What you always wanted to know about networking but were afraid to ask! * How the Internet works * How e-mail, e-learning, and telephony work on the Internet * What makes a network safe * How traffic gets from here to there * Disaster recovery and other ways to keep a network running * How businesses share data * See the world of Cisco networking with this illustrated guide s visual approach to learning * Useful for both novices and networking professionals * Covers a broad variety of internetworking topics-from e-mail to VPNs At last-an illustrated guide to the world of Cisco networking. Cisco Networking Simplified breaks down the complicated world of internetworking into easy-to-understand parts. Learn quickly and easily the fundamentals of a variety of topics, such as security, IP telephony, and quality of service, from the full-color diagrams and clear explanations found in Cisco Networking Simplified.
标签： networking Internet always afraid
上传时间： 2013-12-14
上传用户：520
Collection of key-value pairs. TDictionary represents a generic collection of key-value pairs. This class provides a mapping from a collection of keys to a collection of values. When you create a TDictionary object, you can specify various combinations of initial capacity, equality operation, and initial content. You can add a key that is associated with a corresponding value with the Add or AddOrSetValue methods. You can remove entries with Remove or clear, which removes all key-value pairs. Adding or removing a key-value pair and looking up a key are efficient, close to O(1), because keys are hashed. A key must not be nil (though a value may be nil) and there must be an equality comparison operation for keys. You can test for the presence or keys and values with the TryGetValue, ContainsKey and ContainsValue methods. The Items property lists all Count dictionary entries. You can also set and get values by indexing the Items property. Setting the value this way overwrites any existing value. The class TObjectDictionary inherits from TDictionary and provides an automatic mechanism for freeing objects removed from dictionary entries.
标签： Delphi 泛型
上传时间： 2015-07-01
上传用户：mirage
Guided vehicles (GVs) are commonly used for the internal transportation of loads in warehouses, production plants and terminals. These guided vehicles can be routed with a variety of vehicle dispatching rules in an attempt to meet performance criteria such as minimizing the average load waiting times. In this research, we use simulation models of three companies to evaluate the performance of several real-time vehicle dispatching rules, in part described in the literature. It appears that there is a clear difference in average load waiting time between the different dispatching rules in the different environments. Simple rules, based on load and vehicle proximity (distance-based) perform best for all cases. The penalty