<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[RSS Feed]]></title><description><![CDATA[RSS Feed]]></description><link>http://direct.ecency.com</link><image><url>http://direct.ecency.com/logo512.png</url><title>RSS Feed</title><link>http://direct.ecency.com</link></image><generator>RSS for Node</generator><lastBuildDate>Tue, 14 Apr 2026 12:49:36 GMT</lastBuildDate><atom:link href="http://direct.ecency.com/@shenzehe/rss" rel="self" type="application/rss+xml"/><item><title><![CDATA[学习回归分析篇---之逐步回归分析]]></title><description><![CDATA[向前逐步与向后逐步回归的效应估计差异 1. 前 言 逐步回归有向前逐步（Step forward简记作SF）和向后逐步（Step backwards 简记作 SB）两种。 向前逐步回归与向后逐步回归的计算结果是有差别的，只有当样本相当大时才会一样。 我们现在用模拟实验的方法来验证这一结果。 假设一个实验过程的响应与实验因子之间的关系可用以下函数表达 y=f(x)=0.35+2 x1 - 0.54]]></description><link>http://direct.ecency.com/cn/@shenzehe/5jrfss</link><guid isPermaLink="true">http://direct.ecency.com/cn/@shenzehe/5jrfss</guid><category><![CDATA[cn]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Tue, 07 May 2019 15:52:24 GMT</pubDate><enclosure url="https://images.ecency.com/p/C3TZR1g81UNaPs7vzNXHueW5ZM76DSHWEY7onmfLxcK2iPdh5EPg9TWVDzFP4caWSoDFmADAxoHMhWStVoWkkbens5okCHDJJn1gJuzRxvnuTwQSGKraCmC?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[学习回归分析篇---之一元非线性回归分析方法]]></title><description><![CDATA[如果相关系数不大于相应的相关系数临界值，P 值太小，线性假设不能成立。 过程不是线性的，存在两种可能性：没有相关关系；存在非线性关系。 如果确认过程是非线性的，就应该使用非线性模型。 非线性回归分析的基本思想是把非线性问题转换为线性回归分析问题，从而和线性回归分析一样地处理。 基本方法是通过变换，把一个量转换成另一个量。变成线性回归问题之后，调用线性回归过程估计参数。 然后再将变量反代换回原来的变量。]]></description><link>http://direct.ecency.com/cn/@shenzehe/4gmpp-cny</link><guid isPermaLink="true">http://direct.ecency.com/cn/@shenzehe/4gmpp-cny</guid><category><![CDATA[cn]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Thu, 16 Aug 2018 19:19:15 GMT</pubDate></item><item><title><![CDATA[学习回归分析篇---之一元非线性回归分析实例]]></title><description><![CDATA[下面是某银行的一个存款数据样本，自变量是年份，有四个应变量。 图 5.1. 一元多响应数据样本 我们分别把这些数据描绘在坐标图上，这样管理数据是不是很有意思呢？但是，如果把数据点绘在图上会更清楚，更直观，更一目了然。 图 5.2. 点绘]]></description><link>http://direct.ecency.com/cn/@shenzehe/pfbmf-cny</link><guid isPermaLink="true">http://direct.ecency.com/cn/@shenzehe/pfbmf-cny</guid><category><![CDATA[cn]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Wed, 15 Aug 2018 22:52:03 GMT</pubDate><enclosure url="https://images.ecency.com/p/qjrE4yyfw5pEPvDbJDzhdNXM7mjt1tbr2kM3X28F6SraZeiMz7tHoLZ7gFvrctRCd7St5EbjVB75wSZL6DgsQybSfBv5YUtLR5oZKsuDq9aY4Fg7ZZscdWzz?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[学习回归分析篇---之一元线性回归分析算法]]></title><description><![CDATA[如果系统只有一个自变量 x，一个响应变量 y (可以有多个应变量，为方便起见，暂假定只有一个应变量) 的 n 次试验的试验样本具有下表形式： 假设过程是线性的，那么就可以写出数学模型， yi= a +bxi + ei,(i=1,2,...,n) ------(4.1) 其中 e=(e1,...,en)T 为观察的误差向量，假定它服从正态分布。 这里，a 和 b 是待确定的参数。