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.
標(biāo)簽： networking Internet always afraid
上傳時(shí)間： 2013-12-14
上傳用戶：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.
標(biāo)簽： Delphi 泛型
上傳時(shí)間： 2015-07-01
上傳用戶：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.
標(biāo)簽： Testing and classifying vehicle dispatching rules in three real-world settings
上傳時(shí)間： 2016-04-01
上傳用戶：五塊錢的油條
簡(jiǎn)單命令使用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 ~]$
標(biāo)簽： 簡(jiǎn)單命令使用
上傳時(shí)間： 2016-05-24
上傳用戶：12345678gan
% 生成訓(xùn)練樣本集 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; %輸入矢量的取值范圍矩陣 pr(1:9,2)=1; bpnet=newff(pr,[12 4],{'logsig', 'logsig'}, 'traingdx', 'learngdm'); %建立BP神經(jīng)網(wǎng)絡(luò)， 12個(gè)隱層神經(jīng)元，4個(gè)輸出神經(jīng)元 %tranferFcn屬性 'logsig' 隱層采用Sigmoid傳輸函數(shù) %tranferFcn屬性 'logsig' 輸出層采用Sigmoid傳輸函數(shù) %trainFcn屬性 'traingdx' 自適應(yīng)調(diào)整學(xué)習(xí)速率附加動(dòng)量因子梯度下降反向傳播算法訓(xùn)練函數(shù) %learn屬性 'learngdm' 附加動(dòng)量因子的梯度下降學(xué)習(xí)函數(shù) net.trainParam.epochs=1000;%允許最大訓(xùn)練步數(shù)2000步 net.trainParam.goal=0.001; %訓(xùn)練目標(biāo)最小誤差0.001 net.trainParam.show=10; %每間隔100步顯示一次訓(xùn)練結(jié)果 net.trainParam.lr=0.05; %學(xué)習(xí)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);
上傳時(shí)間： 2016-05-28
上傳用戶：shanqiu
樓術(shù)描述項(xiàng): (1).該項(xiàng)目中"我的電腦"是作者自定義的root節(jié)點(diǎn),沒(méi)有設(shè)定其路徑,所以BeforeExpand事件中會(huì)從它開(kāi)始依次遍歷,但"我的電腦"會(huì)提示"沒(méi)有指定路徑".故需要if(e.Tag.ToString() != "我的電腦")判斷.同時(shí)"我的文檔"需要再次獲取其路徑,依次實(shí)現(xiàn)Add子節(jié)點(diǎn); (2).同時(shí)在"我的文檔"和盤符中需要添加tNode.Nodes.Add("")加載空節(jié)點(diǎn)形成+號(hào),如果沒(méi)有該+號(hào),BeforeExpend事件不會(huì)被調(diào)用,子目錄無(wú)法獲取加載,在BeforeExpand事件調(diào)用TreeViewItems.Add加載其子結(jié)點(diǎn)需要e.Nodes.Clear();清除該結(jié)點(diǎn)的子目錄再加載. (3).提供兩篇類似文章供大家學(xué)習(xí),經(jīng)過(guò)對(duì)比可以發(fā)現(xiàn):第一篇僅從驅(qū)動(dòng)器(C盤)開(kāi)始加載,所以BeforeExpend簡(jiǎn)單展開(kāi)子目錄即可,不需要判斷"我的電腦"和"我的文檔".第二篇含"桌面",因此需要判斷路徑:"C# TreeView磁盤文件,AfterSelect顯示加號(hào)-駱駝祥子" 和"Treeview樹狀顯示文件夾" .同時(shí)補(bǔ)充一篇很優(yōu)秀的文章供大家學(xué)習(xí)"WinForm應(yīng)用：ListView做圖像瀏覽" (4).補(bǔ)充TreeView(樹視圖)事件:更詳細(xì)見(jiàn)"c# 樹狀視圖(TreeView類)". 事件 描述 AfterCheck 在選中節(jié)點(diǎn)復(fù)選框后引發(fā) AfterCollapse 在折疊一個(gè)節(jié)點(diǎn)后引發(fā) AfterExpand 在擴(kuò)展一個(gè)節(jié)點(diǎn)后引發(fā) AfterSelect 在選中一個(gè)節(jié)點(diǎn)后引發(fā) BeforeCheck 在選中節(jié)點(diǎn)復(fù)選框之前引發(fā) BeforeCollapse 在折疊一個(gè)節(jié)點(diǎn)之前引發(fā) BeforeExpand 在擴(kuò)展一個(gè)節(jié)點(diǎn)之前引發(fā) BeforeSelect 在選中一個(gè)節(jié)點(diǎn)之前引發(fā) (5).補(bǔ)充兩個(gè)關(guān)于論壇討論"c#怎樣動(dòng)態(tài)讀取資源文件里的圖片"和"在C＃中怎么調(diào)用Resources文件中的圖片" (6).在《C#典型模塊與項(xiàng)目實(shí)戰(zhàn)大全》(清華大學(xué)出版社-丁士鋒)書中談到,出于對(duì)程序響應(yīng)性能考慮,它先加載盤符結(jié)點(diǎn),沒(méi)有使用遞歸一次性加載所有文件到樹狀列表中,代碼通過(guò)AfterSelect事件和FileSystemWatcher控件,監(jiān)聽(tīng)加載.并使用線程池Task更新加載TreeView,希望大家去學(xué)習(xí). 總結(jié) 該篇通過(guò)TreeView加載了磁盤目錄路徑,并通過(guò)ImageList加載圖標(biāo).那么怎樣實(shí)現(xiàn)閱讀文件夾下文件,獲取其圖標(biāo)、文件大小、擴(kuò)展名等信息,并雙擊打開(kāi)文件呢?下一篇將接著講述.最后希望該文章對(duì)大家有所幫助,文章中很多鏈接都可以供覺(jué)得有用的同學(xué)學(xué)習(xí),感謝上面提到的文章及書籍作者.同時(shí)如果文章中有錯(cuò)誤或不足之處請(qǐng)?jiān)?有問(wèn)題或建議者亦可提出.希望尊重作者勞動(dòng)果實(shí)勿噴.
