In-wheel Motors: Express Comparative Method for PMBL Motors

One of the challenges facing the electric vehicle industry today is the selection and design of a suitable in-wheel motor. Permanent Magnet Brushless (PMBL) motor is a good choice for the in-wheel motor because of its lossless excitation, improved efficiency, reduced weight and low maintenance. The PMBL motors can be further classified as Axial-Flux Twin-Rotor (AFTR) and Radial-Flux Twin-Rotor (RFTR) machines. The objective of this dissertation is to develop a fast method for the selection of appropriate in-wheel motor depending on wheel size. To achieve this, torque equations are developed for a conventional single-rotor cylindrical, twin-rotor axial-flux and twin-rotor radial-flux PMBL motors with slot-less stators based on magnetic circuit theory and the torque ratio for any two motors is expressed as a function of motor diameter and axial length. The theoretical results are verified, on the basis of magnetic field theory, by building the 3-dimensional Finite Element Method (FEM) models of the three types of motors and analyzing them in magnetostatic solver to obtain the average torque of each motor. Later, validation of software is carried out by a prototype single-rotor cylindrical slotted motor which was built for direct driven electric wheelchair application. Further, the block diagram of this in-wheel motor including the supply circuit is built in Simulink to observe the motor dynamics in practical scenario. The results from finite element analysis obtained for all the three PMBL motors indicate a good agreement with the analytical approach. For twin-rotor PMBL motors of diameter 334mm, length 82.5mm with a magnetic loading of 0.7T and current loading of 41.5A-turns/mm, the error between the express comparison method and simulation results, in computation of torque ratio, is about 1.5%. With respect to the single-rotor cylindrical motor with slotless stator, the express method for AFTR PMBL motor yielded an error of 4.9% and that of an RFTR PMBL motor resulted in an error of -7.6%. Moreover, experimental validation of the wheelchair motor gave almost the same torque and similar dynamic performance as the FEM and Simulink models respectively

Empirical Validation of the Usefulness of Information Theory-Based Software Metrics

Software designs consist of software components and their relationships. Graphs are abstraction of software designs. Graphs composed of nodes and hyperedges are attractive for depicting software designs. Measurement of abstractions quantify relationships that exist among components. Most conventional metrics are based on counting. In contrast, this work adopts information theory because design decisions are information. The goal of this research is to show that information theory-based metrics proposed by Allen, namely size, complexity, coupling, and cohesion, can be useful in real-world software development projects, compared to the counting-based metrics. The thesis includes three case studies with the use of global variables as the abstraction. It is observed that one can use the counting metrics for the size and coupling measures and the information metrics for the complexity and cohesion measures

Engineered Cartilage on Chitosan Calcium Phosphate Scaffolds for Osteochondral Defects

Articular cartilage provides an almost frictionless surface for the articulating ends of the bone. Cartilage functions to lubricate and transmit compressive forces resulting from joint loading and impact. If the cartilage is damaged, through traumatic injury or disease, it lacks the ability of self-repairing as the tissue lacks vascular system. If the injuries to articular cartilage are left untreated, they may progress to Osteoarthritis. Osteoarthritis, a degenerative disease, is one of the leading disabilities in the United States. Tissue engineering has the potential to regenerate healthy hyaline cartilage, which can alleviate pain and restore the functions of normal tissue. This study explores the production of engineered cartilage on top of composite calcium phosphate scaffold. The current research is related to a biphasic approach to cartilage tissue engineering — in which one layer supports to form subchondral bone (osteogenesis) and another supports cartilage formation (chondrogenesis). Chondrocyte and bone marrow-derived stem cell attachment to chitosan will be investigated for producing a bilayered construct for osteochondral repair. The main objectives of my research include the following: attachment and proliferation of human mesenchymal stem cells on chitosan calcium phosphate scaffolds, techniques to create a biphasic construct, the effect of coating chitosan calcium phosphate scaffolds with type I collagen and determining the ideal bead size for making chitosan calcium phosphate scaffolds

Engineered Cartilage on Chitosan Calcium Phosphate Scaffolds for Osteochondral Defects

Articular cartilage provides an almost frictionless surface for the articulating ends of the bone. Cartilage functions to lubricate and transmit compressive forces resulting from joint loading and impact. If the cartilage is damaged, through traumatic injury or disease, it lacks the ability of self-repairing as the tissue lacks vascular system. If the injuries to articular cartilage are left untreated, they may progress to Osteoarthritis. Osteoarthritis, a degenerative disease, is one of the leading disabilities in the United States. Tissue engineering has the potential to regenerate healthy hyaline cartilage, which can alleviate pain and restore the functions of normal tissue. This study explores the production of engineered cartilage on top of composite calcium phosphate scaffold. The current research is related to a biphasic approach to cartilage tissue engineering — in which one layer supports to form subchondral bone (osteogenesis) and another supports cartilage formation (chondrogenesis). Chondrocyte and bone marrow-derived stem cell attachment to chitosan will be investigated for producing a bilayered construct for osteochondral repair. The main objectives of my research include the following: attachment and proliferation of human mesenchymal stem cells on chitosan calcium phosphate scaffolds, techniques to create a biphasic construct, the effect of coating chitosan calcium phosphate scaffolds with type I collagen and determining the ideal bead size for making chitosan calcium phosphate scaffolds

