Fanhao Zeng | Searcharxiv

arXiv2026

Intelligent Distributed Optical Fiber Sensing in Transportation Infrastructures: Research Progress, Applications, and Challenges

Distributed optical fiber sensing (DOFS), along with its capabilities of long-range coverage, multi-parameter monitoring, and completely passive detection, emerges as one of the most promising non-destructive detection techniques for structural health monitoring (SHM) and operational assessment of linear transportation infrastructures. In this paper, we provide a state-of-the-art review on DOFS applications across typical linear infrastructure systems, encompassing highways, long-span bridges, rail transit networks, airport runways, and analogous linear structures. The comprehensive discussion consists of four critical research dimensions: 1) optical fiber selection for multi-parameter sensing and robust cable packaging techniques, 2) distributed sensing principles and signal processing algorithms, 3) diverse application scenarios in SHM and related fields, and 4) anomaly detection and event classification methodologies. Building upon the foundational introduction of DOFS technical principles and monitoring solutions for intelligent transportation infrastructure, this paper elaborates on system design approaches, sensing data analytics algorithms, and future research directions.

physics.optics↗

arXiv2025

Curvature accelerated decentralized non-convex optimization for high-dimensional machine learning problems

We consider distributed optimization as motivated by machine learning in a multi-agent system: each agent holds local data and the goal is to minimize an aggregate loss function over a common model, via an interplay of local training and distributed communication. In the most interesting case of training a neural network, the loss functions are non-convex and the high dimension of the model poses challenges in terms of communication and computation. We propose a primal-dual method that leverages second order information in the local training sub-problems in order to accelerate the algorithm. To ease the computational burden, we invoke a quasi-Newton local solver with linear cost in the model dimension. Besides, our method is communication efficient in the sense of requiring to broadcast the local model only once per round. We rigorously establish the convergence of the algorithm and demonstrate its merits by numerical experiments.

math.OC↗