即，未知数是 a 和]]></description><link>http://direct.ecency.com/cn/@shenzehe/6ydbrq-cny</link><guid isPermaLink="true">http://direct.ecency.com/cn/@shenzehe/6ydbrq-cny</guid><category><![CDATA[cn]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Wed, 15 Aug 2018 05:48:45 GMT</pubDate><enclosure url="https://images.ecency.com/p/qjrE4yyfw5pEPvDbJDzhdNXM7mjt1tbr2kM3X28F6SraZkS9RxkZcnEBcQDyQVscBkBjN4ixw2qQjT9RQqej4g3uJcsf4eFMNSShEwSrZyNEPWpk5ddJVCgN?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[学习回归分析篇---之一元线性回归分析应用例]]></title><description><![CDATA[我们首先来研究一个实例。 一个活塞，底部为一个均匀地排布了网状小孔的漏筛，筒里装有粘性流体，流体在重力作用下，会从小孔漏出。 顶部有一个活塞，活塞上部可以加砝码。加载的砝码越重，流体流出越快。 这个装置用来测试粘性流体的流动性能，表征流体的加工性能。这种装置有标准，供测试塑料加工性能之用。 图 3.1 测试粘性流体的流动特性的装置 一次测试的数据样本如表 3.1 所示]]></description><link>http://direct.ecency.com/cn/@shenzehe/cny</link><guid isPermaLink="true">http://direct.ecency.com/cn/@shenzehe/cny</guid><category><![CDATA[cn]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Tue, 14 Aug 2018 23:55:06 GMT</pubDate><enclosure url="https://images.ecency.com/p/qjrE4yyfw5pEPvDbJDzhdNXM7mjt1tbr2kM3X28F6SraZceTwWUaB8LjCqfSKUuhy766nyYa81SFYUKMVQZEYecmanR91AFw979aJqtzRdLzwB4A3LnDk7US?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[学习回归分析篇---之最小二乘回归概述]]></title><description><![CDATA[工艺优化的全部工作就是如何设计试验使得到的这些数据能够代表过程的实际状况， 从而从对这些数据的分析中得到对过程规律的认识，并利用这种认识去找到优化的工艺， 达到优化生产的目的。所谓优化的工艺，在数学上就是一组好的参数，它使生产出来的产品质量好而成本低。 好的开发技术则使实验数少，开发周期短，需要的开发资金少，更快地得到这组优化的参数。]]></description><link>http://direct.ecency.com/cn/@shenzehe/2cbloa</link><guid isPermaLink="true">http://direct.ecency.com/cn/@shenzehe/2cbloa</guid><category><![CDATA[cn]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Sun, 12 Aug 2018 05:30:36 GMT</pubDate><enclosure url="https://images.ecency.com/p/2N61tyyncFaFVtpM8rCsJzDgecVMtkz4jpzBsszXjhqan9w5GrUdrAhbjofpLz8VjcroN1gjbibH6iLkPvniPCsNSbraKEuoSAgr2RLBsx9x2zMaehpf2KKUHjzNf51kznp25t17vv7G?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[学习回归分析篇---之实验数据整理]]></title><description><![CDATA[概要 本篇介绍的是我对回归分析的理解，在我的认识中，这是工艺优化的理论基础之一。 回归分析解决的是将实验数据归纳成为响应变量关于实验因子的预报方程的一门技术。 这个预报方程如果反映了过程的机制，那么，就可以从这个关系找到优化工艺条件。 这就产生了三个问题：如何使预报方程反映过程机制？如何从试验数据找到这个预报方程？如何从预报方程找到优化工艺？这就是工艺优化的数学原理。也就是试验分析与设计的内容。]]></description><link>http://direct.ecency.com/experimental-statistics/@shenzehe/51ovw1</link><guid isPermaLink="true">http://direct.ecency.com/experimental-statistics/@shenzehe/51ovw1</guid><category><![CDATA[experimental-statistics]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Thu, 09 Aug 2018 18:25:51 GMT</pubDate><enclosure url="https://images.ecency.com/p/8th8uW8KLF3eHoH9cVXk2SSr6eVkAARdFgoTu5NcF2rAG1om9mZWn6sMUVgdCz63ZpSFY9i4sb7vdBv1B6rM6fXU5UwfDu89uWXnpoLZPsTMNCpocyjCFWsxFijg88TuvMSrouGZVu1j8vm44uGgUJPHRJgjVg8a6hVZdwwALZjcJkqJWpesfbCtxE?