From the Publisher: Here's a cutting-edge resource that brings you up-to-date with all the recent advances in computational electromagnetics. You get the most-current information available on the multilevel fast m...
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ISBN:
(纸本)1580531520
From the Publisher: Here's a cutting-edge resource that brings you up-to-date with all the recent advances in computational electromagnetics. You get the most-current information available on the multilevel fast multipole algorithm in both the time and frequency domains, as well as the latest developments in fast algorithms for low frequencies and specialized structures, such as the planar and layered media. These algorithms solve large electromagnetics problems with shorter turn around time, using less computer memory. Complex problems that once required a supercomputer to solve, can now be solved on a workstation or personal computer with the innovative methods taught in this resource. The book introduces you to new advances in the perfectly matched layer absorbing boundary conditions, and offers you a thorough understanding of error analysis of numerical methods, fast-forward and inverse solvers for inverse problems, hybridization in computational electromagnetics, and asymptotic waveform evaluation.
The computer aided collimation gamma camera is aimed at breaking down the resolution sensitivity trade-off of the conventional parallel hole collimator. It uses larger and longer holes, having an added linear movement...
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The computer aided collimation gamma camera is aimed at breaking down the resolution sensitivity trade-off of the conventional parallel hole collimator. It uses larger and longer holes, having an added linear movement at the acquisition sequence. A dedicated algolithm including shift and sum, deconvolution. parabolic filtering and rotation is described. Examples of reconstruction are given. This work shows that a simple and fast algorithm, based on a diagonal dominant approximation of the problem carl be derived. Its gives a practical solution to the CACAO reconstruction problem.
This paper proposes the recurrent learning algorithm for designing thr controllers of continuous dynamical systems in the optimal control problems. The designed controllers are in the form of unfolded recurrent neural...
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This paper proposes the recurrent learning algorithm for designing thr controllers of continuous dynamical systems in the optimal control problems. The designed controllers are in the form of unfolded recurrent neural networks embedded with physical laws coming from the classical control techniques. The proposed learning algorithm is characterized by its double-forward-recurrent-loops structure for solving both the temporal recurrent and the structure recurrent problems. The first problem is resulted from the nature of general optimal control problems, where the objective functions are often related to (evaluated at) some specific (instead of all) time steps or system states only, causing missing Learning signals at some time steps or system states. The second problem is due to the high-order discretization of the continuous systems bg the Runge-Kutta method that we perform to increase the control accuracy. This discretization transforms the system into several identical subnetworks interconnected together, like a recurrent neural network expanded in the time axis. Two recurrent learning algorithms with different convergence properties are derived;the first- and second-order learning algorithms. The computations of both algorithms are local and performed efficiently as network signal propagation. We also propose two new nonlinear controller structures for two specific control problems:1) two-dimensional (2-D) guidance problem and 2) optimal PI control problem. Under the training of the proposed recurrent learning algorithms, these two controllers can be easily tuned to be suboptimal for given objective functions. Extensive computer simulations have shown the optimization and generalization abilities of the controllers designed bg the proposed learning scheme.
Data-accumulating algorithms (d-algorithms for short), extensively studied in [12], work on an input considered as a virtually endless stream. The computation terminates when all the currently arrived data have been p...
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Data-accumulating algorithms (d-algorithms for short), extensively studied in [12], work on an input considered as a virtually endless stream. The computation terminates when all the currently arrived data have been processed before another datum arrives. In this paper a finer characterization of the class of d-algorithms is given, and it is shown that this class is identical to the class of on-line algorithms under a proper definition of the latter. The parallel implementation of d-algorithms is then investigated. It is found that, in general, the speedup achieved through parallelism can be made arbitrarily large for almost any such algorithm. On the other hand, we prove that for d-algorithms whose static counterparts manifest only unitary speedup, no improvement is possible through parallel implementation.
A control scheme was developed for the automation of toluene removal in a cyclical bioreactor. Toluene was added to the self-cycling fermenter by diffusion across a silicone membrane. Transient dissolved oxygen, carbo...
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A control scheme was developed for the automation of toluene removal in a cyclical bioreactor. Toluene was added to the self-cycling fermenter by diffusion across a silicone membrane. Transient dissolved oxygen, carbon dioxide evolution, and oxidation-reduction potential (ORP) were screened as potential control variables. Through experimentation, ORP was deemed most effective. Control algorithms based on realtime estimates of the first and second derivatives of the ORP signal were tested. Although both approaches resulted in stable operation of the reactor, average toluene removal efficiencies of 95% were realized when control was based on the second derivative. This was significantly higher than the 77% efficiencies obtained when the control scheme centered on the first derivative of the transient ORP signal. The system developed was self-regulating, ensuring that a high toluene removal rate, on the order of 1.1 g h(-1), was maintained from cycle to cycle.
