For a large number of degrees of freedom and/or large dimension systems, non-linear model based predictive control algorithms based on dual mode control can become intractable. This paper proposes an alternative which...
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For a large number of degrees of freedom and/or large dimension systems, non-linear model based predictive control algorithms based on dual mode control can become intractable. This paper proposes an alternative which deploys the closed-loop paradigm that has proved to be very effective for the case of linear time-varying or uncertain systems. The various attributes and computational advantages of the approach are shown to carry over to the non-linear case.
A methodology is presented to construct an expectation robust algorithm for principal component regression. The presented method is the first multivariate regression method which can resist outliers and which can cope...
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A methodology is presented to construct an expectation robust algorithm for principal component regression. The presented method is the first multivariate regression method which can resist outliers and which can cope with missing elements in the data simultaneously. Simulations and an example illustrate the good statistical properties of the method. (C) 2009 Elsevier B.V. All rights reserved.
Standard pattern-matching methods used for deep packet inspection and network security can be evaded by means of TCP and IP fragmentation. To detect such attacks, intrusion detection systems must reassemble packets be...
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Standard pattern-matching methods used for deep packet inspection and network security can be evaded by means of TCP and IP fragmentation. To detect such attacks, intrusion detection systems must reassemble packets before applying matching algorithms, thus requiring a large amount of memory and time to respond to the threat. In the literature, only a few efforts proposed a method to detect evasion attacks at high speed without reassembly. The aim of this article is to introduce an efficient system for anti-evasion that can be implemented in real devices. It is based on counting Bloom filters and exploits their capabilities to quickly update the string set and deal with partial signatures. In this way, the detection of attacks and almost all of the traffic processing is performed in the fast data path, thus improving the scalability of intrusion detection systems.
Symbolic languages are increasingly being used in the analysis and implementation of control algorithms. Many of these control procedures involve some type of differentiation or Jacobian formulation. Automatic differe...
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Symbolic languages are increasingly being used in the analysis and implementation of control algorithms. Many of these control procedures involve some type of differentiation or Jacobian formulation. Automatic differentiation provides an alternative means of computing this information which is rarely considered in the control literature. This correspondence will discuss the use of automatic differentiation. Numerical results comparing the use of automatic differentiation and symbolic approaches in the context of a particular nonlinear control system will be given.
Novel dual band-notched monopole antenna is proposed in this paper, which is designed by an improved particle swarm optimization algorithm linked with IIFSS. By etching a one order Minkowski fractal slot on the radiat...
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Novel dual band-notched monopole antenna is proposed in this paper, which is designed by an improved particle swarm optimization algorithm linked with IIFSS. By etching a one order Minkowski fractal slot on the radiating patch and introducing a semioctagon-shaped resonator on the back plane, excellent dual notched frequency bands characteristics of the proposed antenna are achieved. The antenna satisfies the VSWR requirement of less than 2.0 in the frequency band between 2.87 and 12 GHz, except two frequency stop-bands of 3.2-4.01 GHz and 5.55-6.1 GHz. The effects of the key parameters on the dual notched bands are also analyzed. A fairly stable radiation pattern over the frequency range of interest can be obtained. Moreover, measured group delay and transmission characteristics indicate that the antenna has good pulse handling capabilities.
The reproducing kernel algorithm is described in order to obtain the efficient analytical-numerical solutions to nonlinear systems of two point, second-order periodic boundary value problems with finitely many singula...
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The reproducing kernel algorithm is described in order to obtain the efficient analytical-numerical solutions to nonlinear systems of two point, second-order periodic boundary value problems with finitely many singularities. The analytical-numerical solutions are obtained in the form of an infinite convergent series for appropriate periodic boundary conditions in the space W-2(3)[0, 1], whilst two smooth reproducing kernel functions are used throughout the evolution of the algorithm to obtain the required nodal values. An efficient computational algorithm is provided to guarantee the procedure and to confirm the performance of the proposed approach. The main characteristic feature of the utilized algorithm is that the global approximation can be established on the whole solution domain, in contrast with other numerical methods like onestep and multistep methods, and the convergence is uniform. Two numerical experiments are carried out to verify the mathematical results, whereas the theoretical statements for the solutions are supported by the results of numerical experiments. Our results reveal that the present algorithm is a very effective and straightforward way of formulating the analytical-numerical solutions for such nonlinear periodic singular systems.
