It is well known that conventional control theories are widely suited for applications where the processes can be reasonably described in advance. However, when the plant's dynamics are hard to characterize precis...
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It is well known that conventional control theories are widely suited for applications where the processes can be reasonably described in advance. However, when the plant's dynamics are hard to characterize precisely or are subject to environmental uncertainties, one may encounter difficulties in applying the conventional controller design methodologies. Despite the difficulty in achieving high control performance, the fine tuning of controller parameters is a tedious task that always requires experts with knowledge in both control theory and process information. Nowadays, more and more studies have focused on the development of adaptive control algorithms that can be directly applied to complex processes whose dynamics are poorly modeled and/or have severe nonlinearities. In this context, the design of a Model-Free Learning Adaptive Control (MFLAC) based oil pseudogradient concepts and optimization procedure by a Particle Swarm Optimization (PSO) approach using constriction coefficient and Henon chaotic sequences (CPSOH) is presented in this paper. PSO is a stochastic global optimization technique inspired by social behavior of bird flocking. The PSO models the exploration of a problem space by a population of particles. Each particle in PSO has a randomized velocity associated to it, which moves through the space of the problem. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed CPSOH introduces chaos mapping which introduces some flexibility in particle movements in each iteration. The chaotic sequences allow also explorations at early stages and exploitations at later stages during the search procedure of CPSOH. Motivation for application of CPSOH approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the plant has different gains for the operational range when designed by trial-and-error by User. Numerical results of the MFLAC with CPSOH tuni
We estimate parameters in the context of a discrete-time hidden Markov model with two latent states and two observed states through a Bayesian approach. We provide a Gibbs sampling algorithm for longitudinal data that...
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We estimate parameters in the context of a discrete-time hidden Markov model with two latent states and two observed states through a Bayesian approach. We provide a Gibbs sampling algorithm for longitudinal data that ensures parameter identifiability. We examine two approaches to start the algorithm for estimation. The first approach generates the initial latent data from transition probability estimates under the false assumption of perfect classification. The second approach requires an initial guess of the classification probabilities and obtains bias-adjusted approximated estimators of the latent transition probabilities based on the observed data. These probabilities are then used to generate the initial latent data set based on the observed data set. Both approaches are illustrated on medical data and the performance of estimates is examined through simulation studies. The approach using bias-adjusted estimators is the best choice of the two options, since it generates a plausible initial latent data set. Our situation is particularly applicable to diagnostic testing, where specifying the range of plausible classification rates may be more feasible than specifying initial values for transition probabilities. (C) 2009 Elsevier B.V. All rights reserved.
The article discusses the difficulties of automating translation services, and the need for human interaction in order to provide acceptable results. As of 2009, even the best automated translator could not produce pu...
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The article discusses the difficulties of automating translation services, and the need for human interaction in order to provide acceptable results. As of 2009, even the best automated translator could not produce publication-quality translation. computer-assisted translation systems, such as translation memory systems, are discussed, and the development and testing of the TransType2 (TT2) system is described. TT2 allows for machine translation to be corrected by a user, and generates improved suggestions based on those corrections.
作者：
Argon, CenkRTP Off
Seagate Technol Coding & Signal Proc Grp Durham NC 27703 USA
Optical orthogonal code (OCC) sequences are assigned to optical code-division multiple-access (OCDMA) network users, who are able to transmit data asynchronously. In this work, we propose a semi-random COC design tech...
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Optical orthogonal code (OCC) sequences are assigned to optical code-division multiple-access (OCDMA) network users, who are able to transmit data asynchronously. In this work, we propose a semi-random COC design technique based on extended sets, where the input parameters are the sequence weight, number of sequences (i.e., users), and a target sequence length. The design method under consideration is able to converge to the desired short OCC lengths given the number of iterations during the execution of the algorithm is sufficiently large. (C) 2008 Elsevier B.V. All rights reserved.
The Levenberg-Marquardt learning algorithm is applied for training a multilayer perception with three hidden layer each with ten neurons in order to carefully map the structure of chaotic time series such as Mackey-Gl...
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The Levenberg-Marquardt learning algorithm is applied for training a multilayer perception with three hidden layer each with ten neurons in order to carefully map the structure of chaotic time series such as Mackey-Glass time series. First the MLP network is trained with 1000 data, and then it is tested with next 500 data. After that the trained and tested network is applied for long-term prediction of next 120 data which come after test data. The prediction is such a way that, the first inputs to network for prediction are the four last data of test data, then the predicted value is shifted to the regression vector which is the input to the network, then after first four-step of prediction, the input regression vector to network is fully predicted values and in continue, each predicted data is shifted to input vector for subsequent prediction. (C) 2008 Elsevier Ltd. All rights reserved.
