arXiv subjects

Haoxiang Yang

Publications and source records attributed to Haoxiang Yang.

16 recordsLinked to original sources

arXiv2026

MemCorr-DP: Counterfactual Correspondence Conditioning for a Diffusion Policy Guided by a Reference

Behavior-cloned visuomotor policies can remain accurate near their training distribution yet fail when object position and camera viewpoint change together. A successful reference trajectory contains the geometry needed to transfer the same interaction, but the policy must align that geometry with the current scene and remain sensitive to it during denoising. To address these challenges, we present MemCorr-DP, a diffusion policy that lifts frozen RoMa v2 matches into explicit 3D relations between the current scene and the reference trajectory. A counterfactual paired objective assigns opposite behaviors the same physical state and noisy action while retaining reference-specific denoising targets. Mixed-condition fine-tuning then adapts the policy from ground-truth geometry to measured correspondence errors. Our strongest evaluation places the Door in the outermost position bands beyond the training support and changes the query camera by $\pm15^\circ$. Under this combined shift, MemCorr-DP achieves 96.67% closed-loop success, compared with 88.00% for a visual Transformer with the same action architecture. Objective ablations and reference interventions show that behavior responds to the selected reference, while matched controls favor the complete relation set over future motion or centroid geometry alone. These results support explicit 3D reference relations as a robust conditioning interface when spatial and viewpoint changes are compounded in the evaluated task.

cs.RO↗

arXiv2026

Wireless Mobile Charging for Emergency Electric Vehicle Routing: A Mixed-Integer and Metaheuristic Framework for In-Motion Energy Transfer

As electric vehicles (EVs) become central to decarbonization efforts, the need for uninterrupted power supply in time-critical logistics, particularly in medical transportation, poses unique challenges for power systems integration. Conventional fixed or mobile charging infrastructure requires vehicle downtime, which makes them unsuitable for nonstop operations such as organ delivery. This work introduces the Wireless Mobile Charging Electric Vehicle Routing Problem, a novel framework in which mobile charging trucks wirelessly transfer energy to moving EVs via inductive coupling, eliminating the need for stationary charging stops. We formulate a mixed-integer programming model that co-optimizes routing and in-motion energy transfer between heterogeneous vehicle fleets under temporal and spatial alignment constraints. To address computational complexity, we develop a hybrid Bitmask Dynamic Programming and Large Neighborhood Search algorithm, tailored to arc-level charging decisions and dual-fleet synchronization. Experiments using real-world hospital logistics data from Singapore demonstrate significant runtime improvements and higher-quality solutions compared with commercial solvers. This study advances computational power system modeling by incorporating dynamic, motion-based energy delivery that is both time- and space-dependent. The results provide actionable insights into planning mobile energy infrastructure, enhancing demand-side flexibility, strengthening resilience in emergency logistics, and integrating wireless charging into urban power systems to support flexible and zero-carbon electrified mobility.

math.OC↗

arXiv2026

Bridging Natural Language and Microgrid Dynamics: A Context-Aware Simulator and Dataset

Addressing the critical need for intelligent, context-aware energy management in renewable systems, we introduce the OpenCEM Simulator and Dataset: the first open-source digital twin explicitly designed to integrate rich, unstructured contextual information with quantitative renewable energy dynamics. Traditional energy management relies heavily on numerical time series, thereby neglecting the significant predictive power embedded in human-generated context (e.g., event schedules, system logs, user intentions). OpenCEM bridges this gap by offering a unique platform comprising both a meticulously aligned, language-rich dataset from a real-world PV-and-battery microgrid installation and a modular simulator capable of natively processing this multi-modal context. The OpenCEM Simulator provides a high-fidelity environment for developing and validating novel control algorithms and prediction models, particularly those leveraging Large Language Models. We detail its component-based architecture, hybrid data-driven and physics-based modelling capabilities, and demonstrate its utility through practical examples, including context-aware load forecasting and the implementation of online optimal battery charging control strategies. By making this platform publicly available, OpenCEM aims to accelerate research into the next generation of intelligent, sustainable, and truly context-aware energy systems.

eess.SY↗

arXiv2025

Large Neighborhood Search and Bitmask Dynamic Programming for Wireless Mobile Charging Electric Vehicle Routing Problems in Medical Transportation

