Algorithm

In Industrial environments, huge amount of data is being generated which in turn collected indatabase anddata warehouses from all involved areas such as planning, process design, materials, assembly, production, quality, process control, scheduling, fault detection,shutdown, customer relation management, and so on. Data Mining has become auseful tool for knowledge acquisition for industrial process of Iron and steel making. Due to the rapid growth in Data Mining, various industries started using data mining technology to search the hidden patterns, which might further be used to the system with the new knowledge which might design new models to enhance the production quality, productivity optimum cost and maintenance etc. The continuous improvement of all steel production process regarding the avoidance of quality deficiencies and the related improvement of production yield is an essential task of steel producer. Therefore, zero defect strategy is popular today and to maintain it several quality assurance techniques areused. The present report explains the methods of data mining and describes its application in the industrial environment and especially, in the steel industry.

For the treatment of industrial wastewater a multitude of processes is available. It is the task of the chemical engineer to choose processes and to build up a treatment sequence suitable for the actual problem. Because of the large number of processes and process combinations, a knowledge-based system may be helpful during process selection and sequencing. The introduced knowledge based system delivers a treatment sequence based on heuristics and cost estimation. Depending on the supplied information a more or less detailed treatment sequence is suggested by the program. The article describes the aim and development of the knowledge based system, gives an overview on the program functions and presents the result of an example program consultation.

fuzzy learning algorithms require only a single pass through the training set. This produces a computationally efficient method of learning. The effectiveness of the fuzzy learning algorithms is compared with that of a feedforward neural network trained with back-propagation. P. Allen, "System engineering training at TRW's system development division," presented at 2nd Ann. Conf.

Wind speed modeling and prediction plays a critical role in wind related engineering studies. However, since the data have random behavior, it is difficult to apply statistical approaches with apriori and deterministic parameters. On the other hand, wind speed data have an important feature; extreme transitions from a wind state to a far different one are rare. Therefore, behavioral modeling is possible. Although several studies focus on global parametrization of wind data behavior, the literature in time-wise modeling and prediction is relatively small. In this study, a novel approach for wind speed modeling using the Mycielski algorithm is demonstrated. The algorithm accurately predicts the time variations of wind speed data in the sense of forecasting future values of wind data by analyzing the repeatedness in the history of the data. The prediction precision of the procedure is tested using wind speed data obtained from three different locations of Turkey (Kayseri, _ Izmir and Antalya). Prediction results with high accuracy are obtained and presented.

A class of single-valued curves in polar coordinates, which we refer to as p-B6zier cur\e, ha> been recently presented by' St]nchez-Reyes and independently discovered by R de Casteljau. From their definition and expression in terms of the Fourier basis it is obvious that every curxe of degree ;; can be expressed as a curve of degree /or;, for any natural value k. In this paper, we provide a formula for degree elevation and we describe a simple and efliciem implementation of it. ,v~ 1998 Elsevier Science B.V.

The present paper investigates the impact of natural ventilation on the thermal-comfort index, assuming the implementation of fuzz), reasoning for visual comfort control. Mathematical models are used to calculate the outdoor climate, the indoor climate and the thermal-comfort index. Finally, a fuzzy-reasoning expert-system, using a linguistic type algorithm, is presented.

Spatial clustering, which groups similar spatial objects into classes, is an important component of spatial data mining [Han and Kamber, Data Mining: Concepts and Techniques, 2000]. Due to its immense applications in various areas, spatial clustering has been highly active topic in data mining researches, with fruitful, scalable clustering methods developed recently. These spatial clustering methods can be classified into four categories: partitioning method, hierarchical method, density-based method and grid-based method. Clustering large data sets of high dimensionality has always been a serious challenge for clustering algorithms. Many recently developed clustering algorithms have attempted to address either handling data with very large number of records or data sets with very high number of dimensions. This new clustering method GCHL (a Grid-Clustering algorithm for High-dimensional very Large spatial databases) combines a novel density-grid based clustering with axis-parallel partitioning strategy to identify areas of high density in the input data space. The algorithm work as well in the feature space of any data set. The method operates on a limited memory buffer and requires at most a single scan through the data. We demonstrate the high quality of the obtained clustering solutions, capability of discovering concave/deeper and convex/higher regions, their robustness to outlier and noise, and GCHL excellent scalability.

