Research on the Applicability of Touchscreens in Manned/Unmanned Aerial Vehicle Cooperative Missions

Abstract

The suitability of touchscreens for human–computer interaction in manned/unmanned aerial vehicle cooperative missions remains uncertain, especially in situations that are time-sensitive with variations in difficulty levels. The purpose of this study is to determine the feasibility of touchscreen applications in manned/unmanned aerial vehicle cooperative missions and the magnitude of the effects of time pressure and task difficulty. In contrast to previous studies, a combination of performance and perceptual load measures was used to divide errors into disposition errors, undetected errors, and miscalculation errors to explore specific error mechanisms, set up typical manned/unmanned aerial vehicle cooperative human–computer interaction tasks, and set up antecedent features for potential factors. Thirty subjects participated in an experiment that required the use of touchscreens or keyboards to perform a human–computer interaction task in a simulated manned/unmanned aerial vehicle cooperative mission. Experiments were set at three task difficulties: low, medium, and high, and were matched to a set time pressure or no time pressure for two seconds for low difficulty, three seconds for medium difficulty, and four seconds for high difficulty. The results showed that the touchscreens improved the participants’ response speed at a time pressure of 2 s or less compared with the use of a general input device; however, the task error rate also increased significantly. The higher the task difficulty was, the worse the performance was and the greater the perceived workload of the participants. The application of touchscreens in dynamic environments subjected the participants to greater physical demands. The performance of participants using a keyboard was no better than that when touchscreens were used during the experiment. Moreover, touchscreens did not significantly improve participant performance. The results support the possibility of using touchscreens in manned/unmanned aerial vehicle cooperative missions.

Advanced Vibration-Based Fault Diagnosis and Vibration Control Methods

“…This Special Issue on “Advanced Vibration-Based Fault Diagnosis and Vibration Control Methods” includes twelve papers ranging across different methods used in the electrical and mechanical industries’ systems. The papers include a good literature review of the current new works on the verification of mechanical properties identification based on the impulse excitation technique and mobile device measurements [ 4 ], the method for bearing fault diagnosis under variable speeds based on envelope spectrum fault characteristic frequency band identification [ 5 ], the interface design of head-worn display applications for condition monitoring in aviation [ 6 ], color recurrence plots for bearing fault diagnoses [ 7 ], the failure mode detection and validation of a shaft-bearing system with common sensors [ 8 ], the strain response and buckling behavior of composite cylindrical shells subjected to external pressure with one end fixed and the other free of boundary conditions [ 9 ], the smartphone-based and data-driven superstructure state prediction method for highway bridges in service [ 10 ], the optimal control algorithm for stochastic systems with parameter drift [ 11 ], the nonlinear vibration control experimental system design of a flexible arm using interactive actuations from shape memory alloy [ 12 ], the design of an active vibration isolation controller with a disturbance observer-based linear quadratic regulator for optical reference cavities [ 13 ], the applicability of touchscreens in manned/unmanned aerial vehicle cooperative missions [ 14 ], and the engineering frequency domain analysis and vibration suppression of flexible aircraft based on an active disturbance rejection controller [ 15 ].…”

“…Touchscreens’ suitability for human–computer interactions in manned/unmanned aerial vehicle cooperative missions remains uncertain, especially when in time-sensitive conditions with variations under difficult levels. To determine the feasibility of touchscreen applications, Xue et al [ 14 ] introduced a combination of performance and perceptual load measures to divide errors into disposition, undetected, and miscalculation errors. This could explore specific error mechanisms.…”

A Non-Intrusive Automated Testing System for Internet of Vehicles App Based on Deep Learning

“…Text detection and recognition for IoV vehicular screens constitute a challenging task. This section will leverage deep learning techniques to achieve efficient and accurate text detection and recognition, providing more intelligent and precise technical support for non-intrusive automated testing systems targeting in-car IoV apps [45][46][47].…”