This text surrounds the development of the electric power SCADA system exactly, aiming at the present condition of the our country electric power charged barbed wire net currently, according to the oneself at the e- lectric power protect the profession after the electricity in seven years of development, design and adjust to try the experience on the scene from following severals carry on the treatise:Is the emergence to the system of SC- ADA and developments to introduce first Carry on the introduction elucidation to applied present condition and the development foregrounds of various terminal equipments communication agreement(rules invite) the next in order Then is the elucidation to the windows the bottom according to the mfc the plait distance environment an- d VC++6.0 plait distance softwares Carry on the more detailed treatise to the realization of the procedure struct- ure frame and the source code again End is the applied case example give examples.
標簽： the development surrounds electric
上傳時間： 2014-10-28
上傳用戶：liuchee
Implemented BFS, DFS and A* To compile this project, use the following command: g++ -o search main.cpp Then you can run it: ./search The input is loaded from a input file in.txt Here is the format of the input file: The first line of the input file shoud contain two chars indicate the source and destination city for breadth first and depth first algorithm. The second line of input file shoud be an integer m indicate the number of connections for the map. Following m lines describe the map, each line represents to one connection in this form: dist city1 city2, which means there is a connection between city1 and city2 with the distance dist. The following input are for A* The following line contains two chars indicate the source and destination city for A* algorithm. Then there is an integer h indicate the number of heuristic. The following h lines is in the form: city dist which means the straight-line distance from the city to B is dist.
標簽： Implemented following compile command
上傳時間： 2014-01-01
上傳用戶：lhc9102
this m file can Find a (near) optimal solution to the Traveling Salesman Problem (TSP) by setting up a Genetic Algorithm (GA) to search for the shortest path (least distance needed to travel to each city exactly once) Notes: 1. Input error checking included 2. Inputs can be specified in any order, so long as the parameter pairs are specified as a parameter , value
標簽： Traveling Salesman solution Problem
上傳時間： 2013-12-22
上傳用戶：ruixue198909
A system simulation environment in Matlab/Simulink of RFID is constructed in this paper. Special attention is emphasized on the analog/RF circuit.Negative effects are concerned in the system model,such as phase noise of the local oscillator,TX-RX coupling,reflection of the environment, AWGN noise,DC offset,I/Q mismatch,etc.Performance of the whole system can be evaluated by changing the coding method,parameters of building blocks,and operation distance.Finally,some simulation results are presented in this paper.
標簽： environment constructed simulation Simulink
上傳時間： 2014-01-09
上傳用戶：zhangliming420
Finds a (near) optimal solution to the Traveling Salesman Problem (TSP) by setting up a Genetic Algorithm (GA) to search for the shortest path (least distance needed to travel to each city exactly once)
標簽： Traveling Salesman solution Problem
上傳時間： 2013-12-04
上傳用戶：從此走出陰霾
Dijkstra算法求最短路徑(C#版) using System using System.Collections using System.Text namespace Greedy { class Marx { private int[] distance private int row private ArrayList ways = new ArrayList() public Marx(int n,params int[] d) { this.row = n distance = new int[row * row] for (int i = 0 i < row * row i++) { this.distance[i] = d[i]
標簽： System using Collections namespace
上傳時間： 2013-12-29
上傳用戶：liglechongchong
Traveling Salesman Problem (TSP) has been an interesting problem for a long time in classical optimization techniques which are based on linear and nonlinear programming. TSP can be described as follows: Given a number of cities to visit and their distances from all other cities know, an optimal travel route has to be found so that each city is visited one and only once with the least possible distance traveled. This is a simple problem with handful of cities but becomes complicated as the number increases.
標簽： interesting Traveling classical Salesman
上傳時間： 2016-02-06
上傳用戶：rocwangdp
How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
標簽： the decision clusters Cluster
上傳時間： 2013-12-21
上傳用戶：gxmm
壓縮解壓算法LZ77算法有許多派生算法(這里面包括 LZSS算法)。它們的算法原理上基本都相同，無論是哪種派生算法，LZ77算法總會包含一個動態窗口（Sliding Window）和一個預讀緩沖器（Read Ahead Buffer）。動態窗口是個歷史緩沖器，它被用來存放輸入流的前n個字節的有關信息。一個動態窗口的數據范圍可以從 0K 到 64K，而LZSS算法使用了一個4K的動態窗口。預讀緩沖器是與動態窗口相對應的，它被用來存放輸入流的前n個字節，預讀緩沖器的大小通常在0 – 258 之間。這個算法就是基于這些建立的。用下n個字節填充預讀緩存器（這里的n是預讀緩存器的大?。?。在動態窗口中尋找與預讀緩沖器中的最匹配的數據，如果匹配的數據長度大于最小匹配長度 （通常取決于編碼器，以及動態窗口的大小，比如一個4K的動態窗口，它的最小匹配長度就是2），那么就輸出一對〈長度（length），距離（distance）〉數組。長度(length)是匹配的數據長度，而距離(distance)說明了在輸入流中向后多少字節這個匹配數據可以被找到。
上傳時間： 2014-01-22
上傳用戶：tzl1975
Basic hack v2.1 by xgx - http://www.ring0.donster.de/ Features: - Smooth Vector Aimbot - Full ESP ( Namen,Weapon,distance,Visible,Far) - polymorph,peb hiding to prevent VAC detection
標簽： Features donster Aimbot Smooth
上傳時間： 2013-12-18
上傳用戶：agent
