pwc-archive/papers-with-abstracts huggingface dataset:
The Unreasonable Effectiveness of Open Science in AI: A Replication Study
Authors: Odd Erik Gundersen, Odd Cappelen, Martin Mølnå, Nicklas Grimstad Nilsen
Date: 2024-12-20 00:00:00
Tasks: Articles

A reproducibility crisis has been reported in science, but the extent to which it affects AI research is not yet fully understood. Therefore, we performed a systematic replication study including 30 highly cited AI studies relying on original materials when available. In the end, eight articles were rejected because they required access to data or hardware that was practically impossible to acquire as part of the project. Six articles were successfully reproduced, while five were partially reproduced. In total, 50% of the articles included was reproduced to some extent. The availability of code and data correlate strongly with reproducibility, as 86% of articles that shared code and data were fully or partly reproduced, while this was true for 33% of articles that shared only data. The quality of the data documentation correlates with successful replication. Poorly documented or miss-specified data will probably result in unsuccessful replication. Surprisingly, the quality of the code documentation does not correlate with successful replication. Whether the code is poorly documented, partially missing, or not versioned is not important for successful replication, as long as the code is shared. This study emphasizes the effectiveness of open science and the importance of properly documenting data work.
The behavioral approach for LPV data-driven representations
Authors: Chris Verhoek, Ivan Markovsky, Sofie Haesaert, Roland Tóth
Date: 2024-12-24 00:00:00
Tasks: Scheduling

In this paper, we present data-driven representations of linear parameter-varying (LPV) systems that can be used for direct data-driven analysis and control of LPV systems. Specifically, we use the behavioral approach for LPV systems to develop a data-driven representation of the finite-horizon behavior of an LPV system that can be represented by a kernel representation with shifted-affine scheduling dependence. Moreover, we provide a necessary and sufficient rank-based test on the available data that concludes whether the data-driven representation fully represents the finite-horizon behavior. The results in this paper allow for direct data-driven analysis and control of LPV systems with stability and performance guarantees. We demonstrate this by also solving the LPV data-driven simulation problem. Moreover, through the use of LPV systems as surrogates for nonlinear systems, our results may serve as a stepping stone towards direct data-driven analysis and control of nonlinear systems.
Koopman operator based identification of nonlinear networks
Authors: Ramachandran Anantharaman, Alexandre Mauroy
Date: 2024-12-24 00:00:00
Tasks: Network Identification

In this work, we develop a method to identify continuous-time nonlinear networked dynamics via the Koopman operator framework. The proposed technique consists of two steps: the first step identifies the neighbors of each node, and the second step identifies the local dynamics at each node from a predefined set of dictionary functions. The technique can be used to either identify the Boolean network of interactions (first step) or to solve the complete network identification problem that amounts to estimating the local node dynamics and the nature of the node interactions (first and second steps). Under a sparsity assumption, the data required to identify the complete network dynamics is significantly less than the total number of dictionary functions describing the dynamics. This makes the proposed approach attractive for identifying large dimensional networks with sparse interconnections. The accuracy and performance of the proposed identification technique are demonstrated with several examples.
Betting vs. Trading: Learning a Linear Decision Policy for Selling Wind Power and Hydrogen
Authors: Yannick Heiser, Farzaneh Pourahmadi, Jalal Kazempour
Date: 2024-12-24 00:00:00
Tasks: Management, Scheduling

We develop a bidding strategy for a hybrid power plant combining co-located wind turbines and an electrolyzer, constructing a price-quantity bidding curve for the day-ahead electricity market while optimally scheduling hydrogen production. Without risk management, single imbalance pricing leads to an all-or-nothing trading strategy, which we term 'betting'. To address this, we propose a data-driven, pragmatic approach that leverages contextual information to train linear decision policies for both power bidding and hydrogen scheduling. By introducing explicit risk constraints to limit imbalances, we move from the all-or-nothing approach to a 'trading" strategy', where the plant diversifies its power trading decisions. We evaluate the model under three scenarios: when the plant is either conditionally allowed, always allowed, or not allowed to buy power from the grid, which impacts the green certification of the hydrogen produced. Comparing our data-driven strategy with an oracle model that has perfect foresight, we show that the risk-constrained, data-driven approach delivers satisfactory performance.
Non-Minimum-Phase Resonant Controller for Active Damping Control: Application to Piezo-Actuated Nanopositioning System
Authors: Aditya M. Natu, S. Hassan HosseinNia
Date: 2024-12-24 00:00:00

