8 resultados para Design gráfico pós-modernista

em Indian Institute of Science - Bangalore - Índia


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An important issue in the design of a distributed computing system (DCS) is the development of a suitable protocol. This paper presents an effort to systematize the protocol design procedure for a DCS. Protocol design and development can be divided into six phases: specification of the DCS, specification of protocol requirements, protocol design, specification and validation of the designed protocol, performance evaluation, and hardware/software implementation. This paper describes techniques for the second and third phases, while the first phase has been considered by the authors in their earlier work. Matrix and set theoretic based approaches are used for specification of a DCS and for specification of the protocol requirements. These two formal specification techniques form the basis of the development of a simple and straightforward procedure for the design of the protocol. The applicability of the above design procedure has been illustrated by considering an example of a computing system encountered on board a spacecraft. A Petri-net based approach has been adopted to model the protocol. The methodology developed in this paper can be used in other DCS applications.

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DNA adopts different conformations not only based on novel base pairs, but also with different chain polarities. Besides several duplex structures (A, B, Z, parallel stranded (ps)-DNA, etc.), DNA also forms higher-order structures like triplex, tetraplex, and i-motif. Each of these structures has its own biological significance. The ps-duplexes have been found to be resistant to certain nucleases and endonucleases. Molecules that promote triple-helix formation have significant potential. These investigations have many therapeutic advantages which may be useful in the regulation of the expression of genes responsible for certain diseases by locking either their transcription (antigene) or translation (antisense). Each DNA minor groove binding ligand (MGBL) interacts with DNA through helical minor groove recognition in a sequence-specific manner, and this interferes with several DNA-associated processes. Incidentally, these ligands interact with some non-B-DNA and with higher-order DNA structures including ps-DNA and triplexes. While the design and recognition of minor grooves of duplex DNA by specific MGBLs have been a topic of many reports, limited information is available on the binding behavior of MGBLs with nonduplex DNA. In this review, we summarize various attempts of the interaction of MGBLs with ps-DNA and DNA triplexes.

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Through the example of a spacecraft equipment deck, which is generally made of honeycomb sandwich construction, it is shown that modal energy distribution can be used as an effective guideline in improving the deck's frequencies to meet the restrictions imposed upon it. The kinetic energy distribution is employed as a basis for redistributing various packages on the deck. Strain energy distribution is used to identify areas which can be stiffened by bonding �doublers� on the face sheets and the doubler thickness is obtained from a sensitivity analysis.

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Based on the an earlier CFD analysis of the performance of the gas-dynamically controlled laser cavity [1]it was found that there is possibility of optimizing the geometry of the diffuser that can bring about reductions in both size and cost of the system by examining the critical dimensional requirements of the diffuser. Consequently,an extensive CFD analysis has been carried out for a range of diffuser configurations by simulating the supersonic flow through the arrangement including the laser cavity driven by a bank of converging – diverging nozzles and the diffuser. The numerical investigations with 3D-RANS code are carried out to capture the flow patterns through diffusers past the cavity that has multiple supersonic jet interactions with shocks leading to complex flow pattern. Varying length of the diffuser plates is made to be the basic parameter of the study. The analysis reveals that the pressure recovery pattern during the flow through the diffuser from the simulation, being critical for the performance of the laser device shows its dependence on the diffuser length is weaker beyond a critical lower limit and this evaluation of this limit would provide a design guideline for a more efficient system configuration.The observation based on the parametric study shows that the pressure recovery transients in the near vicinity of the cavity is not affected for the reduction in the length of the diffuser plates up to its 10% of the initial size, indicating the design in the first configuration that was tested experimentally has a large factor of margin. The flow stability in the laser cavity is found to be unaffected since a strong and stable shock is located at the leading edge of the diffuser plates while the downstream shock and flow patterns are changed, as one would expect. Results of the study for the different lengths of diffusers in the range of 10% to its full length are presented, keeping the experimentally tested configuration used in the earlier study [1] as the reference length. The conclusions drawn from the analysis is found to be of significance since it provides new design considerations based on the understanding of the intricacies of the flow, allowing for a hardware optimization that can lead to substantial size reduction of the device with no loss of performance.

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This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.

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On introduit une nouvelle classe de schémas de renforcement des automates d'apprentissage utilisant les estimations des caractéristiques aléatoires de l'environnement. On montre que les algorithmes convergent en probabilité vers le choix optimal des actions. On présente les résultats de simulation et on suggère des applications à un environnement à plusieurs apprentissages

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Heat and mass transfer studies in a calandria based reactor is quite complex both due to geometry and due to the complex mixing flow. It is challenging to devise optimum operating conditions with efficient but safe working range for such a complex configuration. Numerical study known to be very effective is taken up for investigation. In the present study a 3D RANS code with turbulence model has been used to compute the flow fields and to get the heat transfer characteristics to understand certain design parameters of engineering importance. The angle of injection and of the coolant liquid has a large effect on the heat transfer within the reactor.