Based on quantile regression (QR) and kernel density estimation (KDE), a framework for probability density forecasting of short-term wind speed is proposed in this study. The empirical mode decomposition (EMD) technique is implemented to reduce the noise of raw wind speed series. Both linear QR (LQR) and nonlinear QR (NQR, including quantile regression neural network (QRNN), quantile regression random forest (QRRF), and quantile regression support vector machine (QRSVM)) models are, respectively, utilized to study the de-noised wind speed series. An ensemble of conditional quantiles is obtained and then used for point and interval predictions of wind speed accordingly. After various experiments and comparisons on the real wind speed data at four wind observation stations of China, it is found that the EMD-LQR-KDE and EMD-QRNN-KDE generally have the best performance and robustness in both point and interval predictions. By taking conditional quantiles obtained by the EMD-QRNN-KDE model as the input, probability density functions (PDFs) of wind speed at different times are obtained by the KDE method, whose bandwidth is optimally determined according to the normal reference criterion. It is found that most actual wind speeds lie near the peak of predicted PDF curves, indicating that the probabilistic density prediction by EMD-QRNN-KDE is believable. Compared with the PDF curves of the 90% confidence level, the PDF curves of the 80% confidence level usually have narrower wind speed ranges and higher peak values. The PDF curves also vary with time. At some times, they might be biased, bimodal, or even multi-modal distributions. Based on the EMD-QRNN-KDE model, one can not only obtain the specific PDF curves of future wind speeds, but also understand the dynamic variation of density distributions with time. Compared with the traditional point and interval prediction models, the proposed QR-KDE models could acquire more information about the randomness and uncertainty of the actual wind speed, and thus provide more powerful support for the decision-making work.

In the field of covert data integrity (DI) attacks, considerable attention has focused on two important issues. One is the issue of how to change the state of a plant, and the other is how to avoid being detected by anomaly detectors. A two-loop covert attack is presented to provide an integrated solution for these two issues. As an exploratory attempt to establish the feasibility of machine learning based covert attacks, it applies the least squares support vector machine (LSSVM) to constructing covert attacks. The proposed attack consists of an attack loop and a covert loop, which are based on an attack agent and a covert agent respectively. The attack agent can move the steady state of a target plant to a desired state, and the covert agent can closely imitate the normal steady state of the plant to cover up the attack agent. In particular, the attack is directed to proportional-integral-derivative (PID) algorithms. Experiments are carried out to demonstrate the feasibility of the proposed attack and show the applicability of machine learning methods in constructing covert attacks.

The automation and intelligence of industrial manufacturing is the core of the fourth industrial revolution, and robotic arms and proprietary networked information systems are an integral part of this vision. However, with the benefits come risks that have been overlooked, and robotic arms have become a heavily attacked area. In order to improve the security of the robotic arm system, this paper proposes an intrusion detection method based on a state classification model. The closure operation process of the robotic arm is divided into five consecutive states, while a support vector machine based on the particle swarm optimization algorithm (PSO-H-SVM) classifies the operation state of the robotic arm. In the detection process, the classifier predicts the operation state of the robotic arm in real time, and the detection method determines whether the state transfer meets the logical requirements, and then determines whether the intrusion occurs. In addition, a response mechanism is proposed on the basis of the intrusion detection system to make protection measures for the robotic arm system. Finally, a physical experiment platform was built to test the intrusion detection method. The results showed that the classification accuracy of the PSO-H-SVM algorithm reached 96.02%, and the detection accuracy of the intrusion detection method reached 90%, which verified the effectiveness and reliability of the intrusion detection method.