Using NASA-TLX to evaluate the flight deck design in Design Phase of Aircraft

AbstractSafety is the most important consideration in civil aviation, and human factors directly influence the safety of an aircraft. This paper focused on implementing the human factors by using improved NASA-TLX to enhance the safety of aircraft in the design phase

Human factors quantification via boundary identification of flight performance margin

AbstractA systematic methodology including a computational pilot model and a pattern recognition method is presented to identify the boundary of the flight performance margin for quantifying the human factors. The pilot model is proposed to correlate a set of quantitative human factors which represent the attributes and characteristics of a group of pilots. Three information processing components which are influenced by human factors are modeled: information perception, decision making, and action execution. By treating the human factors as stochastic variables that follow appropriate probability density functions, the effects of human factors on flight performance can be investigated through Monte Carlo (MC) simulation. Kernel density estimation algorithm is selected to find and rank the influential human factors. Subsequently, human factors are quantified through identifying the boundary of the flight performance margin by the k-nearest neighbor (k-NN) classifier. Simulation-based analysis shows that flight performance can be dramatically improved with the quantitative human factors

Cognitive and Action Sequence Prediction using Deductive Reasoning

Early in the process of the development of an aircraft cockpit, although the designers always introduce a set of operational procedures with the expectation that all pilots would follow, it is very difficult to guarantee that the flight crew will do exactly they are expected to do. The deviation of the pilots’ operation from the intended procedures may lead to an unsafe situation, and could also be an indication to the inherent reason for the biases in the pilots’ cognitive process. It became very obvious that a tool that could help to predict a comprehensive set of possible operations that the pilots would operate the aircraft will be very useful both in the flight deck design process and pilot training practices. This paper presents the development of the researches in the “Cognitive and Action Sequence Prediction using Deductive Creation Theory (CASEPREDICT)”. Unlike any human-made system which the response of the system can be predicted to certain degree of accuracy, a human-in-theloop system is always associated with a great deal of uncertainty issues which comes from the cognitive process of human operators

Research on multi granularity underwater situation awareness through virtual physics of artificial intelligence

On the basis of analyzing the development of underwater multi-platform cluster variable granularity situational awareness, this paper studies the system of systems and the synchronization of spatiotemporal evolution for multi granularity underwater situation awareness. Virtual physical artificial intelligence theory and method for multi granularity underwater situation awareness are proposed to establish the dynamic virtual physical neural network embedded with interpretation models of context and knowledge. Driven by spatiotemporal data and context information, multi granularity underwater situation awareness and synchronous situation evolution analysis across domains are realized to make collaborative intelligent decisions for underwater offensive and defensive confrontation tasks. Theoretical analysis and numerical results show that multi granularity underwater situation awareness and synchronous situation evolution analysis can enhance information superiority by integrating cluster resources, with optimal involved functional domains and space-time scales. Our approach can provide a theoretical basis and technical reference for the construction and development of underwater all-around attack and defense systems