pwc-archive/papers-with-abstracts huggingface dataset:
Whale Optimization Algorithm-based Fractional Order Fuzzy Type-II PI Control for Modular Multilevel Converters
Authors: Mohammad Ali Labbaf-Khaniki, Mohammad Manthouri, Amin Hajizadeh
Date: 2024-04-29 00:00:00

Designing a robust controller for Modular Multilevel Converters (MMCs) is crucial to ensure stability and optimal dynamic performance under various operating conditions, including faulty and disturbed scenarios. The primary objective of controlling grid-connected MMCs (GC-MMCs) is to accurately track real and reactive power references while maintaining excellent harmonic performance in the output response. This paper proposes a novel model-free control strategy for GC-MMCs, employing a Fractional Order Proportional-Integral (FOPI) controller and a Fractional Order Fuzzy type-II Proportional-Integral (FOFPI) controller. The FOFPI controller utilizes a type-II Fuzzy Inference System (FIS) to adaptively adjust the proportional and derivative gains during the control process, enabling effective control of the MMC under diverse operating conditions. The type-II FIS, which leverages type-II fuzzy sets, can mitigate uncertainty and nonlinearity in the system. Furthermore, the incorporation of fractional-order mathematics enhances the flexibility of the proposed controllers. To optimize the initial parameters of the proposed controllers, the Whale Optimization Algorithm (WOA), a meta-heuristic algorithm, is employed. The results demonstrate that the proposed controllers exhibit superior performance under voltage disturbance conditions, varying input voltage, and can ensure the stability of the MMC.
A hybrid prognosis approach for robust lifetime control of commercial wind turbines
Authors: Edwin Kipchirchir, Jonathan Liebeton, Dirk Söffker
Date: 2024-04-29 00:00:00
Tasks: Prognosis

Dynamic fluctuations in the wind field to which a wind turbine (WT) is exposed to are responsible for fatigue loads on its components. To reduce structural loads in WTs, advanced control schemes have been proposed. In recent years, prognosis-based lifetime control of WTs has become increasingly important. In this approach, the prognostic controller gains are adapted based on the stateof-health (SOH) of the WT component to achieve the desired lifetime. However, stochastic wind dynamics complicates estimation of the SOH of a WT. More recently, robust controllers have been combined with real-time damage evaluation models to meet prognosis objectives. Most rely on model-based online load cycle counting algorithms to determine fatigue damage, with analytical models providing the degradation estimate. However, most use load measurements that are either unreliable or unavailable in commercial WTs, limiting their practicality. In this contribution, a hybrid prognosis scheme combining data-driven load prediction and model-based damage estimation models for robust lifetime control of commercial WTs is proposed. A data-driven support vector machine (SVM) regression model is trained using loading data obtained from dynamic simulations using a {\mu}-synthesis robust disturbance accommodating controller (RDAC). The regression model uses available WT measurements to predict tower load. Based on this prediction, an online rain-flow counting (RFC) damage evaluation model estimates the damage level and lifetime of the tower. The RDAC controller gains are dynamically adapted to achieve a predefined damage limit and lifetime. The proposed approach is evaluated on a 5 MW reference WT and its performance is compared with a model-based prognosis scheme using ideal WT tower measurement. Results demonstrate the efficacy of the proposed approach to control the fatigue lifetime in WT components.
Three-Dimension Collision-Free Trajectory Planning of UAVs Based on ADS-B Information in Low-Altitude Urban Airspace
Authors: Chao Dong, Yifan Zhang, Ziye Jia, Yiyang Liao, Lei Zhang, Qihui Wu
Date: 2024-04-29 00:00:00
Tasks: Trajectory Planning

