Let X and Y be jointly continuous random varibles with joint density( ) , the conditional probability that X lies between 0 and (1/2) givenX<Y .
时间: 2023-04-01 14:01:21 浏览: 101
根据题意,需要求解条件概率 P(X<1/2|X<Y)。可以使用全概率公式和条件概率公式来求解:
P(X<1/2|X<Y) = P(X<1/2,X<Y) / P(X<Y)
由于 X 和 Y 是联合连续随机变量,可以使用二重积分来计算概率:
P(X<1/2,X<Y) = ∫[,1/2]∫[x,1] f(x,y) dy dx
P(X<Y) = ∫[,1]∫[,y] f(x,y) dx dy
将上述积分带入公式中,即可求解条件概率。
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Minimizing Mission Completion Time of UA Vs by Jointly Optimizing the Flight and Data Collection Trajectory in UA V -Enabled WSNs
您的问题是关于无人机在无线传感器网络中的任务完成时间最小化的问题,需要同时优化飞行和数据收集轨迹。这是一个比较复杂的问题,需要考虑无人机的飞行能力、传感器节点的分布、数据采集的质量要求等多个因素。目前有一些研究正在进行,主要采用优化算法来解决此类问题,如遗传算法、粒子群算法等。同时,也有一些基于机器学习的方法,如强化学习,可以用来优化无人机的飞行轨迹和数据采集策略。
As shown in Figure 1, the proposed network contains four strided convolutional layers and four strided deconvo- lutional layers. The Leaky Rectified Linear Unit (LReLU) with a negative slope of 0.2 is used after each convolutional and deconvolutional layer. The residual group [38] consists of three residual blocks, and 18 residual blocks are used in GRes. The filter size is set as 11×11 pixels in the first convo- lutional layer in the encoder module and 3 × 3 in all the other convolutional and deconvolutional layers. We jointly train the MSBDN and DFF module and use the Mean Squared Error (MSE) as the loss function to constrain the network output and ground truth. The entire training process con- tains 100 epochs optimized by the ADAM solver [28 ] with β1 = 0.9 and β2 = 0.999 with a batch size of 16. The initial learning rate is set as 10−4 with a decay rate of 0.75 after every 10 epochs. All the experiments are conducted on an NVIDIA 2080Ti GPU. The source code and trained models are availabe at https://github.com/BookerDeWitt/MSBDN- DFF 翻译
如图1所示,所提出的网络包含四个步幅卷积层和四个步幅反卷积层。在每个卷积和反卷积层之后使用LReLU(带有负斜率0.2的泄露整流线性单元)。残差组[38]包含三个残差块,GRes中使用18个残差块。在编码器模块的第一个卷积层中,滤波器大小设置为11×11像素,所有其他卷积和反卷积层的滤波器大小设置为3×3像素。我们联合训练MSBDN和DFF模块,并使用均方误差(MSE)作为损失函数,以约束网络输出和真实值之间的差距。整个训练过程包含100个时期,使用批大小为16的ADAM优化器[28 ]进行优化。初始学习率设置为10^-4,每10个时期衰减率为0.75。所有实验都在NVIDIA 2080Ti GPU上进行。源代码和训练模型可在https://github.com/BookerDeWitt/MSBDN-DFF上获得。
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