pwc-archive/papers-with-abstracts huggingface dataset:
Non-Reciprocal Reconfigurable Intelligent Surfaces
Authors: Jiaqi Xu, Haoyu Wang, Rang Liu, Josef A. Nossek, A. Lee Swindlehurst
Date: 2024-11-23 00:00:00

In contrast to conventional RIS, the scattering matrix of a non-reciprocal RIS (NR-RIS) is non-symmetric, leading to differences in the uplink and the downlink components of NR-RIS cascaded channels. In this paper, a physically-consistent device model is proposed in which an NR-RIS is composed of multiple groups of two-port elements inter-connected by non-reciprocal devices. The resulting non-reciprocal scattering matrix is derived for various cases including two-element groups connected with isolators or gyrators, and general three-element groups connected via circulators. Signal models are given for NR-RIS operating in either reflecting-only or simultaneously transmitting and reflecting modes. The problem of NR-RIS design for non-reciprocal beamsteering is formulated for three-element circulator implementations, and numerical results confirm that non-reciprocal beamsteering can be achieved with minimal sidelobe power. We also show that our physically consistent NR-RIS architecture is effective in implementing channel reciprocity attacks, achieving similar performance to that with idealized NR-RIS models.
Beyond inherent robustness: strong stability of MPC despite plant-model mismatch
Authors: Steven J. Kuntz, James B. Rawlings
Date: 2024-11-23 00:00:00

In this technical report, we establish the asymptotic stability of MPC under plant-model mismatch for problems where the origin remains a steady state despite mismatch. This class of problems includes, but is not limited to, inventory management, path-planning, and control of systems in deviation variables. Our results differ from prior results on the inherent robustness of MPC, which guarantee only convergence to a neighborhood of the origin, the size of which scales with the magnitude of the mismatch. For MPC with quadratic costs, continuous differentiability of the system dynamics is sufficient to demonstrate exponential stability of the closed-loop system despite mismatch. For MPC with general costs, a joint comparison function bound and scaling condition guarantee asymptotic stability despite mismatch. The results are illustrated in numerical simulations, including the classic upright pendulum problem. The tools developed to establish these results can address the stability of offset-free MPC, an open and interesting question in the MPC research literature.
Scalable and Optimal Security Allocation in Networks against Stealthy Injection Attacks
Authors: Anh Tung Nguyen, Sribalaji C. Anand, André M. H. Teixeira
Date: 2024-11-22 00:00:00

This paper addresses the security allocation problem in a networked control system under stealthy injection attacks. The networked system is comprised of interconnected subsystems which are represented by nodes in a digraph. An adversary compromises the system by injecting false data into several nodes with the aim of maximally disrupting the performance of the network while remaining stealthy to a defender. To minimize the impact of such stealthy attacks, the defender, with limited knowledge about attack policies and attack resources, allocates several sensors on nodes to impose the stealthiness constraint governing the attack policy. We provide an optimal security allocation algorithm to minimize the expected attack impact on the entire network. Furthermore, under a suitable local control design, the proposed security allocation algorithm can be executed in a scalable way. Finally, the obtained results are validated through several numerical examples.
KPG 193: A Synthetic Korean Power Grid Test System for Decarbonization Studies
Authors: Geonho Song, Jip Kim
Date: 2024-11-22 00:00:00

This paper introduces the 193 bus synthetic Korean power grid (KPG 193), developed using open data sources to address recent challenges of the Korean power system. The KPG 193 test system serves as a valuable platform for decarbonization research, capturing Korean low renewable energy penetration, concentrated urban energy demand, and isolated grid structure. Clustering techniques were applied to preserve key system characteristics while maintaining computational tractability and representativeness. The system includes 193 buses, 123 generators, 407 transmission lines, and incorporates temporal weather datasets. Its feasibility was validated through Unit Commitment (UC) and AC Optimal Power Flow (ACOPF) simulations using 2022 demand and renewable generation data. This test system aims to provide a foundational framework for modeling and analyzing the Korean power grid.
High-Bandwidth, Low-Computational Approach: Estimator-Based Control for Hybrid Flying Capacitor Multilevel Converters Using Multi-Cost Gradient Descent and State Feedforward
Authors: Inhwi Hwang
Date: 2024-11-22 00:00:00