The environment of low-altitude urban airspace is complex and variable due to numerous obstacles, non-cooperative aircrafts, and birds. Unmanned aerial vehicles (UAVs) leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security. However, the timely information of surrounding situation is difficult to acquire by UAVs, which further brings security risks. As a mature technology leveraged in traditional civil aviation, the automatic dependent surveillance-broadcast (ADS-B) realizes continuous surveillance of the information of aircrafts. Consequently, we leverage ADS-B for surveillance and information broadcasting, and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning. In detail, we propose the secure sub-airspaces planning (SSP) algorithm and particle swarm optimization rapidly-exploring random trees (PSO-RRT) algorithm for the UAV trajectory planning in law-altitude airspace. The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory, and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.
Application of Iterative LQR on a Mobile Robot With Simple Dynamics
Authors: Ayoub Aaqaoui, Yousif Mohammed Elsheikh Mohammed
Date: 2024-04-28 00:00:00

The aim in this paper is to apply the iLQR, iterative Linear Quadratic Regulator, to control the movement of a mobile robot following an already defined trajectory. This control strategy has proven its utility for nonlinear systems. As follows up, this work intends to concertize this statement and to evaluate the extent to which the performance is comparatively improved against the ordinary, non-iterative LQR. The method is applied to a differential robot with non-holonomic constraints. The mathematical equations, resulting description and the implementation of this method are explicitly explained, and the simulation studies are conducted in the Matlab and Simulink environment.
Dissipativity-Based Distributed Droop-Free Controller and Communication Topology Co-Design for DC Microgrids
Authors: Mohammad Javad Najafirad, Shirantha Welikala
Date: 2024-04-28 00:00:00

This paper presents a novel dissipativity-based distributed droop-free control approach for the voltage regulation problem in DC microgrids (MGs) comprised of an interconnected set of distributed generators (DGs), loads, and power lines. First, we describe the closed-loop DC MG as a networked system where the sets of DGs and lines (i.e., subsystems) are interconnected via a static interconnection matrix. This interconnection matrix demonstrates how the inputs and outputs of DGs and lines are connected with each other. Each DG has a local controller and a distributed global controller. To design the distributed global controllers, we use the dissipativity properties of the subsystems and formulate a linear matrix inequality (LMI) problem. To support the feasibility of this distributed global controller design, we identify a set of necessary local conditions, which we then enforce in a specifically developed LMI-based local controller design process. In contrast to existing DC MG control solutions that separate distributed controller and communication topology design problems, our approach proposes a unified framework for distributed controller and communication topology co-design. As the co-design process is LMI-based, it can be efficiently implemented and evaluated using existing software tools. The effectiveness of the proposed solution in terms of voltage regulation and current sharing is verified by simulating an islanded DC MG in a MATLAB/Simulink environment under different scenarios, such as load changes and topological constraint changes, and comparing its performance with the recent droop control approach.
Auto-Optimized Maximum Torque Per Ampere Control of IPMSM Using Dual Control for Exploration and Exploitation
Authors: Yuefei Zuo, Yalei Yu, Jun Yang, Wen-Hua Chen
Date: 2024-04-28 00:00:00

In this paper, a maximum torque per ampere (MTPA) control strategy for the interior permanent magnet synchronous motor (IPMSM) using dual control for exploration and exploitation (DCEE). In the proposed method, the permanent magnet flux and the difference between the $d$- and $q$-axis inductance are identified by multiple estimators using the recursive least square method. The initial values of the estimated parameters in different estimators are different. By using multiple estimators, exploration of the operational environment to reduce knowledge uncertainty can be realized. Compared to those MTPA control strategies based on the extremum-seeking method, the proposed method has better dynamic performance when speed or load varies. The effectiveness of the proposed method is verified by simulations.
Finite-time Safety and Reach-avoid Verification of Stochastic Discrete-time Systems
Authors: Bai Xue
Date: 2024-04-28 00:00:00