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[弱相关试验设计之超立方篇（相关博客目录）]]></title><description><![CDATA[本作者一组有关弱相关试验设计之超立方的博客链接，点击即可浏览。 学习极值原理 回归试验设计的质量要素 弱相关试验设计（修订） 介绍正交超立方试验设计表 正交超立方设计的命名约定 用张量积构造正交超立方存在的缺陷 修正一处认识 同 C.D.Lin 等四博士讨论堆叠法构造正交超立方设计问题 同 C.D.Lin 等四博士讨论堆叠法构造正交超立方设计问题(续1) 同 C.D.Lin]]></description><link>http://direct.ecency.com/olhd/@shenzehe/4kkvhf</link><guid isPermaLink="true">http://direct.ecency.com/olhd/@shenzehe/4kkvhf</guid><category><![CDATA[olhd]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Fri, 03 Aug 2018 21:45:45 GMT</pubDate></item><item><title><![CDATA[回归试验设计的质量要素]]></title><description><![CDATA[1. 回归试验设计的质量要素 试验设计的品质直接关系到试验的结果，因此，对试验设计需要有一些品质要求。 20 世纪初，试验设计创立时，基于区组设计，费歇提出了实验设计应遵循的三个原则：随机化，局部控制和重复。这一思想当今仍然有效。但试验设计与数据处理技术较那时有了重大进展。各种数据处理技术对试验设计的要求不尽相同。根据回归分析技术的特点，回归试验设计应该具有随机性，均衡性和适度稠密三项品质要素。 2.]]></description><link>http://direct.ecency.com/experimental-designs/@shenzehe/5py4d2</link><guid isPermaLink="true">http://direct.ecency.com/experimental-designs/@shenzehe/5py4d2</guid><category><![CDATA[experimental-designs]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Fri, 03 Aug 2018 00:27:45 GMT</pubDate><enclosure url="https://images.ecency.com/p/3W72119s5BjVs3Hye1oHX44R9EcpQD5C9xXzj68nJaq3CeHNfhWcimro1eTwXyUPE6eDPwMMyRfUNqG2FTds8n18rCBKkAji9H3vBCzXHbpkQMQd6NCMft?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[学习极值原理]]></title><description><![CDATA[综 述 极值原理是最小二乘回归的理论基础，因为最小二乘回归是最重要的数据处理工具，也是试验设计的理论基础，研究优化论必须了解极值原理。为以后叙述和应用方便，这里引述一些有关资料，个别地方作了引伸。 受 HTML 语言的限制，符号不够规范，请查各种数学分析教程。 1. 函数的极值原理 1.1 单变量函数的极值 若函数 f(x) 在点x0 的双侧邻域中有定义，对于|x-x0|<δ 内的一切点]]></description><link>http://direct.ecency.com/experimental-designs/@shenzehe/6shywz</link><guid isPermaLink="true">http://direct.ecency.com/experimental-designs/@shenzehe/6shywz</guid><category><![CDATA[experimental-designs]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Thu, 02 Aug 2018 05:10:45 GMT</pubDate><enclosure url="https://images.ecency.com/p/RGgukq5E6HBM2jscGd4Sszpv94XxHH2uqxMY9z21vaqHt2DHr9YzP6BcDwYK9yT6YNQ5RNzytm7FWYa5ps5Dgg3fuSeDwrY8JGd7WHJ7tBkSc9JG5wXMzaMJZkAkgPg?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[弱相关超立方阵列集锦]]></title><description><![CDATA[一类试验设计表的命名约定 w17h6o w18h0o w19h7o w20h6o w21h7o w22h0o w23h7o w24h7o w25h7o w26h0o w27h6o w28h7o w29h6o w30h0o w31h6o w32h60 w33h5o w34h0o w35h6o w36h6o w37h6o]]></description><link>http://direct.ecency.com/olhd/@shenzehe/62trdq</link><guid isPermaLink="true">http://direct.ecency.com/olhd/@shenzehe/62trdq</guid><category><![CDATA[olhd]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Wed, 01 Aug 2018 05:09:51 GMT</pubDate></item><item><title><![CDATA[A weak correlation hypercube