64 research outputs found

    Solvi : a visual constraint modeling tool

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    Current Funding Sources List: Natural Sciences and Engineering Research Council of Canada, Canada Award Number: 2020-04401 — Recipient: Miguel A Nacenta. Engineering and Physical Sciences Research Council, United Kingdom Award Number: DTG1796157 — Recipient: Xu Zhu.Discrete constraint problems surface often in everyday life. Teachers might group students with complex considerations and hospital administrators need to produce staff rosters. Constraint programming (CP) provides techniques to efficiently find solutions. However, there remains a key challenge: these techniques are still largely inaccessible because expressing constraint problems requires sophisticated programming and logic skills. In this work we contribute a language and tool that leverage knowledge of how non-experts conceptualize problems to facilitate the expression of constraint models. Additionally, we report the results of a study surveying the advantages and remaining challenges towards making CP accessible to the wider public.Publisher PDFPeer reviewe

    Knowledge acquisition for effective and efficient use of engineering software

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    The problem of effective and efficient use of engineering software can be thought of as a Pareto optimal problem. However, the complexity of modern engineering software precludes the possibility of acquiring complete knowledge of the software's Pareto optimal set. Instead, heuristic knowledge must be acquired. The thesis proposes that heuristic knowledge be acquired via a knowledge acquisition procedure. The use of a knowledge acquisition system, which may be computerised, forms an integral part of this procedure. Two examples of knowledge acquisition illustrate the use of the knowledge acquisition procedure

    Augmented Reality Assistance for Surgical Interventions using Optical See-Through Head-Mounted Displays

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    Augmented Reality (AR) offers an interactive user experience via enhancing the real world environment with computer-generated visual cues and other perceptual information. It has been applied to different applications, e.g. manufacturing, entertainment and healthcare, through different AR media. An Optical See-Through Head-Mounted Display (OST-HMD) is a specialized hardware for AR, where the computer-generated graphics can be overlaid directly onto the user's normal vision via optical combiners. Using OST-HMD for surgical intervention has many potential perceptual advantages. As a novel concept, many technical and clinical challenges exist for OST-HMD-based AR to be clinically useful, which motivates the work presented in this thesis. From the technical aspects, we first investigate the display calibration of OST-HMD, which is an indispensable procedure to create accurate AR overlay. We propose various methods to reduce the user-related error, improve robustness of the calibration, and remodel the calibration as a 3D-3D registration problem. Secondly, we devise methods and develop hardware prototype to increase the user's visual acuity of both real and virtual content through OST-HMD, to aid them in tasks that require high visual acuity, e.g. dental procedures. Thirdly, we investigate the occlusion caused by the OST-HMD hardware, which limits the user's peripheral vision. We propose to use alternative indicators to remind the user of unattended environment motion. From the clinical perspective, we identified many clinical use cases where OST-HMD-based AR is potentially helpful, developed applications integrated with current clinical systems, and conducted proof-of-concept evaluations. We first present a "virtual monitor'' for image-guided surgery. It can replace real radiology monitors in the operating room with easier user control and more flexibility in positioning. We evaluated the "virtual monitor'' for simulated percutaneous spine procedures. Secondly, we developed ARssist, an application for the bedside assistant in robotic surgery. The assistant can see the robotic instruments and endoscope within the patient body with ARssist. We evaluated the efficiency, safety and ergonomics of the assistant during two typical tasks: instrument insertion and manipulation. The performance for inexperienced users is significantly improved with ARssist, and for experienced users, the system significantly enhanced their confidence level. Lastly, we developed ARAMIS, which utilizes real-time 3D reconstruction and visualization to aid the laparoscopic surgeon. It demonstrates the concept of "X-ray see-through'' surgery. Our preliminary evaluation validated the application via a peg transfer task, and also showed significant improvement in hand-eye coordination. Overall, we have demonstrated that OST-HMD based AR application provides ergonomic improvements, e.g. hand-eye coordination. In challenging situations or for novice users, the improvements in ergonomic factors lead to improvement in task performance. With continuous effort as a community, optical see-through augmented reality technology will be a useful interventional aid in the near future

