Sensor Noise and Ecological Interface Design: Effects of Noise Magnitude on Operators’ Performance and Control Strategies
Bibliographic record
Abstract
The purpose of this dissertation was to investigate the effects of the presence and magnitude of sensor noise on operators’ performance and control strategies using an Ecological Interface Design (EID) interface and a Single-Sensor Single-Indicator (SSSI) interface. To assist in the study of this topic, concepts from sensor technology, cognitive psychology, and cognitive engineering were utilized. Three studies were conducted using different types of sensor noise perturbations with DURESS III, a representative thermal-hydraulic process simulation: 1) global random increases in sensor noise magnitude, 2) global gradual increases in sensor noise magnitude, and 3) local gradual increases in sensor noise magnitude. Three displays (P, P+S, and P+F) were used in the studies, motivated by different interface design principles. There were four main findings. First, the EID condition performed significantly better than the SSSI conditions when sensor noise was set to an industry average level. Second, the robustness of the EID interface was compromised by global and large increases in sensor noise magnitude, but no more than the SSSI interface. Third, increasing the magnitude of sensor noise in selected low-level sensors had an impact on the performance and control stability of the EID condition, but no more than the SSSI condition. Fourth, in all three studies, the introduction of uncertainty in the form of sensor noise to both EID and SSSI interfaces forced participants to explore different control strategies. A number of contributions resulted from this research. First, this was the first set of studies to use the DURESS III microworld to investigate the impact of sensor noise on performance and control strategies. Second, this is the first piece of research to empirically assess the impact of different sensor noise magnitudes on the robustness of an EID interface. Third, this dissertation was the first to empirically investigate issues related to increases in sensor noise magnitude to local low-level sensors and their derivations to emergent features. Fourth, these studies constitute the first investigation of changes in control strategies in the context of increases in sensor noise magnitude. The findings are believed to be important for the applicability of EID in industrial settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".