Detecting the Dangerous Area of Toxic Gases with Wireless Sensor Networks
Bibliographic record
Abstract
Petrochemical accidents, e.g., toxic gas leaking and explosion, result in serious damage, so the detection and visualization of the dangerous area of leaking toxic gases is an important research issue for large-scale petrochemical plants. There have been many efforts made to address this issue by using a large number of special monitoring devices. These special devices provide the gas concentration reports within their individual ranges. However, because of the continuity of gas diffusion and the invisibility of toxic gases, it is difficult to detect and visualize the continuous dangerous area of gas diffusion by only using the scattered concentration reports. This paper proposes a scheme to detect and visualize the dangerous area using Wireless Sensor Networks (WSNs). In this proposed scheme, a planarization algorithm is used to planarize a WSN, and based on the planarized network, the boundary area of gas diffusion is calculated to delimitate the dangerous area. This study also verifies the robustness of the proposed scheme in regards to the node failure. The node failure has a special kind of influence on the accuracy of dangerous area detection. This paper also analyzes the impact of 5 planarization algorithms on the accuracy of dangerous area detection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 source (direct Gemma or distilled Codex), 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".