pwc-archive/papers-with-abstracts huggingface dataset:
Stochastic COLREGs Evaluation for Safe Navigation under Uncertainty
Authors: Peter Nicholas Hansen, Dimitrios Papageorgiou, Roberto Galeazzi, Mogens Blanke
Date: 2024-02-08 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Navigate

The encounter situation between marine vessels determines how they should navigate to obey COLREGs, but time-varying and stochastic uncertainty in estimation of angles of encounter, and of closest point of approach, easily give rise to different assessment of situation at two approaching vessels. This may lead to high-risk conditions and could cause collision. This article considers decision making under uncertainty and suggests a novel method for probabilistic interpretation of vessel encounters that is explainable and provides a measure of uncertainty in the evaluation. The method is equally useful for decision support on a manned bridge as on Marine Autonomous Surface Ships (MASS) where it provides input for automated navigation. The method makes formal safety assessment and validation feasible. We obtain a resilient algorithm for machine interpretation of COLREGs under uncertainty and show its efficacy by simulations.
Spreading Information via Social Networks: An Irrelevance Result
Authors: Yu Awaya, Vijay Krishna
Date: 2024-02-07 00:00:00

An informed planner wishes to spread information among a group of agents in order to induce efficient coordination -- say the adoption of a new technology with positive externalities. The agents are connected via a social network. The planner informs a seed and then the information spreads via the network. While the structure of the network affects the rate of diffusion, we show that the rate of adoption is the same for all acyclic networks.
Triangular phase-shift detector for drone precise vertical landing RF systems
Authors: Víctor Araña-Pulido, Eugenio Jiménez-Yguácel, Francisco Cabrera-Almeida, Pedro Quintana-Morales
Date: 2024-02-08 00:00:00

This paper presents a circuit for precise vertical landing of drones based on a three phase-shifts detection of a single frequency transmitted from the landing point. The circuit can be considered as a new navigation sensor that assists in guidance corrections for landing at a specific point. The circuit has three inputs to which the signal transmitted from an oscillator located at the landing point arrives with different delays. The input signals are combined in pairs in each of the three analog phase detectors, after having passed through 3 dB@90 o hybrid couplers that guarantee a theoretical non-ambiguous phase-shift range of +-90 degree. Each output has a voltage that is proportional to the phase-shift between each of the input signals, which in turn depend on the position relative to the landing point. A simple landing algorithm based on phase-shift values is proposed, which could be integrated into the same flight control platform, thus avoiding the need to add additional processing components. To demonstrate the feasibility of the proposed design, a triangular phase-shift detector prototype has been implemented using commercial devices. Calibration and measurements at 2.46 GHz show a dynamic range of 30 dB and a non-ambiguous detection range of +-80 degree in the worst cases. Those specs let us to track the drone during the landing maneuver in an inverted cone formed by a surface with a +-4.19 m radius at 10m high and the landing point.
Underwater MEMS Gyrocompassing: A Virtual Testing Ground
Authors: Daniel Engelsman, Itzik Klein
Date: 2024-02-08 00:00:00

In underwater navigation, accurate heading information is crucial for accurately and continuously tracking trajectories, especially during extended missions beneath the waves. In order to determine the initial heading, a gyrocompassing procedure must be employed. As unmanned underwater vehicles (UUV) are susceptible to ocean currents and other disturbances, the model-based gyrocompassing procedure may experience degraded performance. To cope with such situations, this paper introduces a dedicated learning framework aimed at mitigating environmental effects and offering precise underwater gyrocompassing. Through the analysis of the dynamic UUV signature obtained from inertial measurements, our proposed framework learns to refine disturbed signals, enabling a focused examination of the earth's rotation rate vector. Leveraging recent machine learning advancements, empirical simulations assess the framework's adaptability to challenging underwater conditions. Ultimately, its contribution lies in providing a resilient gyrocompassing solution for UUVs.
Internal Model Control design for systems learned by Control Affine Neural Nonlinear Autoregressive Exogenous Models
Authors: Jing Xie, Fabio Bonassi, Riccardo Scattolini
Date: 2024-02-08 00:00:00