We formulate new optimal (fourth) order one step nodal cubic spline collocation methods for the solution of various elliptic boundary value problems in the unit square. These methods are constructed so that the respective collocation equations can be solved using matrix decomposition algorithms (MDAs). MDAs are fast, direct methods which employ fast Fourier transforms and require O(N 2 log N) operations on an N × N uniform partition of the unit square. The results of numerical experiments exhibit expected global optimal orders of convergence as well as desired superconvergence phenomena.

The Theorema project aims at extending current computer algebra systems by facilities for supporting mathematical proving. The present early-prototype version of the Theorema software system is implemented in Mathematica 3.0. The system consists of a general higher-order predicate logic prover and a collection of special provers that call each other depending on the particular proof situations. The individual provers imitate the proof style of human mathematicians and aim at producing human-readable proofs in natural language presented in nested cells that facilitate studying the computer-generated proofs at various levels of detail. The special provers are intimately connected with the functors that build up the various mathematical domains.

We present a robust null space method for linear equality constrained state space estimation. Exploiting a degeneracy in the estimator statistics, an orthogonal factorization is used to decompose the problem into stochastic and deterministic components, which are then solved separately. The resulting dimension reduction algorithm has enhanced numerical stability, solves the constrained problem completely, and can reduce computational load by reducing the problem size. The new method addresses deficiencies in commonly used pseudo-observation or projection methods, which either do not solve the constrained problem completely or have unstable numerical implementations, due in part to the degeneracy in the estimator statistics. We present a numerical example demonstrating the effectiveness of the new method compared to other current methods.

Evaluating the network reliability is an important topic in the planning, designing, and control of systems. The minimal cut set (MC, an edge set) is one of the major and fundamental tools for evaluating network reliability. A k-out-of-n MC is a special MC in a k-out-of-n network in which some nodes must receive at least k flows from their n input edges, where k is an integer number between 1 and n: In this study, an alternative method is given first to define a MC using a node set (called MCN) in k-out-of-n networks. A very simple algorithm based on some intuitive theorems that characterize the structure of the MCN and the relationship between MC and MCN is developed to solve the k-out-of-n network reliability by finding the k-out-of-n MCs between two special nodes. The proposed algorithm is not only easier to understand and implement, but is also better than the existing algorithm. The correctness of the proposed algorithm will be analyzed and proven. Two examples are illustrated to show how all k-out-of-n MCs are generated and verified in a k-out-of-n network using the proposed algorithm. The reliability of one example is then computing using one example. q

This work addresses the problem of low power design in high-level synthesis in the scenario of the resources operating at multiple voltages. The problem of resource-and-latency constrained scheduling is tackled and a novel methodology for scheduling as well as design space exploration is proposed. The proposed methodology achieves maximal power reduction of functional units by identifying the maximal parallelism of power hungry operators in resource-constrained/time-constrained designs. A novel scheme is devised for recognizing the ''zones'' of parallel operators that result in maximal power savings when rescheduled to lower voltages. The proposed methodology is developed in the framework of a modified stochastic evolution mechanism in order to tame the computational complexity. The proposed scheduling technique is extremely fast and it runs in Oðn 2 Þ; where n is the number of the nodes in the data flow graph of the design. This is the fastest reported time of scheduling algorithms for resourceand-latency constrained scheduling with resources operating at multiple voltages. The algorithm produces results within accuracy of 3-5% of the integer-linear-programming based (i.e., exponential-timecomplexity) method. The details and the analysis of the results on the standard high-level synthesis benchmarks are provided. Further, analysis of schedules is given for considering generation of control signals for switching the resources. This is the first work to consider such analysis besides proposing an efficient algorithm. r

