pwc-archive/papers-with-abstracts huggingface dataset:
Resilient-Economic Coordinated Robust Operation of Integrated Electric-Gas Systems
Authors: Ahmed Rabee Sayed, Cheng Wang, Tianshu Bi
Date: 2023-04-28 00:00:00
Tasks: Decision Making, Scheduling

Interactions between power and gas systems, which are both large and complex, have been gradually intensified during the last decades, predominantly due to the propagation of large fleet natural gas-fired power units (GPUs) and the technological developments of power-to-gas (P2G) facilities. These interactions not only bring significant economic benefits to society but also provide additional operating flexibilities, which are essential to handle fluctuations of the large-scale renewable power generation (RPG) and power system contingencies. Moreover, neglecting these interactions in power system operation may not only result in infeasible operation status in the gas systems but also increase the decision-making operation costs of both systems. previous studies suffered from two significant drawbacks, namely (1) they assumed the existence of only one utility that has full control authority over the power system and gas system; (2) the economic interactions between power systems and gas systems have been neglected, which goes against the current industrial practice. This research revisits the day-ahead resilient and economic operations of power systems considering the economic and physical interactions with gas systems, which are characterized by the modeling of bilateral energy purchase contracts and operational constraints of gas systems. This thesis provides a novel perspective and solution for the resilient and economically coordinated robust operation of integrated electric-gas systems (IEGSs) under uncertainties. The proposed robust scheduling decision frameworks are practically compatible with the existing industrial operations of the IEGSs.
A Resonant Tank Based Approach for Realizing ZPA in Inductive Power Transfer Systems
Authors: Abhilash B, Arun Karuppaswamy B
Date: 2023-05-01 00:00:00

A load-independent constant current (CC) - constant voltage (CV) output is an important requirement of inductive power transfer (IPT) systems for electric vehicle charging applications. Zero phase angle (ZPA) is also a desirable feature, to ensure a lower power rating requirement for the switching converter. CC and CV output along with ZPA can be achieved by using a suitable compensation topology. Equation manipulation techniques can be used for designing the compensation topology. But, it can be mathematically intensive especially for higher order topologies. To overcome this problem, resonant-tank based approaches are adopted in several works to achieve CC and CV conditions. However, equation-based approaches are depended upon either wholly or partly for realizing ZPA. This approach can be tedious and lacks physical insight. The proposed method extends resonant tank approach to achieve ZPA also, besides CC and CV. The need for a separate method to achieve ZPA is eliminated. Further, it simplifies the process in arriving at the constraints that ensure ZPA. As a sample validation, the proposed method is applied to a S-SP compensation topology. The CC-ZPA and CV-ZPA constraints for the S-SP topology are shown to be in line with the ones arrived at using an existing equation-based impedance approach. The simplicity of the proposed method can be observed from the sample validation.
RAPID: Autonomous Multi-Agent Racing using Constrained Potential Dynamic Games
Authors: Yixuan Jia, Maulik Bhatt, Negar Mehr
Date: 2023-04-30 00:00:00
Tasks: Autonomous Racing

In this work, we consider the problem of autonomous racing with multiple agents where agents must interact closely and influence each other to compete. We model interactions among agents through a game-theoretical framework and propose an efficient algorithm for tractably solving the resulting game in real time. More specifically, we capture interactions among multiple agents through a constrained dynamic game. We show that the resulting dynamic game is an instance of a simple-to-analyze class of games. Namely, we show that our racing game is an instance of a constrained dynamic potential game. An important and appealing property of dynamic potential games is that a generalized Nash equilibrium of the underlying game can be computed by solving a single constrained optimal control problem instead of multiple coupled constrained optimal control problems. Leveraging this property, we show that the problem of autonomous racing is greatly simplified and develop RAPID (autonomous multi-agent RAcing using constrained PotentIal Dynamic games), a racing algorithm that can be solved tractably in real-time. Through simulation studies, we demonstrate that our algorithm outperforms the state-of-the-art approach. We further show the real-time capabilities of our algorithm in hardware experiments.
Fixed-time safe tracking control of uncertain high-order nonlinear pure-feedback systems via unified transformation functions
Authors: Chaoqun Guo, Jiangping Hu, Jiasheng Hao, Sergej Celikovsky, Xiaoming Hu
Date: 2023-04-30 00:00:00

