style=\"color:red\">主</span>光亮剂中的有效<span style=\"color:red\">成分</span>.测出某进口光亮剂中的未知<span style=\"color:red\">成分</span>为吡啶翰丙烷磺基内盐(PPS)和丙炔醇乙氧基化合物(PME)二聚体2种化合物.","authors":[{"authorName":"杨晓燕","id":"d6698555-72e7-4f66-98d6-197cec18e2d8","originalAuthorName":"杨晓燕"},{"authorName":"颜流水","id":"755e7f36-1448-4f10-8c13-2f5f30532fdf","originalAuthorName":"颜流水"},{"authorName":"罗旭彪","id":"0d4bf29a-83f5-4428-9a00-e63932ac7405","originalAuthorName":"罗旭彪"},{"authorName":"郑鄂湘","id":"9311062f-cd40-49e4-abfc-4cdeeb4c97e8","originalAuthorName":"郑鄂湘"}],"doi":"","fpage":"72","id":"06394709-af19-450d-8708-fdc40809d240","issue":"6","journal":{"abbrevTitle":"CLBH","coverImgSrc":"journal/img/cover/CLBH.jpg","id":"7","issnPpub":"1001-1560","publisherId":"CLBH","title":"材料保护"},"keywords":[{"id":"f5311699-41de-40b9-be25-ff5d49ef6e22","keyword":"定性<span style=\"color:red\">分析</span>","originalKeyword":"定性分析"},{"id":"ae06c97e-8fdd-4b73-9310-580f5967702e","keyword":"电镀镍","originalKeyword":"电镀镍"},{"id":"b3e51a7f-2b3e-4c99-a2dc-050d96efcb7f","keyword":"<span style=\"color:red\">主</span>光亮剂","originalKeyword":"主光亮剂"},{"id":"8b0010d7-0bbf-4f3e-8984-a1ecfea8f4e7","keyword":"高效液相色谱-质谱","originalKeyword":"高效液相色谱-质谱"}],"language":"zh","publisherId":"clbh200906022","title":"用HPLC-MS/MS法定性<span style=\"color:red\">分析</span>电镀镍<span style=\"color:red\">主</span>光亮剂<span style=\"color:red\">成分</span>","volume":"42","year":"2009"},{"abstractinfo":"针对冶炼过程喷溅特征提取及喷溅预测困难的问题，提出基于小波包变换与<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>的优化参数模型的支持向量机喷溅预测方法。该方法经小波包变换将冶炼喷溅的噪声和氧枪振动信号分解为不同频带的信号。由于不同频带的信号出现相互干扰和堆叠，因此通过<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>将频带能量降维分离成不同频带，进而将这些处理后的信号作为喷溅特征向量。对支持向量机模型参数(C、g)进行遗传算法优化，通过支持向量机对喷溅的分类及预测，验证了该方法的有效性。实验结果表明：经小波包变换和<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>获得的特征信号能够准确地反应喷溅特征，提出的支持向量机方法具有较好的分类性能，喷溅预测准确率较高。","authors":[{"authorName":"韩顺杰","id":"c1cce975-254b-4e86-bc3e-2ced2f306b3b","originalAuthorName":"韩顺杰"},{"authorName":"齐冀樊","id":"0497ca57-642d-4aea-8e02-a034b5c962db","originalAuthorName":"齐冀樊"},{"authorName":"姜玉莲","id":"4eb40fc4-9f5a-438b-9445-08017fc3f076","originalAuthorName":"姜玉莲"},{"authorName":"尤文","id":"b2203b37-6d7c-43b2-a20c-d2f213554688","originalAuthorName":"尤文"}],"doi":"10.13228/j.boyuan.issn1001-0963.20160175","fpage":"21","id":"7771fe93-fefd-4dab-a7c0-2fd48508c14e","issue":"12","journal":{"abbrevTitle":"GTYJXB","coverImgSrc":"journal/img/cover/GTYJXB.jpg","id":"30","issnPpub":"1001-0963","publisherId":"GTYJXB","title":"钢铁研究学报"},"keywords":[{"id":"3c33c157-8fc6-43aa-8caf-81e68619d76e","keyword":"喷溅预测","originalKeyword":"喷溅预测"},{"id":"d1ce25b6-ef11-4795-83b7-9fe751c611fa","keyword":"小波包变换","originalKeyword":"小波包变换"},{"id":"a903d4c0-6ed7-4c6f-b462-bb342c81b76e","keyword":"<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>","originalKeyword":"主成分分析"},{"id":"eb0179e5-b5d7-4c10-8b38-107064b62440","keyword":"遗传算法","originalKeyword":"遗传算法"},{"id":"4cb971f1-9b67-4e45-8e8a-327d78590927","keyword":"支持向量机","originalKeyword":"支持向量机"}],"language":"zh","publisherId":"gtyjxb201612005","title":"基于<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>与遗传算法-支持向量机的喷溅预测方法","volume":"28","year":"2016"},{"abstractinfo":"提出了一种基于<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>(PCA)和模糊模式识别方法的生物分子太赫兹(THz)光谱识别方法,并采用多种典型糖类和氨基酸生物分子的太赫兹透射光谱作为实验介质证明所提方法的可行性和有效性.