This paper presents an estimator-based control framework for hybrid flying capacitor multilevel (FCML) converters, achieving high-bandwidth control and reduced computational complexity. Utilizing a hybrid estimation method that combines closed-loop and open-loop dynamics, the proposed approach enables accurate and fast flying capacitor voltage estimation without relying on isolated voltage sensors or high-cost computing hardware. The methodology employs multi-cost gradient descent and state feedforward algorithms, enhancing estimation performance while maintaining low computational overhead. A detailed analysis of stability, gain setting, and rank-deficiency issues is provided, ensuring robust operation across diverse converter levels and duty cycle conditions. Simulation results validate the effectiveness of the proposed estimator in achieving active voltage balancing and current control with 6-level AC-DC buck FCML, contributing to cost-effective solutions for FCML applications, such as data centers and electric aircraft.
Optimal Energy Dispatch of Grid-Connected Electric Vehicle Considering Lithium Battery Electrochemical Model
Authors: Yuanbo Chen, Kedi Zheng, Yuxuan Gu, Jianxiao Wang, Qixin Chen
Date: 2024-11-22 00:00:00

The grid-connected electric vehicles (EVs) serve as a promising regulating resource in the distribution grid with Vehicle-to-Grid (V2G) facilities. In the day-ahead stage, electric vehicle batteries (EVBs) need to be precisely dispatched and controlled to ensure high efficiency and prevent degradation. This article focuses on considering a refined battery model, i.e. the electrochemical model (EM), in the optimal dispatch of the local energy system with high penetration of EVs which replenish energy through V2G-equipped charge station and battery swapping station (BSS). In this paper, to utilize the EM efficiently, recursive EVB constraints and a corresponding matrix-based state update method are proposed based on EM power characterization. The charging EV state distribution is profiled and a multi-layer BSS model along with binary aggregation is proposed, in order to overcome the computation complexity of combining the refined battery constraints with the mixed integer optimization. Finally, a local energy system scenario is investigated for evaluation. The efficiency and effectiveness of EM consideration are assessed from the perspective of both the system and battery.
Reinterpreting PID Controller From the Perspective of State Feedback and Lumped Disturbance Compensation
Authors: Xinyu Shi
Date: 2024-11-22 00:00:00

This paper analyzes the motion of solutions to non-homogeneous linear differential equations. It further clarifies that a proportional-integral-derivative (PID) controller essentially comprises two parts: a homogeneous controller and a disturbance observer, which are responsible for stabilizing the homogeneous system and compensating for the lumped disturbances (non-homogeneous components) of the system respectively. Based on this framework, the impact of measurement noise on control performance is examined, and a parameter tuning scheme for the traditional PID controller is provided. Finally, as examples, controllers are designed for two representative control problems: a trajectory tracking controller for an underactuated vertical takeoff and landing (VTOL) aircraft in the time domain, and a lateral controller for a vehicle in the distance domain.
Analysis of the impact of heterogeneous platoon for mixed traffic flow: control strategy, fuel consumption and emissions
Authors: Yunxia Wu, Le Li, Zhihong Yao, Yi Wang, Gen Li, Yangsheng Jiang
Date: 2024-11-22 00:00:00

