pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Mode Inverters: A Unified Control Design for Grid-Forming, Grid-Following, and Beyond
Authors: Alireza Askarian, Jaesang Park, Srinivasa Salapaka
Date: 2024-10-11 00:00:00

We present a novel, integrated control framework designed to achieve seamless transitions among a spectrum of inverter operation modes. The operation spectrum includes grid-forming (GFM), grid-following (GFL), static synchronous compensator (STATCOM), energy storage system (ESS), and voltage source inverter (VSI). The proposed control architecture offers guarantees of stability, robustness, and performance regardless of the specific mode. The core concept involves establishing a unified algebraic structure for the feedback control system, where different modes are defined by the magnitude of closed-loop signals. As we demonstrate, this approach results in a two-dimensional continuum of operation modes and enables transition trajectories between operation modes by dynamically adjusting closed-loop variables towards corresponding setpoints. Stability, robustness, and fundamental limitation analyses are provided for the closed-loop system across any mode, as well as during transitions between modes. This design facilitates stable and enhanced on-grid integration, even during GFM operation and weak grid conditions. Ultimately, we demonstrate the key attributes of the proposed framework through simulations and experiments, showcasing its seamless transition in on-grid operation, functionality in islanded mode, and robustness to line impedance uncertainty.
State Feedback System Level Synthesis in Continuous Time
Authors: Yaozhi Du, Jing Shuang Li
Date: 2024-10-10 00:00:00

System level synthesis (SLS) is a controller parameterization technique that facilitates synthesis of structured distributed controllers via convex optimization. Past results on SLS are primarily in the discrete-time setting; this paper extends SLS to the continuous-time setting. We translate the parametrization and associated constraints to continuous-time, and propose a controller design procedure consisting of two steps: (1) selection of poles and (2) optimization over closed-loop responses. We provide SLS parameterizations for continuous-time $\H2$ and $\Hinf$ control, and show that the proposed procedure allows us to design structured $\H2$ and $\Hinf$ controllers via convex optimization. Furthermore, the proposed procedure preserves the scalability and local-disturbance-rejection features of the original discrete-time SLS framework. We verify our findings in simulation -- on a grid of 9 nodes governed by linearized swing equations, our structured distributed controllers perform similarly to the optimal centralized controllers.
Sensor-Based Safety-Critical Control Using an Incremental Control Barrier Function Formulation via Reduced-Order Approximate Models
Authors: Johannes Autenrieb, Hyo-Sang Shin
Date: 2024-10-10 00:00:00

The existing control barrier function literature generally relies on precise mathematical models to guarantee system safety, limiting their applicability in scenarios with parametric uncertainties. While incremental control techniques have shown promise in addressing model uncertainties in flight control applications, translating these approaches to safety-critical control presents significant challenges. This paper bridges this gap by introducing measurement-robust incremental control barrier functions (MRICBFs), which leverage sensor-based reduced-order models to provide formal safety guarantees for uncertain systems. By carefully addressing the challenges of sensor accuracy and approximation errors in the incremental formulation, our approach enables substituting specific model components with real-time sensor measurements while maintaining rigorous safety guarantees. This formulation overcomes the limitations of traditional adaptive control methods that adjust system parameters over time, enabling immediate and reliable safety measures for a class of model uncertainties. The efficacy of MRICBFs is demonstrated in two simulation case studies: a simple first-order system with time-varying sensor biases and a more complex overactuated hypersonic glide vehicle with multiple state constraints.
A four-bodies motorcycle dynamic model for observer design
Authors: Tychique Nzalalemba Kabwangala, Ziad Alkhoury, Jawwad Ahmed, Mihaly Petreczky, Laurentiu Hetel, Lotfi Belkoura
Date: 2024-10-10 00:00:00

