Cátedra abierta al servicio de la comunidad, dos miradas interdisciplinares
La Universidad Tecnológica de Pereira a través de la Vicerrectoría de Investigaciones, Innovación y Extensión tiene como propósito “Definir y direccionar los lineamientos para la investigación institucional que fortalezcan los grupos y semilleros de investigación, a través de la formación de investi...
- Autores:
-
Mejia Hernandez , Juan Camilo
Hernández Arias, Diego Fernando
- Tipo de recurso:
- Book
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Pereira
- Repositorio:
- Repositorio Institucional UTP
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.utp.edu.co:11059/14390
- Acceso en línea:
- https://repositorio.utp.edu.co/home
https://hdl.handle.net/11059/14390
- Palabra clave:
- 300 - Ciencias sociales
Automatización industrial
Procesamiento de datos
Señales técnicas digitales
Narrativa cubana
Homoerotismo
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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Cátedra abierta al servicio de la comunidad, dos miradas interdisciplinares |
| title |
Cátedra abierta al servicio de la comunidad, dos miradas interdisciplinares |
| spellingShingle |
Cátedra abierta al servicio de la comunidad, dos miradas interdisciplinares 300 - Ciencias sociales Automatización industrial Procesamiento de datos Señales técnicas digitales Narrativa cubana Homoerotismo |
| title_short |
Cátedra abierta al servicio de la comunidad, dos miradas interdisciplinares |
| title_full |
Cátedra abierta al servicio de la comunidad, dos miradas interdisciplinares |
| title_fullStr |
Cátedra abierta al servicio de la comunidad, dos miradas interdisciplinares |
| title_full_unstemmed |
Cátedra abierta al servicio de la comunidad, dos miradas interdisciplinares |
| title_sort |
Cátedra abierta al servicio de la comunidad, dos miradas interdisciplinares |
| dc.creator.fl_str_mv |
Mejia Hernandez , Juan Camilo Hernández Arias, Diego Fernando |
| dc.contributor.author.none.fl_str_mv |
Mejia Hernandez , Juan Camilo Hernández Arias, Diego Fernando |
| dc.subject.ddc.none.fl_str_mv |
300 - Ciencias sociales |
| topic |
300 - Ciencias sociales Automatización industrial Procesamiento de datos Señales técnicas digitales Narrativa cubana Homoerotismo |
| dc.subject.lemb.none.fl_str_mv |
Automatización industrial Procesamiento de datos Señales técnicas digitales Narrativa cubana Homoerotismo |
| description |
La Universidad Tecnológica de Pereira a través de la Vicerrectoría de Investigaciones, Innovación y Extensión tiene como propósito “Definir y direccionar los lineamientos para la investigación institucional que fortalezcan los grupos y semilleros de investigación, a través de la formación de investigadores, el desarrollo de programas o proyectos de ciencia, tecnología e innovación, así como la generación de redes y alianzas estratégicas que contribuyan a la creación y apropiación del conocimiento para la sociedad.” Y es por ello que, anualmente, entre otras se realiza la CONVOCATORIA PARA FOMENTAR LA PUBLICACIÓN DE CAPÍTULOS DE LIBRO RESULTADO DE INVESTIGACIÓN CATEDRÁTICOS AÑO 2022, en la cual pueden postular los resultados de los proyectos de investigación finalizados en los últimos cinco años. En esta oportunidad, se publicarán dos capítulos de las Facultades: Ciencias de la Educación y de Ingeniería Mecánica en los cuales se darán a conocer dos tesis de Maestría. Para la Vicerrectoría de Investigaciones, Innovación y Extensión es de suma importancia socializar por medio de este libro por capítulos el conocimiento, teniendo en cuenta que este debe transferirse a través de diferentes medios, puesto que no solo fortalece la academia sino también a la sociedad en general. |
| publishDate |
2022 |
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2022-11-25T14:01:52Z |
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2022-11-25T14:01:52Z |
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2022 |
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Libro |
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http://purl.org/coar/resource_type/c_2f33 |
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Text |
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info:eu-repo/semantics/book |
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info:eu-repo/semantics/acceptedVersion |
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acceptedVersion |
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978-958-722-741-3 |
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Universidad Tecnológica de Pereira |
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Repositorio Institucional Universidad Tecnológica de Pereira |
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https://repositorio.utp.edu.co/home |
