pwc-archive/papers-with-abstracts huggingface dataset:
Testing Topological Data Analysis for Condition Monitoring of Wind Turbines
Authors: Simone Casolo, Alexander Stasik, Zhenyou Zhang, Signe Riemer-Sorensen
Date: 2024-06-24 00:00:00
Tasks: Fault Detection, Topological Data Analysis

We present an investigation of how topological data analysis (TDA) can be applied to condition-based monitoring (CBM) of wind turbines for energy generation. TDA is a branch of data analysis focusing on extracting meaningful information from complex datasets by analyzing their structure in state space and computing their underlying topological features. By representing data in a high-dimensional state space, TDA enables the identification of patterns, anomalies, and trends in the data that may not be apparent through traditional signal processing methods. For this study, wind turbine data was acquired from a wind park in Norway via standard vibration sensors at different locations of the turbine's gearbox. Both the vibration acceleration data and its frequency spectra were recorded at infrequent intervals for a few seconds at high frequency and failure events were labelled as either gear-tooth or ball-bearing failures. The data processing and analysis are based on a pipeline where the time series data is first split into intervals and then transformed into multi-dimensional point clouds via a time-delay embedding. The shape of the point cloud is analyzed with topological methods such as persistent homology to generate topology-based key health indicators based on Betti numbers, information entropy and signal persistence. Such indicators are tested for CBM and diagnosis (fault detection) to identify faults in wind turbines and classify them accordingly. Topological indicators are shown to be an interesting alternative for failure identification and diagnosis of operational failures in wind turbines.
Discrete-time Integral Resonant Control of Negative Imaginary Systems: Application to a High-speed Nanopositioner
Authors: Kanghong Shi, Erfan Khodabakhshi, Prosanto Biswas, Ian R. Petersen, S. O. Reza Moheimani
Date: 2024-06-24 00:00:00

We propose a discrete-time integral resonant control (IRC) approach for negative imaginary (NI) systems, which overcomes several limitations of continuous-time IRC. We show that a discrete-time IRC has a step-advanced negative imaginary property. A zero-order hold-sampled NI system can be asymptotically stabilized using a discrete-time IRC with suitable parameters. A hardware experiment is conducted where a high-speed flexure-guided nanopositioner is efficiently damped using the proposed discrete-time IRC with the discrete-time controller being implemented in FPGA hardware at the sampling rate of 1.25 MHz.
Frequency Constrained MPC for Efficient Grid Side Operation of Wind Power Conversion Systems
Authors: Orcun Karaca, Georgios Darivianakis
Date: 2024-06-23 00:00:00
Tasks: Model Predictive Control

Model predictive control (MPC) has proven its applicability in power conversion control with its fast dynamic response to reference changes while ensuring critical system constraints are satisfied. Even then, the computational burden still remains a challenge for many MPC variants. In this regard, this paper formulates an indirect MPC scheme for grid-side wind converters. A quadratic program with linear constraints is solved in a receding horizon fashion with a subsequent PWM modulator. To facilitate its solution within a few hundreds of microseconds, its decision variables (modulating signals) are restricted to a specific frequency content. This approach limits the increase in problem size due to horizon length. In case studies, the proposed MPC exhibits fast response in faults and operates the converter within its safety limits.
The Analysis and the Performance of the Parallel-Partial Reset Control System
Authors: Xinxin Zhang, S. Hassan HosseinNia
Date: 2024-06-23 00:00:00