This paper explores the use of Control Affine Neural Nonlinear AutoRegressive eXogenous (CA-NNARX) models for nonlinear system identification and model-based control design. The idea behind this architecture is to match the known control-affine structure of the system to achieve improved performance. Coherently with recent literature of neural networks for data-driven control, we first analyze the stability properties of CA-NNARX models, devising sufficient conditions for their incremental Input-to-State Stability ($\delta$ISS) that can be enforced at the model training stage. The model's stability property is then leveraged to design a stable Internal Model Control (IMC) architecture. The proposed control scheme is tested on a real Quadruple Tank benchmark system to address the output reference tracking problem. The results achieved show that (i) the modeling accuracy of CA-NNARX is superior to the one of a standard NNARX model for given weight size and training epochs, (ii) the proposed IMC law provides performance comparable to the ones of a standard Model Predictive Controller (MPC) at a significantly lower computational burden, and (iii) the $\delta$ISS of the model is beneficial to the closed-loop performance.
A Learning-based Model Predictive Control Scheme with Application to Temperature Control Units
Authors: Jing Xie, Léo Simpson, Jonas Asprion, Riccardo Scattolini
Date: 2024-02-08 00:00:00
Tasks: Model Predictive Control

Temperature control is a complex task due to its often unknown dynamics and disturbances. This paper explores the use of Neural Nonlinear AutoRegressive eXogenous (NNARX) models for nonlinear system identification and model predictive control of a temperature control unit. First, the NNARX model is identified from input-output data collected from the real plant, and a state-space representation with known measurable states consisting of past input and output variables is formulated. Second, a tailored model predictive controller is designed based on the trained NNARX network. The proposed control architecture is experimentally tested on the temperature control units manufactured by Tool-Temp AG. The results achieved are compared with those obtained using a PI controller and a linear MPC. The findings illustrate that the proposed scheme achieves satisfactory tracking performance while incurring the lowest energy cost among the compared controllers.
Design and Prototyping of Transmissive RIS-Aided Wireless Communication
Authors: Jianan Zhang, Rujing Xiong, Junshuo Liu, Tiebin Mi, Robert Caiming Qiu
Date: 2024-02-08 00:00:00

Reconfigurable Intelligent Surfaces (RISs) exhibit promising enhancements in coverage and data rates for wireless communication systems, particularly in the context of 5G and beyond. This paper introduces a novel approach by focusing on the design and prototyping of a transmissive RIS, contrasting with existing research predominantly centered on reflective RIS. The achievement of 1-bit transmissive RIS through the antisymmetry configuration of the two PIN diodes, nearly uniform transmission magnitudes but inversed phase states in a wide band can be obtained. A transmissive RIS prototype consisting of 16 $\times$ 16 elements is meticulously designed, fabricated, and subjected to measurement to validate the proposed design. The results demonstrate that the proposed RIS unit cell achieves effective 1-bit phase tuning with minimal insertion loss and a transmission bandwidth of 3 dB exceeding $20\%$ at 5.8GHz. By dynamically modulating the quantized code distributions on the RIS, it becomes possible to construct scanning beams. The experimental outcomes of the RIS-assisted communication system validate that, in comparison to scenarios without RIS, the signal receiving power experiences an increase of approximately 7dB when RIS is deployed to overcome obstacles. This underscores the potential applicability of mobile RIS in practical communication.
A Game-Theoretical Approach for Optimal Supervisory Control of Discrete Event Systems under Energy Constraints
Authors: Peng Lv, ShaoYuan Li, Xiang Yin
Date: 2024-02-08 00:00:00