This paper studies finite-time safety and reach-avoid verification for stochastic discrete-time dynamical systems. The aim is to ascertain lower and upper bounds of the probability that, within a predefined finite-time horizon, a system starting from an initial state in a safe set will either exit the safe set (safety verification) or reach a target set while remaining within the safe set until the first encounter with the target (reach-avoid verification). We introduce novel barrier-like sufficient conditions for characterizing these bounds, which either complement existing ones or fill gaps. Finally, we demonstrate the efficacy of these conditions on two examples.
Impact of Dynamic Operating Envelopes on Distribution Network Hosting Capacity for Electric Vehicles
Authors: Hossein Fani, Md Umar Hashmi, Emilio J. Palacios-Garcia, Geert Deconinck
Date: 2024-04-27 00:00:00

The examination of the maximum number of electric vehicles (EVs) that can be integrated into the distribution network (DN) without causing any operational incidents has become increasingly crucial as EV penetration rises. This issue can be addressed by utilizing dynamic operating envelopes (DOEs), which are generated based on the grid status. While DOEs improve the hosting capacity of the DN for EVs (EV-HC) by restricting the operational parameters of the network, they also alter the amount of energy needed for charging each EV, resulting in a decrease in the quality of service (QoS). This study proposes a network-aware hosting capacity framework for EVs (EV-NAHC) that i) aims to assess the effects of DOEs on active distribution networks, ii) introduces a novel definition for HC and calculates the EV-NAHC based on the aggregated QoS of all customers. A small-scale Belgian feeder is utilized to examine the proposed framework. The results show a substantial increase in the EV-NAHC with low, medium, and high-daily charging energy scenarios.
DNA Calorimetric Force Spectroscopy at Single Base Pair Resolution
Authors: Paolo Rissone, Marc Rico-Pasto, Steve Smith, Felix Ritort
Date: 2024-04-29 00:00:00

DNA hybridization is a fundamental reaction with wide-ranging applications in biotechnology. The nearest-neighbor (NN) model provides the most reliable description of the energetics of duplex formation. Most DNA thermodynamics studies have been done in melting experiments in bulk, of limited resolution due to ensemble averaging. In contrast, single-molecule methods have reached the maturity to derive DNA thermodynamics with unprecedented accuracy. We combine single-DNA mechanical unzipping experiments using a temperature jump optical trap with machine learning methods and derive the temperature-dependent DNA energy parameters of the NN model. In particular, we measure the previously unknown ten heat-capacity change parameters $\Delta C_p$, relevant for thermodynamical predictions throughout the DNA stability range. Calorimetric force spectroscopy establishes a groundbreaking methodology to accurately study nucleic acids, from chemically modified DNA to RNA and DNA/RNA hybrid structures.
A symbolic information approach applied to human intracranial data to characterize and distinguish different congnitive processes
Authors: Ícaro Rodolfo Soares Coelho Da Paz, Pedro F. A. Silva, Helena Bordini de Lucas, Sérgio H. A. Lira, Osvaldo A. Rosso, Fernanda Selingardi Matias
Date: 2024-04-27 00:00:00
Tasks: Time Series

How the human brain processes information during different cognitive tasks is one of the greatest questions in contemporary neuroscience. Understanding the statistical properties of brain signals during specific activities is one promising way to address this question. Here we analyze freely available data from implanted electrocorticography (ECoG) in five human subjects during two different cognitive tasks in the light of information theory quantifiers ideas. We employ a symbolic information approach to determine the probability distribution function associated with the time series from different cortical areas. Then we utilize these probabilities to calculate the associated Shannon entropy and a statistical complexity measure based on the disequilibrium between the actual time series and one with a uniform probability distribution function. We show that an Euclidian distance in the complexity-entropy plane and an asymmetry index for complexity are useful for comparing the two conditions. We show that our method can distinguish visual search epochs from blank screen intervals in different electrodes and patients. By using a multi-scale approach and embedding time delays to downsample the data, we find important time scales in which the relevant information is being processed. We also determine cortical regions and time intervals along the 2-second-long trials that present more pronounced differences between the two cognitive tasks. Finally, we show that the method is useful to distinguish cognitive processes using brain activity on a trial-by-trial basis.
Decoder Decomposition for the Analysis of the Latent Space of Nonlinear Autoencoders With Wind-Tunnel Experimental Data
Authors: Yaxin Mo, Tullio Traverso, Luca Magri
Date: 2024-04-25 00:00:00
Tasks: Decoder, Dimensionality Reduction