A novel and powerful gait recognition algorithm is proposed. The method of procrustes shape analysis is used to produce procrustes mean shape (PMS) as a compressed representation of gait sequence. PMS is regarded as t...
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A novel and powerful gait recognition algorithm is proposed. The method of procrustes shape analysis is used to produce procrustes mean shape (PMS) as a compressed representation of gait sequence. PMS is regarded as the gait signature for classification. Quite different and novel, instead of using the procrustes mean shape distance as a similarity measure, a novel shape descriptor, shape context, to measure the similarity between two PMSs is introduced. Experimental results show that the proposed algorithm outperforms other approaches in terms of recognition accuracy.
I investigated a multi-behavioral game between predators and prey that integrated both pre-encounter and post-encounter behaviors. These behaviors included landscape-scale movements by predators and prey, a type of pr...
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I investigated a multi-behavioral game between predators and prey that integrated both pre-encounter and post-encounter behaviors. These behaviors included landscape-scale movements by predators and prey, a type of prey vigilance that increases immediately after an encounter and then decays over time ('ratcheting vigilance'), and predator management of prey vigilance. I analyzed the game using a computer-based evolutionary algorithm. This algorithm embedded an individual-based model of ecological interactions within a dynamic adaptive process of mutation and selection. I investigated how evolutionarily stable strategies (ESS) varied with the predators' learning ability, killing efficiency, density and rate of movement. I found that when predators learn prey location, random prey movement can be an ESS. Increased predator killing efficiency reduced prey movement, but only if the rate of predator movement was low. Predators countered ratcheting vigilance by delaying their follow-up attacks;however, this delay was reduced in the presence of additional predators. The interdependence of pre-and post-encounter behaviors revealed by the evolutionary algorithm suggests an intricate co-evolution of multi-behavioral predator-prey behavioral strategies.
We obtain dipolar couplings via a novel application of evolutionary algorithms solving for multiple spin-systems simultaneously and automatically from the NMR spectra of several solutes in several nematic and smectic ...
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We obtain dipolar couplings via a novel application of evolutionary algorithms solving for multiple spin-systems simultaneously and automatically from the NMR spectra of several solutes in several nematic and smectic liquid crystal solvents. The order parameters obtained from the dipolar couplings are used to test a novel Hamiltonian that includes two Maier-Saupe nematic terms plus Kobayashi-McMillan smectic A terms. It is shown that this Hamiltonian can rationalize the NMR experiments with physically reasonable smectic order parameters and Hamiltonian prefactors. (C) 2009 Elsevier B.V. All rights reserved.
A novel multimodal biometric recognition algorithm based on a complex common vector (CCV) is proposed. The CCV generalises the common vector method for the complex field to perform feature fusion and classification. T...
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A novel multimodal biometric recognition algorithm based on a complex common vector (CCV) is proposed. The CCV generalises the common vector method for the complex field to perform feature fusion and classification. Theoretical analysis proves that the CCV could produce a unique common vector for every fusion feature in a given class. The iris and the face are used as two distinct biometric modals to test the algorithm. Experimental results show that the proposed algorithm achieves much better performance than other conventional multimodal biometric algorithms.
In this paper we show that all supergravity billiards corresponding to sigma-models on any U/H non-compact-symmetric space and obtained by compactifying supergravity to D = 3 admit a closed form general integral depen...
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In this paper we show that all supergravity billiards corresponding to sigma-models on any U/H non-compact-symmetric space and obtained by compactifying supergravity to D = 3 admit a closed form general integral depending analytically on a complete set of integration constants. The key point in establishing the integration algorithm is provided by an upper triangular embedding of the solvable Lie algebra associated with U/H into sl(N, R) which is guaranteed to exist for all non-compact symmetric spaces and also for homogeneous special geometries non-corresponding to symmetric spaces. In this context we establish a remarkable relation between the end-points of the time-flow and the properties of the Weyl group. The asymptotic states of the developing Universe are in one-to-one correspondence with the elements of the Weyl group which is a property of the Tits-Satake universality classes and not of their single representatives. Furthermore the Weyl group admits a natural ordering in terms of l(T), the number of reflections with respect to the simple roots. The direction of time flows is always front the minimal accessible value of l(T) to the maximum one or vice versa. (C) 2009 Elsevier B.V. All rights reserved.
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