with 36 runs and 6 orthogonal columns]]></title><description><![CDATA[W36h6o Author : He Shenze (Email：heshenze@gmail.com ) Columns 1 to 23 13,22,26,12,07,05,14,05,03,11,29,04,18,12,21,08,23,12,02,09,03,33,04, 34,14,03,28,14,16,34,29,04,10,33,07,25,18,01,16,07,03,24,14,02,10,20,]]></description><link>http://direct.ecency.com/olhd/@shenzehe/a-weak-correlation-hypercube-with-36-runs-and-6-orthogonal-columns</link><guid isPermaLink="true">http://direct.ecency.com/olhd/@shenzehe/a-weak-correlation-hypercube-with-36-runs-and-6-orthogonal-columns</guid><category><![CDATA[olhd]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Wed, 01 Aug 2018 01:51:48 GMT</pubDate><enclosure url="https://images.ecency.com/p/HNWT6DgoBc14riaEeLCzGYopkqYBKxpGKqfNWfgr368M9WWDM9YY2wCMrxu6a4wPbsDSGhCrgxWbuyqfAHNYzwUbmsDYcohEL7s7JMQ9ySs3rjGjo6GYhrL7pG6?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[A weak correlation hypercube with 35 runs and 6 orthogonal columns]]></title><description><![CDATA[W35h6o Author : He Shenze (Email：heshenze@gmail.com ) Columns 1 to 23 20,22,12,33,07,31,23,19,07,02,22,14,15,08,26,09,35,01,34,35,28,21,13, 21,21,32,22,23,16,03,34,11,18,35,35,35,31,06,32,29,32,31,31,18,28,35,]]></description><link>http://direct.ecency.com/ohc/@shenzehe/a-weak-correlation-hypercube-with-35-runs-and-6-orthogonal-columns</link><guid isPermaLink="true">http://direct.ecency.com/ohc/@shenzehe/a-weak-correlation-hypercube-with-35-runs-and-6-orthogonal-columns</guid><category><![CDATA[ohc]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Wed, 01 Aug 2018 01:39:48 GMT</pubDate><enclosure url="https://images.ecency.com/p/HNWT6DgoBc14riaEeLCzGYopkqYBKxpGKqfNWfgr368M9UaeCtDRY6aYhNHAVJstPGe1r393pFqJ2mL3u2wxVEeMmGkU4nQif9rSVc5xYEXPE8jd3P2dS42Dmgr?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[A weak correlation hypercube with 34 runs and 0 orthogonal columns]]></title><description><![CDATA[W34h0o Author : He Shenze (Email：heshenze@gmail.com ) Columns 1 to 23 29,19,28,26,34,02,28,03,20,26,30,04,33,30,12,12,25,30,11,12,27,29,03, 27,25,10,19,14,17,05,33,30,34,04,02,28,08,17,16,04,01,20,11,32,12,12,]]></description><link>http://direct.ecency.com/olhd/@shenzehe/a-weak-correlation-hypercube-with-34-runs-and-0-orthogonal-columns</link><guid isPermaLink="true">http://direct.ecency.com/olhd/@shenzehe/a-weak-correlation-hypercube-with-34-runs-and-0-orthogonal-columns</guid><category><![CDATA[olhd]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Wed, 01 Aug 2018 01:29:21 GMT</pubDate><enclosure url="https://images.ecency.com/p/HNWT6DgoBc14riaEeLCzGYopkqYBKxpGKqfNWfgr368M9VQYhq4Sf56fc1LBdes1jmscC9Q9qgGWoH6kxaw4MtLLb4X7pVYGMctkcWRJCRZFq9ETdPVoLE8GENA?