A multivariate fuzzy c-means method

Fuzzy c-means (FCMs) is an important and popular unsupervised partitioning algorithm used in several application domains such as pattern recognition, machine learning and data mining. Although the FCM has shown good performance in detecting clusters, the membership values for each individual computed to each of the clusters cannot indicate how well the individuals are classified. In this paper, a new approach to handle the memberships based on the inherent information in each feature is presented. The algorithm produces a membership matrix for each individual, the membership values are between zero and one and measure the similarity of this individual to the center of each cluster according to each feature. These values can change at each iteration of the algorithm and they are different from one feature to another and from one cluster to another in order to increase the performance of the fuzzy c-means clustering algorithm. To obtain a fuzzy partition by class of the input data set, a way to compute the class membership values is also proposed in this work. Experiments with synthetic and real data sets show that the proposed approach produces good quality of clustering.     

Workflows for Heliophysics

In this paper we describe how we have introduced workflows into the working practices of a community for whom the concept of workflows is very new, namely the heliophysics community. Heliophysics is a branch of astrophysics which studies the Sun and the interactions between the Sun and the planets, by tracking solar events as they travel throughout the Solar system. Heliophysics produces two major challenges for workflow technology. Firstly it is a systems science where research is currently developed by many different communities who need reliable data models and metadata to be able to work together. Thus it has major challenges in the semantics of workflows. Secondly, the problem of time is critical in heliophysics; the workflows must take account of the propagation of events outwards from the sun. They have to address the four dimensional nature of space and time in terms of the indexing of data. We discuss how we have built an environment for Heliophysics workflows building on and extending the Taverna workflow system and utilising the myExperiment site for sharing workflows. We also describe how we have integrated the workflows into the existing practices of the communities involved in Heliophysics by developing a web portal which can hide the technical details from the users, who can concentrate on the data from their scientific point of view rather than on the methods used to integrate and process the data. This work has been developed in the EU Framework 7 project HELIO, and is being disseminated to the worldwide Heliophysics community, since Heliophysics requires integration of effort on a global scale.     

Real-time inverse dynamics control of parallel manipulators using general-purpose multibody software

This work deals with the problem of computing the inverse dynamics of complex constrained mechanical systems for real-time control applications. The main goal is the control of robotic systems using model-based schemes in which the inverse model itself is obtained using a general purpose multibody software, exploiting the redundant coordinate formalism. The resulting control scheme is essentially equivalent to a classical computed torque control, commonly used in robotics applications. This work proposes to use modern general-purpose multibody software to compute the inverse dynamics of complex rigid mechanisms in an efficient way, so that it suits the requirements of realistic real-time applications as well. This task can be very difficult, since it involves a higher number of equations than the relative coordinates approach. The latter is believed to be less general, and may suffer from topology limitations. The use of specialized linear algebra solvers makes this kind of control algorithms usable in real-time for mechanism models of realistic complexity. Numerical results from the simulation of practical applications are presented, consisting in a “delta” robot and a bio-mimetic 11 degrees of freedom manipulator controlled using the same software and the same algorithm.     

Simple tests for multi-criteria optimality

In this paper we present two tests which can decide whether a given pointx ₀ ^N is locally efficient or not with respect to a given finite set of real valued continuously differentiable functions defined on ^N . Examples indicate that the tests may fail on a nowhere dense set.     

A class of high performance Maekawa-type algorithms for distributed systems under heavy demand

Summary Distributed Mutual Exclusion algorithms have been mainly compared using the number of messages exchanged per critical section execution. In such algorithms, no attention has been paid to the serialization order of the requests. Indeed, they adopt FCFS discipline. Conversely, the insertion of priority serialization disciplines, such as Short-Job-First, Head-Of-Line, Shortest-Remaining-Job-First etc., can be useful in many applications to optimize some performance indices. However, such priority disciplines are prone to starvation. The goal of this paper is to investigate and evaluate the impact of the insertion of a priority discipline in Maekawa-type algorithms. Priority serialization disciplines will be inserted by means of agated batch mechanism which avoids starvation. In a distributed algorithm, such a mechanism needs synchronizations among the processes. In order to highlight the usefulness of the priority based serialization discipline, we show how it can be used to improve theaverage response time compared to the FCFS discipline. The gated batch approach exhibits other advantages: algorithms are inherently deadlock-free and messages do not need to piggyback timestamps. We also show that, under heavy demand, algorithms using gated batch exchange less messages than Maekawa-type algorithms per critical section excution. Roberto Baldoni was born in Rome on February 1, 1965. He received the Laurea degree in electronic engineering in 1990 from the University of Rome La Sapienza and the Ph.D. degree in Computer Science from the University of Rome La Sapienza in 1994. Currently, he is a researcher in computer science at IRISA, Rennes (France). His research interests include operating systems, distributed algorithms, network protocols and real-time multimedia applications. Bruno Ciciani received the Laurea degree in electronic engineering in 1980 from the University of Rome La Sapienza. From 1983 to 1991 he has been a researcher at the University of Rome Tor Vergata. He is currently full professor in Computer Science at the University of Rome La Sapienza. His research activities include distributed computer systems, fault-tolerant computing, languages for parallel processing, and computer system performance and reliability evaluation. He has published in IEEE Trans. on Computers, IEEE Trans. on Knowledge and Data Engineering, IEEE Trans. on Software Engineering and IEEE Trans. on Reliability. He is the author of a book titled Manufactoring Yield Evaluation of VLSI/WSI Systems to be published by IEEE Computer Society Press.This research was supported in part by the Consiglio Nazionale delle Ricerche under grant 93.02294.CT12This author is also supported by a grant of the Human Capital and Mobility project of the European Community under contract No. 3702 CABERNET     

Level Lines of a Polynomial on a Plane

Programming and Computer Software - A method for calculating the location of all types of level lines of a real polynomial on the real plane is proposed. To this end, the critical points and...     

Generation and interpretation of parsimonious predictive models for load forecasting in smart heating networks

Applied Intelligence - Forecasting future heat load in smart district heating networks is a key problem for utility companies that need such predictions for optimizing their operational activities....     

A Novel Particle/Photon Detector Based on a Superconducting Proximity Array of Nanodots

The current frontiers in the investigation of high-energy particles demand for new detection methods. Higher sensitivity to low-energy deposition, high-energy resolution to identify events and improve the background rejection, and large detector masses have to be developed to detect even an individual particle that weakly interacts with ordinary matter. Here, we will describe the concept and the layout of a novel superconducting proximity array which show dynamic vortex Mott insulator to metal transitions, as an ultra-sensitive compact radiation-particle detector.