Soft Computing

A computational approach for the health care market

by Marina Resta

Portfolio Optimization Through Elastic Maps: Some Evidence from the Italian Stock Exchange

by Marina Resta

Assessing the Efficiency of Health Care Providers: A SOM Perspective

by Marina Resta

Social influence and neighbourhood effects in the health care market

by Marina Resta

Arangio S., Bontempi F. (2010), Soft Computing based Multilevel Strategy for Bridge Integrity Monitoring

by stefania Arangio

Computer-Aided Civil and Infrastructure Engineering, 25, 348-362

Stiffness identification and damage localization via differential evolution algorithms

by Sara Casciati

Casciati S. (2008). “Stiffness identification and damage localization via differential evolution algorithms”. Structural Control & Health Monitoring, 15(3), 436-449. ISSN: 1545-2255.

DATA E LUOGO DI PUBBLICAZIONE: April 2008; John Wiley & Sons, Ltd., Chichester PO19 8SQ, W Sussex, England.

ABSTRACT. The goal of structural health monitoring is to identify which discrepancies between the actual behaviour of... more ABSTRACT. The goal of structural health monitoring is to identify which discrepancies between the actual behaviour of a structure and its reference undamaged state are indicative of damage. For this purpose, an objective function, which minimizes the difference between the measured and theoretical modal characteristics of the structure, is formulated. By selecting the stiffness parameters as optimization variables, a differential evolution algorithm is applied to create successive generations that better reflect the measured response, until a certain tolerance is met. At each step of the algorithm, the current modal parameters are recalculated from the new generation of stiffness matrices to estimate the value of the objective function. This procedure represents a favourable path to solve the so-called ‘inverse problem’. Furthermore, the comparison of the identified stiffness matrix with the initial one allows for damage detection and localization. A numerical example, where a generic structure is discretized into finite elements, is provided.

KEYWORDS: damage; element stiffness matrix; differential evolution algorithm; finite element analyses; modal parameters; objective function; optimization problem

A bio-inspired CNN with re-indexing engine for lossless DNA microarray compression and segmentation

by Sebastiano Battiato

Structural Damage Detection using Fuzzy Cognitive Maps and Hebbian Learning

by Ranjan Ganguli

Gas Turbine Diagnostics Using a Soft Computing Approach

by Ranjan Ganguli

Algoritmi evolutivi differenziali in ingegneria strutturale

by Sara Casciati

Fuzzy training in supervised image classification

by Minhe Ji

Co-authored with John R. Jensen

When classifying imagery of reality that seldom presents itself with hard boundaries but transitions and gradual... more When classifying imagery of reality that seldom presents itself with hard boundaries but transitions and gradual interfaces between biophysical phenomena, remote sensing researchers have proposed a fuzzy partition matrix that is more representative of the real situation than the conventional hard classification. In achieving a complete fuzzy approach in supervised classification, this paper discusses the fuzzification of the training stage to improve the classification performance. "Fuzzy training" is proposed to cope with data uncertainty in the early stage of image classification in order to derive statistical parameters that more closely resemble reality. A fuzzy parameter estimator (FPE) was developed with a modified Bezdek's Fuzzy c-Means engine and empirically evaluated. Classification results based on the fuzzy spectral signatures were superior to results obtained using conventional training methods.

Liquid interface: a malleable, transient, direct-touch interface

by Jeffrey Tzu kwan Valino Koh

We present Liquid Interface, a tangible, malleable, 3D, multi- touch interface where actuation, representation and... more We present Liquid Interface, a tangible, malleable, 3D, multi- touch interface where actuation, representation and self configuration occur through the morphing of ferromagnetic fluid. By exploiting the physical properties of ferrofluid, combined with capacitive, multi-touch technology, we are able to produce an interface that allows user to sculpt music in 3D by directly manipulating the ferrofluid in combination with sound.

