944 resultados para CHARGE CONTROL MODEL


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We consider a dense, ad hoc wireless network confined to a small region, such that direct communication is possible between any pair of nodes. The physical communication model is that a receiver decodes the signal from a single transmitter, while treating all other signals as interference. Data packets are sent between source-destination pairs by multihop relaying. We assume that nodes self-organise into a multihop network such that all hops are of length d meters, where d is a design parameter. There is a contention based multiaccess scheme, and it is assumed that every node always has data to send, either originated from it or a transit packet (saturation assumption). In this scenario, we seek to maximize a measure of the transport capacity of the network (measured in bit-meters per second) over power controls (in a fading environment) and over the hop distance d, subject to an average power constraint. We first argue that for a dense collection of nodes confined to a small region, single cell operation is efficient for single user decoding transceivers. Then, operating the dense ad hoc network (described above) as a single cell, we study the optimal hop length and power control that maximizes the transport capacity for a given network power constraint. More specifically, for a fading channel and for a fixed transmission time strategy (akin to the IEEE 802.11 TXOP), we find that there exists an intrinsic aggregate bit rate (Thetaopt bits per second, depending on the contention mechanism and the channel fading characteristics) carried by the network, when operating at the optimal hop length and power control. The optimal transport capacity is of the form dopt(Pmacrt) x Thetaopt with dopt scaling as Pmacrt 1 /eta, where Pmacrt is the available time average transmit power and eta is the path loss exponent. Under certain conditions on the fading distribution, we then pro- - vide a simple characterisation of the optimal operating point.

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A resource interaction based game theoretical model for military conflicts is presented in this paper. The model includes both the spatial decision capability of adversaries (decision regarding movement and subsequent distribution of resources) as well as their temporal decision capability (decision regarding level of allocation of resources for conflict with adversary’s resources). Attrition is decided at present by simple deterministic models. An additional feature of this model is the inclusion of the possibility of a given resource interacting with several resources of the adversary.The decisions of the adversaries is determined by solving for the equilibrium Nash strategies given that the objectives of the adversaries may not be in direct conflict. Examples are given to show the applicability of these models and solution concepts.

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Access control is an important component in the security of communication systems. While cryptography has rightfully been a significant component in the design of large scale communication systems, its relation to access control, especially its complementarity, has not often been brought out in full. With the wide availability of SELinux, a comprehensive model of access control has all the more become important. In many large scale systems, access control and trust management have become important components in the design. In survivable systems, models of group communication systems may have to be integrated with access control models. In this paper, we discuss the problem of integrating various formalisms often encountered in large scale communication systems, especially in connection with dynamic access control policies as well as trust management

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In arriving at the ideal filter transfer function for an active noise control system in a duct, the effect of the auxiliary sources (generally loudspeakers) on the waves generated by the primary source has invariably been neglected in the existing literature, implying a rigid wall or infinite impedance. The present paper presents a fairly general analysis of a linear one-dimensional noise control system by means of block diagrams and transfer functions. It takes into account the passive as well as active role of a terminal primary source, wall-mounted auxiliary source, open duct radiation impedance, and the effects of mean flow and damping. It is proved that the pressure generated by a source against a load impedance can be looked upon as a sum of two pressure waves, one generated by the source against an anechoic termination and the other by reflecting the rearward wave (incident on the source) off the passive source impedance. Application of this concept is illustrated for both the types of sources. A concise closed-form expression for the ideal filter transfer function is thus derived and discussed. Finally, the dynamics of an adaptive noise control system is discussed briefly, relating its standing-wave variables and transfer functions with those of the progressive-wave model presented here.

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The specific objective of this paper is to develop direct digital control strategies for an ammonia reactor using quadratic regulator theory and compare the performance of the resultant control system with that under conventional PID regulators. The controller design studies are based on a ninth order state-space model obtained from the exact nonlinear distributed model using linearization and lumping approximations. The evaluation of these controllers with reference to their disturbance rejection capabilities and transient response characteristics, is carried out using hybrid computer simulation.

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A torque control scheme, based on a direct torque control (DTC) algorithm using a 12-sided polygonal voltage space vector, is proposed for a variable speed control of an open-end induction motor drive. The conventional DTC scheme uses a stator flux vector for the sector identification and then the switching vector to control stator flux and torque. However, the proposed DTC scheme selects switching vectors based on the sector information of the estimated fundamental stator voltage vector and its relative position with respect to the stator flux vector. The fundamental stator voltage estimation is based on the steady-state model of IM and the synchronous frequency of operation is derived from the computed stator flux using a low-pass filter technique. The proposed DTC scheme utilizes the exact positions of the fundamental stator voltage vector and stator flux vector to select the optimal switching vector for fast control of torque with small variation of stator flux within the hysteresis band. The present DTC scheme allows full load torque control with fast transient response to very low speeds of operation, with reduced switching frequency variation. Extensive experimental results are presented to show the fast torque control for speed of operation from zero to rated.

