pwc-archive/papers-with-abstracts huggingface dataset:
Performance Analysis of a Photovoltaic System with Thermoelectric Generator and Phase Change Material; An Experimental Approach
Authors: Tobechukwu Okamkpa, Joshua Okechukwu, Divine Mbachu, Chigbo Mgbemene
Date: 2024-10-17 00:00:00

This study explores the integration of thermoelectric generators (TEGs) and phase change materials (PCMs) to enhance the efficiency of photovoltaic (PV) panels in high-temperature conditions. An AP-PM-20 Polycrystalline PV panel, SP-1848-27145 Bismuth Telluride TEG, and paraffin wax PCM in an aluminum container were used. Four configurations were tested: standalone PV, PV-PCM, PV-TEG-PCM, and PV-PCM-TEG, under identical conditions from 10:30 AM to 6:00 PM at 25-minute intervals. Data on PV and TEG voltage, current, and solar irradiance were collected and analyzed. The results show significant performance improvements: the PV-PCM configuration boosted power output by 68.04%, while PV-PCM-TEG and PV-TEG-PCM configurations improved efficiency by 43.06% and 37.51%, respectively. Efficiency gains relative to the standalone PV system were 33.33% for PV-PCM, 25.76% for PV-PCM-TEG, and 21.21% for PV-TEG-PCM, demonstrating the effectiveness of PCMs and TEGs in enhancing PV performance.
Cooperative Visual Convex Area Coverage using a Tessellation-free Strategy
Authors: Sotiris Papatheodorou, Anthony Tzes
Date: 2024-10-17 00:00:00

The objective in this article is to develop a control strategy for coverage purposes of a convex region by a fleet of Mobile Aerial Agents (MAAs). Each MAA is equipped with a downward facing camera that senses a convex portion of the area while its altitude flight is constrained. Rather than relying on typical Voronoi-like tessellations of the area to be covered, a scheme focusing on the assignment to each MAA of certain parts of the mosaic of the current covered area is proposed. A gradient ascent algorithm is then employed to increase in a monotonic manner the covered area by the MAA-fleet. Simulation studies are offered to illustrate the effectiveness of the proposed scheme.
Dynamic Input Mapping Inversion for Algebraic Loop-Free Control in Hydraulic Actuators
Authors: Alessio Dallabona, Patrik Schermann, Mogens Blanke, Dimitrios Papageorgiou
Date: 2024-10-17 00:00:00

The application of nonlinear control schemes to electro-hydraulic actuators often requires several alterations in the design of the controllers during their implementation. This is to overcome the challenges that frequently arise from the inherent complexity of such control algorithms owning to model nonlinearities. Moreover, advanced control solutions for this type of systems often introduce input algebraic loops and chatter, which considerably degrade the tracking performance. This study presents a nonlinear control architecture for hydraulic actuators that comprises low-complexity modules, based on well-established designs that facilitate robust high performance in tracking without introducing the aforementioned limitations. Specifically, the proposed solution consists of two variants of a position controller for the hydraulic cylinder and a dynamic input-mapping inversion module to avoid algebraic loops in the control input. The stability of the closed-loop system is analysed using arguments from Lyapunov theory for cascaded non-autonomous nonlinear systems. The effectiveness of the proposed solution is evaluated on a high-fidelity simulator of a wind turbine pitch system. Appropriate quantitative metrics are finally defined to evaluate the closed-loop system performance in comparison to state-of-the-art nonlinear design.
Assessing the techno-economic benefits of LEMs for different grid topologies and prosumer shares
Authors: Markus Doepfert, Soner Candas, Hermann Kraus, Peter Tzscheutschler, Thomas Hamacher
Date: 2024-10-17 00:00:00

