pwc-archive/papers-with-abstracts huggingface dataset:
Uncertainty-Aware Guidance for Target Tracking subject to Intermittent Measurements using Motion Model Learning
Authors: Andres Pulido, Kyle Volle, Kristy Waters, Zachary I. Bell, Prashant Ganesh, Jane Shin
Date: 2024-02-01 00:00:00
Tasks: Position, State Estimation, Uncertainty Quantification

This paper presents a novel guidance law for target tracking applications where the target motion model is unknown and sensor measurements are intermittent due to unknown environmental conditions and low measurement update rate. In this work, the target motion model is represented by a transformer neural network and trained by previous target position measurements. This transformer motion model serves as the prediction step in a particle filter for target state estimation and uncertainty quantification. The particle filter estimation uncertainty is utilized in the information-driven guidance law to compute a path for the mobile agent to travel to a position with maximum expected entropy reduction (EER). The computation of EER is performed in real-time by approximating the information gain from the predicted particle distributions relative to the current distribution. Simulation and hardware experiments are performed with a quadcopter agent and TurtleBot target to demonstrate that the presented guidance law outperforms two other baseline guidance methods.
A Review of Carsickness Mitigation: Navigating Challenges and Exploiting Opportunities in the Era of Intelligent Vehicles
Authors: Daofei Li, Tingzhe Yu, Binbin Tang
Date: 2024-02-01 00:00:00

Motion sickness (MS) has long been a common complaint in road transportation. However, in the era of driving automation, MS has become an increasingly significant issue. The future intelligent vehicle is envisioned as a mobile space for work or entertainment, but unfortunately passengers' engagement in non-driving tasks may exacerbate MS. Finding effective MS countermeasures is crucial to ensure a pleasant passenger experience. Nevertheless, due to the complex mechanism of MS, there are numerous challenges in mitigating it, hindering the development of practical countermeasures. To address this, we first review two prevalent theories explaining the mechanism of MS. Subsequently, this paper provides a summary of current subjective and objective approaches for quantifying motion sickness levels. Then, it surveys existing methods for alleviating MS, including passenger adjustment, intelligent vehicle solutions, and motion cues of various modalities. Furthermore, we outline the limitations and remaining challenges of current research and highlight novel opportunities in the context of intelligent vehicles. Finally, we propose an integrated framework for alleviating MS. The findings of this review will enhance our understanding of carsickness and offer valuable insights for future research and practice in MS mitigation within modern vehicles.
Adaptive FRIT-based Recursive Robust Controller Design Using Forgetting Factors
Authors: Satoshi Tsuruhara, Kazuhisa Ito
Date: 2024-02-01 00:00:00

Adaptive FRIT (A-FRIT) with exponential forgetting (EF) has been proposed for time-varying systems to improve the data dependence of FRIT, which is a direct data-driven tuning method. However, the EF-based method is not a reliable controller because it can cause significant degradation of the control performance and instability unless the persistent excitation (PE) condition is satisfied. To solve this problem, we propose a new A-FRIT method based on directional forgetting (DF) and exponential resetting that can forget old data without instability regardless of the PE condition. To confirm the effectiveness of the proposed method, we applied it to artificial muscle control with strong asymmetric hysteresis characteristics and evaluated its robust performance against load changes during the experiment. The experimental results show that the proposed method based on DF achieves high control performance and is robust against changes in the characteristics and/or target trajectory. The proposed method is also practical because it does not require system identification, model structure, or prior experimentation.
Experimental Application of Predictive Cost Adaptive Control to Thermoacoustic Oscillations in a Rijke Tube
Authors: Juan A. Paredes, Dennis S. Bernstein
Date: 2024-02-01 00:00:00
Tasks: Model Predictive Control

Model predictive control (MPC) has been used successfully in diverse applications. As its name suggests, MPC requires a model for predictive optimization. The present paper focuses on the application of MPC to a Rijke tube, in which a heating source and acoustic dynamics interact to produce self-excited oscillations. Since the dynamics of a Rijke tube are difficult to model to a high level of accuracy, the implementation of MPC requires leveraging data from the physical setup as well as knowledge about thermoacoustics, which is labor intensive and requires domain expertise. With this motivation, the present paper uses predictive cost adaptive control (PCAC) for sampled-data control of an experimental Rijke-tube setup. PCAC performs online closed-loop linear model identification for receding-horizon optimization based on the backward propagating Riccati equation. In place of analytical modeling, open-loop experiments are used to create a simple emulation model, which is used for choosing PCAC hyperparameters. PCAC is applied to the Rijke-tube setup under various experimental scenarios.
Using digital twins for managing change in complex projects
Authors: Jennifer Whyte, Ranjith Soman, Rafael Sacks, Neda Mohammadi, Nader Naderpajouh, Wei-Ting Hong, Ghang Lee
Date: 2024-02-01 00:00:00
Tasks: Management

