HKU Scholars Hubhttp://hub.hku.hkThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Wed, 21 Feb 2018 23:40:48 GMT2018-02-21T23:40:48Z503041- Detection of machine failure: Hidden Markov Model approachhttp://hdl.handle.net/10722/74486Title: Detection of machine failure: Hidden Markov Model approach
Authors: Tai, AH; Ching, WK; Chan, LY
Abstract: Hidden Markov Models (HMMs) are widely used in applied sciences and engineering. The potential applications in manufacturing industries have not yet been fully explored. In this paper, we propose to apply HMM to detect machine failure in process control. We propose models for both cases of indistinguishable production units and distinguishable production units. Numerical examples are given to illustrate the effectiveness of the proposed models. © 2008 Elsevier Ltd. All rights reserved.
Thu, 01 Jan 2009 00:00:00 GMThttp://hdl.handle.net/10722/744862009-01-01T00:00:00Z
- Modeling default data via an interactive hidden markov modelhttp://hdl.handle.net/10722/75122Title: Modeling default data via an interactive hidden markov model
Authors: Ching, WK; Siu, TK; Li, LM; Li, T; Li, WK
Abstract: In this paper, we first introduce the use of an interactive hidden Markov model (IHMM) for modeling and analyzing default data in a sector. Under the IHMM, transitions of the hidden risk states of the sector depend on the observed number of bonds in the sector that default in the current time period. This incorporates the feedback effect of the number of defaults on the transitions of the hidden risk states. This feature seems to be more realistic and does not enjoy by the traditional HMMs. We then develop a "dynamic" version of the binomial expansion technique (BET) modulated by the IHMM for modeling the occurrence of defaults of bonds issued by firms in the same sector. Under the BET modulated by the IHMM, the number of bonds defaulting in each time period follows a Markov-modulated binomial distribution with the probability of defaulting of each bond depending on the states of the IHMM, which represent the hidden risk states of the sector. Efficient method will be presented for estimating the model parameters in the BET modulated by the IHMM. We shall compare the hidden risk state process extracted from the IHMM-modulated BET with that extracted from the BET modulated by HMM in order to illustrate the significance of the feedback effect using real data. We shall also present the estimation results for the BET modulated by the IHMM and compare them with those for the BET modulated by the HMM. © Springer Science+Business Media, LLC. 2009.
Thu, 01 Jan 2009 00:00:00 GMThttp://hdl.handle.net/10722/751222009-01-01T00:00:00Z
- Iterative methods for queuing systems with batch arrivals and negative customershttp://hdl.handle.net/10722/75130Title: Iterative methods for queuing systems with batch arrivals and negative customers
Authors: Ching, WK
Abstract: In this paper, we are interested in solving the stationary probability distributions of Markovian queuing systems having batch arrivals and negative customers by using the Preconditioned Conjugate Gradient Squared (PCGS) method. The preconditioner is constructed by exploiting the near-Toeplitz structure of the generator matrix of the system. We proved that under some mild conditions the preconditioned linear systems have singular values clustered around one when the size of the queue tends to infinity. Numerical results indicated that the convergence rate of the proposed method is very fast.
Wed, 01 Jan 2003 00:00:00 GMThttp://hdl.handle.net/10722/751302003-01-01T00:00:00Z
- On the Use of Renewal Theory in Machine Replacement Modelshttp://hdl.handle.net/10722/75138Title: On the Use of Renewal Theory in Machine Replacement Models
Authors: Tai, AH; Ching, WK
Abstract: In this paper, we give examples on how the renewal theory can be applied in modeling machine replacement problem. We begin with a deterministic model to illustrate the concept of a machine cycle, then follow by a stochastic model with a general cost. We then compare two popular replacement policies: the quantitybased replacement policy and time-based replacement policy for a single machine
replacement problem. We also prove an interesting result that the optimal costs of both policies are the same under certain assumptions.
Sat, 01 Jan 2005 00:00:00 GMThttp://hdl.handle.net/10722/751382005-01-01T00:00:00Z
- A random walk on a circular pathhttp://hdl.handle.net/10722/75145Title: A random walk on a circular path
Authors: Ching, WK; Lee, MS
Abstract: This short note introduces an interesting random walk on a circular path with cards of numbers. By using high school probability theory, it is proved that under some assumptions on the number of cards, the probability that a walker will return to a fixed position will tend to one as the length of the circular path tends to infinity.
Sat, 01 Jan 2005 00:00:00 GMThttp://hdl.handle.net/10722/751452005-01-01T00:00:00Z
- A fast algorithm for the spread of HIV in a system of prisonshttp://hdl.handle.net/10722/75157Title: A fast algorithm for the spread of HIV in a system of prisons
Authors: Ching, WK; Cong, Y; Ng, TW; Tai, AH
Abstract: In this paper, we propose a continuous time model for modelling the spread of HIV in a network of prisons. We give some sufficient conditions for the equilibrium points of the system to be stable. We also develop an efficient algorithm based on Newton's method and the Sherman-Morrison-Woodbury formula for computing the equilibrium values of the infectives in each prison. © 2007 Elsevier Ltd. All rights reserved.
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/10722/751572007-01-01T00:00:00Z
- A novel peak detection approach with chemical noise removal using short-time FFT for prOTOF MS data.http://hdl.handle.net/10722/75166Title: A novel peak detection approach with chemical noise removal using short-time FFT for prOTOF MS data.