The transition to electric vehicles (EVs) is critical to achieving sustainable transportation, but challenges such as limited driving range and insufficient charging infrastructure have hindered the widespread adoption of EVs, especially in time-sensitive logistics such as medical transportation. This paper presents a new model to break through this barrier by combining wireless mobile charging technology with optimization. We propose the Wireless Mobile Charging Electric Vehicle Routing Problem (WMC-EVRP), which enables Medical Transportation Electric Vehicles (MTEVs) to be charged while traveling via Mobile Charging Carts (MCTs). This eliminates the time wastage of stopping for charging and ensures uninterrupted operation of MTEVs for such time-sensitive transportation problems. However, in this problem, the decisions of these two types of heterogeneous vehicles are coupled with each other, which greatly increases the difficulty of vehicle routing optimizations. To address this complex problem, we develop a mathematical model and a tailored meta-heuristic algorithm that combines Bit Mask Dynamic Programming (BDP) and Large Neighborhood Search (LNS). The BDP approach efficiently optimizes charging strategies, while the LNS framework utilizes custom operators to optimize the MTEV routes under capacity and synchronization constraints. Our approach outperforms traditional solvers in providing solutions for medium and large instances. Using actual hospital locations in Singapore as data, we validated the practical applicability of the model through extensive experiments and provided important insights into minimizing costs and ensuring the timely delivery of healthcare services.

cs.LG↗

arXiv2025

The Multi-Trip Time-Dependent Mix Vehicle Routing Problem for Hybrid Autonomous Shared Delivery Location and Traditional Door-to-Door Delivery Modes

Rising labor costs and increasing logistical demands pose significant challenges to modern delivery systems. Automated Electric Vehicles (AEVs) could reduce reliance on delivery personnel and increase route flexibility, but their adoption is limited due to varying customer acceptance and integration complexities. Shared Distribution Locations (SDLs) offer an alternative to door-to-door (D2D) delivery by providing a wider delivery window and serving multiple community customers, thereby improving last-mile logistics through reduced delivery time, lower costs, and higher customer satisfaction.This paper introduces the Multi-Trip Time-Dependent Hybrid Vehicle Routing Problem (MTTD-MVRP), a challenging variant of the Vehicle Routing Problem (VRP) that combines Autonomous Electric Vehicles (AEVs) with conventional vehicles. The problem's complexity arises from factors such as time-dependent travel speeds, strict time windows, battery limitations, and driver labor constraints, while integrating both SDLs and D2D deliveries. To solve the MTTD-MVRP efficiently, we develop a tailored meta-heuristic based on Adaptive Large Neighborhood Search (ALNS) augmented with column generation (CG). This approach intensively explores the solution space using problem-specific operators and adaptively refines solutions, balancing high-quality outcomes with computational effort. Extensive experiments show that the proposed method delivers near-optimal solutions for large-scale instances within practical time limits.From a managerial perspective, our findings highlight the importance of integrating autonomous and human-driven vehicles in last-mile logistics. Decision-makers can leverage SDLs to reduce operational costs and carbon footprints while still accommodating customers who require or prefer D2D services.

cs.MA↗

arXiv2024

VELoRA: A Low-Rank Adaptation Approach for Efficient RGB-Event based Recognition

Pattern recognition leveraging both RGB and Event cameras can significantly enhance performance by deploying deep neural networks that utilize a fine-tuning strategy. Inspired by the successful application of large models, the introduction of such large models can also be considered to further enhance the performance of multi-modal tasks. However, fully fine-tuning these models leads to inefficiency and lightweight fine-tuning methods such as LoRA and Adapter have been proposed to achieve a better balance between efficiency and performance. To our knowledge, there is currently no work that has conducted parameter-efficient fine-tuning (PEFT) for RGB-Event recognition based on pre-trained foundation models. To address this issue, this paper proposes a novel PEFT strategy to adapt the pre-trained foundation vision models for the RGB-Event-based classification. Specifically, given the RGB frames and event streams, we extract the RGB and event features based on the vision foundation model ViT with a modality-specific LoRA tuning strategy. The frame difference of the dual modalities is also considered to capture the motion cues via the frame difference backbone network. These features are concatenated and fed into high-level Transformer layers for efficient multi-modal feature learning via modality-shared LoRA tuning. Finally, we concatenate these features and feed them into a classification head to achieve efficient fine-tuning. The source code and pre-trained models will be released on \url{https://github.com/Event-AHU/VELoRA}.

cs.CV↗

arXiv2024

State Space Model for New-Generation Network Alternative to Transformers: A Survey

