CF 170(div2)B

本文深入探讨了AI音视频处理技术,特别是视频分割与语义识别的应用,介绍了如何利用深度学习算法实现高效准确的视频分析。
B. New Problem
time limit per test
2 seconds
memory limit per test
256 megabytes
input
standard input
output
standard output

Coming up with a new problem isn't as easy as many people think. Sometimes it is hard enough to name it. We'll consider a title original if it doesn't occur as a substring in any titles of recent Codeforces problems.

You've got the titles of n last problems — the strings, consisting of lowercase English letters. Your task is to find the shortest original title for the new problem. If there are multiple such titles, choose the lexicographically minimum one. Note, that title of the problem can't be an empty string.

A substring s[l... r] (1 ≤ l ≤ r ≤ |s|) of string s = s1s2... s|s| (where |s| is the length of string s) is string slsl + 1... sr.

String x = x1x2... xp is lexicographically smaller than string y = y1y2... yq, if either p < q and x1 = y1, x2 = y2, ... , xp = yp, or there exists such number r (r < p, r < q), that x1 = y1, x2 = y2, ... , xr = yr and xr + 1 < yr + 1. The string characters are compared by their ASCII codes.

Input

The first line contains integer n (1 ≤ n ≤ 30) — the number of titles you've got to consider. Then follow n problem titles, one per line. Each title only consists of lowercase English letters (specifically, it doesn't contain any spaces) and has the length from 1 to 20, inclusive.

Output

Print a string, consisting of lowercase English letters — the lexicographically minimum shortest original title.

Sample test(s)
Input
5
threehorses
goodsubstrings
secret
primematrix
beautifulyear
Output
j
Input
4
aa
bdefghijklmn
opqrstuvwxyz
c
Output
ab
Note

In the first sample the first 9 letters of the English alphabet (a, b, c, d, e, f, g, h, i) occur in the problem titles, so the answer is letter j.

In the second sample the titles contain 26 English letters, so the shortest original title cannot have length 1. Title aa occurs as a substring in the first title.

 
没看出来串的长度最多为2,傻b了。26^2 > 600。顺便膜拜大湿一记,随便就yy出来了d题,卧槽。。。
#include <cstdio>
#include <cstring>
#include <iostream>
#include <algorithm>
#include <vector>
#include <queue>
#include <string>
#include <map>
using namespace std;
const int maxn = 100 + 5;
const int INF = 1000000000;

string str;
map<string,int> M;

int main(){
    int n;
    while(cin >> n){
        M.clear();
        for(int k = 0;k < n;k++){
            cin >> str;
            int len = str.length();
            for(int i = 0;i < len;i++){
                for(int j = 1;i+j <= len;j++){
                    string s(str,i,j);
                    M[s] = 1;
                }
            }
        }
        char ss[3];
        int tag = 0;
        for(char i = 'a';i <= 'z';i++){
            ss[0] = i;ss[1] = '\0';
            string s(ss);
            if(M[s] == 0){
                cout << s << endl;
                tag = 1;
                break;
            }
        }
        if(tag == 1) continue;
        for(char i = 'a';i <= 'z';i++){
            ss[0] = i;
            if(tag == 1) break;
            for(char j = 'a';j <= 'z';j++){
                ss[1] = j;ss[2] = '\0';
                string s(ss);
                if(M[s] == 0){
                    cout << s << endl;
                    tag = 1;
                    break;
                }
            }
        }
    }
    return 0;
}

【无人机】基于改进粒子群算法的无人机路径规划研究[和遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性和寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度和全局寻优方面的优势。; 适合人群:具备一定Matlab编程基础和优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型和改进思路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比思路,拓展到其他智能优化算法的研究与改进中。
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