    Embracing Machine Learning in Safety Assurance in Healthcare

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    Machine learning (ML) is becoming more widely used in many different sectors, including automotive, aviation and healthcare. ML has a great potential to change society and to improve peoples' lives. However, the prospect of ML also poses many challenges; one of the biggest challenges is safety. Thus, there are two important questions that require urgent answers: (1) Are well-established safety engineering methods still appropriate and effective in assuring the safety of ML in some representative healthcare scenarios? (2) Are there new opportunities for well-established safety engineering methods with the development of ML and why are they specifically good for safety in this domain? In this thesis, the first question is explored from the viewpoint of designing ML models. The second question is explored from two perspectives: explanaibility of ML models in support of safety assurance; and using ML to update safety analysis. Both these questions are addressed in the context of healthcare. In other words, this thesis investigates how ML can be embraced in the safety assurance of healthcare applications. Through exploration of three concrete clinical case studies, the thesis demonstrates that well-established safety engineering methods can be applied to ML systems to integrate safety into their design process in healthcare. It further identifies different ways in which ML can assist well-established safety engineering methods, and concludes that there are many opportunities for greater synergy between ML and safety engineering in healthcare and, potentially, in other domains

    31th International Conference on Information Modelling and Knowledge Bases

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    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

    New Approaches in Automation and Robotics

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    The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book

    Fused mechanomyography and inertial measurement for human-robot interface

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    Human-Machine Interfaces (HMI) are the technology through which we interact with the ever-increasing quantity of smart devices surrounding us. The fundamental goal of an HMI is to facilitate robot control through uniting a human operator as the supervisor with a machine as the task executor. Sensors, actuators, and onboard intelligence have not reached the point where robotic manipulators may function with complete autonomy and therefore some form of HMI is still necessary in unstructured environments. These may include environments where direct human action is undesirable or infeasible, and situations where a robot must assist and/or interface with people. Contemporary literature has introduced concepts such as body-worn mechanical devices, instrumented gloves, inertial or electromagnetic motion tracking sensors on the arms, head, or legs, electroencephalographic (EEG) brain activity sensors, electromyographic (EMG) muscular activity sensors and camera-based (vision) interfaces to recognize hand gestures and/or track arm motions for assessment of operator intent and generation of robotic control signals. While these developments offer a wealth of future potential their utility has been largely restricted to laboratory demonstrations in controlled environments due to issues such as lack of portability and robustness and an inability to extract operator intent for both arm and hand motion. Wearable physiological sensors hold particular promise for capture of human intent/command. EMG-based gesture recognition systems in particular have received significant attention in recent literature. As wearable pervasive devices, they offer benefits over camera or physical input systems in that they neither inhibit the user physically nor constrain the user to a location where the sensors are deployed. Despite these benefits, EMG alone has yet to demonstrate the capacity to recognize both gross movement (e.g. arm motion) and finer grasping (e.g. hand movement). As such, many researchers have proposed fusing muscle activity (EMG) and motion tracking e.g. (inertial measurement) to combine arm motion and grasp intent as HMI input for manipulator control. However, such work has arguably reached a plateau since EMG suffers from interference from environmental factors which cause signal degradation over time, demands an electrical connection with the skin, and has not demonstrated the capacity to function out of controlled environments for long periods of time. This thesis proposes a new form of gesture-based interface utilising a novel combination of inertial measurement units (IMUs) and mechanomyography sensors (MMGs). The modular system permits numerous configurations of IMU to derive body kinematics in real-time and uses this to convert arm movements into control signals. Additionally, bands containing six mechanomyography sensors were used to observe muscular contractions in the forearm which are generated using specific hand motions. This combination of continuous and discrete control signals allows a large variety of smart devices to be controlled. Several methods of pattern recognition were implemented to provide accurate decoding of the mechanomyographic information, including Linear Discriminant Analysis and Support Vector Machines. Based on these techniques, accuracies of 94.5% and 94.6% respectively were achieved for 12 gesture classification. In real-time tests, accuracies of 95.6% were achieved in 5 gesture classification. It has previously been noted that MMG sensors are susceptible to motion induced interference. The thesis also established that arm pose also changes the measured signal. This thesis introduces a new method of fusing of IMU and MMG to provide a classification that is robust to both of these sources of interference. Additionally, an improvement in orientation estimation, and a new orientation estimation algorithm are proposed. These improvements to the robustness of the system provide the first solution that is able to reliably track both motion and muscle activity for extended periods of time for HMI outside a clinical environment. Application in robot teleoperation in both real-world and virtual environments were explored. With multiple degrees of freedom, robot teleoperation provides an ideal test platform for HMI devices, since it requires a combination of continuous and discrete control signals. The field of prosthetics also represents a unique challenge for HMI applications. In an ideal situation, the sensor suite should be capable of detecting the muscular activity in the residual limb which is naturally indicative of intent to perform a specific hand pose and trigger this post in the prosthetic device. Dynamic environmental conditions within a socket such as skin impedance have delayed the translation of gesture control systems into prosthetic devices, however mechanomyography sensors are unaffected by such issues. There is huge potential for a system like this to be utilised as a controller as ubiquitous computing systems become more prevalent, and as the desire for a simple, universal interface increases. Such systems have the potential to impact significantly on the quality of life of prosthetic users and others.Open Acces