Authors: Zhang, S; DeGraba, TJ; Wang, H; Hoehn, GT; Gonzales, DA; Suffredini, AF; Ching, WK; Ng, MK; Zhou, X; Wong, ST
Abstract: Peak detection is a pivotal first step in biomarker discovery from MS data and can significantly influence the results of downstream data analysis steps. We developed a novel automatic peak detection method for prOTOF MS data, which does not require a priori knowledge of protein masses. Random noise is removed by an undecimated wavelet transform and chemical noise is attenuated by an adaptive short-time discrete Fourier transform. Isotopic peaks corresponding to a single protein are combined by extracting an envelope over them. Depending on the S/N, the desired peaks in each individual spectrum are detected and those with the highest intensity among their peak clusters are recorded. The common peaks among all the spectra are identified by choosing an appropriate cut-off threshold in the complete linkage hierarchical clustering. To remove the 1 Da shifting of the peaks, the peak corresponding to the same protein is determined as the detected peak with the largest number among its neighborhood. We validated this method using a data set of serial peptide and protein calibration standards. Compared with MoverZ program, our new method detects more peaks and significantly enhances S/N of the peak after the chemical noise removal. We then successfully applied this method to a data set from prOTOF MS spectra of albumin and albumin-bound proteins from serum samples of 59 patients with carotid artery disease compared to vascular disease-free patients to detect peaks with S/N> or =2. Our method is easily implemented and is highly effective to define peaks that will be used for disease classification or to highlight potential biomarkers.
Thu, 01 Jan 2009 00:00:00 GMThttp://hdl.handle.net/10722/751662009-01-01T00:00:00Z
- Efficient Reconstruction of Piecewise Constant Images Using Nonsmooth Nonconvex Minimizationhttp://hdl.handle.net/10722/75167Title: Efficient Reconstruction of Piecewise Constant Images Using Nonsmooth Nonconvex Minimization
Authors: Nikolova, M; Ng, MK; Zhang, S; Ching, WK
Abstract: We consider the restoration of piecewise constant images where the number of the regions and their
values are not fixed in advance, with a good difference of piecewise constant values between neighboring regions, from noisy data obtained at the output of a linear operator (e.g., a blurring kernel or
a Radon transform). Thus we also address the generic problem of unsupervised segmentation in the
context of linear inverse problems. The segmentation and the restoration tasks are solved jointly
by minimizing an objective function (an energy) composed of a quadratic data-fidelity term and a
nonsmooth nonconvex regularization term. The pertinence of such an energy is ensured by the analytical properties of its minimizers. However, its practical interest used to be limited by the difficulty
of the computational stage which requires a nonsmooth nonconvex minimization. Indeed, the existing
methods are unsatisfactory since they (implicitly or explicitly) involve a smooth approximation
of the regularization term and often get stuck in shallow local minima. The goal of this paper is to
design a method that efficiently handles the nonsmooth nonconvex minimization. More precisely,
we propose a continuation method where one tracks the minimizers along a sequence of approximate
nonsmooth energies {Jε}, the first of which being strictly convex and the last one the original energy
to minimize. Knowing the importance of the nonsmoothness of the regularization term for the segmentation task, each Jε is nonsmooth and is expressed as the sum of an l1 regularization term and
a smooth nonconvex function. Furthermore, the local minimization of each Jε is reformulated as the
minimization of a smooth function subject to a set of linear constraints. The latter problem is solved
by the modified primal-dual interior point method, which guarantees the descent direction at each
step. Experimental results are presented and show the effectiveness and the efficiency of the proposed
method. Comparison with simulated annealing methods further shows the advantage of our method.
Tue, 01 Jan 2008 00:00:00 GMThttp://hdl.handle.net/10722/751672008-01-01T00:00:00Z
- A vector - host epidemic modelhttp://hdl.handle.net/10722/75169Title: A vector - host epidemic model
Authors: Ching, WK; Chung, SK; Lau, YK; Ng, TW; Yung, SP
Tue, 01 Jan 2002 00:00:00 GMThttp://hdl.handle.net/10722/751692002-01-01T00:00:00Z
- An approximation method for solving the steady-state probability distribution of probabilistic Boolean networkshttp://hdl.handle.net/10722/75171Title: An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks
Authors: Ching, WK; Zhang, S; Ng, MK; Akutsu, T
Abstract: Motivation: Probabilistic Boolean networks (PBNs) have been proposed to model genetic regulatory interactions. The steady-state probability distribution of a PBN gives important information about the captured genetic network. The computation of the steady-state probability distribution usually includes construction of the transition probability matrix and computation of the steady-state probability distribution. The size of the transition probability matrix is 2 n-by- 2 n where n is the number of genes in the genetic network. Therefore, the computational costs of these two steps are very expensive and it is essential to develop a fast approximation method. Results: In this article, we propose an approximation method for computing the steady-state probability distribution of a PBN based on neglecting some Boolean networks (BNs) with very small probabilities during the construction of the transition probability matrix. An error analysis of this approximation method is given and theoretical result on the distribution of BNs in a PBN with at most two Boolean functions for one gene is also presented. These give a foundation and support for the approximation method. Numerical experiments based on a genetic network are given to demonstrate the efficiency of the proposed method. © The Author 2007. Published by Oxford University Press. All rights reserved.
