pwc-archive/papers-with-abstracts huggingface dataset:
An Ultra-Wideband Microstrip MIMO Antenna with EBG Loading for WLAN and Sub-6G Applications
Authors: Adel Omrani
Date: 2023-03-01 00:00:00

This manuscript presents an ultra-wideband Microstrip multiple-input-multiple-output (MIMO) antenna covering the 2.4GHz to 6.5 GHz wireless local networks (WLAN) and Sub-6G bands. The applied MIMO antenna is composed of two symmetrical microstrip antenna elements. Each microstrip is designed based on the stepped impedance resonator (SIR) technique and closely positioned with about 0.25lambda of the lower band (2.5 GHz). The proposed MIMO antenna is incorporated with the EBG structure to enhance the radiation characteristic and isolation between two radiating components. The return loss (S11 under -10) covers 2.4 GHz to 6.5 GHz, and the proposed configuration obtains about 20 dB isolation within the 2.4 GHz to 6.5 GHz Sub-6G and WLAN bands, which shows an improvement compared to the initial design of the MIMO antenna. The envelope correlation coefficient (ECC) is under 0.02 over the frequency band. The simulation results offer that the proposed microstrip MIMO antenna system with the different levels of the EBG structure is suitable for WLAN and Sub-6G applications.
Robust consensus control of second-order uncertain multiagent systems with velocity and input constraints (extended version)
Authors: Gang Wang, Zongyu Zuo, Chaoli Wang
Date: 2023-03-01 00:00:00

In this paper, we investigate the consensus problem of second-order multiagent systems under directed graphs. Simple yet robust consensus algorithms that advance existing achievements in accounting for velocity and input constraints, agent uncertainties, and lack of neighboring velocity measurements are proposed. Furthermore, we show that the proposed method can be applied to the consensus control of uncertain robotic manipulators with velocity and control torque constraints. We rigorously prove that the velocity and control inputs stay within prespecified ranges by tuning design parameters a priori and that asymptotic consensus can be achieved through Lyapunov functions and fixed-time stability. Simulations are performed for symmetric and asymmetric constraints to show the efficiency of the theoretical findings.
Online Parameter Estimation using Physics-Informed Deep Learning for Vehicle Stability Algorithms
Authors: Kemal Koysuren, Ahmet Faruk Keles, Melih Cakmakci
Date: 2023-03-01 00:00:00
Tasks: parameter estimation

Physics-informed deep learning is a popular trend in the modeling and control of dynamical systems. This paper presents a novel method for rapid online identification of vehicle cornering stiffness coefficient, a crucial parameter in vehicle stability control models and control algorithms. The new method enables designers to rapidly identify the vehicle front and rear cornering stiffness parameters so that the controller reference gains can be re-adjusted under varying road and vehicle conditions to improve the reference tracking performance of the control system during operation. The proposed method based on vehicle model-based deep learning is compared to other alternatives such as traditional neural network training and identification, and Pacejka model estimation with regression. Our initial findings show that, in comparison to these classical methods, high fidelity estimations can be done with much smaller data sets simple enough to be obtained from a lane-changing or vehicle overtake maneuver. In order to conduct experiments, and collect sensor data, a custom-built 1:8 scaled test vehicle platform is used real-time wireless networking capabilities. The proposed method is applicable to predict derived vehicle parameters such as the understeering coefficient so it can be used in parallel with conventional MIMO controllers. Our $H_{\infty}$ yaw rate regulation controller test results show that the reference gains updated with the proposed online estimation method improve the tracking performance in both simulations and vehicle experiments.
Grid-Forming Control Based On Emulated Synchronous Condenser Strategy Compliant With Challenging Grid Code Requirements
Authors: Julian Freytes, Antoine Rossé, Valentin Costan, Grégoire Prime
Date: 2023-03-01 00:00:00
Tasks: Fault Detection