Rate of convergence of Hermite interpolation based on the roots of certain Jacobi polynomials.

by Victor Ekong

Adaptive Bacterial Foraging Optimization Based Tuning of Optimal PI Speed Controller for PMSM Drive

by Anguluri Rajasekhar

my first paper......springer lncs publication

Speed regulation with conventional PI regulator reduces the speed control precision because of disturbances in Motor... more Speed regulation with conventional PI regulator reduces the speed control precision because of disturbances in Motor and load characteristics, leading to poor performance of whole system. The values so obtained may not give satisfactory results for a wide range of speed. This paper implements, a new tuning algorithm based on the foraging behavior of E-coli Bacteria with an adaptive chemotaxis step, to optimize the coefficients of ‘‘Proportional-Integral’’ (PI) speed controller in a Vector-Controlled Permanent Magnet Synchronous Motor (PMSM) Drive. Through the computer simulations, it is observed that dynamic response of Adaptive bacterial foraging PI (ABF-PI) controller is quite satisfactory. It has good dynamic and static characteristics like low peak overshoot, low Steady state error and less settling time. The ABF technique is compared with ‘‘Gradient descent search’’ method and basic ‘‘Bacterial Foraging’’ Algorithm. The performances of these methods are studied thoroughly using ‘‘ITAE’’ criterion. Simulations are implemented using Industrial Standard MATLAB/SIMULINK.

Controller Tuning Using a Cauchy Mutated Artificial Bee Colony Algorithm

by Anguluri Rajasekhar

Permanent Magnet Synchronous Motors (PMSM) are immensely popular because they can meet the huge capacity needs of... more Permanent Magnet Synchronous Motors (PMSM) are immensely popular because they can meet the huge capacity needs of industrial applications. Speed regulation of PMSM Drives with conventional Proportional-Integral (PI) regulator reduces the speed control precision because of the disturbances in Motor and load characteristics, leading to poor performance of whole system. The values so obtained may not give satisfactory results in a wide range of speed. In this research, we considered the Mathematical model of speed controller for controlling the speed, which can be formulated as an optimization problem subject to various constraints imposed due to motor and other limitation factors. For solving this problem we used a modified version of Artificial Bee Colony (ABC) algorithm known as Cauchy Mutation ABC (C-ABC).We first illustrate the proposed method using various standard benchmark functions and then it is used for tuning PI controller for speed regulation in PMSM drive. Empirical results obtained are compared with the basic version of ABC, which clearly indicates the superior performance of the C-ABC algorithm.

Speed control of PMSM by hybrid genetic Artificial Bee Colony Algorithm

by Anguluri Rajasekhar

Swarm Intelligence is the one of the most efficient and emergent techniques for global optimization. Artificial Bee... more Swarm Intelligence is the one of the most efficient and emergent techniques for global optimization. Artificial Bee Colony Algorithm (ABCA) is one of the new swarm intelligent population-based meta-heuristic approaches, inspired by foraging behavior of bees for function optimization. To enhance the efficiency of ABCA optimizer this paper proposes a novel hybrid approach involving genetic algorithms (GA) and Artificial Bee colony (ABC) algorithms. The proposed method is used for tuning Proportional Integral (PI) speed controller in a vector-controlled Permanent Magnet Synchronous Motor (PMSM) Drive. In this application our tuning method focuses on minimizing the Integral Time Absolute Error (ITAE) criterion. Simulation results and as well as comparisons with other methods like conventional Gradient descent method, Genetic algorithm, and Artificial Bee Colony methods shows the effectiveness of hybrid approach. Simulations are carried out using Industrial standard MATLAB/SIMULINK.

Credal semantics of Bayesian transformations in terms of probability intervals

by Fabio Cuzzolin

The geometry of consonant belief functions: Simplicial complexes of necessity measures

by Fabio Cuzzolin

Two new Bayesian approximations of belief functions based on convex geometry

by Fabio Cuzzolin

Evidential modeling for object pose estimation

by Fabio Cuzzolin