Compared with traditional vehicle longitudinal spacing control strategies, the combination spacing strategy can integrate the advantages of different spacing control strategies. However, the impact mechanism of different combination spacing control strategies on mixed traffic flow has not been analyzed yet. Therefore, this paper proposes various combination spacing control strategies for connected automated vehicles (CAVs). First, a mixed traffic flow model was developed to analyze the characteristics of CAV platoons. On this basis, a probability model of vehicle distribution was derived, and its effectiveness was verified through simulation. Then, multiple spacing combination strategies are proposed based on four spacing control strategies. Finally, numerical experiments were conducted to calculate the average fuel consumption and pollutant emissions of mixed traffic flow under different spacing control strategies, and the impact of platoon spacing control strategies on traffic flow fuel consumption and pollutant emissions was further analyzed. Results show that: (1) the differences in average fuel consumption and pollutant emissions of traffic flow are relatively small under different platoon spacing control strategies under low traffic density (i.e., 15 veh/km); (2) at medium to high traffic densities (i.e., 55-95 veh/km), when the penetration rate of CAVs exceeds 80%, VTG1-CS, VTG2-CS, and CTG-CS strategies can effectively ensure traffic flow stability and safety, and significantly reduce fuel consumption and pollutant emissions.
Path Planning and Task Assignment for Data Retrieval from Wireless Sensor Nodes Relying on Game-Theoretic Learning
Authors: Sotiris Papatheodorou, Michalis Smyrnakis, Tembine Hamidou, Anthony Tzes
Date: 2024-11-21 00:00:00
Tasks: Scheduling

The energy-efficient trip allocation of mobile robots employing differential drives for data retrieval from stationary sensor locations is the scope of this article. Given a team of robots and a set of targets (wireless sensor nodes), the planner computes all possible tours that each robot can make if it needs to visit a part of or the entire set of targets. Each segment of the tour relies on a minimum energy path planning algorithm. After the computation of all possible tour-segments, a utility function penalizing the overall energy consumption is formed. Rather than relying on the NP-hard Mobile Element Scheduling (MES) MILP problem, an approach using elements from game theory is employed. The suggested approach converges fast for most practical reasons thus allowing its utilization in near real time applications. Simulations are offered to highlight the efficiency of the developed algorithm.
Energy Efficient Automated Driving as a GNEP: Vehicle-in-the-loop Experiments
Authors: Viranjan Bhattacharyya, Tyler Ard, Rongyao Wang, Ardalan Vahidi, Yunyi Jia, Jihun Han
Date: 2024-11-21 00:00:00
Tasks: Model Predictive Control, Motion Planning

In this paper, a multi-agent motion planning problem is studied aiming to minimize energy consumption of connected automated vehicles (CAVs) in lane change scenarios. We model this interactive motion planning as a generalized Nash equilibrium problem and formalize how vehicle-to-vehicle intention sharing enables solution of the game between multiple CAVs as an optimal control problem for each agent, to arrive at a generalized Nash equilibrium. The method is implemented via model predictive control (MPC) and compared with an advanced baseline MPC which utilizes unilateral predictions of other agents' future states. A ROS-based in-the-loop testbed is developed: the method is first evaluated in software-in-the-loop and then vehicle-in-the-loop experiments are conducted. Experimental results demonstrate energy and travel time benefits of the presented method in interactive lane change maneuvers.
Effective Analog ICs Floorplanning with Relational Graph Neural Networks and Reinforcement Learning
Authors: Davide Basso, Luca Bortolussi, Mirjana Videnovic-Misic, Husni Habal
Date: 2024-11-20 00:00:00
Tasks: Transfer Learning

Analog integrated circuit (IC) floorplanning is typically a manual process with the placement of components (devices and modules) planned by a layout engineer. This process is further complicated by the interdependence of floorplanning and routing steps, numerous electric and layout-dependent constraints, as well as the high level of customization expected in analog design. This paper presents a novel automatic floorplanning algorithm based on reinforcement learning. It is augmented by a relational graph convolutional neural network model for encoding circuit features and positional constraints. The combination of these two machine learning methods enables knowledge transfer across different circuit designs with distinct topologies and constraints, increasing the \emph{generalization ability} of the solution. Applied to $6$ industrial circuits, our approach surpassed established floorplanning techniques in terms of speed, area and half-perimeter wire length. When integrated into a \emph{procedural generator} for layout completion, overall layout time was reduced by $67.3\%$ with a $8.3\%$ mean area reduction compared to manual layout.
Global and local synchrony of coupled neurons in small-world networks
Authors: Naoki Masuda, Kazuyuki Aihara
Date: 2024-11-25 00:00:00

