OCB: A Generic Benchmark to Evaluate the Performances of Object-Oriented Database Systems

Abstract: We present in this paper a generic object-oriented benchmark (the Object Clustering Benchmark) that has been designed to evaluate the performances of clustering policies in object-oriented databases. OCB is generic because its sample database may be customized to fit the databases introduced by the main existing benchmarks (e.g., OO1). OCB's current form is clustering-oriented because of its clustering-oriented workload, but it can be easily adapted to other purposes. Lastly, OCB's code is compact and easily portable. OCB has been implemented in a real system (Texas, running on a Sun workstation), in order to test a specific clustering policy called DSTC. A few results concerning this test are presented.


Performance Evaluation for Clustering Algorithms in Object-Oriented Database Systems

Abstract: It is widely acknowledged that good object clustering is critical to the performance of object-oriented databases. However, object clustering always involves some kind of overhead for the system. The aim of this paper is to propose a modelling methodology in order to evaluate the performances of different clustering policies. This methodology has been used to compare the performances of three clustering algorithms found in the literature (Cactis, CK and ORION) that we considered representative of the current research in the field of object clustering. The actual performance evaluation was performed using simulation. Simulation experiments we performed showed that the Cactis algorithm is better than the ORION algorithm and that the CK algorithm totally outperforms both other algorithms in terms of response time and clustering overhead.


Iterative Rounding for the Closest String Problem

Abstract: The closest string problem is an NP-hard problem, whose task is to find a string that minimizes maximum Hamming distance to a given set of strings. This can be reduced to an integer program (IP). However, to date, there exists no known polynomial-time algorithm for IP. In 2004, Meneses et al. introduced a branch-and-bound (B & B) method for solving the IP problem. Their algorithm is not always efficient and has the exponential time complexity. In the paper, we attempt to solve efficiently the IP problem by a greedy iterative rounding technique. The proposed algorithm is polynomial time and much faster than the existing B & B IP for the CSP. If the number of strings is limited to 3, the algorithm is provably at most 1 away from the optimum. The empirical results show that in many cases we can find an exact solution. Even though we fail to find an exact solution, the solution found is very close to exact solution.


Clustering Co-occurrence of Maximal Frequent Patterns in Streams

Abstract: One way of getting a better view of data is using frequent patterns. In this paper frequent patterns are subsets that occur a minimal number of times in a stream of itemsets. However, the discovery of frequent patterns in streams has always been problematic. Because streams are potentially endless it is in principle impossible to say if a pattern is often occurring or not. Furthermore the number of patterns can be huge and a good overview of the structure of the stream is lost quickly. The proposed approach will use clustering to facilitate the analysis of the structure of the stream. A clustering on the co-occurrence of patterns will give the user an improved view on the structure of the stream. Some patterns might occur so much together that they should form a combined pattern. In this way the patterns in the clustering will be the largest frequent patterns: maximal frequent patterns. Our approach to decide if patterns occur often together will be based on a method of clustering when only the distance between pairs is known. The number of maximal frequent patterns is much smaller and combined with clustering methods these patterns provide a good view on the structure of the stream.


Clustering with Lattices in the Analysis of Graph Patterns

Abstract: Mining frequent subgraphs is an area of research where we have a given set of graphs (each graph can be seen as a transaction), and we search for (connected) subgraphs contained in many of these graphs. In this work we will discuss techniques used in our framework Lattice2SAR for mining and analysing frequent subgraph data and their corresponding lattice information. Lattice information is provided by the graph mining algorithm gSpan; it contains all supergraph-subgraph relations of the frequent subgraph patterns -- and their supports. Lattice2SAR is in particular used in the analysis of frequent graph patterns where the graphs are molecules and the frequent subgraphs are fragments. In the analysis of fragments one is interested in the molecules where patterns occur. This data can be very extensive and in this paper we focus on a technique of making it better available by using the lattice information in our clustering. Now we can reduce the number of times the highly compressed occurrence data needs to be accessed by the user. The user does not have to browse all the occurrence data in search of patterns occurring in the same molecules. Instead one can directly see which frequent subgraphs are of interest.


NodeTrix: Hybrid Representation for Analyzing Social Networks

Abstract: The need to visualize large social networks is growing as hardware capabilities make analyzing large networks feasible and many new data sets become available. Unfortunately, the visualizations in existing systems do not satisfactorily answer the basic dilemma of being readable both for the global structure of the network and also for detailed analysis of local communities. To address this problem, we present NodeTrix, a hybrid representation for networks that combines the advantages of two traditional representations: node-link diagrams are used to show the global structure of a network, while arbitrary portions of the network can be shown as adjacency matrices to better support the analysis of communities. A key contribution is a set of interaction techniques. These allow analysts to create a NodeTrix visualization by dragging selections from either a node-link or a matrix, flexibly manipulate the NodeTrix representation to explore the dataset, and create meaningful summary visualizations of their findings. Finally, we present a case study applying NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate the capabilities of NodeTrix as both an exploration tool and an effective means of communicating results.


Risk Assessment Algorithms Based On Recursive Neural Networks

Abstract: The assessment of highly-risky situations at road intersections have been recently revealed as an important research topic within the context of the automotive industry. In this paper we shall introduce a novel approach to compute risk functions by using a combination of a highly non-linear processing model in conjunction with a powerful information encoding procedure. Specifically, the elements of information either static or dynamic that appear in a road intersection scene are encoded by using directed positional acyclic labeled graphs. The risk assessment problem is then reformulated in terms of an inductive learning task carried out by a recursive neural network. Recursive neural networks are connectionist models capable of solving supervised and non-supervised learning problems represented by directed ordered acyclic graphs. The potential of this novel approach is demonstrated through well predefined scenarios. The major difference of our approach compared to others is expressed by the fact of learning the structure of the risk. Furthermore, the combination of a rich information encoding procedure with a generalized model of dynamical recurrent networks permit us, as we shall demonstrate, a sophisticated processing of information that we believe as being a first step for building future advanced intersection safety systems


Privacy - an Issue for eLearning? A Trend Analysis Reflecting the Attitude of European eLearning Users

Abstract: Availing services provided via the Internet became a widely accepted means in organising one's life. Beside others, eLearning goes with this trend as well. But, while employing Internet service makes life more convenient, at the same time, it raises risks with respect to the protection of the users' privacy. This paper analyses the attitudes of eLearning users towards their privacy by, initially, pointing out terminology and legal issues connected with privacy. Further, the concept and implementation as well as a result analysis of a conducted study is presented, which explores the problem area from different perspectives. The paper will show that eLearning users indeed care for the protection of their personal information when using eLearning services. However, their attitudes and behaviour slightly differ. In conclusion, we provide first approaches of assisting possibilities for users how to resolve the difference of requirements and their actual activities with respect to privacy protection.


Moving Walkways, Escalators, and Elevators

Abstract: We study a simple geometric model of transportation facility that consists of two points between which the travel speed is high. This elementary definition can model shuttle services, tunnels, bridges, teleportation devices, escalators or moving walkways. The travel time between a pair of points is defined as a time distance, in such a way that a customer uses the transportation facility only if it is helpful. We give algorithms for finding the optimal location of such a transportation facility, where optimality is defined with respect to the maximum travel time between two points in a given set.


Learning to Bluff

Abstract: The act of bluffing confounds game designers to this day. The very nature of bluffing is even open for debate, adding further complication to the process of creating intelligent virtual players that can bluff, and hence play, realistically. Through the use of intelligent, learning agents, and carefully designed agent outlooks, an agent can in fact learn to predict its opponents reactions based not only on its own cards, but on the actions of those around it. With this wider scope of understanding, an agent can in learn to bluff its opponents, with the action representing not an illogical action, as bluffing is often viewed, but rather as an act of maximising returns through an effective statistical optimisation. By using a tee dee lambda learning algorithm to continuously adapt neural network agent intelligence, agents have been shown to be able to learn to bluff without outside prompting, and even to learn to call each others bluffs in free, competitive play.


Soft constraint abstraction based on semiring homomorphism

Abstract: The semiring-based constraint satisfaction problems (semiring CSPs), proposed by Bistarelli, Montanari and Rossi \cite{BMR97}, is a very general framework of soft constraints. In this paper we propose an abstraction scheme for soft constraints that uses semiring homomorphism. To find optimal solutions of the concrete problem, the idea is, first working in the abstract problem and finding its optimal solutions, then using them to solve the concrete problem. In particular, we show that a mapping preserves optimal solutions if and only if it is an order-reflecting semiring homomorphism. Moreover, for a semiring homomorphism $\alpha$ and a problem $P$ over $S$, if $t$ is optimal in $\alpha(P)$, then there is an optimal solution $\bar{t}$ of $P$ such that $\bar{t}$ has the same value as $t$ in $\alpha(P)$.


The Optimization of a Novel Prismatic Drive

Abstract: The design of a mechanical transmission taking into account the transmitted forces is reported in this paper. This transmission is based on Slide-o-Cam, a cam mechanism with multiple rollers mounted on a common translating follower. The design of Slide-o-Cam, a transmission intended to produce a sliding motion from a turning drive, or vice versa, was reported elsewhere. This transmission provides pure-rolling motion, thereby reducing the friction of rack-and-pinions and linear drives. The pressure angle is a relevant performance index for this transmission because it determines the amount of force transmitted to the load vs. that transmitted to the machine frame. To assess the transmission capability of the mechanism, the Hertz formula is introduced to calculate the stresses on the rollers and on the cams. The final transmission is intended to replace the current ball-screws in the Orthoglide, a three-DOF parallel robot for the production of translational motions, currently under development for machining applications at Ecole Centrale de Nantes.


