1,484 research outputs found

    Learning and Control of Dynamical Systems

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    Despite the remarkable success of machine learning in various domains in recent years, our understanding of its fundamental limitations remains incomplete. This knowledge gap poses a grand challenge when deploying machine learning methods in critical decision-making tasks, where incorrect decisions can have catastrophic consequences. To effectively utilize these learning-based methods in such contexts, it is crucial to explicitly characterize their performance. Over the years, significant research efforts have been dedicated to learning and control of dynamical systems where the underlying dynamics are unknown or only partially known a priori, and must be inferred from collected data. However, much of these classical results have focused on asymptotic guarantees, providing limited insights into the amount of data required to achieve desired control performance while satisfying operational constraints such as safety and stability, especially in the presence of statistical noise. In this thesis, we study the statistical complexity of learning and control of unknown dynamical systems. By utilizing recent advances in statistical learning theory, high-dimensional statistics, and control theoretic tools, we aim to establish a fundamental understanding of the number of samples required to achieve desired (i) accuracy in learning the unknown dynamics, (ii) performance in the control of the underlying system, and (iii) satisfaction of the operational constraints such as safety and stability. We provide finite-sample guarantees for these objectives and propose efficient learning and control algorithms that achieve the desired performance at these statistical limits in various dynamical systems. Our investigation covers a broad range of dynamical systems, starting from fully observable linear dynamical systems to partially observable linear dynamical systems, and ultimately, nonlinear systems. We deploy our learning and control algorithms in various adaptive control tasks in real-world control systems and demonstrate their strong empirical performance along with their learning, robustness, and stability guarantees. In particular, we implement one of our proposed methods, Fourier Adaptive Learning and Control (FALCON), on an experimental aerodynamic testbed under extreme turbulent flow dynamics in a wind tunnel. The results show that FALCON achieves state-of-the-art stabilization performance and consistently outperforms conventional and other learning-based methods by at least 37%, despite using 8 times less data. The superior performance of FALCON arises from its physically and theoretically accurate modeling of the underlying nonlinear turbulent dynamics, which yields rigorous finite-sample learning and performance guarantees. These findings underscore the importance of characterizing the statistical complexity of learning and control of unknown dynamical systems.</p

    Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics

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    It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    20th SC@RUG 2023 proceedings 2022-2023

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    Investigating the impact of space weather on the polar atmosphere using rigorous statistical methods