Reset controllers have demonstrated their effectiveness in enhancing performance in precision motion systems. To further exploiting the potential of reset controllers, this study introduces a parallel-partial reset control structure. Frequency response analysis is effective for the design and fine-tuning of controllers in industries. However, conducting frequency response analysis for reset control systems poses challenges due to their nonlinearities. We develop frequency response analysis methods for both the open-loop and closed-loop parallel-partial reset systems. Simulation results validate the accuracy of the analysis methods, showcasing precision enhancements exceeding 100% compared to the traditional describing function method. Furthermore, we design a parallel-partial reset controller within the Proportional-Integral-Derivative (PID) control structure for a mass-spring-damper system. The frequency response analysis of the designed system indicates that, while maintaining the same bandwidth and phase margin of the first-order harmonics, the new system exhibits lower magnitudes of higher-order harmonics, compared to the traditional reset system. Moreover, simulation results demonstrate that the new system achieves lower overshoot and quicker settling time compared to both the traditional reset and linear systems.
Proper Implicit Discretization of the Super-Twisting Controller -- without and with Actuator Saturation
Authors: Richard Seeber, Benedikt Andritsch
Date: 2024-06-23 00:00:00

The discrete-time implementation of the super-twisting sliding mode controller for a plant with disturbances with bounded slope, zero-order hold actuation, and actuator constraints is considered. Motivated by restrictions of existing implicit or semi-implicit discretization variants, a new proper implicit discretization for the super-twisting controller is proposed. This discretization is then extended to the conditioned super-twisting controller, which mitigates windup in presence of actuator constraints by means of the conditioning technique. It is proven that the proposed controllers achieve best possible worst-case performance subject to similarly simple stability conditions as their continuous-time counterparts. Numerical simulations and comparisons demonstrate and illustrate the results.
Characterization of Lightweight GPS Disciplined Oscillators for Distributed UAV Measurement Applications
Authors: Julia Beuster, Carsten Andrich, Sebastian Giehl
Date: 2024-06-23 00:00:00

With an increasing variety of measurement applications using sensing nodes on unmanned aerial vehicles (UAVs), global positioning system (GPS) and global navigation satellite system (GNSS) disciplined oscillators (GPSDOs, GNSSDOs) are an appealing solution for precise wireless inter-device synchronization. Typically evaluated under laboratory conditions by analyzing the 10 MHz and 1 pulse per second (PPS) reference signal stability, these test methods overlook airborne oscillator performance. This paper characterizes lightweight GNSSDO models for flight suitability using a measurement system based on software defined radios (SDRs). We analyze reference signal stability under controlled GNSS signal impairments to predict performance and measurement precision loss in dynamic operational modes. Additionally, we assess behavior under the impacts caused by operating the UAV, as well as typical flight vibrations and accelerations outlined in the standard for payload devices.
State-Compensation-Linearization-Based Stability Margin Analysis for a Class of Nonlinear Systems: A Data-Driven Method
Authors: Jinrui Ren, Quan Quan
Date: 2024-06-23 00:00:00

The classical stability margin analysis based on the linearized model is widely used in practice even in nonlinear systems. Although linear analysis techniques are relatively standard and have simple implementation structures, they are prone to misbehavior and failure when the system is performing an off-nominal operation. To avoid the drawbacks and exploit the advantages of linear analysis methods and frequency-domain stability margin analysis while tackling system nonlinearity, a state-compensation-linearization-based stability margin analysis method is studied in the paper. Based on the state-compensation-linearization-based stabilizing control, the definition and measurement of the stability margin are given. The l2 gain margin and l2 time-delay margin for the closed-loop nonlinear system with state-compensation-linearization-based stabilizing control are defined and derived approximatively by the small-gain theorem in theory. The stability margin measurement can be carried out by the frequency-sweep method in practice. The proposed method is a data-driven method for obtaining the stability margin of nonlinear systems, which is practical and can be applied to practical systems directly. Finally, three numerical examples are given to illustrate the effectiveness of the proposed method.
Voltage enhancement and loss minimization in a radial network through optimal capacitor sizing and placement based on Crow Search Algorithm
Authors: Stephen W. Mathenge, Edwell. T. Mharakurwa, Lucas Mogaka
Date: 2024-06-22 00:00:00

