arXiv subjects

Wassim M. Haddad

Publications and source records attributed to Wassim M. Haddad.

7 recordsLinked to original sources

arXiv2025

CarboNet: A Finite-Time Combustion-Tolerant Compartmental Network for Tropospheric Carbon Control

While governments and international organizations have set the net-zero target to prevent a climate event horizon, practical solutions are lacking mainly because of the impracticability in completely replacing combustion processes. To address the net-zero target problem, in this paper we first design a compartmental network whose states must remain in the nonnegative orthant for physical consistency and in which the carbon dioxide emissions result from the combustion of diesel in vehicles and gas in house heaters. Then, we design both full-state and output-feedback linear-quadratic regulators of the compartmental network to bring the mass of carbon dioxide to the pre-industrial era, which is reached in approximately 25 and 60 days, respectively. The output-feedback controller tolerates for 6 days the combustion taking place in 5,000 vehicles and in 10,000 house heating systems, meets the net-zero target, and nullifies the extraction of finite natural resources. With closed-loop control, the tropospheric temperature stabilizes approximately to the pre-industrial era reference condition, i.e., to 13.5 °C, which is 21.7 °C lower than the steady-state temperature achieved without carbon capture. This work is a first step in designing optimal network control systems for climate stability. Source code is publicly available.

math.DS↗

arXiv2025

Circular Microalgae-Based Carbon Control for Net Zero

The alteration of the climate in various areas of the world is of increasing concern since climate stability is a necessary condition for human survival as well as every living organism. The main reason of climate change is the greenhouse effect caused by the accumulation of carbon dioxide in the atmosphere. In this paper, we design a networked system underpinned by compartmental dynamical thermodynamics to circulate the atmospheric carbon dioxide. Specifically, in the carbon dioxide emitter compartment, we develop an initial-condition-dependent finite-time stabilizing controller that guarantees stability within a desired time leveraging the system property of affinity in the control. Then, to compensate for carbon emissions we show that a cultivation of microalgae with a volume 625 times bigger than the one of the carbon emitter is required. To increase the carbon uptake of the microalgae, we implement the nonaffine-in-the-control microalgae dynamical equations as an environment of a state-of-the-art library for reinforcement learning (RL), namely, Stable-Baselines3, and then, through the library, we test the performance of eight RL algorithms for training a controller that maximizes the microalgae absorption of carbon through the light intensity. All the eight controllers increased the carbon absorption of the cultivation during a training of 200,000 time steps with a maximum episode length of 200 time steps and with no termination conditions. This work is a first step towards approaching net zero as a classical and learning-based network control problem. The source code is publicly available.

math.DS↗

arXiv2024

A Unification Between Deep-Learning Vision, Compartmental Dynamical Thermodynamics, and Robotic Manipulation for a Circular Economy

The shift from a linear to a circular economy has the potential to simultaneously reduce uncertainties of material supplies and waste generation. However, to date, the development of robotic and, more generally, autonomous systems have been rarely integrated into circular economy implementation strategies despite their potential to reduce the operational costs and the contamination risks from handling waste. In addition, the science of circularity still lacks the physical foundations needed to improve the accuracy and the repeatability of the models. Hence, in this paper, we merge deep-learning vision, compartmental dynamical thermodynamics, and robotic manipulation into a theoretically-coherent physics-based research framework to lay the foundations of circular flow designs of materials. The proposed framework tackles circularity by generalizing the design approach of the Rankine cycle enhanced with dynamical systems theory. This differs from state-of-the-art approaches to circular economy, which are mainly based on data analysis, e.g., material flow analysis (MFA). We begin by reviewing the literature of the three abovementioned research areas, then we introduce the proposed unified framework and we report the initial application of the framework to plastics systems along with initial simulation results of reinforcement-learning control of robotic waste sorting. This shows the framework applicability, generality, scalability, and the similarity and difference between the optimization of artificial neural systems and the proposed compartmental networks. Finally, we discuss the still not fully exploited opportunities for robotics in circular economy and the future challenges in the theory and practice of the proposed circularity framework.

cs.RO↗

arXiv2022

Thermodynamical Material Networks for Modeling, Planning, and Control of Circular Material Flows

Waste production, carbon dioxide atmospheric accumulation, and dependence on finite natural resources are expressions of the unsustainability of the current industrial networks that supply fuels, energy, and manufacturing products. In particular, circular manufacturing supply chains and carbon control networks are urgently needed. To model and design these and, in general, any material networks, we propose to generalize the approach used for traditional networks such as water and thermal power systems by using compartmental dynamical thermodynamics and graph theory. The key idea is that the thermodynamic compartments and their connections can be added, removed or modified as needed to achieve a circular material flow. The design methodology is explained and its application is illustrated through examples. In addition, we provide a physics-based definition of circularity and, by implementing a nonlinear compartmental control, we strengthen the connection between "Industry 4.0" and "Sustainability". The paper source code is publicly available.

math.DS↗

arXiv2017

On the Global Dynamics of an Electroencephalographic Mean Field Model of the Neocortex

This paper investigates the global dynamics of a mean field model of the electroencephalogram developed by Liley et al., 2002. The model is presented as a system of coupled ordinary and partial differential equations with periodic boundary conditions. Existence, uniqueness, and regularity of weak and strong solutions of the model are established in appropriate function spaces, and the associated initial-boundary value problems are proved to be well-posed. Sufficient conditions are developed for the phase spaces of the model to ensure nonnegativity of certain quantities in the model, as required by their biophysical interpretation. It is shown that the semigroups of weak and strong solution operators possess bounded absorbing sets for the entire range of biophysical values of the parameters of the model. Challenges towards establishing a global attractor for the model are discussed and it is shown that there exist parameter values for which the constructed semidynamical systems do not possess a compact global attractor due to the lack of the asymptotic compactness property. Finally, using the theoretical results of the paper, instructive insights are provided into the complexity of the behavior of the model and computational analysis of the model.

math.DS↗

arXiv2015

Gait Assessment for Multiple Sclerosis Patients Using Microsoft Kinect

Gait analysis of patients with neurological disorders, including multiple sclerosis (MS), is important for rehabilitation and treatment. The Mircrosoft Kinect sensor, which was developed for motion recognition in gaming applications, is an ideal candidate for an inexpensive system providing the capability for human gait analysis. In this research, we develop a framework to quantify the gait abnormality of MS patients using a Kinect for Windows camera. In addition to the previously introduced gait indices, a novel set of MS gait indices based on the concept of dynamic time warping is introduced. The newly introduced indices can characterize a patient's gait pattern as a whole and quantify a subject's gait distance from the healthy population. We will investigate the correlation of gait indices with the multiple sclerosis walking scale (MSWS) and the clinical ambulation score. This work establishes the feasibility of using the Kinect sensor for clinical gait assessment for MS patients.

cs.CV↗

arXiv2009

Segmentation of Facial Expressions Using Semi-Definite Programming and Generalized Principal Component Analysis

In this paper, we use semi-definite programming and generalized principal component analysis (GPCA) to distinguish between two or more different facial expressions. In the first step, semi-definite programming is used to reduce the dimension of the image data and "unfold" the manifold which the data points (corresponding to facial expressions) reside on. Next, GPCA is used to fit a series of subspaces to the data points and associate each data point with a subspace. Data points that belong to the same subspace are claimed to belong to the same facial expression category. An example is provided.

cs.CV↗