Search results for "intelligence"
showing 10 items of 6959 documents
SEMANTIC WEB TECHNOLOGIES FOR IMPLEMENTING COST-EFFECTIVE AND INTEROPERABLE BUILDING INFORMATION MODELING
2015
International audience; In the field of AEC/FM, BIM has been recognized by industrial and political actors as a powerful tool for resolving data interoperability problems. Coupled with cloud computing and GIS, BIM would allow integrating different information exchange standard into one single digital building model that can be real-time edited by several stakeholders or architects. In this paper, we examine the benefits brought by using Semantic Web technologies in delivering such universal building model. We present how our approach is a step further in reaching the vision of BIM, and how it can serve construction process, operation and maintenance, along with facilities’ lifecycle managem…
A spatially explicit model to simulate soil microbial communities’ dynamics at an agricultural landscape scale
2021
Soil microorganisms play a major role in soil functions and are an efficient indicator to evaluate the impact of agricultural practices on soil quality. Biogeographical studies over wide scales ranging from landscape to countries have concluded that soil microbial abundance and soil prokaryotic richness is following a heterogeneous distribution in space under the dependence of soil properties (e.g. pH, soil texture, organic matter content) and agricultural practices. The goal of this study is the creation of a model that can predict dynamics of soil microbial communities depending on the agricultural management over time. For this, we focus on a monitored landscape (Fénay landscape, 1.200 h…
Bridging Sensing and Decision Making in Ambient Intelligence Environments
2009
Context-aware and Ambient Intelligence environments represent one of the emerging issues in the last decade. In such intelligent environments, information is gathered to provide, on one hand, autonomic and easy to manage applications, and, on the other, secured access controlled environments. Several approaches have been defined in the literature to describe context-aware application with techniques to capture and represent information related to a specified domain. However and to the best of our knowledge, none has questioned the reliability of the techniques used to extract meaningful knowledge needed for decision making especially if the information captured is of multimedia types (image…
Modeling and Coordination of interconnected microgrids using distributed artificial intelligence approaches
2019
As renewable sources penetrate the current electrical system to relief global warming and energy shortage, microgrid (MG) emerges to reduce the impact of intermittent generation on the utility grid. Additionally, it improves the automation and intelligence of the power grid with plug-and-play characteristics. Inserting more MGs into a distribution network promotes the development of the smart grid. Thus MG networks existing in the power system are in prospect. Coordinating them could gain a system with high reliability, low cost, and strong resistance to electrical faults. Achieving these profits relies on developed technologies of communication, control strategy, and corresponding algorith…
Incorporating depth information into few-shot semantic segmentation
2021
International audience; Few-shot segmentation presents a significant challengefor semantic scene understanding under limited supervision.Namely, this task targets at generalizing the segmentationability of the model to new categories given a few samples.In order to obtain complete scene information, we extend theRGB-centric methods to take advantage of complementary depthinformation. In this paper, we propose a two-stream deep neuralnetwork based on metric learning. Our method, known as RDNet,learns class-specific prototype representations within RGB anddepth embedding spaces, respectively. The learned prototypesprovide effective semantic guidance on the corresponding RGBand depth query ima…
Leveraging Uncertainty Estimates to Improve Segmentation Performance in Cardiac MR
2021
International audience; In medical image segmentation, several studies have used Bayesian neural networks to segment and quantify the uncertainty of the images. These studies show that there might be an increased epistemic uncertainty in areas where there are semantically and visually challenging pixels. The uncertain areas of the image can be of a great interest as they can possibly indicate the regions of incorrect segmentation. To leverage the uncertainty information, we propose a segmentation model that incorporates the uncertainty into its learning process. Firstly, we generate the uncertainty estimate (sample variance) using Monte-Carlo dropout during training. Then we incorporate it …
Système de sécurité biométrique multimodal par imagerie, dédié au contrôle d’accès
2019
Research of this thesis consists in setting up efficient and light solutions to answer the problems of securing sensitive products. Motivated by a collaboration with various stakeholders within the Nuc-Track project, the development of a biometric security system, possibly multimodal, will lead to a study on various biometric features such as the face, fingerprints and the vascular network. This thesis will focus on an algorithm and architecture matching, with the aim of minimizing the storage size of the learning models while guaranteeing optimal performances. This will allow it to be stored on a personal support, thus respecting privacy standards.
DPLL with restarts linearly simulates CDCL
2019
If we give DPLL the ability to make restarts and to learn clauses representing the explored search space, it can linearly simulate CDCL solvers.
Automatic Timeline Construction and Analysis For Computer Forensics Purposes
2014
International audience; To determine the circumstances of an incident, investigators need to reconstruct events that occurred in the past. The large amount of data spread across the crime scene makes this task very tedious and complex. In particular, the analysis of the reconstructed timeline, due to the huge quantity of events that occurred on a digital system, is almost impossible and leads to cognitive overload. Therefore, it becomes more and more necessary to develop automatic tools to help or even replace investigators in some parts of the investigation. This paper introduces a multi-layered architecture designed to assist the investigative team in the extraction of information left in…
6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
2001
International audience; no abstract