Future power systems will include high shares of inverter-based generation. There is a general consensus that for allowing this transition, the Grid-Forming (GFM) control approach would be of great value. This article presents a GFM control strategy which is based on the concept of an Emulated Synchronous Condenser in parallel with a controlled current source with an explicit representation of the swing equation. The advantage of this control is that it can cope with challenging grid code requirements such as severe phase jumps, balanced and unbalanced Fault Ride-Through (FRT), main grid disconnection and black start. All these scenarios can be surpassed with a single control structure with no further logic involved (e.g. fault detection to turn on or off different control parts, freezes, etc.). The proposed strategy is evaluated via time-domain simulations of a 2-MW Battery Energy Storage System (BESS).
Low-level Online Control of the Formula 1 Power Unit with Feedforward Cylinder Deactivation
Authors: Marc-Philippe Neumann, Giona Fieni, Camillo Balerna, Pol Duhr, Alberto Cerofolini, Christopher H. Onder
Date: 2023-03-01 00:00:00
Tasks: energy management, Management

Since 2014, the F\'ed\'eration Internationale de l'Automobile has prescribed a parallel hybrid powertrain for the Formula 1 race cars. The complex low-level interactions between the thermal and the electrical part represent a non-trivial and challenging system to be controlled online. We present a novel controller architecture composed of a supervisory controller for the energy management, a feedforward cylinder deactivation controller, and a track region-dependent low-level nonlinear model predictive controller to optimize the engine actuators. Except for the nonlinear model predictive controller, the proposed controller subsystems are computationally inexpensive and are real time capable. The framework is tested and validated in a simulation environment for several realistic scenarios disturbed by driver actions or grip conditions on the track. In particular, we analyze how the control architecture deals with an unexpected gearshift trajectory during an acceleration phase. Further, we demonstrate how an increased maximum velocity trajectory impacts the online low-level controller. Our results show a suboptimality over an entire lap with respect to the benchmark solution of 49 ms and 64 ms, respectively, which we deem acceptable. Compared to the same control architecture with full knowledge of the disturbances, the suboptimality amounted to only 2 ms and 17 ms. For all case studies we show that the cylinder deactivation capability decreases the suboptimality by 7 to 8 ms.
Influence of Spattering on In-process Layer Surface Roughness during Laser Powder Bed Fusion
Authors: Haolin Zhang, Chaitanya Krishna Prasad Vallabh, Xiayun Zhao
Date: 2023-03-01 00:00:00

Laser powder bed fusion (LPBF) holds promise to efficiently produce metal parts. However, LPBF incurs stochastic melt pool (MP) spattering, which would roughen workpiece in-process surface, thus weakening inter-layer bonding and causing issues like porosity, powder contamination, and recoater intervention. Understanding the consequential effect of MP spattering on layer surface remains difficult due to the lack of process monitoring capability for concurrently tracking MP spatters and characterizing layer surfaces. In this work, using our lab-designed LPBF-specific fringe projection profilometry and an off-axis camera, we quantify the correlation between MP spattering and in-process layer surface roughness for the first time to reveal the influence of MP spatters on process anomaly and part defects. A method of extracting and registering MP spattering metrics is developed by machine learning of the in-situ imaging data. Each image is analyzed to obtain the MP center location and the spatter count and ejection angle. These MP spatter signatures are registered for each monitored MP across each layer. Regression modeling is used to correlate registered MP spatter signature and its processing parameters with layer surface topography measured by the in-situ FPP. We find that the attained MP spatter feature profile can help predict the layer surface roughness more accurately (> 50% less error), in contrast to the conventional approaches that would only use nominal process setting. This is because the spatter information can reflect key process changes including the deviations in actual laser scan parameters and their effects. The results corroborate the importance of spatter monitoring and the distinct influence of spattering on layer surface roughness. Our work paves a foundation to thoroughly elucidate and effectively control the role of MP spattering in defect formation during LPBF.
Exponential Consensus of Multiple Agents over Dynamic Network Topology: Controllability, Connectivity, and Compactness
Authors: Qichao Ma, Jiahu Qin, Brian D. O. Anderson, Long Wang
Date: 2023-03-01 00:00:00