MIMO detection employing Markov Chain Monte Carlo

Abstract: We propose a soft-output detection scheme for Multiple-Input-Multiple-Output (MIMO) systems. The detector employs Markov Chain Monte Carlo method to compute bit reliabilities from the signals received and is thus suited for coded MIMO systems. It offers a good trade-off between achievable performance and algorithmic complexity.


Approximate textual retrieval

Abstract: An approximate textual retrieval algorithm for searching sources with high levels of defects is presented. It considers splitting the words in a query into two overlapping segments and subsequently building composite regular expressions from interlacing subsets of the segments. This procedure reduces the probability of missed occurrences due to source defects, yet diminishes the retrieval of irrelevant, non-contextual occurrences.


Bayesian Approach to Neuro-Rough Models

Abstract: This paper proposes a neuro-rough model based on multi-layered perceptron and rough set. The neuro-rough model is then tested on modelling the risk of HIV from demographic data. The model is formulated using Bayesian framework and trained using Monte Carlo method and Metropolis criterion. When the model was tested to estimate the risk of HIV infection given the demographic data it was found to give the accuracy of 62%. The proposed model is able to combine the accuracy of the Bayesian MLP model and the transparency of Bayesian rough set model.


Medical Image Segmentation and Localization using Deformable Templates

Abstract: This paper presents deformable templates as a tool for segmentation and localization of biological structures in medical images. Structures are represented by a prototype template, combined with a parametric warp mapping used to deform the original shape. The localization procedure is achieved using a multi-stage, multi-resolution algorithm de-signed to reduce computational complexity and time. The algorithm initially identifies regions in the image most likely to contain the desired objects and then examines these regions at progressively increasing resolutions. The final stage of the algorithm involves warping the prototype template to match the localized objects. The algorithm is presented along with the results of four example applications using MRI, x-ray and ultrasound images.


Overview of the Netsukuku network

Abstract: Netsukuku is a P2P network system designed to handle a large number of nodes with minimal CPU and memory resources. It can be easily used to build a worldwide distributed, anonymous and not controlled network, separated from the Internet, without the support of any servers, ISPs or authority controls. In this document, we give a generic and non technical description of the Netsukuku network, emphasizing its main ideas and features.


Quantum Shortest Path Netsukuku

Abstract: This document describes the QSPN, the routing discovery algorithm used by Netsukuku. Through a deductive analysis the main proprieties of the QSPN are shown. Moreover, a second version of the algorithm, is presented.


The Netsukuku network topology

Abstract: In this document, we describe the fractal structure of the Netsukuku topology. Moreover, we show how it is possible to use the QSPN v2 on the high levels of the fractal.


ANDNA: the distributed hostname management system of Netsukuku

Abstract: We present the Abnormal Netsukuku Domain Name Anarchy system. ANDNA is the distributed, non hierarchical and decentralised system of hostname management used in the Netsukuku network.


Enhancement of Noisy Planar Nuclear Medicine Images using Mean Field Annealing

Abstract: Nuclear medicine (NM) images inherently suffer from large amounts of noise and blur. The purpose of this research is to reduce the noise and blur while maintaining image integrity for improved diagnosis. The proposed solution is to increase image quality after the standard pre- and post-processing undertaken by a gamma camera system. Mean Field Annealing (MFA) is the image processing technique used in this research. It is a computational iterative technique that makes use of the Point Spread Function (PSF) and the noise associated with the NM image. MFA is applied to NM images with the objective of reducing noise while not compromising edge integrity. Using a sharpening filter as a post-processing technique (after MFA) yields image enhancement of planar NM images.


The Multiobjective Optimization of a Prismatic Drive

Abstract: The multiobjective optimization of Slide-o-Cam is reported in this paper. Slide-o-Cam is a cam mechanism with multiple rollers mounted on a common translating follower. This transmission provides pure-rolling motion, thereby reducing the friction of rack-and-pinions and linear drives. A Pareto frontier is obtained by means of multiobjective optimization. This optimization is based on three objective functions: (i) the pressure angle, which is a suitable performance index for the transmission because it determines the amount of force transmitted to the load vs. that transmitted to the machine frame; (ii) the Hertz pressure used to evaluate the stresses produced on the contact surface between cam and roller; and (iii) the size of the mechanism, characterized by the number of cams and their width.


Satisfiability Parsimoniously Reduces to the Tantrix(TM) Rotation Puzzle Problem

Abstract: Holzer and Holzer (Discrete Applied Mathematics 144(3):345--358, 2004) proved that the Tantrix(TM) rotation puzzle problem is NP-complete. They also showed that for infinite rotation puzzles, this problem becomes undecidable. We study the counting version and the unique version of this problem. We prove that the satisfiability problem parsimoniously reduces to the Tantrix(TM) rotation puzzle problem. In particular, this reduction preserves the uniqueness of the solution, which implies that the unique Tantrix(TM) rotation puzzle problem is as hard as the unique satisfiability problem, and so is DP-complete under polynomial-time randomized reductions, where DP is the second level of the boolean hierarchy over NP.


An Independent Evaluation of Subspace Face Recognition Algorithms

Abstract: This paper explores a comparative study of both the linear and kernel implementations of three of the most popular Appearance-based Face Recognition projection classes, these being the methodologies of Principal Component Analysis, Linear Discriminant Analysis and Independent Component Analysis. The experimental procedure provides a platform of equal working conditions and examines the ten algorithms in the categories of expression, illumination, occlusion and temporal delay. The results are then evaluated based on a sequential combination of assessment tools that facilitate both intuitive and statistical decisiveness among the intra and interclass comparisons. The best categorical algorithms are then incorporated into a hybrid methodology, where the advantageous effects of fusion strategies are considered.


On Isotropic Sets of Points in the Plane. Application to the Design of Robot Archirectures

Abstract: Various performance indices are used for the design of serial manipulators. One method of optimization relies on the condition number of the Jacobian matrix. The minimization of the condition number leads, under certain conditions, to isotropic configurations, for which the roundoff-error amplification is lowest. In this paper, the isotropy conditions, introduced elsewhere, are the motivation behind the introduction of isotropic sets of points. By connecting together these points, we define families of isotropic manipulators. This paper is devoted to planar manipulators, the concepts being currently extended to their spatial counterparts. Furthermore, only manipulators with revolute joints are considered here.


The Kinematic Analysis of a Symmetrical Three-Degree-of-Freedom Planar Parallel Manipulator

Abstract: Presented in this paper is the kinematic analysis of a symmetrical three-degree-of-freedom planar parallel manipulator. In opposite to serial manipulators, parallel manipulators can admit not only multiple inverse kinematic solutions, but also multiple direct kinematic solutions. This property produces more complicated kinematic models but allows more flexibility in trajectory planning. To take into account this property, the notion of aspects, i.e. the maximal singularity-free domains, was introduced, based on the notion of working modes, which makes it possible to separate the inverse kinematic solutions. The aim of this paper is to show that a non-singular assembly-mode changing trajectory exist for a symmetrical planar parallel manipulator, with equilateral base and platform triangle.


Uniqueness Domains in the Workspace of Parallel Manipulators

Abstract: This work investigates new kinematic features of parallel manipulators. It is well known that parallel manipulators admit generally several direct kinematic solutions for a given set of input joint values. The aim of this paper is to characterize the uniqueness domains in the workspace of parallel manipulators, as well as their image in the joint space. The study focuses on the most usual case of parallel manipulators with only one inverse kinematic solution. The notion of aspect introduced for serial manipulators in [Borrel 86] is redefined for such parallel manipulators. Then, it is shown that it is possible to link several solutions to the forward kinematic problem without meeting a singularity, thus meaning that the aspects are not uniqueness domains. An additional set of surfaces, namely the characteristic surfaces, are characterized which divide the workspace into basic regions and yield new uniqueness domains. This study is illustrated all along the paper with a 3-RPR planar parallel manipulator. An octree model of spaces is used to compute the joint space, the workspace and all other newly defined sets.


The Kinematic design of a 3-dof Hybrid Manipulator

Abstract: This paper focuses on the kinematic properties of a new three-degree-of-freedom hybrid manipulator. This manipulator is obtained by adding in series to a five-bar planar mechanism (similar to the one studied by Bajpai and Roth) a third revolute passing through the line of centers of the two actuated revolute joints of the above linkage. The resulting architecture is hybrid in that it has both serial and parallel links. Fully-parallel manipulators are known for the existence of particularly undesirable singularities (referred to as parallel singularities) where control is lost [4] and [6]. On the other hand, due to their cantilever type of kinematic arrangement, fully serial manipulators suffer from a lack of stiffness and from relatively large positioning errors. The hybrid manipulator studied is intrinsically stiffer and more accurate. Furthermore, since all actuators are located on the first axis, the inertial effects are considerably reduced. In addition, it is shown that the special kinematic structure of our manipulator has the potential of avoiding parallel singularities by a suitable choice of the "working mode", thus leading to larger workspaces. The influence of the different structural dimensions (e.g. the link lengths) on the kinematic and mechanical properties are analysed in view of the optimal design of such hybrid manipulators.


Definition sets for the Direct Kinematics of Parallel Manipulators

Abstract: The aim of this paper is to characterize the uniqueness domains in the workspace of parallel manipulators, as well as their image in the joint space. The notion of aspect introduced for serial manipulators in [Borrel 86] is redefined for such parallel manipulators. Then, it is shown that it is possible to link several solutions to the direct kinematic problem without meeting a singularity, thus meaning that the aspects are not uniqueness domains. Additional surfaces are characterized in the workspace which yield new uniqueness domains. An octree model of spaces is used to compute the joint space, the workspace and all other newly defined sets. This study is illustrated all along the paper with a 3-RPR planar parallel manipulator.