Turbulent flows are chaotic and multi-scale dynamical systems, which have large numbers of degrees of freedom. Turbulent flows, however, can be modelled with a smaller number of degrees of freedom when using the appropriate coordinate system, which is the goal of dimensionality reduction via nonlinear autoencoders. Autoencoders are expressive tools, but they are difficult to interpret. The goal of this paper is to propose a method to aid the interpretability of autoencoders. This is the decoder decomposition. First, we propose the decoder decomposition, which is a post-processing method to connect the latent variables to the coherent structures of flows. Second, we apply the decoder decomposition to analyse the latent space of synthetic data of a two-dimensional unsteady wake past a cylinder. We find that the dimension of latent space has a significant impact on the interpretability of autoencoders. We identify the physical and spurious latent variables. Third, we apply the decoder decomposition to the latent space of wind-tunnel experimental data of a three-dimensional turbulent wake past a bluff body. We show that the reconstruction error is a function of both the latent space dimension and the decoder size, which are correlated. Finally, we apply the decoder decomposition to rank and select latent variables based on the coherent structures that they represent. This is useful to filter unwanted or spurious latent variables, or to pinpoint specific coherent structures of interest. The ability to rank and select latent variables will help users design and interpret nonlinear autoencoders.
Advancing low-field MRI with a universal denoising imaging transformer: Towards fast and high-quality imaging
Authors: Zheren Zhu, Azaan Rehman, Xiaozhi Cao, Congyu Liao, Yoo Jin Lee, Michael Ohliger, Hui Xue, Yang Yang
Date: 2024-04-30 00:00:00
Tasks: Denoising, Diversity

Recent developments in low-field (LF) magnetic resonance imaging (MRI) systems present remarkable opportunities for affordable and widespread MRI access. A robust denoising method to overcome the intrinsic low signal-noise-ratio (SNR) barrier is critical to the success of LF MRI. However, current data-driven MRI denoising methods predominantly handle magnitude images and rely on customized models with constrained data diversity and quantity, which exhibit limited generalizability in clinical applications across diverse MRI systems, pulse sequences, and organs. In this study, we present ImT-MRD: a complex-valued imaging transformer trained on a vast number of clinical MRI scans aiming at universal MR denoising at LF systems. Compared with averaging multiple-repeated scans for higher image SNR, the model obtains better image quality from fewer repetitions, demonstrating its capability for accelerating scans under various clinical settings. Moreover, with its complex-valued image input, the model can denoise intermediate results before advanced post-processing and prepare high-quality data for further MRI research. By delivering universal and accurate denoising across clinical and research tasks, our model holds great promise to expedite the evolution of LF MRI for accessible and equal biomedical applications.
Networks And Productivity -- A Study In Economic Scholars During COVID-19
Authors: Hanqiao Zhang, Joy D. Xiuyao Yang
Date: 2024-04-29 00:00:00

The COVID-19 pandemic has disrupted traditional academic collaboration patterns, offering a unique opportunity to analyze the influence of peer effects and collaboration dynamics on research productivity. Using a novel network dataset, this paper investigates the role of peer effects on the productivity of economists, as measured by their publication count, during both pre-pandemic and pandemic periods. The results indicate that peer effects were significant in the pre-pandemic period but not during the pandemic. Additionally, the study sheds light on gender and race differences. These findings enhance our understanding of how research collaboration impacts knowledge production and provide insights that may inform policies aimed at promoting collaboration and boosting research productivity within the academic community.
Flock2: A model for orientation-based social flocking
Authors: Rama Carl Hoetzlein
Date: 2024-04-27 00:00:00
Tasks: model