In this paper, a fixed-time safe control problem is investigated for an uncertain high-order nonlinear pure-feedback system with state constraints. A new nonlinear transformation function is firstly proposed to handle both the constrained and unconstrained cases in a unified way. Further, a radial basis function neural network is constructed to approximate the unknown dynamics in the system and a fixed-time dynamic surface control (FDSC) technique is developed to facilitate the fixed-time control design for the uncertain high-order pure-feedback system. Combined with the proposed unified transformation function and the FDSC technique, an adaptive fixed-time control strategy is proposed to guarantee the fixed-time tracking. The proposed fixed-time control strategy can guarantee uniform control structure when addressing both constrained and unconstrained situations. Numerical examples are presented to demonstrate the proposed fixed-time tracking control strategy.
MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches
Authors: Verena Häberle, Linbin Huang, Xiuqiang He, Eduardo Prieto-Araujo, Roy S. Smith, Florian Dörfler
Date: 2023-04-29 00:00:00
Tasks: subspace methods

A fast and accurate grid impedance measurement of three-phase power systems is crucial for online assessment of power system stability and adaptive control of grid-connected converters. Existing grid impedance measurement approaches typically rely on pointwise sinusoidal injections or sequential wideband perturbations to identify a nonparametric grid impedance curve via fast Fourier computations in the frequency domain. This is not only time-consuming, but also inaccurate during time-varying grid conditions, while on top of that, the identified nonparametric model cannot be immediately used for stability analysis or control design. To tackle these problems, we propose to use parametric system identification techniques (e.g., prediction error or subspace methods) to obtain a parametric impedance model directly from time-domain current and voltage data. Our approach relies on injecting wideband excitation signals in the converter's controller and allows to accurately identify the grid impedance in closed loop within one injection and measurement cycle. Even though the underlying parametric system identification techniques are well-studied in general, their utilization in a grid impedance identification setup poses specific challenges, is vastly underexplored, and has not gained adequate attention in urgent and timely power systems applications. To this end, we demonstrate in numerical experiments how the proposed parametric approach can accomplish a significant improvement compared to prevalent nonparametric methods.
Distributed State Estimation for Linear Time-Varying Systems with Sensor Network Delays
Authors: Sanjay Chandrasekaran, Vishnu Varadan, Siva Vignesh Krishnan, Florian Dörfler, Mohammad H. Mamduhi
Date: 2023-04-29 00:00:00
Tasks: State Estimation

Distributed sensor networks often include a multitude of sensors, each measuring parts of a process state space or observing the operations of a system. Communication of measurements between the sensor nodes and estimator(s) cannot realistically be considered delay-free due to communication errors and transmission latency in the channels. We propose a novel stability-based method that mitigates the influence of sensor network delays in distributed state estimation for linear time-varying systems. Our proposed algorithm efficiently selects a subset of sensors from the entire sensor nodes in the network based on the desired stability margins of the distributed Kalman filter estimates, after which, the state estimates are computed only using the measurements of the selected sensors. We provide comparisons between the estimation performance of our proposed algorithm and a greedy algorithm that exhaustively selects an optimal subset of nodes. We then apply our method to a simulative scenario for estimating the states of a linear time-varying system using a sensor network including 2000 sensor nodes. Simulation results demonstrate the performance efficiency of our algorithm and show that it closely follows the achieved performance by the optimal greedy search algorithm.
Tutorial: Implementando Controladores Preditivos Não Lineares através do Ferramental LPV
Authors: Marcelo Menezes Morato, Amir Naspolini, Julio Elias Normey-Rico
Date: 2023-04-28 00:00:00
Tasks: Model Predictive Control

Recent works have demonstrated how Linear Parameter Varying Model Predictive Control (LPV MPC) algorithms are able to control nonlinear systems with precision and reduced computational load. Specifically, these schemes achieve comparable performances to state-of-the-art nonlinear MPCs, while requiring the solution of only one quadratic programming problem (thus being real-time capable). In this tutorial paper, we provide a step-by-step overview of how to implement such LPV MPC algorithms, covering from modelling to stability aspects. For illustration purposes, we consider a realistic implementation for a gas-lift petroleoum extraction process, comparing the LPV approach with the becnhmark nonlinear MPC software CasADi.
Model Predictive Control of Wind Turbines with Piecewise-Affine Power Coefficient Approximation
Authors: Arnold Sterle, Aaron Grapentin, Christian A. Hans, Jörg Raisch
Date: 2023-04-28 00:00:00
Tasks: Model Predictive Control

In this paper, an offset-free bilinear model predictive control approach for wind turbines is presented. State-of-the-art controllers employ different control loops for pitch angle and generator torque which switch depending on wind conditions. In contrast, the presented controller is based on one unified control law that works for all wind conditions. The inherent nonlinearity of wind turbines is addressed through a piecewise-affine approximation of the power coefficient, which is modelled in a mixed-integer fashion. The presented controller is compared to a state-of-the-art baseline controller in a numerical case study using OpenFAST. Simulation results show that the presented controller ensures accurate reference power tracking. Additionally, damage equivalent loads are reduced for higher wind speeds.
Low-Cost Throttle-By-Wire-System Architecture For Two-Wheeler Vehicles
Authors: Jannis Kreß, Jens Rau, Hektor Hebert, Fernando Perez-Peña, Karsten Schmidt, Arturo Morgado-Estévez
Date: 2023-04-28 00:00:00
Tasks: Position, Vocal Bursts Valence Prediction

