Computational Science - ICCS 2004

We consider the realization of matrix-matrix multiplication and propose a hierarchical algorithm implemented in a task-parallel way using multiprocessor tasks on distributed memory. The algorithm has been designed to minimize the communication overhead while showing large locality of memory references. The task-parallel realization makes the algorithm especially suited for cluster of SMPs since tasks can then be mapped to the different cluster nodes in order to efficiently exploit the cluster architecture. Experiments on current cluster machines show that the resulting execution times are competitive with state-of-the-art methods like PDGEMM.

On cc-NUMA multi-processors, the non-uniformity of main memory latencies motivates the need for co-location of threads and data. We call this special form of data locality, geographical locality, as one aspect of the non-uniformity is the physical distance between the cc-NUMA nodes. We compare the well established first-touch strategy to an application-initiated page migration strategy as means of increasing the geographical locality for a set of important scientific applications.The main conclusions of the study are: (1) that geographical locality is important for the performance of the applications, (2) that application-initiated migration outperforms the first-touch scheme in almost all cases, and in some cases even results in performance which is close to what is obtained if all threads and data are allocated on a single node.

A cache oblivious matrix transposition algorithm is implemented and analyzed using simulation and hardware performance counters. Contrary to its name, the cache oblivious matrix transposition algorithm is found to exhibit a complex cache behavior with a cache miss ratio that is strongly dependent on the associativity of the cache. In some circumstances the cache behavior is found to be worst than that of a naïve transposition algorithm. While the total size is an important factor in determining cache usage efficiency, the sub-block size, associativity, and cache line replacement policy are also shown to be very important.

We give a hybrid algorithm for solving non-linear polynomial systems. It is based on a branch-and-prune algorithm, combined with classical numerical methods, symbolic methods and interval methods. For some kinds of problems, Gather-and-Sift method, a symbolic method proposed by L. Yang, was used to reduce the dependency of variables or occurrences of the same variable, then interval methods were used to isolate the real roots. Besides these, there are some intelligent judgments which can improve the system’s efficiency significantly. The algorithm presented here works rather efficiently for some kinds of tests.

For the nonlinear eigenvalue problem T(λ)x=0 we consider a Jacobi–Davidson type iterative projection method. The resulting projected nonlinear eigenvalue problems are solved by inverse iteration. The method is applied to a rational eigenvalue problem governing damped vibrations of a structure.

matcont is a matlab continuation package for the interactive numerical study of a range of parameterized nonlinear problems. We discuss a recent addition to the package, namely the continuation of branch points of limit cycles in three parameters which is not available in any other package. This includes the exact location of the BPC points and branch switching. The algorithm is important in the numerical study of symmetry and we illustrate it in the case of the famous Lorenz model for the atmospheric circulation.

In this paper we prove the analytic connection between Support Vector Machines (SVM) and Regularization Theory (RT) and show, based on this prove, a new on-line parametric model for time series forecasting based on Vapnik-Chervonenkis (VC) theory. Using the latter strong connection, we propose a regularization operator in order to obtain a suitable expansion of radial basis functions (RBFs) and expressions for updating neural parameters. This operator seeks for the “flattest” function in a feature space, minimizing the risk functional. Finally we mention some modifications and extensions that can be applied to control neural resources and select relevant input space.

In this paper, we improve A-P iterative algorithm, and use the algorithm to implement the reconstruction from weighted samples, and obtain explicit convergence rate of the algorithm in spline subspaces.

Mathematical modeling of growing multicellular structures creates the problem of solving systems of equations in which not only the values of variables, but the equations themselves, may change over time. We consider this problem in the framework of Lindenmayer systems, a standard formalism for modeling plants, and show how parametric context-sensitive L-systems can be used to numerically solve growing systems of coupled differential equations. We illustrate our technique with a developmental model of the multicellular bacterium Anabaena.

In this paper we develop a family of A-stable one-step methods with high derivatives by means of a collocation technique. We present construction details and a theory to justify such sort of methods. We also concentrate on an effective way of their practical implementation.

The main purpose of this paper is to investigate the local error for the sampling problem in diverse situations. We find that the local error is heavily depending on the asymptotic behavior of the sampling function. By virtue of evaluating the decay of the sampling function, we give a local error estimation for uniform and non-uniform sampling in multiresolution analysis (MRA) and in shift-invariant spaces.

Two aspects of work are addressed in this paper. The first is concerned with a mathematical model for mean flow and hydraulics in the strait of Gibraltar. The model is based on the two-dimensional shallow water equations. The second aspect of work is devoted to developing a robust numerical method for solving such equations. We introduce a fully implicit semi-Lagrangian method which maintains stability even if large time steps are used in computations and reduces artificial numerical dispersion. Preliminary results obtained for a dam-break problem show that our method is able to provide stable and accurate solutions.

Efficiency of the preconditioning methodology for an iterative solution of discrete stress analysis problems is studied in this article. The preconditioning strategy is based on space decomposition and subspace correction framework. The principle idea is to decompose a global discrete system into a sequence of scalar subproblems, corresponding to the different Cartesian coordinates of the displacement vector. The scalar subproblems can be treated by a host of direct and iterative techniques, however we restrict ourselves to a “black-box” application of the direct sparse solvers and the scalar algebraic multigrid (AMG) method. The subspace correction is performed in either block diagonal or block lower triangular fashion. The efficiency and potential limitations of the proposed preconditioning methodology are studied on stress analysis for 2D and 3D model problems from microfabrication technology.

A direct solver for diffraction problems is presented in this paper. The solver is based on the fast Fourier transform (FFT) and the successive elimination of lines which we call SEL. In the previous paper, we showed the numerical algorithm by use of SEL and proved that the limit function of approximate solutions satisfied the diffraction problem in the sense of distribution. In this paper, the above numerical algorithm is improved with FFT and we show that the calculation speed is faster than the previous one.

There are two nasty classical problems of synonymy and polysemy in the filtering systems of Chinese documents. To deal with these two problems, we would ideally like to represent documents not by words, but by the semantic relations between words. Non-negative matrix factorization (NMF) is applied to dimensionality reduction of the words space. NMF is distinguished from the latent semantic indexing (LSI) by its non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. Also, NMF computation is based on the simple iterative algorithm; it is therefore advantageous for applications involving large sparse matrices. The experimental results show that, comparing with LSI, NMF method not only improves filtering precision markedly, but also has the merits of fast computing speed and less memory occupancy.

