Adaptive algorithms: a bibliographic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The analysis of a research work developed in the company C.V.G CARBONORCA of Venezuela is presented, which has two gas purification plants for the cooking area, designed to purify the gas that comes from the cooking ovens. Each plant is made up of solenoid valves, pneumatic valves, transmitters, process mimic panel and a supervisory system. All these elements are governed by a SIEMENS S5-115U PLC which is in a state of obsolescence, which is why the replacement of these automata by ALLEN BRADLEY ContolLogix automata was designed, in order to guarantee continuity in operations in plant. The research was done with a descriptive design of the field experimental type. A code for each gas treatment plant was obtained in RSLOGIX 5000 v17.00.00 and the update of the database of the supervisory system. The operation of the program was also verified through a simulation of the plant in a supervisory system, the deployment of which was designed for this purpose.
 Keywords: Automation, Modernization, ControlLogix, Supervisory System, Mimic Panel
 References
 [1]M. Simao, N. Mendes, O. Gibaru y P. Neto, «A Review on Electromyography Decoding and Pattern Recognition for Human-Machine Interaction,» IEEE Access, vol. 7, pp. 39564 - 39582, 2019.
 [2]Instituto de Estadística de la Organización de las Naciones Unidas para la Educación, la Ciencia y la Tecnología, «Clasificación Internacional Normalizada de la Educación CINE,» UNESCO Institute for Statistics, Montréal, 2011.
 [3]Y. Zheng y H. Xiaogang, «Interference Removal From Electromyography Based on Independent Component Analysis,» IEEE Trans Neural Syst Rehabil Eng, vol. 27, nº 5, pp. 887-894, Mayo 2019.
 [4]B. Afsharipour, F. Petracca, M. Gasparini y R. Merletti, «Spatial distribution of surface EMG on trapezius and lumbar muscles of violin and cello players in single note playing,» Journal Electromyography Kinesiology, vol. 31, pp. 144 - 153, 2016.
 [5]M. Niegowski, M. Zivanovic, M. Gómez y P. Lecumberri, «Unsupervised learning technique for surface electromyogram denoising from power line interference and baseline wander,» de 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italia, 2015.
 [6]S. D. Soedirdjo, K. Ullah y R. Merletti, «Power line interference attenuation in multi-channel sEMG signals: Algorithms and analysis,» de Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2015.
 [7]A. Phinyomark, F. Quaine, S. Charbonnier, C. Serviere, F. Tarpin-Bernard y Y. Laurillau, «Feature extraction of the first difference of EMG time series for EMG pattern recognition,» Computer Methods and Programs in Biomedicine, vol. 117, nº 2, pp. 247-256, Noviembre 2014.
 [8]M. Malboubi, F. Razzazi, M. Aliyari y A. DAvari, «Power line noise elimination from EMG signals using adaptive Laguerre filter with fuzzy step size,» de 17th Iranian Conference of Biomedical Engineering (ICBME), Isfahan, Iran, 2010.
 [9]C. Luca, L. Gilmore, M. Kuznetsov y S. Roy, «Filtering the surface EMG signal: Movement artifact and baseline noise contamination,» J. Biomech, pp. 1573-1582, 28 Mayo 2010.
 [10]R. Mello, L. Oliveira y J. Nadal, «Digital Butterworth filter for subtracting noise from low magnitude surface electromyogram,» Comput Methods Programs Biomed, vol. 1, nº 87, pp. 28-35, 2007.
 [11]A. Botter y T. Vieira, «Filtered virtual reference: A new method for the reduction of power line interference with minimal distortion of monopolar surface EMG,» IEEE Transactions on Biomedical Engineering, vol. 62, nº 11, pp. 2638 - 2647, 2015.
 [12]J. R. Potvin y S. H. Brown, «Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates,» J. Electromyogr. Kinesiol., vol. 14, nº 3, pp. 389-399, 2004.
 [13]D. T. Mewett, K. J. Reynolds y H. Nazeran, «Reducing power line interference in digitised electromyogram recordings by spectrum interpolation,» Med. Biol. Eng. Comput., vol. 4, nº 42, pp. 524-531, 2004.
 [14]D. T. Mewett, H. Nazeran y K. J. Reynolds, «Removing power line noise from recorded EMG,» de 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, 2001.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 0.001 |
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 it