arXiv subjects

Homayoun Hamedmoghadam

Publications and source records attributed to Homayoun Hamedmoghadam.

12 recordsLinked to original sources

arXiv2026

Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations

Research, innovation and practical capital investment have been increasing rapidly toward the realization of autonomous physical agents. This includes industrial and service robots, unmanned aerial vehicles, embedded control devices, and a number of other realizations of cybernetic/mechatronic implementations of intelligent autonomous devices. In this paper, we consider a stylized version of robotic care, which would normally involve a two-level Reinforcement Learning procedure that trains a policy for both lower level physical movement decisions as well as higher level conceptual tasks and their sub-components. In order to deliver greater safety and reliability in the system, we present the general formulation of this as a two-level optimization scheme which incorporates control at the lower level, and classical planning at the higher level, integrated with a capacity for learning. This synergistic integration of multiple methodologies -- control, classical planning, and RL -- presents an opportunity for greater insight for algorithm development, leading to more efficient and reliable performance. Here, the notion of reliability pertains to physical safety and interpretability into an otherwise black box operation of autonomous agents, concerning users and regulators. This work presents the necessary background and general formulation of the optimization framework, detailing each component and its integration with the others.

math.OC↗

arXiv2026

Dynamic access pricing control for fair and stable resource sharing

We consider the co-existence of users with different price sensitivities under dynamic (or surge) pricing that regulates how they access a shared resource. To balance supply and demand in such settings, some dynamic pricing systems adopt a monotonically increasing price scheme which is well known to be socially regressive, driving out price-sensitive users in favor of price-insensitive ones. Here, we propose a new dynamic pricing scheme based on a non-monotonic price function that allows all user classes to coexist more fairly when price-insensitive users are present, but automatically reverts to conventional dynamic pricing when they leave the system, even though the exact price-sensitivity composition is unobservable. The resulting system consists of nonlinear ODEs, which we further investigate to characterize its equilibria and their domains of attraction, first for a single price-sensitive class competing with a price-insensitive one and then for the general $n$-class case. Numerical simulations demonstrate the effectiveness of the proposed design.

math.OC↗

arXiv2026

Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning

Denoising wearable electroencephalogram (EEG) is inherently challenging since neural activity is not only subtle but also inseparable from spectrally overlapping noise artifacts. Classical signal processing methods, relying on fixed or heuristic rules, cannot handle the time-varying pervasive artifacts in wearable EEGs. Deep learning methods, on the other hand, show promise in decomposition-free EEG denoising using highly expressive neural networks, but the training requires artifact-free EEG, which is inherently unobtainable. To address this, we propose Intelligent Partitioning for Self-supervised Denoising (iPSD). Our method eliminates the need for clean references by learning to partition an input EEG segment into independent noisy realizations with the same underlying signal. This enables self-supervision of deep learning denoisers, even in zero-shot settings where only a single EEG segment to be denoised is available. We validate iPSD through extensive experiments, including validations on wearable EEG from in-ear sensors. The results show that iPSD achieves state-of-the-art performance, most notably under extremely low signal-to-noise ratios (down to -10 dB) and challenging artifacts (e.g., EMG), with spectral fidelity orders of magnitude higher than competitive baselines.

cs.LG↗

arXiv2026

Robust Dynamic Pricing and Admission Control with Fairness Guarantees

Dynamic pricing is commonly used to regulate congestion in shared service systems. This paper is motivated by the fact that in the presence of users with varying price sensitivity (responsiveness), conventional monotonic pricing can lead to unfair outcomes by disproportionately excluding price-elastic users, particularly under high or uncertain demand. We therefore develop a fairness-oriented mechanism under demand uncertainty. The paper's contributions are twofold. First, we show that when fairness is imposed as a hard state constraint, the optimal (revenue maximizing) pricing policy is generally non-monotonic in demand. This structural result departs fundamentally from standard surge pricing rules and reveals that price reduction under heavy load may be necessary to maintain equitable access. Second, we address the problem that price elasticity among heterogeneous users is unobservable. To solve it, we develop a robust dynamic pricing and admission control framework that enforces capacity and fairness constraints for all user type distributions consistent with aggregate measurements. By integrating integral High Order Control Barrier Functions (iHOCBFs) with a robust optimization framework under uncertain user-type distribution, we obtain a controller that guarantees forward invariance of safety and fairness constraints while optimizing revenue. Numerical experiments demonstrate improved fairness and revenue performance relative to monotonic surge pricing policies.

eess.SY↗

arXiv2025

Learning Network Dismantling Without Handcrafted Inputs

The application of message-passing Graph Neural Networks has been a breakthrough for important network science problems. However, the competitive performance often relies on using handcrafted structural features as inputs, which increases computational cost and introduces bias into the otherwise purely data-driven network representations. Here, we eliminate the need for handcrafted features by introducing an attention mechanism and utilizing message-iteration profiles, in addition to an effective algorithmic approach to generate a structurally diverse training set of small synthetic networks. Thereby, we build an expressive message-passing framework and use it to efficiently solve the NP-hard problem of Network Dismantling, virtually equivalent to vital node identification, with significant real-world applications. Trained solely on diversified synthetic networks, our proposed model -- MIND: Message Iteration Network Dismantler -- generalizes to large, unseen real networks with millions of nodes, outperforming state-of-the-art network dismantling methods. Increased efficiency and generalizability of the proposed model can be leveraged beyond dismantling in a range of complex network problems.

cs.LG↗

arXiv2024

Reinforcement Learning with Adaptive Regularization for Safe Control of Critical Systems

