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Lihui Peng

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4 papers

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EAAI Journal 2026 Journal Article

Two-stage automated design of railway vertical alignments with topography-driven Fourier transform and constrained A-Star search

Taoran Song
Hao Pu
Hong Zhang
Paul Schonfeld
Lihui Peng

Vertical alignment design is crucial for a railway project since it largely determines its construction investment, lifecycle costs, and various other impacts. However, among the theoretically-infinite numbers of possible alternatives, it is difficult to optimize a vertical alignment matching the drastically-undulating terrain line along the railway, while also considering large structure tradeoffs (such as bridges and tunnels) and various design constraints. To solve this problem, a two-stage method is proposed for automated vertical alignment optimization. In stage I, the railway terrain line is converted to a spatial wave and modelled through the spectral signature analysis of a topography-driven Fast Fourier Transform (FFT). Afterward, by identifying the key terrain characteristic locations based on the derived Fourier series function, feasible search regions for vertical alignment design are determined by processing specific design constraints. For stage II, an A-Star algorithm is customized for vertical alignment search. First, A-Star nodes are discretized within the above feasible search spaces. Then, a comprehensive constraint-handling operator is devised to guarantee a solution’s feasibility during optimization. Moreover, a deterministic simulation algorithm is integrated to create a weighted directed graph for A-Star path generation. Ultimately, the developed method is applied to a complex mountain railway alignment case. The algorithm performances of the two stages are both discussed in detail.

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EAAI Journal 2025 Journal Article

A flow rate estimation method for gas–liquid two-phase flow based on filter-enhanced convolutional neural network

Yuxiao Jiang
Yinyan Liu
Lihui Peng
Yi Li

Accurate estimation of flow rate in gas–liquid two-phase flow is crucial for various industrial processes. How to accurately estimate flow rate remains a challenging problem. Previously, deep learning-based methods focused on a few human-set points with single task learning. In addition, the data were not denoised. In this study, a flow rate estimation method based on a filter-enhanced convolutional neural network (FECNN) is proposed for gas–liquid two-phase flow. The method leverages multimodal data from a Venturi tube and an electrical capacitance tomography (ECT) sensor as input, utilizing multilayer perceptron (MLP) to fuse data. Subsequently, a learnable filter module is employed to attenuate noise adaptively, followed by multiscale convolutional neural network (MSCNN) extraction of flow rate features at different scales. Finally, the method enables estimate each single-phase flow rate simultaneously through multi-task learning (MTL). The adaptive noise attenuation capabilities of the learnable filter module are demonstrated, and the ability of the proposed MSCNN to capture multiscale flow rate features through multiple comparative experiments is shown. Additionally, a qualitative comparison with recent flow rate estimation methods is provided. Overall, this study demonstrates the effectiveness and superiority of the proposed FECNN in flow rate estimation.

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TMLR Journal 2025 Journal Article

A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches

Ruibo Ming
Zhewei Huang
Jingwei Wu
Zhuoxuan Ju
Daxin Jiang
Jianming Hu
Lihui Peng
Shuchang Zhou

Future Frame Synthesis (FFS), the task of generating subsequent video frames from context, represents a core challenge in machine intelligence and a cornerstone for developing predictive world models. This survey provides a comprehensive analysis of the FFS landscape, charting its critical evolution from deterministic algorithms focused on pixel-level accuracy to modern generative paradigms that prioritize semantic coherence and dynamic plausibility. We introduce a novel taxonomy organized by algorithmic stochasticity, which not only categorizes existing methods but also reveals the fundamental drivers—advances in architectures, datasets, and computational scale—behind this paradigm shift. Critically, our analysis identifies a bifurcation in the field's trajectory: one path toward efficient, real-time prediction, and another toward large-scale, generative world simulation. By pinpointing key challenges and proposing concrete research questions for both frontiers, this survey serves as an essential guide for researchers aiming to advance the frontiers of visual dynamic modeling.

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EAAI Journal 2024 Journal Article

A 3D-RRT-star algorithm for optimizing constrained mountain railway alignments

Hao Pu
Xinjie Wan
Taoran Song
Paul Schonfeld
Lihui Peng

Railway alignment development in mountainous regions is a complex problem, especially when there are numerous obstacles in the study area. Obtaining a feasible solution that satisfies all the obstacle constraints requires considerable computing resources and time. To solve this problem, a graph-based shortest path method, i. e. , three-dimensional rapidly exploring random tree star (3D-RRT-star), is customized. Three main innovations are included in this method: (1) A heuristic sampling process is proposed to avoid getting the RRT search trapped into local ranges and overlooking possible path alternatives by combining a specially designed RRT node sampling method and a railway spatially-reachable analysis. (2) A multi-level constraint discretization approach is proposed to describe the obstacles in the study area, while procedures are developed to tackle the obstacle constraints dynamically during the search process. (3) An evolutionary search method integrating a sampling strategy and constraint handling operator is devised for generating a set of dissimilar RRT paths, which are finally refined into railway alignments satisfying curve constraints. Ultimately, the proposed method is applied to a realistic railway case. The experimental results confirm that it can yield a better alignment than the best manually obtained solution. Furthermore, its search efficiency is compared to a previous optimization method for this problem and the results reveal significant improvement.

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