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/10722/751712007-01-01T00:00:00Z
- A new multiple regression approach for the construction of genetic regulatory networkshttp://hdl.handle.net/10722/75188Title: A new multiple regression approach for the construction of genetic regulatory networks
Authors: Zhang, SQ; Ching, WK; Tsing, NK; Leung, HY; Guo, D
Abstract: 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.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/10722/751882010-01-01T00:00:00Z
- On Computing Prestige in a Network with Negative Relationshttp://hdl.handle.net/10722/75192Title: On Computing Prestige in a Network with Negative Relations
Authors: Tai, AH; Ching, WK; Cheung, WS
Abstract: The computation of network prestige is an important issue in studying networks such as the Internet and social networks. A number of iterative methods have been proposed for the measurement of prestige of symmetric or asymmetric network. The PageRank algorithm has been successfully applied in the computation of ranking of webpages and data mining in the Internet. In this paper, we propose a revised PageRank algorithm for the computation of prestige of a general networks and extend it to handle the case of negative relations.
Sat, 01 Jan 2005 00:00:00 GMThttp://hdl.handle.net/10722/751922005-01-01T00:00:00Z
- Circulant preconditioners for stochastic automata networkshttp://hdl.handle.net/10722/75198Title: Circulant preconditioners for stochastic automata networks
Authors: Chan, RH; Ching, WK
Abstract: Stochastic Automata Networks (SANs) are widely used in modeling communication systems, manufacturing systems and computer systems. The SAN approach gives a more compact and efficient representation of the network when compared to the stochastic Petri nets approach. To find the steady state distribution of SANs, it requires solutions of linear systems involving the generator matrices of the SANs. Very often, direct methods such as the LU decomposition are inefficient because of the huge size of the generator matrices. An efficient algorithm should make use of the structure of the matrices. Iterative methods such as the conjugate gradient methods are possible choices. However, their convergence rates are slow in general and preconditioning is required. We note that the MILU and MINV based preconditioners are not appropriate because of their expensive construction cost. In this paper, we consider preconditioners obtained by circulant approximations of SANs. They have low construction cost and can be inverted efficiently. We prove that if only one of the automata is large in size compared to the others, then the preconditioned system of the normal equations will converge very fast. Numerical results for three different SANs solved by CGS are given to illustrate the fast convergence of our method.
Sat, 01 Jan 2000 00:00:00 GMThttp://hdl.handle.net/10722/751982000-01-01T00:00:00Z
- Fast inversion of triangular Toeplitz matriceshttp://hdl.handle.net/10722/75203Title: Fast inversion of triangular Toeplitz matrices
Authors: Lin, FR; Ching, WK; Ng, MK
Abstract: In this paper, we present an approximate inversion method for triangular Toeplitz matrices based on trigonometric polynomial interpolation. To obtain an approximate inverse of high accuracy for a triangular Toeplitz matrix of size n, our algorithm requires two fast Fourier transforms (FFTs) and one fast cosine transform of 2n-vectors. We then revise the approximate method proposed by Bini (SIAM J. Comput. 13 (1984) 268). The complexity of the revised Bini algorithm is two FFTs of 2n-vectors. © 2004 Published by Elsevier B.V.
Thu, 01 Jan 2004 00:00:00 GMThttp://hdl.handle.net/10722/752032004-01-01T00:00:00Z
- Inverse Toeplitz preconditioners for Hermitian Toeplitz systemshttp://hdl.handle.net/10722/75215Title: Inverse Toeplitz preconditioners for Hermitian Toeplitz systems
Authors: Lin, FR; Ching, WK
Abstract: In this paper we consider solving Hermitian Toeplitz systems T nx = b by using the preconditioned conjugate gradient (PCG) method. Here the Toeplitz matrices Tn are assumed to be generated by a non-negative continuous 2π-periodic function f, i.e. Tn = script J sign[f]. It was proved in (Linear Algebra Appl. 1993; 190:181) that if f is positive then the spectrum of script J signn[1/f]script J sign n[f] is clustered around 1. We prove that the trigonometric polynomial qn (s) (s ≥ 2, cf. (2) and (3)) converges to 1/f uniformly as n → ∞ under the condition that 1/f is in Wiener class. It follows that the computational cost of the PCG method can be reduced by replacing 1/f with qN (2) where N < n. We also extend our method to construct efficient preconditioners for Tn when f has finite zeros of even orders. We prove that with our preconditioners, the preconditioned matrix has spectrum clustered around 1. It follows that the PCG methods converge very fast when applied to solve the preconditioned systems. Numerical results are given to demonstrate the efficiency of our preconditioners. Copyright © 2004 John Wiley & Sons, Ltd.