Nanopositioning systems frequently encounter limitations in control bandwidth due to their lightly damped resonance behavior. This paper presents a novel Non-Minimum-Phase Resonant Controller (NRC) aimed at active damping control within dual closed-loop architectures, specifically applied to piezo-actuated nanopositioning systems. The control strategy is structured around formulated objectives for shaping sensitivity functions to meet predetermined system performance criteria. Leveraging non-minimum-phase characteristics, the proposed NRC accomplishes complete damping and the bifurcation of double resonant poles at the primary resonance peak through a constant-gain design accompanied by tunable phase variation. The NRC demonstrates robustness against frequency variations of the resonance arising from load changes and is also capable of damping higher-order flexural modes simultaneously. Furthermore, by establishing high gains at low frequencies within the inner closed-loop and integrating it with a conventional PI tracking controller, the NRC achieves substantial dual closed-loop bandwidths that can exceed the first resonance frequency. Moreover, the NRC significantly diminishes the effect of low-frequency reference signals on real feedback errors while effectively rejecting disturbances proximate to the resonance frequency. All contributions are thoroughly formulated and exemplified mathematically, with the controller's performance confirmed through an experimental setup utilizing an industrial nanopositioning system. The experimental results indicate dual closed-loop bandwidths of 895 Hz and 845 Hz, characterized by $\pm$3 dB and $\pm$1 dB bounds, respectively, that surpass the resonance frequency of 739 Hz.
SCKF-LSTM Based Trajectory Tracking for Electricity-Gas Integrated Energy System
Authors: Liang Chen, Yang Li, Jun Cai, Songlin Gu, Ying Yan
Date: 2024-12-24 00:00:00
Tasks: Time Series Prediction

This paper introduces a novel approach for tracking the dynamic trajectories of integrated natural gas and power systems, leveraging a Kalman filter-based structure. To predict the states of the system, the Holt's exponential smoothing techniques and nonlinear dynamic equations of gas pipelines are applied to establish the power and gas system equations, respectively. The square-root cubature Kalman filter algorithm is utilized to address the numerical challenges posed by the strongly nonlinear system equations. The boundary conditions in the gas system include the flow balances at sink nodes, and the mass flow rates of loads have to be predicted at each computation step. For the prediction of load mass flows, the long short-term memory network is employed, known for its effectiveness in time series prediction. Consequently, a combined method based on the square-root cubature Kalman filter and the long short-term memory network is proposed for tracking integrated gas and power systems. To evaluate the tracking performances of the proposed method, the IEEE-39 bus power system and GasLib-40 node gas system are used to form the testing system. Simulation results demonstrate high precision in tracking the dynamic states of power and gas systems. Two indexes are introduced for a numerical analysis of the tracking results, indicating that the accuracy of this method surpasses that of traditional measurements.
Comprehensive Analysis and Experimental Design of High-Gain DC-DC Boost Converter Topologies
Authors: Webster Adepoju, Mary Sanyaolu
Date: 2024-12-24 00:00:00
Tasks: Experimental Design

Global demand for clean and eco-friendly energy sources has inspired decades of far-reaching research in power generation from renewable energy sources. Solar cells, wind, and tidal sources are limited in output power generation compared to the fast-rising power requirements of most industrial applications. Besides, the efficiency of conventional DC-DC boost converters is significantly low due to the presence of parasitic elements culminating in switching losses. This study presents three high-gain boost converter topologies for optimizing the limited voltage generation by solar Photovoltaic cells. The three converters are realized based on a modification of a classical Cuk converter. Simply put, the first proposed converter is realized by the inclusion of one capacitor and one inductor to the classical topology. Similarly, the addition of 3 capacitors, 2 diodes, and one inductor leads to the practical realization of the second proposed topology. Similarly, the third proposed topology consists of additional 3 diodes and 3 capacitors. Based on this estimation, the first, second, and third proposed high gain modified Cuk converter topologies generate output voltages 10 times, 20 times, and 29 times the input voltage respectively when the switching device is gated at 90% duty ratio. Theoretical/mathematical analysis validates the Lt-spice numerical simulation results of all the proposed converters. Furthermore, experimental prototype results were compared with Lt-spice estimation to determine the accuracy of the converters.
Simulation-based Approach for Fast Optimal Control of a Stefan Problem with Application to Cell Therapy
Authors: Prakitr Srisuma, George Barbastathis, Richard D. Braatz
Date: 2024-12-24 00:00:00
Tasks: Model Predictive Control

This article describes a new, efficient way of finding control and state trajectories in optimal control problems by reformulation as a system of differential-algebraic equations (DAEs). The optimal control and state vectors can be obtained via simulation of the resulting DAE system with the selected DAE solver, eliminating the need for an optimization solver. Our simulation-based approach is demonstrated and benchmarked against various optimization-based algorithms via four case studies associated with the optimization and control of a Stefan problem for cell therapy. The simulation-based approach is faster than every optimization-based method by more than an order of magnitude while giving similar/better accuracy in all cases. The solution obtained from the simulation-based approach is guaranteed to be optimal provided that at least one constraint or algebraic equation resulting from the reformulation remains active at all times. The proposed technique offers an efficient and reliable framework for optimal control, serving as a promising alternative to the traditional techniques in applications where speed is crucial, e.g., real-time online model predictive control.
Future Pathways for EVTOLs: A Design Optimization Perspective
Authors: Johannes Janning, Sophie F. Armanini, Urban Fasel
Date: 2024-12-24 00:00:00

