990 resultados para Vehicle Services.


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The worldwide research in nanoelectronics is motivated by the fact that scaling of MOSFETs by conventional top down approach will not continue for ever due to fundamental limits imposed by physics even if it is delayed for some more years. The research community in this domain has largely become multidisciplinary trying to discover novel transistor structures built with novel materials so that semiconductor industry can continue to follow its projected roadmap. However, setting up and running a nanoelectronics facility for research is hugely expensive. Therefore it is a common model to setup a central networked facility that can be shared with large number of users across the research community. The Centres for Excellence in Nanoelectronics (CEN) at Indian Institute of Science, Bangalore (IISc) and Indian Institute of Technology, Bombay (IITB) are such central networked facilities setup with funding of about USD 20 million from the Department of Information Technology (DIT), Ministry of Communications and Information Technology (MCIT), Government of India, in 2005. Indian Nanoelectronics Users Program (INUP) is a missionary program not only to spread awareness and provide training in nanoelectronics but also to provide easy access to the latest facilities at CEN in IISc and at IITB for the wider nanoelectronics research community in India. This program, also funded by MCIT, aims to train researchers by conducting workshops, hands-on training programs, and providing access to CEN facilities. This is a unique program aiming to expedite nanoelectronics research in the country, as the funding for projects required for projects proposed by researchers from around India has prior financial approval from the government and requires only technical approval by the IISc/ IITB team. This paper discusses the objectives of INUP, gives brief descriptions of CEN facilities, the training programs conducted by INUP and list various research activities currently under way in the program.

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An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A I-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.

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A controller design for vibration control and alignment maintenance at critical location is developed in a generic launch vehicle whose equipment bay (EB) houses the main inertial platform. The controller uses active control to reduce the attitude disturbance in the attitude at the EB due to elastic deformation. The vibration energy is redistributed by the technique of vibration confinement, which enables the response at the EB to reach its steady state faster in the remaining portion of the structure. (AIAA)

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The protein-protein docking programs typically perform four major tasks: (i) generation of docking poses, (ii) selecting a subset of poses, (iii) their structural refinement and (iv) scoring, ranking for the final assessment of the true quaternary structure. Although the tasks can be integrated or performed in a serial order, they are by nature modular, allowing an opportunity to substitute one algorithm with another. We have implemented two modular web services, (i) PRUNE: to select a subset of docking poses generated during sampling search (http://pallab.serc.iisc.ernet.in/prune) and (ii) PROBE: to refine, score and rank them (http://pallab.serc.iisc.ernet.in/probe). The former uses a new interface area based edge-scoring function to eliminate > 95% of the poses generated during docking search. In contrast to other multi-parameter-based screening functions, this single parameter based elimination reduces the computational time significantly, in addition to increasing the chances of selecting native-like models in the top rank list. The PROBE server performs ranking of pruned poses, after structure refinement and scoring using a regression model for geometric compatibility, and normalized interaction energy. While web-service similar to PROBE is infrequent, no web-service akin to PRUNE has been described before. Both the servers are publicly accessible and free for use.

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We describe in some detail the process of development of a dynamic model of a three wheeled vehicle using ADAMS-CAR. We first describe the rigid body model, and then the modeling of structural flexibilities. The aim of this report is to document procedural details of such modeling, with a view to presenting more research and development oriented investigations in the future. The contents of this report may also be of interest to practicing engineers engaged in multi-body dynamics modeling of wheeled vehicles.

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Trajectory optimization of a generic launch vehicle is considered in this paper. The trajectory from launch point to terminal injection point is divided in to two segments. The first segment deals with launcher clearance and vertical raise of the vehicle. During this phase, a nonlinear feedback guidance loop is incorporated to assure vertical raise in presence of thrust misalignment, centre of gravity offset, wind disturbance etc. and possibly to clear obstacles as well. The second segment deals with the trajectory optimization, where the objective is to ensure desired terminal conditions as well as minimum control effort and minimum structural loading in the high dynamic pressure region. The usefulness of this dynamic optimization problem formulation is demonstrated by solving it using the classical Gradient method. Numerical results for both the segments are presented, which clearly brings out the potential advantages of the proposed approach.