Motivated by the need to predict dangerous scenarios, this article introduces a non-linear dynamic model for motorcycles consisting of four rigid bodies. Using Jourdain's principle, the model incorporates both longitudinal and lateral dynamics, targeting a balance between numerical complexity and accuracy of representation. The paper further employs the model to design a Luenberger observer based on linear quadratic regulator theory, for estimating physical states based on sensor measurements. In turn, the state estimates are useful for predicting dangerous scenarios (lowside, highside, fall). The relevance of the approach is demonstrated through simulations of various rectilinear trajectories and a lane-changing scenario using BikeSim simulator.
The Impact of Grid Storage on Balancing Costs and Carbon Emissions in Great Britain
Authors: Seyyed Mostafa Nosratabadi, Iacopo Savelli, Volkan Kumtepeli, Phil Grunewald, Marko Aunedi, David A. Howey, Thomas Morstyn
Date: 2024-10-10 00:00:00
Tasks: Management

Grid energy storage can help to balance supply and demand, but its financial viability and operational carbon emissions impact is poorly understood because of the complexity of grid constraints and market outcomes. We analyse the impact of several technologies (Li-ion and flow batteries, pumped hydro, hydrogen) on Great Britain balancing mechanism, the main market for supply-demand balancing and congestion management. We find that, for many locations and technologies, financially optimal operation of storage for balancing can result in higher carbon emissions. For example, the extra emissions associated with a 1 MW 2-hour duration Li-ion battery in winter vary between +230 to -71 kgCO2/h. Although storage enable higher usage of renewables, it can also unlock additional demand leading to greater use of gas. In addition, balancing services alone are presently insufficient for financial viability of storage projects. This work highlights the need for market reform aligning financial incentives with environmental impacts.
A Visual Cooperative Localization Method for Airborne Magnetic Surveying Based on a Manifold Sensor Fusion Algorithm Using Lie Groups
Authors: Liang Liu, Xiao Hu, Wei Jiang, Guanglei Meng, Zhujun Wang, Taining Zhang
Date: 2024-10-10 00:00:00
Tasks: Sensor Fusion

Recent advancements in UAV technology have spurred interest in developing multi-UAV aerial surveying systems for use in confined environments where GNSS signals are blocked or jammed. This paper focuses airborne magnetic surveying scenarios. To obtain clean magnetic measurements reflecting the Earth's magnetic field, the magnetic sensor must be isolated from other electronic devices, creating a significant localization challenge. We propose a visual cooperative localization solution. The solution incorporates a visual processing module and an improved manifold-based sensor fusion algorithm, delivering reliable and accurate positioning information. Real flight experiments validate the approach, demonstrating single-axis centimeter-level accuracy and decimeter-level overall 3D positioning accuracy.
Design and Characterization of High Efficiency Single-stage Electromagnetic Coil Guns
Authors: Sophia Chen, Annie Peng, Ava Chen, Takyiu Liu
Date: 2024-10-10 00:00:00

This study presents several novel approaches to improve the efficiency of a single-stage coil gun. Conventional designs typically feature a uniformly wound solenoid and a ferrite projectile. For our research, we constructed a microcontroller-based prototype to test several new enhancements, including the use of a bipolar current pulse, a stepped multilayer coil with non-uniform winding densities, and the replacement of conventional ferrite projectiles with a neodymium permanent magnet. These modifications were designed to reduce energy loss and improve projectile acceleration by changing magnetic field strength and effectively controlling the magnetic flux. The experimental results show that the proposed methods resulted in significant efficiency improvements, with the varying current pulse and stepped coil design providing enhanced magnetic force at key points in the projectile's path, and the permanent magnet projectile contributing to higher velocities and efficiencies by leveraging the current pulses. Our findings suggest that combining these enhancements significantly improves coil gun performance, achieving higher velocities and efficiencies. These findings can be applied to future coil gun developments, such as multi-stage coil gun systems.
Fabrication-Aware Inverse Design For Shape Optimization
Authors: Shaheer Khan, Mustafa Hammood, Nicolas A. F. Jaeger, Lukas Chrostowski
Date: 2024-10-09 00:00:00