Complex systems are not entirely decomposable, hence interdependences arise at the interfaces in complex projects. When changes occur, significant risks arise at these interfaces as it is hard to identify, manage and visualise the systemic consequences of changes. Particularly problematic are the interfaces in which there are multiple interdependencies, which occur where the boundaries between design components, contracts and organisation coincide, such as between design disciplines. In this paper, we propose an approach to digital twin-based interface management, through an underpinning state-of-the-art review of the existing technical literature and a small pilot to identify the characteristics of future data-driven solutions. We set out an approach to digital twin-based interface management and an agenda for research on advanced methodologies for managing change in complex projects. This agenda includes the need to integrate work on identifying systems interfaces, change propagation and visualisation, and the potential to significantly extend the limitations of existing solutions by using developments in the digital twin, such as linked data, semantic enrichment, network analyses, natural language processing (NLP)-enhanced ontology and machine learning.
A study on the dynamic model of a three-phase grid-connected inverter and an innovative method for its verification
Authors: Arash Esfandiari, Mohammad Naser Hashemnia
Date: 2024-01-31 00:00:00

The ever-increasing use of renewable energy sources has underlined the role of power electronic converters as an interface between these resources and the power grid. One application of these converters is in three-phase inverters utilized in a solar power plant to inject active/reactive power to the grid. The dynamic model of power electronic converters is necessary for investigating the overall system stability and the design of the controller for the converters. Generally, the inverter dynamic model is needed to investigate the dynamic behavior of inverters in different applications. This paper is a study of the dynamical model of the grid-connected voltage source inverter, which is extracted by the state-space averaging (SSA) method. This model is verified by applying the values of the operating point to the inverter in Matlab Simulink environment. To attain the steady-state operating point, the zero component of the duty ratio of the converter is required. To obtain this component, the matrix form of the converter's average equations is used. Overally, using the above methods provides a more efficient and clear understanding of the dynamic model of the converter.
Arithmetic Average Density Fusion -- Part IV: Distributed Heterogeneous Fusion of RFS and LRFS Filters via Variational Approximation
Authors: Tiancheng Li, Haozhe Liang, Guchong Li, Jesús García Herrero, Quan Pan
Date: 2024-01-31 00:00:00

This paper, the fourth part of a series of papers on the arithmetic average (AA) density fusion approach and its application for target tracking, addresses the intricate challenge of distributed heterogeneous multisensor multitarget tracking, where each inter-connected sensor operates a probability hypothesis density (PHD) filter, a multiple Bernoulli (MB) filter or a labeled MB (LMB) filter and they cooperate with each other via information fusion. Earlier papers in this series have proven that the proper AA fusion of these filters is all exactly built on averaging their respective unlabeled/labeled PHDs. Based on this finding, two PHD-AA fusion approaches are proposed via variational minimization of the upper bound of the Kullback-Leibler divergence between the local and multi-filter averaged PHDs subject to cardinality consensus based on the Gaussian mixture implementation, enabling heterogeneous filter cooperation. One focuses solely on fitting the weights of the local Gaussian components (L-GCs), while the other simultaneously fits all the parameters of the L-GCs at each sensor, both seeking average consensus on the unlabeled PHD, irrespective of the specific posterior form of the local filters. For the distributed peer-to-peer communication, both the classic consensus and flooding paradigms have been investigated. Simulations have demonstrated the effectiveness and flexibility of the proposed approaches in both homogeneous and heterogeneous scenarios.
A Time-varying Shockwave Speed Model for Trajectory Reconstruction using Lagrangian and Eulerian Observations
Authors: Yifan Zhang, Anastasios Kouvelas, Michail A. Makridis
Date: 2024-01-29 00:00:00

Inference of detailed vehicle trajectories is crucial for applications such as traffic flow modeling, energy consumption estimation, and traffic flow optimization. Static sensors can provide only aggregated information, posing challenges in reconstructing individual vehicle trajectories. Shockwave theory is used to reproduce oscillations that occur between sensors. However, as the emerging of connected vehicles grows, probe data offers significant opportunities for more precise trajectory reconstruction. Existing methods rely on Eulerian observations (e.g., data from static sensors) and Lagrangian observations (e.g., data from probe vehicles) incorporating shockwave theory and car-following modeling. Despite these advancements, a prevalent issue lies in the static assignment of shockwave speed, which may not be able to reflect the traffic oscillations in a short time period caused by varying response times and vehicle dynamics. Moreover, energy consumption estimation is largely ignored. In response, this paper proposes a novel framework that integrates Eulerian and Lagrangian observations for trajectory reconstruction. The approach introduces a calibration algorithm for time-varying shockwave speed. The calibrated shockwave speed of the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory. Additionaly, vehicle and driver dynamics are introduced to optimize the trajectory and estimate energy consumption. The proposed method is evaluated using real-world datasets, demonstrating superior performance in terms of trajectory accuracy, reproducing traffic oscillations, and estimating energy consumption.
Real-Time Electromagnetic Estimation for Reluctance Actuators
Authors: Edgar Ramirez-Laboreo, Eduardo Moya-Lasheras, Carlos Sagues
Date: 2024-01-24 00:00:00

