Feature extraction plays an important role in constructing artificial intel-ligence(AI)models of industrial control systems(ICSs).Three challenges in this field are learning effective representation from high-dimensio...Feature extraction plays an important role in constructing artificial intel-ligence(AI)models of industrial control systems(ICSs).Three challenges in this field are learning effective representation from high-dimensional features,data heterogeneity,and data noise due to the diversity of data dimensions,formats and noise of sensors,controllers and actuators.Hence,a novel unsupervised learn-ing autoencoder model is proposed for ICS data in this paper.Although traditional methods only capture the linear correlations of ICS features,our deep industrial representation learning model(DIRL)based on a convolutional neural network can mine high-order features,thus solving the problem of high-dimensional and heterogeneous ICS data.In addition,an unsupervised denoising autoencoder is introduced for noisy ICS data in DIRL.Training the denoising autoencoder allows the model to better mitigate the sensor noise problem.In this way,the represen-tative features learned by DIRL could help to evaluate the safety state of ICSs more effectively.We tested our model with absolute and relative accuracy experi-ments on two large-scale ICS datasets.Compared with other popular methods,DIRL showed advantages in four common indicators of AI algorithms:accuracy,precision,recall,and F1-score.This study contributes to the effective analysis of large-scale ICS data,which promotes the stable operation of ICSs.展开更多
In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network(DNN) to learn the Q-function of reinforcem...In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network(DNN) to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output. Based on the obtained DNN,we can find the appropriate signal timing policies by implicitly modeling the control actions and the change of system states.We explain the possible benefits and implementation tricks of this new approach. The relationships between this new approach and some existing approaches are also carefully discussed.展开更多
基金This study is supported by The National Key Research and Development Program of China:“Key measurement and control equipment with built-in information security functions”(Grant No.2018YFB2004200)Independent Subject of State Key Laboratory of Robotics“Research on security industry network construction technology for 5G communication”(No.2022-Z13).
文摘Feature extraction plays an important role in constructing artificial intel-ligence(AI)models of industrial control systems(ICSs).Three challenges in this field are learning effective representation from high-dimensional features,data heterogeneity,and data noise due to the diversity of data dimensions,formats and noise of sensors,controllers and actuators.Hence,a novel unsupervised learn-ing autoencoder model is proposed for ICS data in this paper.Although traditional methods only capture the linear correlations of ICS features,our deep industrial representation learning model(DIRL)based on a convolutional neural network can mine high-order features,thus solving the problem of high-dimensional and heterogeneous ICS data.In addition,an unsupervised denoising autoencoder is introduced for noisy ICS data in DIRL.Training the denoising autoencoder allows the model to better mitigate the sensor noise problem.In this way,the represen-tative features learned by DIRL could help to evaluate the safety state of ICSs more effectively.We tested our model with absolute and relative accuracy experi-ments on two large-scale ICS datasets.Compared with other popular methods,DIRL showed advantages in four common indicators of AI algorithms:accuracy,precision,recall,and F1-score.This study contributes to the effective analysis of large-scale ICS data,which promotes the stable operation of ICSs.
文摘针对低干噪比条件下灵巧干扰识别准确率不高与干扰特征难以积累导致的网络需要重新训练问题,本文将信号的平滑伪Wigner⁃Ville分布的时频二维图像作为输入,提出了一种基于动态权重的知识积累(Dy⁃namic Weighted Knowledge Accumulation method based on Convolutional Neural Network,DWKA⁃CNN)灵巧干扰识别方法,利用通道特征注意力机制,提升了低干噪比下模型的干扰识别能力,通过均值最近邻分层屏蔽网络权重,实现了在单一网络中的知识积累,与当前典型基于深度学习的灵巧干扰识别方法相比,无需每次重新训练即可学习多项干扰识别任务。并且仿真实验表明,与现有典型算法相比,该算法模型在7种雷达灵巧干扰分类数据集上的平均识别准确率显著提升,在低干噪比条件下分类性能优秀。
基金supported by National Natural Science Foundation of China(6153301971232006,61233001)
文摘In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network(DNN) to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output. Based on the obtained DNN,we can find the appropriate signal timing policies by implicitly modeling the control actions and the change of system states.We explain the possible benefits and implementation tricks of this new approach. The relationships between this new approach and some existing approaches are also carefully discussed.