for this is a relatively high maximum load waiting time. A distance-based rule with time truncation, giving more priority to loads that have to wait longer than a time threshold, appears to yield the best possible overall performance. A rule that particularly considers load-waiting time performs poor overall. We also show that using little pre-arrival information of loads leads to a significant improvement in the performance of the dispatching rules without changing their performance ranking.
标签： Testing and classifying vehicle dispatching rules in three real-world settings
上传时间： 2016-04-01
上传用户：五块钱的油条
简单命令使用grep等的使用 [zorro@isch ~]$ history 1 ifconfig 2 su 3 exit 4 ls 5 cd Desktop/ 6 ls 7 tar zxcf VMwareTools-8.4.5-324285.tar.gz 8 tar zxvf VMwareTools-8.4.5-324285.tar.gz 9 cd vmware-tools-distrib/ 10 ls 11 ./vmware-install.pl 12 su 13 ls 14 cd .. 15 ls 16 rm VMwareTools-8.4.5-324285.tar.gz 17 rm -r vmware-tools-distrib 18 ls 19 make 20 ls 21 cd redis/ 22 quit 23 ls 24 ca redis/ 25 cd redis/ 26 cd redis-2.8.17 27 make 28 cd redis-2.8.17 29 ls 30 cd redis-2.8.17 31 cd str 32 cd src 33 ls 34 ./redis-cli 35 ls 36 cd redis-2.8.17 tar.gz 37 make 38 cd src 39 ./redis-server .. /redis.conf 40 ./redis-cli 41 ./redis-server ../redis.conf 42 vi test1.sh 43 ./test1.sh 44 vi test.sh 45 ./test.sh 46 ls 47 chmod 777 test.sh 48 ./test.sh 49 vi express 50 $ grep –n ‘the’ express 51 clear 52 grep -n 'the' express 53 vi express 54 grep -n 'the' express 55 grep -vn 'the'express 56 grep -vn 'the' express 57 grep -in 'the' express 58 vi test2.c 59 grep -l 'the' *.c 60 grep -n 't[ae]st' express 61 grep -n 'oo' express 62 grep -n '[^g]oo' express 63 grep -n '[a^z]oo' express 64 grep -n '[0^9]' express 65 grep -n '^the' express 66 vi express 67 sed -e 'd' express 68 sed -e '1d' express 69 sed -e '1~7d' express 70 sed -e '$d' express 71 sed -e '1，/^$/d' express 72 ls 73 cd 74 pwd 75 history [zorro@isch ~]$
标签： 简单命令使用
上传时间： 2016-05-24
上传用户：12345678gan
% 生成训练样本集 clear all; clc; P=[110 0.807 240 0.2 15 1 18 2 1.5; 110 2.865 240 0.1 15 2 12 1 2; 110 2.59 240 0.1 12 4 24 1 1.5; 220 0.6 240 0.3 12 3 18 2 1; 220 3 240 0.3 25 3 21 1 1.5; 110 1.562 240 0.3 15 3 18 1 1.5; 110 0.547 240 0.3 15 1 9 2 1.5]; 0 1.318 300 0.1 15 2 18 1 2]; T=[54248 162787 168380 314797; 28614 63958 69637 82898; 86002 402710 644415 328084; 230802 445102 362823 335913; 60257 127892 76753 73541; 34615 93532 80762 110049; 56783 172907 164548 144040]; @907 117437 120368 130179]; m=max(max(P)); n=max(max(T)); P=P'/m; T=T'/n; %-------------------------------------------------------------------------% pr(1:9,1)=0; %输入矢量的取值范围矩阵 pr(1:9,2)=1; bpnet=newff(pr,[12 4],{'logsig', 'logsig'}, 'traingdx', 'learngdm'); %建立BP神经网络， 12个隐层神经元，4个输出神经元 %tranferFcn属性 'logsig' 隐层采用Sigmoid传输函数 %tranferFcn属性 'logsig' 输出层采用Sigmoid传输函数 %trainFcn属性 'traingdx' 自适应调整学习速率附加动量因子梯度下降反向传播算法训练函数 %learn属性 'learngdm' 附加动量因子的梯度下降学习函数 net.trainParam.epochs=1000;%允许最大训练步数2000步 net.trainParam.goal=0.001; %训练目标最小误差0.001 net.trainParam.show=10; %每间隔100步显示一次训练结果 net.trainParam.lr=0.05; %学习速率0.05 bpnet=train(bpnet,P,T); %------------------------------------------------------------------------- p=[110 1.318 300 0.1 15 2 18 1 2]; p=p'/m; r=sim(bpnet,p); R=r'*n; display(R);