    Modélisation des émissions conduites de mode commun d'une chaîne électromécanique. Optimisation paramétrique de l'ensemble convertisseur filtres sous contraintes CEM.

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    Au cours de ces dernières décennies, les avionneurs n’ont cessé d’augmenter la puissance électrique embarquée à bord des avions. Cette intensification de l’usage de l’électricité, dans le but de rationaliser les énergies secondaires de l’avion (pneumatique, hydraulique, mécanique) constitue le fondement du concept de l’avion plus électrique. Une des contreparties de l’augmentation du nombre de charges électriques réside dans le fait qu’elles doivent fonctionner dans le même environnement électromagnétique, ce qui engendre des problèmes de compatibilité. Cette discipline a été traitée jusqu’à présent en fin de développement d’un système, avant l’étape de la certification et de l’intégration sur avion. La prise en compte de ces contraintes dès la phase de conception, via l’estimation des perturbations électromagnétiques conduites et rayonnées par simulation, peut permettre d’importants gains de temps et de coûts en réduisant les phases d’essais. La première étape de ce projet de recherche est la mise en place d’une approche de modélisation compatible avec les processus d’optimisation. Il est alors indispensable de prendre en compte l’ensemble des sous-systèmes qui composent la chaîne électromécanique, à savoir les RSILs, les câbles, le convertisseur et le moteur. L’approche de modélisation choisie est de type directe ; elle consiste à représenter la chaîne électromécanique dans la base de mode commun par des quadripôles. Ce modèle générique permet d’estimer les courants de mode commun directement dans le domaine fréquentiel en différents points du système. Par ailleurs, afin d’être compétitif vis-à-vis des autres vecteurs d’énergie présents sur avion, la densité de puissance des systèmes électriques doit être drastiquement augmentée. L’introduction des semi conducteurs grands gaps à base de Carbure de Silicium (SiC) permet de contribuer à l’augmentation de la densité de puissance des électroniques de puissance. Cependant, dans ces travaux de thèse, nous veillons à la non régression des performances au niveau système et notamment vis-à-vis de l’impact des émissions électromagnétiques conduites de mode commun. Une fois les modèles en émission établis, diverses solutions de filtrage sont étudiées : filtrage passif externe et interne. Une démarche d’optimisation multi-objectifs (masse, pertes) et multi contraintes (qualité réseau, stabilité, CEM, thermique, etc.) est proposée. Des études de sensibilité mettent en évidence les variables de conception ayant le plus d’impact sur les émissions conduites. Cette approche permet le dimensionnement optimal des composants de l’onduleur (module de puissance, dissipateur, filtres de mode commun et de mode différentiel, paramètres de la commande rapprochée). Les résultats obtenus grâce à l’algorithme génétique employé permettent de construire des courbes de tendance utiles pour l’aide au dimensionnement

    Research on Teaching and Learning In Biology, Chemistry and Physics In ESERA 2013 Conference

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    This paper provides an overview of the topics in educational research that were published in the ESERA 2013 conference proceedings. The aim of the research was to identify what aspects of the teacher-student-content interaction were investigated frequently and what have been studied rarely. We used the categorization system developed by Kinnunen, Lampiselkä, Malmi and Meisalo (2016) and altogether 184 articles were analyzed. The analysis focused on secondary and tertiary level biology, chemistry, physics, and science education. The results showed that most of the studies focus on either the teacher’s pedagogical actions or on the student - content relationship. All other aspects were studied considerably less. For example, the teachers’ thoughts about the students’ perceptions and attitudes towards the goals and the content, and the teachers’ conceptions of the students’ actions towards achieving the goals were studied only rarely. Discussion about the scope and the coverage of the research in science education in Europe is needed.Peer reviewe
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