In power systems, the distribution network is pivotal for consumers because it is the final stage in the delivery of electricity from the generation plants to the end users. Reactive power demand from consumers can result in challenges like low power factor, diminished voltage, and heightened power losses. The primary challenge encountered when utilizing a radial system as a distribution network is the voltage drop, which leads to distortion in the voltage profile across the entire network. This research focuses on optimizing network performance by appropriately sizing and placing capacitors based on the Crow Search Algorithm (CSA). The results from this approach were compared with Particle Swarm Optimization (PSO), Artificial bee colony, Cultural Algorithm, Firefly Algorithm, Genetic Algorithm, Invasive Weed Optimization, and Teacher Learner Based Optimization methods illustrated in the study where CSA's effectiveness, demonstrated a notable 30.41% and 29.33% overall reduction in active and reactive power losses compared to the base case. The lowest bus voltage, notably at bus 18 which happens to be the longest in the IEEE 33 Radial Distribution Network (RDN) topology, improved from 0.8820 in the base case to 0.908 with CSA, showcasing a 32.9% enhancement in voltage deviation. This optimization led to a significant 3.841pu reduction in capacitor cost, highlighting CSA's efficacy in enhancing RDN performance and cost efficiency.
On Physics-Informed Neural Network Control for Power Electronics
Authors: Peifeng Hui, Chenggang Cui, Pengfeng Lin, Amer M. Y. M. Ghias, Xitong Niu, Chuanlin Zhang
Date: 2024-06-22 00:00:00

Considering the growing necessity for precise modeling of power electronics amidst operational and environmental uncertainties, this paper introduces an innovative methodology that ingeniously combines model-driven and data-driven approaches to enhance the stability of power electronics interacting with grid-forming microgrids. By employing the physics-informed neural network (PINN) as a foundation, this strategy merges robust data-fitting capabilities with fundamental physical principles, thereby constructing an accurate system model. By this means, it significantly enhances the ability to understand and replicate the dynamics of power electronics systems under complex working conditions. Moreover, by incorporating advanced learning-based control methods, the proposed method is enabled to make precise predictions and implement the satisfactory control laws even under serious uncertain conditions. Experimental validation demonstrates the effectiveness and robustness of the proposed approach, highlighting its substantial potential in addressing prevalent uncertainties in controlling modern power electronics systems.
Fault-tolerant control of random switching topology multi-agent system based on event triggering
Authors: Ouyang Lingcong, Yang Kaijun
Date: 2024-06-22 00:00:00

In this paper, the formation control of multi-agent systems in random switching communication topology is studied, and the problem of excessive bandwidth and low control efficiency among multi-agents is solved. For nonlinear multi-agent systems, a sliding mode formation control algorithm with event-triggered random switching communication topology is proposed. Firstly, a fault-tolerant control strategy based on stochastic system model is designed to solve the problem of low controller efficiency and controller failure during the formation of multi-agent systems. Compared with the traditional multi-agent system dynamic model, the stochastic system model has stronger universality, which improves the efficiency of the controller. Secondly, in order to deal with the problem of high communication load and frequency between agents during formation in random switching communication topology, the sample-based event triggering controller is introduced into the controller, which effectively reduces the communication frequency and reduces the impact of communication delay. Finally, the stability and robustness of event-triggered random switching topology formation control are verified by computer simulation.
Background results for robust minmax control of linear dynamical systems
Authors: James B. Rawlings, Davide Mannini, Steven J. Kuntz
Date: 2024-06-21 00:00:00
Tasks: LEMMA

The purpose of this note is to summarize the arguments required to derive the results appearing in robust minmax control of linear dynamical systems using a quadratic stage cost. The main result required in robust minmax control is Proposition 20.a. Moreover, the solution to the trust-region problem given in Proposition 15 and Lemma 16 may be of more general interest. This revised (second) version provides the following corrections and extensions of the previous (first) version. 1. The optimal u and w formulas in the original Corollary 13, Proposition 14, Corollary 19, and Proposition 20 have been corrected in this revision. 2. Corollary 13 and Proposition 14 are combined in the revised Proposition 14.a. 3. Corollary 19 and Proposition 20 are combined in the revised Proposition 20.a. 4. The revised Proposition 12.a is a generalization of the previous Proposition 12. 5. Propositions 5.a, 5.b, and 5.c are new in this revision. 6. Figure 1 has been revised to illustrate the revised Proposition 14.a.
Necessary and sufficient condition for neutral-type delay systems: Polynomial approximations
Authors: Gerson Portilla, Mathieu Bajodek, Sabine Mondié
Date: 2024-06-21 00:00:00

