This paper addresses the problem of Age-of-Information (AoI) in UAV-assisted
networks. Our objective is to minimize the expected AoI across devices by
optimizing UAVs' stopping locations and device selection probabilities. To
tackle this problem, we first derive a closed-form expression of the expected
AoI that involves the probabilities of selection of devices. Then, we formulate
the problem as a non-convex minimization subject to quality of service
constraints. Since the problem is challenging to solve, we propose an Ensemble
Deep Neural Network (EDNN) based approach which takes advantage of the dual
formulation of the studied problem. Specifically, the Deep Neural Networks
(DNNs) in the ensemble are trained in an unsupervised manner using the
Lagrangian function of the studied problem. Our experiments show that the
proposed EDNN method outperforms traditional DNNs in reducing the expected AoI,
achieving a remarkable reduction of $29.5\%$.

该论文研究了在无人机辅助网络中的信息时代（Age-of-Information，AoI）问题，并通过优化无人机的停止位置和设备选择概率来最小化设备之间的平均 AoI。为了解决这个问题，论文首先推导了一个包括设备选择概率的平均 AoI 的闭合形式表达式。然后，将该问题建模为一个带有服务质量约束的非凸最小化问题。由于该问题难以求解，论文提出了一种基于集成深度神经网络（Ensemble Deep Neural Network，EDNN）的方法，该方法利用了所研究问题的双重形式化。具体而言，集成中的深度神经网络（Deep Neural Networks，DNNs）通过使用所研究问题的 Lagrangian 函数进行无监督方式的训练。实验结果表明，所提出的 EDNN 方法在减少平均 AoI 方面优于传统的 DNN 方法，实现了 29.5% 的显著降低。

使用集成深度神经网络在无人机辅助网络中最小化信息年龄

Ensemble DNN for Age-of-Information Minimization in UAV-assisted  Networks

This paper reports the methods and techniques we have developed for classify
dermoscopic images (task 1) of the ISIC 2019 challenge dataset for skin lesion
classification, our approach aims to use ensemble deep neural network with some
powerful techniques to deal with unbalance data sets as its the main problem
for this challenge in a move to increase the performance of CNNs model.

本文介绍了我们用于分类 ISIC 2019 挑战数据集皮损 dermoscopic 照片的方法和技术，我们的方法旨在使用合奏深度神经网络及其一些强大的技术来处理不平衡数据集，以提高 CNN 模型性能。