Improved Analysis of Kannan's Shortest Lattice Vector Algorithm

Abstract: The security of lattice-based cryptosystems such as NTRU, GGH and Ajtai-Dwork essentially relies upon the intractability of computing a shortest non-zero lattice vector and a closest lattice vector to a given target vector in high dimensions. The best algorithms for these tasks are due to Kannan, and, though remarkably simple, their complexity estimates have not been improved since more than twenty years. Kannan's algorithm for solving the shortest vector problem is in particular crucial in Schnorr's celebrated block reduction algorithm, on which are based the best known attacks against the lattice-based encryption schemes mentioned above. Understanding precisely Kannan's algorithm is of prime importance for providing meaningful key-sizes. In this paper we improve the complexity analyses of Kannan's algorithms and discuss the possibility of improving the underlying enumeration strategy.


Artificial Neural Networks and Support Vector Machines for Water Demand Time Series Forecasting

Abstract: Water plays a pivotal role in many physical processes, and most importantly in sustaining human life, animal life and plant life. Water supply entities therefore have the responsibility to supply clean and safe water at the rate required by the consumer. It is therefore necessary to implement mechanisms and systems that can be employed to predict both short-term and long-term water demands. The increasingly growing field of computational intelligence techniques has been proposed as an efficient tool in the modelling of dynamic phenomena. The primary objective of this paper is to compare the efficiency of two computational intelligence techniques in water demand forecasting. The techniques under comparison are the Artificial Neural Networks (ANNs) and the Support Vector Machines (SVMs). In this study it was observed that the ANNs perform better than the SVMs. This performance is measured against the generalisation ability of the two.


A New Three-DOF Parallel Mechanism: Milling Machine Applications

Abstract: This paper describes a new parallel kinematic architecture for machining applications, namely, the orthoglide. This machine features three fixed parallel linear joints which are mounted orthogonally and a mobile platform which moves in the Cartesian x-y-z space with fixed orientation. The main interest of the orthoglide is that it takes benefit from the advantages of the popular PPP serial machines (regular Cartesian workspace shape and uniform performances) as well as from the parallel kinematic arrangement of the links (less inertia and better dynamic performances), which makes the orthoglide well suited to high-speed machining applications. Possible extension of the orthoglide to 5-axis machining is also investigated.


Tracking User Attention in Collaborative Tagging Communities

Abstract: Collaborative tagging has recently attracted the attention of both industry and academia due to the popularity of content-sharing systems such as CiteULike, del.icio.us, and Flickr. These systems give users the opportunity to add data items and to attach their own metadata (or tags) to stored data. The result is an effective content management tool for individual users. Recent studies, however, suggest that, as tagging communities grow, the added content and the metadata become harder to manage due to an ease in content diversity. Thus, mechanisms that cope with increase of diversity are fundamental to improve the scalability and usability of collaborative tagging systems. This paper analyzes whether usage patterns can be harnessed to improve navigability in a growing knowledge space. To this end, it presents a characterization of two collaborative tagging communities that target scientific literature: CiteULike and Bibsonomy. We explore three main directions: First, we analyze the tagging activity distribution across the user population. Second, we define new metrics for similarity in user interest and use these metrics to uncover the structure of the tagging communities we study. The structure we uncover suggests a clear segmentation of interests into a large number of individuals with unique preferences and a core set of users with interspersed interests. Finally, we offer preliminary results that demonstrate that the interest-based structure of the tagging community can be used to facilitate content usage as communities scale.


Recognizing Partial Cubes in Quadratic Time

Abstract: We show how to test whether a graph with n vertices and m edges is a partial cube, and if so how to find a distance-preserving embedding of the graph into a hypercube, in the near-optimal time bound O(n^2), improving previous O(nm)-time solutions.


Fuzzy Artmap and Neural Network Approach to Online Processing of Inputs with Missing Values

Abstract: An ensemble based approach for dealing with missing data, without predicting or imputing the missing values is proposed. This technique is suitable for online operations of neural networks and as a result, is used for online condition monitoring. The proposed technique is tested in both classification and regression problems. An ensemble of Fuzzy-ARTMAPs is used for classification whereas an ensemble of multi-layer perceptrons is used for the regression problem. Results obtained using this ensemble-based technique are compared to those obtained using a combination of auto-associative neural networks and genetic algorithms and findings show that this method can perform up to 9% better in regression problems. Another advantage of the proposed technique is that it eliminates the need for finding the best estimate of the data, and hence, saves time.


Optimal Cache-Oblivious Mesh Layouts

Abstract: A mesh is a graph that divides physical space into regularly-shaped regions. Meshes computations form the basis of many applications, e.g. finite-element methods, image rendering, and collision detection. In one important mesh primitive, called a mesh update, each mesh vertex stores a value and repeatedly updates this value based on the values stored in all neighboring vertices. The performance of a mesh update depends on the layout of the mesh in memory. This paper shows how to find a memory layout that guarantees that the mesh update has asymptotically optimal memory performance for any set of memory parameters. Such a memory layout is called cache-oblivious. Formally, for a $d$-dimensional mesh $G$, block size $B$, and cache size $M$ (where $M=\Omega(B^d)$), the mesh update of $G$ uses $O(1+|G|/B)$ memory transfers. The paper also shows how the mesh-update performance degrades for smaller caches, where $M=o(B^d)$. The paper then gives two algorithms for finding cache-oblivious mesh layouts. The first layout algorithm runs in time $O(|G|\log^2|G|)$ both in expectation and with high probability on a RAM. It uses $O(1+|G|\log^2(|G|/M)/B)$ memory transfers in expectation and $O(1+(|G|/B)(\log^2(|G|/M) + \log|G|))$ memory transfers with high probability in the cache-oblivious and disk-access machine (DAM) models. The layout is obtained by finding a fully balanced decomposition tree of $G$ and then performing an in-order traversal of the leaves of the tree. The second algorithm runs faster by almost a $\log|G|/\log\log|G|$ factor in all three memory models, both in expectation and with high probability. The layout obtained by finding a relax-balanced decomposition tree of $G$ and then performing an in-order traversal of the leaves of the tree.


Strategies for the Design of a Slide-o-Cam Transmission

Abstract: The optimization of the pressure angle in a cam-follower transmission is reported in this paper. This transmission is based on Slide-o-Cam, a cam mechanism with multiple rollers mounted on a common translating follower. The design of Slide-o-Cam, a transmission intended to produce a sliding motion from a turning drive, or vice versa, was reported elsewhere. This transmission provides pure-rolling motion, thereby reducing the friction of rack-and-pinions and linear drives. The pressure angle is a suitable performance index for this transmission because it determines the amount of force transmitted to the load vs. that transmitted to the machine frame. Two alternative design strategies are studied, namely, (i) increase the number of lobes on each cam or (ii) increase the number of cams. This device is intended to replace the current ball-screws in Orthoglide, a three-DOF parallel robot for the production of translational motions, currently under development at Ecole Centrale de Nantes for machining applications.


Regions of Feasible Point-to-Point Trajectories in the Cartesian Workspace of Fully-Parallel Manipulators

Abstract: The goal of this paper is to define the n-connected regions in the Cartesian workspace of fully-parallel manipulators, i.e. the maximal regions where it is possible to execute point-to-point motions. The manipulators considered in this study may have multiple direct and inverse kinematic solutions. The N-connected regions are characterized by projection, onto the Cartesian workspace, of the connected components of the reachable configuration space defined in the Cartesian product of the Cartesian space by the joint space. Generalized octree models are used for the construction of all spaces. This study is illustrated with a simple planar fully-parallel manipulator.


The Design of Parallel Kinematic Machine Tools Using Kinetostatic Performance Criteria

Abstract: Most industrial machine tools have a serial kinematic architecture, which means that each axis has to carry the following one, including its actuators and joints. High Speed Machining highlights some drawbacks of such architectures: heavy moving parts require from the machine structure high stiffness to limit bending problems that lower the machine accuracy, and limit the dynamic performances of the feed axes. That is why PKMs attract more and more researchers and companies, because they are claimed to offer several advantages over their serial counterparts, like high structural rigidity and high dynamic capacities. Indeed, the parallel kinematic arrangement of the links provides higher stiffness and lower moving masses that reduce inertia effects. Thus, PKMs have better dynamic performances. However, the design of a parallel kinematic machine tool (PKMT) is a hard task that requires further research studies before wide industrial use can be expected. Many criteria need to be taken into account in the design of a PKMT. We pay special attention to the description of kinetostatic criteria that rely on the conditioning of the Jacobian matrix of the mechanism. The organisation of this paper is as follows: next section introduces general remarks about PKMs, then is explained why PKMs can be interesting alternative machine tool designs. Then are presented existing PKMTs. An application to the design of a small-scale machine tool prototype developed at IRCCyN is presented at the end of this paper.


Mining Patterns with a Balanced Interval

Abstract: In many applications it will be useful to know those patterns that occur with a balanced interval, e.g., a certain combination of phone numbers are called almost every Friday or a group of products are sold a lot on Tuesday and Thursday. In previous work we proposed a new measure of support (the number of occurrences of a pattern in a dataset), where we count the number of times a pattern occurs (nearly) in the middle between two other occurrences. If the number of non-occurrences between two occurrences of a pattern stays almost the same then we call the pattern balanced. It was noticed that some very frequent patterns obviously also occur with a balanced interval, meaning in every transaction. However more interesting patterns might occur, e.g., every three transactions. Here we discuss a solution using standard deviation and average. Furthermore we propose a simpler approach for pruning patterns with a balanced interval, making estimating the pruning threshold more intuitive.