In this paper, we investigate the problem of optimal supervisory control for the discrete event systems under energy constraints. We consider that the execution of events consumes energy and the energy can be replenished at specific reload states. When the energy level drops below zero, the system will be crashed. To capture the above scenario, we introduce a new model, called consumption discrete event system (cDES). Our objective is to find the minimal initial energy value and synthesize an optimal supervisor ensuring that the energy will never be exhausted. To solve this problem, we propose a game-theoretical approach by converting the cDES as a consumption two-player graph game (cTPG) and reformulate the optimal supervisory control problem in game theory. In particular, we demonstrate that the converted game can be decomposed into independent reachability games related to reload vertices, which can be solved by a fixed point iterative algorithm proposed in this paper. Through iteratively removing unsafe reload vertices and solving reachability games for the remaining reload vertices, a solution can be found.
Techno-Economic Modeling and Safe Operational Optimization of Multi-Network Constrained Integrated Community Energy Systems
Authors: Ze Hu, Ka Wing Chan, Ziqing Zhu, Xiang Wei, Weiye Zheng, Siqi Bu
Date: 2024-02-08 00:00:00
Tasks: Safe Reinforcement Learning

The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the operational optimization of ICES is hindered by the physical constraints of heterogeneous networks including electricity, natural gas, and heat. These challenges are difficult to address due to the non-linearity of network constraints and the high complexity of multi-network coordination. This paper, therefore, proposes a novel Safe Reinforcement Learning (SRL) algorithm to optimize the multi-network constrained operation problem of ICES. Firstly, a comprehensive ICES model is established considering integrated demand response (IDR), multiple energy devices, and network constraints. The multi-network operational optimization problem of ICES is then presented and reformulated as a constrained Markov Decision Process (C-MDP) accounting for violating physical network constraints. The proposed novel SRL algorithm, named Primal-Dual Twin Delayed Deep Deterministic Policy Gradient (PD-TD3), solves the C-MDP by employing a Lagrangian multiplier to penalize the multi-network constraint violation, ensuring that violations are within a tolerated range and avoid over-conservative strategy with a low reward at the same time. The proposed algorithm accurately estimates the cumulative reward and cost of the training process, thus achieving a fair balance between improving profits and reducing constraint violations in a privacy-protected environment with only partial information. A case study comparing the proposed algorithm with benchmark RL algorithms demonstrates the computational performance in increasing total profits and alleviating the network constraint violations.
Control of AC-AC interlinking converters for multi-grids
Authors: Jeremy Watson, Ioannis Lestas
Date: 2024-02-07 00:00:00

This paper considers the control of AC-AC inter-linking converters (ILCs) in a multi-grid network. We overview the control schemes in the literature and propose a passivity framework for the stabilization of multi-grid networks, considering both AC grid-following and AC grid-forming behavior for the ILC connections. We then analyze a range of AC/AC interlinking converter control methods derived from the literature and propose suitable controllers for this purpose including both AC grid-forming and grid-following behavior. The controller we propose is partially grid-forming; in particular, it is based on a combination of a grid-following and a grid-forming converter to improve the stability properties of the network. Simulation results and theoretical analysis confirm that the proposed ILC control designs are appropriate for the multi-grid network.
Safe Human-UAS Collaboration Abstraction
Authors: Hossein Rastgoftar
Date: 2024-02-07 00:00:00
Tasks: Decision Making, Motion Planning

This paper studies the problem of safe humanuncrewed aerial system (UAS) collaboration in a shared work environment. By considering human and UAS as co-workers, we use Petri Nets to abstractly model evolution of shared tasks assigned to human and UAS co-workers. Particularly, the Petri Nets places represent work stations; therefore, the Petri Nets transitions can formally specify displacements between the work stations. The first objective is to incorporate uncertainty regarding the intentions of human co-workers into motion planning for UAS, when UAS co-workers closely interact with human co-workers. To this end, the proposed Petri Nets model uses conflict constructs to represent situations at which UAS deals with incomplete knowledge about human co-worker intention. The second objective is then to plan the motion of the UAS in a resilient and safe manner, in the presence of non-cooperative human co-workers. In order to achieve this objective, UAS equipped with onboard perception and decision-making capabilities are able to, through real-time processing of in-situ observation, predict human intention, quantify human distraction, and apply a non-stationary Markov Decision model to safely plan UAS motion in the presence of uncertainty.
ASCENT: A Context-Aware Spectrum Coexistence Design and Implementation Toolset for Policymakers in Satellite Bands
Authors: Ta-seen Reaz Niloy, Saurav Kumar, Aniruddha Hore, Zoheb Hassan, Carl Dietrich, Eric W. Burger, Jeffrey H. Reed, Vijay K. Shah
Date: 2024-02-07 00:00:00

