This paper addresses the problem of a low-speed missile intercepting a hypersonic vehicle in the longitudinal plane. Firstly, based on the concept of the zero of the angular rate of the line-of-sight (LOS) angle, the guidance system is established by defining the LOS angular rate as the state variable. Secondly, in view of the difficulty of precisely measuring the external disturbance caused by the hypersonic vehicle’s maneuver in the guidance system, a non-homogeneous disturbance observer is designed to precisely estimate the disturbance information. Then, by introducing the fractional-order operator into the sliding surface, a fractional-order fast power reaching (FOFPR) guidance law is proposed based on the fast power reaching law. Simulation examples are carried out in two different maneuver modes of the hypersonic vehicle: the bang-bang maneuver mode and sinusoidal maneuver mode. Besides, comparative experiments are conducted with the proportional navigation (PN) and the integer-order fast power reaching (IOFPR) guidance laws. Finally, the simulation results demonstrate the superiority of the effectiveness of the proposed guidance law.
This article is mainly to study the realization of travel recommendations for different users through deep learning under global information management. The personalized travel route recommendation is realized by establishing personalized travel dynamic interest (PTDR) algorithm and distributed lock manager (DLM) model. It is hoped that this model can provide more complete data information of tourist destinations on the basis of the past, and can also meet the needs of users. The innovation of this article is to compare and analyze with a large number of baseline algorithms, highlighting the superiority of this model in personalized travel recommendation. In addition, the model incorporates the topic factor features, geographic factor features, and user preference features to make the data more in line with user needs and improve the efficiency and applicability of the model. It is hoped that the plan proposed in this article can help users make choices of tourist destinations more conveniently.
Carbon emission reduction has become a common hot topic around the world. Although the previous literature has proven that the asymmetric information and fairness concerns would influence the operational strategy for low-carbon supply chain, it hardly touched the asymmetric information of fairness concerns, which contradicted practical observations and experimental evidence. Incorporating the asymmetric information of fairness concerns, this paper investigates a low-carbon supply chain consisting of a manufacturer and a retailer with discrete types including selfish S-type and fairness-concerned F-type. The manufacturer can observe and thereby know the behavioral type of the retailer in the scenario of symmetric information, while it cannot in the scenario of asymmetric information. In the approach of game theory, the optimal carbon emission reducing strategy and pricing strategy in the symmetric scenario and asymmetric scenario are achieved successively. By comparing the above two scenarios, the impacts stemming from the asymmetric information of fairness concerns at the individual level and systematic level are analyzed, respectively. A case study is offered before concluding some implications for the supply chain management. The findings include the following: Firstly, the asymmetric information of fairness concerns enhances the carbon emission reduction significantly. Although the fairness concerns alone decrease the carbon emission reduction, the asymmetric information increases with the dominating power. Secondly, the asymmetric information of fairness concerns raises the wholesale price and retail price dramatically. Although the impact of either fairness concerns or asymmetric information randomly changes with the behavioral type and information structure, their interactive impacts are stable and change smoothly. Thirdly, the asymmetric information of fairness concerns promotes a fairer profit distribution, while either fairness concerns or asymmetric information alone hardly changes the overall profit of the low-carbon supply chain.
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