This paper investigates the performance of a lowcost Throttle-by-Wire-System (TbWS) for two-wheeler applications. Mopeds/scooters are still restricted environmentally harmful. Throttle-by-Wire-Systems can contribute to environmental protection by replacing conventional restrictors. Its consisting of an anisotropic magnetoresistance (AMR) throttle position sensor and a position controlled stepper motor driven throttle valve actuator. The decentralized throttle position sensor is operating contactless and acquires redundant data. Throttle valve actuation is realized through a position controlled stepper motor, sensing its position feedback by Hall effect. Using a PI-controller, the stepper motors position is precisely set. Both units are transceiving data by a CAN bus. Furthermore, fail-safe functions, plausibility checks, calibration algorithms and energy saving modes have been implemented. Both modules have been evaluated by Hardware-in-the-Loop testing in terms of reliability and measuring/positioning performance before the system was integrated into a Peugeot Kisbee 50 4T (Euro 5/injected). Finally, the sensor unit comes with a measurement deviation of less than 0.16% whereas the actuator unit can approach throttle valve positions with a deviation of less than 0.37%. The actuators settling time does not exceed 0.13s while stable, step-loss free and noiseless operation.
Regret Optimal Control for Uncertain Stochastic Systems
Authors: Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros, Giancarlo Ferrari-Trecate
Date: 2023-04-28 00:00:00

We consider control of uncertain linear time-varying stochastic systems from the perspective of regret minimization. Specifically, we focus on the problem of designing a feedback controller that minimizes the loss relative to a clairvoyant optimal policy that has foreknowledge of both the system dynamics and the exogenous disturbances. In this competitive framework, establishing robustness guarantees proves challenging as, differently from the case where the model is known, the clairvoyant optimal policy is not only inapplicable, but also impossible to compute without knowledge of the system parameters. To address this challenge, we embrace a scenario optimization approach, and we propose minimizing regret robustly over a finite set of randomly sampled system parameters. We prove that this policy optimization problem can be solved through semidefinite programming, and that the corresponding solution retains strong probabilistic out-of-sample regret guarantees in face of the uncertain dynamics. Our method naturally extends to include satisfaction of safety constraints with high probability. We validate our theoretical results and showcase the potential of our approach by means of numerical simulations.
Mobile Network Slicing under Demand Uncertainty: A Stochastic Programming Approach
Authors: Anousheh Gholami, Nariman Torkzaban, John S. Baras
Date: 2023-04-27 00:00:00

Network slicing enables the deployment of multiple dedicated virtual sub-networks, i.e. slices on a shared physical infrastructure. Unlike traditional one-size-fits-all resource provisioning schemes, each network slice (NS) in 5G is tailored to the specific service requirements of a group of customers. An end-to-end (E2E) mobile NS orchestration requires the simultaneous provisioning of computing, storage, and networking resources across the core network (CN) and the radio access network (RAN). Constant temporospatial changes in mobile user demand profiles further complicate the E2E NSs resource provisioning beyond the limits of the existing best-effort schemes that are only effective under accurate demand forecasts for all slices. This paper proposes a practical two-time-scale resource provisioning framework for E2E network slicing under demand uncertainty. At each macro-scale instance, we assume that only the spatial probability distribution of the NS demands is available. We formulate the NSs resource allocation problem as a stochastic mixed integer program (SMIP) with the objective of minimizing the total resource cost at the CN and the RAN. At each microscale instance, utilizing the exact slice demand profiles, a linear program is solved to jointly minimize the unsupported traffic and the resource cost at the RAN. We verify the effectiveness of our resource allocation scheme through numerical experiments.
Bearing-Based Network Localization Under Randomized Gossip Protocol
Authors: Nhat-Minh Le-Phan, Minh Hoang Trinh, Phuoc Doan Nguyen
Date: 2023-04-27 00:00:00
Tasks: Position

In this paper, we consider a randomized gossip algorithm for the bearing-based network localization problem. Let each sensor node be able to obtain the bearing vectors and communicate its position estimates with several neighboring agents. Each update involves two agents, and the update sequence follows a stochastic process. Under the assumption that the network is infinitesimally bearing rigid and contains at least two beacon nodes, we show that when the updating step-size is properly selected, the proposed algorithm can successfully estimate the actual sensor nodes' positions with probability one. The randomized update provides a simple, distributed, and cost-effective method for localizing the network. The theoretical result is supported with a simulation of a 1089-node sensor network.
Rapid Object Detection using a Boosted Cascade of Simple Features
Authors: Paul Viola, Michael Jones
Date: 2003-04-15 00:00:00
Proceeding: CVPR 2003 4
Tasks: Face Detection, Object, object-detection, Object Detection