Rapid technological advances are resulting in a greater use of data intensive applications. For this reason and that of the alarming growth in electronic crime, security and availability are critical factors that must be considered in designing a system. This paper proposes a novel data dispersal/encryption scheme to improve both the availability and security of distributed storage system by using the Singular Values Decomposition (SVD) theorem. It handles data represented by any size matrix, and it also allows complete recovery of original data even though stored data are partially damaged. Analysis shows that it improves the availability about 10% compared with an efficient existing scheme for both read and write operation, while it allows secure storage simultaneously.

This paper concerns an adaptive finite element method for the elliptic obstacle problem. We consider the formulation of the problem as an elliptic variational inequation. The adaptive algorithm (modified Uzawa adaptive method)we construct is based on a combination of the Uzawa method associated with the corresponding multivalued operator and a convergent adaptive method for the linear problem. As our main result we show that if the adaptive method for the linear problem is convergent, then the adaptive modified Uzawa method is convergent as well. A numerical experiment shows the studied properties of the method.

We present a model of fracture formation on surfaces of bi-layered materials. The model makes it possible to synthesize patterns of fractures induced by growth or shrinkage of one layer with respect to another. We use the finite element methods (FEM) to obtain numerical solutions. This paper improves the standard FEM with techniques needed to efficiently capture growth and fractures.

Finite elements are an effective method to solve partial differential equations. However, the high computation time and memory needs, especially for 3-dimensional finite elements, restrict the usage of sequential realizations and require efficient parallel algorithms and implementations to compute real-life problems in reasonable time. Adaptivity together with parallelism can reduce execution time significantly, however may introduce additional difficulties like hanging nodes and refinement level hierarchies. This paper presents a parallel adaptive, 3-dimensional, hexahedral finite element method on distributed memory machines. It reduces communication and encapsulates communication details like actual data exchange and communication optimizations by a modular structure.

The paper presents modular design principles and an implementation for computational kernels of parallel adaptive finite element codes. The main idea is to consider separately sequential modules and to add several specific modules for parallel execution. The paper describes main features of the proposed architecture and some technical details of implementation. Advanced capabilities of finite element codes, like higher order and discontinuous discretizations, multi-level solvers and dynamic parallel adaptivity, are taken into account. A prototype code implementing described ideas is also presented.

We investigate a load balancing strategy that uses a model of the computational behavior of a parallel solver to correct an initial partition of data.

Computational analysis of unidirectional transient problems in multiscale heterogeneous media using specially adopted homogenization technique and the Finite Element Method is described below. Multiresolutional homogenization being the extension of the classical micro-macro traditional approach is used to calculate effective parameters of the composite. Effectiveness of the method is compared against previous techniques thanks to the FEM solution of some engineering problems with real material parameters and with their homogenized values. Further computational studies are necessary in this area, however application of the multiresolutional technique is justified by the natural multiscale character of composites.

In this paper, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. The performance of the proposed gFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

In this study, a new architecture and comprehensive design methodology of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) are introduced and a series of numeric experiments are carried out. The gHFNN architecture results from a synergistic usage of the hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). FNN contributes to the formation of the premise part of the overall network structure of the gHFNN. The consequence part of the gHFNN is designed using PNN.

The paper addresses the problem of using contextual information by neural nets solving problems of contextual nature. The model of a context-dependent neuron is unique in the fact that allows weights to change according to some contextual variables even after the learning process has been completed. The structures of context-dependent neural nets are outlined, the Vapnik-Chervonenkis dimension of the single context-dependent neuron and multilayer net shortly discussed, decision boundaries are analyzed and compared with the traditional nets. The main goal of the article is to present highly effective contextual training algorithms for the considered models of neural nets.

This study proposes a new approach to feature selection and the identification of underlaying factors. The goal of this method is to visualize and extract information from complex and high dimensional data sets. The model proposed is an extension of Maximum Likelihood Hebbian Learning [14], [5], [15] based on a family of cost functions, which maximizes the likelihood of identifying a specific distribution in the data while minimizing the effect of outliers [7], [10]. We demonstrate a hierarchical extension method which provides an interactive method for identifying possibly hidden structure in the dataset. We have applied this method to study the thermal evolution of several construction materials under different thermal and humidity environmental conditions.

In this paper, a novel method for intrusion detection is presented. The presented method uses a clustering method based on the transformed features, which can enhance the effectiveness of the clustering. Clustering is used in anomaly detection systems to separate attack and normal samples. In general, separating attack and normal samples in the original input space is not an easy task. For better separation of the samples, a transformation that maps input data into a different feature space should be performed. In this paper, we propose a novel method for obtaining proper transformation function that reflects the characteristics of the given domain. The transformation function is obtained from the hidden layer of the trained three-layer neural network. Experiments over network connection records from KDD CUP 1999 data set are used to evaluate the proposed method. The result of the experiment clearly shows the outstanding performance of the proposed method.

Classical prediction fire schemes do not match the real fire propagation, basically, because of the complexity of the physical models involved, the need for a great amount of computation and the difficulties of providing accurate input parameters. We describe an enhanced prediction scheme, which uses recent fire history and optimization techniques to predict near future propagation. The proposed method takes advantage of the computational power offered by distributed systems to accelerate the optimization process at real time.

In a high resolution SAR-satellite mission it is very important to predict the flight trajectory of the satellite with a very high precision. Typically a SAR-satellite operates in near earth orbits. At this altitude the orbit is influenced by several physical phenomena (gravity, atmosphere, and air friction). The model to simulate these orbits should represent these influences sufficiently well. A detailed model has been build on the basis of the newest CHAMP gravity model and the MSIS86 atmosphere model. As a result of the complexity and the mathematical characteristics of this model it is necessary to implement a high order ODE-solver. Three different kinds of error are investigated to evaluate the predictive power of the model: numerical error due to the approximation scheme, power series truncation error on the gravity model, and statistical error due to parameter sensitivity. To this end an extensive sensitivity analysis of thousands of model parameters is carried out.

This paper describes a novel approach of combining Monte Carlo simulation and neural nets. This hybrid approach is applied to model discrete transportation systems, with the accurate but computationally expensive Monte Carlo simulation used to train a neural net. Once trained the neural net can efficiently, but less accurately provide functional analysis and reliability predictions. No restriction on the system structure and on a kind of distribution is the main advantage of the proposed approach. The results of reliability and functional analysis can be used as a basis for economic aspects discussion related to the discrete transport system. The presented decision problem is practically essential for defining an organization of vehicle maintenance.

In this paper a gas mixture model is developed to study anisotropic hydrogen and water vapour flow in anode of the PEM fuel cell. Dependence of the distribution of concentrations and fluxes of the of gas components in anisotropic porous layer is investigated. First full partial differential equations describing mass transport for permeability and diffusivity tensors based on Darcy’s and Fick’s laws are developed. Next this set of nonlinear equations together with appropriate nonlinear boundary conditions using finite element method was solved. At the end an illustrative example is given.