In the post-deep learning era, the Transformer architecture has demonstrated its powerful performance across pre-trained big models and various downstream tasks. However, the enormous computational demands of this architecture have deterred many researchers. To further reduce the complexity of attention models, numerous efforts have been made to design more efficient methods. Among them, the State Space Model (SSM), as a possible replacement for the self-attention based Transformer model, has drawn more and more attention in recent years. In this paper, we give the first comprehensive review of these works and also provide experimental comparisons and analysis to better demonstrate the features and advantages of SSM. Specifically, we first give a detailed description of principles to help the readers quickly capture the key ideas of SSM. After that, we dive into the reviews of existing SSMs and their various applications, including natural language processing, computer vision, graph, multi-modal and multi-media, point cloud/event stream, time series data, and other domains. In addition, we give statistical comparisons and analysis of these models and hope it helps the readers to understand the effectiveness of different structures on various tasks. Then, we propose possible research points in this direction to better promote the development of the theoretical model and application of SSM. More related works will be continuously updated on the following GitHub: https://github.com/Event-AHU/Mamba_State_Space_Model_Paper_List.

cs.LG↗

arXiv2024

Multistage Stochastic Program for Mitigating Power System Risks under Wildfire Disruptions

The frequency of wildfire disasters has surged five-fold in the past 50 years due to climate change. Preemptive de-energization is a potent strategy to mitigate wildfire risks but substantially impacts customers. We propose a multistage stochastic programming model for proactive de-energization planning, aiming to minimize economic loss while accomplishing a fair load delivery. We model wildfire disruptions as stochastic disruptions with varying timing and intensity, introduce a cutting-plane decomposition algorithm, and test our approach on the RTS-GLMC test case. Our model consistently offers a robust and fair de-energization plan that mitigates wildfire damage costs and minimizes load-shedding losses, particularly when pre-disruption restoration is considered.

math.OC↗

arXiv2024

Uncertainty-aware Bridge based Mobile-Former Network for Event-based Pattern Recognition

The mainstream human activity recognition (HAR) algorithms are developed based on RGB cameras, which are easily influenced by low-quality images (e.g., low illumination, motion blur). Meanwhile, the privacy protection issue caused by ultra-high definition (HD) RGB cameras aroused more and more people's attention. Inspired by the success of event cameras which perform better on high dynamic range, no motion blur, and low energy consumption, we propose to recognize human actions based on the event stream. We propose a lightweight uncertainty-aware information propagation based Mobile-Former network for efficient pattern recognition, which aggregates the MobileNet and Transformer network effectively. Specifically, we first embed the event images using a stem network into feature representations, then, feed them into uncertainty-aware Mobile-Former blocks for local and global feature learning and fusion. Finally, the features from MobileNet and Transformer branches are concatenated for pattern recognition. Extensive experiments on multiple event-based recognition datasets fully validated the effectiveness of our model. The source code of this work will be released at https://github.com/Event-AHU/Uncertainty_aware_MobileFormer.

cs.CV↗

arXiv2024

Distributionally Robust Frequency-Constrained Microgrid Scheduling Towards Seamless Islanding

Unscheduled islanding events of microgrids result in the transition between grid-connected and islanded modes and induce a sudden and unknown power imbalance, posing a threat to frequency security. To achieve seamless islanding, we propose a distributionally robust frequency-constrained microgrid scheduling model considering unscheduled islanding events. This model co-optimizes unit commitments, power dispatch, upward/downward primary frequency response reserves, virtual inertia provisions from renewable energy sources (RESs), deloading ratios of RESs, and battery operations, while ensuring the system frequency security during unscheduled islanding. We establish an affine relationship between the actual power exchange and RES uncertainty in grid-connected mode, describe RES uncertainty with a Wasserstein-metric ambiguity set, and formulate frequency constraints under uncertain post-islanding power imbalance as distributionally robust quadratic chance constraints, which are further transformed by a tight conic relaxation. We solve the proposed mixed-integer convex program and demonstrate its effectiveness through case studies.

math.OC↗

arXiv2023

A Review of Sequential Decision Making via Simulation

Optimization via simulation has been well established to find optimal solutions and designs in complex systems. However, it still faces modeling and computational challenges when extended to the multi-stage setting. This survey reviews the models and methodologies of single-stage optimization via simulation and multi-stage stochastic programming. These are necessary theoretical components to push forward the development of sequential decision making via simulation. We identify the key challenge of sequential decision making via simulation as the appropriate modeling of the stage-wise value function, for which we survey the state-of-the-art meta-models and their potential solution algorithms.

