Search results for "Mach"

showing 10 items of 3360 documents

Deep learning for dehazing: Benchmark and analysis

2018

International audience; We compare a recent dehazing method based on deep learning , Dehazenet, with traditional state-of-the-art approach, on benchmark data with reference. Dehazenet estimates the depth map from a single color image, which is used to inverse the Koschmieder model of imaging in the presence of haze. In this sense, the solution is still attached to the Koschmieder model. We demonstrate that this method exhibits the same limitation than other inversions of this imaging model.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][STAT.ML] Statistics [stat]/Machine Learning [stat.ML][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[STAT.ML]Statistics [stat]/Machine Learning [stat.ML][ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI][ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML][ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM]
researchProduct

Automated uncertainty quantification analysis using a system model and data

2015

International audience; Understanding the sources of, and quantifying the magnitude of, uncertainty can improve decision-making and, thereby, make manufacturing systems more efficient. Achieving this goal requires knowledge in two separate domains: data science and manufacturing. In this paper, we focus on quantifying uncertainty, usually called uncertainty quantification (UQ). More specifically, we propose a methodology to perform UQ automatically using Bayesian networks (BN) constructed from three types of sources: a descriptive system model, physics-based mathematical models, and data. The system model is a high-level model describing the system and its parameters; we develop this model …

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]generic modeling environment[SPI] Engineering Sciences [physics]Computer scienceuncertainty quantificationMachine learningcomputer.software_genre01 natural sciencesData modelingSystem model[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]010104 statistics & probability03 medical and health sciences[SPI]Engineering Sciences [physics][ SPI ] Engineering Sciences [physics]Sensitivity analysis0101 mathematicsUncertainty quantification[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]030304 developmental biologyautomation0303 health sciencesMathematical modelbusiness.industryConditional probabilityBayesian networkmeta-modelMetamodelingBayesian networkProbability distributionData miningArtificial intelligencebusinesscomputer
researchProduct

AN ONTOLOGY-BASED RECOMMENDER SYSTEM USING HIERARCHICAL MULTICLASSIFICATION FOR ECONOMICAL E-NEWS

2014

International audience; This paper focuses on a recommender system of economic news articles. Its objectives are threefold: (i) automatically multi-classify new economic articles, (ii) recommend articles by comparing profiles of users and multi-classification of articles, and (iii) managing the vocabulary of the economic news domain to improve the system based on seamlessly intervention of documentalists. In this paper we focus on the automatic multi-classification of the articles, managed by inference process of ontologies, and the enrichment of the documentalist-oriented ontology which provides the necessary capabilities to the DL reasoner for automatic multi-classification.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]recommender system[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]Multi-label classification[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO.INFO-IT ] Computer Science [cs]/Information Theory [cs.IT]machine learning[INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-IT] Computer Science [cs]/Information Theory [cs.IT][INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]e-newsontology[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]ComputingMilieux_MISCELLANEOUS
researchProduct

Advanced 3D movement analysis algorithms for robust functional capacity assessment.

2017

SummaryObjectives: We developed a novel system for in home functional capacities assessment in frail older adults by analyzing the Timed Up and Go movements. This system aims to follow the older people evolution, potentially allowing a forward detection of motor decompensation in order to trigger the implementation of rehabilitation. However, the pre-experimentations conducted on the ground, in different environments, revealed some problems which were related to KinectTM operation. Hence, the aim of this actual study is to develop methods to resolve these problems.Methods: Using the KinectTM sensor, we analyze the Timed Up and Go test movements by measuring nine spatio-temporal parameters, …

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR]Computer science02 engineering and technologyTimed Up and Go testcomputer.software_genreCorrelation0302 clinical medicineHealth Information ManagementMICROSOFT KINECT0202 electrical engineering electronic engineering information engineering[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO][ SDV.IB ] Life Sciences [q-bio]/Bioengineeringsitting posture recognition[ INFO.INFO-RB ] Computer Science [cs]/Robotics [cs.RO]FALLSVideo processingPatient self-care home care and e-healthComputer Science Applications3D real-time video processing020201 artificial intelligence & image processing[SDV.IB]Life Sciences [q-bio]/BioengineeringAlgorithmsClinical testsCapacity assessmentGO TESTskin detectionFrail ElderlyMovementFrail Older AdultsPostureHealth InformaticsMachine learning03 medical and health sciencesRobustness (computer science)[ SDV.MHEP ] Life Sciences [q-bio]/Human health and pathologyclinical informaticsHumansVALIDITYOLDER-ADULTSSimulationAgedMonitoring Physiologicbusiness.industryMovement analysisMOTOR STRATEGIESArtificial intelligencebusinesscomputer030217 neurology & neurosurgery[SDV.MHEP]Life Sciences [q-bio]/Human health and pathologyApplied clinical informatics
researchProduct

Scheduling independent stochastic tasks on heterogeneous cloud platforms

2019

International audience; This work introduces scheduling strategies to maximize the expected number of independent tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The cloud platform is composed of several types of virtual machines (VMs), where each type has a unitexecution cost that depends upon its characteristics. The amount of budget spent during the execution of a task on a given VM is the product of its execution length by the unit execution cost of that VM. The execution lengths of tasks follow a variety of standard probability distributions (exponential, uniform, halfnormal, etc.), which is known beforehand and whose mean and stand…

[INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC]020203 distributed computingComputer scienceStochastic processbusiness.industryDistributed computing[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]Processor schedulingCloud computing02 engineering and technologycomputer.software_genreScheduling (computing)Virtual machine0202 electrical engineering electronic engineering information engineeringTask analysisProbability distribution020201 artificial intelligence & image processing[INFO]Computer Science [cs][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]InterruptHeuristicsbusinesscomputer
researchProduct

Investigating the Relationship Between Community-aware and Classical Centrality Measures

2021

International audience

[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI][INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]ComputingMilieux_MISCELLANEOUS[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]
researchProduct

An Empirical Comparison of Centrality and Hierarchy Measures in Complex Networks

2020

International audience

[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI][INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]ComputingMilieux_MISCELLANEOUS[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]
researchProduct

Assessing the Relationship Between Centrality and Hierarchy in Complex Networks

2020

International audience

[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI][INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]ComputingMilieux_MISCELLANEOUS[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]
researchProduct

Hierarchy and Centrality: Two Sides of The Same Coin?

2020

International audience

[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI][INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]ComputingMilieux_MISCELLANEOUS[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]
researchProduct

Classical versus Community-aware Centrality Measures: An Empirical Study

2021

International audience

[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI][INFO]Computer Science [cs][INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG][INFO] Computer Science [cs]ComputingMilieux_MISCELLANEOUS[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]
researchProduct