This paper investigates the problem of securing exponentially fast consensus (exponential consensus for short) for identical agents with finite-dimensional linear system dynamics over dynamic network topology. Our aim is to find the weakest possible conditions that guarantee exponentially fast consensus using a Lyapunov function consisting of a sum of terms of the same functional form. We first investigate necessary conditions, starting by examining the system (both agent and network) parameters. It is found that controllability of the linear agents is necessary for reaching consensus. Then, to work out necessary conditions incorporating the network topology, we construct a set of Laplacian matrix-valued functions. The precompactness of this set of functions is shown to be a significant generalization of existing assumptions on network topology, including the common assumption that the edge weights are bounded piecewise constant functions or continuous functions. With the aid of such a precompactness assumption and restricting the Lyapunov function to one consisting of a sum of terms of the same functional form, we prove that a joint $(\delta, T)$-connectivity condition on the network topology is necessary for exponential consensus. Finally, we investigate how the above two ``necessities'' work together to guarantee exponential consensus. To partially address this problem, we define a synchronization index to characterize the interplay between agent parameters and network topology. Based on this notion, it is shown that by designing a proper feedback matrix and under the precompactness assumption, exponential consensus can be reached globally and uniformly if the joint $(\delta,T)$-connectivity and controllability conditions are satisfied, and the synchronization index is not less than one.
Automated control and optimisation of laser driven ion acceleration
Authors: B. Loughran, M. J. V. Streeter, H. Ahmed, S. Astbury, M. Balcazar, M. Borghesi, N. Bourgeois, C. B. Curry, S. J. D. Dann, S. DiIorio, N. P. Dover, T. Dzelzanis, O. C. Ettlinger, M. Gauthier, L. Giuffrida, G. D. Glenn, S. H. Glenzer, J. S. Green, R. J. Gray, G. S. Hicks, C. Hyland, V. Istokskaia, M. King, D. Margarone, O. McCusker, P. McKenna, Z. Najmudin, C. Parisuaña, P. Parsons, C. Spindloe, D. R. Symes, A. G. R. Thomas, F. Treffert, N. Xu, C. A. J. Palmer
Date: 2023-03-01 00:00:00
Tasks: Bayesian Optimisation

The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimisation of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimisation. Here, an automated, HRR-compatible system produced high fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimisation of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually-optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimisation of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.
Ensemble flow reconstruction in the atmospheric boundary layer from spatially limited measurements through latent diffusion models
Authors: Alex Rybchuk, Malik Hassanaly, Nicholas Hamilton, Paula Doubrawa, Mitchell J. Fulton, Luis A. Martínez-Tossas
Date: 2023-03-01 00:00:00

Due to costs and practical constraints, field campaigns in the atmospheric boundary layer typically only measure a fraction of the atmospheric volume of interest. Machine learning techniques have previously successfully reconstructed unobserved regions of flow in canonical fluid mechanics problems and two-dimensional geophysical flows, but these techniques have not yet been demonstrated in the three-dimensional atmospheric boundary layer. Here, we conduct a numerical analogue of a field campaign with spatially limited measurements using large-eddy simulation. We pose flow reconstruction as an inpainting problem, and reconstruct realistic samples of turbulent, three-dimensional flow with the use of a latent diffusion model. The diffusion model generates physically plausible turbulent structures on larger spatial scales, even when input observations cover less than 1% of the volume. Through a combination of qualitative visualization and quantitative assessment, we demonstrate that the diffusion model generates meaningfully diverse samples when conditioned on just one observation. These samples successfully serve as initial conditions for a large-eddy simulation code. We find that diffusion models show promise and potential for other applications for other turbulent flow reconstruction problems.
Methods and measures for investigating microscale motility
Authors: Karen Grace Bondoc-Naumovitz, Hannah Laeverenz-Schlogelhofer, Rebecca N. Poon, Alexander K. Boggon, Samuel A. Bentley, Dario Cortese, Kirsty Y. Wan
Date: 2023-02-28 00:00:00
Tasks: Navigate