The shift towards decentralized and renewable energy sources has introduced significant challenges to traditional power systems, necessitating innovative market designs. Local energy markets present a viable solution for integrating distributed energy resources such as photovoltaic systems, electric vehicles, and heat pumps within various grid topologies. This study investigates the techno-economic benefits of local energy markets compared to conventional market designs, focusing on their impact on average energy prices and operational peak power, using a self-developed agent-based energy system simulation tool. Through comprehensive simulations across the countryside, rural, suburban, and urban grid topologies with varying penetration levels of the distributed energy resources, totaling 400 simulation setups, we demonstrate that local energy markets can enhance economic efficiency and grid stability with 99 % of the scenarios boasting lower average energy prices and 80 % lower operational peak power levels. Our findings suggest that local energy markets can play a role in the future energy system, especially in areas with high shares of PV and HP, provided that additional infrastructure, management costs, and bureaucratic complexity are kept to a minimum.
A Critical Review of Proton Exchange Membrane Fuel Cells Matter Transports and Voltage Polarisation for Modelling
Authors: Raphaël Gass, Zhongliang Li, Rachid Outbib, Samir Jemei, Daniel Hissel
Date: 2024-10-17 00:00:00
Tasks: Articles

Technologies based on the use of hydrogen are promising for future energy requirements in a more sustainable world. Consequently, modelling fuel cells is crucial, for instance, to optimize their control to achieve excellent performance, to test new materials and configurations on a limited budget, or to consider their degradation for improved lifespan. To develop such models, a comprehensive study is required, encompassing both well-established and the latest governing laws on matter transport and voltage polarisation for Proton Exchange Membrane Fuel Cells (PEMFCs). Recent articles often rely on outdated or inappropriate equations, lacking clear explanations regarding their background. Indeed, inconsistent understanding of theoretical and experimental choices or model requirements hinders comprehension and contributes to the misuse of these equations. Additionally, specific researches are needed to construct more accurate models. This study aims to offer a comprehensive understanding of the current state-of-the-art in PEMFC modeling. It clarifies the corresponding governing equations, their usage conditions, and assumptions, thus serving as a foundation for future developments. The presented laws and equations are applicable in most multi-dimensional, dynamic, and two-phase PEMFC models.
Cyber C2: Achieving Scrutability and Agency in Cyberspace Operations
Authors: Daniel Salmond, Van Nguyen, Anton V. Uzunov, Natalia Nikolova, Prajakta Desai, Ross Kyprianou
Date: 2024-10-16 00:00:00

Our thesis is that operating in cyberspace is challenging because cyberspace exhibits extreme variety, high malleability, and extreme velocity. These properties make cyberspace largely inscrutable and limits one's agency in cyberspace, where agency is the ability to exert influence to transform the state or behaviour of the environment. With this thesis, we explore the nature of cyberspace, command and control (C2), and diagnose the challenges for cyber C2, with treatment to follow in future work.
A Communication Consistent Approach to Signal Temporal Logic Task Decomposition in Multi-Agent Systems
Authors: Gregorio Marchesini, Siyuan Liu, Lars Lindemann, Dimos V. Dimarogonas
Date: 2024-10-16 00:00:00

We consider the problem of decomposing a global task assigned to a multi-agent system, expressed as a formula within a fragment of Signal Temporal Logic (STL), under range-limited communication. Given a global task expressed as a conjunction of local tasks defined over the individual and relative states of agents in the system, we propose representing task dependencies among agents as edges of a suitably defined task graph. At the same time, range-limited communication naturally induces the definition of a communication graph that defines which agents have access to each other's states. Within these settings, inconsistencies arise when a task dependency between a pair of agents is not supported by a corresponding communication link due to the limited communication range. As a result, state feedback control laws previously derived to achieve the tasks' satisfaction can not be leveraged. We propose a task decomposition mechanism to distribute tasks assigned to pairs of non-communicating agents in the system as conjunctions of tasks defined over the relative states of communicating agents, thus enforcing consistency between task and communication graphs. Assuming the super-level sets of the predicate functions composing the STL tasks are bounded polytopes, our task decomposition mechanism can be cast as a parameter optimization problem and solved via state-of-the-art decentralized convex optimization algorithms. To guarantee the soundness of our approach, we present various conditions under which the tasks defined in the applied STL fragment are unsatisfiable, and we show sufficient conditions such that our decomposition approach yields satisfiable global tasks after decomposition.
Optimal Network Expansion Planning Considering Uncertain Dynamic Thermal Line Rating
Authors: Arash Baharvandi, Duong Tung Nguyen
Date: 2024-10-16 00:00:00

