51 research outputs found

    Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

    Get PDF
    Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

    Program and Abstracts Celebration of Student Scholarship, 2013

    Get PDF
    Program and Abstracts from the Celebration of Student Scholarship on April 24, 2013

    Program and Abstracts Celebration of Student Scholarship, 2013

    Get PDF
    Program and Abstracts from the Celebration of Student Scholarship on April 24, 2013

    The influence of dopamine on prediction, action and learning

    Get PDF
    In this thesis I explore functions of the neuromodulator dopamine in the context of autonomous learning and behaviour. I first investigate dopaminergic influence within a simulated agent-based model, demonstrating how modulation of synaptic plasticity can enable reward-mediated learning that is both adaptive and self-limiting. I describe how this mechanism is driven by the dynamics of agentenvironment interaction and consequently suggest roles for both complex spontaneous neuronal activity and specific neuroanatomy in the expression of early, exploratory behaviour. I then show how the observed response of dopamine neurons in the mammalian basal ganglia may also be modelled by similar processes involving dopaminergic neuromodulation and cortical spike-pattern representation within an architecture of counteracting excitatory and inhibitory neural pathways, reflecting gross mammalian neuroanatomy. Significantly, I demonstrate how combined modulation of synaptic plasticity and neuronal excitability enables specific (timely) spike-patterns to be recognised and selectively responded to by efferent neural populations, therefore providing a novel spike-timing based implementation of the hypothetical ‘serial-compound’ representation suggested by temporal difference learning. I subsequently discuss more recent work, focused upon modelling those complex spike-patterns observed in cortex. Here, I describe neural features likely to contribute to the expression of such activity and subsequently present novel simulation software allowing for interactive exploration of these factors, in a more comprehensive neural model that implements both dynamical synapses and dopaminergic neuromodulation. I conclude by describing how the work presented ultimately suggests an integrated theory of autonomous learning, in which direct coupling of agent and environment supports a predictive coding mechanism, bootstrapped in early development by a more fundamental process of trial-and-error learning

    A framework for automatic modeling of underground excavations in homogeneous rock mass

    Get PDF
    Determining the optimum excavation sequence in mining or civil engineering requires using stress analysis methods to repeatedly solve large models. Time consuming preparation of the model and lengthy computations, often measured in days, can have major impacts on successful ongoing operation of an underground mine, where stope failures can cost millions of dollars and perhaps result in closure of the mine. Widespread acceptance of new tunneling methods such as NATM which depend heavily on numerical stress analysis tools and the fact that the effects of excavation at the face of the tunnel are distinctively three dimensional necessitates the use of 3D numerical analysis of these problems. A framework was developed to facilitate efficient modeling of underground excavations and to create an optimal 3D mesh by reducing the number of surface and volume elements while keeping the result of stress analysis accurate enough at the region of interest, where a solution is sought. Fewer surface and volume elements means fewer degrees of freedom in the numerical model. The reduction in number of degrees of freedom directly translates to savings in computational time and resources. The mesh refinement algorithm is driven by a set of criteria that are functions of distance and visibility of points from the region of interest and the framework can be easily extended by adding new types of criteria. A software application was developed to realize the proposed framework and it was applied to a number of mining and civil engineering problems to investigate the applicability, accuracy and efficiency of the framework. The optimized mesh produced by the framework reduced the time to solution significantly and the accuracy of the results obtained from the optimized mesh is comparable to the accuracy of the input data for mining engineering problems

    Proceedings of the 2017 Coal Operators\u27 Conference

    Get PDF
    Proceedings of the 2017 Coal Operators\u27 Conference. All papers in these proceedings are peer reviewed. ISBN: 978174128261

    A formal descriptive theory of software-based creative practice

    Get PDF
    PhDCreative artefacts, from concert posters to architectural plans, are often created in entirely software-based workflows. Software tools can be easily made to record all user interactions, thereby capturing the observable part of creative practice. Although recording software-based creative practice is easy, analysing it is much harder. This is especially true if one wishes to analyse the cognitive process that underlies the recorded creative practice. There are currently no clear methods for the analysis of recorded creative practice, nor are there any suitable theories of the cognition underlying creative practice that can serve as the basis for the development of such methods. This thesis develops a formal descriptive theory of the cognition underlying software-based creative practice, with the aim of informing the development of analysis of recorded creative practice. The theory, called the Software-based Creative Practice Framework (SbCPF), fits with extended and predictive views of cognition. It characterises creative practice as a process of iteratively working from an abstract idea to a concrete artefact, whereby the required lowlevel detail to decide on action is imagined in flight, during practice. Furthermore, it argues that this iterative just-in-time imagination is necessary, because of the predictive nature of the mind. The SbCPF was developed through the use of a novel method for the analysis of creative practice displayed in video tutorials. This method is based on Grounded Theory, Rhetorical Structure Theory, Gesture Theory, Category Theory, and a novel taxonomy describing the relation of action to speech. The method is applied to produce a grounded theory of the creative practice of 3D modelling and animation with the Blender software. The grounded theory forms the basis of the aforementioned formal theory. Finally, the formal theory is further illustrated, evaluated, and explored by way of implementing a computational model.Queen Mary University of London, and the EPSRC Centre for Doctoral Training in Media and Arts Technology EP/G03723X/

    Ubiquitäre Systeme (Seminar) und Mobile Computing (Proseminar) SS 2019 : Mobile und Verteilte Systeme Ubiquitous Computing. Teil XIX

    Get PDF
    Die Seminarreihe Mobile Computing und Ubiquitäre Systeme existiert seit dem Wintersemester 2013/2014. Seit diesem Semester findet das Proseminar Mobile Computing am Lehrstuhl fur Pervasive Computing System statt. Die Arbeiten des Proseminars werden seit dem mit den Arbeiten des zweiten Seminars des Lehrstuhls, dem Seminar Ubiquitäre Systeme, zusammengefasst und gemeinsam veröffentlicht. Die Seminarreihe Ubiquitäre Systeme hat eine lange Tradition in der Forschungsgruppe TECO. Im Wintersemester 2010/2011 wurde die Gruppe Teil des Lehrstuhls für Pervasive Computing Systems. Seit dem findet das Seminar Ubiquitäre Systeme in jedem Semester statt. Ebenso wird das Proseminar Mobile Computing seit dem Wintersemester 2013/2014 in jedem Semester durchgeführt. Seit dem Wintersemester 2003/2004 werden die Seminararbeiten als KIT-Berichte veröffentlicht. Ziel der gemeinsamen Seminarreihe ist die Aufarbeitung und Diskussion aktueller Forschungsfragen in den Bereichen Mobile und Ubiquitous Computing. Dieser Seminarband fasst die Arbeiten der Seminare des Sommersemesters 2019 zusammen. Wir danken den Studierenden für ihren besonderen Einsatz, sowohl während des Seminars als auch bei der Fertigstellung dieses Bandes

    Faculty Publications and Creative Works 2003

    Get PDF
    Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. It serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM
    corecore