This work embodies the overspeed protection and safe headway control into an iterative learning control (ILC) based train trajectory tracking algorithm to satisfy the high safety requirement of high-speed railways. First, a D-type ILC scheme with overspeed protection is proposed. Then, a corresponding coordinated ILC scheme with multiple trains is studied to keep the safe headway. Finally, the control scheme under traction/braking force constraint is also considered for this proposed ILC-based train trajectory tracking strategy. Rigorous theoretical analysis has shown that the proposed control schemes can guarantee the asymptotic convergence of train speed and position to its desired profiles without requirement of the physical model aside from some mild assumptions on the system. Effectiveness is further evaluated through simulations.Note to Practitioners-This work was motivated by the outstanding repetitive operation pattern and the high safety requirements of high-speed railways. By utilizing the repetitive operation feature of a train, iterative learning control strategy is applied to improve control performance by learning repetitive information. To avoid excessive speed, which is regarded as a severe unsafe factor for the high-speed train, an overspeed penalty is added as a state constraint. To keep safe headway between adjacent trains, the adjacent trains' information is used to construct the coordinated iterative learning control strategy. To further enhance the applicability, the corresponding control scheme under traction/braking force constraint is also presented. In addition, this approach is a data driven model-free control method. The above method can also be applied to other repetitive operation systems with state constraint, input constraint, or requirement of coordination operation. Although the feasibility and effectiveness of this ILC-based train control method has been proved through the rigorous theoretical analyses without requirement of the train dynamics model as long as some mild reasonable assumptions are satisfied, and the simulation research as well, the field experiments have not been carried out yet. In our future research, we will focus on this issue and address some other practical concerns, such as the operation-depended uncertainties and disturbances in train operations.

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This paper demonstrates that polarization plays an important role in the formation of interference patterns, pattern contrasts, and periods in four-beam interference lithography. Three different polarization modes are presented to study the effects of polarization on four-beam laser interference based on theoretical analysis, simulations, and experiments. A four-beam laser interference system was set up to modify the silicon surface. It was found that the secondary periodicity or modulation was the result of the misaligned or unequal incident angles only in the case of the TE-TE-TM-TM mode. The resulting patterns have shown a good correspondence with the theoretical analysis and simulations.

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Abstract:In this study, we present the design and implementation of Arabic text classification in regard to university students' opinions through different algorithms such as Support Vector Machine (SVM) and Naive Bayes (NB). The aim of the study is to develop a framework to analyse Twitter "tweets" as having negative, positive or neutral sentiments in education or, in other words, to illustrate the relationship between the sentiments conveyed in Arabic tweets and the students' learning experiences at universities. Two experiments were carried out, one using negative and positive classes only and the other one with a neutral class. The results show that in Arabic, a sentiments SVM with an n-gram feature achieved higher accuracy than NB both with using negative and positive classes only and with the neutral class.