Search results for "machine"

Generalized power-angle control for grid-forming converters: A structural analysis

2022

Several control schemes have been recently proposed and studied as grid-forming controls for power converters. In all these schemes, the power-angle control loop is the part which defines the fundamental capabilities of the grid-forming control: that control loop governs in fact the inherent synchronization mechanism of the power converter, the power sharing with the other generation sources in the system and the oscillatory characteristics of the converter-based resource. This article introduces a general formulation for the power-angle control characterizing the grid-forming concept for power converters. The generalized power-angle control is based on a polynomial fraction formulation, an…

Machine Learning-Based Classification of Vector Vortex Beams.

2020

Structured light is attracting significant attention for its diverse applications in both classical and quantum optics. The so-called vector vortex beams display peculiar properties in both contexts due to the non-trivial correlations between optical polarization and orbital angular momentum. Here we demonstrate a new, flexible experimental approach to the classification of vortex vector beams. We first describe a platform for generating arbitrary complex vector vortex beams inspired to photonic quantum walks. We then exploit recent machine learning methods -- namely convolutional neural networks and principal component analysis -- to recognize and classify specific polarization patterns. O…

Language Documentation and Descriptive Linguistics

2021

Comparative study to predict toxic modes of action of phenols from molecular structures.

2013

Quantitative structure-activity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. M…

Langage et Apprentissage en Interaction pour des Assistants Numériques Autonomes - Une Approche Développementale

2021

The rapid development of digital assistants (DA) opens the way to new modes of interaction. Some DA allows users to personalise the way they respond to queries, in particular by teaching them new procedures. This work proposes to use machine learning methods to enrich the linguistic and procedural generalisation capabilities of these systems. The challenge is to reconcile rapid learning skills, necessary for a smooth user experience, with a sufficiently large generalisation capacity. Though this is a natural human ability, it remains out-of-reach for artificial systems and this leads us to approach these issues from the perspective of developmental Artificial Intelligence. This work is thus…

Development of handcrafted and deep based methods for face and facial expression recognition

2021

The research objectives of this thesis concern the development of new concepts for image segmentation and region classification for image analysis. This involves implementing new descriptors, whether color, texture, or shape, to characterize regions and propose new deep learning architectures for the various applications linked to facial analysis. We restrict our focus on face recognition and person-independent facial expressions classification tasks, which are more challenging, especially in unconstrained environments. Our thesis lead to the proposal of many contributions related to facial analysis based on handcrafted and deep architecture.We contributed to face recognition by an effectiv…

Artificial intelligence for image-guided prostate brachytherapy procedures

2020

Radiotherapy procedures aim at exposing cancer cells to ionizing radiation. Permanently implanting radioactive sources near to the cancer cells is a typical technique to cure early-stage prostate cancer. It involves image acquisition of the patient, delineating the target volumes and organs at risk on different medical images, treatment planning, image-guided radioactive seed delivery, and post-implant evaluation. Artificial intelligence-based medical image analysis can benefit radiotherapy procedures. It can help to facilitate and improve the efficiency of the procedures by automatically segmenting target organs and extrapolating clinically relevant information. However, manual delineation…

Sequential Mining Classification

2017

Sequential pattern mining is a data mining technique that aims to extract and analyze frequent subsequences from sequences of events or items with time constraint. Sequence data mining was introduced in 1995 with the well-known Apriori algorithm. The algorithm studied the transactions through time, in order to extract frequent patterns from the sequences of products related to a customer. Later, this technique became useful in many applications: DNA researches, medical diagnosis and prevention, telecommunications, etc. GSP, SPAM, SPADE, PrefixSPan and other advanced algorithms followed. View the evolution of data mining techniques based on sequential data, this paper discusses the multiple …

Machine learning for rapid mapping of archaeological structures made of dry stones – Example of burial monuments from the Khirgisuur culture, Mongoli…

2020

11 pages; International audience; The present study proposes a workflow to extract from orthomosaics the enormous amount of dry stones used by past societies to construct funeral complexes in the Mongolian steppes. Several different machine learning algorithms for binary pixel classification (i.e. stone vs non-stone) were evaluated. Input features were extracted from high-resolution orthomosaics and digital elevation models (both derived from aerial imaging). Comparative analysis used two colour spaces (RGB and HSV), texture features (contrast, homogeneity and entropy raster maps), and the topographic position index, combined with nine supervised learning algorithms (nearest centroid, naive…

Levantamiento 3D para el estudio arqueológico y la reconstrucción virtual del Santuario de Isis en la antigua Lilybaeum (Italia)

2020

[EN] In recent years, the use of three-dimensional (3D) models in cultural and archaeological heritage for documentation and dissemination purposes has increased. New geomatics technologies have significantly reduced the time spent on fieldwork surveys and data processing. The archaeological remains can be documented and reconstructed in a digital 3D environment thanks to the new 3D survey technologies. Furthermore, the products generated by modern surveying technologies can be reconstructed in a virtual environment on effective archaeological bases and hypotheses coming from a detailed 3D data analysis. However, the choice of technologies that should be used to get the best results for dif…