Sat, 01 Jan 2005 00:00:00 GMThttp://hdl.handle.net/10722/752152005-01-01T00:00:00Z
- A weighted q-gram method for glycan structure classificationhttp://hdl.handle.net/10722/75218Title: A weighted q-gram method for glycan structure classification
Authors: Li, L; Ching, WK; Yamaguchi, T; AokiKinoshita, KF
Abstract: Background: Glycobiology pertains to the study of carbohydrate sugar chains, or glycans, in a particular cell or organism. Many computational approaches have been proposed for analyzing these complex glycan structures, which are chains of monosaccharides. The monosaccharides are linked to one another by glycosidic bonds, which can take on a variety of comformations, thus forming branches and resulting in complex tree structures. The q-gram method is one of these recent methods used to understand glycan function based on the classification of their tree structures. This q-gram method assumes that for a certain q, different q-grams share no similarity among themselves. That is, that if two structures have completely different components, then they are completely different. However, from a biological standpoint, this is not the case. In this paper, we propose a weighted q-gram method to measure the similarity among glycans by incorporating the similarity of the geometric structures, monosaccharides and glycosidic bonds among q-grams. In contrast to the traditional q-gram method, our weighted q-gram method admits similarity among q-grams for a certain q. Thus our new kernels for glycan structure were developed and then applied in SVMs to classify glycans.Results: Two glycan datasets were used to compare the weighted q-gram method and the original q-gram method. The results show that the incorporation of q-gram similarity improves the classification performance for all of the important glycan classes tested.Conclusion: The results in this paper indicate that similarity among q-grams obtained from geometric structure, monosaccharides and glycosidic linkage contributes to the glycan function classification. This is a big step towards the understanding of glycan function based on their complex structures. © 2010 Li et al; licensee BioMed Central Ltd.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/10722/752182010-01-01T00:00:00Z
- An optimization algorithm for clustering using weighted dissimilarity measureshttp://hdl.handle.net/10722/75220Title: An optimization algorithm for clustering using weighted dissimilarity measures
Authors: Chan, EY; Ching, WK; Ng, MK; Huang, JZ
Abstract: One of the main problems in cluster analysis is the weighting of attributes so as to discover structures that may be present. By using weighted dissimilarity measures for objects, a new approach is developed, which allows the use of the k-means-type paradigm to efficiently cluster large data sets. The optimization algorithm is presented and the effectiveness of the algorithm is demonstrated with both synthetic and real data sets. © 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Thu, 01 Jan 2004 00:00:00 GMThttp://hdl.handle.net/10722/752202004-01-01T00:00:00Z
- Stochastic Neural Network Models for Quadratic Assignment Problemshttp://hdl.handle.net/10722/75228Title: Stochastic Neural Network Models for Quadratic Assignment Problems
Authors: Ching, WK
Tue, 01 Jan 2002 00:00:00 GMThttp://hdl.handle.net/10722/752282002-01-01T00:00:00Z
- Machine repairing models for production systemshttp://hdl.handle.net/10722/75233Title: Machine repairing models for production systems
Authors: Ki Ching, W
Abstract: In this paper we consider manufacturing systems of m identical unreliable machines producing one type of product. The operating time of each machine is exponentially distributed. The repairing process of a machine requires more than one phase. In each phase, the repair time is exponentially distributed and more than one operator may be required for fixing a broken machine. Here we consider two models of manufacturing systems. In the first model, there are r operators assigned in one server to repair a broken machine. The repairing rate in each phase depends on the number of operators there. This is a generalized model discussed in Buzacott and Shanthikumar [11]. We then consider a two-phase repairing model. Two groups of operators are assigned in the two phases. Each operator can handle one broken machine in each phase individually. This model is a generalization of Eben-Chaime's model [8]. Average profits are derived for both models and can be optimized by suitable allocation of the number of machines and operators in the systems. © 2001 Elsevier Science B.V.
Mon, 01 Jan 2001 00:00:00 GMThttp://hdl.handle.net/10722/752332001-01-01T00:00:00Z
- Super-resolution image reconstruction using multisensorshttp://hdl.handle.net/10722/75238Title: Super-resolution image reconstruction using multisensors
Authors: Ching, WK; Ng, MK; Sze, KN; Yau, AC
Abstract: Super-resolution image reconstruction refers to obtaining an image at a resolution higher than that of a camera (sensor) used in recording the image. In this paper, we present a new joint minimization model in which an objective function is set up consisting of three terms: the data fitting term, the regularization terms for the reconstructed image and the observed low-resolution images. An alternating minimization iterative algorithm is proposed and developed to reconstruct the image. We give a convergence analysis of the alternating minimization iterative algorithm and show that it converges for H 1-norm regularization Functional. Numerical examples are given to illustrate the effectiveness of the joint minimization model and the efficiency of the algorithm. Copyright ©2004 John Wiley & Sons, Ltd.
Sat, 01 Jan 2005 00:00:00 GMThttp://hdl.handle.net/10722/752382005-01-01T00:00:00Z
- Boundary value methods for solving transient solutions of Markovian queueing networkshttp://hdl.handle.net/10722/75239Title: Boundary value methods for solving transient solutions of Markovian queueing networks
Authors: Chan, RH; Ma, KC; Ching, WK
Abstract: In this paper, we consider Boundary Value Methods (BVMs) for finding transient solutions of Markovian queueing networks. Algebraic Multigrid (AMG) methods with modified restriction operator are applied to solve the resulting system of linear equations. Numerical examples are given to demonstrate the efficiency of our proposed method. © 2004 Elsevier Inc. All rights reserved.
Sun, 01 Jan 2006 00:00:00 GMThttp://hdl.handle.net/10722/752392006-01-01T00:00:00Z
- Circulant Preconditioning for Unreliable Manufacturing Systems with Batch Arrivalshttp://hdl.handle.net/10722/75258Title: Circulant Preconditioning for Unreliable Manufacturing Systems with Batch Arrivals
Authors: Ching, WK
Sat, 01 Jan 2000 00:00:00 GMThttp://hdl.handle.net/10722/752582000-01-01T00:00:00Z
- On Modeling SARS in Hong Konghttp://hdl.handle.net/10722/75259Title: On Modeling SARS in Hong Kong
Authors: Ching, WK; Chung, SK; Ng, TW
Wed, 01 Jan 2003 00:00:00 GMThttp://hdl.handle.net/10722/752592003-01-01T00:00:00Z
- A semi-supervised regression model for mixed numerical and categorical variableshttp://hdl.handle.net/10722/75264Title: A semi-supervised regression model for mixed numerical and categorical variables
Authors: Ng, MK; Chan, EY; So, MMC; Ching, WK
Abstract: In this paper, we develop a semi-supervised regression algorithm to analyze data sets which contain both categorical and numerical attributes. This algorithm partitions the data sets into several clusters and at the same time fits a multivariate regression model to each cluster. This framework allows one to incorporate both multivariate regression models for numerical variables (supervised learning methods) and k-mode clustering algorithms for categorical variables (unsupervised learning methods). The estimates of regression models and k-mode parameters can be obtained simultaneously by minimizing a function which is the weighted sum of the least-square errors in the multivariate regression models and the dissimilarity measures among the categorical variables. Both synthetic and real data sets are presented to demonstrate the effectiveness of the proposed method. © 2006 Pattern Recognition Society.