Comparative study on Double-Rotor PM brushless motors with cylindrical and disc type slot-less stator

Among brushless permanent magnet machines, the torus motors (also called Axial Flux Double-Rotor Permanent Magnet (AFTR PM) motors) are most compact and highly efficient. A cylindrical counterpart of this motor is a newly proposed Radial Flux Double-Rotor Permanent Magnet (RFTR PM) motor. The objectives of this thesis are to optimize the magnetic circuit of both AFTR PM and RFTR PM motors and to compare their electromechanical parameters on the basis of the results obtained from magnetic field simulation using Finite Element Method (FEM). To reach these objectives, FEM models are developed for both the motors, for particular given data. Applying the magnetic field simulation with the help of FEMM 4.0 software package, optimized stator and rotor core dimensions were determined as well as electromechanical parameters such as electromechanical torque, electromotive force, resistance and inductance of the stator windings. Next, the efficiency and torque to volume ratio along with the torque to mass ratio were calculated. Comparing the parameters of both motors, the following conclusions are obtained: • Both slot-less motors developed electromagnetic torque with very low torque ripple contents. • The torque to mass ratio of RFTR PM motor is almost equal to the torque per mass of AFTR PM machine. • AFTR PM motor is more compact than its cylindrical counterpart because its torque to volume ratio is higher. • The efficiency of RFTR PM motor is relatively higher than that of AFTR PM motor, particularly if multi disc motor is considered, mainly due to the smaller percentage of end connection in the entire volume of the winding

EFFECT OF DIETARY CHOLESTEROL ON BRAIN CHOLESTEROL IN DEVELOPING RATS

Cholesterol is a significant component of brain and is found in both neuronal membranes and myelin. All mammalian milks provide cholesterol during periods of maximal brain growth and myelination. However, infant formulas contain only traces of cholesterol. The purpose of this study is to determine if dietary cholesterol alters rat brain composition during development. Pregnant Long-Evans rats were randomly assigned (12/group) to a semi-synthetic diet with or without cholesterol. On postnatal day 1(P1), litters were culled to eight and weaned on P17. Litters received the same concentration of cholesterol as their dam from P17 to P32. On P32, pups were sacrificed. The left brain cortices were analyzed for cholesterol and protein concentration. Developing rats exposed to the cholesterol diet had an increase in left brain cortex size (p=0.003), cholesterol (p=0.006) and protein concentration (p=0.0005). Exposure to exogenous cholesterol increased brain cholesterol and protein concentration in developing rats

Designing a Datawarehousing and Business Analytics Course Using Experiential Learning Pedagogy

Experiential learning refers to learning from experience or learning by doing. Universities have explored various forms for implementing experiential learning such as apprenticeships, internships, cooperative education, practicums, service learning, job shadowing, fellowships and community activities. However, very little has been done in systematically trying to integrate experiential learning to the main stream academic curriculum. Over the last two years, at the authors’ university, a new program titled UNI-X was launched to achieve this. Combining academic curriculum with experiential learning pedagogy, provides a challenging environment for students to use their disciplinary knowledge and skills to tackle real world problems and issues through inter-disciplinary approaches and activities. A course designated as UNI-X involves external partners from corporate, non-profit or government-sector organisations. The course requires the student to learn knowledge and skills during the classroom sessions and then to apply them in solving a real-world project proposed by the partners. The instructor along with the industry partner plays an active role in the project design, mentoring and assessment. In this paper contribution, the authors share their experience in designing a Data Warehousing and Business Analytics (DWBA) course to include experiential learning activities. The paper describes in detail the content, pedagogy, in-course project, challenges and lessons learned when introducing experiential learning into an existing course. Thus providing one pathway for Information Systems (IS) professors to adapt their analytics course to include experiential learning activities

Incorporating Analytics into a Business Process Modelling Course

Embedding analytics is about integrating data analytics into operational systems that are part of an organization’s business processes. Currently, most organizations focus on automation business processes and enhancing productivity. However, going forward, in order to stay competitive, organizations have to go beyond automating their processes, by making them more intelligent, by embedding analytics into their processes and business applications. Therefore, there is need for enhancing the knowledge and skills of BPM professionals with know-how on improving a business process by embedding analytics into the workflow. In this paper contribution, the authors share their experience on how an existing process modelling, analysis and solution designing course was modified in order to incorporate the analytics component. The paper describes in detail the content, pedagogy, and lessons learned when introducing analytics into an existing business process modelling course. Thus providing one pathway for IS professors to adapt their current process modelling, enterprise systems and BPM courses to include analytics

