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Enregistrement W2083501222 · doi:10.1518/001872000779698213

Why Fluid Dynamics Matters for Display Design in Process Control: Commentary on Bennett and Malek

2000· letter· en· W2083501222 sur OpenAlexafffund
Kim J. Vicente, C. Ross Ethier

Notice bibliographique

RevueHuman Factors The Journal of the Human Factors and Ergonomics Society · 2000
Typeletter
Langueen
DomainePsychology
ThématiqueHuman-Automation Interaction and Safety
Établissements canadiensUniversity of Toronto
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésProcess (computing)Control (management)Dynamics (music)Human factors and ergonomicsPoison controlPsychologyComputer scienceHuman–computer interactionArtificial intelligenceMedical emergencyMedicine

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION As its name implies, cognitive engineering requires attention to properties of both human cognition and engineering systems. Bennett and Malek (this issue) have done an exemplary job of investigating properties of human cognition that are pertinent to design of animated mimic displays for process control systems. In so doing, however, they may have inadvertently overlooked some potentially vital properties of real engineering systems (e.g., how fluid flows in a piping network under normal and, especially, abnormal situations). A closer look at fluid mechanical factors reveals that using animation to depict flow rate will sometimes provide operators with misleading feedback that could negatively affect plant safety. FLOW PATTERNS IN A PIPING NETWORK Implications for Animated Mimic Displays Bennett and Malek (this issue) motivated their research on animated mimic displays by need to support fault management behavior. In their discussion authors return to this issue, stating that the inclusion of animation in mimic displays could ... improve detection and diagnosis of faults (p. 448). However, two studies conducted evaluated performance on a quantitative psychophysical task, not a fault management task. It is not clear how results from an elemental task of quantitative judgments of velocity generalize to more complex relational task of fault management. Therefore, it is of interest to consider implications of using an animated mimic display in a complex piping network, typical of that found in real-world applications. Although usability of a design can only be assessed empirically, its usefulness can be evaluated analytically by identifying control requirements associated with a problem (Rouse, 1990). In this case these requirements can be examined by reviewing how fluid flows in a piping network. We initially consider simplest case (shown in Figure la) of fluid flowing through a pipe controlled by a valve (VA), resulting in a sensed flow rate (FA). Breakdown of Normal Expectations about Flow Rate Conservation of mass requires that instantaneous flow rate of an incompressible fluid in a rigid pipe be same at every location along a pipe segment. A pipe segment is defined as any continuous length of pipe uninterrupted by branch or feeder pipes. Clearly, flow rate will change across a branch or feeder point, as fluid will leave or enter pipe at such a location. Under normal operating conditions, it is therefore appropriate to represent flow in an entire pipe segment based on output of a single flow sensor. A display of type advocated by Bennett and Malek (this issue) would be effective in this case. However, consider more critical and demanding case of a pipe break or leak. Depending on location and severity of leak or break, flow rate along pipe could change drastically as a function of spatial location. For example, flow rate downstream of flow sensor location could be much less than that at flow sensor itself because of leaking fluid. However, an animated mimic display like that shown in Figure 1b would erroneously suggest that fluid is flowing at same flow rate all along pipe segment. It would do so because display extrapolates from flow datum collected at a single location to create a very compelling and attention-grabbing animated representation of what is normally true (i.e., constant flow rate all along pipe). This normal relationship is an inference because we do not have flow sensors all along pipe. During some faults, this inference is incorrect, and animated display may discourage operators from entertaining valid hypotheses about where pipe break or leak might be. The resulting situation is analogous to that observed in Three Mile Island (TMI) control room (Rubinstein, 1979). …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,021
score de la tête « metaresearch » (Gemma)0,078
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,053
Score d'incertitude au seuil0,116

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0210,078
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0010,002
Études des sciences et des technologies0,0070,018
Communication savante0,0080,013
Science ouverte0,0110,004
Intégrité de la recherche0,0530,077
Charge utile insuffisante (le modèle a refusé de juger)0,0050,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,301
Écart entre enseignants0,272 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2000
Routes d'admission2
Résumé présentoui

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