User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Apprentissage par renforcement et systèmes distribués : Application à l\u27apprentissage de la marche d\u27un robot hexapode

Le but de cette thèse est d\u27étudier et de proposer des techniques d\u27apprentissage par renforcement pour l\u27apprentissage de la marche d\u27un robot marcheur hexapode. L\u27hypothèse sur laquelle repose ce travail est que des marches peuvent être obtenues lorsque la commande des mouvements est distribuée au niveau de chaque patte plutôt que d\u27être centralisée. Une approche distribuée de l\u27apprentissage par renforcement de type Q-learning a été retenue dans laquelle les agents (les contrôleurs de mouvement) contribuant à une même tâche mènent leur propre apprentissage en tenant compte ou non de l\u27existence des autres agents. Différentes simulations et tests on été menés avec pour objectif la génération de marches périodiques stables. La marche apparaît comme un phénomène émergeant des mouvements individuels des pattes. L\u27influence des paramètres d\u27apprentissage sur les marches obtenues est étudiée. Sont aussi traités des problèmes de tolérances aux fautes et de manque d\u27information sur l\u27état du robot. Enfin il est vérifié en simulation que, avec les algorithmes développés, le robot apprend à rattraper une trajectoire prédéfinie tout en contrôlant sa posture

Adaptive Sliding Mode Control Improved by Fuzzy-PI Controller: Applied to Magnetic Levitation System

This study mainly concerns the use of Fuzzy-PI adaptive sliding control (Fuzzy-ASMC) to force the stat space of MAGLEV to track a desired trajectory. The usage of adaptive sliding mode control allows the MAGLEV to operate in an uncertain environment and in the presence of external disturbances. The Fuzzy-PI schema is designed to improve the performance of adaptive sliding mode control and reduce the main drawback caused by the discontinuous term of this method, which is the well-known chattering phenomenon. The results of our study prove the effectiveness of the proposed approach in achieving desired performances

Distributed control of multi-actor systems: Reinforcement signal by Shannon’s entropy

International audienceThis paper presents a control of multi-actor systems (or multi-sensors) functioning in an unknown environment. These actors are reactive entities able to react to the stimuli coming from the environment and to choose between several actions. In order to improve their behaviour (i.e. in order to choose the good action) in the course of time, the system multi-actors must be able to use reinforcement learning. This signal of reinforcement is, until now, a signal whose values are a priori defined. We propose to raise this "a priori" while using the Shannon’s entropy to measure the coherence of the choice of the action by the transformation of the reinforcement signal table. This stage, of local training will allow the improvement of the control of the global system and coordination between the various actors. The results of the simulation show that the actor can learn to control its trajectory efficiently

Control of mobile robot navigation under the virtual world Matlab-Gazebo

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An Intelligent Optimization Algorithm for Scheduling the Required SIL Using Neural Network

The purpose of safety analysis is to ensure that hazards and risks that could be a possible source of harm and damage are reduced well enough by dealing with all phases of the safety lifecycle and design of suitable safety barriers. It is known that any error or failure to perform the function of each proposed safety barrier can cause extreme damage to the environment, facilities and humans, and even loss of life. Therefore, it is necessary to ensure the effectiveness of the study or analysis. However, even with the major development in control system fields the problems of uncertainties, classification and optimization are still considered unsolved issues. In recent years several tools are developed based on artificial intelligence to deal with such difficulties. In this work, an approach based on Artificial Neural Networks (ANN) is developed to schedule the SIL values of the safety integrity functions (SIF) of an industrial-fired heater. The SIFs are first deduced from HAZOP study for the fired heater. The SIL risk of the consequences related to personnel health and safety, the economic SIL and environment SIL are considered as inputs of the multilayer network with a predefined hard limit activation function

Nonlinear Control of a differential wheeled mobile robot in real time-Turtlebot 2

International audienceThe paper presents a control architecture in order to control the trajectory tracking of a robot a mobile robot with non-holonomic differential wheels (DWMR) using the kinematic model of the DWMR with different trajectories. a description of the robot is presented followed by a hard robot model and a brief presentation of the ROS system to control the real robot thereafter. a nonlinear controller that provides stability according to Lyapunov function is formulated followed by a 3D simulation under Gazebo software and real simulation on the robot with different trajectories. Finally we presented the results which clearly shows the efficiency of our control approach followed by prospects for future work

Nonlinear Control of a differential wheeled mobile robot in real time-Turtlebot 2

International audienceThe paper presents a control architecture in order to control the trajectory tracking of a robot a mobile robot with non-holonomic differential wheels (DWMR) using the kinematic model of the DWMR with different trajectories. a description of the robot is presented followed by a hard robot model and a brief presentation of the ROS system to control the real robot thereafter. a nonlinear controller that provides stability according to Lyapunov function is formulated followed by a 3D simulation under Gazebo software and real simulation on the robot with different trajectories. Finally we presented the results which clearly shows the efficiency of our control approach followed by prospects for future work

A Comparative Study of STPA Hierarchical Structures in Risk Analysis: The case of a Complex Multi-Robot Mobile System

International audienceAutonomous multi-robot systems are among the most complex systems to control, especially when those robots navigate in fully hazardous and dynamic environments such as chemical analysis laboratories which include dangerous and harmful products (poisonous, flammable, explosive...). This paper presents an approach for systems-complex and theoretic safety assessment, also it considers their coordinating, cooperating and collaborating using different control architectures (centralized, hierarchical and modified hierarchical). We classified at first those control architectures according to their properties using Bowtie analysis method, and then we used a systems-theoretic hazard analysis technique (STPA) to identify the potential safety hazard scenarios and their causal factors