The aerial flocking of birds, or murmurations, has fascinated observers while presenting many challenges to behavioral study and simulation. We examine how the periphery of murmurations remain well bounded and cohesive. We also investigate agitation waves, which occur when a flock is disturbed, developing a plausible model for how they might emerge spontaneously. To understand these behaviors a new model is presented for orientation-based social flocking. Previous methods model inter-bird dynamics by considering the neighborhood around each bird, and introducing forces for avoidance, alignment, and cohesion as three dimensional vectors that alter acceleration. Our method introduces orientation-based social flocking that treats social influences from neighbors more realistically as a desire to turn, indirectly controlling the heading in an aerodynamic model. While our model can be applied to any flocking social bird we simulate flocks of starlings, Sturnus vulgaris, and demonstrate the possibility of orientation waves in the absence of predators. Our model exhibits spherical and ovoidal flock shapes matching observation. Comparisons of our model to Reynolds' on energy consumption and frequency analysis demonstrates more realistic motions, significantly less energy use in turning, and a plausible mechanism for emergent orientation waves.
Quantum Patch-Based Autoencoder for Anomaly Segmentation
Authors: Maria Francisca Madeira, Alessandro Poggiali, Jeanette Miriam Lorenz
Date: 2024-04-26 00:00:00
Tasks: Anomaly Segmentation, Quantum Machine Learning, Segmentation

Quantum Machine Learning investigates the possibility of quantum computers enhancing Machine Learning algorithms. Anomaly segmentation is a fundamental task in various domains to identify irregularities at sample level and can be addressed with both supervised and unsupervised methods. Autoencoders are commonly used in unsupervised tasks, where models are trained to reconstruct normal instances efficiently, allowing anomaly identification through high reconstruction errors. While quantum autoencoders have been proposed in the literature, their application to anomaly segmentation tasks remains unexplored. In this paper, we introduce a patch-based quantum autoencoder (QPB-AE) for image anomaly segmentation, with a number of parameters scaling logarithmically with patch size. QPB-AE reconstructs the quantum state of the embedded input patches, computing an anomaly map directly from measurement through a SWAP test without reconstructing the input image. We evaluate its performance across multiple datasets and parameter configurations and compare it against a classical counterpart.
Identification by non-Gaussianity in structural threshold and smooth transition vector autoregressive models
Authors: Savi Virolainen
Date: 2024-04-30 00:00:00

Linear structural vector autoregressive models can be identified statistically without imposing restrictions on the model if the shocks are mutually independent and at most one of them is Gaussian. We show that this result extends to structural threshold and smooth transition vector autoregressive models incorporating a time-varying impact matrix defined as a weighted sum of the impact matrices of the regimes. We also discuss the problem of labelling the shocks, estimation of the parameters, and stationarity the model. The introduced methods are implemented to the accompanying R package sstvars. Our empirical application studies the effects of the climate policy uncertainty shock on the U.S. macroeconomy. In a structural logistic smooth transition vector autoregressive model with two regimes, we find that a positive climate policy uncertainty shock decreases production and increases inflation in times of both low and high economic policy uncertainty, but its inflationary effects are stronger in the periods of high economic policy uncertainty.
A Locally Robust Semiparametric Approach to Examiner IV Designs
Authors: Lonjezo Sithole
Date: 2024-04-29 00:00:00
Tasks: valid

I propose a locally robust semiparametric framework for estimating causal effects using the popular examiner IV design, in the presence of many examiners and possibly many covariates relative to the sample size. The key ingredient of this approach is an orthogonal moment function that is robust to biases and local misspecification from the first step estimation of the examiner IV. I derive the orthogonal moment function and show that it delivers multiple robustness where the outcome model or at least one of the first step components is misspecified but the estimating equation remains valid. The proposed framework not only allows for estimation of the examiner IV in the presence of many examiners and many covariates relative to sample size, using a wide range of nonparametric and machine learning techniques including LASSO, Dantzig, neural networks and random forests, but also delivers root-n consistent estimation of the parameter of interest under mild assumptions.