Academic literature on the topic 'Feature stationarity'
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Journal articles on the topic "Feature stationarity"
Conni, Michele, and Hilda Deborah. "Texture Stationarity Evaluation with Local Wavelet Spectrum." London Imaging Meeting 2020, no. 1 (September 29, 2020): 24–27. http://dx.doi.org/10.2352/issn.2694-118x.2020.lim-20.
Full textNing, Jing, Mingkuan Fang, Wei Ran, Chunjun Chen, and Yanping Li. "Rapid Multi-Sensor Feature Fusion Based on Non-Stationary Kernel JADE for the Small-Amplitude Hunting Monitoring of High-Speed Trains." Sensors 20, no. 12 (June 18, 2020): 3457. http://dx.doi.org/10.3390/s20123457.
Full textNi, Sihan, Zhongyi Wang, Yuanyuan Wang, Minghao Wang, Shuqi Li, and Nan Wang. "Spatial and Attribute Neural Network Weighted Regression for the Accurate Estimation of Spatial Non-Stationarity." ISPRS International Journal of Geo-Information 11, no. 12 (December 13, 2022): 620. http://dx.doi.org/10.3390/ijgi11120620.
Full textGao, Yuqing, Khalid M. Mosalam, Yueshi Chen, Wei Wang, and Yiyi Chen. "Auto-Regressive Integrated Moving-Average Machine Learning for Damage Identification of Steel Frames." Applied Sciences 11, no. 13 (June 30, 2021): 6084. http://dx.doi.org/10.3390/app11136084.
Full textEntezami, Alireza, and Hashem Shariatmadar. "Damage localization under ambient excitations and non-stationary vibration signals by a new hybrid algorithm for feature extraction and multivariate distance correlation methods." Structural Health Monitoring 18, no. 2 (January 30, 2018): 347–75. http://dx.doi.org/10.1177/1475921718754372.
Full textFang, Yan, TaiSheng Zeng, and Tianrong Song. "Classification Method of EEG Based on Evolutionary Algorithm and Random Forest for Detection of Epilepsy." Journal of Medical Imaging and Health Informatics 10, no. 5 (May 1, 2020): 979–83. http://dx.doi.org/10.1166/jmihi.2020.3050.
Full textFRANK, T. D., and S. MONGKOLSAKULVONG. "ON STRONGLY NONLINEAR AUTOREGRESSIVE MODELS: IMPLICATIONS FOR THE THEORY OF TRANSIENT AND STATIONARY RESPONSES OF MANY-BODY SYSTEMS." Fluctuation and Noise Letters 12, no. 04 (December 2013): 1350022. http://dx.doi.org/10.1142/s0219477513500223.
Full textHidalgo, Javier, and Pedro C. L. Souza. "A TEST FOR WEAK STATIONARITY IN THE SPECTRAL DOMAIN." Econometric Theory 35, no. 03 (July 20, 2018): 547–600. http://dx.doi.org/10.1017/s0266466618000191.
Full textvan Doorn, Erik A., and Pauline Schrijner. "Geomatric ergodicity and quasi-stationarity in discrete-time birth-death processes." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 37, no. 2 (October 1995): 121–44. http://dx.doi.org/10.1017/s0334270000007621.
Full textCai, Jianhua. "Feature extraction of rolling bearing fault signal based on local mean decomposition and Teager energy operator." Industrial Lubrication and Tribology 69, no. 6 (November 13, 2017): 872–80. http://dx.doi.org/10.1108/ilt-12-2015-0200.
Full textDissertations / Theses on the topic "Feature stationarity"
Wood, Mark. "Discriminant analysis using wavelet derived features." Thesis, University of Aberdeen, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.252149.
Full textSchwalbe, Karsten, and Karl Heinz Hoffmann. "Performance Features of a Stationary Stochastic Novikov Engine." Universitätsbibliothek Chemnitz, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-232585.
Full textYaseen, Muhammad Usman. "Identification of cause of impairment in spiral drawings, using non-stationary feature extraction approach." Thesis, Högskolan Dalarna, Datateknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:du-6473.
Full textBruni, Matteo. "Incremental Learning of Stationary Representations." Doctoral thesis, 2021. http://hdl.handle.net/2158/1237986.
Full textVinson, Robert G. "Rotating machine diagnosis using smart feature selection under non-stationary operating conditions." Diss., 2015. http://hdl.handle.net/2263/43764.