In a chamber of the heart, large-scale vortices are shown to exist as the result of the dynamic blood flow and unique morphological changes of the chamber wall. As the cardiovascular flow varies over a cardiac cycle, there is a need for a robust quantification method to analyze its vorticity and circulation. We attempt to measure vortex characteristics by means of two-dimensional vorticity maps and vortex circulation. First, we develop vortex component analysis by segmenting the vortices using an data clustering algorithm before histograms of their vorticity distribution are generated. The next stage is to generate the statistics of the vorticity maps for each phase of the cardiac cycle to allow analysis of the flow. This is followed by evaluating the circulation of each segmented vortex. The proposed approach is dedicated to examining vortices within the human heart chamber. The vorticity field can indicate the strength and number of large-scale vortices in the chamber. We provide the results of the flow analysis after vorticity map segmentation and the statistical properties that characterize the vorticity components. The success of the cardiac measurement and analysis is illustrated by a case study of the right atrium. Our investigation shows that it is possible to utilize a data clustering algorithm to segment vortices after vorticity mapping, and that the vorticity and circulation analysis of a chamber vorticity can provide new insights into the blood flow within the cardiovascular structure.

In this paper different optimization of Ad-hoc routing algorithm is surveyed and a new method using training based optimization algorithm for reducing the complexity of routing algorithms is suggested. A binary matrix is assigned to each node in the network and gets updated after each data transfer using the protocols.

The use of optimization algorithm in routing algorithm can reduce the complexity of routing to the least amount possible.

Combining both spatial and intensity information in image, we present an MRI brain image segmentation approach based on multiresolution edge detection, region selection, and intensity threshold methods. The detection of white matter structure in brain is emphasized in this paper. First, a multi-resolution brain image representation and segmentation procedure based on a multi-scale image filtering method is presented. Given the nature of the structural connectivity and intensity homogeneity of brain tissues, region-based methods such as region growing and subtraction to segment the brain tissue structure from the multi-resolution images are utilized. From the segmented structure, the region-of-interest (ROI) image in the structure region is derived, and then a modified segmentation of the ROI based on an automatic threshold method using our threshold selection criterion is presented. Examples on both T1 and T2 weighted MRI brain image segmentation is presented, showing finer brain tissue structures. ᭧

A robot has to construct a complete map of an unknown environment modeled as an undirected connected graph. The task is to explore all nodes and edges of the graph using the minimum number of edge traversals. The penalty of an exploration Ž Ž. Ž .. algorithm running on a graph G s V G , E G is the worst-case number of < Ž .< traversals in excess of the lower bound E G that it must perform in order to explore the entire graph. We give an exploration algorithm whose penalty is Ž< Ž .<. O V G for every graph. We also show that some natural exploration algorithms cannot achieve this efficiency.

Fractional-pixel accuracy Motion Estimation (ME) has been shown to result in higher quality reconstructed image sequences in hybrid video coding systems. However, the higher quality is achieved by notably increased Motion Field (MF) bitrate and more complex computations. In this paper, new half-pixel block matching ME algorithms are proposed to improve the rate-distortion characteristics of low bitrate video communications. The proposed methods tend to decrease the required video bandwidth, while improving the motion compensation quality. The key idea is to put a deeper focus on the search origin of the ME process, based on center-bias characteristics of low bitrate video MFs. To employ the bene ts of Mesh-based ME (MME), the introduced algorithms are also examined in the framework of a fast MME scheme. Experimental results show the e ciency of the proposed schemes, especially when employed in the MME approach, so that a reduction of more than 20% in the MF bitrate is achieved when employing typical QCIF formatted image sequences.