This paper introduces ASCENT (context Aware Spectrum Coexistence Design and Implementation) toolset, an advanced context-aware terrestrial satellite spectrum sharing toolset designed for researchers, policymakers, and regulators. It serves two essential purposes (a) evaluating the potential for harmful interference to primary users in satellite bands and (b) facilitating the analysis, design, and implementation of diverse regulatory policies on spectrum usage and sharing. Notably, ASCENT implements a closed-loop feedback system that allows dynamic adaptation of policies according to a wide range of contextual factors (e.g., weather, buildings, summer/winter foliage, etc.) and feedback on the impact of these policies through realistic simulation. Specifically, ASCENT comprises the following components (i) interference evaluation tool for evaluating interference at the incumbents in a spectrum-sharing environment while taking the underlying contexts, (ii) dynamic spectrum access (DSA) framework for providing context-aware instructions to adapt networking parameters and control secondary terrestrial network's access to the shared spectrum band according to context aware prioritization, (iii) Context broker to acquire essential and relevant contexts from external context information providers; and (iv) DSA Database to store dynamic and static contexts and the regulator's policy information. The closed-loop feedback system of ASCENT is implemented by integrating these components in a modular software architecture. A case study of sharing the lower 12 GHz Ku band (12.2-12.7 GHz) with the 5G terrestrial cellular network is considered, and the usability of ASCENT is demonstrated by dynamically changing exclusion zone's radius in different weather conditions.
Position and Speed Control of Brushless DC Motors Using Sensorless Techniques and Application Trends
Authors: Jose-Carlos Gamazo-Real, Ernesto Vazquez-Sanchez, Jaime Gomez-Gil
Date: 2024-02-07 00:00:00
Tasks: Position

This paper provides a technical review of position and speed sensorless methods for controlling Brushless Direct Current (BLDC) motor drives, including the background analysis using sensors, limitations and advances. The performance and reliability of BLDC motor drivers have been improved because the conventional control and sensing techniques have been improved through sensorless technology. Then, in this paper sensorless advances are reviewed and recent developments in this area are introduced with their inherent advantages and drawbacks, including the analysis of practical implementation issues and applications. The study includes a deep overview of state-of-the-art back-EMF sensing methods, which includes Terminal Voltage Sensing, Third Harmonic Voltage Integration, Terminal Current Sensing, Back-EMF Integration and PWM strategies. Also, the most relevant techniques based on estimation and models are briefly analysed, such as Sliding-mode Observer, Extended Kalman Filter, Model Reference Adaptive System, Adaptive observers (Full-order and Pseudoreduced-order) and Artificial Neural Networks.
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
Authors: Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier, Carolina Cuesta-Lazaro, Simon Ding, Axel Lapel, Pablo Lemos, Christopher C. Lovell, T. Lucas Makinen, Chirag Modi, Viraj Pandya, Shivam Pandey, Lucia A. Perez, Benjamin Wandelt, Greg L. Bryan
Date: 2024-02-06 00:00:00
Tasks: All, Benchmarking, Efficient Exploration

This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for implementing various neural architectures, training schemata, priors, and density estimators in a manner easily adaptable to any research workflow. It includes comprehensive validation metrics to assess posterior estimate coverage, enhancing the reliability of inferred results. Additionally, the pipeline is easily parallelizable and is designed for efficient exploration of modeling hyperparameters. To demonstrate its capabilities, we present real applications across a range of astrophysics and cosmology problems, such as: estimating galaxy cluster masses from X-ray photometry; inferring cosmology from matter power spectra and halo point clouds; characterizing progenitors in gravitational wave signals; capturing physical dust parameters from galaxy colors and luminosities; and establishing properties of semi-analytic models of galaxy formation. We also include exhaustive benchmarking and comparisons of all implemented methods as well as discussions about the challenges and pitfalls of ML inference in astronomical sciences. All code and examples are made publicly available at https://github.com/maho3/ltu-ili.
Coarse graining correlation matrices according to macrostructures: Financial markets as a paradigm
Authors: M. Mijaíl Martínez-Ramos, Parisa Majari, Andres R. Cruz-Hernández, Hirdesh K. Pharasi, Manan Vyas
Date: 2024-02-08 00:00:00