In this paper the method of computation of weak magnetic fields in the presence of anisotropic shields it is described. The formulation is based on vector magnetic potential and finite element formulation. Investigation of influence of anisotropic ratio on shielding effectiveness of low level magnetic fields is investigated. At the end some illustrative example in 3D is given and numerical results are compared with experimental data.

We summarize a theoretical study for modeling functionalization of single-wall carbon nanotubes (SWCNTs), specifically first principles density functional theory calculations, as compared to semi-empirical or simplified hierarchical methods. We focus on the assessment of the methods to be applied to obtain reliable results and gain a fundamental understanding of the diazotization and ozonolysis of SWCNTs. Computational challenges encountered are highlighted.

Shadow Hybrid Monte Carlo (SHMC) is a new method for sampling the phase space of large biological molecules. It improves sampling by allowing larger time steps and system sizes in the molecular dynamics (MD) step of Hybrid Monte Carlo (HMC). This is achieved by sampling from high order approximations to the modified Hamiltonian, which is exactly integrated by a symplectic MD integrator. SHMC requires extra storage, modest computational overhead, and a reweighting step to obtain averages from the canonical ensemble. Numerical experiments are performed on biological molecules, ranging from a small peptide with 66 atoms to a large solvated protein with 14281 atoms. Experimentally, SHMC achieves an order magnitude speedup in sampling efficiency for medium sized proteins.

We present a Monte Carlo method for approximating the solution of conservation laws. A relaxation method is used to transform the conservation law to a kinetic form that can be interpreted in a probabilistic manner. A Monte Carlo algorithm is then used to simulate the kinetic approximation. The method we present in this paper is simple to formulate and to implement, and can be straightforwardly extended to higher dimensional conservation laws. Numerical experiments are carried out using Burgers equation subject to both smooth and nonsmooth initial data.

Gillespie’s Direct Method Algorithm (1977), is a well-known exact stochastic algorithm for simulating coupled reactions that requires the use of random numbers to calculate which reaction occurs next and when it occurs. However this algorithm is serial in design. For complex chemical systems, this will involve computationally intensive requirements with long simulation runs. This paper looks at decreasing execution times by attempting to parallelize this algorithm through splitting the computational domain into smaller units which will result in smaller computations and thus faster executions.

A new method is presented for simulation of deformable objects that consist of rigid and flexible structural elements using the control based Singularly Perturbed Sliding Manifold (SPSM) approach. The problem is multi-scale due to its rigid and flexible components and forms a set of differential-algebraic equations. The problem is formulated as a set of ODEs with inequality constraints by allowing some deviations in the rigid links. The SPSM approach is particularly well suited for such problems. It is shown that this method can handle inconsistent initial conditions, and it allows the user to systematically approximate the equations due to its robustness properties. The inherent attractivity of the sliding dynamics enables the method to handle sudden changes in position, velocity or acceleration while satisfying geometrical constraints. The desired level of accuracy in constraint errors is achieved in a finite time and thereafter. Moreover, the new approach is explicit and capable of performing multi-rate and real-time simulations. Finally, it is shown that the SPSM approach to simulation requires the inversion of a smaller matrix than comparable implicit methods. The result is significantly improved performance for computationally expensive applications such as cloth animation.

The key to the successful simulation of deformable objects is to model the realistic behavior of deformation when they are influenced by intricate contact conditions and geometric constraints. This paper describes constraint-based contact modeling between deformable objects using a nonlinear finite element method. In contrast to the penalty force based approaches, constraint-based enforcement of contact provide accuracy and freedom from finding proper penalty coefficients. This paper is focused on determining contact regions and calculating reaction forces at appropriate nodes and elements within the contact regions. The displacement and deformation of all nodes are dynamically updated based on the contact reaction forces. Our constraint based contact force computation method guarantees tight error bound at the contact regions and maintains hard constraints without overshoot or oscillation at the boundaries. In addition, the proposed method doesn’t require us to choose proper penalty coefficients, thus greater numerical stability can be achieved and generally large integration steps can be utilized for the ODE solver. Contact conditions are formulated as nonlinear equality and inequality constraints and the force computation is cast into a nonlinear optimization problem. Our rigid-to-deformable and deformable-to-deformable contact simulation demonstrates that the non-penetration constraints are well maintained.

Determining the binding sites in protein-nucleic acid complexes is essential to the complete understanding of protein-nucleic acid interactions and to the development of new drugs. We have developed a set of algorithms for analyzing protein-nucleic acid interactions and for predicting potential binding sites in protein-nucleic acid complexes. The algorithms were used to analyze the hydrogen-bonding interactions in protein-RNA and protein-DNA complexes. The analysis was done both at the atomic and residue level, and discovered several interesting interaction patterns and differences between the two types of nucleic acids. The interaction patterns were used for predicting potential binding sites in new protein-RNA complexes.

Information on protein-protein interactions provides valuable insight into the underlying mechanism of protein functions. However, the accuracy of protein-protein interaction data obtained by high-throughput experimental methods is low, and thus requires a rigorous assessment of their reliability. This paper proposes a computational method for predicting the unknown function of a protein interacting with a protein with known function, and presents the experimental results of applying the method to the protein-protein interactions in yeast and human. This method can also be used to assess the reliability of the protein-protein interaction data.

Magainin-2 is a natural peptide that kills bacteria at concentrations that are harmless to animal cells. Molecular modelling methods were applied to investigate basic mechanisms of magainin-2 amide (M2a) membrane selectivity. Three computer models of a lipid matrix of either animal or bacterial membrane containing M2a were constructed and simulated. Specific interactions between membrane lipids and M2a peptides, responsible for M2a selectivity are elucidated.

When a discrete granular layer on a uniform substrate is tapped from beneath the material piles up into discrete heaps which gradually coarsen. We investigate the relaxation dynamics of the heaping process. We present a non-linear phenomenological partial differential equation to describe the formation of the heaplets. This equation is derived from the continuity equation for a diffusive powder system and from a constitutive equation giving the current as the sum of three terms: the first proportional to the gradient of the height profile with a limiting factor, the second related to the average curvature of the heap surface and the third related to the Gaussian curvature.

This paper presents a FE-analysis of shear localization in granular bodies with a finite element method based on a hypoplastic constitutive law. The law can reproduce essential features of granular bodies in dependence on the void ratio, pressure level and deformation direction. To simulate the formation of a spontaneous shear zone inside of cohesionless sand during plane strain compression, a hypoplastic law was extended by polar, non-local and gradient terms. The effects of 3 different models on the thickness of a shear zone was investigated.