Children's difficulty in point-and-click tasks using indirect pointing devices such as the mouse has been documented in several studies. This difficulty is manifested in a lack of control near the target, which often results in children clicking inaccurately. This paper presents and evaluates PointAssist, a tool that helps children in pointing tasks by detecting the type of motion that occurs when children have difficulty pointing at a target, and triggering a precision mode that slows the speed of the mouse cursor in those cases. We conducted a study with 30 four year old participants who completed point-and-click tasks with and without PointAssist. PointAssist provided participants with significant advantages in terms of click accuracy, enabling them to be as accurate as 18 to 22 year olds in a previous study with a very similar setup.

Quelques résultats de convergence pour l'algorithme de Howard Résumé : Nousétudions des résultats de convergence pour l'algorithme de Howard appliqué a la résolution du problème min a∈A (B a x − b a) = 0 où B a est une matrice, b a est un vecteur (éventuellement de dimension infinie), et A est un ensemble compact. Nous obtenons une convergence surlinéaire, sous une hypothèse de monotonie des matrices B a. Dans le cas particulier d'un problème d'obstacle de la forme: min(Ax − b, x − g) = 0 où A est une matrice N × N monotone, nous prouvons que l'algorithme de Howard converge en N itérations. De plus, nousétablissons l'équivalence entre l'algorithme de Howard et la méthode primal-dual (M.

One of the most important problem for solving the linear system Ax = b, by using the iterative methods, is to use a good stopping criterion and to determine the common significant digits between each corresponding components of computed solution and exact solution. In this paper, for a certain class of iterative methods, we propose a way to determine the number of common significant digits of x m and x, where x m and x are computed solution at iteration m and exact solution, respectively. By using the CADNA library which allows us to estimate the round-off error effect on any computed result, we also propose a good stopping criterion which is able to stop the process as soon as a satisfactory informatical solution is obtained. Numerical examples are used to show the good numerical properties.

In this paper, we propose a hybrid approach of Arabic scripts web page language identification based on decision tree and ARTMAP approaches. We use the decision tree approach to find the general identities of a web document, be it an Arabic script-based or a non-Arabic-based. Then, we use the selected representations of identified pages from the decision tree approach as

Texture segmentation is one of the early steps towards identifying surfaces and objects in an image. Textures considered here are de ned in terms of primitives called tokens. In this paper we h a ve developed a texture segmentation algorithm based on the Voronoi tessellation. The algorithm rst builds the Voronoi tessellation of the tokens that make up the textured image. It then computes a feature vector for each V oronoi polygon. These feature vectors are used in a probabilistic relaxation labeling on the tokens, to identify the interior and the border regions of the textures. The algorithm has successfully segmented binary images containing textures whose primitives have identical second-order statistics and a n umber of gray level texture images.

The a priori signal-to-noise ratio (SNR) plays an important role in many speech enhancement algorithms. In this paper we present a data-driven approach to a priori SNR estimation. It may be used with a wide range of speech enhancement techniques, such as, e.g., the minimum mean square error (MMSE) (log) spectral amplitude estimator, the super Gaussian joint maximum a posteriori (JMAP) estimator, or the Wiener filter. The proposed SNR estimator employs two trained artificial neural networks, one for speech presence, one for speech absence. The classical decision-directed a priori SNR estimator by Ephraim and Malah is broken down into its two additive components, which now represent the two input signals to the neural networks. Both output nodes are combined to represent the new a priori SNR estimate. As an alternative to the neural networks, also simple lookup tables are investigated. Employment of these data-driven nonlinear a priori SNR estimators reduces speech distortion, particularly in speech onset, while retaining a high level of noise attenuation in speech absence.