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    I de senere år har det vært en økning i observasjonsbaserte, re-analytiske og modellbaserte studier som viser korrelasjoner mellom dag-til-dag og år-til-år solaktivitet og klima-/vær-mønstre. Det overordnede målet med avhandlingen er å undersøke to solklima-mekanismer, den Kjemisk-Dynamiske koblingen og Mansurov-effekten. Den Kjemisk-Dynamiske koblingen er knyttet til ioniseringen av den øvre atmosfæren (¡50 km) som skjer ved energisk partikkelnedbør (EPP). Dette resulterer i produksjon av nitrogen- og hydrogenoksider (NOx og HOx). Disse molekylene bryter effektivt ned ozon, og kan derfor endre strålingsbalansen i atmosfæren, noe som igjen potensielt kan føre til en kaskadeeffekt av dynamisk induserte atmosfæriske værendringer i polaratmosfæren. Mansurov-effekten er knyttet til det interplanetariske magnetfeltet (IMF) og dets evne til å modulere den globale elektriske kretsen (GEC). Dette antas å videre påvirke den polare troposfæren gjennom å endre de fysiske prosessene bak dannelse og vekst av skyer. Effekten antas å være nesten umiddelbar, noe som gir en fysisk forbindelse mellom verdensrommet og den nedre del av Jordens atmosfære. Begge mekanismene har blitt studert ved hjelp av sofistikerte statistiske analysemetoder. For den Kjemisk-Dynamiske koblingen, bruker vi SOCOL3-MPIOM-modellen for å sammenligne temperaturforskjeller i den nordlige atmosfæren i modellkjøringen med og uten EPP. Analysen bygger på en nylig studie som viser at EPP hovedsakelig påvirker den nordlige atmosfæriske temperaturen rett før og under forstyrrede forhold i den stratosfæriske polare jetstrøm. Vi finner svært signifikante temperaturresponser rett før hendelser karakterisert som små stratosfæriske oppvarminger, forhold assosiert med en svekket polar jetstrøm og økt bølgeaktivitet. De største temperaturforskjellene er synlig i februar, men bare for den siste halvdel (1955–2008) av simuleringsperioden (1900–2008). Funnene antyder at den Kjemisk-Dynamiske koblingen kan spille en avgjørende rolle i stratosfæriske forhold om vinteren og bekrefter eksistensen av den Kjemisk-Dynamiske koblingen i modellen. Ved å bruke data fra OMNIweb og ERA5 re-analyse over tidsperioden 1968–2020, undersøkes forbindelsen mellom IMF By og polart atmosfærisk trykk på havnivå. I motsetning til tidligere publiserte studier om Mansurov-effekten, finner vi ingen signifikant respons etter å ha tatt hensyn til autokorrelasjon og kontrollert for falsk deteksjonsandel (false discovery rate). Tidligere studier har også fremhevet en 27-dagers syklisk trykkrespons i sine resultater som indirekte bevis for en fysisk forbindelse. Vi demonstrerer at denne periodiske trykkresponsen oppstår som et resultat av de statistiske metodene som er brukt, og kan derfor ikke brukes som en indikator på en fysisk sammenheng. Videre oppdages en hittil ukjent robust og statistisk signifikant korrelasjon mellom IMF By og polart atmosfærisk trykk ved havnivå. Korrelasjonen er tydelig i perioden mars-april-mai på begge halvkuler, men med en tilsynelatende ufysisk timing med hensyn til Mansurov-effekten. I alt fremhever resultatene det generelle behovet for grundig statistisk testing, samt behovet for varsomhet når man bruker spesifikke metoder sammen med periodiske og autokorrelerte variabler.Recent years have seen a surge in observational, re-analysis, and model-based studies providing evidence of statistical correlations between day-to-day to interannual solar activity and climate/weather patterns. The overarching objective of this thesis is to delve into the theory of two solar-climate mechanisms, the Chemical-Dynamical coupling and the Mansurov effect. The Chemical-Dynamical coupling is linked to the ionization of the upper atmosphere (¡50 km) by energetic particle precipitation (EPP), resulting in the production of odd nitrogen and hydrogen oxides (NOx and HOx). These compounds are effective ozone depleters, and can alter the radiative balance of the atmosphere, potentially leading to a cascading effect in dynamically induced atmospheric weather changes observable in the polar atmosphere. The Mansurov effect is related to the interplanetary magnetic field (IMF) and its ability to modulate the global electric circuit (GEC), which is further assumed to impact the polar troposphere through cloud generation processes. It is hypothesised to occur nearly instantaneously, providing a physical link between near-Earth-space and the lower atmosphere. These topics will be studied with sophisticated statistical analysis methods. For the Chemical-Dynamical coupling, we use the SOCOL3-MPIOM model to compare the northern polar atmospheric temperature differences in ensemble members with and without EPP. The analyses builds on recent re-analysis evidence showing that EPP mostly impacts the northern polar atmospheric temperature right before and during disturbed Polar Vortex (PV) conditions. We find highly significant temperature responses during conditions set up by minor Sudden Stratospheric Warmings (SSW), associated with disturbed polar vortex and enhanced planetary wave activity. The largest anomalies are seen in February, and only for the latter half (1955–2008) of the simulation period (1900–2008). The findings suggest that during winter, the Chemical-Dynamical coupling could play a crucial role in stratospheric conditions and confirms the existence of the chemical-dynamical link in the model. By using ERA5 atmospheric re-analysis data and OMNIweb IMF data spanning 1968–2020, the connection between the IMF By and polar surface pressure is investigated. Contrary to prior published studies on the Mansurov effect, no significant response is found after accounting for autocorrelation and multiple hypothesis testing. In addition, prior studies highlight a 27-day cyclic pressure response as indirect evidence of a physical link. However, we show that this periodic pressure behaviour occurs as a statistical artefact of the methods, and is not a reliable indicator of a causal connection. Furthermore, a new robust and statistically significant correlation is determined between the IMF By and polar surface pressure. It is found in the time-period March-April-May for both hemispheres, but with an unphysical timing with respect to the Mansurov hypothesis. The analyses highlight the general need for rigorous statistical testing, as well as the need for caution when deploying certain methodologies with periodic and highly autocorrelated variables.Doktorgradsavhandlin

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    20th SC@RUG 2023 proceedings 2022-2023

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    Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation

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    Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices. One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers. A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks. A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation
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