mathematical description of the planar graph coloring problem

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mathematical description of the planar graph coloring problem

    Short-term scheduling of cascade reservoirs using an immune algorithm-based particle swarm optimization Original Research Article

    Computers & Mathematics with Applications, In Press, Corrected Proof, Available online 6 August 2011

    Xiang Fu, Anqiang Li, Liping Wang, Changming Ji

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     Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0 Original Research Article

    Computer Methods and Programs in Biomedicine, Volume 98, Issue 1, April 2010, Pages 55-65

    Caroline Bazzoli, Sylvie Retout, France Mentr??

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     The Graph Sandwich Problem for P4-sparse graphs Original Research Article

    Discrete Mathematics, Volume 309, Issue 11, 6 June 2009, Pages 3664-3673 Simone Dantas, Sulamita Klein, C??lia P. de Mello, Aurora Morgana

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     Mathematical modelling in science and mathematics education Original Research Article

    Computer Physics Communications, Volume 182, Issue 1, January 2011, Pages 8-10

    V?ªtor Duarte Teodoro, Rui Gomes Neves

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     Restricted second order information for the solution of optimal control problems using control vector parameterization Original Research Article

    Journal of Process Control, Volume 12, Issue 2, February 2002, Pages


    Eva Balsa Canto, Julio R. Banga, Antonio A. Alonso, Vassilios S. Vassiliadis

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    A new scheme using a Truncated Newton algorithm with and exact Hessian-search direction vector product is presented for the solution of optimal control problems. The derivation of formulae for second order parametric sensitivity analysis of differential-algebraic equations is presented, following earlier published work [V.S. Vassiliadis, E. Balsa-Canto, J.R. Banga, Second order sensitivities of general dynamic systems with application to optimal control problems. Chem. Eng. Sci. 54 (17) (1999) 3851?C3860]. An original result in this work is the derivation of Hessian matrix-vector product forms which are shown to have the same computational complexity as the evaluation of first order sensitivities. This result for optimal control Hessian-vector products using control vector parameterization is shown to be a very effective way to solve optimal control problems. It is also noted that this work introduces the use of suitable Truncated Newton solvers which can exploit the exact vector products in using conjugate gradient iterations to converge the Newton equations. Such a solver is the TN algorithm of Nash [(S.G. Nash-Newton type minimization via the Lanczos method. SIAM J. Num. Anal. 21, (1984) 770?C778)]. Because no full Hessian update is necessary it is demonstrated that the resulting optimal control solver performs very well for a very large number of degrees of freedom, limited only by the necessity for many right-hand-side calculations in the first and second order sensitivity equations (the Hessian vector product). It is also demonstrated by several case studies that the scheme is capable of starting far from the solution and yet arrive there in almost invariant performance.

    Article Outline


    1. Introduction

    2. Problem definition and theoretical analysis

    2.1. Second order information for dynamic systems

    3. Application to optimal control problems

    4. Truncated Newton method for the solution of the master NLP 5. Implementation details

    6. Case studies

    6.1. van der Pol oscillator

    6.2. Nonlinear CSTR

    6.2.1. Three control variables case

6.2.2. Four control variables case

    6.3. Industrial sterilization of canned foods

    7. Conclusions and future work


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     Counter-propagation neural networks in Matlab

    Chemometrics and Intelligent Laboratory Systems, Volume 90, Issue 1, 15 January 2008, Pages 84-91

    Igor Kuzmanovski, Marjana Novi?

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    The counter-propagation neural networks have been widely used by the chemometricians for more than fifteen years. This valuable tool for data analysis has been applied for solving many different chemometric problems. In this paper the implementation of counter-propagation neural networks in Matlab environment is described. The program presented here is an extension of Self-Organizing Maps Toolbox for Matlab that is not widely used by chemometricians. This program coupled with the excellent visualization tools available in Self-Organizing Maps Toolbox and with other valuable functions in this environment could be of great interest for analysis of chemical data. The use of the program is demonstrated on the development of the regression and classification models.