S\'eparation des Solutions aux Mod\`eles G\'eom\'etriques Direct et Inverse pour les Manipulateurs Pleinement Parall\`eles

Abstract: This article provides a formalism making it possible to manage the solutions of the direct and inverse kinematic models of the fully parallel manipulators. We introduce the concept of working modes to separate the solutions from the opposite geometrical model. Then, we define, for each working mode, the aspects of these manipulators. To separate the solutions from the direct kinematics model, we introduce the concept of characteristic surfaces. Then, we define the uniqueness domains, as being the greatest domains of the workspace in which there is unicity of solutions. The principal applications of this work are the design, the trajectory planning.


On the Kinetostatic Optimization of Revolute-Coupled Planar Manipulators

Abstract: Proposed in this paper is a kinetostatic performance index for the optimum dimensioning of planar manipulators of the serial type. The index is based on the concept of distance of the underlying Jacobian matrix to a given isotropic matrix that is used as a reference model for purposes of performance evaluation. Applications of the index fall in the realm of design, but control applications are outlined. The paper focuses on planar manipulators, the basic concepts being currently extended to their three-dimensional counterparts.


IDF revisited: A simple new derivation within the Robertson-Sp\"arck Jones probabilistic model

Abstract: There have been a number of prior attempts to theoretically justify the effectiveness of the inverse document frequency (IDF). Those that take as their starting point Robertson and Sparck Jones's probabilistic model are based on strong or complex assumptions. We show that a more intuitively plausible assumption suffices. Moreover, the new assumption, while conceptually very simple, provides a solution to an estimation problem that had been deemed intractable by Robertson and Walker (1997).


Artificial Intelligence for Conflict Management

Abstract: Militarised conflict is one of the risks that have a significant impact on society. Militarised Interstate Dispute (MID) is defined as an outcome of interstate interactions, which result on either peace or conflict. Effective prediction of the possibility of conflict between states is an important decision support tool for policy makers. In a previous research, neural networks (NNs) have been implemented to predict the MID. Support Vector Machines (SVMs) have proven to be very good prediction techniques and are introduced for the prediction of MIDs in this study and compared to neural networks. The results show that SVMs predict MID better than NNs while NNs give more consistent and easy to interpret sensitivity analysis than SVMs.


Control of Complex Systems Using Bayesian Networks and Genetic Algorithm

Abstract: A method based on Bayesian neural networks and genetic algorithm is proposed to control the fermentation process. The relationship between input and output variables is modelled using Bayesian neural network that is trained using hybrid Monte Carlo method. A feedback loop based on genetic algorithm is used to change input variables so that the output variables are as close to the desired target as possible without the loss of confidence level on the prediction that the neural network gives. The proposed procedure is found to reduce the distance between the desired target and measured outputs significantly.


Kinematic Calibration of the Orthoglide-Type Mechanisms

Abstract: The paper proposes a novel calibration approach for the Orthoglide-type mechanisms based on observations of the manipulator leg parallelism during motions between the prespecified test postures. It employs a low-cost measuring system composed of standard comparator indicators attached to the universal magnetic stands. They are sequentially used for measuring the deviation of the relevant leg location while the manipulator moves the TCP along the Cartesian axes. Using the measured differences, the developed algorithm estimates the joint offsets that are treated as the most essential parameters to be adjusted. The sensitivity of the measurement methods and the calibration accuracy are also studied. Experimental results are presented that demonstrate validity of the proposed calibration technique.


The Design of a Novel Prismatic Drive for a Three-DOF Parallel-Kinematics Machine

Abstract: The design of a novel prismatic drive is reported in this paper. This transmission is based on Slide-O-Cam, a cam mechanism with multiple rollers mounted on a common translating follower. The design of Slide-O-Cam was reported elsewhere. This drive thus provides pure-rolling motion, thereby reducing the friction of rack-and-pinions and linear drives. Such properties can be used to design new transmissions for parallel-kinematics machines. In this paper, this transmission is optimized to replace ball-screws in Orthoglide, a three-DOF parallel robot optimized for machining applications.


Calibration of quasi-isotropic parallel kinematic Machines: Orthoglide

Abstract: The paper proposes a novel approach for the geometrical model calibration of quasi-isotropic parallel kinematic mechanisms of the Orthoglide family. It is based on the observations of the manipulator leg parallelism during motions between the specific test postures and employs a low-cost measuring system composed of standard comparator indicators attached to the universal magnetic stands. They are sequentially used for measuring the deviation of the relevant leg location while the manipulator moves the TCP along the Cartesian axes. Using the measured differences, the developed algorithm estimates the joint offsets and the leg lengths that are treated as the most essential parameters. Validity of the proposed calibration technique is confirmed by the experimental results.


Evolving Symbolic Controllers

Abstract: The idea of symbolic controllers tries to bridge the gap between the top-down manual design of the controller architecture, as advocated in Brooks' subsumption architecture, and the bottom-up designer-free approach that is now standard within the Evolutionary Robotics community. The designer provides a set of elementary behavior, and evolution is given the goal of assembling them to solve complex tasks. Two experiments are presented, demonstrating the efficiency and showing the recursiveness of this approach. In particular, the sensitivity with respect to the proposed elementary behaviors, and the robustness w.r.t. generalization of the resulting controllers are studied in detail.


Design of a 3 Axis Parallel Machine Tool for High Speed Machining: The Orthoglide

Abstract: The Orthoglide project aims at designing a new 3-axis machine tool for High Speed Machining. Basis kinematics is a 3 degree-of-freedom translational parallel mechanism. This basis was submitted to isotropic and manipulability constraints that allowed the optmization of its kinematic architecture and legs architecture. Thus, several leg morphologies are convenient for the chosen mechanism. We explain the process that led us to the choice we made for the Orthoglide. A static study is presented to show how singular configurations of the legs can cause stiffness problems.


The Isoconditioning Loci of Planar Three-DOF Parallel Manipulators

Abstract: The subject of this paper is a special class of parallel manipulators. First, we analyze a family of three-degree-of-freedom manipulators. Two Jacobian matrices appear in the kinematic relations between the joint-rate and the Cartesian-velocity vectors, which are called the "inverse kinematics" and the "direct kinematics" matrices. The singular configurations of these matrices are studied. The isotropic configurations are then studied based on the characteristic length of this manipulator. The isoconditioning loci of all Jacobian matrices are computed to define a global performance index to compare the different working modes.


A Novel method for the design of 2-DOF Parallel mechanisms for machining applications

Abstract: Parallel Kinematic Mechanisms (PKM) are interesting alternative designs for machine tools. A design method based on velocity amplification factors analysis is presented in this paper. The comparative study of two simple two-degree-of-freedom PKM dedicated to machining applications is led through this method: the common desired properties are the largest square Cartesian workspace for given kinetostatic performances. The orientation and position of the Cartesian workspace are chosen to avoid singularities and to produce the best ratio between Cartesian workspace size and mechanism size. The machine size of each resulting design is used as a comparative criterion.


Design of a Three-Axis Isotropic Parallel Manipulator for Machining Applications: The Orthoglide

Abstract: The orthoglide is a 3-DOF parallel mechanism designed at IRCCyN for machining applications. It features three fixed parallel linear joints which are mounted orthogonally and a mobile platform which moves in the Cartesian x-y-z space with fixed orientation. The orthoglide has been designed as function of a prescribed Cartesian workspace with prescribed kinetostatic performances. The interesting features of the orthoglide are a regular Cartesian workspace shape, uniform performances in all directions and good compactness. A small-scale prototype of the orthoglide under development is presented at the end of this paper.


Workspace Analysis of the Orthoglide using Interval Analysis

Abstract: This paper addresses the workspace analysis of the orthoglide, a 3-DOF parallel mechanism designed for machining applications. This machine features three fixed parallel linear joints which are mounted orthogonally and a mobile platform which moves in the Cartesian x-y-z space with fixed orientation. The workspace analysis is conducted on the bases of prescribed kinetostatic performances. The interesting features of the orthoglide are a regular Cartesian workspace shape, uniform performances in all directions and good compactness. Interval analysis based methods for computing the dextrous workspace and the largest cube enclosed in this workspace are presented.


P\'eriph\'eriques haptiques et simulation d'objets, de robots et de mannequins dans un environnement de CAO-Robotique : eM-Virtual Desktop

Abstract: This paper presents the development of a new software in order to manage objects, robots and mannequins in using the possibilities given by the haptic feedback of the Phantom desktop devices. The haptic device provides 6 positional degree of freedom sensing but three degrees force feedback. This software called eM-Virtual Desktop is integrated in the Tecnomatix's solution called eM-Workplace. The eM-Workplace provides powerful solutions for planning and designing of complex assembly facilities, lines and workplaces. In the digital mockup context, the haptic interfaces can be used to reduce the development cycle of products. Three different loops are used to manage the graphic, the collision detection and the haptic feedback according to theirs own frequencies. The developed software is currently tested in industrial context by a European automotive constructor.


Predicting the Presence of Internet Worms using Novelty Detection

Abstract: Internet worms cause billions of dollars in damage yearly, affecting millions of users worldwide. For countermeasures to be deployed timeously, it is necessary to use an automated system to detect the spread of a worm. This paper discusses a method of determining the presence of a worm, based on routing information currently available from Internet routers. An autoencoder, which is a specialized type of neural network, was used to detect anomalies in normal routing behavior. The autoencoder was trained using information from a single router, and was able to detect both global instability caused by worms as well as localized routing instability.


