Recently, public attention is thoroughly aroused as to the security threats of Wireless Network Control System (WNCS), which can seriously disrupt the system operation. In order to achieve the attack effect that each ...
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Recently, public attention is thoroughly aroused as to the security threats of Wireless Network Control System (WNCS), which can seriously disrupt the system operation. In order to achieve the attack effect that each sensor is damaged and maximize the terminal estimation error covariance, it is necessary to study an attack system from the attacker's perspective. In this paper, we establish an attack system, which includes: the multi-sensor importance evaluation model, the time allocation of jamming attack, and the attack rules. Specifically, we firstly establish the wireless network control system model and the jamming attack model. Then, according to the transmission data and channel parameter information which is intercepted by the attackers, we establish an evaluation model of sensor based on the meanimpactvalue (MIV) algorithm. Then, based on the evaluation results of each sensor, we establish a distribution model of the number of attacks on each sensor. Then, we perform two jamming attack rules(continuous attack rule and good-sensor-late-attack rule)to attack each sensor. Finally, we use the attack system to conduct digital simulation experiments in first-order and high-order system. There is no different between the MIV-based sensor evaluation method in the multi-sensor importance evaluation experiment and sensor performance evaluation based on estimation error. In the jamming attack time allocation experiment, effect that every sensor was attacked had been achieved. In the attack rule experiment, we compare the experimental results of "continuous attack" and " discontinuous attack", and the result shows that the effect of "continuous attack" is better than that of "intermittent attack". Similarly, we have conducted comparative experiments on all attack strategies, and the results show that " good-sensor-late-attack " strategy has the best effect. The effectiveness of the attack system is proved by digital simulation experiment.
Accurate prediction of steam coal prices is important for stabilizing the coal trading market and formulating coal use strategies scientifically. In this paper, a new decomposition integration model (VADM) is proposed...
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Accurate prediction of steam coal prices is important for stabilizing the coal trading market and formulating coal use strategies scientifically. In this paper, a new decomposition integration model (VADM) is proposed to predict coal prices by combining the variational modal decomposition (VMD), arithmetic optimization algorithm (AOA), deep temporal convolutional network (DeepTCN), and mean impact value algorithm (MIV). Firstly, the AOA optimization algorithm is used to improve the VMD, AOA-VMD was obtained. It is used to decompose the steam coal price series. Then, the decomposed subsequences are predicted for the prediction of steam coal prices by using DeepTCN. Finally, the MIV algorithm is applied to analyze the impact of different factors on the price of steam coal. It is found that: the steam coal price sub-series decomposed by AOA-VMD are smoother and more linear compared with the original series;the errors in forecasting steam coal prices are significantly reduced after considering newly proposed factors, interest rates, such as the overnight Shanghai interbank offered rate and the six-month treasury bond yield;the MAPE, MASE and SMAPE of the VADM model all show different degrees of decline compared with benchmark models. The forecasting effect of VADM model is better than the benchmark model in terms of stability and accuracy, and can be used for short-term forecasting of coal prices.
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