Several modeling, estimation, and control strategies have been recently presented for simple reluctance devices like solenoid valves and electromagnetic switches. In this paper, we present a new algorithm to online estimate the flux linkage and the electrical time-variant parameters of these devices, namely the resistance and the inductance, only by making use of discrete-time measurements of voltage and current. The algorithm, which is robust against measurement noise, is able to deal with temperature variations of the device and provides accurate estimations during the motion of the armature. Additionally, an integral {estimator} that uses the start of each operation of the actuator as reset condition has been also implemented for comparative purposes. The performances of both estimation methods are studied and compared by means of simulations and experimental tests, and the benefits of our proposal are emphasized. Possible uses of the estimates and further modeling developments are also described and discussed.
Hybrid Dynamical Model for Reluctance Actuators Including Saturation, Hysteresis and Eddy Currents
Authors: Edgar Ramirez-Laboreo, Maurice G. L. Roes, Carlos Sagues
Date: 2024-01-24 00:00:00

A novel hybrid dynamical model for single-coil, short-stroke reluctance actuators is presented in this paper. The model, which is partially based on the principles of magnetic equivalent circuits, includes the magnetic phenomena of hysteresis and saturation by means of the generalized Preisach model. In addition, the eddy currents induced in the iron core are also considered, and the flux fringing effect in the air is incorporated by using results from finite element simulations. An explicit solution of the dynamics without need of inverting the Preisach model is derived, and the hybrid automaton that results from combining the electromagnetic and motion equations is presented and discussed. Finally, an identification method to determine the model parameters is proposed and experimentally illustrated on a real actuator. The results are presented and the advantages of our modeling method are emphasized.
Optimized Task Assignment and Predictive Maintenance for Industrial Machines using Markov Decision Process
Authors: Ali Nasir, Samir Mekid, Zaid Sawlan, Omar Alsawafy
Date: 2024-01-20 00:00:00
Tasks: Decision Making, Management

This paper considers a distributed decision-making approach for manufacturing task assignment and condition-based machine health maintenance. Our approach considers information sharing between the task assignment and health management decision-making agents. We propose the design of the decision-making agents based on Markov decision processes. The key advantage of using a Markov decision process-based approach is the incorporation of uncertainty involved in the decision-making process. The paper provides detailed mathematical models along with the associated practical execution strategy. In order to demonstrate the effectiveness and practical applicability of our proposed approach, we have included a detailed numerical case study that is based on open source milling machine tool degradation data. Our case study indicates that the proposed approach offers flexibility in terms of the selection of cost parameters and it allows for offline computation and analysis of the decision-making policy. These features create and opportunity for the future work on learning of the cost parameters associated with our proposed model using artificial intelligence.
A Queueing Model for the Ambulance Ramping Problem with an Offload Zone
Authors: Josef Zuk, David Kirszenblat
Date: 2024-01-12 00:00:00

This work develops a methodology for studying the effect of an offload zone on the ambulance ramping problem using a multi-server, multi-class non-preemptive priority queueing model that can be treated analytically. A prototype model for the ambulance/emergency-department interface is constructed, which is then implemented as a formal discrete event simulation, and is run as a regenerative steady-state simulation for empirical estimation of the ambulance queue-length and waiting-time distributions. The model is also solved by analytical means for explicit and exact representations of these distributions, which are subsequently tested against simulation results. A number of measures of performance is extracted, including the mean and 90th percentiles of the ambulance queue length and waiting time, as well as the average number of ambulance days lost per month due to offload delay (offload delay rate). Various easily computable approximations are proposed and tested. In particular, a closed-form, purely algebraic expression that approximates the dependence of the offload delay rate on the capacity of the offload zone is proposed. It can be evaluated directly from model input parameters and is found to be, for all practical purposes, indistinguishable from the exact result.
Extreme value statistics of nerve transmission delay
Authors: Satori Tsuzuki
Date: 2024-02-01 00:00:00

