5 resultados para slots (or shelf space)
em Bucknell University Digital Commons - Pensilvania - USA
Resumo:
The object consists of a disc with 20 or so images of an object/person around the edges, each slightly in a different position and space. Extending from the edges of the disc is a shutter: there are slots that one looks through with a solid part in between that blocks some of our view when in rotation to give the illusion of movement. A mirror is also used with the device. The user spins the wheel, while looking at the mirror and seeing the reflection of the phenakistoscope. The shutter blocks some of the image so that what we see appears to be moving, or animated. (Leskosky, 178)
Resumo:
The size of the Zoetrope was roughly between a foot or two of cubic space, although the exact dimensions vary from model to model. The materials used to create the Zoetrope were fairly basic; the wooden platform served as the stabilizer, the cylinder was mounted on a wooden or metal pole that elevated the viewing platform and the cylinder itself, we can deduce, was made of a flexible paper-like material that allowed slots to be cut into it. The band with the painted or sketched images would be made of a similar if not identical material as it has to change form to fit inside its corresponding cylinder.
Resumo:
The US penitentiary at Lewisburg, Pennsylvania, was retrofitted in 2008 to offer the country’s first federal Special Management Unit (SMU) program of its kind. This model SMU is designed for federal inmates from around the country identified as the most intractably troublesome, and features double-celling of inmates in tiny spaces, in 23-hour or 24-hour a day lockdown, requiring them to pass through a two-year program of readjustment. These spatial tactics, and the philosophy of punishment underlying them, contrast with the modern reform ideals upon which the prison was designed and built in 1932. The SMU represents the latest punitive phase in American penology, one that neither simply eliminates men as in the premodern spectacle, nor creates the docile, rehabilitated bodies of the modern panopticon; rather, it is a late-modern structure that produces only fear, terror, violence, and death. This SMU represents the latest of the late-modern prisons, similar to other supermax facilities in the US but offering its own unique system of punishment as well. While the prison exists within the system of American law and jurisprudence, it also manifests features of Agamben’s lawless, camp-like space that emerges during a state of exception, exempt from outside scrutiny with inmate treatment typically beyond the scope of the law.
Resumo:
The US penitentiary at Lewisburg, Pennsylvania, was retrofitted in 2008 to offer the country's first federal Special Management Unit (SMU) program of its kind. This model SMU is designed for federal inmates from around the country identified as the most intractably troublesome, and features double-celling of inmates in tiny spaces, in 23-hour or 24-hour a day lockdown, requiring them to pass through a two-year program of readjustment. These spatial tactics, and the philosophy of punishment underlying them, contrast with the modern reform ideals upon which the prison was designed and built in 1932. The SMU represents the latest punitive phase in American penology, one that neither simply eliminates men as in the premodern spectacle, nor creates the docile, rehabilitated bodies of the modern panopticon; rather, it is a late-modern structure that produces only fear, terror, violence, and death. This SMU represents the latest of the late-modern prisons, similar to other supermax facilities in the US but offering its own unique system of punishment as well. While the prison exists within the system of American law and jurisprudence, it also manifests features of Agamben's lawless, camp-like space that emerges during a state of exception, exempt from outside scrutiny with inmate treatment typically beyond the scope of the law
Resumo:
Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.