math.OC↗

arXiv2023

Multi-period Power System Risk Minimization under Wildfire Disruptions

Natural wildfire becomes increasingly frequent as climate change evolves, posing a growing threat to power systems, while grid failures simultaneously fuel the most destructive wildfires. Preemptive de-energization of grid equipment is effective in mitigating grid-induced wildfires but may cause significant power outages during natural wildfires. This paper proposes a novel two-stage stochastic program for planning preemptive de-energization and solves it via an enhanced Lagrangian cut decomposition algorithm. We model wildfire events as stochastic disruptions with random magnitude and timing. The stochastic program maximizes the electricity delivered while proactively de-energizing components over multiple time periods to reduce wildfire risks. We use a cellular automaton process to sample grid failure and wildfire scenarios driven by realistic risk and environmental factors. We test our method on an augmented version of the RTS-GLMC test case in Southern California and compare it with four benchmark cases, including deterministic, wait-and-see, and robust optimization formulations as well as a comparison with prior wildfire risk optimization. Our method reduces wildfire damage costs and load-shedding losses, and our nominal plan is robust against uncertainty perturbation.

math.OC↗

arXiv2023

Active Neural Mapping

We address the problem of active mapping with a continually-learned neural scene representation, namely Active Neural Mapping. The key lies in actively finding the target space to be explored with efficient agent movement, thus minimizing the map uncertainty on-the-fly within a previously unseen environment. In this paper, we examine the weight space of the continually-learned neural field, and show empirically that the neural variability, the prediction robustness against random weight perturbation, can be directly utilized to measure the instant uncertainty of the neural map. Together with the continuous geometric information inherited in the neural map, the agent can be guided to find a traversable path to gradually gain knowledge of the environment. We present for the first time an active mapping system with a coordinate-based implicit neural representation for online scene reconstruction. Experiments in the visually-realistic Gibson and Matterport3D environment demonstrate the efficacy of the proposed method.

cs.CV↗

arXiv2021

Observation of heat scaling across a first-order quantum phase transition in a spinor condensate

Heat generated as a result of the breakdown of an adiabatic process is one of the central concepts of thermodynamics. In isolated systems, the heat can be defined as an energy increase due to transitions between distinct energy levels. Across a second-order quantum phase transition (QPT), the heat is predicted theoretically to exhibit a power-law scaling, but it is a significant challenge for an experimental observation. In addition, it remains elusive whether a power-law scaling of heat can exist for a first-order QPT. Here we experimentally observe a power-law scaling of heat in a spinor condensate when a system is linearly driven from a polar phase to an antiferromagnetic phase across a first-order QPT. We experimentally evaluate the heat generated during two non-equilibrium processes by probing the atom number on a hyperfine energy level. The experimentally measured scaling exponents agree well with our numerical simulation results. Our work therefore opens a new avenue to experimentally and theoretically exploring the properties of heat in non-equilibrium dynamics.

cond-mat.quant-gas↗

arXiv2019

Robust Optimization for Electricity Generation

We consider a robust optimization problem in an electric power system under uncertain demand and availability of renewable energy resources. Solving the deterministic alternating current optimal power flow (ACOPF) problem has been considered challenging since the 1960s due to its nonconvexity. Linear approximation of the AC power flow system sees pervasive use, but does not guarantee a physically feasible system configuration. In recent years, various convex relaxation schemes for the ACOPF problem have been investigated, and under some assumptions, a physically feasible solution can be recovered. Based on these convex relaxations, we construct a robust convex optimization problem with recourse to solve for optimal controllable injections (fossil fuel, nuclear, etc.) in electric power systems under uncertainty (renewable energy generation, demand fluctuation, etc.). We propose a cutting-plane method to solve this robust optimization problem, and we establish convergence and other desirable properties. Experimental results indicate that our robust convex relaxation of the ACOPF problem can provide a tight lower bound.

math.OC↗

arXiv2019

Analyzing Client Behavior in a Syringe Exchange Program

Multiple syringe exchange programs serve the Chicago metropolitan area, providing support for drug users to help prevent infectious diseases. Using data from one program over a ten-year period, we study the behavior of its clients, focusing on the temporal process governing their visits to service locations and on their demographics. We construct a phase-type distribution with an affine relationship between model parameters and features of an individual client. The phase-type distribution governs inter-arrival times between reoccurring visits of each client and is informed by characteristics of a client including age, gender, ethnicity, and more. The inter-arrival time model is a sub-model in a simulation that we construct for the larger system, which allows us to provide a personalized prediction regarding the client's time-to-return to a service location so that better intervention decisions can be made with the help of simulation.

stat.AP↗