Deep active localization

Des progrès considérables ont été réalisés en robotique mobile au cours des dernières décennies et ces robots sont maintenant capables d’effectuer des tâches qu’on croyait au- paravant impossibles. Un facteur critique qui a permis aux robots d’accomplir ces diverses tâches difficiles est leur capacité à déterminer où ils se trouvent dans un environnement donné (localisation). On parvient à une automatisation plus poussée en laissant le robot choisir ses propres actions au lieu de faire appel à un téléopérateur humain. Cependant, la détermination précise de la pose (position + orientation) du robot et l’adaptation de cette capacité à des environnements plus vastes constituent depuis longtemps un défi dans le do- maine de la robotique mobile. Les approches traditionnelles à cette tâche de " localisation active " utilisent un critère théorique de l’information pour la sélection des actions ainsi que des modèles perceptuels faits à la main. Avec une augmentation constante des capacités de calcul disponibles au cours des trois dernières décennies, l’algorithme back-propagation a trouvé son utilisation dans des réseaux neuronaux beaucoup plus profonds et dans de nombreuses applications. En l’absence de données labellisées, le paradigme de l’apprentissage par le renforcement (RL) a récemment connu beaucoup de succès en ce qu’il apprend en interagissant avec l’environnement. Cepen- dant, il n’est pas pratique pour un algorithme RL d’apprendre raisonnablement bien à partir de l’expérience limitée du monde réel. C’est pourquoi il est courant d’entraîner l’agent dans un simulateur puis de transférer efficacement l’apprentissage dans de vrais robots. Dans cette thèse, nous proposons une méthode différentiable de bout en bout afin d’ap- prendre à choisir des mesures informatives pour la localisation de robots, qui peut être entraînée entièrement en simulation et ensuite transférée sur le robot réel sans aucun ajus- tement. Pour ce faire, on s’appuie sur les progrès récents de l’apprentissage profond et des paradigmes d’apprentissage de renforcement, combinés aux techniques de randomisation des domaine. Le système est composé de deux modules d’apprentissage : un réseau neuronal convolutionnel pour la perception, et un module de planification utilisant l’apprentissage profond par renforcement. Nous utilisons une approche multi-échelles dans le modèle per- ceptuel puisque la sélection d’action à l’aide de l’apprentissage par renforcement nécessite une précision de la position inférieure à la précision nécessaire au contrôle du robot. Nous démontrons que le système résultant surpasse les approches traditionnelles, en termes de perception et de planification. Nous démontrons également la robustesse de notre approche vis-à-vis différentes configurations de cartes et d’autres facteurs de nuisance par l’utilisa- tion de la randomisation de domaine au cours de l’entraînement. Le code a été publié : https://github.com/montrealrobotics/dal et est compatible avec le framework OpenAI gym, ainsi qu’avec le simulateur Gazebo.Mobile robots have made significant advances in recent decades and are now able to perform tasks that were once thought to be impossible. One critical factor that has enabled robots to perform these various challenging tasks is their ability to determine where they are located in a given environment (localization). Further automation is achieved by letting the robot choose its own actions instead of a human teleoperating it. However, determining its pose (position + orientation) precisely and scaling this capability to larger environments has been a long-standing challenge in the field of mobile robotics. Traditional approaches to this task of active localization use an information-theoretic criterion for action selection and hand-crafted perceptual models. With a steady rise in available computation in the last three decades, the back-propagation algorithm found its use in much deeper neural networks and in numerous applications. When labelled data is not available, the paradigm of reinforcement learning (RL) is used, where it learns by interacting with the environment. However, it is impractical for most RL algorithms to learn reasonably well from just the limited real world experience. Hence, it is common practice to train the RL based models in a simulator and efficiently transfer (without any significant loss of performance) these trained models into real robots. In this thesis, we propose an end-to-end differentiable method for learning to take in- formative actions for robot localization that is trainable entirely in simulation and then transferable onto real robot hardware with zero refinement. This is achieved by leveraging recent advancements in deep learning and reinforcement learning combined with domain randomization techniques. The system is composed of two learned modules: a convolu- tional neural network for perception, and a deep reinforcement learned planning module. We leverage a multi-scale approach in the perceptual model since the accuracy needed to take actions using reinforcement learning is much less than the accuracy needed for robot control. We demonstrate that the resulting system outperforms traditional approaches for either perception or planning. We also demonstrate our approach’s robustness to different map configurations and other nuisance parameters through the use of domain randomization in training. The code has been released: https://github.com/montrealrobotics/dal and is compatible with the OpenAI gym framework, as well as the Gazebo simulator

Rapid Transition of a Technical Course from Face-to-Face to Online

Just like most universities around the world, due to the coronavirus disease of 2019 (COVID-19) pandemic, senior management at Singapore Management University decided to move all courses to a virtual, online, synchronous mode and gave instructors a short notice period—one week—to make this transition. In this paper, we describe the challenges we faced, the practical solutions we adopted, and the lessons we learnt in rapidly transitioning a face-to-face master’s degree course in text analytics and applications into a virtual, online, course format that could deliver a quality learning experience