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/10722/752642007-01-01T00:00:00Z
- Iterative Methods for Re-manufacturing Systemshttp://hdl.handle.net/10722/75268Title: Iterative Methods for Re-manufacturing Systems
Authors: Ching, WK; Yuen, WO
Tue, 01 Jan 2002 00:00:00 GMThttp://hdl.handle.net/10722/752682002-01-01T00:00:00Z
- A hybrid algorithm for queueing systemshttp://hdl.handle.net/10722/75269Title: A hybrid algorithm for queueing systems
Authors: Yuen, WO; Ching, WK; Ng, MK
Abstract: In this paper, we propose a hybrid algorithm based on [12] for solving linear systems of equations. The hybrid algorithm combines the evolutionary algorithm and the successive over-relaxation (SOR) method. The evolutionary algorithm allows the relaxation parameter w to be adaptive in the SOR method. We prove the convergence of the hybrid algorithm for strictly diagonal dominant linear systems. We then apply it to solve the steady-state probability distributions of Markovian queueing systems. Numerical examples are given to demonstrate the fast convergence rate of the method. © Springer-Verlag 2004.
Thu, 01 Jan 2004 00:00:00 GMThttp://hdl.handle.net/10722/752692004-01-01T00:00:00Z
- Algorithms for finding small attractors in boolean networkshttp://hdl.handle.net/10722/75285Title: Algorithms for finding small attractors in boolean networks
Authors: Zhang, SQ; Hayashida, M; Akutsu, T; Ching, WK; Ng, MK
Abstract: A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in O( 1.19 n) time for K=2, which is much faster than the naive O( 2n) time algorithm, where n is the number of genes and K is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/10722/752852007-01-01T00:00:00Z
- Circulant approximation for preconditioning in stochastic automata networkshttp://hdl.handle.net/10722/75289Title: Circulant approximation for preconditioning in stochastic automata networks
Authors: Ching, WK; Zhou, XY
Abstract: Stochastic Automata Networks (SANs) are widely used in modeling practical systems such as queueing systems, communication systems, and manufacturing systems. For the performance analysis purposes, one needs to calculate the steady-state distributions of SANs. Usually, the steady-state distributions have no close form solutions and cannot be obtained efficiently by direct methods such as LU decomposition due to the huge size of the generator matrices. An efficient numerical method should make use of the tensor structure of SANs' generator matrices. The generalized Conjugate Gradient (CG) methods are possible choices though their convergence rates are slow in general. To speed up the convergence rate, preconditioned CG methods are considered in this paper. In particular, circulant based preconditioners for the SANs are constructed. The preconditioners presented in this paper are easy to construct and can be inverted efficiently. Numerical examples of practical SANs are also given to illustrate the fast convergence rate of the method.
Sat, 01 Jan 2000 00:00:00 GMThttp://hdl.handle.net/10722/752892000-01-01T00:00:00Z
- Generating probabilistic Boolean networks from a prescribed stationary distributionhttp://hdl.handle.net/10722/75296Title: Generating probabilistic Boolean networks from a prescribed stationary distribution
Authors: Zhang, SQ; Ching, WK; Chen, X; Tsing, NK
Abstract: Modeling gene regulation is an important problem in genomic research. Boolean networks (BN) and its generalization probabilistic Boolean networks (PBNs) have been proposed to model genetic regulatory interactions. BN is a deterministic model while PBN is a stochastic model. In a PBN, on one hand, its stationary distribution gives important information about the long-run behavior of the network. On the other hand, one may be interested in system synthesis which requires the construction of networks from the observed stationary distribution. This results in an inverse problem which is ill-posed and challenging. Because there may be many networks or no network having the given properties and the size of the inverse problem is huge. In this paper, we consider the problem of constructing PBNs from a given stationary distribution and a set of given Boolean Networks (BNs). We first formulate the inverse problem as a constrained least squares problem. We then propose a heuristic method based on Conjugate Gradient (CG) algorithm, an iterative method, to solve the resulting least squares problem. We also introduce an estimation method for the parameters of the PBNs. Numerical examples are then given to demonstrate the effectiveness of the proposed methods. © 2010 Elsevier Inc. All rights reserved.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/10722/752962010-01-01T00:00:00Z
- On Optimal Screening and Quarantining Policy in a Network of Prisonshttp://hdl.handle.net/10722/75308Title: On Optimal Screening and Quarantining Policy in a Network of Prisons
Authors: Bai, ZJ; Ching, WK; Cong, Y; Ng, TW
Abstract: In this paper, we propose mathematical models for the spread of HIV in a network of prisons. We study the effect of both screening prisoners and quarantining infectives. Efficient algorithms based on Newton's method are then developed for computing the equilibrium values of the infectives in each prison. We also give an optimization formulation for obtaining the optimal screening and quarantine policy. The models and algorithms developed can be extended to model the spread of a disease in a general network of connected zones.