In this work we present a novel algorithm which detects contacts between spherical particles in 2D and 3D. We estimate efficiency of this algorithm throughout analysis of the execution time. We also compare our results with the Linked Cell Method.

The vortex–in–cell method for three-dimensional, viscous flow was presented. A viscous splitting algorithm was used. Initially the Euler inviscid equation was solved. Following that, the viscous effect was taken into account by the solution of the diffusion equation. The diffusion equation was then solved by the particle strength exchange (PSE) method. Validation of the method was tested by simulation of the leap-frogging phenomenon for two vortex rings moving along a common axis of symmetry and the reconnection phenomenon of two colliding vortex rings for viscous flow.

Particle compound material is the composition of different particles with inhomogeneous and non-uniform properties. Particle compound material is the most complicated engineering material whose properties vary according to the applications, method of manufacturing and ratio of its ingredients. The quasi-homogeneous materials like building materials and constituents of tablets, pellets etc are some of the examples of particle compounds.The 2 Dimensional Finite Element Analysis was done with single plate compressive stressing on the particle compound material to have the idea of the stress distributions during stressing. Then the method was followed by the Discrete Element Method (DEM) for further analysis.The study of crack propagating mechanism in particle compound was represented by a model material which resembles the high strength materials, pressed agglomerates and more. The paper analyses the cracking of the particle compounds, here concrete ball, with respect to continuum and discrete approach.

The dynamic mathematical models were greatly developed to describe the motion of the sucker-rod pumping system since 1960s and various mathematic models based on different assumptions were presented. This paper does a study in three sides. First, a mathematical model is presented to describe the sucker-rod string with longitudinal and transverse vibrations, and coupled with longitudinal vibration of the tubing and fluid columns in a deviated well. Second, the relations of several kinds of mathematical models in sucker-rod pumping systems are discussed, and the model made in this paper, when based on different assumptions, can become different models made by people these years, which are presented in important references. Third, a method of characteristics is used to transform the set of partial differential equations which describe the vibration of the sucker-rod string, and coupled with vibrations of tubing and liquid columns in the sucker-rod pumping system. Through the transformation, a diagonal partial differential equations set is obtained. Hence a relatively complex model is transformed into a reduced model which is easy to solve. This model has basic meaning for using the technique of pattern recognition to make automatic diagnosis of sucker rod pumping system.

This paper describes a new parallel algorithm to compute the optical flow of a video sequence. A previous sequential algorithm has been distributed over a cluster. It has been implemented in a cluster with 8 nodes connected by means of a Gigabit Ethernet. On this architecture, the algorithm, that computes the optical flow of every image on the sequence, is able of processing 10 images of 720x576 pixels per second.

Modified conjugate directional filtering (MCDF) is a method proposed by Guo and Watson recently for digital data and image processing. By using MCDF, directionally filtered results in conjugate directions can be not only merged into one image that shows the maximum linear features in the two conjugate directions, but also further manipulated by a number of predefined generic MCDF operations for different purposes. Although a number of cases have been used to test the usefulness of several proposed MCDF operations, and the results are ‘visually’ better than some conventional methods, however, no quantified analytical results on its effectiveness have been obtained. This has been the major obstacle on the decision whether it is worth developing a usable MCDF system. This paper firstly outlines a FFT-based analytical design for conducting the tests, and then presents the results of applying this analytical design to the analysis of MCDF(add1) operation for an image of digital terrain model in central Australia. The test verifies that the MCDF(add1) operation indeed overcomes the two weaknesses of using the conventional directional filtering in image processing, i.e., separation in presentation of processed results in different directions, and significant loss in low-frequency components. Therefore, the MCDF method is worth for further development.

VolumePro is a real-time volume rendering hardware for consumer PCs. It cannot be used for the applications requiring perspective projection such as virtual endoscopy since it provides only orthographic projection. Several methods have been proposed to approximate perspective projection by decomposing a volume into slabs and applying successive orthographic projection to them. However it takes a lot of time since entire region of each slab should be processed, even though some of the region does not contribute to final image. In this paper, we propose an efficient perspective projection method that exploits several subvolumes with cropping feature of VolumePro. It reduces the rendering time in comparison to slab-based method without image quality deterioration since it processes only the parts contained in the view frustum.

An approach for reconstructing wireframe models of curvilinear objects from three orthographic views is discussed. Our main stress is on the method of generating three-dimensional (3D) conic edges from two-dimensional (2D) projection conic curves, which is the pivotal work for reconstructing curvilinear objects from three orthographic views. In order to generate 3D conic edges, a five-point method is firstly utilized to obtain the algebraic representations of all 2D projection curves in each view, and then all algebraic forms are converted to the corresponding geometric forms analytically. Thus the locus of a 3D conic edge can be derived from the geometric forms of the relevant conic curves in three views. Finally, the wireframe model is created after eliminating all redundant elements generated in previous reconstruction process. The approach extends the range of objects to be reconstructed and imposes no restriction on the axis of the quadric surface.

This paper presents an adaptive modification of the Edge spinning method for polygonization of implicit surfaces. The method insists on the shape of triangles and the accuracy of resulting approximation as well. The main advantages of the triangulation presented are simplicity and the stable features that can be used for next expanding. The implementation is not complicated and only the standard data structures are used. The presented algorithm is based on the surface tracking scheme and it is compared with the other algorithms which are based on the similar principle, such as the Marching cubes and the Marching triangles algorithms.

This paper presents two visualization techniques suitable for huge oceanography time-varying volume datasets on high-performance graphics workstations. We first propose an off-line parallel rendering algorithm that merges volume ray-casting and on-the-fly isocontouring. This hybrid technique is quite effective in producing fly-through movies of high resolution. We also describe an interactive rendering algorithm that exploits multi-piped graphics hardware. Through this technique, it is possible to achieve interactive-time frame rates for huge time-varying volume data streams. While both techniques have been originally developed on an SGI visualization system, they can be also ported to commodity PC cluster environments with great ease.

This paper presents a new free-form surface representation scheme, which we call Sphere-Spin-Image(SSI), and its application to 3D face recognition. An SSI, associated with a point on the surface, is a 2D histogram constructed from a neighborhood surface of the point using position information, which captures the characteristic of local shape. Thus, a free-form surface can be represented by a series of SSIs. Correlation coefficient is used as similarity metric for comparing SSIs. During face recognition, the SSIs of points on face surface are computed, and recognition task is achieved by SSI-comparison-based voting method. To reduce computational cost, only some particular points of face surface are involved in voting. With face database consisting of 31 different pose models, experimental result of equal error rate 8.32% demonstrates performance of the proposed method.

