Exploiting World Knowledge inDiscourse Processing – A Comparison of Feature -Based and Inference-Based Approaches–

Tohoku University乾健太郎課

A numerical study of fin and jet propulsions involving fluid-structure interactions

Fish swimming is elegant and efficient, which inspires humans to learn from them to design high-performance artificial underwater vehicles. Research on aquatic locomotion has made extensive progress towards a better understanding of how aquatic animals control their flexible body and fin for propulsion. Although the structural flexibility and deformation of the body and fin are believed to be important features to achieve optimal swimming performance, studies on high-fidelity deformable body and fin with complex material behavior, such as non-uniform stiffness distributions, are rare. In this thesis, a fully coupled three-dimensional high-fidelity fluid-structure interaction (FSI) solver is developed to investigate the flow field evolution and propulsion performance of caudal fin and jet propulsion involving body and/or fin deformation. Within this FSI solver, the fluid is resolved by solving unsteady and viscous Navier-Stokes equations based on the finite volume method with a multi-block grid system. The solid dynamics are solved by a nonlinear finite element method. The coupling between the two solvers is achieved in a partitioned approach in which convergence check and sub-iteration are implemented to ensure numerical stability and accuracy. Validations are conducted by comparing the simulation results of classical benchmarks with previous data in the literature, and good agreements between them are obtained. The developed FSI solver is then applied to study the bio-inspired fin and jet propulsion involving body deformation. Specifically, the effect of non-uniform stiffness distributions of fish body and/or fin, key features of fish swimming which have been excluded in most previous studies, on the propulsive performance is first investigated. Simulation results of a sunfish-like caudal fin model and a tuna-inspired swimmer model both show that larger thrust and propulsion efficiency can be achieved by a non-uniform stiffness distribution (e.g., increased by 11.2% and 9.9%, respectively, for the sunfish-like model) compared with a uniform stiffness profile. Despite the improved propulsive e performance, a bionic variable fish body stiffness does not yield fish-like midline kinematics observed in real fish, suggesting that fish movement involves significant active control that cannot be replicated purely by passive deformations. Subsequent studies focus on the jet propulsion inspired by squid locomotion using the developed numerical solver. Simulation results of a two-dimensional inflation-deflation jet propulsion system, whose inflation is actuated by an added external force that mimics the muscle constriction of the mantle and deflation is caused by the release of elastic energy of the structure, suggest larger mean thrust production and higher efficiency in high Reynolds number scenarios compared with the cases in laminar flow. A unique symmetry-breaking instability in turbulent flow is found to stem from irregular internal body vortices, which cause symmetry breaking in the wake. Besides, a three-dimensional squid-like jet propulsion system in the presence of background flow is studied by prescribing the body deformation and jet velocity profiles. The effect of the background flow on the leading vortex ring formation and jet propulsion is investigated, and the thrust sources of the overall pulsed jet are revealed as well. Finally, FSI analysis on motion control of a self-propelled flexible swimmer in front of a cylinder utilizing proportional-derivative (PD) control is conducted. The amplitude of the actuation force, which is applied to the swimmer to bend it to produce thrust, is dynamically tuned by a feedback PD controller to instruct the swimmer to swim the desired distance from an initial position to a target location and then hold the station there. Despite the same swimming distance, a swimmer whose departure location is closer to the cylinder requires less energy consumption to reach the target and hold the position there.Fish swimming is elegant and efficient, which inspires humans to learn from them to design high-performance artificial underwater vehicles. Research on aquatic locomotion has made extensive progress towards a better understanding of how aquatic animals control their flexible body and fin for propulsion. Although the structural flexibility and deformation of the body and fin are believed to be important features to achieve optimal swimming performance, studies on high-fidelity deformable body and fin with complex material behavior, such as non-uniform stiffness distributions, are rare. In this thesis, a fully coupled three-dimensional high-fidelity fluid-structure interaction (FSI) solver is developed to investigate the flow field evolution and propulsion performance of caudal fin and jet propulsion involving body and/or fin deformation. Within this FSI solver, the fluid is resolved by solving unsteady and viscous Navier-Stokes equations based on the finite volume method with a multi-block grid system. The solid dynamics are solved by a nonlinear finite element method. The coupling between the two solvers is achieved in a partitioned approach in which convergence check and sub-iteration are implemented to ensure numerical stability and accuracy. Validations are conducted by comparing the simulation results of classical benchmarks with previous data in the literature, and good agreements between them are obtained. The developed FSI solver is then applied to study the bio-inspired fin and jet propulsion involving body deformation. Specifically, the effect of non-uniform stiffness distributions of fish body and/or fin, key features of fish swimming which have been excluded in most previous studies, on the propulsive performance is first investigated. Simulation results of a sunfish-like caudal fin model and a tuna-inspired swimmer model both show that larger thrust and propulsion efficiency can be achieved by a non-uniform stiffness distribution (e.g., increased by 11.2% and 9.9%, respectively, for the sunfish-like model) compared with a uniform stiffness profile. Despite the improved propulsive e performance, a bionic variable fish body stiffness does not yield fish-like midline kinematics observed in real fish, suggesting that fish movement involves significant active control that cannot be replicated purely by passive deformations. Subsequent studies focus on the jet propulsion inspired by squid locomotion using the developed numerical solver. Simulation results of a two-dimensional inflation-deflation jet propulsion system, whose inflation is actuated by an added external force that mimics the muscle constriction of the mantle and deflation is caused by the release of elastic energy of the structure, suggest larger mean thrust production and higher efficiency in high Reynolds number scenarios compared with the cases in laminar flow. A unique symmetry-breaking instability in turbulent flow is found to stem from irregular internal body vortices, which cause symmetry breaking in the wake. Besides, a three-dimensional squid-like jet propulsion system in the presence of background flow is studied by prescribing the body deformation and jet velocity profiles. The effect of the background flow on the leading vortex ring formation and jet propulsion is investigated, and the thrust sources of the overall pulsed jet are revealed as well. Finally, FSI analysis on motion control of a self-propelled flexible swimmer in front of a cylinder utilizing proportional-derivative (PD) control is conducted. The amplitude of the actuation force, which is applied to the swimmer to bend it to produce thrust, is dynamically tuned by a feedback PD controller to instruct the swimmer to swim the desired distance from an initial position to a target location and then hold the station there. Despite the same swimming distance, a swimmer whose departure location is closer to the cylinder requires less energy consumption to reach the target and hold the position there