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[A weak correlation hypercube with 33 runs and 5 orthogonal columns]]></title><description><![CDATA[W33h5o Author : He Shenze (Email：heshenze@gmail.com ) Columns 1 to 23 18,29,23,10,26,11,18,06,16,23,22,01,19,24,02,33,27,10,32,31,12,29,25, 27,03,12,11,09,14,28,31,30,32,31,11,30,04,10,27,29,31,11,14,18,10,22,]]></description><link>http://direct.ecency.com/ohc/@shenzehe/a-weak-correlation-hypercube-with-33-runs-and-5-orthogonal-columns</link><guid isPermaLink="true">http://direct.ecency.com/ohc/@shenzehe/a-weak-correlation-hypercube-with-33-runs-and-5-orthogonal-columns</guid><category><![CDATA[ohc]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Wed, 01 Aug 2018 01:15:42 GMT</pubDate><enclosure url="https://images.ecency.com/p/HNWT6DgoBc14riaEeLCzGYopkqYBKxpGKqfNWfgr368M9Wc1pWaGkitSNoeauC1nY7sMHoK8wuhJxW3SewzkSkox4npRY6JzDN4bzSFk1ffnaVqQ31wWtW8PL6z?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[A weak correlation hypercube with 32 runs and 6 orthogonal columns]]></title><description><![CDATA[W32h6o Author : He Shenze (Email：heshenze@gmail.com ) Columns 1 to 23 02,04,09,18,10,20,03,09,20,07,05,05,28,29,06,30,08,21,05,11,29,19,29, 10,27,19,30,32,10,24,11,07,01,08,26,10,31,15,01,10,28,18,09,10,26,16,]]></description><link>http://direct.ecency.com/ohc/@shenzehe/a-weak-correlation-hypercube-with-32-runs-and-6-orthogonal-columns</link><guid isPermaLink="true">http://direct.ecency.com/ohc/@shenzehe/a-weak-correlation-hypercube-with-32-runs-and-6-orthogonal-columns</guid><category><![CDATA[ohc]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Wed, 01 Aug 2018 01:05:03 GMT</pubDate><enclosure url="https://images.ecency.com/p/HNWT6DgoBc14riaEeLCzGYopkqYBKxpGKqfNWfgr368M9VRQsL4PmWRLTYpaq2EMwnBKbHkSYK62Wn6HPpTvRs7XPCL7fySXb9GcZpYBUrrjgvTsfpmbXqr8UuC?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[A weak correlation hypercube with 31 runs and 6 orthogonal columns]]></title><description><![CDATA[W31h6o Author : He Shenze (Email：heshenze@gmail.com ) Columns 1 to 23 09,29,16,15,18,17,03,18,29,28,30,26,12,15,11,08,26,05,09,04,19,01,26, 16,30,05,13,07,28,06,09,17,02,16,02,01,14,10,25,17,29,23,24,09,05,14,]]></description><link>http://direct.ecency.com/olhd/@shenzehe/a-weak-correlation-hypercube-with-31-runs-and-6-orthogonal-columns</link><guid isPermaLink="true">http://direct.ecency.com/olhd/@shenzehe/a-weak-correlation-hypercube-with-31-runs-and-6-orthogonal-columns</guid><category><![CDATA[olhd]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Wed, 01 Aug 2018 00:35:21 GMT</pubDate><enclosure url="https://images.ecency.com/p/HNWT6DgoBc14riaEeLCzGYopkqYBKxpGKqfNWfgr368M9UTMmYGatFrvPNgSXbtbggQbMKmiJtuf9GrzmGQ2VTd5wcisxWku8eQX8goJwNcn1vSULBM6xjK6LAi?