Formula spotting aims to simultaneously detect and recognize formulas in documents, with broad applications in intelligent document parsing, mathematical reasoning, and more. Although existing methods that first detect and then recognize have achieved prominent results, they still suffer from semantic confusion from similar character structures, semantic loss from bounding box perturbation, and visual interference from non-formula regions. To address these issues, we propose a Synergy Perception and Representation Mining Network. This network facilitates explicit interaction between the RoI features of the detection module and the semantic features of the recognition module, leveraging additional visual priors to distinguish subtle differences in similar characters. Moreover, to better perceive the boundary character structure of formulas and filter out irrelevant visual interference, a Formula Representation Mining module is proposed. This module employs progressive attention mining to achieve a complementarity between semantic information and visual context without disrupting the linguistic priors of the formulas. Additionally, to enhance the efficiency of formula decoding, we propose a parallel mask, allowing the network to output multiple LaTeX tokens simultaneously in a single prediction step. To evaluate the effectiveness of our method in formula spotting, two novel datasets: Formula-7K and Exam-1K are established. To the best of our knowledge, they are the first formula spotting datasets. Experimental results on Formula-7K and Exam-1K validate the generality and effectiveness of the proposed method. Code is available at https://github.com/hongen123/SynRMFormer.

The following operation of multiple high-speed trains (HSTs) with high speed and great density based on the moving block and virtual coupling control system can significantly improve the performance of railway systems concerning operation safety and line capacity. However, it is difficult for HST’s conventional control approach to satisfy the service demand, including safety, energy-saving, and ride comfort, due to the significantly increasing longitudinal interaction between adjacent trains in the following operating conditions. In this paper, the characteristic model of multiple HSTs’ following operation was formulated to effectively and accurately describe train control constraints derived from the coupling relationship among adjacent trains’ operation states. Then, the cooperative model predictive control (CMPC) strategy with a multiobjective rolling optimization scheme is developed. The overall line capacity, energy consumption, and ride comfort of train platoons in the railway section are defined as optimization indexes. Furthermore, some sufficient conditions for the consensus stability of the CMPC of HST are given, based on the proposed multiple HSTs following characteristic model and cooperative optimal control strategy. Simulation experiments are carried out by using HST’s operation data collected from real-world runs. Results demonstrate the effectiveness and feasibility of the proposed modeling and control method.

Efficient and safe following operation of a high-speed train (HST) under the moving block system (MBS) has been the trend for high-speed railway (HSR) transportation. However, due to the complex operating environment and changeable operation state of HST during the following operation process, it becomes inapplicable for conventional train control approaches to satisfy HST's multi-objective operation demand including safety, punctuality, energy efficiency, and ride comfort. To resolve this problem, in this paper, practical models for the real-world characteristics of HSR's line parameters and HST's following control, as well as the multi-objective optimization model with some novel metrics, are established for achieving the optimal following control of HST under MBS. Besides, the multi-objective particle swarm optimization algorithm is modified with the preference information of control sensitivity and energy efficiency, so as to efficiently obtain the optimal following control strategy based on the real-time data of HST like velocity, location, and speed restriction. Furthermore, the convergence performance of our proposed method is demonstrated by comparative tests which also help select the proper algorithm parameters. Finally, the efficiency and feasibility of the proposed framework are illustrated by showing some experimental results.

Multiobjective operation optimization for high-speed trains (HSTs) is hardly implemented by manual operation due to the increasing operational complexity and environmental uncertainties. In this paper, a T-S fuzzy bilinear model is established based on the nonlinear dynamics of the HST. A new adaptive predictive control approach based on the T-S fuzzy bilinear model of HST control is proposed with consideration of security, punctuality, and energy efficiency. In view of the model's adaptability and the approach's real-time performance, a lazy learning algorithm is used to adjust the parameters of the model and controller online while the model prediction error exceeds a given threshold. Simulation results based on the real HST running data show that the proposed approach contributes a significant improvement in HSTs' tracking precision and energy efficiency.