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

2023 IMSAloquium

Welcome to IMSAloquium 2023. This is IMSA’s 36 th year of leading in educationalinnovation, and the 35th year of the IMSA Student Inquiry and Research (SIR) Program.https://digitalcommons.imsa.edu/archives_sir/1033/thumbnail.jp

On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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

31th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Evolutionary design of deep neural networks

Mención Internacional en el título de doctorFor three decades, neuroevolution has applied evolutionary computation to the optimization of the topology of artificial neural networks, with most works focusing on very simple architectures. However, times have changed, and nowadays convolutional neural networks are the industry and academia standard for solving a variety of problems, many of which remained unsolved before the discovery of this kind of networks. Convolutional neural networks involve complex topologies, and the manual design of these topologies for solving a problem at hand is expensive and inefficient. In this thesis, our aim is to use neuroevolution in order to evolve the architecture of convolutional neural networks. To do so, we have decided to try two different techniques: genetic algorithms and grammatical evolution. We have implemented a niching scheme for preserving the genetic diversity, in order to ease the construction of ensembles of neural networks. These techniques have been validated against the MNIST database for handwritten digit recognition, achieving a test error rate of 0.28%, and the OPPORTUNITY data set for human activity recognition, attaining an F1 score of 0.9275. Both results have proven very competitive when compared with the state of the art. Also, in all cases, ensembles have proven to perform better than individual models. Later, the topologies learned for MNIST were tested on EMNIST, a database recently introduced in 2017, which includes more samples and a set of letters for character recognition. Results have shown that the topologies optimized for MNIST perform well on EMNIST, proving that architectures can be reused across domains with similar characteristics. In summary, neuroevolution is an effective approach for automatically designing topologies for convolutional neural networks. However, it still remains as an unexplored field due to hardware limitations. Current advances, however, should constitute the fuel that empowers the emergence of this field, and further research should start as of today.This Ph.D. dissertation has been partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917. This research stay has been partially co-funded by the Spanish Ministry of Education, Culture and Sports under FPU short stay grant with identifier EST15/00260.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: María Araceli Sanchís de Miguel.- Secretario: Francisco Javier Segovia Pérez.- Vocal: Simon Luca