This paper examines the integrated generation and transmission expansion planning problem to address the growing challenges associated with increasing power network loads. The proposed approach optimizes the operation and investment costs for new generation units and transmission lines, while also considering the environmental benefits of integrating renewable energy sources (RES) and the impact of electric vehicle (EV) charging on the grid. The inherent uncertainties in demand, EV charging loads, and RES generation are managed using a hybrid stochastic-robust optimization approach. Additionally, the model integrates Dynamic Thermal Line Rating (DTLR) to improve the efficiency and resilience of transmission lines. The framework also tackles the uncertainty related to DTLR, incorporating a heuristic linearization technique to reduce model complexity. The effectiveness of the proposed model and techniques is evaluated through simulations conducted on two case studies: the modified IEEE 6-bus system and the IEEE 24-bus Reliability Test System.
Modeling, Prediction and Risk Management of Distribution System Voltages with Non-Gaussian Probability Distributions
Authors: Yuanhai Gao, Xiaoyuan Xu, Zheng Yan, Mohammad Shahidehpour, Bo Yang, Xinping Guan
Date: 2024-10-16 00:00:00
Tasks: Management, Prediction

High renewable energy penetration into power distribution systems causes a substantial risk of exceeding voltage security limits, which needs to be accurately assessed and properly managed. However, the existing methods usually rely on the joint probability models of power generation and loads provided by probabilistic prediction to quantify the voltage risks, where inaccurate prediction results could lead to over or under estimated risks. This paper proposes an uncertain voltage component (UVC) prediction method for assessing and managing voltage risks. First, we define the UVC to evaluate voltage variations caused by the uncertainties associated with power generation and loads. Second, we propose a Gaussian mixture model-based probabilistic UVC prediction method to depict the non-Gaussian distribution of voltage variations. Then, we derive the voltage risk indices, including value-at-risk (VaR) and conditional value-at-risk (CVaR), based on the probabilistic UVC prediction model. Third, we investigate the mechanism of UVC-based voltage risk management and establish the voltage risk management problems, which are reformulated into linear programming or mixed-integer linear programming for convenient solutions. The proposed method is tested on power distribution systems with actual photovoltaic power and load data and compared with those considering probabilistic prediction of nodal power injections. Numerical results show that the proposed method is computationally efficient in assessing voltage risks and outperforms existing methods in managing voltage risks. The deviation of voltage risks obtained by the proposed method is only 15% of that by the methods based on probabilistic prediction of nodal power injections.
Design and Analysis of a Metamaterial-Inspired Absorber for data rate in 52% RF-to-DC conversion Efficiency Dual-band SWIPT system
Authors: Zhengchen Dong, Xin Jiang, Adel Barakat, Ramesh K. Pokharel
Date: 2024-10-16 00:00:00

This paper proposes a novel metamaterial-inspired absorber designed to enhance the data rate in 52% RF to DC conversion simultaneous wireless information and power transfer system (SWIPT) through biological tissue. The proposed absorber includes split-ring resonators(SRRs) and demonstrates significant permeability characteristics, with both the real and imaginary parts being negative and close to -1. It also improves isolation by around 5dB in a WPT distance of 9mm. A 5mm thick phantom is used for biological tissue in this study. Experimental results exhibits that the SWIPT systems including a rectifier that converts 52% RF to DC efficiency in a WPT distance of 9mm embedding this absorber between power and signal ports at Tx side results in a 5dB improvement in isolation performance. By using proposed absorber, it enables a 7MB/s improvement of data rate and allows signals to be transmitted with 5dBm weaker power than without absorber SWIPT system.
RTI-NMPC for Control of Autonomous Vehicles Using Implicit Discretization Methods
Authors: Matheus Wagner, Julio E. Normey-Rico
Date: 2024-10-16 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Model Predictive Control