In this paper, we present two new articulated motion analysis and object tracking approaches: the decentralized articulated object tracking method and the hierarchical articulated object tracking method. The first approach avoids the common practice of using a high-dimensional joint state representation for articulated object tracking. Instead, we introduce a decentralized scheme and model the interpart interaction within an innovative Bayesian framework. Specifically, we estimate the interaction density by an efficient decomposed interpart interaction model. To handle severe self-occlusions, we further extend the first approach by modeling high-level interunit interaction and develop the second algorithm within a consistent hierarchical framework. Preliminary experimental results have demonstrated the superior performance of the proposed approaches on real-world videos in both robustness and speed compared with other articulated object tracking methods.

ABSTRACT
This work aims at developing a generic and anisotropic point error model, which is capable of computing magnitude and
direction of a priori random errors, described in the form of error ellipsoids for each individual point of the cloud. The
direct TLS observations are the range (ρ), vertical (α) and horizontal (θ) angles, each of which is in fact associated with a
priori precision value. A practical methodology was designed and performed in real-world test environments to
determine these precision values. The methodology has two experimental parts. The first part is a static and repetitive
measurement configuration for the determination of a priori precisions of the vertical (𝜎𝛼) and horizontal (𝜎𝜃) angles. The
second part is the measurement of a test stand which contains four plates in white, light grey, dark grey and black colors,
for the determination of a priori precisions of the range observations (𝜎𝜌). The test stand measurement is performed in a
recursive manner so that sensor-to-object distance, incidence angle and surface reflectivity are parameterized. The
experiment was conducted with three TLSs, namely Faro Focus 3D X330, Riegl VZ400 and Z+F 5010x in the same
location and atmospheric conditions. This procedure was followed by the computation of error ellipsoids of each point
using the law of variance-covariance propagation. The direction and size of the error ellipsoids were computed by the
principal components transformation. Validation of the proposed error model was performed in real world scenarios,
which revealed feasibility of the model.
Keywords: TLS (terrestrial laser scanner), range, incidence angle, reflectance, error ellipsoid, anisotropic, error model,
variance-covariance propagation

Lagrange interpolation by polynomials in several variables is studied through a nite di erences approach. We establish an interpolation formula analogous to that of Newton and a remainder formula, both of them in terms of the nite di erences. We prove that the nite di erence admits an integral representation involving simplex spline functions. In particular, this provides a remainder formula for Lagrange interpolation of degree n of a function f , which is a sum of integrals of certain (n + 1){st directional derivatives of f multiplied by simplex spline functions. We also provide two algorithms for the computation of Lagrange interpolants which use only addition, scalar multiplication, and point evaluation of polynomials.

A new way of looking at permutation distance-preserving mappings (DPMs) is presented by making use of a graph representation. The properties necessary to make such a graph distance-preserving, are also investigated. Further, this new knowledge is used to analyze previous constructions, as well as to construct a new general mapping algorithm for a previous multilevel construction.

Single frequency estimation is a long-studied problem with application domains including radar, sonar, telecommunications, astronomy and medicine. One method of estimation, called phase unwrapping, attempts to estimate the frequency by performing linear regression on the phase of the received signal. This procedure is complicated by the fact that the received phase is 'wrapped' modulo 2 and therefore must be 'unwrapped' before the regression can be performed. In this paper, we propose an estimator that performs phase unwrapping in the least squares sense. The estimator is shown to be strongly consistent and its asymptotic distribution is derived. We then show that the problem of computing the least squares phase unwrapping is related to a problem in algorithmic number theory known as the nearest lattice point problem. We derive a polynomial time algorithm that computes the least squares estimator. The results of various simulations are described for different values of sample size and SNR.

