Kouamana Bousson

Model predictive control approach to global air collision avoidance

Purpose – Most of the existing approaches for flight collision avoidance are concerned with local traffic alone for which the separation is based on the pairwise analysis of aircraft trajectory trends, which is not efficient with regard to some flight path requirements along waypoints. The purpose of this paper is to deal with the global collision avoidance problem which aims at separating aircraft taking into consideration the global traffic in a given area instead of considering them pairwise. It aims to model the concept of global collision avoidance and propose a validated algorithm for the purpose in the framework of free‐flight.Design/methodology/approach – The collision avoidance procedure computes online the appropriate speed and heading for each aircraft, at each sampling time‐instant, to generate a collision‐free flight trajectory along scheduled waypoints. The method accounts for automatic assignment of priority indexes that are updated from one control time horizon to the next. The paper consi...

Single Gridpoint Dynamic Programming For Trajectory Optimization

A Dynamic Programming (DP) method is presented in the framework of trajectory optimization. The method is based on the use of a single state gridpoint at each stage, and on the successive reductions of the value of the cost-to-go at each iteration up to convergence. The method has the advantage of requiring less computation memory and time than Iterative Dynamic Programming 10-12 that has been the best extension to dynamic programming known so far. The main contribution of the proposed method is to provide an efficient dynamic programming method for high dimension systems and for both classes of smooth and nonsmooth cost functions. Numerical examples are presented, and the results are shown to be much better than those obtained through Iterative Dynamic Programming.

Medical decision-making inspired from aerospace multisensor data fusion concepts

In recent years, Internet-delivered treatments have been largely used for pain monitoring, offering healthcare professionals and patients the ability to interact anywhere and at any time. Electronic diaries have been increasingly adopted as the preferred methodology to collect data related to pain intensity and symptoms, replacing traditional pen-and-paper diaries. This article presents a multisensor data fusion methodology based on the capabilities provided by aerospace systems to evaluate the effects of electronic and pen-and-paper diaries on pain. We examined English-language studies of randomized controlled trials that use computerized systems and the Internet to collect data about chronic pain complaints. These studies were obtained from three data sources: BioMed Central, PubMed Central and ScienceDirect from the year 2000 until 30 June 2012. Based on comparisons of the reported pain intensity collected during pre- and post-treatment in both the control and intervention groups, the proposed multisensor data fusion model revealed that the benefits of technology and pen-and-paper are qualitatively equivalent . We conclude that the proposed model is suitable, intelligible, easy to implement, time efficient and resource efficient.

Time‐varying parameter estimation with application to trajectory tracking

Purpose – This paper is concerned with an online parameter estimation algorithm for nonlinear uncertain time‐varying systems for which no stochastic information is available.Design/methodology/approach – The estimation procedure, called nonlinear learning rate adaptation (NLRA), computes an individual adaptive learning rate for each parameter instead of using a single adaptive learning rate for all the parameters as done in stochastic approximation, each individual learning rate being controlled by a meta‐learning rate rule for the sake of minimizing the measurement prediction error. The method does not require stochastic information about the system model and the measurement noise covariance matrices contrarily to the Kalman filtering. Numerical results about aircraft navigation trajectory tracking show that the method is able to estimate reliably time‐varying parameters even in presence of measurement noise.Findings – The proposed algorithm is practically insensitive to changes in the meta‐learning rate...

Sequential parameter identification method for nonlinear systems

In this paper we propose an accurate online parameter identification algorithm for nonlinear time-varying systems. For such systems, techniques based on cross-validation to achieve regularization or model selection are not possible, and classical least square techniques are not reliable when the dynamics of the system are highly nonlinear. To overcome these problems, an identification algorithm devised from Suttons dynamic learning rate techniques and based on a learning window and forgetting factor criterion has been used. In doing so, the proposed algorithm avoids the need for heuristic choices of the initial conditions and noise covariance matrices required by Kalman filtering. The performance of the proposed method is demonstrated on aircraft flight dynamics parameter identification in the horizontal plane.

Classification techniques on computerized systems to predict and/or to detect Apnea

BACKGROUND AND OBJECTIVE Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios. METHODS This study aims to systematically review the literature on systems for the detection and/or prediction of apnea events using a classification model. RESULTS Forty-five included studies revealed a combination of classification techniques for the diagnosis of apnea, such as threshold-based (14.75%) and machine learning (ML) models (85.25%). In addition, the ML models, were clustered in a mind map, include neural networks (44.26%), regression (4.91%), instance-based (11.47%), Bayesian algorithms (1.63%), reinforcement learning (4.91%), dimensionality reduction (8.19%), ensemble learning (6.55%), and decision trees (3.27%). CONCLUSIONS A classification model should provide an auto-adaptive and no external-human action dependency. In addition, the accuracy of the classification models is related with the effective features selection. New high-quality studies based on randomized controlled trials and validation of models using a large and multiple sample of data are recommended.

Safe flight envelope for overhead line inspection

The present work aims to improve the safety of overhead power line inspection and similar tasks. Due to the proximity of objects imposed by the inspection missions, the appliance of Extended Kalman Filter for quadrotor attitude control in a supervised environment was proposed. Verification of electrical lines and their infrastructure is an essential activity to ensure the undisturbed operation of the electrical grid and supply energy to final consumers. Development of versatile monitoring systems to inspect high voltage overhead lines represents an invaluable element for the maintenance of these, for corrective actions and especially preventive.

Qualitative reasoning methods for CELSS modeling

Qualitative Reasoning (QR) is a branch of Artificial Intelligence that arose from research on engineering problem solving. This paper describes the major QR methods and techniques, which, we believe, are capable of addressing some of the problems that are emphasized in the literature and posed by CELSS modeling, simulation, and control at the supervisory level.

Artificial Neural Learning Based on Big Data Process for eHealth Applications

The complexity of the clinical context requires systems with the capability to make decisions based on reduced sets of data. Moreover, the adoption of mobile and ubiquitous devices could provide personal health-related information. In line with this, eHealth application faces several challenges so as to provide accurate and reliable data to both healthcare professionals and patients. This chapter focuses on computational learning on the healthcare systems presenting different classification processes to obtain knowledge from data. Finally, a case study based on a radial basis function neural network aiming the estimation of ECG waveform is explained. The presented model revealed its adaptability and suitability to support clinical decision making. However, complementary studies should be addressed to enable the model to predict the upper and lower points related to upward and downward deflections.

RNAV and RNP AR Approach Systems: The Case for Pico Island Airport

This paper deals with the viability analysis of the implementation of RNAV and RNP AR approaches to Pico Island airport in the Azorean archipelago. The main objective is to prove that this new type of approach technology can be implemented in the Azorean islands airports in accordance with the ICAO rules providing an increase in safety and lowering approach minima thus reducing the costs associated with the operation and maintenance of the traditional approach systems.