Width and wavelength-tunable optical pulse train generation based on four-wave mixing in highly nonlinear photonic crystal fiber

Author name used in this publication: M. S. DemokanAuthor name used in this publication: H. Y. Tam2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

B-spline recurrent neural network and its application to modelling of non-linear dynamic systems

A new recurrent neural network based on B-spline function approximation is presented. The network can be easily trained and its training converges more quickly than that for other recurrent neural networks. Moreover, an adaptive weight updating algorithm for the recurrent network is proposed. It can speed up the training process of the network greatly and its learning speed is more quickly than existing algorithms, e.g., back-propagation algorithm. Examples are presented comparing the adaptive weight updating algorithm and the constant learning rate method, and illustrating its application to modelling of nonlinear dynamic system.published_or_final_versio

Mode couplings in superstructure fiber Bragg gratings

Author name used in this publication: A-Ping ZhangAuthor name used in this publication: Xiao-Ming Tao2001-2002 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Stable and broad bandwidth multiwavelength fiber ring laser incorporating a highly nonlinear photonic crystal fiber

Author name used in this publication: M. S. DemokanAuthor name used in this publication: H. Y. Tam2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

State estimation with measurement error compensation using neural network

For a system with redundant sensors, the estimated state from the Kalman filter is biased if sensor mounting error existed. To remove this bias, the mounting errors must be compensated first before using the Kalman filter. It is shown that only the projection part of the sensors errors in the measurement space needs to be compensated. If the state of a system is unavailable, a neurofuzzy network can be used to estimate the compensation term. This method is simpler, as it does not require a model for the errors as that proposed in [2]. A sub-optimal Kalman filter with measurement compensation that restrains each row of the Kalman gain matrix to be in the measurement space is also derived. An example is presented to illustrate the performance of the proposed methods.published_or_final_versio

Game-Theoretic Approach to Tourism Supply Chain Coordination under Demand Uncertainty For Package Holidays

Demand uncertainty is one of the most significant characteristics of the tourism industry. In a typical tourism supply chain (TSC) for package holidays, multiple tour operators reserve rooms from a hotel chain in advance according to their demand predictions. Discrepancies between demand predictions and actual demand lead to shortages or unused room reservations, which inevitably leads to reduced profits for the tour operators concerned. This article examines different TSC coordination strategies to determine how they can be used to help alleviate such negative effects. A game-theoretic approach is used to analyze the different coordination relationships between TSC players. Two coordination programs are discussed. The first is a horizontal coordination program in which tour operators exchange shortages or unused reservations with each other. The second is a vertical coordination program in which tour operators trade shortages or unused reservations with hotel chains. Game models are established and analyzed for the two coordination strategies and uncoordinated conditions, respectively. The analytical results suggest that both coordination strategies can be used to reduce the negative impacts of the demand uncertainty. The results also show that the horizontal coordination is preferred to the vertical coordination when the competition among tour operators is fierce.published_or_final_versio

Nonlinear observer design with unknown nonlinearity via B-spline network approach

A novel approach is proposed to the state estimation of a class of nonlinear systems which consist of known linear part and unknown nonlinear part. A linear observer is first designed then a nonlinear compensation term in the nonlinear observer is determined using the proposed “deconvolution method”. The B-spline neural network is used to model the estimated compensation term. Three simulation examples are given to compare the effectiveness of the proposed approach and some analytical approaches.published_or_final_versio

Realizing serine/threonine ligation: scope and limitations and mechanistic implication thereof

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A new multiple regression approach for the construction of genetic regulatory networks

Objective: Re-construction of a genetic regulatory network from a given time-series gene expression data is an important research topic in systems biology. One of the main difficulties in building a genetic regulatory network lies in the fact that practical data set has a huge number of genes vs. a small number of sampling time points. In this paper, we propose a new linear regression model that may overcome this difficulty for uncovering the regulatory relationship in a genetic network. Methods: The proposed multiple regression model makes use of the scale-free property of a real biological network. In particular, a filter is constructed by using this scale-free property and some appropriate statistical tests to remove redundant interactions among the genes. A model is then constructed by minimizing the gap between the observed and the predicted data. Results: Numerical examples based on yeast gene expression data are given to demonstrate that the proposed model fits the practical data very well. Some interesting properties of the genes and the underlying network are also observed. Conclusions: In conclusion, we propose a new multiple regression model based on the scale-free property of real biological network for genetic regulatory network inference. Numerical results using yeast cell cycle gene expression dataset show the effectiveness of our method. We expect that the proposed method can be widely used for genetic network inference using high-throughput gene expression data from various species for systems biology discovery. © 2009 Elsevier B.V.postprin

Object-Based Rendering and 3D reconstruction Using a Moveable Image-Based System

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