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一個半監督具有動態關聯域適應遷移學習 使用WiFi訊號的人類活動識別

為了解決Toyer City的問題，作者李淳宇 這樣論述：

Human activity recognition without equipment plays a vital role in smart home applications, freeing humans from the shackles of wearable devices. In this paper, by using the channel state information (CSI) of the WiFi signal, a semi-supervised transfer learning with dynamic associate domain adaptat

ion is proposed for human activity recognition. In order to improve the CSI quality and denoising of CSI, we carried out missing packet filling, burst noise removal, Background estimation, feature extraction, feature enhancement, and data augmentation in the data preprocessing stage. This paper cons

iders the problem of environment-independent human activity recognition, also known as domain adaptation. The pre-trained model is trained from the source domain by collecting a complete labeled data set of all CSI of human activity patterns. Then the pre-trained model is transferred to the target e

nvironment through the semi-supervised transfer learning stage. Therefore, when humans move to different target domains, partial labeled data set of the target domain are required for fine-tuning. In this paper, we propose a dynamic associate domain adaptation called DADA. By modifying the existing

associate domain adaptation algorithm, the target domain can provide a dynamic ratio of labeled data set/unlabeled data set, while the existing associate domain adaptation algorithm only allow target domains with the unlabeled data set. The advantage of DADA is that it provides a dynamic strategy to

eliminate different effects on different environments. In addition, we further designed an attention-based DenseNet model, or AD, as our training network, which is modified by existed DenseNet by adding the attention function. The solution we proposed was simplified to DADA-AD throughout the paper.

The experimental results show that for domain adaptation in different domains, the accuracy of human activity recognition of the DADA-AD scheme is 97.4%. It also shows that DADA-AD has advantages over existing semi-supervised learning schemes.

優化通訊和計算資源分配的網路切片以降低延遲使用孿生生成對抗網路與深度強化學習於5G混合式雲端接取網絡

為了解決Toyer City的問題，作者洪湘晴 這樣論述：

1 Introduction..................................... 11.1 Introduction..................................... 12 Related Work..................................... 62.1 Related works.................................... 62.2 Motivation...................................... 73 Preliminaries..............

....................... 83.1 System model.................................... 83.2 Problem formulation................................ 93.3 Basic idea...................................... 134 Twin-GAN-based DRL for communication and computational resource allocations in network slicing ............

.......................... 164.1 Services clustering phase........................ 184.2 The bandwidth allocation of Twin-GAN-based DRL phase.......................... 204.3 The VNF split of Twin-GAN-based DRL phase............................ 294.4 Optimization of bandwidth allocation and VNF split

............................ 355 Experimental Results............................ 395.1 Total delay................................. 415.2 Spectrum Efficiency.................................. 425.3 Computational Rate.................................. 455.4 System Utility............................

..... 466 Conclusion................................. 48