上传时间： 2016-05-28
上传用户：shanqiu
楼术描述项: (1).该项目中"我的电脑"是作者自定义的root节点,没有设定其路径,所以BeforeExpand事件中会从它开始依次遍历,但"我的电脑"会提示"没有指定路径".故需要if(e.Tag.ToString() != "我的电脑")判断.同时"我的文档"需要再次获取其路径,依次实现Add子节点; (2).同时在"我的文档"和盘符中需要添加tNode.Nodes.Add("")加载空节点形成+号,如果没有该+号,BeforeExpend事件不会被调用,子目录无法获取加载,在BeforeExpand事件调用TreeViewItems.Add加载其子结点需要e.Nodes.clear();清除该结点的子目录再加载. (3).提供两篇类似文章供大家学习,经过对比可以发现:第一篇仅从驱动器(C盘)开始加载,所以BeforeExpend简单展开子目录即可,不需要判断"我的电脑"和"我的文档".第二篇含"桌面",因此需要判断路径:"C# TreeView磁盘文件,AfterSelect显示加号-骆驼祥子" 和"Treeview树状显示文件夹" .同时补充一篇很优秀的文章供大家学习"WinForm应用：ListView做图像浏览" (4).补充TreeView(树视图)事件:更详细见"c# 树状视图(TreeView类)". 事件 描述 AfterCheck 在选中节点复选框后引发 AfterCollapse 在折叠一个节点后引发 AfterExpand 在扩展一个节点后引发 AfterSelect 在选中一个节点后引发 BeforeCheck 在选中节点复选框之前引发 BeforeCollapse 在折叠一个节点之前引发 BeforeExpand 在扩展一个节点之前引发 BeforeSelect 在选中一个节点之前引发 (5).补充两个关于论坛讨论"c#怎样动态读取资源文件里的图片"和"在C＃中怎么调用Resources文件中的图片" (6).在《C#典型模块与项目实战大全》(清华大学出版社-丁士锋)书中谈到,出于对程序响应性能考虑,它先加载盘符结点,没有使用递归一次性加载所有文件到树状列表中,代码通过AfterSelect事件和FileSystemWatcher控件,监听加载.并使用线程池Task更新加载TreeView,希望大家去学习. 总结 该篇通过TreeView加载了磁盘目录路径,并通过ImageList加载图标.那么怎样实现阅读文件夹下文件,获取其图标、文件大小、扩展名等信息,并双击打开文件呢?下一篇将接着讲述.最后希望该文章对大家有所帮助,文章中很多链接都可以供觉得有用的同学学习,感谢上面提到的文章及书籍作者.同时如果文章中有错误或不足之处请原谅,有问题或建议者亦可提出.希望尊重作者劳动果实勿喷.
上传时间： 2016-08-15
上传用户：baobao9437
已知系统函数为H(z)=1/[(1-0.2z^-1)(1-0.3z^-1)(1+0.4z^-1)]。试用长除法求h(n)的6点输出。 答案：clc;clear all;b=1;a=poly([0.2,0.3,-0.4]);x=deconv([1,zeros(1,6+4-1-1)],a)
上传时间： 2017-10-21
上传用户：zhouhua
简单的实现JPEG图像压缩编码方法一 clear all; RGB=imread('C:\Users\Administrator\Desktop\123.bmp');%读取内存中bmp格式的彩色图像 I=rgb2gray(RGB);%将彩色图像转换为灰度图像 I1=im2double(I);%将图像变换为双精度格式 T=dctmtx(8);%处理后返回一个8*8阶的DCT矩阵 B1=blkproc(I1,[8 8],'P1*x*P2',T,T');%对图像的每个8*8子块应用矩阵式‘P1*x*P2(像素块的处理函数，x是形式参数)进行处理，P1=T，P2=T’ mask=[1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ];%选取10个DCT系数重构图像(DCT具有能量集中的性质，数据集中在左上角，故进行图像压缩时离散余弦变换矩阵可以舍弃右下角的高频数据) B2=blkproc(B1,[8 8],'P1.*x',mask);%舍弃每个块中的高频系数，达到压缩的目的 I2=blkproc(B2,[8 8],'P1*x*P2',T',T);%余弦反变换，重构图像 subplot(2,2,1);imshow(RGB);%原彩色图像 subplot(2,2,2);imshow(I);%灰度图像 subplot(2,2,3);imshow(I1);%双精度灰度图像 subplot(2,2,4);imshow(I2);%重构图像
标签： matlab
上传时间： 2018-03-15
上传用户：wlmelody
BP神经网络matlab源程序代码 %% 该代码为 BP 神经网络的预测代码 % 清空环境变量 clc clear % %% 网络结构建立 % 读取数据 load data input output input=data(;,1;2;3;4;5); output=data(;,5); %节点个数 %训练数据和预测数据 %选连样本输入输出数据归一化 %构建网络 net=newff(inputn,outputn,hiddennum); %网络进化参数 %网络训练 % 预测数据归一化
上传时间： 2018-04-10
上传用户：45808330
This is a great introductory book for manipulators. There are clear examples to help walk through kinematics, dynamics and control.
标签： Robotics Planning modeling Control and
上传时间： 2019-10-28
上传用户：xiangpingfly100
