Quantitative Modeling of Human Performance in Complex, Dynamic Systems (1990) / Chapter Skim
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3. Applications
3. Applications
Pages 52-71
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From page 52... ...
The fourth is a broad class of human-machine interactions wherein the human operator does not perform the task directly, but instead supervises one or more automatic control systems that execute the direct control. The latter area, which includes autopilots in aircraft, semiautomated nuclear or chemical plant control, and robots in factories, space, or undersea, poses new challenges for human performance modeling.
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From page 53... ...
Flight Control Background The expansion of operational envelopes and mission requirements for flight vehicles that occurred in the past two to three decades, and the resulting increase in task difficulty and pilot workload, have shmulated a strong need for systematic means of analyzing the pilot-vehicle system and predicting closed-loop performance and workload. This, in turn, has led to substantial efforts aimed at developing quantitative engineering models for the human pilot performing closed-loop manual control tasks.
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From page 54... ...
Current Issues The evolutions of aircraft, control and display systems, and mission requirements are posing new problems in control: innovative aircraft configurations with different dynamic characteristics and, especially, with highly augmented controls; new types of control including six degrees of freedom controls; and different paths to fly. These new systems are not wholly understood, to say the least, and there have been persistent difficulties in design, including pilot-induced oscillations, excessive pilot workload and inadequate pilot-vehicle interfaces.
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From page 55... ...
Prediction of crew workload is a complex and labor-intensive task One of the first published models developed for this purpose was based on a task network approach (Siegel, Miehle, and Federman, 19623. It calculated the times required for discrete operator actions from an extension of information theory.
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From page 56... ...
Ask time distn~utions and priorities were inputs to the models; worldoad emerged from a comparison of task times with available tune. As part of this working groups' effort, the three companies that reported using workload models in 1978, plus six other airframe contractors, were contacted to update the status of aircrew workload modeling.
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From page 57... ...
Some investigators feel that a clean distinction should be made between human operator perfonnance requirements, such as result Dom task analysis, and human operator mental effort expended (i.e., a trained operator might perform a task with time and cognitive resources left over, whereas a novice may be fully occupied)
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From page 58... ...
FAIR equipment and tasks, and a proposed Update version with more integrated and automated FLIR equipment and tasks. Comparison of HOS simulation results for the baseline and prototype versions confirmed actual fleet results which had shown that performance of the normal ECM tasks would be significanth,r degraded and performance of the FL-JR tasks would rarely be successfully completed in the protoWpe aircraft.
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From page 59... ...
lIUMAN PERFORMANCE MOOEI5 IN NUCLEAR POWER OPERATIONS Background The number of human performance modeling simulations actually applied within the nuclear industry is at present very small. Although considerable theoretical work has been done (e.g., Shendan, Jenkins, and Kisner, 1982)
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From page 60... ...
The OATS also utilizes probability trees to structure operator actions but has much larger units of analysis, usually plant functions rather than operator tasks (U.S. Nuclear Regulatory Commission, 1984~.
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From page 62... ...
Human simulation methods currently do not have an effective interface to normal plant user environments. A comparison of where the approaches are deficient provides addi.
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lb answer that question, better data are needed. Because obtaining data is difficult, the use of human performance modeling techniques is slow, particularly for rare accident events.
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From page 64... ...
Another area is the use of human operator models in design specification, particularly for purposes of increasing human reliability. The final area concerns what can be done to eliminate confusion and increase correct diagnosis probabilities, given the occurrence of a misdiagnosed event.
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From page 65... ...
Therefore, the choice among the models in Able 3-1 depends on the level of performance to be modeled. Summary It would seem feasible to use fine-grained models to produce the SEQUENCES to meet the task analysis requirements of the global models.
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From page 66... ...
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From page 67... ...
HUMAN PERFORMANCE MODELS IN SUPERVISORY CONTROL Background Supervisory control is an example of an important merging class of human operator activity in which HPMs are needed but for which proven models do not now Ernst. Simply stated, supervisory control refers to all the activities of the human superior who interacts via a computer with a complex semiautomatic process.
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From page 68... ...
1 ' -- - 1 o ~ o o in V ~ ~ ~ ~ 0 cn A c: ~ ~ ct o ~ ~ ~ ~ ct ~ o o An u, tn ct ~ ~ o it ° in ~ - ~ o a' Q ~ cn c,n ~ 1.
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From page 69... ...
From Figure 3-1, it is clear that a model of supervisory control must be a model of the entire system, not just the human. The situation is similar to that found in models of the human operator in manual control systems, where the particular realization of the model depends in each case on the properties of the rest of the system.
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From page 70... ...
However, none of these models supervisory control. If other models of planning, monitonng, and fault management existed, they might be used to predict behavior in supervisory control, provided that the state of displayed information was also known.
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From page 71... ...
Summary Supervisory control is an emergent class of systems wherein humans supervise computers and computers perform the direct control. It poses new demands for integrated human performance modeling, inherently demanding component models of high-level activities such as planning, teaching, monitoring, failure detection/inte~vention, and learning.
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