    Article Outline

    1. Introduction

    2. Counter-propagation neural networks algorithm

    3. Data sets

    4. Software specifications and requirements

    5. Demonstration

    5.1. Regression demo

    5.2. Classification demo

    6. Conclusion

    7. Validation


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     Software Quality Estimation with Case-Based Reasoning Review Article Advances in Computers, Volume 62, 2004, Pages 249-291

    Taghi M. Khoshgoftaar, Naeem Seliya

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    The software quality team of a software project often strives to predict the operational quality of software modules prior to software deployment. A timely software quality prediction can be used for enacting any preventive actions so as to reduce software faults from occurring during system operations. This is especially important for high-assurance systems where software reliability is very critical. The two most commonly used models for software quality estimation are, software fault prediction and software quality classification. Generally, such models use software metrics as predictors of a software module's quality, which is either represented by the expected number of faults or a class membership to quality-based groups. This study presents a comprehensive methodology for building software quality estimation models with case-based reasoning (cbr), a computational intelligence technique that is suited for experience-based analysis. A cbr system is a practical option for software quality modeling, because it uses an organization's previous experience with its software development process to estimate the quality of a currently under-development software project. In the context of software metrics and quality data collected from a high-assurance software system, software fault prediction and software quality classification models are built. The former predicts the number of faults in software modules, while the latter predicts the class membership of the modules into the fault-prone and not fault-prone groups. This study presents in-depth details for the cbr models so as to facilitate a comprehensive understanding of the cbr technology as applied to software quality estimation.

    Article Outline

    1. Introduction

    2. Software Quality Estimation with CBR

    2.1. Similarity Functions

    2.1.1. City Block Distance

    2.1.2. Euclidean Distance

    2.1.3. Mahalanobis Distance

    2.2. Quantitative Solution Algorithms

    2.2.1. Unweighted Average

    2.2.2. Inverse Distance Weighted Average

    2.3. Qualitative Solution Algorithms

    2.3.1. Majority Voting Classification Rule

    2.3.2. Data Clustering Classification Rule

    3. Modeling Methodology

3.1. Case-Based Reasoning Modeling Process

    3.2. Performance Evaluation for Software Fault Prediction 3.3. Performance Evaluation for Software Quality Classification 4. Case Study Description

    5. Results and Analysis

    5.1. Software Fault Prediction Results

    5.2. Software Quality Classification Results

    5.2.1. Majority Voting-Based Classification Model

    5.2.2. Data Clustering-Based Classification Model

    5.2.3. Majority Voting vs. Data Clustering

    5.3. Threats to Validity

    6. Conclusion


    Appendix A. Two-Way Analysis of Variance Models

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     A note on universally optimal matrices and field independence of the minimum rank of a graph Original Research Article

    Linear Algebra and its Applications, Volume 433, Issue 3, 1 September 2010, Pages 585-594

    Liang-Hao Huang, Gerard J. Chang, Hong-Gwa Yeh

     Close preview | Related articles | Related reference work articles AbstractAbstract | ReferencesReferencesAbstract For a simple graph G on n vertices, the minimum rank of G over a field F, written as mrF(G), is defined to be the smallest possible rank among all n?Án symmetric matrices over F whose (i,j)th entry (for i?Ùj) is nonzero whenever {i,j} is an edge in G and is zero otherwise. A symmetric integer matrix A such that every off-diagonal entry is 0, 1, or -1 is called a universally optimal matrix if, for all fields F, the rank of A over F is the minimum rank of the graph of A over F. Recently, Dealba et al. [L.M. Dealba, J. Grout, L. Hogben, R. Mikkelson, K. Rasmussen, Universally optimal matrices and field independence of the minimum rank of a graph, Electron. J. Linear Algebra 18 (2009) 403?C419] initiated the study of universally optimal matrices and established field independence or dependence of minimum rank for some families of graphs. In the present paper, more results on universally optimal matrices and field independence or dependence of the minimum rank of a graph are presented, and some results of Dealba et al. [5] are improved. Purchase