Robust Multi-Cellular Developmental Design

Abstract: This paper introduces a continuous model for Multi-cellular Developmental Design. The cells are fixed on a 2D grid and exchange "chemicals" with their neighbors during the growth process. The quantity of chemicals that a cell produces, as well as the differentiation value of the cell in the phenotype, are controlled by a Neural Network (the genotype) that takes as inputs the chemicals produced by the neighboring cells at the previous time step. In the proposed model, the number of iterations of the growth process is not pre-determined, but emerges during evolution: only organisms for which the growth process stabilizes give a phenotype (the stable state), others are declared nonviable. The optimization of the controller is done using the NEAT algorithm, that optimizes both the topology and the weights of the Neural Networks. Though each cell only receives local information from its neighbors, the experimental results of the proposed approach on the 'flags' problems (the phenotype must match a given 2D pattern) are almost as good as those of a direct regression approach using the same model with global information. Moreover, the resulting multi-cellular organisms exhibit almost perfect self-healing characteristics.


The Optimal Design of Three Degree-of-Freedom Parallel Mechanisms for Machining Applications

Abstract: The subject of this paper is the optimal design of a parallel mechanism intended for three-axis machining applications. Parallel mechanisms are interesting alternative designs in this context but most of them are designed for three- or six-axis machining applications. In the last case, the position and the orientation of the tool are coupled and the shape of the workspace is complex. The aim of this paper is to use a simple parallel mechanism with two-degree-of-freedom (dof) for translational motions and to add one leg to have one-dof rotational motion. The kinematics and singular configurations are studied as well as an optimization method. The three-degree-of-freedom mechanisms analyzed in this paper can be extended to four-axis machines by adding a fourth axis in series with the first two.


Classification of one family of 3R positioning Manipulators

Abstract: The aim of this paper is to classify one family of 3R serial positioning manipulators. This categorization is based on the number of cusp points of quaternary, binary, generic and non-generic manipulators. It was found three subsets of manipulators with 0, 2 or 4 cusp points and one homotopy class for generic quaternary manipulators. This classification allows us to define the design parameters for which the manipulator is cuspidal or not, i.e., for which the manipulator can or cannot change posture without meeting a singularity, respectively.


An Approximation Algorithm for Shortest Descending Paths

Abstract: A path from s to t on a polyhedral terrain is descending if the height of a point p never increases while we move p along the path from s to t. No efficient algorithm is known to find a shortest descending path (SDP) from s to t in a polyhedral terrain. We give a simple approximation algorithm that solves the SDP problem on general terrains. Our algorithm discretizes the terrain with O(n^2 X / e) Steiner points so that after an O(n^2 X / e * log(n X /e))-time preprocessing phase for a given vertex s, we can determine a (1+e)-approximate SDP from s to any point v in O(n) time if v is either a vertex of the terrain or a Steiner point, and in O(n X /e) time otherwise. Here n is the size of the terrain, and X is a parameter of the geometry of the terrain.


Logic Column 18: Alternative Logics: A Book Review

Abstract: This article discusses two books on the topic of alternative logics in science: "Deviant Logic", by Susan Haack, and "Alternative Logics: Do Sciences Need Them?", edited by Paul Weingartner.


Machine and Component Residual Life Estimation through the Application of Neural Networks

Abstract: This paper concerns the use of neural networks for predicting the residual life of machines and components. In addition, the advantage of using condition-monitoring data to enhance the predictive capability of these neural networks was also investigated. A number of neural network variations were trained and tested with the data of two different reliability-related datasets. The first dataset represents the renewal case where the failed unit is repaired and restored to a good-as-new condition. Data was collected in the laboratory by subjecting a series of similar test pieces to fatigue loading with a hydraulic actuator. The average prediction error of the various neural networks being compared varied from 431 to 841 seconds on this dataset, where test pieces had a characteristic life of 8,971 seconds. The second dataset was collected from a group of pumps used to circulate a water and magnetite solution within a plant. The data therefore originated from a repaired system affected by reliability degradation. When optimized, the multi-layer perceptron neural networks trained with the Levenberg-Marquardt algorithm and the general regression neural network produced a sum-of-squares error within 11.1% of each other. The potential for using neural networks for residual life prediction and the advantage of incorporating condition-based data into the model were proven for both examples.


The Orthoglide: Kinematics and Workspace Analysis

Abstract: The paper addresses kinematic and geometrical aspects of the Orthoglide, a three-DOF parallel mechanism. This machine consists of three fixed linear joints, which are mounted orthogonally, three identical legs and a mobile platform, which moves in the Cartesian x-y-z space with fixed orientation. New solutions to solve inverse/direct kinematics are proposed and a detailed workspace analysis is performed taking into account specific joint limit constraints.


Subjective Evaluation of Forms in an Immersive Environment

Abstract: User's perception of product, by essence subjective, is a major topic in marketing and industrial design. Many methods, based on users' tests, are used so as to characterise this perception. We are interested in three main methods: multidimensional scaling, semantic differential method, and preference mapping. These methods are used to built a perceptual space, in order to position the new product, to specify requirements by the study of user's preferences, to evaluate some product attributes, related in particular to style (aesthetic). These early stages of the design are primordial for a good orientation of the project. In parallel, virtual reality tools and interfaces are more and more efficient for suggesting to the user complex feelings, and creating in this way various levels of perceptions. In this article, we present on an example the use of multidimensional scaling, semantic differential method and preference mapping for the subjective assessment of virtual products. These products, which geometrical form is variable, are defined with a CAD model and are proposed to the user with a spacemouse and stereoscopic glasses. Advantages and limitations of such evaluation is next discussed..


Realistic Rendering of Kinetostatic Indices of Mechanisms

Abstract: The work presented in this paper is related to the use of a haptic device in an environment of robotic simulation. Such device introduces a new approach to feel and to understand the boundaries of the workspace of mechanisms as well as its kinetostatic properties. Indeed, these concepts are abstract and thus often difficult to understand for the end-users. To catch his attention, we propose to amplify the problems of the mechanisms in order to help him to take the good decisions.


A New Concept of Modular Parallel Mechanism for Machining Applications

Abstract: The subject of this paper is the design of a new concept of modular parallel mechanisms for three, four or five-axis machining applications. Most parallel mechanisms are designed for three- or six-axis machining applications. In the last case, the position and the orientation of the tool are coupled and the shape of the workspace is complex. The aim of this paper is to use a simple parallel mechanism with two-degree-of-freedom (dof) for translation motions and to add one or two legs to add one or two-dofs for rotation motions. The kinematics and singular configurations are studied for each mechanism.


A Workspace based Classification of 3R Orthogonal Manipulators

Abstract: A classification of a family of 3-revolute (3R) positioning manipulators is established. This classification is based on the topology of their workspace. The workspace is characterized in a half-cross section by the singular curves of the manipulator. The workspace topology is defined by the number of cusps and nodes that appear on these singular curves. The design parameters space is shown to be partitioned into nine subspaces of distinct workspace topologies. Each separating surface is given as an explicit expression in the DH-parameters.


Singularity Surfaces and Maximal Singularity-Free Boxes in the Joint Space of Planar 3-RPR Parallel Manipulators

Abstract: In this paper, a method to compute joint space singularity surfaces of 3-RPR planar parallel manipulators is first presented. Then, a procedure to determine maximal joint space singularity-free boxes is introduced. Numerical examples are given in order to illustrate graphically the results. This study is of high interest for planning trajectories in the joint space of 3-RPR parallel manipulators and for manipulators design as it may constitute a tool for choosing appropriate joint limits and thus for sizing the link lengths of the manipulator.


Kinematics analysis of the parallel module of the VERNE machine

Abstract: The paper derives the inverse and forward kinematic equations of a spatial three-degree-of-freedom parallel mechanism, which is the parallel module of a hybrid serial-parallel 5-axis machine tool. This parallel mechanism consists of a moving platform that is connected to a fixed base by three non-identical legs. Each leg is made up of one prismatic and two pair spherical joint, which are connected in a way that the combined effects of the three legs lead to an over-constrained mechanism with complex motion. This motion is defined as a simultaneous combination of rotation and translation.


Does P=NP?

Abstract: This paper has been withdrawn Abstract: This paper has been withdrawn by the author due to the publication.


An Algorithm for Computing Cusp Points in the Joint Space of 3-RPR Parallel Manipulators

Abstract: This paper presents an algorithm for detecting and computing the cusp points in the joint space of 3-RPR planar parallel manipulators. In manipulator kinematics, cusp points are special points, which appear on the singular curves of the manipulators. The nonsingular change of assembly mode of 3-RPR parallel manipulators was shown to be associated with the existence of cusp points. At each of these points, three direct kinematic solutions coincide. In the literature, a condition for the existence of three coincident direct kinematic solutions was established, but has never been exploited, because the algebra involved was too complicated to be solved. The algorithm presented in this paper solves this equation and detects all the cusp points in the joint space of these manipulators.


Typer la d\'e-s\'erialisation sans s\'erialiser les types

Abstract: In this paper, we propose a way of assigning static type information to unmarshalling functions and we describe a verification technique for unmarshalled data that preserves the execution safety provided by static type checking. This technique, whose correctness is proven, relies on singleton types whose values are transmitted to unmarshalling routines at runtime, and on an efficient checking algorithm able to deal with sharing and cycles.