We analyze correlation structures in financial markets by coarse graining the Pearson correlation matrices according to market sectors to obtain Guhr matrices using Guhr's correlation method according to Ref. [P. Rinn {\it et. al.}, Europhysics Letters 110, 68003 (2015)]. We compare the results for the evolution of market states and the corresponding transition matrices with those obtained using Pearson correlation matrices. The behavior of market states is found to be similar for both the coarse grained and Pearson matrices. However, the number of relevant variables is reduced by orders of magnitude.
Microscopic models for the large-scale spread of SARS-CoV-2 virus: A Statistical Mechanics approach
Authors: Marzia Bisi, Silvia Lorenzani
Date: 2024-02-08 00:00:00

In this work, we derive a system of Boltzmann-type equations to describe the spread of SARS-CoV-2 virus at the microscopic scale, that is by modeling the human-to-human mechanisms of transmission. To this end, we consider two populations, characterized by specific distribution functions, made up of individuals without symptoms (population $1$) and infected people with symptoms (population $2$). The Boltzmann operators model the interactions between individuals within the same population and among different populations with a probability of transition from one to the other due to contagion or, vice versa, to recovery. In addition, the influence of innate and adaptive immune systems is taken into account. Then, starting from the Boltzmann microscopic description we derive a set of evolution equations for the size and mean state of each population considered. Mathematical properties of such macroscopic equations, as equilibria and their stability, are investigated and some numerical simulations are performed in order to analyze the ability of our model to reproduce the characteristic features of Covid-19.
High Dimensional Factor Analysis with Weak Factors
Authors: Jungjun Choi, Ming Yuan
Date: 2024-02-08 00:00:00
Tasks: Matrix Completion

This paper studies the principal components (PC) estimator for high dimensional approximate factor models with weak factors in that the factor loading ($\boldsymbol{\Lambda}^0$) scales sublinearly in the number $N$ of cross-section units, i.e., $\boldsymbol{\Lambda}^{0\top} \boldsymbol{\Lambda}^0 / N^\alpha$ is positive definite in the limit for some $\alpha \in (0,1)$. While the consistency and asymptotic normality of these estimates are by now well known when the factors are strong, i.e., $\alpha=1$, the statistical properties for weak factors remain less explored. Here, we show that the PC estimator maintains consistency and asymptotical normality for any $\alpha\in(0,1)$, provided suitable conditions regarding the dependence structure in the noise are met. This complements earlier result by Onatski (2012) that the PC estimator is inconsistent when $\alpha=0$, and the more recent work by Bai and Ng (2023) who established the asymptotic normality of the PC estimator when $\alpha \in (1/2,1)$. Our proof strategy integrates the traditional eigendecomposition-based approach for factor models with leave-one-out analysis similar in spirit to those used in matrix completion and other settings. This combination allows us to deal with factors weaker than the former and at the same time relax the incoherence and independence assumptions often associated with the later.
Difference-in-Differences Estimators with Continuous Treatments and no Stayers
Authors: Clément de Chaisemartin, Xavier D'Haultfœuille, Gonzalo Vazquez-Bare
Date: 2024-02-08 00:00:00

Many treatments or policy interventions are continuous in nature. Examples include prices, taxes or temperatures. Empirical researchers have usually relied on two-way fixed effect regressions to estimate treatment effects in such cases. However, such estimators are not robust to heterogeneous treatment effects in general; they also rely on the linearity of treatment effects. We propose estimators for continuous treatments that do not impose those restrictions, and that can be used when there are no stayers: the treatment of all units changes from one period to the next. We start by extending the nonparametric results of de Chaisemartin et al. (2023) to cases without stayers. We also present a parametric estimator, and use it to revisit Desch\^enes and Greenstone (2012).