Search Results for author: Amir G. Aghdam

Found 7 papers, 0 papers with code

Low-Frequency Load Identification using CNN-BiLSTM Attention Mechanism

no code implementations14 Nov 2023 Amanie Azzam, Saba Sanami, Amir G. Aghdam

Non-intrusive Load Monitoring (NILM) is an established technique for effective and cost-efficient electricity consumption management.

Event Detection Management +1

A Distributed Strategy to Maximize Coverage in a Heterogeneous Sensor Network in the Presence of Obstacles

no code implementations17 Sep 2023 Hesam Mosalli, Amir G. Aghdam

In this paper, an efficient deployment strategy is proposed for a network of mobile and static sensors with nonidentical sensing and communication radii.

Position

Generalized Power Iteration with Application to Distributed Connectivity Estimation of Asymmetric Networks

no code implementations8 Aug 2023 M. Mehdi Asadi, Mohammad Khosravi, Hesam Mosalli, Stephane Blouin, Amir G. Aghdam

The problem of connectivity assessment in an asymmetric network represented by a weighted directed graph is investigated in this article.

Connectivity Estimation

Risk-Constrained Control of Mean-Field Linear Quadratic Systems

no code implementations14 Jul 2023 Masoud Roudneshin, Saba Sanami, Amir G. Aghdam

We address the risk constraint by bounding the cumulative one-stage variance of the state penalty of all players.

Fast-Image2Point: Towards Real-Time Point Cloud Reconstruction of a Single Image using 3D Supervision

no code implementations20 Sep 2022 AmirHossein Zamani, Amir G. Aghdam, Kamran Ghaffari T

Despite considerable progress in 3D reconstruction systems in recent years, applying them to real-time systems such as navigation systems in autonomous vehicles is still challenging due to the high complexity and computational demand of the existing methods.

Autonomous Vehicles Point cloud reconstruction

Receding Horizon Control in Deep Structured Teams: A Provably Tractable Large-Scale Approach with Application to Swarm Robotics

no code implementations20 Oct 2021 Jalal Arabneydi, Amir G. Aghdam

The problem is formulated as a linear quadratic deep structured team, where the decision-makers wish to track a global target cooperatively while considering their local targets.

Reinforcement Learning in Linear Quadratic Deep Structured Teams: Global Convergence of Policy Gradient Methods

no code implementations29 Nov 2020 Vida Fathi, Jalal Arabneydi, Amir G. Aghdam

In such systems, agents are partitioned into a few sub-populations wherein the agents in each sub-population are coupled in the dynamics and cost function through a set of linear regressions of the states and actions of all agents.

Policy Gradient Methods

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