     Dynamic Assessment of Algebraic Learning in Predicting Third Graders' Development of Mathematical Problem Solving Original Research Article

    Journal of Educational Psychology, Volume 100, Issue 4, November 2008, Pages 829-850

    Lynn S. Fuchs, Donald L. Compton, Douglas Fuchs, Kurstin N. Hollenbeck, Caitlin F. Craddock, Carol L. Hamlett

     Close preview | Related articles | Related reference work articles AbstractAbstract | ReferencesReferencesDynamic assessment (DA) involves helping students learn a task and indexing responsiveness to that instruction as a measure of learning potential. The purpose of this study was to explore the utility of a DA of algebraic learning in predicting third graders' development of mathematics problem solving. In the fall, 122 third-grade students were assessed on language, nonverbal reasoning, attentive behavior, calculations, word-problem skill, and DA. On the basis of random assignment, students received 16 weeks of validated instruction on word problems or received 16 weeks of conventional instruction on word problems. Then, students were assessed on word-problem measures proximal and distal to instruction. Structural equation measurement models showed that DA measured a distinct dimension of pretreatment ability and that proximal and distal word-problem measures were needed to account for outcome. Structural equation modeling showed that instruction (conventional vs. validated) and pretreatment calculation skills were sufficient to account for math word-problem outcome proximal to instruction; by contrast, language, pretreatment word-problem skill, and DA were needed to forecast learning on word-problem outcomes more distal to instruction. Findings are discussed in terms of responsiveness-to-intervention models for preventing and identifying learning disabilities. Purchase


     Writing with speech recognition: The adaptation process of professional writers with and without dictating experience Original Research Article

    Interacting with Computers, Volume 17, Issue 6, December 2005, Pages 736-772

    Mari?lle Leijten, Luuk Van Waes

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    This paper describes the adaptation and writing process of writers who have started using speech recognition systems for writing business texts. The writers differ in their previous writing experience. They either have previous classical dictating experience or they are used to writing their texts with a word processor. To gather the process

    data for this study we chose complementary research methods. First the participants were asked to fill in a questionnaire and given instruction about the speech recognition system. Then they were observed five times using the speech recognition system during their day-to-day work. Finally, they also filled in a logging questionnaire after each task. The quantitative analysis of the use of the writing mode shows that those participants who had no previous dictating experience, tend to use the voice input more extensively, both for formulating and reviewing. This result is confirmed in the more detailed case analysis. The other analyses in the case study?ªi.e. repair, revision, and pause analysis-refine the differences in the organization of the writing process between the writers, and show that the speech recognition mode seems to create a writing environment that is open for different writing profiles.

Article Outline

    1. Introduction

    2. Related research: writing processes

    2.1. Speech recognition and writing

    2.2. Research questions

    3. Description of the research project

    3.1. Participants

    3.2. Design and procedure

    3.3. Materials

    3.4. Selection of data

    4. Analysis

    4.1. Categorization model

    4.1.1. Writing modes

    4.1.2. Repairs Technical problems Intention and outcome Cause Number of attempts Writing mode Level and remoteness of correction Direction of correction Absolute time and interval Revisions Writing mode Level and remoteness of revision Direction of revision Absolute time and writing phase

    4.1.3. Pauses Duration of pauses Number of pauses Temporal location of pauses

    4.2. Transcription model

    5. Quantitative study: writing modes

    6. Case study

    6.1. Writing modes

    6.2. Repairs

    6.3. Revisions

    6.4. Pauses

    6.5. Transcriptions

    7. Conclusions

    8. Discussion and further research


    Appendix: Original transcripts in Dutch

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     A faster exact schedulability analysis for fixed-priority scheduling Original Research Article

    Journal of Systems and Software, Volume 79, Issue 12, December 2006, Pages 1744-1753