DWEB: A Data Warehouse Engineering Benchmark

Abstract: Data warehouse architectural choices and optimization techniques are critical to decision support query performance. To facilitate these choices, the performance of the designed data warehouse must be assessed. This is usually done with the help of benchmarks, which can either help system users comparing the performances of different systems, or help system engineers testing the effect of various design choices. While the TPC standard decision support benchmarks address the first point, they are not tuneable enough to address the second one and fail to model different data warehouse schemas. By contrast, our Data Warehouse Engineering Benchmark (DWEB) allows to generate various ad-hoc synthetic data warehouses and workloads. DWEB is fully parameterized to fulfill data warehouse design needs. However, two levels of parameterization keep it relatively easy to tune. Finally, DWEB is implemented as a Java free software that can be interfaced with most existing relational database management systems. A sample usage of DWEB is also provided in this paper.


DOEF: A Dynamic Object Evaluation Framework

Abstract: In object-oriented or object-relational databases such as multimedia databases or most XML databases, access patterns are not static, i.e., applications do not always access the same objects in the same order repeatedly. However, this has been the way these databases and associated optimisation techniques like clustering have been evaluated up to now. This paper opens up research regarding this issue by proposing a dynamic object evaluation framework (DOEF) that accomplishes access pattern change by defining configurable styles of change. This preliminary prototype has been designed to be open and fully extensible. To illustrate the capabilities of DOEF, we used it to compare the performances of four state of the art dynamic clustering algorithms. The results show that DOEF is indeed effective at determining the adaptability of each dynamic clustering algorithm to changes in access pattern.


Decision tree modeling with relational views

Abstract: Data mining is a useful decision support technique that can be used to discover production rules in warehouses or corporate data. Data mining research has made much effort to apply various mining algorithms efficiently on large databases. However, a serious problem in their practical application is the long processing time of such algorithms. Nowadays, one of the key challenges is to integrate data mining methods within the framework of traditional database systems. Indeed, such implementations can take advantage of the efficiency provided by SQL engines. In this paper, we propose an integrating approach for decision trees within a classical database system. In other words, we try to discover knowledge from relational databases, in the form of production rules, via a procedure embedding SQL queries. The obtained decision tree is defined by successive, related relational views. Each view corresponds to a given population in the underlying decision tree. We selected the classical Induction Decision Tree (ID3) algorithm to build the decision tree. To prove that our implementation of ID3 works properly, we successfully compared the output of our procedure with the output of an existing and validated data mining software, SIPINA. Furthermore, since our approach is tuneable, it can be generalized to any other similar decision tree-based method.


Warehousing Web Data

Abstract: In a data warehousing process, mastering the data preparation phase allows substantial gains in terms of time and performance when performing multidimensional analysis or using data mining algorithms. Furthermore, a data warehouse can require external data. The web is a prevalent data source in this context. In this paper, we propose a modeling process for integrating diverse and heterogeneous (so-called multiform) data into a unified format. Furthermore, the very schema definition provides first-rate metadata in our data warehousing context. At the conceptual level, a complex object is represented in UML. Our logical model is an XML schema that can be described with a DTD or the XML-Schema language. Eventually, we have designed a Java prototype that transforms our multiform input data into XML documents representing our physical model. Then, the XML documents we obtain are mapped into a relational database we view as an ODS (Operational Data Storage), whose content will have to be re-modeled in a multidimensional way to allow its storage in a star schema-based warehouse and, later, its analysis.


Web data modeling for integration in data warehouses

Abstract: In a data warehousing process, the data preparation phase is crucial. Mastering this phase allows substantial gains in terms of time and performance when performing a multidimensional analysis or using data mining algorithms. Furthermore, a data warehouse can require external data. The web is a prevalent data source in this context, but the data broadcasted on this medium are very heterogeneous. We propose in this paper a UML conceptual model for a complex object representing a superclass of any useful data source (databases, plain texts, HTML and XML documents, images, sounds, video clips...). The translation into a logical model is achieved with XML, which helps integrating all these diverse, heterogeneous data into a unified format, and whose schema definition provides first-rate metadata in our data warehousing context. Moreover, we benefit from XML's flexibility, extensibility and from the richness of the semi-structured data model, but we are still able to later map XML documents into a database if more structuring is needed.


Mixing the Objective Caml and C# Programming Models in the .Net Framework

Abstract: We present a new code generator, called O'Jacare.net, to inter-operate between C# and Objective Caml through their object models. O'Jacare.net defines a basic IDL (Interface Definition Language) that describes classes and interfaces in order to communicate between Objective Caml and C#. O'Jacare.net generates all needed wrapper classes and takes advantage of static type checking in both worlds. Although the IDL intersects these two object models, O'Jacare.net allows to combine features from both.


Actin - Technical Report

Abstract: The Boolean satisfiability problem (SAT) can be solved efficiently with variants of the DPLL algorithm. For industrial SAT problems, DPLL with conflict analysis dependent dynamic decision heuristics has proved to be particularly efficient, e.g. in Chaff. In this work, algorithms that initialize the variable activity values in the solver MiniSAT v1.14 by analyzing the CNF are evolved using genetic programming (GP), with the goal to reduce the total number of conflicts of the search and the solving time. The effect of using initial activities other than zero is examined by initializing with random numbers. The possibility of countering the detrimental effects of reordering the CNF with improved initialization is investigated. The best result found (with validation testing on further problems) was used in the solver Actin, which was submitted to SAT-Race 2006.


A note on module-composed graphs

Abstract: In this paper we consider module-composed graphs, i.e. graphs which can be defined by a sequence of one-vertex insertions v_1,...,v_n, such that the neighbourhood of vertex v_i, 2<= i<= n, forms a module (a homogeneous set) of the graph defined by vertices v_1,..., v_{i-1}. We show that module-composed graphs are HHDS-free and thus homogeneously orderable, weakly chordal, and perfect. Every bipartite distance hereditary graph, every (co-2C_4,P_4)-free graph and thus every trivially perfect graph is module-composed. We give an O(|V_G|(|V_G|+|E_G|)) time algorithm to decide whether a given graph G is module-composed and construct a corresponding module-sequence. For the case of bipartite graphs, module-composed graphs are exactly distance hereditary graphs, which implies simple linear time algorithms for their recognition and construction of a corresponding module-sequence.


Unfolding Manhattan Towers

Abstract: We provide an algorithm for unfolding the surface of any orthogonal polyhedron that falls into a particular shape class we call Manhattan Towers, to a nonoverlapping planar orthogonal polygon. The algorithm cuts along edges of a 4x5x1 refinement of the vertex grid.


Wireless Networking to Support Data and Voice Communication Using Spread Spectrum Technology in The Physical Layer

Abstract: Wireless networking is rapidly growing and becomes an inexpensive technology which allows multiple users to simultaneously access the network and the internet while roaming about the campus. In the present work, the software development of a wireless LAN(WLAN) is highlighted. This WLAN utilizes direct sequence spread spectrum (DSSS) technology at 902MHz RF carrier frequency in its physical layer. Cost effective installation and antijaming property of spread spectrum technology are the major advantages of this work.


HMM Speaker Identification Using Linear and Non-linear Merging Techniques

Abstract: Speaker identification is a powerful, non-invasive and in-expensive biometric technique. The recognition accuracy, however, deteriorates when noise levels affect a specific band of frequency. In this paper, we present a sub-band based speaker identification that intends to improve the live testing performance. Each frequency sub-band is processed and classified independently. We also compare the linear and non-linear merging techniques for the sub-bands recognizer. Support vector machines and Gaussian Mixture models are the non-linear merging techniques that are investigated. Results showed that the sub-band based method used with linear merging techniques enormously improved the performance of the speaker identification over the performance of wide-band recognizers when tested live. A live testing improvement of 9.78% was achieved


Principal Component Analysis and Automatic Relevance Determination in Damage Identification

Abstract: This paper compares two neural network input selection schemes, the Principal Component Analysis (PCA) and the Automatic Relevance Determination (ARD) based on Mac-Kay's evidence framework. The PCA takes all the input data and projects it onto a lower dimension space, thereby reduc-ing the dimension of the input space. This input reduction method often results with parameters that have significant influence on the dynamics of the data being diluted by those that do not influence the dynamics of the data. The ARD selects the most relevant input parameters and discards those that do not contribute significantly to the dynamics of the data being modelled. The ARD sometimes results with important input parameters being discarded thereby compromising the dynamics of the data. The PCA and ARD methods are implemented together with a Multi-Layer-Perceptron (MLP) network for fault identification in structures and the performance of the two methods is as-sessed. It is observed that ARD and PCA give similar accu-racy levels when used as input-selection schemes. There-fore, the choice of input-selection scheme is dependent on the nature of the data being processed.


Using artificial intelligence for data reduction in mechanical engineering

Abstract: In this paper artificial neural networks and support vector machines are used to reduce the amount of vibration data that is required to estimate the Time Domain Average of a gear vibration signal. Two models for estimating the time domain average of a gear vibration signal are proposed. The models are tested on data from an accelerated gear life test rig. Experimental results indicate that the required data for calculating the Time Domain Average of a gear vibration signal can be reduced by up to 75% when the proposed models are implemented.


Evolutionary Optimisation Methods for Template Based Image Registration

Abstract: This paper investigates the use of evolutionary optimisation techniques to register a template with a scene image. An error function is created to measure the correspondence of the template to the image. The problem presented here is to optimise the horizontal, vertical and scaling parameters that register the template with the scene. The Genetic Algorithm, Simulated Annealing and Particle Swarm Optimisations are compared to a Nelder-Mead Simplex optimisation with starting points chosen in a pre-processing stage. The paper investigates the precision and accuracy of each method and shows that all four methods perform favourably for image registration. SA is the most precise, GA is the most accurate. PSO is a good mix of both and the Simplex method returns local minima the most. A pre-processing stage should be investigated for the evolutionary methods in order to improve performance. Discrete versions of the optimisation methods should be investigated to further improve computational performance.