运用PCA方法对生物分子太赫兹光谱数据做降维处理,提取样品太赫兹光谱特征信息;用获得的<span style=\"color:red\">主</span><span style=\"color:red\">成分</span>得分矩阵代替原始太赫兹光谱数据输入到模糊模式识别<span style=\"color:red\">分析</span>模型中,运用基于择近原则的模糊模式识别方法对待定样品进行分类识别.结果表明以生物分子的太赫兹光谱作为数据特征,采用PCA与模糊识别相结合的方法实现生物分子的检测和识别是可行的,为太赫兹光谱技术用于生物分子的鉴定和识别提供了一种新的有效<span style=\"color:red\">分析</span>方法.","authors":[{"authorName":"陈涛","id":"77d48001-e251-44cf-8a74-8977e94eb3ba","originalAuthorName":"陈涛"}],"doi":"10.3969/j.issn.1007-5461.2016.04.002","fpage":"392","id":"564e4e91-5d7f-4126-9cb3-06fda19ade0b","issue":"4","journal":{"abbrevTitle":"LZDZXB","coverImgSrc":"journal/img/cover/LZDZXB.jpg","id":"53","issnPpub":"1007-5461","publisherId":"LZDZXB","title":"量子电子学报   "},"keywords":[{"id":"7c0b932e-bb60-4349-a766-689ff78d343a","keyword":"光谱学","originalKeyword":"光谱学"},{"id":"39304a4c-33ae-4483-ad72-3789fe487a2a","keyword":"太赫兹光谱","originalKeyword":"太赫兹光谱"},{"id":"4daf79df-bcc4-4fe0-9474-51aac1537d4a","keyword":"生物分子","originalKeyword":"生物分子"},{"id":"a718bf51-82b2-48c6-b35f-e990cec60f6f","keyword":"<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>","originalKeyword":"主成分分析"},{"id":"10d1acb4-1a32-4307-9ef5-43e9bb562963","keyword":"模糊模式识别","originalKeyword":"模糊模式识别"}],"language":"zh","publisherId":"lzdzxb201604002","title":"基于<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>和模糊识别的生物分子太赫兹光谱识别","volume":"33","year":"2016"},{"abstractinfo":"采用<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>法(PCA)研究了滚塑包装箱用高密度聚乙烯(HDPE)在四个地区特定环境条件下,1年期内5个气象因子及6个力学指标的变化规律,<span style=\"color:red\">分析</span>了力学指标对气象因子的敏感度差异及其相关关系.结果表明:气象因子敏感度<span style=\"color:red\">分析</span>发现冲击强度与弯曲模量对气象因子最敏感,拉伸强度敏感度较强但地区差异大,弯曲强度、硬度和拉伸模量敏感度低.相关关系<span style=\"color:red\">分析</span>发现月总辐射量和总降水量与冲击强度变化呈协同相关,月平均气压呈抵抗相关,月平均相对湿度呈弱相关.弯曲模量变化与气象因子相关水平弱于冲击强度,且与各气象因子相关水平大致相同.","authors":[{"authorName":"郭骏骏","id":"391d3f76-1052-4daf-9aa9-b42fe31125f7","originalAuthorName":"郭骏骏"},{"authorName":"晏华","id":"38bf11cf-9e77-4a68-9e3c-cfc8bbd2e22c","originalAuthorName":"晏华"},{"authorName":"胡志德","id":"3e4a07bb-4515-4968-9530-bb9c9cdf3b80","originalAuthorName":"胡志德"},{"authorName":"杨健健","id":"bf047920-56c9-4fb2-b32a-968a4fe5fb49","originalAuthorName":"杨健健"}],"doi":"10.11868/j.issn.1001-4381.2015.01.017","fpage":"96","id":"162f987c-73f7-4efe-90e6-9727cecc60a9","issue":"1","journal":{"abbrevTitle":"CLGC","coverImgSrc":"journal/img/cover/CLGC.jpg","id":"9","issnPpub":"1001-4381","publisherId":"CLGC","title":"材料工程"},"keywords":[{"id":"b96fb6eb-4e79-483f-9043-7c7b03c29f6e","keyword":"<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>法","originalKeyword":"主成分分析法"},{"id":"8457a3a4-f170-417b-84b0-74290835e605","keyword":"环境适应性","originalKeyword":"环境适应性"},{"id":"fff52053-9ae2-44f6-8c5c-0a28db4a3761","keyword":"环境敏感度","originalKeyword":"环境敏感度"},{"id":"54a597c7-68f8-49a3-8ddc-69aed96b8eaa","keyword":"相关关系","originalKeyword":"相关关系"}],"language":"zh","publisherId":"clgc201501017","title":"基于<span style=\"color:red\">主</span><span style=\"color:red\">成分分析</span>的高密度聚乙烯环境适应行为研究","volume":"43","year":"2015"}],"totalpage":9153,"totalrecord":91524}