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/10722/753082007-01-01T00:00:00Z
- Non-linear Optimisation and Traffic Demand Estimationhttp://hdl.handle.net/10722/75311Title: Non-linear Optimisation and Traffic Demand Estimation
Authors: Ching, WK
Tue, 01 Jan 2002 00:00:00 GMThttp://hdl.handle.net/10722/753112002-01-01T00:00:00Z
- On convergent probability of a random walkhttp://hdl.handle.net/10722/75312Title: On convergent probability of a random walk
Authors: Lee, YF; Ching, WK
Abstract: This note introduces an interesting random walk on a straight path with cards of random numbers. The method of recurrent relations is used to obtain the convergent probability of the random walk with different initial positions. © 2006 Taylor & Francis.
Sun, 01 Jan 2006 00:00:00 GMThttp://hdl.handle.net/10722/753122006-01-01T00:00:00Z
- Discrete wavelet transforms for Toeplitz matriceshttp://hdl.handle.net/10722/75316Title: Discrete wavelet transforms for Toeplitz matrices
Authors: Lin, FR; Ching, WK; Ng, MK
Abstract: In this paper, we discuss discrete wavelet transforms for Toeplitz matrices and block-Toeplitz-Toeplitz-block matrices. The main contribution of this paper is to give the Toeplitz-like structure of the wavelet transformed Toeplitz matrices, and show that the computational cost for such structure is O(k3ln) where n is the size of the Toeplitz matrix, k is the order of the wavelet and l is the level used in the wavelet transform. The comparison between the wavelet transformed Toeplitz matrices and the Fourier transformed Toeplitz matrices is also given. © 2003 Elsevier Science Inc. All rights reserved.
Wed, 01 Jan 2003 00:00:00 GMThttp://hdl.handle.net/10722/753162003-01-01T00:00:00Z
- A note on the paper: Optimizing web servers using page rank prefetching for clustered accesseshttp://hdl.handle.net/10722/75317Title: A note on the paper: Optimizing web servers using page rank prefetching for clustered accesses
Authors: Ching, WK
Abstract: The page rank algorithm used for ranking importance of web pages by Google is discussed. Some efficient adaptive power methods are developed recently for computation of page rank. Safronov and parashar proposed to use page rank as a prefetching technique for access to web-page clusters. The effectiveness of the method is proved by presenting examples.
Sat, 01 Jan 2005 00:00:00 GMThttp://hdl.handle.net/10722/753172005-01-01T00:00:00Z
- Construction and Control of Genetic Regulatory Networks: A Multivariate Markov Chain Approachhttp://hdl.handle.net/10722/75318Title: Construction and Control of Genetic Regulatory Networks: A Multivariate Markov Chain Approach
Authors: Zhang, SQ; Ching, WK; Jiao, Y; Wu, LY; Chan, RH
Abstract: In the post-genomic era, the construction and control of genetic regulatory networks using gene expression data is a hot research topic. Boolean networks (BNs) and its extension Probabilistic Boolean Networks (PBNs) have been served as an effective tool for this purpose. However, PBNs are difficult to be used in practice when the number of genes is large because of the huge computational cost. In this paper, we propose a simplified multivariate Markov model for approximating a PBN The new model can preserve the strength of PBNs, the ability to capture the inter-dependence of the genes in the network, qnd at the same time reduce the complexity of the network and therefore the computational cost. We then present an optimal control model with hard constraints for the purpose of control/intervention of a genetic regulatory network. Numerical experimental examples based on the yeast data are given to demonstrate the effectiveness of our proposed model and control policy.
Tue, 01 Jan 2008 00:00:00 GMThttp://hdl.handle.net/10722/753182008-01-01T00:00:00Z
- Inventory Models for Supply Planning Under Stochastic Lead Time and Demandhttp://hdl.handle.net/10722/75320Title: Inventory Models for Supply Planning Under Stochastic Lead Time and Demand
Authors: Ching, WK; Wong, KK
Tue, 01 Jan 2002 00:00:00 GMThttp://hdl.handle.net/10722/753202002-01-01T00:00:00Z
- Control of Boolean networks: Hardness results and algorithms for tree structured networkshttp://hdl.handle.net/10722/75321Title: Control of Boolean networks: Hardness results and algorithms for tree structured networks
Authors: Akutsu, T; Hayashida, M; Ching, WK; Ng, MK
Abstract: Finding control strategies of cells is a challenging and important problem in the post-genomic era. This paper considers theoretical aspects of the control problem using the Boolean network (BN), which is a simplified model of genetic networks. It is shown that finding a control strategy leading to the desired global state is computationally intractable (NP-hard) in general. Furthermore, this hardness result is extended for BNs with considerably restricted network structures. These results justify existing exponential time algorithms for finding control strategies for probabilistic Boolean networks (PBNs). On the other hand, this paper shows that the control problem can be solved in polynomial time if the network has a tree structure. Then, this algorithm is extended for the case where the network has a few loops and the number of time steps is small. Though this paper focuses on theoretical aspects, biological implications of the theoretical results are also discussed. © 2006 Elsevier Ltd. All rights reserved.
