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.
標簽： networking Internet always afraid
上傳時間： 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.
標簽： Delphi 泛型
上傳時間： 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.
標簽： Testing and classifying vehicle dispatching rules in three real-world settings
上傳時間： 2016-04-01
上傳用戶：五塊錢的油條
簡單命令使用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 ~]$
標簽： 簡單命令使用
上傳時間： 2016-05-24
上傳用戶：12345678gan
% 生成訓練樣本集 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神經網絡， 12個隱層神經元，4個輸出神經元 %tranferFcn屬性 'logsig' 隱層采用Sigmoid傳輸函數 %tranferFcn屬性 'logsig' 輸出層采用Sigmoid傳輸函數 %trainFcn屬性 'traingdx' 自適應調整學習速率附加動量因子梯度下降反向傳播算法訓練函數 %learn屬性 'learngdm' 附加動量因子的梯度下降學習函數 net.trainParam.epochs=1000;%允許最大訓練步數2000步 net.trainParam.goal=0.001; %訓練目標最小誤差0.001 net.trainParam.show=10; %每間隔100步顯示一次訓練結果 net.trainParam.lr=0.05; %學習速率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);
上傳時間： 2016-05-28
上傳用戶：shanqiu
樓術描述項: (1).該項目中"我的電腦"是作者自定義的root節(jié)點,沒有設定其路徑,所以BeforeExpand事件中會從它開始依次遍歷,但"我的電腦"會提示"沒有指定路徑".故需要if(e.Tag.ToString() != "我的電腦")判斷.同時"我的文檔"需要再次獲取其路徑,依次實現Add子節(jié)點; (2).同時在"我的文檔"和盤符中需要添加tNode.Nodes.Add("")加載空節(jié)點形成+號,如果沒有該+號,BeforeExpend事件不會被調用,子目錄無法獲取加載,在BeforeExpand事件調用TreeViewItems.Add加載其子結點需要e.Nodes.clear();清除該結點的子目錄再加載. (3).提供兩篇類似文章供大家學習,經過對比可以發(fā)現:第一篇僅從驅動器(C盤)開始加載,所以BeforeExpend簡單展開子目錄即可,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-駱駝祥子" 和"Treeview樹狀顯示文件夾" .同時補充一篇很優(yōu)秀的文章供大家學習"WinForm應用：ListView做圖像瀏覽" (4).補充TreeView(樹視圖)事件:更詳細見"c# 樹狀視圖(TreeView類)". 事件 描述 AfterCheck 在選中節(jié)點復選框后引發(fā) AfterCollapse 在折疊一個節(jié)點后引發(fā) AfterExpand 在擴展一個節(jié)點后引發(fā) AfterSelect 在選中一個節(jié)點后引發(fā) BeforeCheck 在選中節(jié)點復選框之前引發(fā) BeforeCollapse 在折疊一個節(jié)點之前引發(fā) BeforeExpand 在擴展一個節(jié)點之前引發(fā) BeforeSelect 在選中一個節(jié)點之前引發(fā) (5).補充兩個關于論壇討論"c#怎樣動態(tài)讀取資源文件里的圖片"和"在C＃中怎么調用Resources文件中的圖片" (6).在《C#典型模塊與項目實戰(zhàn)大全》(清華大學出版社-丁士鋒)書中談到,出于對程序響應性能考慮,它先加載盤符結點,沒有使用遞歸一次性加載所有文件到樹狀列表中,代碼通過AfterSelect事件和FileSystemWatcher控件,監(jiān)聽加載.并使用線程池Task更新加載TreeView,希望大家去學習. 總結 該篇通過TreeView加載了磁盤目錄路徑,并通過ImageList加載圖標.那么怎樣實現閱讀文件夾下文件,獲取其圖標、文件大小、擴展名等信息,并雙擊打開文件呢?下一篇將接著講述.最后希望該文章對大家有所幫助,文章中很多鏈接都可以供覺得有用的同學學習,感謝上面提到的文章及書籍作者.同時如果文章中有錯誤或不足之處請原諒,有問題或建議者亦可提出.希望尊重作者勞動果實勿噴.
上傳時間： 2016-08-15
上傳用戶：baobao9437
已知系統函數為H(z)=1/[(1-0.2z^-1)(1-0.3z^-1)(1+0.4z^-1)]。試用長除法求h(n)的6點輸出。 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)
上傳時間： 2017-10-21
上傳用戶：zhouhua
簡單的實現JPEG圖像壓縮編碼方法一 clear all; RGB=imread('C:\Users\Administrator\Desktop\123.bmp');%讀取內存中bmp格式的彩色圖像 I=rgb2gray(RGB);%將彩色圖像轉換為灰度圖像 I1=im2double(I);%將圖像變換為雙精度格式 T=dctmtx(8);%處理后返回一個8*8階的DCT矩陣 B1=blkproc(I1,[8 8],'P1*x*P2',T,T');%對圖像的每個8*8子塊應用矩陣式‘P1*x*P2(像素塊的處理函數，x是形式參數)進行處理，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DCT系數重構圖像(DCT具有能量集中的性質，數據集中在左上角，故進行圖像壓縮時離散余弦變換矩陣可以舍棄右下角的高頻數據) B2=blkproc(B1,[8 8],'P1.*x',mask);%舍棄每個塊中的高頻系數，達到壓縮的目的 I2=blkproc(B2,[8 8],'P1*x*P2',T',T);%余弦反變換，重構圖像 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);%重構圖像
標簽： matlab
上傳時間： 2018-03-15
上傳用戶：wlmelody
BP神經網絡matlab源程序代碼 %% 該代碼為 BP 神經網絡的預測代碼 % 清空環(huán)境變量 clc clear % %% 網絡結構建立 % 讀取數據 load data input output input=data(;,1;2;3;4;5); output=data(;,5); %節(jié)點個數 %訓練數據和預測數據 %選連樣本輸入輸出數據歸一化 %構建網絡 net=newff(inputn,outputn,hiddennum); %網絡進化參數 %網絡訓練 % 預測數據歸一化
上傳時間： 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.
標簽： Robotics Planning modeling Control and
上傳時間： 2019-10-28
上傳用戶：xiangpingfly100