Recent efforts in the development of autonomous driving technology have induced great advancements in perception, planning and control systems. Model predictive control is one of the most popular advanced control methods, but its application to nonlinear systems still depends on the development of computationally efficient methods. This work presents a nonlinear model predictive control formulation based on real-time iteration using an implicit discretization of the system's dynamics, with the objective of achieving greater prediction accuracy and lower computational cost when dealing with stiff dynamical systems, as is the case for vehicle dynamics. The proposed method is described and later evaluated on a simulation scenario considering modeling errors and external disturbances. The presented results demonstrate the effectiveness of the method when it comes to tracking a given trajectory and its low computational burden, measured in terms of execution time.
System-Level Analysis of Module Uncertainty Quantification in the Autonomy Pipeline
Authors: Sampada Deglurkar, Haotian Shen, Anish Muthali, Marco Pavone, Dragos Margineantu, Peter Karkus, Boris Ivanovic, Claire J. Tomlin
Date: 2024-10-15 00:00:00
Tasks: Autonomous Driving, Decision Making, Decision Making Under Uncertainty, Uncertainty Quantification

We present a novel perspective on the design, use, and role of uncertainty measures for learned modules in an autonomous system. While in the current literature uncertainty measures are produced for standalone modules without considering the broader system context, in our work we explicitly consider the role of decision-making under uncertainty in illuminating how "good'" an uncertainty measure is. Our insights are centered around quantifying the ways in which being uncertainty-aware makes a system more robust. Firstly, we use level set generation tools to produce a measure for system robustness and use this measure to compare system designs, thus placing uncertainty quantification in the context of system performance and evaluation metrics. Secondly, we use the concept of specification generation from systems theory to produce a formulation under which a designer can simultaneously constrain the properties of an uncertainty measure and analyze the efficacy of the decision-making-under-uncertainty algorithm used by the system. We apply our analyses to two real-world and complex autonomous systems, one for autonomous driving and another for aircraft runway incursion detection, helping to form a toolbox for an uncertainty-aware system designer to produce more effective and robust systems.
Parallel Batch Scheduling With Incompatible Job Families Via Constraint Programming
Authors: Jorge A. Huertas, Pascal Van Hentenryck
Date: 2024-10-15 00:00:00
Tasks: Scheduling

This paper addresses the incompatible case of parallel batch scheduling, where compatible jobs belong to the same family, and jobs from different families cannot be processed together in the same batch. The state-of-the-art constraint programming (CP) model for this problem relies on specific functions and global constraints only available in a well established commercial CP solver. This paper expands the literature around this problem by proposing four new CP models that can be implemented in commercial and open-source solvers: a new model that relies on automaton constraints, and three alternative models that integrate assignment and scheduling decisions with different strategies and global constraints. Extensive computational experiments on standard test cases under multiple objectives and multiple solvers demonstrate the implementation flexibility and competitive performance of the proposed models.
Derivation of macroscopic epidemic models from multi-agent systems
Authors: Mattia Zanella
Date: 2024-10-11 00:00:00

We present a systematic review of some basic results on the derivation of classical epidemiological models from simple agent-based dynamics. The evolution of large populations is coupled with the dynamics of the contact distribution, providing insights into how individual behaviors impact macroscopic epidemiological trends. The resulting set of equations incorporates local characteristics of the operator governing the emergence of a family of contact distributions. To validate the proposed approach, we provide several numerical results based on asymptotic preserving methods, demonstrating their effectiveness in capturing the multi-scale nature of the problem and ensuring robust performance across different regimes.
First experimental study of multiple orientation muon tomography, with image optimization in sparse data environments
Authors: Jesus J. Valencia, Adam A. Hecht, C. L. Morris, E. Guardincerri, D. Poulson, J. Bacon, J. M. Durham
Date: 2024-10-08 00:00:00
Tasks: Object