Synchronous firing of neurons is thought to play important functional roles such as feature binding and switching of cognitive states. Although synchronization has mainly been investigated using model neurons with simple connection topology so far, real neural networks have more complex structures. Here we examine behavior of pulse-coupled leaky integrate-and-fire neurons with various network structures. We first show that the dispersion of the number of connections for neurons influences dynamical behavior even if other major topological statistics are kept fixed. The rewiring probability parameter representing the randomness of networks bridges two spatially opposite frameworks: precise local synchrony and rough global synchrony. Finally, cooperation of the global connections and the local clustering property, which is prominent in small-world networks, reinforces synchrony of distant neuronal groups receiving coherent inputs.
Stabilization of macroscopic dynamics by fine-grained disorder in many-species ecosystems
Authors: Juan Giral Martínez, Silvia De Monte, Matthieu Barbier
Date: 2024-11-22 00:00:00

A central feature of complex systems is the relevance and entanglement of different levels of description. For instance, the dynamics of ecosystems can be alternatively described in terms of large ecological processes and classes of organisms, or of individual species and their relations. Low-dimensional heuristic 'macroscopic' models that are widely used to capture ecological relationships -- and commonly evidence out-of equilibrium regimes -- implicitly assume that species-level 'microscopic' heterogeneity can be neglected. Here, we address the stability of such macroscopic descriptions to the addition of disordered microscopic interactions. We find that increased heterogeneity can stabilize collective as well as species fluctuations -- contrary to the well-known destabilizing effect of disorder on fixed points. We analytically find the conditions for the existence of heterogeneity-driven equilibria, and relate their stability to a mismatch in microscopic time scales. This may shed light onto the empirical observation that many-species ecosystems often appear stable at aggregated levels despite highly diverse interactions and large fluctuations at the species level.
Novel Cisplatin-Magnetoliposome Complex Shows Enhanced Antitumor Activity via Hyperthermia
Authors: M. Carmen Jiménez-López, Ana Carolina Moreno-Maldonado, Natividad Martín-Morales, Francisco OValle, M. Ricardo Ibarra, Gerardo F. Goya, Ignacio J. Molina
Date: 2024-11-13 00:00:00

There are several methods to improve cancer patient survival rates by inducing hyperthermia in tumor tissues, which involves raising their temperature above 41{\deg}C. These methods utilize different energy sources to deliver heat to the target region, including light, microwaves or radiofrequency electromagnetic fields. We have developed a new, magnetically responsive nanocarrier, consisting of liposomes loaded with magnetic nanoparticles and cis-diamminedichloroplatinum (II) (CDDP), commonly known as Cisplatin. The resulting magnetoliposome (ML) is rapidly internalized by lung and pancreas tumor cell lines, stored in intracellular vesicles, and capable of inducing hyperthermia under magnetic fields. The ML has no significant toxicity both in vitro and in vivo and, most importantly, enhances cell death by apoptosis after magnetic hyperthermia. Remarkably, mice bearing induced lung tumors, treated with CDDP-loaded nanocarriers and subjected to an applied electromagnetic field, showed an improved survival rate over those treated with either soluble CDDP or hyperthermia alone. Therefore, our approach of magnetic hyperthermia plus CDDP-ML significantly enhances in vitro cell death and in vivo survival of treated animals.
Data-driven Modeling of Granular Chains with Modern Koopman Theory
Authors: Atoosa Parsa, James Bagrow, Corey S. O'Hern, Rebecca Kramer-Bottiglio, Josh Bongard
Date: 2024-11-01 00:00:00

