Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven method has been developed to predict RUL due to its ability to deal with abundant complex data. In this paper, a novel scheme based on a health indicator (HI) and a hybrid deep neural network (DNN) model is proposed to predict RUL by analyzing equipment degradation. Explicitly, HI obtained by polynomial regression is combined with a convolutional neural network (CNN) and long short-term memory (LSTM) neural network to extract spatial and temporal features for efficacious prognostics. More specifically, valid data selected from the raw sensor data are transformed into a one-dimensional HI at first. Next, both the preselected data and HI are sequentially fed into the CNN layer and LSTM layer in order to extract high-level spatial features and long-term temporal dependency features. Furthermore, a fully connected neural network is employed to achieve a regression model of RUL prognostics. Lastly, validated with the aid of numerical and graphic results by an equipment RUL dataset from the Commercial Modular Aero-Propulsion System Simulation(C-MAPSS), the proposed scheme turns out to be superior to four existing models regarding accuracy and effectiveness.

Precise and reliable forecasting of short-term electricity load is essential to the development of smart grids. Particularly, deep neural networks (DNNs) are widely utilized for the prediction of shortterm electricity load due to their automatic feature extraction ability. However, these available stacked deeplearning models may lose some temporal features or spatial features of original input data. To capture more comprehensive information, in this paper, we present an integration scheme based on empirical mode decomposition (EMD), similar day methods, and DNNs to perform short-term load forecasting. It is especially worth noting that the electricity price is also an important factor for load variation, which is considered in our proposed scheme. Specifically, there are two primary layers: a feature extraction layer and a forecasting layer. In the feature extraction layer, EMD is applied to decompose load time series into several components, which are arranged into the 2-D input matrix of the convolutional neural network (CNN). Both the output vectors of the CNN and the raw load sequences are fed into the long short-term memory (LSTM) layer. Therefore, the whole EMD based CNN-LSTM approach extracts multimodal spatial-temporal features from input data. Meanwhile, the electricity price data is utilized to obtain multimodal spatial-temporal features in the same way. Additionally, the day and hour information and loads of similar days are to augment extra features for prediction. In the forecasting layer, the forecasting task is accomplished through a fully-connected neural network based on the outputs of the feature extraction layer. Leveraging these techniques enables our proposed scheme to extract more latent features, which significantly improve the accuracy. In order to demonstrate the performance of our proposed scheme, related experiments are conducted on actual data from the electricity market in Singapore. Compared to other available models, our proposed scheme is superior in graphic and numerical results.

This paper proposes a novel network anomaly detection framework based on data balance and feature selection. Different from the previous binary classification of network intrusion, the network anomaly detection strategy proposed in this paper solves the problem of multiple classification of network intrusion. Regarding the common data imbalance of a network intrusion detection set, a resampling strategy generated by random sampling and Borderline SMOTE data is developed for data balance. According to the features of the intrusion detection dataset, feature selection is carried out based on information gain rate. Experiments are carried out on three basic machine learning algorithms (K-nearest neighbor algorithm (KNN), decision tree (DT), random forest (RF)), and the optimal feature selection scheme is obtained.

In this paper, we investigate the physical layer security of a full-duplex base station (BS) aided system in the worst case, where an uplink transmitter (UT) and a downlink receiver (DR) are both equipped with a single antenna, while a powerful eavesdropper is equipped with multiple antennas. For securing the confidentiality of signals transmitted from the BS and UT, an artificial noise (AN) aided secrecy beamforming scheme is proposed, which is robust to the realistic imperfect state information of both the eavesdropping channel and the residual self-interference channel. Our objective function is that of maximizing the worst-case sum secrecy rate achieved by the BS and UT, through jointly optimizing the beamforming vector of the confidential signals and the transmit covariance matrix of the AN. However, the resultant optimization problem is non-convex and non-linear. In order to efficiently obtain the solution, we transform the non-convex problem into a sequence of convex problems by adopting the block coordinate descent algorithm. We invoke a linear matrix inequality for finding its Karush-Kuhn-Tucker (KKT) solution. In order to evaluate the achievable performance, the worst-case secrecy rate is derived analytically. Furthermore, we construct another secrecy transmission scheme using the projection matrix theory for performance comparison. Our simulation results show that the proposed robust secrecy transmission scheme achieves substantial secrecy performance gains, which verifies the efficiency of the proposed method.

In this paper, a novel short-term load forecasting method amalgamated with quantile regression random forest is proposed. Comprised with point forecasting, it is capable of quantifying the uncertainty of power load. Firstly, a bespoke 2D data preprocessing taking advantage of empirical mode decomposition (EMD) is presented. It can effectively assist subsequent point forecasting models to extract spatial features hidden in the 2D load matrix. Secondly, by exploiting multimodal deep neural networks (DNN), three short-term load point forecasting models are conceived. Furthermore, a tailor-made multimodal spatial–temporal feature extraction is proposed, which integrates spatial features, time information, load, and electricity price to obtain more covert features. Thirdly, relying on quantile regression random forest, the probabilistic forecasting method is proposed, which exploits the results from the above three short-term load point forecasting models. Lastly, the experimental results demonstrate that the proposed method outperforms its conventional counterparts.