Inverse design (ID) is a computational method that systematically explores a design space to find optimal device geometries based on specific performance criteria. In silicon photonics, ID often leads to devices with design features that degrade significantly due to the fabrication process, limiting the applicability of these devices in scalable silicon photonic fabrication. We demonstrate a solution to this performance degradation through fabrication-aware inverse design (FAID), integrating lithography models for deep-ultraviolet (DUV) lithography and electron beam lithography (EBL) into the shape optimization approach of ID. A Y-branch and an SWG-to-strip converter were generated and fabricated with this new approach. Simulated and measured results verify that the FAID yields devices with up to 0.6 dB lower insertion loss per device. The modified workflow enables designers to use ID to generate devices that adjust for process bias predicted by lithography models.
Robust Lateral Control of a Convoy of Autonomous & Connected Vehicles with Limited Preview
Authors: Mengke Liu, Neelkamal Somisetty, Sivakumar Rathinam, Swaroop Darbha
Date: 2020-11-06 00:00:00

This paper addresses the lateral control of Autonomous & Connected Vehicles (ACVs) convoys during Emergency Lane Change (ELC) maneuvers. These maneuvers are initiated in response to emergency cues from either the front or rear of the convoy, responding to the need to avoid obstacles or facilitate the passage of other vehicles. The primary objective of this study is to develop a lateral control scheme for ACVs based on the available information. The foundational assumption in this study is the existence of reliable connectivity among ACVs, wherein each subsequent ACV possesses information concerning the GPS position traces of both the lead and immediately preceding vehicles within the convoy. This connectivity facilitates the construction of a composite ELC trajectory that synthesizes this information, serving as a "discretized" preview of the trajectory to be tracked. The procedural steps include constructing this composite trajectory, determining cross-track error, heading, and yaw rate errors relative to it, and subsequently formulating a lateral control strategy. Furthermore, the paper presents findings on the lateral string stability of ACV convoys across various scenarios, encompassing changes in longitudinal velocity and scenarios where lead vehicle information is unavailable. Numerical and experimental results validate the efficacy of the proposed lateral control scheme for ACV convoys.
Blind and robust reconstruction of adaptive optics point spread functions for asteroid deconvolution and moon detection
Authors: Anthony Berdeu, Férréol Soulez, Kate Minker, Benoit Carry, Guillaume Bourdarot, Antoine Kaszczyc, Maud Langlois
Date: 2024-10-11 00:00:00

Initially designed to detect and characterize exoplanets, extreme adaptive optics systems (AO) open a new window on the solar system by resolving its small bodies. Nonetheless, despite the always increasing performances of AO systems, the correction is not perfect, degrading their image and producing a bright halo that can hide faint and close moons. Using a reference point spread function (PSF) is not always sufficient due to the random nature of the turbulence. In this work, we present our method to overcome this limitation. It blindly reconstructs the AO-PSF directly in the data of interest, without any prior on the instrument nor the asteroid's shape. This is done by first estimating the PSF core parameters under the assumption of a sharp-edge and flat object, allowing the image of the main body to be deconvolved. Then, the PSF faint extensions are reconstructed with a robust penalization optimization, discarding outliers on-the-fly such as cosmic rays, defective pixels and moons. This allows to properly model and remove the asteroid's halo. Finally, moons can be detected in the residuals, using the reconstructed PSF and the knowledge of the outliers learned with the robust method. We show that our method can be easily applied to different instruments (VLT/SPHERE, Keck/NIRC2), efficiently retrieving the features of AO-PSFs. Compared with state-of-the-art moon enhancement algorithms, moon signal is greatly improved and our robust detection method manages to discriminate faint moons from outliers.
Content-Based Image Retrieval Using COSFIRE Descriptors with application to Radio Astronomy
Authors: Steven Ndungu, Trienko Grobler, Stefan J. Wijnholds, George Azzopardi
Date: 2024-09-27 00:00:00
Tasks: Astronomy, Content-Based Image Retrieval, Image Retrieval, Retrieval