Based on the analysis of the virtual assembly planning, this paper presents a new assembly planning method, which is named Intelligent Virtual Assembly Planning. The architecture of the IVAP system consists of four main components: Intelligent Assembly Planning Environment, Virtual Assembly Planning Environment, Data/Knowledge Acquisition Interface, and Inner Interactive Interface. The integrated assembly object model used in the system is described in detail in the paper.

Rational functions are often used to model and to interpolate frequency-domain data. The data samples, required to obtain an accurate model, can be computationally expensive to simulate. Using the frequency derivatives of the data significantly reduces the number of support samples, since they provide additional information during the modeling process. They are particularly useful when the data is sparse and the samples are selected adaptively.

As it is not realistic to expect that all users, especially specialists in the graphic business, use complex low-level parallel programs to speed up image processing, we have developed a plugin for the highly acclaimed GIMP which enables to invoke a series of filter operations in a pipeline in parallel on a set of images loaded by the plugin. We present the software developments, test scenarios and experimental results on cluster grid systems possibly featuring single-processors and SMP nodes and being used by other users at the same time. Behind the GUI, the plugin invokes a smart DAMPVM cluster grid shell which spawns processes on the best nodes in the cluster, taking into account their loads including other user processes. This enables to select the fastest nodes for the stages in the pipeline. We show by experiment that the approach prevents scenarios in which other user processes or even slightly more loaded processors become the bottlenecks of the whole pipeline. The parallel mapping is completely transparent to the end user who interacts only with the GUI. We present the results achieved with the GIMP plugin using the smart cluster grid shell as well as a simple round robin scheduling and prove the former solution to be superior.

Performance of parallel scientific applications is often heavily influenced by various mathematical kernels like linear algebra software that needs to be highly optimised for each particular platform. Parallel multi-dimensional Fast Fourier Transforms (FFT) fall into this category too. In this paper we describe a systematic methodology for benchmarking parallel application kernels using the ZENTURIO experiment management tool. We report comparative results on benchmarking two three dimensional FFT kernels on a Beowulf cluster.

We developed the interactive Java applet which made it possible to prove and illustrate by Brownian numerical experiments the Central Limit Theorem (CLT); the theorem which yet constitutes the basis for the Gaussian stochastic processes and the reference case for the non-Gaussian ones. Our approach emphasizes the contrast between theoretical complexity and simplicity provided by our probabilistic simulations. We argue that the present approach should be the preliminary stage for the advanced educational process before the analytical stage is developed. We stress that the Gaussian probability distribution function (PDF) is a stable as distinguished, e.g., from the delta-Dirac, Poisson and t-Student ones which were also considered for comparison. The latter distribution was chosen so as to have all the moments higher than the second order diverging so as to verify the validity of CLT. As our experiments were performed in real time we were able to visualize the convergence of the processes (versus the size of the statistical ensemble of Brownian experiments) both to the variance of the sum of independent identically distributed random variables (which linearly increases with the number of the latter) and to the Gaussian PDF (which is the asymptotic distribution of this sum independently of the single-step PDF used here). We hope that our experimental approach will inspire students to undertake their own studies, e.g., to consider the non-Gaussian processes where CLT is violated which is the modern trend in statistical physics and its applications.

This paper presents a new checkpoint recovery protocol for Distributed Shared Memory (DSM) systems with read-write objects. It is based on independent checkpointing integrated with a coherence protocol for causal consistency model. That integration results in high availability of shared objects and ensures fast restoration of consistent state of the DSM in spite of multiple node failures, introducing little overhead. Moreover, in case of network partitioning, the extended protocol ensures that all the processes in majority partition of the DSM system can continuously access all the objects.

This paper presents various graphical techniques developed by us to support research of some very hard combinatorial optimization (CO) problems. Thanks to increased efficiency of algorithms and high power of modern PC, we are able to make visualization of the solution space and search trajectories, which in order, allows us to analyze and design modern approximate approaches quickly running towards good solution. We provide unique graphic representations of solution spaces and search trajectories of modern approximate algorithms dedicated for the job-shop problem, the one of the hardest problem in CO.

Human bodies defend themselves against harmful invaders by the Natural Immune System (NIS). The NIS possesses a huge set of detectors against many different invaders. Based on the essential principles of the NIS, the idea of Artificial Immune System (AIS) can be used for protecting computer resources. The AIS can detect the nonself-activity using a self-adopted detector set. The detector set is generated randomly and evolved by a Genetic Algorithm (GA). However, the random generation and the blind GA adoption decrease the performance of detector set and generate unnecessary redundancy. To overcome this problem, we propose a more efficient detector generation and evolution scheme. Using the proposed idea, the AIS can produce more effective detector set with time advantages.

Since the first computer virus has been found, scanning detection has been used as a primarily method in virus detection systems. As computer viruses and worms become more complex and sophisticated, the scanning detection method is no longer able to detect various forms of viruses and worms effectively. Many anti-virus researchers proposed various detection methods including artificial immune system to cope with these characteristics of viruses and worms. This paper discusses some principle of artificial immune system and proposes artificial immune based virus detection system that can detect unknown viruses.

This paper presents novel developments of a programming methodology – in a form of the programming paradigms – for the group of hardware description languages (HDL). On the one hand, this group of languages is very similar to other high-level programming languages. From the other hand a description, which then must fit specific hardware arrays, needs to be coded in a special way with particular attention devoted to the code structure. In this paper it will be shown that the specific programming rules can make this process easier and more efficient. Also clarity of the code can be greatly improved, what is very important in the case of a team work. The complete set of the VHDL programming rules is presented, as well as examples of increased coding efficiency. This approach is much more general and stands in opposition to the other literature sources in which only selective programming advices are provided. Presented ideas were verified in many large telecommunication projects developed on the Xilinx’ ISE Foundation platform.

Fuzzy Cognitive Maps constitute an attractive modeling approach that encompasses advantageous features. The most pronounces are the flexibility in system design, model and control, the comprehensive operation and the abstractive representation of complex systems. The main deficiencies of FCMs are the critical dependence on the expert’s opinion and the potential convergence to undesired steady states. In order to overcome these deficiencies a possible solution is the utilization of learning methods to adapt the cause-effect relationships of the FCM model. This research work examines the formulation of a weight adaptation method suitable for FCMs based on nonlinear Hebbian-type learning algorithm. A process control problem is presented and its control is investigated using the adaptation technique.

