TC716D2T3

题目

Problem Statement

Treeland is a country with n cities, numbered 0 through n-1. Obviously, the topology of Treeland is a tree: there are exactly n-1 bidirectional roads, each connecting two cities in such a way that it is possible to travel from any city to any other city. You are given the description of the road network: the vector p with n-1 elements. For each valid index i, there is a road that connects the cities i+1 and p[i]. The distance between two cities is the smallest number of roads you need to use in order to travel from one city to the other. Rabbit Hanako had a trip in Treeland. She started her trip on day 0 in the city x[0] = 0. On each of the following m days she chose a city x[i] (possibly the same as her current city) and traveled there by using the only direct route. Each day she wrote down the distance she traveled - i.e., on day i she wrote down the distance d[i] between the cities x[i-1] and x[i]. After the trip was over, she constructed a set D that contained all values d[i] she wrote down. For example, if d[1], d[2], …, d[m] are {1, 0, 0, 1, 3, 5, 0} then the set D will be {0, 1, 3, 5}. You know that Hanako’s trip had the form described above, but you don’t know the value m and the values x[i]. You are given a vector < int> S. Check whether it is possible that S = D. In other words, check whether there are values m and x[1] … x[m] such that the set of distances D will contain exactly the same elements as the given vector < int> S. Return “Possible” if such a trip exists and “Impossible” if it does not.
Definition

Class:
JumpDistancesOnTreeEasy
Method:
isPossible
Parameters:
vector , vector
Returns:
string
Method signature:
string isPossible(vector p, vector S)
(be sure your method is public)
Limits

Time limit (s):
2.000
Memory limit (MB):
256
Stack limit (MB):
256
Constraints

p will contain between 1 and 50 elements, inclusive.

For each i, 0 <= p[i] <= i.

S will contain between 1 and 50 elements, inclusive.

Each element in S will be between 0 and |p|, inclusive.

For each i, S[i] < S[i+1].
Examples
0)

{0,1,1,0,4,4}
{2,4}
Returns: “Possible”
The tree is like:

     0 
   /   \
  1     4
 / \   / \
2  3  5   6

One of the possible route is: 0, 2, 3, 6, 5. The distance for each day is {2, 2, 4, 2} so S = {2, 4}.
1)

{0,1,1,0,4,4}
{1,2,3,4,5}
Returns: “Impossible”
The maximal possible distance between any two cities is 4, so you will never have 5 in D.
2)

{0,1,1,0,4,4}
{3,4}
Returns: “Impossible”

3)

{0,1,2,3,4,0,6,7,8,9}
{2,4,6,8,10}
Returns: “Impossible”

4)
{0}
{1}
Returns: “Possible”

题意:

给你一棵树,所有的边距离为1,让你从根节点出发,经过若干个节点。将每次行走的距离记录下来记为集合D(去重)。现在给出集合S,问是否存在合法的路径使得D=S。

思路:

先跑一遍Floyd得出每个点之间的距离。如果存在某个点能到达的最大距离>=S的最大值,则显然只要到达该点就能满足题意,相反的,如果从根节点出发无法到达这种点,则无解。

代码:



那你帮我写一个完整的显控7.1中GT-070-32MT-8TC型号的plc梯形图,要求完成的功能为,两个温度检测,需要可以在触摸屏上设置采样时间与加热周期,单位都为S,以及在触摸屏上设置设定温度,两个温控的设定温度分开设置,还要设置允许启动的加热温差,单位都为摄氏度,当实际温度与检测温度的差值在加热温差范围内,机器允许启动,要求使用自己计算的PID算法进行温度检测,使用合理的比例系数、积分系数和微分系数,机器启动后电机开始转动,电机与调速器相连,由调速器控制电机开机与关机以及速度,另外还有两个电磁阀,一个电磁阀为双孔,控制两个刀架的起落,要求两个刀架起落需同步,一个单孔电磁阀控制压带,另外还有一个外界计数器,计数器为电机形式,正转正向计数,反转负向计数,这可能为计数器自带功能,计数器计数值需要在触摸屏上显示出来,另外在触摸屏上可以设置目标计数值,达到计数值后机器停止,再加入清零按钮,点击后计数清零,再加入寸动功能以及寸动功能的开关,寸动功能开启后,启动机器时刀架下落一次后电机转动,刀架抬起后电机停止转动,再次下落再次转动,压带并不参与寸动,寸动功能关闭后,启动机器时刀架一直下落,电机一直转动,另外还有脚踏开关,与启动按钮功能相同,脚踏压下时即为启动,脚踏松开即为停止,触摸屏上还需有加热开启按钮,刀架开启按钮,(两个刀架一个开启按钮),以及压带开启按钮,以及寸动功能开启按钮,另外温控功能方面,加入恢复出厂设置按钮,按下后,采样时间恢复初始值1.5s、加热周期恢复初始值1.5s、设定温度恢复初始值120摄氏度,加热温差恢复初始值10摄氏度,外界部件有一个plc和触摸屏一体机,一个电源,一个继电器,一个电机与调速器,两个温控与两个发热管,一个双孔电磁阀,一个单孔电磁阀,梯形图要连贯,,不用分主程序与子程序,全写在主程序即可。
08-05
资源下载链接为: https://pan.quark.cn/s/d37d4dbee12c A:计算机视觉,作为人工智能领域的关键分支,致力于赋予计算机系统 “看懂” 世界的能力,从图像、视频等视觉数据中提取有用信息并据此决策。 其发展历程颇为漫长。早期图像处理技术为其奠基,后续逐步探索三维信息提取,与人工智能结合,又经历数学理论深化、机器学习兴起,直至当下深度学习引领浪潮。如今,图像生成和合成技术不断发展,让计算机视觉更深入人们的日常生活。 计算机视觉综合了图像处理、机器学习、模式识别和深度学习等技术。深度学习兴起后,卷积神经网络成为核心工具,能自动提炼复杂图像特征。它的工作流程,首先是图像获取,用相机等设备捕获视觉信息并数字化;接着进行预处理,通过滤波、去噪等操作提升图像质量;然后进入关键的特征提取和描述环节,提炼图像关键信息;之后利用这些信息训练模型,学习视觉模式和规律;最终用于模式识别、分类、对象检测等实际应用。 在实际应用中,计算机视觉用途极为广泛。在安防领域,能进行人脸识别、目标跟踪,保障公共安全;在自动驾驶领域,帮助车辆识别道路、行人、交通标志,实现安全行驶;在医疗领域,辅助医生分析医学影像,进行疾病诊断;在工业领域,用于产品质量检测、机器人操作引导等。 不过,计算机视觉发展也面临挑战。比如图像生成技术带来深度伪造风险,虚假图像和视频可能误导大众、扰乱秩序。为此,各界积极研究检测技术,以应对这一问题。随着技术持续进步,计算机视觉有望在更多领域发挥更大作用,进一步改变人们的生活和工作方式 。
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