The morphologies of astronomical sources are highly complex, making it essential not only to classify the identified sources into their predefined categories but also to determine the sources that are most similar to a given query source. Image-based retrieval is essential, as it allows an astronomer with a source under study to ask a computer to sift through the large archived database of sources to find the most similar ones. This is of particular interest if the source under study does not fall into a "known" category (anomalous). Our work uses the trainable COSFIRE (Combination of Shifted Filter Responses) approach for image retrieval. COSFIRE filters are automatically configured to extract the hyperlocal geometric arrangements that uniquely describe the morphological characteristics of patterns of interest in a given image; in this case astronomical sources. This is achieved by automatically examining the shape properties of a given prototype source in an image, which ultimately determines the selectivity of a COSFIRE filter. We further utilize hashing techniques, which are efficient in terms of required computation and storage, enabling scalability in handling large data sets in the image retrieval process. We evaluated the effectiveness of our approach by conducting experiments on a benchmark data set of radio galaxies, containing 1,180 training images and 404 test images. Notably, our approach achieved a mean average precision of 91% for image retrieval, surpassing the performance of the competing DenseNet-based method. Moreover, the COSFIRE filters are significantly more computationally efficient, requiring $\sim\!14\times$ fewer operations than the DenseNet-based method.
AI Foundation Model for Heliophysics: Applications, Design, and Implementation
Authors: Sujit Roy, Talwinder Singh, Marcus Freitag, Johannes Schmude, Rohit Lal, Dinesha Hegde, Soumya Ranjan, Amy Lin, Vishal Gaur, Etienne Eben Vos, Rinki Ghosal, Badri Narayana Patro, Berkay Aydin, Nikolai Pogorelov, Juan Bernabe Moreno, Manil Maskey, Rahul Ramachandran
Date: 2024-09-30 00:00:00

Deep learning-based methods have been widely researched in the areas of language and vision, demonstrating their capacity to understand long sequences of data and their usefulness in numerous helio-physics applications. Foundation models (FMs), which are pre-trained on a large-scale datasets, form the basis for a variety of downstream tasks. These models, especially those based on transformers in vision and language, show exceptional potential for adapting to a wide range of downstream applications. In this paper, we provide our perspective on the criteria for designing an FM for heliophysics and associated challenges and applications using the Solar Dynamics Observatory (SDO) dataset. We believe that this is the first study to design an FM in the domain of heliophysics.
Adaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials Growth
Authors: Leonardo Sabattini, Annalisa Coriolano, Corneel Casert, Stiven Forti, Edward S. Barnard, Fabio Beltram, Massimiliano Pontil, Stephen Whitelam, Camilla Coletti, Antonio Rossi
Date: 2024-10-10 00:00:00

Two-dimensional (2D) materials are poised to revolutionize current solid-state technology with their extraordinary properties. Yet, the primary challenge remains their scalable production. While there have been significant advancements, much of the scientific progress has depended on the exfoliation of materials, a method that poses severe challenges for large-scale applications. With the advent of artificial intelligence (AI) in materials science, innovative synthesis methodologies are now on the horizon. This study explores the forefront of autonomous materials synthesis using an artificial neural network (ANN) trained by evolutionary methods, focusing on the efficient production of graphene. Our approach demonstrates that a neural network can iteratively and autonomously learn a time-dependent protocol for the efficient growth of graphene, without requiring pretraining on what constitutes an effective recipe. Evaluation criteria are based on the proximity of the Raman signature to that of monolayer graphene: higher scores are granted to outcomes whose spectrum more closely resembles that of an ideal continuous monolayer structure. This feedback mechanism allows for iterative refinement of the ANN's time-dependent synthesis protocols, progressively improving sample quality. Through the advancement and application of AI methodologies, this work makes a substantial contribution to the field of materials engineering, fostering a new era of innovation and efficiency in the synthesis process.
An Optical Method for Evaluating the Mechanical Properties of Wires Under Impact Tensile Load
Authors: Phichai Youplao, Hadi Nasbey, Akihiro Takita, Prin Nachaisit, Yusaku Fujii
Date: 2024-10-12 00:00:00
Tasks: Friction