This paper describes a novel approach based on fuzzy logic technique for smoothing process on the unstructured meshes in 2D. The proposed method works local on triangulation. Therefore, remeshing does not require. It is known that other smoothing operations such as laplacian smoothing , optimization based smoothing, angle based smoothing, and hybrid smoothing operations work global. That is, in these methods, meshing is achieved, afterwards remeshing is done for smoothing. Whereas, the goal of presented method is to find the best fit location of node while meshing is done. However, remeshing process is avoided.

During the last years the use of intelligent strategies for tuning Proportional-Integral-Derivative (PID) controllers has been growing. The evolutionary strategies have won an important place thanks to their flexibility. In this paper, the automatic tuning of systems with scarce initial information and integrative and unstable dynamics, through a genetic approach is presented. The advantages of the proposed approach were highlighted through the comparison with the Ziegler-Nichols modified closed loop method, and the Visioli genetic approach. The proposed methodology goal is to expand the intelligent tuning application to a wider range of processes (covering systems with oscillatory or unstable modes).

Biologists today are striving to solve multidisciplinary, complex systems biology questions. To successfully address these questions, software tools must be created to allow scientists to capture data and information, to share this information, and to analyze the data as elements of a complete system. At Pacific Northwest National Laboratory, we are creating the Computational Cell Environment, a biology-centered collaborative problem-solving environment with the goal of providing data retrieval, management, and analysis through all aspects of biological study. A horizontal prototype called SysBioPSE, demonstrates this vision. Our initial work is centered on developing the Distributed Data Management and Analysis subsystem, which is a specific tool for retrieving data from multiple heterogeneous data stores, providing storage facilities that support pedigree tracking and data and information analysis under a common user interface. With time, many such individual subsystems will be developed and integrated to fulfill the Computational Cell Environment vision.

Depth recovery is one of the classical problems of computer vision. Many methods have been developed that address this issue. However, each of them has specific behavior depending on the image contents, acquisition system, computation time, etc. The variety of methods is due to the non uniqueness of the matching process involved in depth recovery from a pair of images of a given scene. The presented method relies on tensor representation of local structures in images. This representation allows for extraction of the local phase, signal intensity and coherence measure of a local neighborhood in different scales. This operator showed to be very useful in detection of locally oriented structures and corners in single digital images – presented at the previous ICCS. In this paper, the tensor operator is used for area-based matching of stereo images. Due to tensor transformation of the input images the more reliable matching was achieved. The method was tested with many different classes of stereo images. In this paper we present and discuss the experimental results and also details of implementation.

The principal aim of this work is the presentation of a symbolic calculation computer analysis for exploring electromagnetic fields for not inertial observer. Based on Frölicher-Nijenhuis super-Lie $\mathbb R $-algebra, we developed a learning environment for axiomatic classical electromagnetics and electrodynamics. A collection of programs developed on Mathematical programming environment has been builded for the $\mathbb N $-graded Graßmann Algebra, $\mathbb Z $-graded endomorphisms and graded commutators.

It is well-known that the classical univariate orthogonal polynomials give rise to highly efficient Gaussian quadrature rules. We show how the classical orthogonal polynomials can be generalized to a multivariate setting and how this generalization leads to Gaussian cubature rules for specific families of multivariate polynomials.The multivariate homogeneous orthogonal functions that we discuss here satisfy a unique slice projection property: they project to univariate orthogonal polynomials on every one-dimensional subspace spanned by a vector from the unit hypersphere. We therefore call them spherical orthogonal polynomials.

In this paper we interpret the Berlekamp-Massey algorithm (BMA) for synthesis of linear feedback shift register (LFSR) as an algorithm computing Pade approximants for Laurent series over arbitrary field. This interpretation of the BMA is based on a iterated procedure for computing of the sequence of polynomials orthogonal to some sequence of polynomial spaces with scalar product depending on the given Laurent series. It is shown that the BMA is equivalent to the Euclidean algorithm of a conversion of Laurent series in continued fractions.

In recent paper [5] a procedure was developed to control a step size for Runge-Kutta methods. Here, we present a new version of that step size selection to make it better in exceptional cases when the old version does not work appropriately.

In recent papers [6]–[10] the technique for local and global errors estimation and the local-global step size control have been presented to solve both ordinary differential equations and semi-explicit index 1 differential-algebraic systems by multistep methods with any automatically obtained reasonable accuracy. Now we describe the object oriented library INTEGRATOR (ver. 1.0) built in C++ for portability and performance across a wide class of machine architectures. Also we give some computational tests and a comparison with the software implemented the well-known Gear’s method which has been included into MATLAB (ver. 6.2, release 12).

In this paper, we study the reconstructing of signals based on regular and irregular incremental integral samples in some non-bandlimited space-—spline subspace, and we obtain reconstructing formulas with a new method.

The article presents two approaches to the WZ factorization – specific ones for solving Markov chains – and the results of their vectorization and parallelization.

One of the global approach for solving the two dimensional inverse parabolic problem is the predictor corrector which takes place for evaluating the pair (u,p) and adjusting the evaluation for the desired accuracy. In this work we will present a new parallel algorithm(of non iterative type) for solving two or higher dimensional inverse control problem.

In this paper we present a-posteriori error estimator for the mixed formulation of linear parabolic problem, and we use them in designing an efficient adaptive algorithm. Our space-time discretization consist of lowest order Raviart-Thomas finite element over graded meshes, and discontinuous Galerkin method with varying time-steps.

In this paper we show an analysis of different parallel numerical libraries for solving the linear systems associated to the electron continuity equation from the 3D simulation of the semiconductor devices. We use domain decomposition techniques, such as Additive Schwarz, Multicolor SOR or Schur Complement methods, in order to find the best method of resolution considering the minimization of the execution time. The results were obtained in a Beowulf system with Myrinet 2000 network with MPI standard for message–passing communication.

In the rapidly growing field of computational methods within the discipline of Metabolic Engineering, the simulation of instationary13C labeling experiments is a new research focus. The underlying mathematical model is a high-dimensional cascaded system of differential equations and must be exploited to obtain efficient simulation algorithms. Additionally the sensitivity matrices of the system have to be computed as efficient as possible. For this purpose several ways for parallel implementation are introduced, compared and discussed.

The recursive spectral bisection for the k-way graph partition has been underestimated because it tries to balance the bipartition strictly. However, by loosening the balancing constraint, the spectral bisection can identify clusters efficiently. We propose a k-way graph partitioning algorithm based on clustering using recursive spectral bisection. After a graph is divided into a partition, the partition is adjusted in order to meet the balancing constraint. Experimental results show that the clustering based k-way partitioning generates partitions with 83.8~ 108.4% cutsets compared to the strict recursive spectral bisections or multi-level partitions.