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https://hdl.handle.net/11059/14390 |
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978-958-722-741-3 Universidad Tecnológica de Pereira Repositorio Institucional Universidad Tecnológica de Pereira |
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https://repositorio.utp.edu.co/home https://hdl.handle.net/11059/14390 |
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spa |
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Trabajos de investigación |
| dc.relation.references.none.fl_str_mv |
Acharya, R., Molinari, F., & Sree, V. (2012). Automated diagnosis of epileptic EEG using entropies. Biomedical Signal Processing and Control, 7, 401-408. Ahirrao, N., Bhosle, S., & Nehete, D. (2018). Dynamics and Vibration Measurements in Engines. Procedia Manufacturing, 20, 434-439. Arvanitis, S. (2017). A note on the limit theory of a Dickey Fuller unit root test with heavy tailed innovations. Statistics and Probability Letters, 126, 198-204. Aziz, W., Arif, M., & Fate, Z. (2005). Multiscale Permutation Entropy of Physiological Time Series. akistan Section Multitopic Conference, 53, 1-6 Bandt, C., & Pompe, B. (2002). Permutation Entropy: A Natural Complexity Measure for Time Series. Physical review letters, 88, 74-102. Berger, D., Pietra, V., & Pietra, S. (1996). A maxi mumentropy approach to NLP. Computational Linguistics, 2, 39-71. Chazal, P., Dwyer, R., & Odwyer, A. (2004). Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering, 51, 1196-1206. Chen, Y., & Pun, C. S. (2019). A bootstrap-based KPSS test for functional time series. Journal of Multivariate Analysis, 174, 10- 45. Costa, M., Goldberger, A., & Peng, C. K. (2002). Multiscale entropy analysis of complex physiologic time series. Physical review letters, 89, 068102 Daza, S., Arias, J., Godino, J., & Saenz, N. (2009). Dynamic feature extraction: an application to voice pathology detection. Intelligent Automation and Soft Computing, 15, 667–682. Derya, E. (2008). Wavelet mixture of experts network structure for EEG signals classification. Expert Systems with Applications, 34, 1954-1962. Escobari, D., Garcia, S., & Mellado, C. (2017). Identifying bubbles in Latin American equity markets: Phillips-Perron-based tests and linkages. Emerging Markets Review, 33, 90-101 Fatin, E., Naomie, S., & Arief, H. (2016). Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Computer Methods and Programs in Biomedicine, 127, 52-63. Gaoxiang, O., Jing, L., Xianzeng, L., & Xiaoli, L. (2013). Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis. Epilepsy Research, 104, 246-252. Goldsworthy, R. (2019). Temporal envelope cues and simulations of cochlear implant signal processing. Speech Communication, 92, 252-258. Guler, N., Derya, E., & Guler, I. (2005). Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Systems with Applications, 29, 506-514. Guyon, I., Elisseeff, A., & Pack, L. (2003). An Introduction to Variable and Feature Selection. J. Mach. Learn. Res., 3, 1157–1182. Heng, W., Nor, M., & Cao, W. (1998). Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition. Applied Acoustics, 53, 211-226. Hernández, J., Álvarez, A., & Orozco, A. (2017). Estimación de características relevantes para el monitoreo de condición de motores de combustión interna a partir de senales de vibración. TecnoLógicas, 20, 39-55. Hu, Y., Palreddy, S., & Tompkins, W. (1997). A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Transactions on Biomedical Engineering, 44, 891-900 Hugh, M., Farhang, H., & Litle, S. (1995). Application of Statistical Moments to Bearing Failure Detection. Appl. Acoust, 44, 67-77 Jaouherl, B., Fnaiech, N., & Lotfi, S. (2015). Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics, 89, 16-27 Pengbo, Y., Pengjian, S., & Aijing, L. (2017). Financial time series analysis based on effective phase transfer entropy. Physica A: Statistical Mechanics and its Applications, 468, 398-408. Penny, B., Geoff, J., & Ganesh, S. (2017). Classification trees for poverty mapping. Computational Statistics Data Analysis, 115, 53-66. Philip, M., Katri, S., & Nieminen, V. (2018). Scent Classification by K Nearest Neighbors using Ion-Mobility Spectrometry Measurements. Expert Systems with Applications, 15, 199-210. Pincus, S. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88, 2297–2301. Platero, C. (1999). Introducción al procesamiento digital de señales. Universidad Politécnica de Madrid, 1, 1-5. Prakash, O. M., Singh, V., & Kumar, P. (2012). Signature extraction from acoustic signals and its application for ANN based engine fault diagnosis. Signal and Imaging Systems Engineering, 5, 220- 216. Rajendra, A., Vinitha, S., & Peng, C. (2012). Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework. Expert Systems with Applications, 39, 9072- 9078. Rashid, A., Ghanbar, A., & Tavares, C. (2016). Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals. Pattern Recognition Letters, 70, 45-51. Ren, W., & Han, M. (2018). Classification of EEG Signals Using Hybrid Feature Extraction and Ensemble Extreme Learning Machine. Neural Processing Letters, 5, 1-21. Richman, J., Moorman, R., & Pardo, C. (2000). Physiological time series analysis using approximate entropy and sample entropy. American journal of physiology. Heart and circulatory physiology, 278, 39-49. Seung, Y., Jinju, J., Gwonhyu, J., & Jiwoo, Y. (2019). A novel supportive assessment for comprehensive aggression using EEG and ECG. Neuroscience Letters, 694, 136-142. Shai, G., Yosi, K., & ZhiWu, L. (2019). Iterative spectral independent component analysis. Signal Processing, 155, 368-376. Shao, H., Jiang, H., King, Z., & Ying, G. (2017). An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowledge-Based Systems, 119, 200-220. Sikonja, R., & Kononenko, I. (2003). Theoretical and empirical analysis of relieff and rrelieff. Machine learning,, 53, 23–69 Sohail, A., Chighoub, F., & ZhiWu, L. (2018). Spectral analysis of the stochastic time-fractional-KdV equation. Alexandria Engineering Studholme, C., Hill, D., & Daji, H. (1999). An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition, 32, 71-86. Tiebing, L., Wenpo, Y., & Min, W. (2017). Multiscale permutation entropy analysis of electrocardiogram. Physica A: Statistical Mechanics and its Applications, 471, 492-498 Tiwari, R., Gupta, V., & Kankar, P. (2015). Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier. Journal of Vibration and Control, 21, 461-467. Umut, O., Mahmut, H., & Mahmut, O. (2011). EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Systems with Applications, 38, 13475- 13481. Vencalek, O., & Pokotylo, O. (2018). Depth-weighted Bayes classification. Computational Statistics Data Analysis, 123, 1-12. Vignolo, J. (2008). Introducción al procesamiento digital de señales. Pontificia Universidad Catolica de Valparaiso, 1, 17-19. Wei, Z., Wang, Y., Shuilong, H., & Jiading, B. (2017). A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection. Knowledge-Based Systems, 116, 1–12. Wieczorkowska, A., Kubera, T., & Krzysztof, S. (2018). Spectral features for audio based vehicle and engine classification. Journal of Intelligent Information Systems, 50, 265-290. William, P., & Hoffman, M. (2011). Identification of bearing faults using time domain zero-crossings. Mechanical Systems and Signal Processing, 25, 3078-3088. Yakup, K., & Damla, K. (2012). Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients. Computer 46 Cátedra abierta al servicio de la comunidad, dos miradas interdisciplinares Inicio Inicio Methods and Programs in Biomedicine, 105, 257-267. Yang, H., Twen, A., & Jansh, A. (2004). Art kohonen neural network for fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 18, 645–657 Ye, C., Vijaya, K., & Tavares, C. (2012). Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Transactions on Biomedical Engineering, 59, 2930-2941. Yimei, D., Jiayi, H., Yue, W., Shijian, C., & Pengjian, S. (2019). Generalized entropy plane based on permutation entropy and distribution entropy analysis for complex time series. Physica A: Statistical Mechanics and its Applications, 520, 217-231. Yuedong, S., & Pietro, L. (2010). A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. J. Biomedical Science and Engineering, 34, 556-567 Yuwono, M., & Qin, Y. (2016). Automatic bearing fault diagnosis using particle swarm clustering and Hidden Markov Model. Engineering Applications of Artificial Intelligence, 47, 88-100 Zhang, S. (2019). Bayesian copula spectral analysis for stationary time series. Computational Statistics and Data Analysis, 133, 166-179. Zhang, X., Liang, Y., & Zang, Y. (2015). A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement, 69, 164-179. Zheng, J., Cheng, J., & Yang, Y. (2013). A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy. Mechanism and Machine Theory, 70, 441-453. Zheng, P., Yang, H., Shubao, Y., & Junsheng, C. (2018). Generalized composite multiscale permutation entropy and laplacian score based rolling bearing fault diagnosis. Mechanical Systems and Signal Processing, 99, 229–243. Zhiwen, L., Hongrui, C., & Xuefeng, C. (2013). Multi-fault classification Juan Camilo Mejía Hernández based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings. Neurocomputing, 99, 399- 410. |
