Search results for "network"

showing 10 items of 7718 documents

Developing National Career and Workforce Development Systems and Policies with Structured International Co-operation : Structures, Processes and Acti…

2017

This chapter describes the work undertaken by European Lifelong Guidance Policy Network, ELGPN during 2007-15, including the progress of lifelong guidance policy adaptation and implementation processes of the member countries at national, regional and local levels. In an evaluation of ELGPN’s work, members report that participation in the Network enriched their awareness of possible responses to common challenges and given them fresh perspectives and new insights into their national provision. A key strength of the Network was the strong ownership of its activities expressed by the national delegations. ELGPN member countries stress the importance of continuing structured European co-operat…

networksopastuslifelong educationurakehitysworkforce development systems
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Hyper-flexible Convolutional Neural Networks based on Generalized Lehmer and Power Means

2022

Convolutional Neural Network is one of the famous members of the deep learning family of neural network architectures, which is used for many purposes, including image classification. In spite of the wide adoption, such networks are known to be highly tuned to the training data (samples representing a particular problem), and they are poorly reusable to address new problems. One way to change this would be, in addition to trainable weights, to apply trainable parameters of the mathematical functions, which simulate various neural computations within such networks. In this way, we may distinguish between the narrowly focused task-specific parameters (weights) and more generic capability-spec…

neural networkCognitive NeuroscienceLehmer meansyväoppiminenneuroverkotMachine LearningflexibilitykoneoppiminenPower meanArtificial Intelligenceconvolutionadversarial robustnesspoolingNeural Networks Computeractivation functionconvolutionalgeneralization
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Performance Evaluation for a Sustainable Supply Chain Management System in the Automotive Industry Using Artificial Intelligence

2020

Increasing the sustainability of a system can be achieved by evaluating the system, identifying the issues and their root cause and solving them. Performance evaluation translates into key performance indicators (KPIs) with a high impact on increasing overall efficacy and efficiency. As the pool of KPIs has increased over time in the context of evaluating the supply chain management (SCM) system&rsquo

neural networkComputer scienceSupply chainAutomotive industryBioengineeringContext (language use)010501 environmental scienceslcsh:Chemical technologyrisk management01 natural scienceslcsh:Chemistry0502 economics and businessChemical Engineering (miscellaneous)lcsh:TP1-1185Risk management0105 earth and related environmental sciencesSupply chain managementbusiness.industryProcess Chemistry and Technology05 social sciencesdata miningRoot causeartificial intelligenceperformance evaluationlcsh:QD1-999key performance indicatorManagement systemArtificial intelligencePerformance indicatorbusiness050203 business & managementProcesses
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Noise effect in a FitzHugh-Nagumo circuit driven by a bichromatic signal

2013

We analyze the response of a nonlinear circuit exactly ruled by the FitzHugh-Nagumo equations. This circuit is submitted to a bichromatic signal including a high frequency and a low frequency. In absence of noise, we show that for an appropriate amplitude of the high frequency driving, the response of the circuit estimated at the low frequency can be optimized via the phenomenon of vibrational resonance. Next, we show that under certain conditions, noise can contribute to the effect of vibrational resonance. Colored noise is also considered. Our experimental results are confirmed by a numerical analysis.

neural networkStochastic resonance[PHYS.PHYS.PHYS-BIO-PH]Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph]Acoustics01 natural sciencesNoise (electronics)010305 fluids & plasmas[NLIN.NLIN-PS]Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]Noise generator[ PHYS.PHYS.PHYS-BIO-PH ] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph][NLIN.NLIN-PS] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]0103 physical sciencesPhase noise[ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]010306 general physicsComputingMilieux_MISCELLANEOUSPhysics[PHYS.PHYS.PHYS-BIO-PH] Physics [physics]/Physics [physics]/Biological Physics [physics.bio-ph]Noise spectral densityQuantum noiseNoise floor[SPI.TRON] Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/ElectronicsBurst noiseQuantum electrodynamicsnonlinear circuitStochastic resonance2013 22nd International Conference on Noise and Fluctuations (ICNF)
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Detector-based visual analysis of time-series data