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/10722/753212007-01-01T00:00:00Z
- Optimal service capacity in a multiple-server queueing system: A game theory approachhttp://hdl.handle.net/10722/75323Title: Optimal service capacity in a multiple-server queueing system: A game theory approach
Authors: Ching, WK; Choi, SM; Huang, M
Abstract: The economic behavior of service providers in a competitive environment is a very important and interesting research topic. A two-server service network has been proposed by Kalai et al. [14] for this purpose. Their model actually aims at studying both the role and impact of service capacity in capturing larger market share. The market share is important in maximizing individual's long-run expected profit. A Markovian queueing system of two servers was employed in their model for the captured problem. The obvious advantage of such a model is that it is mathematically tractable. They then further formulated the problem as a two-person strategic game and analyzed the equilibrium solutions. The main aim of this paper is to extend the results of their two-server queueing model to the case of a general multiple-server queueing model. Here we will focus on the case when the queueing system is stable. It is found that when the marginal cost of service capacity is low relatively to the revenue per customer, a unique Nash equilibrium exists, in which all servers choose the same service capacity and the expected waiting times are finite.
Thu, 01 Jan 2009 00:00:00 GMThttp://hdl.handle.net/10722/753232009-01-01T00:00:00Z
- Manufacturing Systems with Fuzzy Backlog Costhttp://hdl.handle.net/10722/75331Title: Manufacturing Systems with Fuzzy Backlog Cost
Authors: Ching, WK
Tue, 01 Jan 2002 00:00:00 GMThttp://hdl.handle.net/10722/753312002-01-01T00:00:00Z
- A Note on the Bias Reduction Method in the Valuations of Exotic Optionshttp://hdl.handle.net/10722/75333Title: A Note on the Bias Reduction Method in the Valuations of Exotic Options
Authors: Ching, WK
Tue, 01 Jan 2002 00:00:00 GMThttp://hdl.handle.net/10722/753332002-01-01T00:00:00Z
- On Measuring Average Speed in a Circulating Systemhttp://hdl.handle.net/10722/75339Title: On Measuring Average Speed in a Circulating System
Authors: Ching, WK
Abstract: The paper considers measuring speeds of vehicles which move in a circular track. By using the Schwarz inequality, it is shown that the average speed of the vehicles is over-estimated when the vehicles are observed at a fixed point in the track. It is also illustrated that the relative error is the square of an important statistical quantity, the coefficient of variation of the distribution of vehicle speeds. The results are interesting and the techniques used are elementary mathematics and statistics.
Mon, 01 Jan 2001 00:00:00 GMThttp://hdl.handle.net/10722/753392001-01-01T00:00:00Z
- Classroom note: Building simple hidden Markov modelshttp://hdl.handle.net/10722/75347Title: Classroom note: Building simple hidden Markov models
Authors: Ching, WK; Ng, MK
Abstract: Hidden Markov models (HMMs) are widely used in bioinformatics, speech recognition and many other areas. This note presents HMMs via the framework of classical Markov chain models. A simple example is given to illustrate the model. An estimation method for the transition probabilities of the hidden states is also discussed.
Thu, 01 Jan 2004 00:00:00 GMThttp://hdl.handle.net/10722/753472004-01-01T00:00:00Z
- An improved multivariate Markov chain model for credit riskhttp://hdl.handle.net/10722/75374Title: An improved multivariate Markov chain model for credit risk
Authors: Ching, WK; Siu, TK; Li, LM; Jiang, H; Li, T; Li, WK
Abstract: In this paper we use Ching's multivariate Markov chain model to model the dependency of rating transitions of several credit entities. The model is an enhancement of the multivariate Markov chain model for ratings considered by Siu et al. Our model is more parsimonious, flexible and empirically competent than the model used by Siu et al. We adopt an efficient method to calibrate the model parameters and formulate the estimation problem as a linear programming problem that can easily be solved using spreadsheets. We compare the estimation results and the computational efficiency of the enhanced model with that also empirically investigate the effect of incorporating both positive and negative associations on portfolio credit risks.
Thu, 01 Jan 2009 00:00:00 GMThttp://hdl.handle.net/10722/753742009-01-01T00:00:00Z
- CpG/CpNpG motifs in the coding region are preferred sites for mutagenesis in the breast cancer susceptibility geneshttp://hdl.handle.net/10722/75377Title: CpG/CpNpG motifs in the coding region are preferred sites for mutagenesis in the breast cancer susceptibility genes
Authors: Cheung, LWT; Lee, YF; Ng, TW; Ching, WK; Khoo, US; Ng, MKP; Wong, AST
Abstract: The range of BRCA1/BRCA2 gene mutations is diverse and the mechanism accounting for this heterogeneity is obscure. To gain insight into the endogenous mutational mechanisms involved, we evaluated the association of specific sequences (i.e. CpG/CpNpG motifs, homonucleotides, short repeats) and mutations within the genes. We classified 1337 published mutations in BRCA1 (1765 BRCA2 mutations) for each specific sequence, and employed computer simulation combined with mathematical calculations to estimate the true underlying tendency of mutation occurrence. Interestingly, we found no mutational bias to homonucleotides and repeats in deletions/insertions and substitutions but striking bias to CpG/CpNpG in substitutions in both genes. This suggests that methylation-dependent DNA alterations would be a major mechanism for mutagenesis. © 2007 Federation of European Biochemical Societies.
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/10722/753772007-01-01T00:00:00Z
- On Multi-dimensional Markov Chain Modelshttp://hdl.handle.net/10722/75385Title: On Multi-dimensional Markov Chain Models
Authors: Ching, WK; Zhang, SQ; Ng, MK
Abstract: Markov chain models are commonly used to model categorical data sequences. In the paper, we propose a multi-dimensional Markov chain model for modeling high dimensional categorical data sequences. In particular, the model is practical when there are limited data available. We then test the model with some practical sales demand data. Numerical results indicate the proposed model when compared to the existing models has comparable performance but has much less number of model parameters.