Reinforcement Learning (RL) is a powerful method for controlling dynamic systems, but its learning mechanism can lead to unpredictable actions that undermine the safety of critical systems. Here, we propose RL with Adaptive Regularization (RL-AR), an algorithm that enables safe RL exploration by combining the RL policy with a policy regularizer that hard-codes the safety constraints. RL-AR performs policy combination via a "focus module," which determines the appropriate combination depending on the state--relying more on the safe policy regularizer for less-exploited states while allowing unbiased convergence for well-exploited states. In a series of critical control applications, we demonstrate that RL-AR not only ensures safety during training but also achieves a return competitive with the standards of model-free RL that disregards safety.

cs.LG↗

arXiv2024

On Optimal Battery Sizing for Electric Vehicles

In this paper, we introduce a quantitative framework to optimize electric vehicle (EV) battery capacities, considering two criteria: upfront vehicle cost and charging inconvenience cost. For this purpose, we (1) develop a comprehensive model for charging inconvenience costs, incorporating both charging time and detours, improving on existing studies, (2) show, through extensive simulations and analytical models, how charging inconvenience cost is affected by different battery capacity and charging infrastructure configurations, (3) introduce an optimisation framework to determine optimal battery capacities based on charging inconvenience and vehicle cost, and (4) show that optimal battery capacities can be influenced by strategic investments in charging infrastructure and tax/incentive policies. The proposed framework provides actionable insights into the sustainable design of EV systems, supporting the development of cost-effective and convenient electric mobility solutions.

eess.SY↗

arXiv2024

Tree Proof-of-Position Algorithms

We present a novel class of proof-of-position algorithms: Tree-Proof-of-Position (T-PoP). This algorithm is decentralised, collaborative and can be computed in a privacy preserving manner, such that agents do not need to reveal their position publicly. We make no assumptions of honest behaviour in the system, and consider varying ways in which agents may misbehave. Our algorithm is therefore resilient to highly adversarial scenarios. This makes it suitable for a wide class of applications, namely those in which trust in a centralised infrastructure may not be assumed, or high security risk scenarios. Our algorithm has a worst case quadratic runtime, making it suitable for hardware constrained IoT applications. We also provide a mathematical model that summarises T-PoP's performance for varying operating conditions. We then simulate T-PoP's behaviour with a large number of agent-based simulations, which are in complete agreement with our mathematical model, thus demonstrating its validity. T-PoP can achieve high levels of reliability and security by tuning its operating conditions, both in high and low density environments. Finally, we also present a mathematical model to probabilistically detect platooning attacks.

cs.DS↗

arXiv2024

Quantifying indirect and direct vaccination effects arising in the SIR model

Vaccination campaigns have both direct and indirect effects that act to control an infectious disease as it spreads through a population. Indirect effects arise when vaccinated individuals block disease transmission in any infection chains they are part of, and this in turn can benefit both vaccinated and unvaccinated individuals. Indirect effects are difficult to quantify in practice, but here, working with the Susceptible-Infected-Recovered (SIR) model, they are analytically calculated in important cases, through pivoting on the Final Size formula for epidemics. Their relationship to herd immunity is also clarified. Furthermore, we identify the important distinction between quantifying indirect effects of vaccination at the "population level" versus the "per capita" individual level, which often results in radically different conclusions. As an important example, the analysis unpacks why population-level indirect effect can appear significantly larger than its per capita analogue. In addition, we consider a recently proposed epidemiological non-pharamaceutical intervention used over COVID-19, referred to as "shielding", and study its impact in our mathematical analysis. The shielding scheme is extended by inclusion of limited vaccination.

q-bio.PE↗

arXiv2023

Electric Vehicle E-hailing Fleet Dispatching and Charge Scheduling

With recent developments in vehicle and battery technologies, electric vehicles (EVs) are rapidly getting established as a sustainable alternative to traditional fossil-fuel vehicles. This has made the large-scale electrification of ride-sourcing operations a practical viability, providing an opportunity for a leap toward urban sustainability goals. Despite having a similar driving range to fossil-fuel vehicles, EVs are disadvantaged by their long charging times which compromises the total fleet service time. To efficiently manage an EV fleet, the operator needs to address the charge scheduling problem as part of the dispatch strategy. This paper introduces a probabilistic matching method which evaluates the optimal trip and charging decisions for a fully electrified e-hailing fleet, with the goal of maximising the operator's expected market profit. In the midst of the technological transition towards autonomous vehicles, it is also critical to include stochastic driver behaviours in transport models as presented in this paper. Since drivers may either comply with trip dispatching or choose to reject a charging trip order considering the additional fees, contrary to the commonly assumed fleet autonomy, the proposed method designs an incentivisation scheme (charging discounts) to encourage driver compliance so that the planned charging trips and the associated profit can be realised.

eess.SY↗

arXiv2019

A simple contagion process describes spreading of traffic jams in urban networks

The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two novel macroscopic characteristics of network traffic, namely congestion propagation rate \b{eta} and congestion dissipation rate μ. We describe the dynamics of congestion propagation and dissipation using these new parameters, \b{eta}, and μ, embedded within a system of ordinary differential equations, analogous to the well-known Susceptible-Infected-Recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time.

physics.soc-ph↗

arXiv2018

Australia's long-term electricity demand forecasting using deep neural networks

Accurate prediction of long-term electricity demand has a significant role in demand side management and electricity network planning and operation. Demand over-estimation results in over-investment in network assets, driving up the electricity prices, while demand under-estimation may lead to under-investment resulting in unreliable and insecure electricity. In this manuscript, we apply deep neural networks to predict Australia's long-term electricity demand. A stacked autoencoder is used in combination with multilayer perceptrons or cascade-forward multilayer perceptrons to predict the nation-wide electricity consumption rates for 1-24 months ahead of time. The experimental results show that the deep structures have better performance than classical neural networks, especially for 12-month to 24-month prediction horizon.

cs.NE↗