    Wan-Chen Lu, Jen-Wei Hsieh, Wei-Kuan Shih, Tei-Wei Kuo

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    Real-time scheduling for task sets has been studied, and the corresponding schedulability analysis has been developed. Due to the considerable overheads required to precisely analyze the schedulability of a task set (referred to as exact schedulability analysis), the trade-off between precision and efficiency is widely studied. Many efficient but imprecise (i.e., sufficient but not necessary) analyses are discussed in the literature. However, how to precisely and efficiently analyze the schedulability of task sets remains an important issue. The Audsley??s Algorithm was shown to be effective in exact schedulability analysis for task sets under rate-monotonic scheduling (one of the optimal fixed-priority scheduling algorithms). This paper focuses on reducing the runtime overhead of the Audsley??s Algorithm. By properly partitioning a task set into two subsets and differently treating these two subsets during each iteration, the number of iterations required for analyzing the schedulability of the task set can be significantly reduced. The capability of the proposed algorithm was evaluated and compared to related works, which revealed up to a 55.5% saving in the runtime

    overhead for the Audsley??s Algorithm when the system was under a heavy load.

    Article Outline

    1. Introduction

    2. Task model and definitions

    3. A faster exact schedulability analysis

    3.1. Overview

    3.2. Enhanced Audsley??s algorithm (EAA)

    3.3. Properties

    4. Performance evaluation

    4.1. Metrics, experimental setup, and data sets

    4.2. Experimental results

    4.2.1. EAA compared with the Audsley??s algorithm

    4.2.2. Initial value improvement

    5. Conclusion and future work

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     Hierarchical multi-objective evacuation routing in stadium using ant colony optimization approach Original Research Article Journal of Transport Geography, Volume 19, Issue 3, May 2011, Pages 443-451

    Zhixiang Fang, Xinlu Zong, Qingquan Li, Qiuping Li, Shengwu Xiong

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    Evacuation planning is a fundamental requirement to ensure that most people can be evacuated to a safe area when a natural accident or an intentional act happens in a stadium environment. The central challenge in evacuation planning is to determine the optimum evacuation routing to safe areas. We describe the evacuation network within a stadium as a hierarchical directed network. We propose a multi-objective optimization approach to solve the evacuation routing problem on the basis of this hierarchical directed network. This problem involves three objectives that need to be achieved simultaneously, such as minimization of total evacuation time, minimization of total evacuation distance and minimal cumulative congestion degrees in an evacuation process. To solve this problem, we designed a modified ant colony optimization (ACO) algorithm, implemented it in the MATLAB software environment, and tested it using a stadium at the Wuhan Sports Center in China. We demonstrate that the algorithm can solve the problem, and has a better evacuation performance in terms of organizing evacuees?? space?Ctime paths than the ACO algorithm, the kth shortest path

    algorithm and the second generation of non-dominated sorting genetic algorithm were used to improve the results from the kth shortest path algorithm.

    Article Outline

    1. Introduction

    2. Related work

    3. Problem formulation

    3.1. Notation

    3.2. Mathematical formulation

    4. Methodology

    4.1. Hierarchical heuristic searching strategy of an ant 4.2. Binary pheromone updating strategy

    4.3. Proposed algorithm

    5. Computational experiments

    5.1. Experimental design

    5.2. Result analysis

    6. Conclusions


    Appendix. Appendix

    A.1. Pareto optimal set in a multi-objective optimization problem References Purchase

Research highlights

    ? A destination-oriented evacuation network organization and two critical strategies (the hierarchical heuristic searching strategy of an ant, and the binary pheromone updating strategy) are adopted to improve the searching efficiency of an intelligent ant??s path in finding evacuation routings. ? Evacuation time, distance and congestion degree are both embedded into evacuation routing research, which makes space-time evacuation paths more practical and reasonable. ? The proposed multi-objective approach provides an alternative perspective to solve evacuation routing problem in stadium.


     National achievements in control theory: The aerospace perspective Original Research Article

    Annual Reviews in Control, Volume 29, Issue 1, 2005, Pages 13-31 V.F. Krotov, A.B. Kurzhanski

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    It is well known that among the first motivations for modern control theory were dynamic optimization problems in rocket launching and navigation in aerospace. These problems had become especially important

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