计算机科学
特征工程
人工智能
特征(语言学)
直方图
构造(python库)
特征模型
组分(热力学)
模式识别(心理学)
数据挖掘
深度学习
核(代数)
机器学习
图像(数学)
组合数学
物理
哲学
热力学
程序设计语言
软件
语言学
数学
作者
Yang Zhou,Zhoujia Yang,Qiang Sun,Chengqing Yu,Chengming Yu
出处
期刊:Optik
[Elsevier]
日期:2023-02-01
卷期号:273: 170443-170443
被引量:1
标识
DOI:10.1016/j.ijleo.2022.170443
摘要
Optical network performance monitoring technology, which can effectively identify physical impairment in the network, is of great significance to ensure network stability and prevent accidents. In order to establish a high-precision performance monitoring framework for optical networks, a three-step feature engineering and deep attention mechanism approach is proposed in the paper. The main modeling process consists of the following three steps: In Step I, the AAH (Asynchronous Amplitude Histogram) method is used to initially extract features from the original data. In Step II, the KPCA (Kernel Principal Component Analysis) method and the Q-learning method are utilized to reduce the feature dimension and transmit the optimized feature to the downstream predictor. In step III, the downstream predictor based on the LSTM (long short-term memory network) and attention mechanism can effectively construct the mapping between features and labels and realize data prediction. After several groups of comparative experiments, the following conclusions can be obtained: (a) Ablation experiments and component comparisons demonstrate that the proposed framework is able to effectively combine feature engineering and predictors, which can get excellent results. (b) The optical network performance detection framework proposed in the paper achieves better results than three SOTA (state-of-the-art) models and fifteen alternative frameworks.
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