The dynamic properties of materials utilized in architecture or engineering applications can significantly affect their performance under dynamic or impact loading conditions. To evaluate such behavior, force transducers are commonly employed in testing. However, the calibration of force transducers is typically limited to static conditions and relies solely on gravitational forces exerted on standard masses. Thus, assessing the uncertainty in force measurements using force transducers during dynamic loading conditions remains a challenging task, presenting a significant obstacle in accurately characterizing the dynamic behavior of materials. In this work, an optical technique to evaluate the mechanical properties of wires subjected to impact tensile loads is presented. The wire under test is subjected to an impact tensile load by applying the inertial force of a rigid mass, which is supported by utilizing an aerostatic linear bearing with sufficiently small friction. The inertial force applied to the wire can be determined by multiplying the mass of the rigid mass by its acceleration, where the acceleration can be measured employing a Michelson type optical interferometer. The performance of the proposed method is demonstrated through experiments and analysis of the dynamic characteristics of a tungsten wire under impact tensile loading conditions.
EPi-cKANs: Elasto-Plasticity Informed Kolmogorov-Arnold Networks Using Chebyshev Polynomials
Authors: Farinaz Mostajeran, Salah A Faroughi
Date: 2024-10-12 00:00:00
Tasks: Kolmogorov-Arnold Networks, Sand

Multilayer perceptron (MLP) networks are predominantly used to develop data-driven constitutive models for granular materials. They offer a compelling alternative to traditional physics-based constitutive models in predicting nonlinear responses of these materials, e.g., elasto-plasticity, under various loading conditions. To attain the necessary accuracy, MLPs often need to be sufficiently deep or wide, owing to the curse of dimensionality inherent in these problems. To overcome this limitation, we present an elasto-plasticity informed Chebyshev-based Kolmogorov-Arnold network (EPi-cKAN) in this study. This architecture leverages the benefits of KANs and augmented Chebyshev polynomials, as well as integrates physical principles within both the network structure and the loss function. The primary objective of EPi-cKAN is to provide an accurate and generalizable function approximation for non-linear stress-strain relationships, using fewer parameters compared to standard MLPs. To evaluate the efficiency, accuracy, and generalization capabilities of EPi-cKAN in modeling complex elasto-plastic behavior, we initially compare its performance with other cKAN-based models, which include purely data-driven parallel and serial architectures. Furthermore, to differentiate EPi-cKAN's distinct performance, we also compare it against purely data-driven and physics-informed MLP-based methods. Lastly, we test EPi-cKAN's ability to predict blind strain-controlled paths that extend beyond the training data distribution to gauge its generalization and predictive capabilities. Our findings indicate that, even with limited data and fewer parameters compared to other approaches, EPi-cKAN provides superior accuracy in predicting stress components and demonstrates better generalization when used to predict sand elasto-plastic behavior under blind triaxial axisymmetric strain-controlled loading paths.
Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
Authors: Zhidong Gao, Zhenxiao Zhang, Yu Zhang, Tongnian Wang, Yanmin Gong, Yuanxiong Guo
Date: 2024-09-29 00:00:00
Tasks: Federated Learning, Scheduling, Stochastic Optimization

Federated learning (FL) enables edge devices to collaboratively train a machine learning model without sharing their raw data. Due to its privacy-protecting benefits, FL has been deployed in many real-world applications. However, deploying FL over mobile edge networks with constrained resources such as power, bandwidth, and computation suffers from high training latency and low model accuracy, particularly under data and system heterogeneity. In this paper, we investigate the optimal client scheduling and resource allocation for FL over mobile edge networks under resource constraints and uncertainty to minimize the training latency while maintaining the model accuracy. Specifically, we first analyze the impact of client sampling on model convergence in FL and formulate a stochastic optimization problem that captures the trade-off between the running time and model performance under heterogeneous and uncertain system resources. To solve the formulated problem, we further develop an online control scheme based on Lyapunov-based optimization for client sampling and resource allocation without requiring the knowledge of future dynamics in the FL system. Extensive experimental results demonstrate that the proposed scheme can improve both the training latency and resource efficiency compared with the existing schemes.