2015

neural networkaikasarjatvisualisointimittausgraphical user interfaceknowledge discoverychange-point detectiondata miningneuroverkotvisual analyticsuser interactioncontextaikasarja-analyysimittaustekniikkavisual data explorationkäyttöliittymätihminen-konejärjestelmätenergiantuotantolaitoksetklusterianalyysitiedonlouhintaenergiantuotantobiovoimalatvisualizationclustering
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Mixtures identification of chemical compounds on the basis of their IR spectra by artificial intelligence

2012

Infrared (IR) spectrometric identification of individual chemical compounds from their mixtures is still a challenging process. Therefore, we developed a method in which we use the IR “Fingerprint” spectra of a particular chemical substance followed by artificial intelligence (AI) – based analysis to correctly characterise components of relatively simple chemical mixtures. We describe here the assembly of tools developed especially for this purpose as well as the artificial neural network design together with the requirements that must be met for its proper functioning. To test our approach, we used a mixture of amphetamine and creatinine which are difficult to identify in mixtures by stand…

neural networkchemical compoundsinfrared spectroscopyartificial intelligenceEcological Chemistry and Engineering. A
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SEAM PUCKERING EVALUATION METHOD FOR SEWING PROCESS

2014

The paper presents an automated method for the assessment and classification of puckering defects detected during the preproduction control stage of the sewing machine or product inspection. In this respect, we have presented the possible causes and remedies of the wrinkle nonconformities. Subjective factors related to the control environment and operators during the seams evaluation can be reduced using an automated system whose operation is based on image processing. Our implementation involves spectral image analysis using Fourier transform and an unsupervised neural network, the Kohonen Map, employed to classify material specimens, the input images, into five discrete degrees of quality…

neural networklcsh:ManufacturespuckerDiscrete Fourier Transformseamslcsh:TS1-2301image processingAnnals of the University of Oradea: Fascicle of Textiles, Leatherwork
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NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation

2023

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing indirect immunofluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is very different from the conventional neural network approaches but has an equivalent quantitative and qu…

neuron physiology networksSettore INF/01 - Informaticabiomedical imaging; explainable ai; neuron physiology networks; computer-aided analysis; image segmentation; pattern analysispattern analysisElectrical and Electronic Engineeringbiomedical imagingcomputer-aided analysisimage segmentationBiochemistryInstrumentationAtomic and Molecular Physics and Opticsexplainable aiAnalytical ChemistrySensors
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Unsupervised representation learning of spontaneous MEG data with nonlinear ICA

2023

Funding Information: We wish to thank the reviewers and editors for the useful comments to improve the paper a lot. We thank Dr. Hiroshi Morioka for the useful discussion at the beginning of the project. L.P. was funded in part by the European Research Council (No. 678578 ). A.H. was supported by a Fellowship from CIFAR, and the Academy of Finland. The authors acknowledge the computational resources provided by the Aalto Science-IT project, and also wish to thank the Finnish Grid and Cloud Infrastructure (FGCI) for supporting this project with computational and data storage resources. | openaire: EC/H2020/678578/EU//HRMEG Resting-state magnetoencephalography (MEG) data show complex but stru…

neuropalautenon-stationarityMEGsignaalinkäsittelyCognitive Neurosciencesyväoppiminensignaalianalyysineurofeedbackunsupervised learningdeep generative modelkoneoppiminenNeurologyresting-state networkmagnetoencephalography (MEG)nonlinear independent component analysis (ICA)NeuroImage
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Ghost stochastic resonance in FitzHugh–Nagumo circuit

2014

International audience; The response of a neural circuit submitted to a bi-chromatic stimulus and corrupted by noise is investigated. In the presence of noise, when the spike firing of the circuit is analysed, a frequency not present at the circuit input appears. For a given range of noise intensities, it is shown that this ghost frequency is almost exclusively present in the interspike interval distribution. This phenomenon is for the first time shown experimentally in a FitzHugh-Nagumo circuit.

noise[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingInterval distribution[ NLIN.NLIN-CD ] Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD][ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingStochastic ResonanceComputer Science::Hardware ArchitectureComputer Science::Emerging Technologies[NLIN.NLIN-PS]Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingElectronic engineering[ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]Electrical and Electronic EngineeringMathematicsCircuit noiseQuantitative Biology::Neurons and CognitionArtificial neural networkStochastic processMathematical analysisneural networksFitzhugh nagumo[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/ElectronicsHarmonics[NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]Nonlinear network analysis[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingElectronics Letters
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