Position-sensorless control of permanent-magnet-assisted synchronous reluctance motor

The sensorless control of permanent-magnet-assisted synchronous reluctance (PMASR) motors is investigated, in order to conjugate the advantages of the sensorless control with full exploitation of the allowed operating area, for a given inverter. An additional pulsating flux is injected in the d-axis direction at low and zero speed, while it is dropped out, at large speed, to save voltage and additional loss. A flux-observer-based control scheme is used, which includes an accurate knowledge of the motor magnetic behavior. This leads, in general, to good robustness against load variations, by counteracting the magnetic cross saturation effect. Moreover, it allows an easy and effective correspondence between the wanted torque and flux and the set values of the chosen control variables, that is d-axis flux and q-axis current. Experimental verification of the proposed method is given, both steady-state and dynamic performance are outlined. A prototype PMASR motor will be used to this aim, as part of a purposely assembled prototype drive, for light traction application (electric scooter

The k.p method and its application to graphene, carbon nanotubes and graphene nanoribbons: the Dirac equation

The k.p method is a semi-empirical approach which allows to extrapolate the band structure of materials from the knowledge of a restricted set of parameters evaluated in correspondence of a single point of the reciprocal space. In the first part of this review article we give a general description of this method, both in the case of homogeneous crystals (where we consider a formulation based on the standard perturbation theory, and Kane's approach) and in the case of non-periodic systems (where, following Luttinger and Kohn, we describe the single-band and multi-band envelope function method and its application to heterostructures). The following part of our review is completely devoted to the application of the k.p method to graphene and graphene-related materials. Following Ando's approach, we show how the application of this method to graphene results in a description of its properties in terms of the Dirac equation. Then we find general expressions for the probability density and the probability current density in graphene and we compare this formulation with alternative existing representations. Finally, applying proper boundary conditions, we extend the treatment to carbon nanotubes and graphene nanoribbons, recovering their fundamental electronic properties.Comment: 96 pages, 14 figures, updated journal URL. Please cite as: P. Marconcini, M. Macucci, "The k.p method and its application to graphene, carbon nanotubes and graphene nanoribbons: the Dirac equation", Riv. Nuovo Cimento, Vol. 34, Issue N. 8-9, pp. 489-584 (2011), DOI: 10.1393/ncr/i2011-10068-1 . Downloadable also from Springer at https://link.springer.com/article/10.1393/ncr/i2011-10068-

Locke and Hume’s philosophical theory of color is investigated through a case study of Esref Armagan, an artist born blind

This article investigates Locke and Hume’s philosophical theory of color, through a study of the fine art practice of Esref Armagan, a Turkish artist who was born blind. The philosophical theory of color is important to the cultural history of blindness, as it has been used to justify early curricula in schools for the blind. This study is based on the following research question: Can people who are born blind understand color in the fine arts? The study is part of a grounded methodology study of art practices and visual impairment, whose findings informed a participatory study of museum access. This article examines part of the study’s first phase, and focuses on the practice of the blind Turkish artist, Esref Armagan. Data was collected through a translated correspondence interview with Esref Armagan, and an examination of research articles focusing on Armagan’s drawing skills. The study’s data is analyzed using Anderson, Krathwohl, and Bloom’s (2001) learning hierarchy. It is found that Armagan has an extensive knowledge of color and other visual concepts, developed symbolically. What is more, not only does Armagan have a knowledge of color, but he can use this knowledge creatively in accordance with Anderson et al.’s (2001) highest level of learning (level 6). The article concludes that Locke and Hume’s philosophical theory of color can be challenged in the context of the creative fine arts, as Armagan could develop unique, creative images using color. Therefore, our application of the philosophical theory of color on the education of students with visual impairments, and the pedagogical and andragogical practice based on these theories, should be questioned

Estimating Correspondences of Deformable Objects “In-the-wild”

During the past few years we have witnessed the development of many methodologies for building and fitting Statistical Deformable Models (SDMs). The construction of accurate SDMs requires careful annotation of images with regards to a consistent set of landmarks. However, the manual annotation of a large amount of images is a tedious, laborious and expensive procedure. Furthermore, for several deformable objects, e.g. human body, it is difficult to define a consistent set of landmarks, and, thus, it becomes impossible to train humans in order to accurately annotate a collection of images. Nevertheless, for the majority of objects, it is possible to extract the shape by object segmentation or even by shape drawing. In this paper, we show for the first time, to the best of our knowledge, that it is possible to construct SDMs by putting object shapes in dense correspondence. Such SDMs can be built with much less effort for a large battery of objects. Additionally, we show that, by sampling the dense model, a part-based SDM can be learned with its parts being in correspondence. We employ our framework to develop SDMs of human arms and legs, which can be used for the segmentation of the outline of the human body, as well as to provide better and more consistent annotations for body joints

Estimating correspondences of deformable objects "in-the-wild"

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordDuring the past few years we have witnessed the development of many methodologies for building and fitting Statistical Deformable Models (SDMs). The construction of accurate SDMs requires careful annotation of images with regards to a consistent set of landmarks. However, the manual annotation of a large amount of images is a tedious, laborious and expensive procedure. Furthermore, for several deformable objects, e.g. human body, it is difficult to define a consistent set of landmarks, and, thus, it becomes impossible to train humans in order to accurately annotate a collection of images. Nevertheless, for the majority of objects, it is possible to extract the shape by object segmentation or even by shape drawing. In this paper, we show for the first time, to the best of our knowledge, that it is possible to construct SDMs by putting object shapes in dense correspondence. Such SDMs can be built with much less effort for a large battery of objects. Additionally, we show that, by sampling the dense model, a part-based SDM can be learned with its parts being in correspondence. We employ our framework to develop SDMs of human arms and legs, which can be used for the segmentation of the outline of the human body, as well as to provide better and more consistent annotations for body joints.Engineering and Physical Sciences Research Council (EPSRC)TekesEuropean Community Horizon 202