Nerve transmission delay is an important topic in neuroscience. Spike signals fired or received at the dendrites of a neuron travel from the axon to the presynaptic cell. The spike signal triggers a chemical reaction at the synapse, wherein a presynaptic cell transfers neurotransmitters to the postsynaptic cell, and regenerates electrical signals by a chemical reaction process through ion channels and transmits it to neighboring neurons. In the context of describing the complex physiological reaction process as a stochastic process, this study aimed to show that the distribution of the maximum time interval of spike signals follows extreme order statistics. By considering the statistical variance in the time constant of the Leaky Integrate-and-Fire model, which is a deterministic time evolution model of spike signals, we enabled randomness in the time interval of spike signals. When the time constant follows an exponential distribution function, the time interval of the spike signal also follows an exponential distribution. In this case, our theory and simulations confirmed that the histogram of the maximum time interval follows the Gumbel distribution, which is one of the three types of extreme value statistics. We also confirmed that the histogram of the maximum time interval follows a Fr\'{e}chet distribution when the time interval of the spike signal follows a Pareto distribution. These findings confirm that nerve transmission delay can be described using extreme value statistics and could, therefore, be used as a new indicator for transmission delay.
EU-28's progress towards the 2020 renewable energy share. A club convergence analysis
Authors: María José Presno, Manuel Landajo
Date: 2024-02-01 00:00:00

This paper assesses the convergence of the EU-28 countries towards their common goal of 20% in the renewable energy share indicator by year 2020. The potential presence of clubs of convergence towards different steady state equilibria is also analyzed from both the standpoints of global convergence to the 20% goal and specific convergence to the various targets assigned to Member States. Two clubs of convergence are detected in the former case, each corresponding to different RES targets. A probit model is also fitted with the aim of better understanding the determinants of club membership, that seemingly include real GDP per capita, expenditure on environmental protection, energy dependence, and nuclear capacity, with all of them having statistically significant effects. Finally, convergence is also analyzed separately for the transport, heating and cooling, and electricity sectors.
Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models
Authors: Victor Chernozhukov, Iván Fernández-Val, Chen Huang, Weining Wang
Date: 2024-02-01 00:00:00
Tasks: Time Series

The Arellano-Bond estimator is a fundamental method for dynamic panel data models, widely used in practice. However, the estimator is severely biased when the data's time series dimension $T$ is long due to the large degree of overidentification. We show that weak dependence along the panel's time series dimension naturally implies approximate sparsity of the most informative moment conditions, motivating the following approach to remove the bias: First, apply LASSO to the cross-section data at each time period to construct most informative (and cross-fitted) instruments, using lagged values of suitable covariates. This step relies on approximate sparsity to select the most informative instruments. Second, apply a linear instrumental variable estimator after first differencing the dynamic structural equation using the constructed instruments. Under weak time series dependence, we show the new estimator is consistent and asymptotically normal under much weaker conditions on $T$'s growth than the Arellano-Bond estimator. Our theory covers models with high dimensional covariates, including multiple lags of the dependent variable, common in modern applications. We illustrate our approach by applying it to weekly county-level panel data from the United States to study opening K-12 schools and other mitigation policies' short and long-term effects on COVID-19's spread.
Stochastic convergence in per capita CO$_2$ emissions. An approach from nonlinear stationarity analysis
Authors: María José Presno, Manuel Landajo, Paula Fernández González
Date: 2024-02-01 00:00:00

This paper studies stochastic convergence of per capita CO$_2$ emissions in 28 OECD countries for the 1901-2009 period. The analysis is carried out at two aggregation levels, first for the whole set of countries (joint analysis) and then separately for developed and developing states (group analysis). A powerful time series methodology, adapted to a nonlinear framework that allows for quadratic trends with possibly smooth transitions between regimes, is applied. This approach provides more robust conclusions in convergence path analysis, enabling (a) robust detection of the presence, and if so, the number of changes in the level and/or slope of the trend of the series, (b) inferences on stationarity of relative per capita CO$_2$ emissions, conditionally on the presence of breaks and smooth transitions between regimes, and (c) estimation of change locations in the convergence paths. Finally, as stochastic convergence is attained when both stationarity around a trend and $\beta$-convergence hold, the linear approach proposed by Tomljanovich and Vogelsang (2002) is extended in order to allow for more general quadratic models. Overall, joint analysis finds some evidence of stochastic convergence in per capita CO$_2$ emissions. Some dispersion in terms of $\beta$-convergence is detected by group analysis, particularly among developed countries. This is in accordance with per capita GDP not being the sole determinant of convergence in emissions, with factors like search for more efficient technologies, fossil fuel substitution, innovation, and possibly outsources of industries, also having a crucial role.