The rapid development of advanced urban air mobility, particularly electric vertical take-off and landing (eVTOL) aircraft, requires interdisciplinary approaches involving the future urban air mobility ecosystem. Operational cost efficiency, regulatory aspects, sustainability, and environmental compatibility must be incorporated directly into the preliminary design of aircraft and across operational and regulatory strategies. In this work, we present a novel multidisciplinary design optimization framework for the preliminary design of eVTOL aircraft. The framework optimizes conventional design elements of eVTOL aircraft over a generic mission and integrates a comprehensive operational cost model to directly capture economic incentives of the designed system through profit modeling for operators. We compare the optimized eVTOL system with various competing road, rail, and air transportation modes in terms of sustainability, cost, and travel time. We investigate four objective-specific eVTOL optimization designs in a broad scenario space, mapping regulatory, technical, and operational constraints to generate a representation of potential urban air mobility ecosystem conditions. The analysis of an optimized profit-maximizing eVTOL, cost-minimizing eVTOL, sustainability-maximizing eVTOL, and a combined figure of merit maximizing eVTOL design highlights significant trade-offs in the area of profitability, operational flexibility, and sustainability strategies. This underlines the importance of incorporating multiple operationally tangential disciplines into the design process.
Dynamic Power Management in Modular Reconfigurable Battery Systems with Energy and Power Modules
Authors: Pouyan Pourhadi, Nima Tashakor, Mahdi Bayati, Stefan Goetz
Date: 2024-12-23 00:00:00
Tasks: Management

Integrating power electronics with batteries can offer many advantages, including load sharing and balancing with parallel connectivity. However, parallel batteries with differing voltages and power profiles can cause large circulating currents and uncontrolled energy transfers, risking system instability. To overcome these challenges, we propose a novel modular reconfigurable topology for AC batteries with coupled inductors between adjacent submodules. This approach dynamically manages energy distribution between energy and power modules. Under normal conditions, energy modules operate in series or parallel configurations to deliver stable voltage and current, ensuring efficient power delivery. During heavy loads, such as during electric-vehicle acceleration, power modules seamlessly meet additional power demands beyond the energy modules' capacity. The use of mutual inductors reduces inductance along the load path for faster response times while providing sufficient inductance to regulate circulating currents and enable efficient energy transfer among modules. The system's AC load further complicates control, necessitating a simple yet effective feedforward-feedback control strategy to maintain satisfactory performance.
Reinforcement Learning for Motor Control: A Comprehensive Review
Authors: Danial Kazemikia
Date: 2024-12-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Electric motors are crucial in many applications, but traditional control methods struggle with nonlinearities, parameter uncertainties, and external disturbances. Reinforcement Learning (RL) offers a promising solution as a data-driven approach that can learn optimal control strategies without an explicit model. This review paper examines the current state of RL in motor control, exploring various RL algorithms and applications. The review highlights RL's advantages, including model-free control, adaptability to changing conditions, and the ability to optimize for complex objectives. It also addresses challenges in applying RL to motor control, such as sim-to-real transfer, safety and stability concerns, scalability, and computational complexity. By providing a comprehensive overview of the field, this review aims to deepen understanding of RL's potential to revolutionize motor control and drive advancements across industries.
Estimating Potential Demand and Customers' Perception of Service Value in a Two-station Service System
Authors: Nishant Mangre, Jiesen Wang
Date: 2024-11-21 00:00:00

The potential demand in the market and customers' perception of service value are crucial factors in pricing strategies, resource allocation, and other operational decisions. However, this information is typically private and not readily accessible. In this paper, we analyze a service system operating across two stations, each with its own customer flow. Customers arriving at the system are informed of the waiting times at both stations and can choose to either join the local station, switch to the other station, or balk. Our objective is to estimate the arrival rates at each station and customers' perceived service value based on the observed workloads at both stations. A significant challenge arises from the inability to observe balking customers and the lack of distinction between local arrivals and customers switching from the other station, as the switching cost is unknown. To address this issue, we employ maximum likelihood estimation and validate the effectiveness of the estimator through a series of simulations.
An Ultra-Wideband Study of Vegetation Impact on Upper Midband / FR3 Communication
Authors: Naveed A. Abbasi, Tathagat Pal, Kelvin Arana, Vikram Vasudevan, Jorge Gomez-Ponce, Young-Han Nam, Charlie Zhang, Andreas F. Molisch
Date: 2024-12-20 00:00:00