We consider the categorical concepts of a ‘network of networks’: (a) each node is a host network (1-network or 1-graph) and super-links are analogous to a graph-functor, i.e. this is (1,1)-network; (b) 2-network where there are 2-links among 1-links. The general notion of network-morphism is proposed.

This paper presents an in-place pseudo linear average case radix sorting algorithm. The proposed algorithm, MSL (Map Shuffle Loop) is a modification of the ARL algorithm. The MSL permutation loop is faster than the ARL counterpart since it searches for the root of the next permutation cycle group by group. The permutation cycle loop maps a key to its target group and shuffles the input array. The performance of MSL is compared with Java quicksort, as well as MSD and LSD radix sorting algorithms.

We study the cycles generated by the chip firing game associated with n-cube orientations. We consider a particular class of partitions of vertices of n-cubes called left cyclic partitions that induce parallel periodic evolutions. Using this combinatorical model, we show that cycles generated by parallel evolutions are of even lengths from 2 to 2n on H _n (n≥ 1), and of odd lengths different from 3 and ranging from 1 to 2n − 1-1 on H _n (n≥ 4). However, the question weather there exist parallel evolutions with period greater that 2n remains opened.

The paper presents a highly optimized implementation of a multifrontal solver for linear systems arising in the FEM simulation of multi-physics problems related to the behaviour of porous media. The solver features a careful preprocessing phase that is crucial to considerably speed up both system assembly and Gaussian elimination. When run on a number of relevant test cases, the proposed solver compares very favourably with both its previous unifrontal counterpart and two general multifrontal solvers well known in the literature.

The analytical and numerical modelling of interaction between the mud layer at the sea bottom and the surface waves have been presented. In the simulations the theory for linear water wave movement in a two-layer viscous fluid system has been considered. The upper layer represents water and the lower layer represents fluid-mud. The type of the bottom material over which waves are propagating is assumed to be similar to a viscous fluid, characterized by a viscosity and density greater than the overlying fluid. It is assumed that the two fluids are incompressible and isotropic, and the rigid strata is smooth and impermeable. At the surface the height of the surface wave is specified and the forced interfacial wave is determined. Developed model solves the equations of motion for an incompressible fluid by composite finite difference-finite element approximations on a staggered scheme. Results of analytical and numerical solutions are compared with the experimental results and favourable agreement has been obtained.

Computational modelling of biological flow can have many in medicine and biomechanical engineering. Stenosis of blood vessels is the main cause of vascular disease. A mathematical model of pulsating flow through stenotic blood vessels has been formulated. The numerical simulations using finite element analysis show that the axial velocity profile is controlled by the shape of the stenotic plagues and the pulsating pressure gradient. Instability due to the vessel narrowing is closely related non-uniform stress distribution. In addition, the peak value of the shear stress may directly affect the degree and location of the rupture.

Heterogeneity of granular materials triggers shear zone formation. In the paper, the FE-analysis of the effect of heterogeneity on the formation of a spontaneous shear zone inside of granular material during a plane strain compression test is presented. The numerical calculations are performed with a finite element method on the basis of a hypoplastic constitutive law extended by polar quantities: rotations, curvatures, couple stresses and a mean grain diameter used as a characteristic length. The heterogeneity in the granular body is assumed in the form of spots with a different initial void ratio. The spots are single and distributed randomly or stochastically.

A simple model of chain containing Dirac’s delta-like potentials is used to calculate the density of states in systems with structural disorder. The crystal case of equally spaced scattering centers may be seen as a reference case suitable both for numerical calculations as well as analytical ones. The structural disorder means distortion of ideal lattice seen as fluctuation of distances between adjacent sites. Such disorder is sometimes referred to as amorphous and may lead to smearing off the band boundaries resulting in a tail-like density of states.

The purpose of this paper is to present a economic system, such as the base to estimate the mathematical exactness of the System Dynamics Method. An interesting point of view is to examine the reaction of such system to the sinusoidal characteristic of the market sale of a product and to estimate the mathematical preciseness of the method solution.

In this paper the Penna model is reconsidered. With computer simulations we check how the control parameters of the model influence the size of the stable population.

Air pollution models usually start from the computation of the velocity field of a fluid. In this paper, we present a model for computing that field based on the contribution of the observed wind flow and the vertical buoyancy or momentum plume rise defined by a Gaussian plume model. This initial velocity field is adjusted to verify incompressibility and impermeability conditions by using a mass consistent model. In this environmental modelling that occur in a three-dimensional domain defined over complex terrain, a mesh generator capable of adapting itself to the topographical data and to the numerical solution is essential. Here, the unstructured tetrahedral meshes are generated by combining the use of a refinement/derefinement algorithm for two-dimensional domains and a tetrahedral mesh generator based on Delaunay triangulation. Occasionally in this process, low quality or even inverted elements may appear. A simultaneous untangling and smoothing procedure allows to optimise the resulting meshes. Once we have constructed the adapted mesh in accordance with the geometrical characteristics of our domain, we use an adaptive local refinement in the plume trajectory. Finally, this model is applied in a test problem.

The subject of this article concerns some aspects of the generation of 3D surface meshes, widely used in many scientific applications. This work contains a description of data structure used in the developed mesh generator and issues related to the management of this structure. The proper organization of this structure coupled with the appropriately selected algorithms, allow to achieve the satisfactory efficiency of the mesh generation process.

We present a novel method based on a recently proposed extension to a negative feedback network which uses simple Hebbian learning to self-organise called Maximum Likelihood Hebbian learning [2]. We use the kernel version of the ML algorithm on data from a spectroscopic analysis of a stained glass rose window in a Spanish cathedral. It is hoped that in classifying the origin and date of each segment it will help in the restoration of this and other historical stain glass windows.

Research activities in the computer reaction modeling and synthesis design have led to the development of a number of computer systems that are successively used as a research tools to study the organic synthesis problems. One of such systems is the CSB (Chemical Sense Builder), which combines four different logic- and knowledge-based models to generate possible structural modes of chemical conversions. In these paper we briefly discuss the architecture and specially the learning methods implemented in the CSB. They exploit different sources of knowledge and chemical reaction data for the classification, generalization, and derivation of rules in a form of the graph transformation schemes. Any transformation is general description of a collection of similar reactions from the reaction data base. Application of these learned rules in the course of reaction simulation enables us to predict – for a given set of reacting molecules – all possible reaction courses, having responses in real chemistry.