Due to the high penetrating power of cosmic ray muons, they can be used to probe very thick and dense objects. As charged particles, they can be tracked by ionization detectors, determining the position and direction of the muons. With detectors on either side of an object, particle direction changes can be used to extract scattering information within an object. This can be used to produce a scattering intensity image within the object related to density and atomic number. Such imaging is typically performed with a single detector-object orientation, taking advantage of the more intense downward flux of muons, producing planar imaging with some depth-of-field information in the third dimension. Several simulation studies have been published with multi-orientation tomography, which can form a three-dimensional representation faster than a single orientation view. In this work we present the first experimental multiple orientation muon tomography study. Experimental muon-scatter based tomography was performed using a concrete filled steel drum with several different metal wedges inside, between detector planes. Data was collected from different detector-object orientations by rotating the steel drum. The data collected from each orientation were then combined using two different tomographic methods. Results showed that using a combination of multiple depth-of-field reconstructions, rather than a traditional inverse Radon transform approach used for CT, resulted in more useful images for sparser data. As cosmic ray muon flux imaging is rate limited, the imaging techniques were compared for sparse data. Using the combined depth-of-field reconstruction technique, fewer detector-object orientations were needed to reconstruct images that could be used to differentiate the metal wedge compositions.
IceDiff: High Resolution and High-Quality Sea Ice Forecasting with Generative Diffusion Prior
Authors: Jingyi Xu, Siwei Tu, Weidong Yang, Shuhao Li, Keyi Liu, Yeqi Luo, Lipeng Ma, Ben Fei, Lei Bai
Date: 2024-10-10 00:00:00

Variation of Arctic sea ice has significant impacts on polar ecosystems, transporting routes, coastal communities, and global climate. Tracing the change of sea ice at a finer scale is paramount for both operational applications and scientific studies. Recent pan-Arctic sea ice forecasting methods that leverage advances in artificial intelligence has made promising progress over numerical models. However, forecasting sea ice at higher resolutions is still under-explored. To bridge the gap, we propose a two-staged deep learning framework, IceDiff, to forecast sea ice concentration at finer scales. IceDiff first leverages an independently trained vision transformer to generate coarse yet superior forecasting over previous methods at a regular 25km x 25km grid. This high-quality sea ice forecasting can be utilized as reliable guidance for the next stage. Subsequently, an unconditional diffusion model pre-trained on sea ice concentration maps is utilized for sampling down-scaled sea ice forecasting via a zero-shot guided sampling strategy and a patch-based method. For the first time, IceDiff demonstrates sea ice forecasting with the 6.25km x 6.25km resolution. IceDiff extends the boundary of existing sea ice forecasting models and more importantly, its capability to generate high-resolution sea ice concentration data is vital for pragmatic usages and research.
Compressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model
Authors: Qian Liu, Bing Gong, Xiaoran Zhuang, Xiaohui Zhong, Zhiming Kang, Hao Li
Date: 2024-10-10 00:00:00
Tasks: Image Compression

The rapid advancement of artificial intelligence (AI) in weather research has been driven by the ability to learn from large, high-dimensional datasets. However, this progress also poses significant challenges, particularly regarding the substantial costs associated with processing extensive data and the limitations of computational resources. Inspired by the Neural Image Compression (NIC) task in computer vision, this study seeks to compress weather data to address these challenges and enhance the efficiency of downstream applications. Specifically, we propose a variational autoencoder (VAE) framework tailored for compressing high-resolution datasets, specifically the High Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) with a spatial resolution of 1 km. Our framework successfully reduced the storage size of 3 years of HRCLDAS data from 8.61 TB to just 204 GB, while preserving essential information. In addition, we demonstrated the utility of the compressed data through a downscaling task, where the model trained on the compressed dataset achieved accuracy comparable to that of the model trained on the original data. These results highlight the effectiveness and potential of the compressed data for future weather research.