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[A weak correlation hypercube with 30 runs and 0 orthogonal columns]]></title><description><![CDATA[W30h0o Author : He Shenze (Email：heshenze@gmail.com ) Columns 1 to 23 02,02,09,12,08,13,07,30,24,11,12,23,12,13,12,30,13,25,21,01,22,15,24, 15,19,19,27,29,06,20,05,21,13,29,29,26,02,14,09,04,29,06,11,06,10,08,]]></description><link>http://direct.ecency.com/olhd/@shenzehe/a-weak-correlation-hypercube-with-30-runs-and-0-orthogonal-columns</link><guid isPermaLink="true">http://direct.ecency.com/olhd/@shenzehe/a-weak-correlation-hypercube-with-30-runs-and-0-orthogonal-columns</guid><category><![CDATA[olhd]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Wed, 01 Aug 2018 00:23:15 GMT</pubDate><enclosure url="https://images.ecency.com/p/HNWT6DgoBc14riaEeLCzGYopkqYBKxpGKqfNWfgr368M9VQqyWQUkCFjG42gd7jG5oM4fkSWgoMn5fgpfEWv6kq5vz8e2NGT2bXAgijngSr2Wzo8kUYtQ21uYgN?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[A weak correlation hypercube with 29 runs and 6 orthogonal columns]]></title><description><![CDATA[W29h6 Author : He Shenze (Email：heshenze@gmail.com ) Columns 1 to 23 20,09,09,05,26,23,22,24,16,08,02,01,11,29,04,14,17,19,06,19,26,02,10, 05,29,23,20,15,29,03,29,14,17,11,24,23,26,26,05,27,08,04,21,12,12,13,]]></description><link>http://direct.ecency.com/olhd/@shenzehe/a-weak-correlation-hypercube-with-29-runs-and-6-orthogonal-columns</link><guid isPermaLink="true">http://direct.ecency.com/olhd/@shenzehe/a-weak-correlation-hypercube-with-29-runs-and-6-orthogonal-columns</guid><category><![CDATA[olhd]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Tue, 31 Jul 2018 23:52:51 GMT</pubDate><enclosure url="https://images.ecency.com/p/HNWT6DgoBc14riaEeLCzGYopkqYBKxpGKqfNWfgr368M9UhTqtTcvikFNFtyNNU3kR2NXvCin7FzvohSWZGL7t6VYFrWdpaE4MF3Jy2RKgGnCpfM1W5tLQor5jC?format=match&amp;mode=fit" length="0" type="false"/></item><item><title><![CDATA[A weak correlation hypercube with 26 runs and 0 orthogonal columns]]></title><description><![CDATA[W26H0o Author : He Shenze (Email：heshenze@gmail.com ) 17,18,08,26,22,24,16,25,05,11,19,08,24,23,20,13,11,18,14,01,23,20,17,13,02, 21,13,07,11,15,12,11,15,16,03,01,26,23,01,24,06,21,01,11,17,20,24,10,17,13,]]></description><link>http://direct.ecency.com/olhd/@shenzehe/a-weak-correlation-hypercube-with-26-runs-and-0-orthogonal-columns</link><guid isPermaLink="true">http://direct.ecency.com/olhd/@shenzehe/a-weak-correlation-hypercube-with-26-runs-and-0-orthogonal-columns</guid><category><![CDATA[olhd]]></category><dc:creator><![CDATA[shenzehe]]></dc:creator><pubDate>Tue, 31 Jul 2018 05:51:18 GMT</pubDate><enclosure 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