It is often hard to write programs that are efficient yet reusable. For example, an efficient implementation of Gaussian elimination should be specialized to the structure and known static properties of the input matr...
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It is often hard to write programs that are efficient yet reusable. For example, an efficient implementation of Gaussian elimination should be specialized to the structure and known static properties of the input matrix. The most profitable optimizations, such as choosing the best pivoting or memoization, cannot be expected of even an advanced compiler because they are specific to the domain, but expressing these optimizations directly makes for ungainly source code. Instead, a promising and popular way to reconcile efficiency with reusability is for a domain expert to write code generators. Two pillars of this approach are types and effects. Typed multilevel languages such as MetaOCaml ensure safety and early error reporting: a well-typed code generator neither goes wrong nor generates code that goes wrong. Side effects such as state and control ease correctness and expressivity: An effectful generator can resemble the textbook presentation of an algorithm, as is familiar to domain experts, yet insert let for memoization and if for bounds checking, as is necessary for efficiency. Together, types and effects enable structuring code generators as compositions of modules with well-defined interfaces, and hence scaling to large programs. However, blindly adding effects renders multilevel types unsound. We introduce the first multilevel calculus with control effects and a sound type system. We give small-step operational semantics as well as a one-pass continuation-passing-style translation. For soundness, our calculus restricts the code generator's effects to the scope of generated binders. Even with this restriction, we can finally write efficient code generators for dynamic programming and numerical methods in direct style, like in algorithm textbooks, rather than in continuation-passing or monadic style.
We consider M (greater-than-or-equal-to 2) transmitting stations sending packets to a single receiver over a slotted time-multiplexed link. For each phase consisting of T consecutive slots, the receiver dynamically al...
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We consider M (greater-than-or-equal-to 2) transmitting stations sending packets to a single receiver over a slotted time-multiplexed link. For each phase consisting of T consecutive slots, the receiver dynamically allocates these slots among the M transmitters. The cost per slot for holding a packet may vary among the transmitters, and may be interpreted in terms of multiple classes of messages. Our objective is to characterize policies that minimize the discounted and long-term average costs due to holding packets at the M stations, based on delayed information on the numbers of packets being held at the respective transmitters. We derive properties of optimal (discounted) policies that reduce the computational complexity of the optimal How control algorithm. For M = 2, we show that the minimal total cost is convex and submodular in the state, and we prove the following properties of optimal policies: 1) when the state at transmitter i increases by unity while the state at the other transmitter j is fixed, the optimal allocation is either unchanged, or increases by one at transmitter i and decreases hy one at transmitter j;and 2) the optimal policy is of the threshold type. We use these properties to show that the optimization reduces to the calculation of optimal allocations for a finite number of states. In addition, for each such state (excluding the origin), property 1) implies a significant reduction in the computation of optimal allocations. As an application, we further characterize optimal policies when the message generation at the transmitter of higher priority is stochastically larger than the message generation at the other. Under additional restrictions on the average arrival rate and the second moment of the number of arrivals per slot, similar results are derived for optimal policies with time-average costs.
A field-programmable gate array (FPGA)-based recurrent wavelet neural network (RWNN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM). First, the structure and operating pri...
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A field-programmable gate array (FPGA)-based recurrent wavelet neural network (RWNN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM). First, the structure and operating principles of the LUSM are introduced. Since the dynamic characteristics and motor parameters of the LUSM are non-linear and time-varying, an RWNN controller is designed to improve the control performance for the precision tracking of various reference trajectories. The network structure and its on-line learning algorithm using delta adaptation law of the RWNN are described in detail. Moreover, the connective weights, translations and dilations of the RWNN are trained on-line. Furthermore, to guarantee the convergence of the tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RWNN. In addition, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. Finally, the effectiveness of the proposed control system is verified by some experimental results.
Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural network dedicated to an extremely fast learning. However, the performance of ELM in structural impact localization is u...
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Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural network dedicated to an extremely fast learning. However, the performance of ELM in structural impact localization is unknown yet. In this paper, a comparison study of ELM with least squares support vector machine (LSSVM) is presented for the application on impact localization of a plate structure with surface-mounted piezoelectric sensors. Both basic and kernel-based ELM regression models have been developed for the location prediction. Comparative studies of the basic ELM, kernel-based ELM, and LSSVM models are carried out. Results show that the kernel-based ELM requires the shortest learning time and it is capable of producing suboptimal localization accuracy among the three models. Hence, ELM paves a promising way in structural impact detection.
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