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<a href="/2020/04/09/sqlServer-update-to-kafka/" class="post-title-link" itemprop="url">配置 Kafka 实时获取 SQL Server 的数据更新记录</a>
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<time title="Created: 2020-04-09 10:52:17 / Modified: 11:10:41" itemprop="dateCreated datePublished" datetime="2020-04-09T10:52:17+08:00">2020-04-09</time>
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<span class="post-meta-item-text">Symbols count in article: </span>
<span>3.2k</span>
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<h2 id="背景"><a href="#背景" class="headerlink" title="背景"></a>背景</h2><p>SQLServer 为实时更新数据同步提供了 CDC 机制，类似于 Mysql 的 binlog，将数据更新操作维护到一张 CDC 表中。<br>开启 cdc 的源表在插入 INSERT、更新 UPDATE 和删除 DELETE 活动时会插入数据到日志表中。cdc 通过捕获进程将变更数据捕获到变更表中，通过 cdc 提供的查询函数，可以捕获这部分数据。</p>
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<a href="/2019/01/19/million-face-search/" class="post-title-link" itemprop="url">千万级别人脸检索测试小结</a>
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<h1 id="项目背景"><a href="#项目背景" class="headerlink" title="项目背景"></a>项目背景</h1><p>市公安局为了解各企业人脸识别系统的实际性能表现，评估现有人脸识别系统对公安实战应用需求的满足程度，为后期人脸识别技术系统建设选型提供数据依据，举行了此次人脸识别系统性能测试，全部委托软件测试中心组织此次测试。</p>
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<a href="/2019/01/01/install-tensorflow/" class="post-title-link" itemprop="url">TensorFlow源码安装教程</a>
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<p>直接使用pip安装Tensorflow时发现安装的Tensorflow只支持cuda7.5，而我的服务器上安装的是cuda8.0，由于服务器上有其他程序在运行，不能重新安装cuda，所以只好使用源码自己编译Tensorflow，然后安装。 </p>
<p>安装环境：<br>Ubuntu 14.04.4 LTS (GNU/Linux 3.13.0-24-generic x86_64) + CUDA 8.0</p>
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<h2 id="0-项目来源"><a href="#0-项目来源" class="headerlink" title="0 项目来源"></a>0 项目来源</h2><p>今年8月我入职我司的信息系统部门，我司要求新员工必须要跨部门轮岗实习，我被分配到了人事。人事的带教老师听说我是搞人工智能的，提出了需求：判断一张照片中的人是不是我司的员工。这个需求和我在学校做的工作类似，感觉难度不大，所以答应下来。此项目一来可以作为一个工具供人事部门使用，二来也可以作为轮岗实习的项目成果。</p>
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<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 显示逻辑卷组</span></span><br><span class="line">$ sudo vgdisplay</span><br><span class="line"><span class="comment"># 显示逻辑卷</span></span><br><span class="line">$ sudo lvdisplay</span><br><span class="line"><span class="comment"># 显示磁盘</span></span><br><span class="line">$ sudo fdisk -l</span><br><span class="line"><span class="comment"># 创建物理卷</span></span><br><span class="line">$ sudo pvcreate /dev/sdb</span><br><span class="line"><span class="comment"># 显示物理卷信息</span></span><br><span class="line">$ sudo pvdisplay</span><br><span class="line"><span class="comment"># 物理卷/dev/sdb加入卷组vg-root</span></span><br><span class="line">$ sudo vgextend ubuntu-vg /dev/sdb</span><br><span class="line"><span class="comment"># 增大逻辑卷root 1T</span></span><br><span class="line">$ sudo lvextend -L +1T /dev/ubuntu-vg/root</span><br><span class="line"><span class="comment"># 调整文件系统大小</span></span><br><span class="line">$ sudo resize2fs /dev/ubuntu-vg/root</span><br><span class="line"><span class="comment"># 查看结果</span></span><br><span class="line">$ df -h</span><br></pre></td></tr></table></figure>
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<p>原文链接：<a href="http://www.restapitutorial.com/lessons/whatisrest.html" target="_blank" rel="noopener">http://www.restapitutorial.com/lessons/whatisrest.html</a></p>
<h1 id="什么是REST？"><a href="#什么是REST？" class="headerlink" title="什么是REST？"></a>什么是REST？</h1><p>REST全称Representational State Transfer。REST结构包含六大约束，这些约束规范最早是Roy Fielding在他的<a href="http://www.ics.uci.edu/~fielding/pubs/dissertation/rest_arch_style.htm" target="_blank" rel="noopener">博士论文</a>中提出，并且他在论文中定义了RESTful风格的基础。</p>
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<h1 id="引言"><a href="#引言" class="headerlink" title="引言"></a>引言</h1><p>高维矢量的欧氏距离计算应用广泛，尤其是在近邻搜索方面。由于维度灾难，近邻搜索是非常耗时耗力的。在一个D维的欧式空间$R^{D}$中，近邻搜索就是在n个矢量组成的有限集合$y\subset R^{D}$中找出元素$NN(x)$，并且该元素到查询矢量$x\in R^{D}$的距离最小：$$NN(x)=arg\min_{y\subset \cal{Y}}d(x,y)$$</p>
<p>现已提出的几种多维索引方法都在某种程度上减少了搜索时间，例如KD-tree或者其他分支和限定技术。然而，对于高维搜索空间，这些方法还不如蛮力计算矢量距离的方法高效。</p>
<p>已经有大量的文献提出算法用以解决近似近邻搜索执行过程中产生的问题，这些算法的关键性思想是“近似”，即找出可能性最大的元素而不是可能性为1的元素。</p>
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<h1 id="功能目标"><a href="#功能目标" class="headerlink" title="功能目标"></a>功能目标</h1><p>一个简单的安卓app，按下按钮弹出对话框，调用.so文件输出”Hello from JNI!”。<br>完整的項目<a href="https://github.com/HulkSun/JNITest" target="_blank" rel="noopener">代碼</a>。</p>
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