When modelling the buckling phenomenon of thin plates in presence of an obstacle, we obtain a variational inequality with two unknowns that are the buckling load and the corresponding buckling mode. Using the finite elements method, the continuous problem is approximated by a discrete problem. Then, an algorithm for computing the buckling critical load and the corresponding unilateral buckling mode of the plate is suggested. The last part of the paper is devoted to some numerical results obtained for the same rectangular plate but three different kinds of obstacle. RÉSUMÉ. On s'intéresse, dans ce papier, au flambement unilatéral d'une plaque mince en présence d'un obstacle. Après approximation par éléments finis, un algorithme, pour calculer la charge critique de flambement ainsi que le mode correspondant, est proposé. La fin du papier est consacrée à des résultats numériques obtenus pour la même plaque rectangulaire, mais trois différents types d'obstacle. KEYWORDS: unilateral buckling, buckling critical load, buckling mode, finite element method, algorithm, eigenvalue problem. MOTS-CLÉS : flambement unilatéral, charge critique de flambement, mode de flambement, méthode d'éléments finis, algorithme, problème aux valeurs propres.

In this paper, we present a multiagent algorithm for the frequency assignment problem in cellular radio networks. The algorithm, that has been successfully applied to GSM networks, efficiently assigns frequencies to each radio cell satisfying the constraints given by a compatibility matrix.

Acoustic signals recorded simultaneously in a reverberant environment can be described as sums of differently convolved sources. The task of source separation is to identify the multiple channels and possibly to invert those in order to obtain estimates of the underlying sources. We tackle the problem by explicitly exploiting the nonstationarity of the acoustic sources. Changing cross-correlations at multiple times give a sufficient set of constraints for the unknown channels. A least squares optimization allows us to estimate a forward model, identifying thus the multi-path channel. In the same manner we can find an FIR backward model, which generates well separated model sources. Furthermore, for more than three channels we have sufficient conditions to estimate underlying additive sensor noise powers. We show good performance in real room environments and demonstrate the algorithm's utility for automatic speech recognition.

Illumination invariance remains the most researched, yet the most challenging aspect of automatic face recognition. In this paper we investigate the discriminative power of colour-based invariants in the presence of large illumination changes between training and test data, when appearance changes due to cast shadows and non-Lambertian effects are significant. Specifically, there are three main contributions: (i) we employ a more sophisticated photometric model of the camera and show how its parameters can be estimated, (ii) we derive several novel colour-based face invariants, and (iii) on a large database of video sequences we examine and evaluate the largest number of colour-based representations in the literature. Our results suggest that colour invariants do have a substantial discriminative power which may increase the robustness and accuracy of recognition from low resolution images.

Binary image compression is desirable for a wide range of applications, such as digital libraries, map archives, fingerprint databases, facsimile, etc. In this paper, we present a new highly efficient algorithm for lossless binary image compression. The proposed algorithm introduces a new method, direct redundancy elimination, to efficiently exploit the two-dimensional redundancy of an image, as well as a novel

In this work, we study coding over a class of two-user broadcast channels (BCs) with additive white Gaussian noise and multiplicative fading known at the receivers only. Joint decoding of low-density paritycheck (LDPC) codes is analyzed. The message update rule at the mapping node linking the users' codes is derived and is found to exhibit an interesting soft interference cancellation property. High performance codes are found using the differential evolution optimization technique and extrinsic information transfer analysis adapted to our multiuser setting. The optimized codes have rates very close to the boundary of the achievable region for binary constrained input for both faded and unfaded channels. Simulation results for moderate block lengths show that our codes operate within less than 1 dB of their respective threshold.

"This paper introduces the theory and hardware implementation of two new algorithms for computing a single component of the discrete Fourier transform.
In terms of multiplicative complexity, both algorithms are more efficient, in general, than the well known Goertzel Algorithm."

The first step has been taken in the effort to develop a microscopic simulation model of mixed motor vehicle and bicycle traffic over an entire network. The primary objective was to develop the main algorithm of BICycle SIMulator and translate it into a working computer program. To achieve the goal, the gaps in the existing literature were filled by data collected at the University of Delaware campus in Newark. An equation of mixed bicycle and motor vehicle following was developed and used for the representation of longitudinal interaction of vehicles. The program was implemented in C+plus; programming language and tested on real-world data. The resulting software is the first in the world to allow the inclusion of bicyclists in the analysis of urban transportation by the means of microscopic simulation.