Motility is an essential factor for an organism's survival and diversification. With the advent of novel single-cell technologies, analytical frameworks and theoretical methods, we can begin to probe the complex lives of microscopic motile organisms and answer the intertwining biological and physical questions of how these diverse lifeforms navigate their surroundings. Herein, we give an overview of different experimental, analytical, and mathematical methods used to study a suite of microscale motility mechanisms across different scales encompassing molecular-, individual- to population-level. We identify transferable techniques, pressing challenges, and future directions in the field. This review can serve as a starting point for researchers who are interested in exploring and quantifying the movements of organisms in the microscale world.
AI and the FCI: Can ChatGPT Project an Understanding of Introductory Physics?
Authors: Colin G. West
Date: 2023-03-02 00:00:00
Tasks: Chatbot, College Physics, Conceptual Physics, Language Modelling, Large Language Model

ChatGPT is a groundbreaking ``chatbot"--an AI interface built on a large language model that was trained on an enormous corpus of human text to emulate human conversation. Beyond its ability to converse in a plausible way, it has attracted attention for its ability to competently answer questions from the bar exam and from MBA coursework, and to provide useful assistance in writing computer code. These apparent abilities have prompted discussion of ChatGPT as both a threat to the integrity of higher education and conversely as a powerful teaching tool. In this work we present a preliminary analysis of how two versions of ChatGPT (ChatGPT3.5 and ChatGPT4) fare in the field of first-semester university physics, using a modified version of the Force Concept Inventory (FCI) to assess whether it can give correct responses to conceptual physics questions about kinematics and Newtonian dynamics. We demonstrate that, by some measures, ChatGPT3.5 can match or exceed the median performance of a university student who has completed one semester of college physics, though its performance is notably uneven and the results are nuanced. By these same measures, we find that ChatGPT4's performance is approaching the point of being indistinguishable from that of an expert physicist when it comes to introductory mechanics topics. After the completion of our work we became aware of Ref [1], which preceded us to publication and which completes an extensive analysis of the abilities of ChatGPT3.5 in a physics class, including a different modified version of the FCI. We view this work as confirming that portion of their results, and extending the analysis to ChatGPT4, which shows rapid and notable improvement in most, but not all respects.
Initial experiences with Direct Imaging of Neuronal Activity (DIANA) in humans
Authors: Shota Hodono, Reuben Rideaux, Timo van Kerkoerle, Martijn A. Cloos
Date: 2023-03-01 00:00:00
Tasks: Specificity

Functional MRI (fMRI) has been widely used to study activity patterns in the human brain. It infers neuronal activity from the associated hemodynamic response, which fundamentally limits its spatial and temporal specificity. In mice, the Direct Imaging of Neuronal Activity (DIANA) method revealed MRI signals that correlated with intracellular voltage changes, showing high spatial and temporal specificity. In this work we attempted DIANA in humans. Four visual paradigms were tested, exploring different stimulus types (flickering noise patterns, and naturalistic images) and stimulus durations (50-200ms). Regions of interest (ROI) were derived from traditional fMRI acquisitions and anatomical scans. When using small manually drawn ROI, signals were detected that resembled possible functional activity. However, increasing the stimulus duration did not lead to corroborating signal changes. Moreover, these signals disappeared when averaged over larger functionally or anatomically derived ROI. Further analysis of the data highlighted, DIANA's sensitivity to inflow effects and subject motion. Therefore, care should be taken not to mistake artifacts for neuronal activity. Although we did not yet observe clear DIANA signals in humans, it is possible that a higher spatial resolution is needed to separate signals with opposite signs in different laminar compartments. However, obtaining such data may be particularly challenging because repetitive experiments with short interstimulus intervals can be strenuous for the subjects. To obtain better data, improvements in sequence and stimulus designs are needed to maximize the DIANA signal and minimize confounds. However, without a clear understanding of DIANA's biophysical underpinnings it is difficult to do so. Therefore, it may be more effective to first study DIANA's biophysical underpinnings in a controlled, yet biologically revenant, setting.
Indirect reciprocity with stochastic rules
Authors: Yohsuke Murase, Christian Hilbe
Date: 2023-03-02 00:00:00