The theory of unit memory repetitive processes is used to investigate local convergence and stability properties of algorithms fbr the solution of discrete optimal control problems. in particular, the properties are a...
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The theory of unit memory repetitive processes is used to investigate local convergence and stability properties of algorithms fbr the solution of discrete optimal control problems. in particular, the properties are addressed of a method for finding the correct solution of an optimal control problem where the model used for optimisation is different from reality. Limit profile and stability concepts of unit memory linear repetitive process theory are employed to demonstrate optimality and to obtain necessary and sufficient conditions for convergence. Two main stability theorems are obtained from different approaches and their equivalence is proved: The theoretical results are verified through simulation and numerical analysis, and it is demonstrated that repetitive process theory provides a useful tool for the analysis of iterative algorithms for the solution of dynamic optimal control problems.
This paper describes an experimental investigation of adaptive control algorithms applied to aeroacoustic instabilities. The study is carried out on a cold how experimental rig, designed to reproduce the essential fea...
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This paper describes an experimental investigation of adaptive control algorithms applied to aeroacoustic instabilities. The study is carried out on a cold how experimental rig, designed to reproduce the essential features of acoustically coupled vortex shedding. This mechanism is the source of thrust oscillations in large segmented solid rocket motors. It is also found in a wide variety of combustion instabilities. Two adaptive control strategies are investigated and selected experimental results are reported. These results show the feasibility of control. The effect of the controller on the instability mechanism is analyzed and improvements to the control strategy are proposed. (C) 2000 Academic Press.
The article highlights the success of pair programming based on a 'pair programming survey,' in context of Robert L. Fulgham's essay titled 'All I Really Need to Know I Learned in Kindergarten.' Th...
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The article highlights the success of pair programming based on a 'pair programming survey,' in context of Robert L. Fulgham's essay titled 'All I Really Need to Know I Learned in Kindergarten.' The article reports that when it comes to programming practices, studies show two heads are almost better than one. Pair programming is a practice in which two programmers work side-by-side at one computer, continuously collaborating on the same design, algorithm, code, or test. Anecdotal and initial statistical evidence indicates pair programming is highly beneficial, which is also attributed by Extreme programming. In the case of transition to pair programming from solitary programming, to avoid hesitation and to work successfully, some principles referred to are sharing of the work, sharing of key board typing and continuous analysis, avoiding negative thoughts, leaving ego etc. As a final thought, the article points out that making transition to pair programming involves breaking down some personal barriers. The success lies in hands of them in understanding the value of intercommunication skills, in confidently sharing the work and accepting the ownership of the partner's work. INSET: All I Really Need to Know I Learned in Kindergarten..
Hierarchical decompositions of graphs are interesting for algorithmic purposes, There are several types of hierarchical decompositions. Tree decompositions are the best known ones. On graphs of tree-width at most k, i...
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Hierarchical decompositions of graphs are interesting for algorithmic purposes, There are several types of hierarchical decompositions. Tree decompositions are the best known ones. On graphs of tree-width at most k, i.e., that have tree decompositions of width at most k, where k is fixed, every decision or optimization problem expressible in monadic second-order logic has a linear algorithm. We prove that this is also the case for graphs of clique-width at most k, where this complexity measure is associated with hierarchical decompositions of another type, and where logical formulas are no longer allowed to use edge set quantifications. We develop applications to several classes of graphs that include cographs and are, like cographs, defined by forbidding subgraphs with "too many" induced paths with four vertices.
In this paper the continuous repetitive controller is designed to reject the periodic disturbance and track periodic reference signal for rotating mechanisms. The stability condition is derived and the system is shown...
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In this paper the continuous repetitive controller is designed to reject the periodic disturbance and track periodic reference signal for rotating mechanisms. The stability condition is derived and the system is shown to satisfy the internal model principle, which assures that the control output tracks a class of reference commands without a steady-state error if the generator for the references is included in the stable closed-loop system. In order to satisfy this principle, a periodic signal generator is placed in the feedback loop to reject periodic disturbances and to track repetitive reference signals. The best control method will be chosen from the viewpoint of suitability in the practical problems. Finally, the repetitive control algorithm is applied to both the slider-crank and quick-return mechanisms. (C) 2000 Elsevier Science Ltd. All rights reserved.
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