The behavior of a 'pilot-automaton-aircraft-operating environment' system (the System) in off-nominal situations with multiple risks can be unpredictably dangerous. Most multifactorial flight scenarios (corner cases) are considered as theoretically improbable. Such anomalies do nonetheless occur in operations and can lead to inconceivable accidents - 'black swan' events.
For the last few years, a great deal of interest has been paid to crew monitoring systems in order to address potential safety problems during a flight. They aim at detecting any degraded physiological and/or cognitive state of an aircraft pilot or crew, such as visual tunneling, also called inattentional blindness. Indeed, they might have a negative impact on the performance to pursue the mission with adequate flight safety levels. One of the usual approaches consists in using sensors to collect physiological signals which are then analyzed. Two main families exist to process the signals. The first one combines feature extraction and machine learning whereas the second is based on deep-learning approaches which require a large amount of labeled data. In this work, we focused on the first family.
The aircraft production rate is now increasing and requires to keep the production tools as close as possible from the assembly work area. As production sites cannot be extended as much as the rate increases, this has created the need for developing innovative & efficient line side equipment, which fulfils storage capacity, ergonomical accessibility, easy handling & quick load unload performance for all aircraft part assemblies. This paper will focus on the development and the integration into the production on our innovative solutions on Line Side Equipment . The Line Side Equipment is custom designed and built for manual or semi-automated assembly lines. It offers a wide range of solutions such as dedicated storage areas, trolleys, easy acces, tool kits & smart cabinets.
Data Science and Machine Learning are buzzwords in our everyday lives, as is evident from its applications, such as voice recognition features in vehicles and on cell phones, automatic facial and traffic sign recognition, etc. Analyzing big data on the basis of internet searches, pattern recognition, and learning algorithms, provides deep understanding of the behaviour of processes, systems, nature, and ultimately the people. The already implementable idea of autonomous driving is nearly a reality for many drivers today with the aid of “lane keeping assistance” and “adaptive cruise control systems” in the vehicle. The drift towards connected, autonomous, and artificially intelligent systems that constantly learns from big data and is able to make best-suited decisions, is progressing in ways that is fundamental to the growth of automotive industry. The paper envisages the future of connected and- autonomous-vehicles (CAVs) as computers-on-wheels.
This highly interactive workshop focuses training on negotiation strategy and skills. This is not the manipulative, win-lose negotiation approach frequently taught today, where the winner eventually spends time and effort protecting his negotiated advantage against erosion, while the loser continually exploits loopholes and shortcuts to recover lost ground. Traditional negotiation is a wary dance based on mistrust, the true cost of which is lost in quality and brain fatigue - usually for someone other than the negotiator - over the life of the agreement.