A combined algorithm for denoising GNSS-RTK positioning solutions with application to displacement monitoring of a super-high-rise building
Bibliographic record
Abstract
Abstract Given that global navigation satellite system (GNSS)-based real-time kinematic (GNSS-RTK) monitoring accuracy is easily interfered with by residual errors, a combined algorithm is proposed for signal denoising with application to the GNSS-RTK positioning solutions of a super-high-rise building; namely, the Tianjin Radio and Television Tower in China. The proposed denoising algorithm is a combination of the Butterworth high-pass filter and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), after which the intrinsic mode function components are selected based on the effective coefficient, the correlation coefficient and the power spectral density. After signal filtering, the structural dynamic deformation is 36.0 mm in the horizontal directions and 48.4 mm in the up-direction. Two orders of natural frequencies of the Tianjin Radio and Television Tower are successfully identified (i.e. 0.1583 Hz and 1 Hz). In addition, the relative error of the fundamental frequency is less than 0.44% compared with previous studies. The results reveal the reliability of the combined Butterworth–CEEMDAN algorithms for dealing with GNSS-RTK measurements. Moreover, the improvement in the GNSS-RTK sampling frequency is conducive to extracting more helpful monitoring information. Furthermore, the dynamic deformation data in this paper indicate that the Tianjin Radio and Television Tower is operating normally.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".