A new necessary and sufficient stability test in a tractable number of operations for linear neutral-type delay systems is introduced. It is developed in the Lyapunov-Krasovskii framework via functionals with prescribed derivatives. The necessary conditions, which stem from substituting any polynomial approximation of the functional argument, reduce to a quadratic form of monomials whose matrix is independent of the coefficients of the approximation under consideration. In the particular case of Chebyshev polynomials, the functional approximation error is quantified, leading to an estimate of the order of approximation such that the positive semi-definiteness of the functional is verified. Some examples illustrate the obtained results.
Miniature fluorescence sensor for quantitative detection of brain tumour
Authors: Jean Pierre Ndabakuranye, James Belcourt, Deepak Sharma, Cathal D. O'Connell, Victor Mondal, Sanjay K. Srivastava, Alastair Stacey, Sam Long, Bobbi Fleiss, Arman Ahnood
Date: 2024-06-21 00:00:00
Tasks: Specificity

Fluorescence-guided surgery has emerged as a vital tool for tumour resection procedures. As well as intraoperative tumour visualisation, 5-ALA-induced PpIX provides an avenue for quantitative tumour identification based on ratiometric fluorescence measurement. To this end, fluorescence imaging and fibre-based probes have enabled more precise demarcation between the cancerous and healthy tissues. These sensing approaches, which rely on collecting the fluorescence light from the tumour resection site and its remote spectral sensing, introduce challenges associated with optical losses. In this work, we demonstrate the viability of tumour detection at the resection site using a miniature fluorescence measurement system. Unlike the current bulky systems, which necessitate remote measurement, we have adopted a millimetre-sized spectral sensor chip for quantitative fluorescence measurements. A reliable measurement at the resection site requires a stable optical window between the tissue and the optoelectronic system. This is achieved using an antifouling diamond window, which provides stable optical transparency. The system achieved a sensitivity of 92.3% and specificity of 98.3% in detecting a surrogate tumour at a resolution of 1 x 1 mm2. As well as addressing losses associated with collecting and coupling fluorescence light in the current remote sensing approaches, the small size of the system introduced in this work paves the way for its direct integration with the tumour resection tools with the aim of more accurate interoperative tumour identification.
Testing for Restricted Stochastic Dominance under Survey Nonresponse with Panel Data: Theory and an Evaluation of Poverty in Australia
Authors: Rami V. Tabri, Mathew J. Elias
Date: 2024-06-22 00:00:00
Tasks: Survey

This paper lays the groundwork for a unifying approach to stochastic dominance testing under survey nonresponse that integrates the partial identification approach to incomplete data and design-based inference for complex survey data. We propose a novel inference procedure for restricted $s$th-order stochastic dominance, tailored to accommodate a broad spectrum of nonresponse assumptions. The method uses pseudo-empirical likelihood to formulate the test statistic and compares it to a critical value from the chi-squared distribution with one degree of freedom. We detail the procedure's asymptotic properties under both null and alternative hypotheses, establishing its uniform validity under the null and consistency against various alternatives. Using the Household, Income and Labour Dynamics in Australia survey, we demonstrate the procedure's utility in a sensitivity analysis of temporal poverty comparisons among Australian households.
Identification and Estimation of Causal Effects in High-Frequency Event Studies
Authors: Alessandro Casini, Adam McCloskey
Date: 2024-06-21 00:00:00
Tasks: regression