Option Pricing Using Bayesian Neural Networks

Abstract: Options have provided a field of much study because of the complexity involved in pricing them. The Black-Scholes equations were developed to price options but they are only valid for European styled options. There is added complexity when trying to price American styled options and this is why the use of neural networks has been proposed. Neural Networks are able to predict outcomes based on past data. The inputs to the networks here are stock volatility, strike price and time to maturity with the output of the network being the call option price. There are two techniques for Bayesian neural networks used. One is Automatic Relevance Determination (for Gaussian Approximation) and one is a Hybrid Monte Carlo method, both used with Multi-Layer Perceptrons.


A Tighter Analysis of Setcover Greedy Algorithm for Test Set

Abstract: Setcover greedy algorithm is a natural approximation algorithm for test set problem. This paper gives a precise and tighter analysis of performance guarantee of this algorithm. The author improves the performance guarantee $2\ln n$ which derives from set cover problem to $1.1354\ln n$ by applying the potential function technique. In addition, the author gives a nontrivial lower bound $1.0004609\ln n$ of performance guarantee of this algorithm. This lower bound, together with the matching bound of information content heuristic, confirms the fact information content heuristic is slightly better than setcover greedy algorithm in worst case.


Scalability and Optimisation of a Committee of Agents Using Genetic Algorithm

Abstract: A population of committees of agents that learn by using neural networks is implemented to simulate the stock market. Each committee of agents, which is regarded as a player in a game, is optimised by continually adapting the architecture of the agents using genetic algorithms. The committees of agents buy and sell stocks by following this procedure: (1) obtain the current price of stocks; (2) predict the future price of stocks; (3) and for a given price trade until all the players are mutually satisfied. The trading of stocks is conducted by following these rules: (1) if a player expects an increase in price then it tries to buy the stock; (2) else if it expects a drop in the price, it sells the stock; (3)and the order in which a player participates in the game is random. The proposed procedure is implemented to simulate trading of three stocks, namely, the Dow Jones, the Nasdaq and the S&P 500. A linear relationship between the number of players and agents versus the computational time to run the complete simulation is observed. It is also found that no player has a monopolistic advantage.


Finite Element Model Updating Using Response Surface Method

Abstract: This paper proposes the response surface method for finite element model updating. The response surface method is implemented by approximating the finite element model surface response equation by a multi-layer perceptron. The updated parameters of the finite element model were calculated using genetic algorithm by optimizing the surface response equation. The proposed method was compared to the existing methods that use simulated annealing or genetic algorithm together with a full finite element model for finite element model updating. The proposed method was tested on an unsymmetri-cal H-shaped structure. It was observed that the proposed method gave the updated natural frequen-cies and mode shapes that were of the same order of accuracy as those given by simulated annealing and genetic algorithm. Furthermore, it was observed that the response surface method achieved these results at a computational speed that was more than 2.5 times as fast as the genetic algorithm and a full finite element model and 24 times faster than the simulated annealing.


Dynamic Model Updating Using Particle Swarm Optimization Method

Abstract: This paper proposes the use of particle swarm optimization method (PSO) for finite element (FE) model updating. The PSO method is compared to the existing methods that use simulated annealing (SA) or genetic algorithms (GA) for FE model for model updating. The proposed method is tested on an unsymmetrical H-shaped structure. It is observed that the proposed method gives updated natural frequencies the most accurate and followed by those given by an updated model that was obtained using the GA and a full FE model. It is also observed that the proposed method gives updated mode shapes that are best correlated to the measured ones, followed by those given by an updated model that was obtained using the SA and a full FE model. Furthermore, it is observed that the PSO achieves this accuracy at a computational speed that is faster than that by the GA and a full FE model which is faster than the SA and a full FE model.


Modeling and Controlling Interstate Conflict

Abstract: Bayesian neural networks were used to model the relationship between input parameters, Democracy, Allies, Contingency, Distance, Capability, Dependency and Major Power, and the output parameter which is either peace or conflict. The automatic relevance determination was used to rank the importance of input variables. Control theory approach was used to identify input variables that would give a peaceful outcome. It was found that using all four controllable variables Democracy, Allies, Capability and Dependency; or using only Dependency or only Capabilities avoids all the predicted conflicts.


Scheduling Dags under Uncertainty

Abstract: This paper introduces a parallel scheduling problem where a directed acyclic graph modeling $t$ tasks and their dependencies needs to be executed on $n$ unreliable workers. Worker $i$ executes task $j$ correctly with probability $p_{i,j}$. The goal is to find a regimen $\Sigma$, that dictates how workers get assigned to tasks (possibly in parallel and redundantly) throughout execution, so as to minimize the expected completion time. This fundamental parallel scheduling problem arises in grid computing and project management fields, and has several applications. We show a polynomial time algorithm for the problem restricted to the case when dag width is at most a constant and the number of workers is also at most a constant. These two restrictions may appear to be too severe. However, they are fundamentally required. Specifically, we demonstrate that the problem is NP-hard with constant number of workers when dag width can grow, and is also NP-hard with constant dag width when the number of workers can grow. When both dag width and the number of workers are unconstrained, then the problem is inapproximable within factor less than 5/4, unless P=NP.


Ontology-Supported and Ontology-Driven Conceptual Navigation on the World Wide Web

Abstract: This paper presents the principles of ontology-supported and ontology-driven conceptual navigation. Conceptual navigation realizes the independence between resources and links to facilitate interoperability and reusability. An engine builds dynamic links, assembles resources under an argumentative scheme and allows optimization with a possible constraint, such as the user's available time. Among several strategies, two are discussed in detail with examples of applications. On the one hand, conceptual specifications for linking and assembling are embedded in the resource meta-description with the support of the ontology of the domain to facilitate meta-communication. Resources are like agents looking for conceptual acquaintances with intention. On the other hand, the domain ontology and an argumentative ontology drive the linking and assembling strategies.


A Technical Report On Grid Benchmarking using ATLAS V.O

Abstract: Grids include heterogeneous resources, which are based on different hardware and software architectures or components. In correspondence with this diversity of the infrastructure, the execution time of any single job, as well as the total grid performance can both be affected substantially, which can be demonstrated by measurements. Running a simple benchmarking suite can show this heterogeneity and give us results about the differences over the grid sites.


Double Sided Watermark Embedding and Detection with Perceptual Analysis

Abstract: In our previous work, we introduced a double-sided technique that utilizes but not reject the host interference. Due to its nice property of utilizing but not rejecting the host interference, it has a big advantage over the host interference schemes in that the perceptual analysis can be easily implemented for our scheme to achieve the locally bounded maximum embedding strength. Thus, in this work, we detail how to implement the perceptual analysis in our double-sided schemes since the perceptual analysis is very important for improving the fidelity of watermarked contents. Through the extensive performance comparisons, we can further validate the performance advantage of our double-sided schemes.


Towards Informative Statistical Flow Inversion

Abstract: A problem which has recently attracted research attention is that of estimating the distribution of flow sizes in internet traffic. On high traffic links it is sometimes impossible to record every packet. Researchers have approached the problem of estimating flow lengths from sampled packet data in two separate ways. Firstly, different sampling methodologies can be tried to more accurately measure the desired system parameters. One such method is the sample-and-hold method where, if a packet is sampled, all subsequent packets in that flow are sampled. Secondly, statistical methods can be used to ``invert'' the sampled data and produce an estimate of flow lengths from a sample. In this paper we propose, implement and test two variants on the sample-and-hold method. In addition we show how the sample-and-hold method can be inverted to get an estimation of the genuine distribution of flow sizes. Experiments are carried out on real network traces to compare standard packet sampling with three variants of sample-and-hold. The methods are compared for their ability to reconstruct the genuine distribution of flow sizes in the traffic.


A Branch and Cut Algorithm for the Halfspace Depth Problem

Abstract: The concept of data depth in non-parametric multivariate descriptive statistics is the generalization of the univariate rank method to multivariate data. Halfspace depth is a measure of data depth. Given a set S of points and a point p, the halfspace depth (or rank) k of p is defined as the minimum number of points of S contained in any closed halfspace with p on its boundary. Computing halfspace depth is NP-hard, and it is equivalent to the Maximum Feasible Subsystem problem. In this thesis a mixed integer program is formulated with the big-M method for the halfspace depth problem. We suggest a branch and cut algorithm. In this algorithm, Chinneck's heuristic algorithm is used to find an upper bound and a related technique based on sensitivity analysis is used for branching. Irreducible Infeasible Subsystem (IIS) hitting set cuts are applied. We also suggest a binary search algorithm which may be more stable numerically. The algorithms are implemented with the BCP framework from the COIN-OR project.


A Closed-Form Method for LRU Replacement under Generalized Power-Law Demand

Abstract: We consider the well known \emph{Least Recently Used} (LRU) replacement algorithm and analyze it under the independent reference model and generalized power-law demand. For this extensive family of demand distributions we derive a closed-form expression for the per object steady-state hit ratio. To the best of our knowledge, this is the first analytic derivation of the per object hit ratio of LRU that can be obtained in constant time without requiring laborious numeric computations or simulation. Since most applications of replacement algorithms include (at least) some scenarios under i.i.d. requests, our method has substantial practical value, especially when having to analyze multiple caches, where existing numeric methods and simulation become too time consuming.


