Abstract
Monitoring responsibilities include checking the automated operating system to make judgments and provide solutions. A loss of vigilance will lead to accidents if care is not taken. Therefore, emergency situations need to be quickly detected. The purpose of this study was to develop an intelligent fuzzy control system by using fuzzy sets to evaluate and improve the performance of supervisors. There are two input variables: fuzzy set \(\tilde{S}\), which represents the linguistic notion “hit” when supervisor action is needed, and the fuzzy set Ñ, which represents the linguistic notion “false alarm” when no action is needed. The evaluation was extended from a two-value logic to a multi-value logic by using membership functions. The experimental results show that the fuzzy control used to evaluate the domain of decision response, i.e., the differences among a Type I error (miss), a Type II error (false alarm) and an appropriate reaction, was effective. Therefore, the traditional two-values logic was expanded to the multiple-values performance evaluation to clearly describe the difference in the judgment needed when monitoring “also this also other” work. In addition an alarm signal was produced by the fuzzy system for reminding participant’s attention. According to the results, the alarm was adapted to call the operator’s attention when a situation needed action to improve the supervisory performance. The results show that the effect of the fuzzy control alarm system for improving supervisory performance is significant. Additionally, the wide interval defined in fuzzy set would be more efficient to call participant’s attention and improve performance significantly than narrow.
Similar content being viewed by others
References
Bult JH, van Putten B, Schifferstein HN, Roozen JP, Voragen AG, Kroeze JH (2004) Modeling panel detection frequencies by queuing system theory: an application in gas chromatography olfactometry. Percept Psychophys 66:1125–1146
Davies DR, Parasuraman R (1982) The psychology of vigilance. American Elsevier, New York
Donaldson W (1922) Measuring recognition memory. J Exp Psychol Gen 121:275–277
Driankov D, Hellendoorn H, Reinfrank M (1996) An introduction to fuzzy control, 2nd edn. Springer, Berlin
Egan JP, Greengerg GZ, Schulman AI (1961) Operating characteristics, signal detectability and the method of free response. J Acoust Soc Am 33:993–1007
Green DM, Swets JA (1988) Signal detection theory and psychophysics. Wiley, New York
Grier JB (1971) Nonparametric indexes for sensitivity and bias: computing formulas. Psychol Bull 75:424–429
Helton WS, Russell PN (2013) Visuospatial and verbal working memory load: effects on visuospatial vigilance. Exp Brain Res 224:429–436
Helton WS, Russell PN (2015) Rest is best: the role of rest and task interruptions on vigilance. Cognition 134:165–173
Karwowski W (1992) The human world of fuzziness, human entropy, and the need for general fuzzy systems theory. J Jpn Soc Fuzzy Theory Syst 4:591–609
Karwowski W, Grobelny J, Yang Y, Lee WG (1999) Applications of fuzzy systems in human factors. In: Zimmermman H (ed) Handbook of fuzzy sets and possibility theory. Kluwer, Boston, pp 589–621
Kenneth RB, Lloyd K, James PT (1986) Handbook of perception and human performance, 2nd edn. Wiley, New York
Lane JD, Phillips-Bute BG (1998) Caffeine deprivation affects vigilance performance and mood. Physiol Behav 65:171–175
Langner R, Eickhoff SB (2012) Sustaining attention to simple tasks: a meta-analytic review of the neural mechanisms of vigilant attention. Psychol Bull 139:870–900
Loo SK, Hale TS, Macion J, Hanada G, McGough JJ, McCracken JT (2009) Cortical activity patterns in ADHD during arousal, activation and sustained attention. Neuropsychologia 47:2114–2119
Mackie RR, Wylie CD, Smith, MJ (1994) Countering loss of vigilance in sonar watch standing using signal injection and performance feedback. Ergonomics 37:1157–1184
Macmillan NA, Creelman CD (2005) Detection theory: a user’s guide, 2nd edn. Erlbaum, Mahwah
McLeod RW (2015) Automation and supervisory control. In: McLeod RW (ed) Designing for human reliability: human factors engineering in the oil, gas, and process industries. Elsevier, Waltham, pp 159–169
Molloy R, Parasuraman R (1996) Monitoring an automated system for a single failure: vigilance and task complexity effects. Hum Factors 38:311–322
Parasuraman R (1985) Detection and identification of abnormalities in chest X-rays: effects of reader skill, disease prevalence, and reporting standards. In: Eberts RE, Eberts CG (eds) Trends in ergonomics/human/factors II. North-Holland, Amsterdam, pp 59–66
Parasuraman R, Molloy R, Singh IL (1993) Performance consequences of automation-induced “complacency”. Int J Aviat Psychol 3:1–23
Park JK, Choi SS, Hong JH, Chang SH (1997) Development of the effectiveness measure for an advanced alarm system using signal detection theory. IEEE Trans Nucl Sci 44:163–172
Ponce P, Molina A, Grammatikou D (2016) Design based on fuzzy signal detection theory for a semi-autonomous assisting robot in children autism therapy. Comput Hum Behav 55:28–42
Proctor RW, Zandt TV (1994) Human factors in simple and complex systems. Allyn and Bacon, Needham Heights
Repperger DW, Phillips CA (2009) The human role in automation. In: Nof SY (ed) Springer handbook of automation. Springer, Berlin, Heidelberg, pp 295–304
Rogé J, Kielbasa L, Muzet A (2002) Deformation of the useful visual field with state of vigilance, task priority, and central task complexity. Percept Mot Skills 95:118–130
Rose CL, Murphy LB, Schickedantz B, Tucci J (2001) The effects of event rate and signal probability on children’s vigilance. J Clin Exp Neuropsychol 23:215–224
See JE, Warm JS, Dember WN, Howe SR (1997) Vigilance and signal detection theory: an empirical evaluation of five measures of response bias. Hum Factors 39:14–29
Sheridan TB, Parasuraman R (2005) Human-automation interaction. Rev Hum Factors Ergon 1:89–129
Silverstein SM, Light G, Palumbo DR (1998) The sustained attention test: a measure of attentional disturbance. Comput Hum Behav 14:463–475
Snodgrass JG, Corwin J (1988) Pragmatics of measuring recognition memory: applications to dementia and amnesia. J Exp Psychol Gen 117:34–50
Stanislaw H, Todorov N (1999) Calculation of signal detection theory measures. Behav Res Methods Instrum Comput 31:137–149
Szalma JL, Hancock PA (2013) A signal improvement to signal detection analysis: fuzzy SDT on the ROCs. J Exp Psychol Hum Percept Perform 39:1741–1762
Szalma JL, Warm JS, Matthews G, Dember WN, Weiler EM, Meier A (2004) Effects of sensory modality and task duration on performance, workload, and stress in sustained attention. Hum Factors 46:219–233
Warm JS, Jerison H (1984) The psychophysics of vigilance. In: Warm JS (ed) Sustained attention in human performance. Wiley, England, pp 15–60
Watson CS, Nichols TL (1976) Detectability of auditory signals presented without defined observation intervals. J Acoust Soc Am 59:655–668
Wickens CD (1992) Engineering psychology and human performance. HarperCollins, New York
Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 3:28–44
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, CL., Uang, ST. & Kuo, SC. An intelligent fuzzy control system with adapted interval for improving the supervisory performance in automation. Oper Res Int J 18, 689–709 (2018). https://doi.org/10.1007/s12351-017-0336-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12351-017-0336-3