Externally driven dense packings of particles can exhibit nonlinear wave phenomena that are not described by effective medium theory or linearized approximate models. Such nontrivial wave responses can be exploited to design sound-focusing/scrambling devices, acoustic filters, and analog computational units. At high amplitude vibrations or low confinement pressures, the effect of nonlinear particle contacts becomes increasingly noticeable, and the interplay of nonlinearity, disorder, and discreteness in the system gives rise to remarkable properties, particularly useful in designing structures with exotic properties. In this paper, we build upon the data-driven methods in dynamical system analysis and show that the Koopman spectral theory can be applied to granular crystals, enabling their phase space analysis beyond the linearizable regime and without recourse to any approximations considered in the previous works. We show that a deep neural network can map the dynamics to a latent space where the essential nonlinearity of the granular system unfolds into a high-dimensional linear space. As a proof of concept, we use data from numerical simulations of a two-particle system and evaluate the accuracy of the trajectory predictions under various initial conditions. By incorporating data from experimental measurements, our proposed framework can directly capture the underlying dynamics without imposing any assumptions about the physics model. Spectral analysis of the trained surrogate system can help bridge the gap between the simulation results and the physical realization of granular crystals and facilitate the inverse design of materials with desired behaviors.
Optimal switching strategies in multi-drug therapies for chronic diseases
Authors: Juan Magalang, Javier Aguilar, Jose Perico Esguerra, Édgar Roldán, Daniel Sanchez-Taltavull
Date: 2024-11-25 00:00:00

Antimicrobial resistance is a threat to public health with millions of deaths linked to drug resistant infections every year. To mitigate resistance, common strategies that are used are combination therapies and therapy switching. However, the stochastic nature of pathogenic mutation makes the optimization of these strategies challenging. Here, we propose a two-scale stochastic model that considers the effective evolution of therapies in a multidimensional efficacy space, where each dimension represents the efficacy of a specific drug in the therapy. The diffusion of therapies within this space is subject to stochastic resets, representing therapy switches. The boundaries of the space, inferred from coarser pathogen-host dynamics, can be either reflecting or absorbing. Reflecting boundaries impede full recovery of the host, while absorbing boundaries represent the development of antimicrobial resistance, leading to therapy failure. We derive analytical expressions for the average absorption times, accounting for both continuous and discrete genomic changes using the frameworks of Langevin and Master equations, respectively. These expressions allow us to evaluate the relevance of times between drug-switches and the number of simultaneous drugs in relation to typical timescales for drug resistance development. We also explore realistic scenarios where therapy constraints are imposed to the number of administered therapies and/or their costs, finding non-trivial optimal drug-switching protocols that maximize the time before antimicrobial resistance develops while reducing therapy costs.
Complexity synchronization analysis of neurophysiological data: Theory and methods
Authors: Ioannis Schizas, Sabrina Sullivan, Scott E. Kerick, Korosh Mahmoodi, J. Cortney Bradford, David L. Boothe, Piotr J. Franaszczuk, Paolo Grigolini, Bruce J. West
Date: 2024-11-21 00:00:00
Tasks: Time Series

We apply modified diffusion entropy analysis (MDEA) to assess multifractal dimensions of ON time series (ONTS) and complexity synchronization (CS) analysis to infer information transfer among ONs that are part of a network of organ networks (NoONs). The purpose of this paper is to advance the validation, standardization, and repeatability of MDEA and CS analysis of heterogeneous neurophysiological time series data. Results from processing these datasets show that the complexity of brain, heart, and lung ONTS significantly co-vary over time during cognitive task performance but that certain principles, guidelines, and strategies for the application of MDEA analysis need consideration.
Dynamic Causal Models of Time-Varying Connectivity
Authors: Johan Medrano, Karl J. Friston, Peter Zeidman
Date: 2024-11-25 00:00:00
Tasks: Computational Efficiency

This paper introduces a novel approach for modelling time-varying connectivity in neuroimaging data, focusing on the slow fluctuations in synaptic efficacy that mediate neuronal dynamics. Building on the framework of Dynamic Causal Modelling (DCM), we propose a method that incorporates temporal basis functions into neural models, allowing for the explicit representation of slow parameter changes. This approach balances expressivity and computational efficiency by modelling these fluctuations as a Gaussian process, offering a middle ground between existing methods that either strongly constrain or excessively relax parameter fluctuations. We validate the ensuing model through simulations and real data from an auditory roving oddball paradigm, demonstrating its potential to explain key aspects of brain dynamics. This work aims to equip researchers with a robust tool for investigating time-varying connectivity, particularly in the context of synaptic modulation and its role in both healthy and pathological brain function.