Growing demand for high data rates is driving interest in the upper mid-band (FR 3) spectrum (6-24 GHz). While some propagation measurements exist in literature, the impact of vegetation on link performance remains under-explored. This study examines vegetation-induced losses in an urban scenario across 6-18 GHz. A simple method for calculating vegetation depth is introduced, along with a model that quantifies additional attenuation based on vegetation depth and frequency, divided into 1 GHz sub-bands. We see that excess vegetation loss increases with vegetation depth and higher frequencies. These findings provide insights for designing reliable, foliage-aware communication networks in FR 3.
Coordinated Power Smoothing Control for Wind Storage Integrated System with Physics-informed Deep Reinforcement Learning
Authors: Shuyi Wang, Huan Zhao, Yuji Cao, Zibin Pan, Guolong Liu, Gaoqi Liang, Junhua Zhao
Date: 2024-12-17 00:00:00
Tasks: Deep Reinforcement Learning, Multi-agent Reinforcement Learning

The Wind Storage Integrated System with Power Smoothing Control (PSC) has emerged as a promising solution to ensure both efficient and reliable wind energy generation. However, existing PSC strategies overlook the intricate interplay and distinct control frequencies between batteries and wind turbines, and lack consideration of wake effect and battery degradation cost. In this paper, a novel coordinated control framework with hierarchical levels is devised to address these challenges effectively, which integrates the wake model and battery degradation model. In addition, after reformulating the problem as a Markov decision process, the multi-agent reinforcement learning method is introduced to overcome the bi-level characteristic of the problem. Moreover, a Physics-informed Neural Network-assisted Multi-agent Deep Deterministic Policy Gradient (PAMA-DDPG) algorithm is proposed to incorporate the power fluctuation differential equation and expedite the learning process. The effectiveness of the proposed methodology is evaluated through simulations conducted in four distinct scenarios using WindFarmSimulator (WFSim). The results demonstrate that the proposed algorithm facilitates approximately an 11% increase in total profit and a 19% decrease in power fluctuation compared to the traditional methods, thereby addressing the dual objectives of economic efficiency and grid-connected energy reliability.
Finite size scaling of survival statistics in metapopulation models
Authors: Alice Doimo, Giorgio Nicoletti, Davide Bernardi, Prajwal Padmanabha
Date: 2024-12-24 00:00:00

Spatial metapopulation models are fundamental to theoretical ecology, enabling to study how landscape structure influences global species dynamics. Traditional models, including recent generalizations, often rely on the deterministic limit of stochastic processes, assuming large population sizes. However, stochasticity - arising from dispersal events and population fluctuations - profoundly shapes ecological dynamics. In this work, we extend the classical metapopulation framework to account for finite populations, examining the impact of stochasticity on species persistence and dynamics. Specifically, we analyze how the limited capacity of local habitats influences survival, deriving analytical expressions for the finite-size scaling of the survival probability near the critical transition between survival and extinction. Crucially, we demonstrate that the deterministic metapopulation capacity plays a fundamental role in the statistics of survival probability and extinction time moments. These results provide a robust foundation for integrating demographic stochasticity into classical metapopulation models and their extensions.
Moving boundaries: An appreciation of John Hopfield
Authors: William Bialek
Date: 2024-12-23 00:00:00

The 2024 Nobel Prize in Physics was awarded to John Hopfield and Geoffrey Hinton, "for foundational discoveries and inventions that enable machine learning with artificial neural networks." As noted by the Nobel committee, their work moved the boundaries of physics. This is a brief reflection on Hopfield's work, its implications for the emergence of biological physics as a part of physics, the path from his early papers to the modern revolution in artificial intelligence, and prospects for the future.
ORAN Drives Higher Returns on Investments in Urban and Suburban Regions
Authors: Priyanka Sharma, Edward J. Oughton, Aleksan Shanoyan
Date: 2024-12-24 00:00:00

This paper provides the first incentive analysis of open radio access networks (ORAN) using game theory. We assess strategic interactions between telecom supply chain stakeholders: mobile network operators (MNOs), network infrastructure suppliers (NIS), and original equipment manufacturers (OEMs) across three procurement scenarios: (i) Traditional, (ii) Predatory as monolithic radio access networks (MRAN), and (iii) DirectOEM as ORAN. We use random forest and gradient boosting models to evaluate the optimal margins across urban, suburban, and rural U.S. regions. Results suggest that ORAN deployment consistently demonstrates higher net present value (NPV) of profits in urban and suburban regions, outperforming the traditional procurement strategy by 11% to 31%. However, rural areas present lower NPVs across all scenarios, with significant variability at the county level. This analysis offers actionable insights for telecom investment strategies, bridging technical innovation with economic outcomes and addressing strategic supply chain dynamics through a game-theoretic lens.