Self-similar growth and fractality are important properties found in many real-world networks,
which could guide the modeling of network evolution and the anticipation of new links …

Modern power grids are faced with a series of challenges, such as the ever-increasing
demand for renewable energy sources, extensive urbanization, climate and energy crisis …

A time series can often be characterized using machine learning techniques, which require
feature vectors as input. The quality of the feature vectors reflects the accuracy of the utilized …

Nowadays, modern man-made infrastructures are being upgraded with information and
communication technologies that form large wireless networks. Such large wireless …

Network softwarization, which shifts hardware-centric functions to software implementations,
is essential for enhancing the agility of cellular and non-cellular wireless networks. This …

Convolutional neural networks (CNNs) are often favored for their strong learning abilities in
tackling automatic intelligent models. The classification of time series data streams spans …

可视图是将时间序列转换成复杂网络的重要方法之一, 也是连接非线性信号分析和复杂网络之间
的全新视角, 在经济金融, 生物医学, 工业工程等领域均应用广泛. 可视图的拓扑结构继承了原始 …

CMI-Net: a Unified Framework for Physiological Time Series Classification with Incomplete
Modalities Page 1 P osted on 7 Sep 2020 — CC-BY 4.0 — h ttps://doi.org/10.36227/tech rxiv.24099339.v1 …

In today's world, modern infrastructures are being equipped with information and
communication technologies to create large IoT networks. It is essential to monitor these …

To achieve progressive and accurate decision-making for long-term time series data while
meeting the needs of privacy-friendly and early, this paper proposes a universal framework …