We provide precise conditions for nonparametric identification of causal effects by high-frequency event study regressions, which have been used widely in the recent macroeconomics, financial economics and political economy literatures. The high-frequency event study method regresses changes in an outcome variable on a measure of unexpected changes in a policy variable in a narrow time window around an event or a policy announcement (e.g., a 30-minute window around an FOMC announcement). We show that, contrary to popular belief, the narrow size of the window is not sufficient for identification. Rather, the population regression coefficient identifies a causal estimand when (i) the effect of the policy shock on the outcome does not depend on the other shocks (separability) and (ii) the surprise component of the news or event dominates all other shocks that are present in the event window (relative exogeneity). Technically, the latter condition requires the policy shock to have infinite variance in the event window. Under these conditions, we establish the causal meaning of the event study estimand corresponding to the regression coefficient and the consistency and asymptotic normality of the event study estimator. Notably, this standard linear regression estimator is robust to general forms of nonlinearity. We apply our results to Nakamura and Steinsson's (2018a) analysis of the real economic effects of monetary policy, providing a simple empirical procedure to analyze the extent to which the standard event study estimator adequately estimates causal effects of interest.
Velocity, Holding Time and Lifespan of Cryptocurrency in Transactions
Authors: Yu Zhang, Mostafa Chegeni, Claudio Tessone
Date: 2024-06-24 00:00:00

The measurement of the velocity of money is still a significant topic. In this paper, we proposed a method to calculate the velocity of money by combining the holding-time distribution and lifespan distribution. By derivation, the velocity of money equals the holding-time distribution's value at zero. When we have much holding-time data, this problem can be converted to a regression problem. After a numeric simulation, we find that the calculating accuracy is high even if we used only a small part of the holding time data, which implies a potential application in measuring the velocity of money in reality, such as digital money. We also tested the methods on Cardano and found that the method can also provide a reasonable estimation of velocity in some cases.
Large Language Models in Student Assessment: Comparing ChatGPT and Human Graders
Authors: Magnus Lundgren
Date: 2024-06-24 00:00:00
Tasks: Prompt Engineering

This study investigates the efficacy of large language models (LLMs) as tools for grading master-level student essays. Utilizing a sample of 60 essays in political science, the study compares the accuracy of grades suggested by the GPT-4 model with those awarded by university teachers. Results indicate that while GPT-4 aligns with human grading standards on mean scores, it exhibits a risk-averse grading pattern and its interrater reliability with human raters is low. Furthermore, modifications in the grading instructions (prompt engineering) do not significantly alter AI performance, suggesting that GPT-4 primarily assesses generic essay characteristics such as language quality rather than adapting to nuanced grading criteria. These findings contribute to the understanding of AI's potential and limitations in higher education, highlighting the need for further development to enhance its adaptability and sensitivity to specific educational assessment requirements.
Revealing risk preferences Evidence from Turkeys 2023 Earthquake
Authors: Emily Quiroga, Michael Tanner
Date: 2024-06-22 00:00:00

The study on risk preferences and its potential changes amid natural catastrophes has been subject of recent study, producing contradictory findings. An often proposed explanation specifically distinguishes between the opposite effect of realized and unrealized losses on risk preferences. Moreover, higher-order risk preferences and its relation to post-disaster behaviors remain unexplored, despite potential theoretical implications. We address these gaps in the literature by conducting experiments with 600 individuals post Turkeys 2023 catastrophic earthquake, specifically heavily affected individuals who are displaced, those who are not and a control group. Results indicate higher risk-taking in heavily affected individuals when compared to unaffected individuals. Our results are specifically driven by affected females. We find no pre existing differences in risk preferences between earthquake and control areas using 2012 data. Within the heavily affected group of individuals, higher house damage, our proxy for realized losses, increases risk aversion. Regarding higher-order risk preferences for individuals heavily affected by the earthquake, we find that prudence is positively associated with selfprotective behaviors after the earthquake, specifically internal migration and/or displacement. While precautionary savings shows initially no correlation to prudence, a positive association emerges when considering that prudence is also related to occupational choices, with individuals with stable incomes and who save being more prudent. Our results contribute insights into how disasters influence risk preferences, specifically aiming to address contradictory findings in the literature, while presenting novel evidence on the relationship between prudence and post-natural disaster behaviors.