On the Hopcroft's minimization algorithm

Abstract: We show that the absolute worst case time complexity for Hopcroft's minimization algorithm applied to unary languages is reached only for de Bruijn words. A previous paper by Berstel and Carton gave the example of de Bruijn words as a language that requires O(n log n) steps by carefully choosing the splitting sets and processing these sets in a FIFO mode. We refine the previous result by showing that the Berstel/Carton example is actually the absolute worst case time complexity in the case of unary languages. We also show that a LIFO implementation will not achieve the same worst time complexity for the case of unary languages. Lastly, we show that the same result is valid also for the cover automata and a modification of the Hopcroft's algorithm, modification used in minimization of cover automata.


A first-order Temporal Logic for Actions

Abstract: We present a multi-modal action logic with first-order modalities, which contain terms which can be unified with the terms inside the subsequent formulas and which can be quantified. This makes it possible to handle simultaneously time and states. We discuss applications of this language to action theory where it is possible to express many temporal aspects of actions, as for example, beginning, end, time points, delayed preconditions and results, duration and many others. We present tableaux rules for a decidable fragment of this logic.


Multi-Dimensional Recurrent Neural Networks

Abstract: Recurrent neural networks (RNNs) have proved effective at one dimensional sequence learning tasks, such as speech and online handwriting recognition. Some of the properties that make RNNs suitable for such tasks, for example robustness to input warping, and the ability to access contextual information, are also desirable in multidimensional domains. However, there has so far been no direct way of applying RNNs to data with more than one spatio-temporal dimension. This paper introduces multi-dimensional recurrent neural networks (MDRNNs), thereby extending the potential applicability of RNNs to vision, video processing, medical imaging and many other areas, while avoiding the scaling problems that have plagued other multi-dimensional models. Experimental results are provided for two image segmentation tasks.


Mean Field Models of Message Throughput in Dynamic Peer-to-Peer Systems

Abstract: The churn rate of a peer-to-peer system places direct limitations on the rate at which messages can be effectively communicated to a group of peers. These limitations are independent of the topology and message transmission latency. In this paper we consider a peer-to-peer network, based on the Engset model, where peers arrive and depart independently at random. We show how the arrival and departure rates directly limit the capacity for message streams to be broadcast to all other peers, by deriving mean field models that accurately describe the system behavior. Our models cover the unit and more general k buffer cases, i.e. where a peer can buffer at most k messages at any one time, and we give results for both single and multi-source message streams. We define coverage rate as peer-messages per unit time, i.e. the rate at which a number of peers receive messages, and show that the coverage rate is limited by the churn rate and buffer size. Our theory introduces an Instantaneous Message Exchange (IME) model and provides a template for further analysis of more complicated systems. Using the IME model, and assuming random processes, we have obtained very accurate equations of the system dynamics in a variety of interesting cases, that allow us to tune a peer-to-peer system. It remains to be seen if we can maintain this accuracy for general processes and when applying a non-instantaneous model.


CDMA Technology for Intelligent Transportation Systems

Abstract: Scientists and Technologists involved in the development of radar and remote sensing systems all over the world are now trying to involve themselves in saving of manpower in the form of developing a new application of their ideas in Intelligent Transport system(ITS). The world statistics shows that by incorporating such wireless radar system in the car would decrease the world road accident by 8-10% yearly. The wireless technology has to be chosen properly which is capable of tackling the severe interferences present in the open road. A combined digital technology like Spread spectrum along with diversity reception will help a lot in this regard. Accordingly, the choice is for FHSS based space diversity system which will utilize carrier frequency around 5.8 GHz ISM band with available bandwidth of 80 MHz and no license. For efficient design, the radio channel is characterized on which the design is based. Out of two available modes e.g. Communication and Radar modes, the radar mode is providing the conditional measurement of the range of the nearest car after authentication of the received code, thus ensuring the reliability and accuracy of measurement. To make the system operational in simultaneous mode, we have started the Software Defined Radio approach for best speed and flexibility.


RADAR Imaging in the Open field At 300 MHz-3000 MHz Radio Band

Abstract: With the technological growth of broadband wireless technology like CDMA and UWB, a lots of development efforts towards wireless communication system and Imaging radar system are well justified. Efforts are also being imparted towards a Convergence Technology.. the convergence between a communication and radar technology which will result in ITS (Intelligent Transport System) and other applications. This encourages present authors for this development. They are trying to utilize or converge the communication technologies towards radar and to achieve the Interference free and clutter free quality remote images of targets using DS-UWB wireless technology.


Scientific citations in Wikipedia

Abstract: The Internet-based encyclopaedia Wikipedia has grown to become one of the most visited web-sites on the Internet. However, critics have questioned the quality of entries, and an empirical study has shown Wikipedia to contain errors in a 2005 sample of science entries. Biased coverage and lack of sources are among the "Wikipedia risks". The present work describes a simple assessment of these aspects by examining the outbound links from Wikipedia articles to articles in scientific journals with a comparison against journal statistics from Journal Citation Reports such as impact factors. The results show an increasing use of structured citation markup and good agreement with the citation pattern seen in the scientific literature though with a slight tendency to cite articles in high-impact journals such as Nature and Science. These results increase confidence in Wikipedia as an good information organizer for science in general.


Parallelized approximation algorithms for minimum routing cost spanning trees

Abstract: We parallelize several previously proposed algorithms for the minimum routing cost spanning tree problem and some related problems.


Improvements to the Psi-SSA representation

Abstract: Modern compiler implementations use the Static Single Assignment representation as a way to efficiently implement optimizing algorithms. However this representation is not well adapted to architectures with a predicated instruction set. The Psi-SSA representation extends the SSA representation such that standard SSA algorithms can be easily adapted to an architecture with a fully predicated instruction set. A new pseudo operation, the Psi operation, is introduced to merge several conditional definitions into a unique definition.


Best insertion algorithm for resource-constrained project scheduling problem

Abstract: This paper considers heuristics for well known resource-constrained project scheduling problem (RCPSP). First a feasible schedule is constructed using randomized best insertion algorithm. The construction is followed by a local search where a new solution is generated as follows: first we randomly delete m activities from the list, which are then reinserted in the list in consecutive order. At the end of run, the schedule with the minimum makespan is selected. Experimental work shows very good results on standard test instances found in PSPLIB


Elementary transformation analysis for Array-OL

Abstract: Array-OL is a high-level specification language dedicated to the definition of intensive signal processing applications. Several tools exist for implementing an Array-OL specification as a data parallel program. While Array-OL can be used directly, it is often convenient to be able to deduce part of the specification from a sequential version of the application. This paper proposes such an analysis and examines its feasibility and its limits.


Sequential mechanism design

Abstract: In the customary VCG (Vickrey-Clarke-Groves) mechanism truth-telling is a dominant strategy. In this paper we study the sequential VCG mechanism and show that other dominant strategies may then exist. We illustrate how this fact can be used to minimize taxes using examples concerned with Clarke tax and public projects.


From Nondeterministic B\"uchi and Streett Automata to Deterministic Parity Automata

Abstract: In this paper we revisit Safra's determinization constructions for automata on infinite words. We show how to construct deterministic automata with fewer states and, most importantly, parity acceptance conditions. Determinization is used in numerous applications, such as reasoning about tree automata, satisfiability of CTL*, and realizability and synthesis of logical specifications. The upper bounds for all these applications are reduced by using the smaller deterministic automata produced by our construction. In addition, the parity acceptance conditions allows to use more efficient algorithms (when compared to handling Rabin or Streett acceptance conditions).


On tractability and congruence distributivity

Abstract: Constraint languages that arise from finite algebras have recently been the object of study, especially in connection with the Dichotomy Conjecture of Feder and Vardi. An important class of algebras are those that generate congruence distributive varieties and included among this class are lattices, and more generally, those algebras that have near-unanimity term operations. An algebra will generate a congruence distributive variety if and only if it has a sequence of ternary term operations, called Jonsson terms, that satisfy certain equations. We prove that constraint languages consisting of relations that are invariant under a short sequence of Jonsson terms are tractable by showing that such languages have bounded relational width.


Response Prediction of Structural System Subject to Earthquake Motions using Artificial Neural Network

Abstract: This paper uses Artificial Neural Network (ANN) models to compute response of structural system subject to Indian earthquakes at Chamoli and Uttarkashi ground motion data. The system is first trained for a single real earthquake data. The trained ANN architecture is then used to simulate earthquakes with various intensities and it was found that the predicted responses given by ANN model are accurate for practical purposes. When the ANN is trained by a part of the ground motion data, it can also identify the responses of the structural system well. In this way the safeness of the structural systems may be predicted in case of future earthquakes without waiting for the earthquake to occur for the lessons. Time period and the corresponding maximum response of the building for an earthquake has been evaluated, which is again trained to predict the maximum response of the building at different time periods. The trained time period versus maximum response ANN model is also tested for real earthquake data of other place, which was not used in the training and was found to be in good agreement.


Fault Classification using Pseudomodal Energies and Neuro-fuzzy modelling

Abstract: This paper presents a fault classification method which makes use of a Takagi-Sugeno neuro-fuzzy model and Pseudomodal energies calculated from the vibration signals of cylindrical shells. The calculation of Pseudomodal Energies, for the purposes of condition monitoring, has previously been found to be an accurate method of extracting features from vibration signals. This calculation is therefore used to extract features from vibration signals obtained from a diverse population of cylindrical shells. Some of the cylinders in the population have faults in different substructures. The pseudomodal energies calculated from the vibration signals are then used as inputs to a neuro-fuzzy model. A leave-one-out cross-validation process is used to test the performance of the model. It is found that the neuro-fuzzy model is able to classify faults with an accuracy of 91.62%, which is higher than the previously used multilayer perceptron.