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/10722/753852007-01-01T00:00:00Z
- Generating probabilistic Boolean networks from a prescribed transition probability matrixhttp://hdl.handle.net/10722/75388Title: Generating probabilistic Boolean networks from a prescribed transition probability matrix
Authors: Ching, WK; Chen, X; Tsing, NK
Abstract: Probabilistic Boolean networks (PBNs) have received much attention in modeling genetic regulatory networks. A PBN can be regarded as a Markov chain process and is characterised by a transition probability matrix. In this study, the authors propose efficient algorithms for constructing a PBN when its transition probability matrix is given. The complexities of the algorithms are also analysed. This is an interesting inverse problem in network inference using steady-state data. The problem is important as most microarray data sets are assumed to be obtained from sampling the steady-state. © 2009 © The Institution of Engineering and Technology.
Thu, 01 Jan 2009 00:00:00 GMThttp://hdl.handle.net/10722/753882009-01-01T00:00:00Z
- A note on the stability of Toeplitz matrix inversion formulashttp://hdl.handle.net/10722/75389Title: A note on the stability of Toeplitz matrix inversion formulas
Authors: Wen, YW; Ng, MK; Ching, WK; Liu, H
Abstract: In this paper, we consider the stability of the algorithms emerging from Toeplitz matrix inversion formulas. We show that if the Toeplitz matrix is nonsingular and well-conditioned, then they are numerically forward stable. © 2004 Elsevier Ltd. All rights reserved.
Thu, 01 Jan 2004 00:00:00 GMThttp://hdl.handle.net/10722/753892004-01-01T00:00:00Z
- Block diagonal and schur complement preconditioners for block-toeplitz systems with small size blockshttp://hdl.handle.net/10722/75407Title: Block diagonal and schur complement preconditioners for block-toeplitz systems with small size blocks
Authors: Ching, WK; Ng, MK; Wen, YW
Abstract: In this paper we consider the solution of Hermitian positive definite block-Toeplitz systems with small size blocks. We propose and study block diagonal and Schur complement preconditioners for such block-Toeplitz matrices. We show that for some block-Toeplitz matrices, the spectra of the preconditioned matrices are uniformly bounded except for a fixed number of outliers where this fixed number depends only on the size of the block. Hence, conjugate gradient type methods, when applied to solving these preconditioned block-Toeplitz systems with small size blocks, converge very fast. Recursive computation of such block diagonal and Schur complement preconditioners is considered by using the nice matrix representation of the inverse of a block-Toeplitz matrix. Applications to block-Toeplitz systems arising from least squares filtering problems and queueing networks are presented. Numerical examples are given to demonstrate the effectiveness of the proposed method. © 2007 Society for Industrial and Applied Mathematics.
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/10722/754072007-01-01T00:00:00Z
- A parasite vector-host epidemic model for TSE propagationhttp://hdl.handle.net/10722/75415Title: A parasite vector-host epidemic model for TSE propagation
Authors: Ng, TW; Turinici, G; Ching, WK; Chung, SK; Danchin, A
Abstract: Background: Transmissible spongiform encephalopathies (TSEs) are a family of diseases that infect mammals. They are explained by cross-contamination through an unknown route or from infection of food contaminated with prion proteins (PrPs), natural proteins that take an infectious form contributing to the slow destruction of the animal brain. While the extreme resistance of PrPs to denaturation and proteolysis accounts for a route from the mouth to the brain, the possible role of another route of contamination is explored here. Many diseases are spread by vectors, as seen in plague, typhus, malaria, or dengue. The situation where PrPs would be transmitted by a vector and, from the characteristics of outbreaks, proposed hypotheses about the biological nature of such vectors are explored. Material/Methods: The nontrivial situation where contamination by the vector prevents infection by making the host immune to further vector contamination was analyzed. To investigate the nature of a possible vector, the spread of a disease in a closed population of hosts and vectors where the number of hosts is constant and the vectors multiply in the host was modeled mathematically. In this model, the disease is caused by an infective agent and is spread by a vector, while direct host-to-host spread is not permitted. Results: Concrete values of the parameters of the model were computed from simulation of the BSE outbreak in the UK as a possible example of the process. Conclusions: Microbial vector-borne diseases might play an unexpected role in the spread of epidemics, warranting further exploration. © Med Sci Monit, 2007.
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/10722/754152007-01-01T00:00:00Z
- Markovian approximation for manufacturing systems of unreliable machines in tandemhttp://hdl.handle.net/10722/75419Title: Markovian approximation for manufacturing systems of unreliable machines in tandem
Authors: Ching, WK
Abstract: This paper studies production planning of manufacturing systems of unreliable machines in tandem. The manufacturing system considered here produces one type of product. The demand is assumed to be a Poisson process and the processing time for one unit of product in each machine is exponentially distributed. A broken machine is subject to a sequence of repairing processes. The up time and the repairing time in each phase are assumed to be exponentially distributed. We study the manufacturing system by considering each machine as an individual system with stochastic supply and demand. The Markov Modulated Poisson Process (MMPP) is applied to model the process of supply. Numerical examples are given to demonstrate the accuracy of the proposed method. We employ (s, S) policy as production control. Fast algorithms are presented to solve the average running costs of the machine system for a given (s, S) policy and hence the approximated optimal (s, S) policy.
Mon, 01 Jan 2001 00:00:00 GMThttp://hdl.handle.net/10722/754192001-01-01T00:00:00Z