In this paper we present a method for extracting relevant words from a document taking into account linguistic information. Form a set of words manually labelled as relevant or not and with the aid of a Machine Learning algorithm, we build a classifier which is able to decide which words from a unseen document must be regarded as relevant. This system is compared with some classical methods (based just on statistical information).

In this paper, Brain Emotional Learning Based Intelligent controller (BELBIC) is applied to Active Queue management (AQM). This type of controller is insensitive to noise and variance of the parameters, thus it is suitable to time varying network systems. Simulation results show the robust performance of BELBIC against the disturbance.

In this paper we present a hybrid algorithm based on Particle Swarm Optimization (PSO) and Simulated Annealing (SA) approaches and apply it to two-dimensional non-guillotine cutting stock problem. The probability of trapping at the local optimum during the searching process can be reduced using the hybrid algorithm. Meanwhile, we propose a converting approach which is similar to the Bottom Left (BL) algorithm to map the cutting pattern to the actual layout. Finally, we implement the proposed algorithm on several test problems. The simulated results show that the performance of the hybrid algorithm is better than that of the standard PSO.

Multi Expression Programming (MEP) is used for evolving a Traveling Salesman Problem (TSP) heuristic for graphs satisfying triangle inequality. Evolved MEP heuristic is compared with Nearest Neighbor Heuristic (NN) and Minimum Spanning Tree Heuristic (MST) on some difficult problems in TSPLIB. The results emphasizes that evolved MEP heuristic is better than the compared algorithm for the considered test problems.

The multiobjective 0/1 knapsack problem is a generalization of the 0/1 knapsack problem in which multiple knapsacks are considered. A new evolutionary algorithm for solving multiobjective 0/1 knapsack problem is proposed in this paper. This algorithm used a ε -dominance relation for direct comparison of two solutions. Several numerical experiments are performed using the best recent algorithms proposed for this problem. Experimental results clearly show that the proposed algorithm outperforms the existing evolutionary approaches for this problem.

This paper presents a genetic evolution system, for target movement prediction, which includes functions inferring opponents’ strategic movements and displaying such predicted movements in an interactive 3D visualization space. To speed up the analysts’ ability to access and integrate information, the prediction approach generates new movements based on past behaviors and application of an inheritance mechanism. It applies Genetic Algorithms (GAs) learning techniques to evolve new individuals in the population of movements in order to converge the evolution process toward optimal movements. The approach is implemented into the GEM (Genetic Evolution of Movement) system and its performance has been experimentally evaluated.

The quality of Radial Basis Functions (RBF) and other nonlinear learning networks such as Multi Layer Perceptrons (MLP) depend significantly on issues in architecture, learning algorithms, initialisation heuristics and regularization techniques. Little attention has been given to the effect of mixture transfer functions in RBF networks on model quality and efficiency of parameter optimisation. We propose Universal Basis Functions (UBF) with flexible activation functions which are parameterised to change their shape smoothly from one functional form to another. This way they can cover bounded and unbounded subspaces depending on data distribution. We define UBF and apply them to a number of classification and function approximation tasks. We find that the UBF approach outperforms traditional RBF with the Hermite data set, a noisy Fourier series and a non f-separable classification problem, however it does not improve statistically significant on the Mackey-Glass chaotic time series. The paper concludes with comments and issues for future research.

A PID Controller has been used to operate this system because of its implementational advantages. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. This paper focuses on tuning of the PID Controller with disturbance rejection using immune network algorithm. To decide the performance of response, an ITSE (Integral of time weighted squared error) is used in this paper.

In this study, we present information granulation (IG)-based optimization approach to the design of fuzzy controller. The design procedure dwells on the use of evolutionary computing (genetic algorithms) and the estimation of the polynomial model that is carried out by effectively combining Hard C-Means Clustering (HCM) Method with Least Mean Square (LSM) method. The developed approach is applied to a nonlinear system such as an inverted pendulum where we show the results of comprehensive numerical studies and carry out a detailed comparative analysis

Dead time processes exist widely in many types of systems such as chemical processes, and the main steam temperature control system of the thermal power plant. A PID Controllers have been used to operate these systems. However, it is very difficult to achieve an optimal PID gain with no experience since the gain of the PID controller has to be manually tuned by trial and error. This paper suggests a tuning method of the PID Controller for a process with long dead time using an immune algorithm typed neural network, through computer simulation. Tuning results of immune algorithms based neural network are compared with the results of genetic algorithm.

In this paper a software environment, called DAGC, is described. The main idea behind the design of DAGC is to facilitate research works in design and development of genetic clustering algorithms for automatic re-modularization of software systems. Within the DAGC environment, clustering algorithms may be assembled or modified by simply selecting the parts from an extendable list of components. Using this distinguishing feature of the DAGC framework, a new algorithm with a new encoding and crossover operator was evolved.

Over the past few decades there has been a growing interest in the use of biology as a source of inspiration for solving computational problems. The motivation of this field is primarily to extract useful metaphors from natural biological systems, in order to create effective computational solutions to complex problems in a wide range of domain areas. The more notable developments have been the neural networks inspired by the working of the brain, and the evolutionary algorithms inspired by neo-Darwinian theory of evolution. This paper presents the theory of an immune network model, and it tries to apply to solve signal classification problems.

In this paper, adaptive identification and control of nonlinear dynamical systems are investigated using Two Synaptic Weight Neural Networks (TSWNN). The identification algorithm has the properties of rapid convergence and persistent adaptability that make it suitable for real-time control. A nonlinear example is simulated to demonstrate the effectiveness of the identification and control algorithms.

In this paper, Dynamic structure neural network controller based on feedback linearization is proposed. The proposed method can adapt the neural network structure dynamically while it can guarantee the stability and tracking precision of system.

Fault tolerant neural network architecture, based on Multilayer Perceptron (MPL) is presented. We modify the conventional Back error propagation (BP) algorithm to be applied to this architecture with the least learning degradation for fault tolerant nodes. Simulation results for random s-a-0 faults demonstrating the fault tolerance improvement are presented.

This work shows the application of Parametric Model Identification method which is Recursive Least Square for the estimation of nonlinear model parameters. The model of the bioreactor dynamics was assumed in the form of regression model. DMC (Dynamic Matrix Control) and PID Control systems of S. cerevisiae yeast production in a batch bioreactor have been investigated experimentally to achieve the optimal operating condition. The performance of these control algorithms was compared with each other.

The computational science education community in the United States can be perceived at a variety of levels: a community of stakeholders, educators, colleagues, or learners. Each level is illustrated by examples and their roles are discussed.