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A nonlinear suboptimal guidance law is presented in this paper for successful interception of ground targets by air-launched missiles and guided munitions. The main feature of this guidance law is that it accurately satisfies terminal impact angle constraints in both azimuth as well as elevation simultaneously. In addition, it is capable of hitting the target with high accuracy as well as minimizing the lateral acceleration demand. The guidance law is synthesized using recently developed model predictive static programming (MPSP). Performance of the proposed MPSP guidance is demonstrated using three-dimensional (3-D) nonlinear engagement dynamics by considering stationary, moving, and maneuvering targets. Effectiveness of the proposed guidance has also been verified by considering first. order autopilot lag as well as assuming inaccurate information about target maneuvers. Multiple munitions engagement results are presented as well. Moreover, comparison studies with respect to an augmented proportional navigation guidance (which does not impose impact angle constraints) as well as an explicit linear optimal guidance (which imposes the same impact angle constraints in 3-D) lead to the conclusion that the proposed MPSP guidance is superior to both. A large number of randomized simulation studies show that it also has a larger capture region.

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In this paper we investigate the effect of terminal substituents on the dynamics of spin and charge transport in donor-acceptor substituted polyenes [D-(CH)(x)-A] chains, also known as push-pull polyenes. We employ a long-range correlated model Hamiltonian for the D-(CH)(x)-A system, and time-dependent density matrix renormalization group technique for time propagating the wave packet obtained by injecting a hole at a terminal site, in the ground state of the system. Our studies reveal that the end groups do not affect spin and charge velocities in any significant way, but change the amount of charge transported. We have compared these push-pull systems with donor-acceptor substituted polymethine imine (PMI), D-(CHN)(x)-A, systems in which besides electron affinities, the nature of p(z) orbitals in conjugation also alternate from site to site. We note that spin and charge dynamics in the PMIs are very different from that observed in the case of push-pull polyenes, and within the time scale of our studies, transport of spin and charge leads to the formation of a ``quasi-static'' state.

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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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A terrestrial biosphere model with dynamic vegetation capability, Integrated Biosphere Simulator (IBIS2), coupled to the NCAR Community Atmosphere Model (CAM2) is used to investigate the multiple climate-forest equilibrium states of the climate system. A 1000-year control simulation and another 1000-year land cover change simulation that consisted of global deforestation for 100 years followed by re-growth of forests for the subsequent 900 years were performed. After several centuries of interactive climate-vegetation dynamics, the land cover change simulation converged to essentially the same climate state as the control simulation. However, the climate system takes about a millennium to reach the control forest state. In the absence of deep ocean feedbacks in our model, the millennial time scale for converging to the original climate state is dictated by long time scales of the vegetation dynamics in the northern high latitudes. Our idealized modeling study suggests that the equilibrium state reached after complete global deforestation followed by re-growth of forests is unlikely to be distinguishable from the control climate. The real world, however, could have multiple climate-forest states since our modeling study is unlikely to have represented all the essential ecological processes (e. g. altered fire regimes, seed sources and seedling establishment dynamics) for the reestablishment of major biomes.

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Joint experimental and theoretical work is presented on two quadrupolar D-pi-A-pi-D chromophores characterized by the same bulky donor (D) group and two different central cores. The first chromophore, a newly synthesized species with a malononitrile-based acceptor (A) group, has a V-shaped structure that makes its absorption spectrum very broad, covering most of the visible region. The second chromophore has a squaraine-based core and therefore a linear structure, as also evinced from its absorption spectra. Both chromophores show an anomalous red shift of the absorption band upon increasing solvent polarity, a feature that is ascribed to the large, bulky structure of the moleCules. For these molecules, the basic description of polar solvation in terms of a uniform reaction field fails. Indeed, a simple extension of the model to account for two independent reaction fields associated with the two molecular arms quantitatively reproduces the observed linear absorption and fluorescence as well as fluorescence anisotropy spectra, fully rationalizing their nontrivial dependence on solvent polarity. The model derived from the analysis of linear spectra is adopted to predict nonlinear spectra and specifically hyper-Rayleigh scattering and two-photon absorption spectra. In polar solvents, the V-shaped chromophore is predicted to have a large HRS response in a wide spectral region (approximately 600-1300 nm). Anomalously large and largely solvent-dependent HRS responses for the linear chromophores are ascribed to symmetry lowering induced by polar solvation and amplified in this bulky system by the presence of two reaction fields.

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A modeling framework is presented in this paper, integrating hydrologic scenarios projected from a General Circulation Model (GCM) with a water quality simulation model to quantify the future expected risk. Statistical downscaling with a Canonical Correlation Analysis (CCA) is carried out to develop the future scenarios of hydro-climate variables starting with simulations provided by a GCM. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold quality level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) presented in an earlier study is then used to develop adaptive policies to address the projected water quality risks. Application of the proposed methodology is demonstrated with the case study of Tunga-Bhadra river in India. The results showed that the projected changes in the hydro-climate variables tend to diminish DO levels, thus increasing the future risk levels of LWQ. (C) 2012 Elsevier B.V. All rights reserved.