0000000000080034

AUTHOR

Kirsi Majava

showing 4 related works from this author

A Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production

2007

This article proposes a Memetic Differential Evolution (MDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. The MDE is an adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution (DE) with the exploitative features of two local searchers. The local searchers are adaptively activated by means of a novel control parameter which measures fitness diversity within the population. Numerical results show that the DE framework is efficient for the class of problems under study and employment of exploitative local searchers is helpful in supporting the DE explorative mechanism in avoid…

Engineeringeducation.field_of_studyFinite impulse responsebusiness.industryProcess (engineering)PopulationEvolutionary algorithmMachine learningcomputer.software_genreFilter designDifferential evolutionMemetic algorithmArtificial intelligencebusinesseducationcomputerDigital filter
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Robust refinement of initial prototypes for partitioning-based clustering algorithms

2007

Non-uniqueness of solutions and sensitivity to erroneous data are common problems to large-scale data clustering tasks. In order to avoid poor quality of solutions with partitioning-based clustering methods, robust estimates (that are highly insensitive to erroneous data values) are needed and initial cluster prototypes should be determined properly. In this paper, a robust density estimation initialization method that exploits the spatial median estimate to the prototype update is presented. Besides being insensitive to noise and outliers, the new method is also computationally comparable with other traditional methods. The methods are compared by numerical experiments on a set of syntheti…

Set (abstract data type)Computer scienceCorrelation clusteringOutlierInitializationSensitivity (control systems)Density estimationNoise (video)Data miningCluster analysiscomputer.software_genrecomputerRecent Advances in Stochastic Modeling and Data Analysis
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An enhanced memetic differential evolution in filter design for defect detection in paper production.

2008

This article proposes an Enhanced Memetic Differential Evolution (EMDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. Defect detection is handled by means of two Gabor filters and their design is performed by the EMDE. The EMDE is a novel adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution with the exploitative features of three local search algorithms employing different pivot rules and neighborhood generating functions. These local search algorithms are the Hooke Jeeves Algorithm, a Stochastic Local Search, and Simulated Annealing. The local search algorithms are adap…

PaperQuality ControlMathematical optimizationPopulationEvolutionary algorithmmultimeme algorithmsdigital filter designArtificial IntelligenceImage Interpretation Computer-AssistedFIR filterHumansIndustryLocal search (optimization)Computer Simulationmemetic algorithmseducationMetaheuristicMathematicsProbabilityedge detectioneducation.field_of_studyElectronic Data ProcessingStochastic ProcessesModels Statisticalbusiness.industrydifferential evolutionpaper productionModels TheoreticalComputational MathematicsFilter designDifferential evolutionSimulated annealingMemetic algorithmbusinessAlgorithmsSoftware
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Optimization-based techniques for image restoration

2001

Kohinanpoisto on olennainen osa kuvankäsittelyä. FL Kirsi Majavan väitöskehittää uuden menetelmän, jolla paljon häiriötä sisältävästä kuvasta löytyvät sekä jyrkät rajat että sileät osat. Ne ovat perinteisen kuvankäsittelyn ongelma-alueita. Työssä kehitetään myös uusia, tehokkaita numeerisia ratkaisumenetelmiä erilaisille kohinanpoistotehtäville. Jos kuvia halutaan analysoida automaattisesti tietokoneella, on usein välttämätöntä aloittaa kuvankäsittely poistamalla kuvasta kohinaa ja muita vääristymiä. Jos kuvasta halutaan automaattisesti löytää ja tunnistaa siinä esiintyvät objektit, on lisäksi olennaista, että kohinanpoisto säilyttää mahdollisimman tarkasti objektien reunat. Usein kuvankäsi…

optimointinumeeriset menetelmätkohinakuvankäsittelyentistäminenkuvatvalokuvatretusointi
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