To achieve an efficient and stable operation of blast furnaces in the steel industry while retaining the proficient human operators’ skills, a human–machine interface based on an ecological interface design (EID) was developed. EID is an interface design framework that reduces the cognitive workload of human operators by providing essential information on the controlled system in an intuitive way. The developed interface allows the operators to explore the possible control actions, by presenting the future predictions of the controlled variables when hypothetical control actions are taken, using a transient model. In addition, a graphical representation of the mass and energy balance that links the manipulated variables and controlled variables is provided to raise the situation awareness of the blast furnace operation. The developed interface is beneficial to determine appropriate control actions that maintain hot metal temperature and production rate near the target values and keep pressure drop below the upper bound while reducing carbon intensity and production costs.

We tested whether pilots would detect low-salient controllability problems more quickly during manual compared to automated flight. Using a moving-base simulator and a Piper Seneca aerodynamic model, airline pilots (n = 20) performed scenarios in which either a gradually ensuing single-engine failure or an icing accumulation occurred. Both scenarios were performed once during manual flight and once during automated flight, and were alternated with distraction scenarios. The icing accumulation was detected marginally significantly more quickly during manual flight, while there was no significant difference for the engine failure. Problems in manual flight were, as expected, most likely discovered from aircraft motions or control forces. Interestingly, there were several late detections during manual flight which appeared to be caused by subconscious manual corrections. In automated flight, the engine failure was discovered most often from the engine manifold pressure indication, while the icing accumulation was most often discovered from control column movement. The results therefore underline the importance of using back-driven controls, and further indicate that manual flight does not necessarily improve detection of problems that occur without display indications.

Previous studies have indicated that the attitude director indicator (ADI) used in commercial aviation is suboptimal in representing the bank angle direction, which can lead to confusion, roll reversal errors and increased workload. Confusion about the bank angle direction has been implied in several cases of loss of control in-flight (LOC-I). In the current study, we therefore tested whether bank angle representation can be improved by adding non-disruptive visual depth cues to the ADI. An enhanced ADI was created, in which three monocular cues were added: atmospheric haze (i.e. a gradient in color towards the horizon), a shadow line under the aircraft symbol, and perspective lines on the ground. Airline pilots (n = 25) were tasked with rolling back to level 96 times from unforeseen (30 or -30 degrees) bank angles after experiencing either matching or mismatching (disorienting) roll motion cues in a motion-base simulator. There was no outside visibility and pilots responded using the ADI only. Roll reversal errors and reaction times were compared within-subject between the enhanced and baseline ADI, which were both based on the B747. Pilots were tasked to respond immediately upon presentation of the display, so that their initial interpretation of bank angle direction could be measured. There was no significant difference in roll reversal errors, and a significant increase in reaction times, when using the enhanced ADI compared to the baseline ADI. This suggests that pilots had slightly more difficulty with reading the bank angle with the enhanced ADI. Of the pilots, 56% preferred the enhanced ADI over the baseline display as it is, 8% had no preference and 36% preferred the baseline ADI. The most valued addition was the perspective lines on the ground, which pilots remarked would also be helpful in recovering extreme attitudes. The most-heard concerns were about potential clutter caused by the added cues, and difficulty with accurate reading of the pitch angle due to the shadow lines. In conclusion, according to the pilots' feedback, the addition of depth cues to the ADI appears promising, but it should be tested using more challenging tasks. Further design changes also appear needed to prevent clutter and facilitate quick reading of the aircraft attitude.

This erratum applies to the following published paper [1]. In Fig. 10(e) and (f) of the published version of the paper, the measured values for the t _f and T _l,f (both having values around 1 s for the considered dataset) were interchanged. This erratum includes both the published and corrected versions of Fig. 10 and the related paragraph in the paper's Results section. PUBLISHED VERSION In preview tasks, bandwidth changes yield only minor adaptations in the model parameters, see Fig. 10. Only the average look-ahead time t _f decreases slightly with bandwidth from around 1.05 to 0.9 s, Fig. 10(e) but with substantial between-participant variability, as indicated by the overlapping confidence intervals. The lower t _f may not reflect a systematic adaptation to the bandwidth, but a more subtle adaptation to minimize the errors due to the additional high-amplitude sinusoids at 2.5 and 4 rad/s, see also Fig. 8c. The general way in which participants use the available preview for control is, however, not affected by the target signal bandwidth: the target response gain (K _f ≈ 0.95, Fig. 10(d)) and lag time-constant (T _{l, f} ≈ 1.15 s, Fig. 10(f)) are approximately invariant. The estimated control dynamics in Fig. 12 show that the target trajectory is tracked almost perfectly at all frequencies below 4 rad/s, mostly because the phase lead due to τ _f allows for synchronizing the CE output with the target signal (as opposed to pursuit tasks, see Fig. 11, bottom right). Therefore, different-bandwidth target signals provide no incentive for HCs to strongly adapt their control behavior in preview tasks. (Figure Presneted) CORRECTED VERSION In preview tasks, bandwidth changes yield only minor adaptations in the model parameters, see Fig. 10. Only the average target smoothing time-constant T _{l, f} decreases slightly with bandwidth [from around 1.05 to 0.9 s, Fig. 10(f)], but with substantial between-participant variability, as indicated by the overlapping confidence intervals. The lower T _{l, f} indicates that slightly more smoothing is applied to reduce tracking of the more high-amplitude high-frequency sinusoids in the 2.5 and 4 rad/s bandwidth signals through the feedforward response, see also Fig. 8(c). The general way in which participants use the available preview for control is, however, not affected by the target signal bandwidth: the target response gain [K _f ≈ 0.95, Fig. 10(d)] and look-ahead time [τ _f ≈ 1.15 s, Fig. 10(e)] are approximately invariant. The estimated control dynamics in Fig. 12 show that the target trajectory is tracked almost perfectly at all frequencies below 4 rad/s, mostly because the phase lead due to τ _f allows for synchronizing the CE output with the target signal (as opposed to pursuit tasks, see Fig. 11, bottom right). Therefore, different-bandwidth target signals provide no incentive for HCs to strongly adapt their control behavior in preview tasks. (Figure presented).