上傳時(shí)間： 2016-08-15
上傳用戶：baobao9437
已知系統(tǒng)函數(shù)為H(z)=1/[(1-0.2z^-1)(1-0.3z^-1)(1+0.4z^-1)]。試用長(zhǎng)除法求h(n)的6點(diǎn)輸出。 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)
上傳時(shí)間： 2017-10-21
上傳用戶：zhouhua
簡(jiǎn)單的實(shí)現(xiàn)JPEG圖像壓縮編碼方法一 Clear all; RGB=imread('C:\Users\Administrator\Desktop\123.bmp');%讀取內(nèi)存中bmp格式的彩色圖像 I=rgb2gray(RGB);%將彩色圖像轉(zhuǎn)換為灰度圖像 I1=im2double(I);%將圖像變換為雙精度格式 T=dctmtx(8);%處理后返回一個(gè)8*8階的DCT矩陣 B1=blkproc(I1,[8 8],'P1*x*P2',T,T');%對(duì)圖像的每個(gè)8*8子塊應(yīng)用矩陣式‘P1*x*P2(像素塊的處理函數(shù)，x是形式參數(shù))進(jìn)行處理，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 ];%選取10個(gè)DCT系數(shù)重構(gòu)圖像(DCT具有能量集中的性質(zhì)，數(shù)據(jù)集中在左上角，故進(jìn)行圖像壓縮時(shí)離散余弦變換矩陣可以舍棄右下角的高頻數(shù)據(jù)) B2=blkproc(B1,[8 8],'P1.*x',mask);%舍棄每個(gè)塊中的高頻系數(shù)，達(dá)到壓縮的目的 I2=blkproc(B2,[8 8],'P1*x*P2',T',T);%余弦反變換，重構(gòu)圖像 subplot(2,2,1);imshow(RGB);%原彩色圖像 subplot(2,2,2);imshow(I);%灰度圖像 subplot(2,2,3);imshow(I1);%雙精度灰度圖像 subplot(2,2,4);imshow(I2);%重構(gòu)圖像
標(biāo)簽： matlab
上傳時(shí)間： 2018-03-15
上傳用戶：wlmelody
BP神經(jīng)網(wǎng)絡(luò)matlab源程序代碼 %% 該代碼為 BP 神經(jīng)網(wǎng)絡(luò)的預(yù)測(cè)代碼 % 清空環(huán)境變量 clc Clear % %% 網(wǎng)絡(luò)結(jié)構(gòu)建立 % 讀取數(shù)據(jù) load data input output input=data(;,1;2;3;4;5); output=data(;,5); %節(jié)點(diǎn)個(gè)數(shù) %訓(xùn)練數(shù)據(jù)和預(yù)測(cè)數(shù)據(jù) %選連樣本輸入輸出數(shù)據(jù)歸一化 %構(gòu)建網(wǎng)絡(luò) net=newff(inputn,outputn,hiddennum); %網(wǎng)絡(luò)進(jìn)化參數(shù) %網(wǎng)絡(luò)訓(xùn)練 % 預(yù)測(cè)數(shù)據(jù)歸一化
標(biāo)簽： matlab BP神經(jīng)網(wǎng)絡(luò) 源程序 代碼
上傳時(shí)間： 2018-04-10
上傳用戶：45808330
This is a great introductory book for manipulators. There are Clear examples to help walk through kinematics, dynamics and control.
標(biāo)簽： Robotics Planning modeling Control and
上傳時(shí)間： 2019-10-28
上傳用戶：xiangpingfly100
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