Cooperation is a crucial aspect of social life, yet understanding the nature of cooperation and how it can be promoted is an ongoing challenge. One mechanism for cooperation is indirect reciprocity. According to this mechanism, individuals cooperate to maintain a good reputation. This idea is embodied in a set of social norms called the ``leading eight''. When all information is publicly available, these norms have two major properties. Populations that employ these norms are fully cooperative, and they are stable against invasion by alternative norms. In this paper, we extend the framework of the leading eight in two directions. First, we allow social norms to be stochastic. Such norms allow individuals to evaluate others with certain probabilities. Second, we consider norms in which also the reputations of passive recipients can be updated. Using this framework, we characterize all evolutionarily stable norms that lead to full cooperation in the public information regime. When only the donor's reputation is updated, and all updates are deterministic, we recover the conventional model. In that case, we find two classes of stable norms: the leading eight and the `secondary sixteen'. Stochasticity can further help to stabilize cooperation when the benefit of cooperation is comparably small. Moreover, updating the recipients' reputations can help populations to recover more quickly from errors. Overall, our study highlights a remarkable trade-off between the evolutionary stability of a norm and its robustness with respect to errors. Norms that correct errors quickly require higher benefits of cooperation to be stable.
Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters
Authors: Vira Semenova
Date: 2023-03-02 00:00:00
Tasks: Classification, regression

This paper proposes a novel framework of aggregated intersection of regression functions, where the target parameter is obtained by averaging the minimum (or maximum) of a collection of regression functions over the covariate space. Such quantities include the lower and upper bounds on distributional effects (Frechet-Hoeffding, Makarov) and the optimal welfare in the statistical treatment choice problem. The proposed estimator -- the envelope score estimator -- is shown to have an oracle property, where the oracle knows the identity of the minimizer for each covariate value. I apply this result to the bounds in the Roy model and the Horowitz-Manski-Lee bounds with a discrete outcome. The proposed approach performs well empirically on the data from the Oregon Health Insurance Experiment.
Consumer Welfare under Individual Heterogeneity
Authors: Charles Gauthier, Sebastiaan Maes, Raghav Malhotra
Date: 2023-03-01 00:00:00

Welfare effects of price changes are often estimated with cross-sections; however, such data do not identify demand models with heterogeneous consumers. We develop a method that leverages moments of demand to construct a local approximation to the curvature of the expenditure function, which is essential for welfare analysis. Our method applies to arbitrary cross-sections and delivers a nonparametric approximation to the entire distribution of consumer welfare, robust to unobserved preference heterogeneity. We provide moment- and quantile-based estimation strategies that are simple to implement.
Transition Probabilities and Moment Restrictions in Dynamic Fixed Effects Logit Models
Authors: Kevin Dano
Date: 2023-02-28 00:00:00
Tasks: valid

Dynamic logit models are popular tools in economics to measure state dependence. This paper introduces a new method to derive moment restrictions in a large class of such models with strictly exogenous regressors and fixed effects. We exploit the common structure of logit-type transition probabilities and elementary properties of rational fractions, to formulate a systematic procedure that scales naturally with model complexity (e.g the lag order or the number of observed time periods). We detail the construction of moment restrictions in binary response models of arbitrary lag order as well as first-order panel vector autoregressions and dynamic multinomial logit models. Identification of common parameters and average marginal effects is also discussed for the binary response case. Finally, we illustrate our results by studying the dynamics of drug consumption amongst young people inspired by Deza (2015).