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Mejia Hernandez , Juan CamiloHernández Arias, Diego Fernando2022-11-25T14:01:52Z2022-11-25T14:01:52Z2022La Universidad Tecnológica de Pereira a través de la Vicerrectoría de Investigaciones, Innovación y Extensión tiene como propósito “Definir y direccionar los lineamientos para la investigación institucional que fortalezcan los grupos y semilleros de investigación, a través de la formación de investigadores, el desarrollo de programas o proyectos de ciencia, tecnología e innovación, así como la generación de redes y alianzas estratégicas que contribuyan a la creación y apropiación del conocimiento para la sociedad.” Y es por ello que, anualmente, entre otras se realiza la CONVOCATORIA PARA FOMENTAR LA PUBLICACIÓN DE CAPÍTULOS DE LIBRO RESULTADO DE INVESTIGACIÓN CATEDRÁTICOS AÑO 2022, en la cual pueden postular los resultados de los proyectos de investigación finalizados en los últimos cinco años. En esta oportunidad, se publicarán dos capítulos de las Facultades: Ciencias de la Educación y de Ingeniería Mecánica en los cuales se darán a conocer dos tesis de Maestría. Para la Vicerrectoría de Investigaciones, Innovación y Extensión es de suma importancia socializar por medio de este libro por capítulos el conocimiento, teniendo en cuenta que este debe transferirse a través de diferentes medios, puesto que no solo fortalece la academia sino también a la sociedad en general.CONTENIDO Introducción ..................................................................................................................5 Capitulo 1. Desarrollo de una metodología para la caracterización y clasificación de señales no estacionarias usando mediciones de Entropía de Permutación Multiescalar / Development of a methodology for the characterization and classification of non-stationary signals using Multiscalar Permutation Entropy measurements.................................9 Juan Camilo Mejía Hernández Capitulo 2. Reinaldo Arenas: escritura disidente y reescritura distópica / Reinaldo Arenas: dissident writing and dystopian rewrite......................................51 Diego Fernando Hernández Arias88 Páginasapplication/pdf978-958-722-741-3Universidad Tecnológica de PereiraRepositorio Institucional Universidad Tecnológica de Pereirahttps://repositorio.utp.edu.co/homehttps://hdl.handle.net/11059/14390spaUniversidad Tecnológica de PereiraPereiraTrabajos de investigaciónAcharya, R., Molinari, F., & Sree, V. (2012). Automated diagnosis of epileptic EEG using entropies. Biomedical Signal Processing and Control, 7, 401-408.Ahirrao, N., Bhosle, S., & Nehete, D. (2018). Dynamics and Vibration Measurements in Engines. Procedia Manufacturing, 20, 434-439.Arvanitis, S. (2017). A note on the limit theory of a Dickey Fuller unit root test with heavy tailed innovations. Statistics and Probability Letters, 126, 198-204.Aziz, W., Arif, M., & Fate, Z. (2005). Multiscale Permutation Entropy of Physiological Time Series. akistan Section Multitopic Conference, 53, 1-6Bandt, C., & Pompe, B. (2002). Permutation Entropy: A Natural Complexity Measure for Time Series. Physical review letters, 88, 74-102.Berger, D., Pietra, V., & Pietra, S. (1996). A maxi mumentropy approach to NLP. Computational Linguistics, 2, 39-71.Chazal, P., Dwyer, R., & Odwyer, A. (2004). Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering, 51, 1196-1206.Chen, Y., & Pun, C. S. (2019). A bootstrap-based KPSS test for functional time series. Journal of Multivariate Analysis, 174, 10- 45.Costa, M., Goldberger, A., & Peng, C. K. (2002). Multiscale entropy analysis of complex physiologic time series. Physical review letters, 89, 068102Daza, S., Arias, J., Godino, J., & Saenz, N. (2009). Dynamic feature extraction: an application to voice pathology detection. Intelligent Automation and Soft Computing, 15, 667–682.Derya, E. (2008). Wavelet mixture of experts network structure for EEG signals classification. Expert Systems with Applications, 34, 1954-1962.Escobari, D., Garcia, S., & Mellado, C. (2017). Identifying bubbles in Latin American equity markets: Phillips-Perron-based tests and linkages. Emerging Markets Review, 33, 90-101Fatin, E., Naomie, S., & Arief, H. (2016). Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Computer Methods and Programs in Biomedicine, 127, 52-63.Gaoxiang, O., Jing, L., Xianzeng, L., & Xiaoli, L. (2013). Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis. Epilepsy Research, 104, 246-252.Goldsworthy, R. (2019). Temporal envelope cues and simulations of cochlear implant signal processing. Speech Communication, 92, 252-258.Guler, N., Derya, E., & Guler, I. (2005). Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Systems with Applications, 29, 506-514.Guyon, I., Elisseeff, A., & Pack, L. (2003). An Introduction to Variable and Feature Selection. J. Mach. Learn. Res., 3, 1157–1182.Heng, W., Nor, M., & Cao, W. (1998). Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition. Applied Acoustics, 53, 211-226.Hernández, J., Álvarez, A., & Orozco, A. (2017). Estimación de características relevantes para el monitoreo de condición de motores de combustión interna a partir de senales de vibración. TecnoLógicas, 20, 39-55.Hu, Y., Palreddy, S., & Tompkins, W. (1997). A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Transactions on Biomedical Engineering, 44, 891-900Hugh, M., Farhang, H., & Litle, S. (1995). Application of Statistical Moments to Bearing Failure Detection. Appl. Acoust, 44, 67-77Jaouherl, B., Fnaiech, N., & Lotfi, S. (2015). Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics, 89, 16-27Pengbo, Y., Pengjian, S., & Aijing, L. (2017). Financial time series analysis based on effective phase transfer entropy. Physica A: Statistical Mechanics and its Applications, 468, 398-408.Penny, B., Geoff, J., & Ganesh, S. (2017). Classification trees for poverty mapping. Computational Statistics Data Analysis, 115, 53-66.Philip, M., Katri, S., & Nieminen, V. (2018). Scent Classification by K Nearest Neighbors using Ion-Mobility Spectrometry Measurements. Expert Systems with Applications, 15, 199-210.Pincus, S. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88, 2297–2301.Platero, C. (1999). Introducción al procesamiento digital de señales. Universidad Politécnica de Madrid, 1, 1-5.Prakash, O. M., Singh, V., & Kumar, P. (2012). Signature extraction from acoustic signals and its application for ANN based engine fault diagnosis. Signal and Imaging Systems Engineering, 5, 220- 216.Rajendra, A., Vinitha, S., & Peng, C. (2012). Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework. Expert Systems with Applications, 39, 9072- 9078.Rashid, A., Ghanbar, A., & Tavares, C. (2016). Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals. Pattern Recognition Letters, 70, 45-51.Ren, W., & Han, M. (2018). Classification of EEG Signals Using Hybrid Feature Extraction and Ensemble Extreme Learning Machine. Neural Processing Letters, 5, 1-21.Richman, J., Moorman, R., & Pardo, C. (2000). Physiological time series analysis using approximate entropy and sample entropy. American journal of physiology. Heart and circulatory physiology, 278, 39-49.Seung, Y., Jinju, J., Gwonhyu, J., & Jiwoo, Y. (2019). A novel supportive assessment for comprehensive aggression using EEG and ECG. Neuroscience Letters, 694, 136-142.Shai, G., Yosi, K., & ZhiWu, L. (2019). Iterative spectral independent component analysis. Signal Processing, 155, 368-376.Shao, H., Jiang, H., King, Z., & Ying, G. (2017). An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowledge-Based Systems, 119, 200-220.Sikonja, R., & Kononenko, I. (2003). Theoretical and empirical analysis of relieff and rrelieff. Machine learning,, 53, 23–69Sohail, A., Chighoub, F., & ZhiWu, L. (2018). Spectral analysis of the stochastic time-fractional-KdV equation. Alexandria EngineeringStudholme, C., Hill, D., & Daji, H. (1999). An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition, 32, 71-86.Tiebing, L., Wenpo, Y., & Min, W. (2017). Multiscale permutation entropy analysis of electrocardiogram. Physica A: Statistical Mechanics and its Applications, 471, 492-498Tiwari, R., Gupta, V., & Kankar, P. (2015). Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier. Journal of Vibration and Control, 21, 461-467.Umut, O., Mahmut, H., & Mahmut, O. (2011). EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Systems with Applications, 38, 13475- 13481.Vencalek, O., & Pokotylo, O. (2018). 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Multi-fault classification Juan Camilo Mejía Hernández based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings. Neurocomputing, 99, 399- 410.Manifiesto (Manifestamos) en este documento la voluntad de autorizar a la Biblioteca Jorge Roa Martínez de la Universidad Tecnológica de Pereira la publicación en el Repositorio institucional (http://biblioteca.utp.edu.co), la versión electrónica de la OBRA titulada: ________________________________________________________________________________________________ ________________________________________________________________________________________________ ________________________________________________________________________________________________ La Universidad Tecnológica de Pereira, entidad académica sin ánimo de lucro, queda por lo tanto facultada para ejercer plenamente la autorización anteriormente descrita en su actividad ordinaria de investigación, docencia y publicación. 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La autorización otorgada se ajusta a lo que establece la Ley 23 de 1982. Con todo, en mi (nuestra) condición de autor (es) me (nos) reservo (reservamos) los derechos morales de la OBRA antes citada con arreglo al artículo 30 dehttps://repositorio.utp.edu.coRepositorio 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