This paper describes a machine learning approach for visual object detection which is capable of processing images
extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first
is the introduction of a new image representation called the “Integral linage” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely eflcient class@ers[5]. The third contribution is a method for combining increasingly more complex classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specijic focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest, In the domain of face detection the system

yields detection rates comparable to the best previous sys-
tems. Used in real-time applications, the detector runs at

15 frames per second without resorting to image differenc-
ing or skin color detection.
Spatial dynamics of synergistic coinfection in rock-paper-scissors models
Authors: J. Menezes, E. Rangel
Date: 2023-04-30 00:00:00

We investigate the spatial dynamics of two disease epidemics reaching a three-species cyclic model. Regardless of their species, all individuals are susceptible to being infected with two different pathogens, which spread through person-to-person contact. The occurrence of coinfection leads to a synergistic increase in the risk of hosts dying due to complications from either disease. Our stochastic simulations show that departed areas inhabited by hosts of a single pathogen arise from random initial conditions. The single-disease spatial domains are bordered by interfaces of coinfected hosts whose dynamics are curvature-driven. Our findings show that the coarsening dynamics of the interface network are controlled by the fluctuations of coinfection waves invading the single-disease territories. As the coinfection mortality grows, the dynamics of the interface network attain the scaling regime. We discover that organisms' infection risk is maximised if the coinfection increases the death due to disease in $30\%$, and minimised as the network dynamics reach the scaling regime, with species populations being maximum. Our conclusions may help ecologists understand the dynamics of epidemics and their impact on the stability of ecosystems.
Restoring Original Signal From Pile-up Signal using Deep Learning
Authors: C. H. Kim, S. Ahn, K. Y. Chae, J. Hooker, G. V. Rogachev
Date: 2023-04-24 00:00:00
Tasks: Deep Learning

Pile-up signals are frequently produced in experimental physics. They create inaccurate physics data with high uncertainty and cause various problems. Therefore, the correction to pile-up signals is crucially required. In this study, we implemented a deep learning method to restore the original signals from the pile-up signals. We showed that a deep learning model could accurately reconstruct the original signal waveforms from the pile-up waveforms. By substituting the pile-up signals with the original signals predicted by the model, the energy and timing resolutions of the data are notably enhanced. The model implementation significantly improved the quality of the particle identification plot and particle tracks. This method is applicable to similar problems, such as separating multiple signals or correcting pile-up signals with other types of noises and backgrounds.
Accurate ignition detection of solid fuel particles using machine learning
Authors: Tao Li, Zhangke Liang, Andreas Dreizler, Benjamin Böhm
Date: 2023-04-20 00:00:00

In the present work, accurate determination of single-particle ignition is focused on using high-speed optical diagnostics combined with machine learning approaches. Ignition of individual particles in a laminar flow reactor are visualized by simultaneous 10 kHz OH-LIF and DBI measurements. Two coal particle sizes of 90-125{\mu}m and 160-200{\mu}m are investigated in conventional air and oxy-fuel conditions with increasing oxygen concentrations. Ignition delay times are first evaluated with threshold methods, revealing obvious deviations compared to the ground truth detected by the human eye. Then, residual networks (ResNet) and feature pyramidal networks (FPN) are trained on the ground truth and applied to predict the ignition time.~Both networks are capable of detecting ignition with significantly higher accuracy and precision. Besides, influences of input data and depth of networks on the prediction performance of a trained model are examined.~The current study shows that the hierarchical feature extraction of the convolutions networks clearly facilitates data evaluation for high-speed optical measurements and could be transferred to other solid fuel experiments with similar boundary conditions.
Gel phantom data for dynamic X-ray tomography
Authors: Tommi Heikkilä, Hanna Help, Alexander Meaney
Date: 2020-03-05 00:00:00

This is the documentation for dynamic X-ray tomography measurements of a gel phantom diffused with potassium iodide contrast agent. The measured data and additional materials are available at http://www.fips.fi/dataset.php and Zenodo (DOI:10.5281/zenodo.7876521) for open use by the scientific community, as long as the data and this documentation at arXiv are appropriately referenced. The files contain: (1) 17 consecutive measurements of the gel phantom organized into sinograms at two different resolutions (binning levels) and some additional metadata which can be used to create matrix-free forward operators and filtered back-projection reconstructions; (2) the first measurement using a greater number of projections, and an additional measurement of an 18th time step using very dense angular sampling, for example to be used for reference reconstructions; (3) short example codes to showcase how the data could be used to test and validate reconstruction algorithms.