Full textDissertation (MEng)--University of Pretoria, 2015.
Mechanical and Aeronautical Engineering
Unrestricted
Su, Shun-Chi, and 蘇順吉. "Studies on underwater acoustic stationary and transient signals spectrum features." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/20487262396994551309.
Full text中正理工學院
電機工程研究所
86
Underwater acoustic signals are non-linear, time-varying, and with low signal-to-noise ratio. These properties make the signal analysis difficulty and complex. For resolving targets through the underwater acoustic signals, effective methods are proposed in this thesis to process underwater acoustic signals, Base on these methods, an signal acoustic recognition system is also designed. Traditionally, the Fourier transform (FT) and Morlet wavelet transform (MWT) are the main tool for stationary and transient signals spectrum analysis, respectively. Here in, a modify power spectrum density (PSD) function is used to extract the critical features for stationary underwater acoustic signals, A multi-scaling MWT kernel is also proposed which can depict the underwater transient spectrum successfully. To illustrate the effectiveness of these two novel design methods, some experiments are taken to perform by using simulation and recorded real underwater acoustic signals. Experimented results show that the proposed methods can detect and analyze both stationary and transient underwater acoustic signals successfully. An underwater acoustic signals analysis is also implemented on Matlab base personal computer to detect, analyze, and recognize targets by stationary signal features. It is hoped that an automatic underwater targets recognition system can be realized by methods discussed in this thesis in the future.
Chang, Chia-Chi, and 張家齊. "The feature extraction and quantitative assessment of non-stationary medical signal based on Hilbert-Huang transform – Cardiovascular autoregulation for example." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/74009753049420916719.
Full text國立交通大學
資訊科學與工程研究所
102
In 2008, world health organization estimated that there are 17.3 million people died from cardiovascular diseases (CVDs) and CVDs is one of the ten leading causes of death in Taiwan. CVDs is preventable compared to cancers and can be detected by cardiovascular monitoring. The health care in cardiovascular circulation becomes important now a day. The portable healthcare device becomes mature owing to the developments of several techniques, including wireless data transfer, system on chip, and wearable sensor network. The requirement of health care device becomes huge owing to aging population. Recent non-invasive cardiovascular monitoring system focuses on the development of electrocardiogram, including the specific process chip and the evaluation index of cardiac function, but the research of cardiovascular auto-regulation is relatively rare. Traditional studies investigated that the auto-regulation could be assessed by heart rate variability (HRV). Most of the HRV studies adopted Fourier transform or wavelet transform as spectral analysis method. These methods have good properties to extract and present the characteristics of stationary signal based on their own mathematical fundamental basis, such as sinusoid function or wavelet function, but the characteristics of medical signal are usually non-stationary. Recently, N.E. Huang proposed a novel adaptive method, called Hilbert-Huang transform (HHT). HHT has good capability for non-stationary characterization without information loss and extracts the intrinsic features in multiple scales. The extraction method of HHT, called empirical mode decomposition (EMD), extracts the intrinsic features of signal based on signal's fluctuations, such as the envelop of signal, and is adaptive for different target signals. The aim of this research is to 1) extract the intrinsic features of blood pulse signal by HHT; 2) quantitatively assess the non-stationary features in multiple time scales; 3) examine the usefulness of the assessment in clinic. The results showed that the blood pulse signal could be decomposed into four different intrinsic features in four physiological time scales, including noise, pulse wave morphology, short-term trend, long-term trend. By analysis of arterial blood pressure (ABP), the reflection wave could be enhanced by EMD and the results were consistent with traditional studies. The pulse wave could be extracted from ABP by EMD, and instantaneous pulse rate (iPR) was estimated by normalized Hilbert transform. The results showed that the iPR presents the characteristics of respiration and cardiovascular auto-regulation. In short-term ABP trend study, the individual ABP regulation was extracted adaptively by EMD. This method helps for the exploration of individual optimal frequency band in auto-regulation assessment. This research also designed and implemented the prototype of cardiovascular auto-regulation monitoring system based on embedded system development and network programming. The progress of this research contains several parts. Currently, there are several non-linear approach for cardiovascular auto-regulation analysis, such as detrend fluctuation analysis and multiscale entropy. Though, the iPR can be used as the estimator of cardiovascular auto-regulation function, the relationship between iPR and CVDs needs further investigation. Besides, the ABP signal is hard to get in daily life, the replacement of ABP signal by blood pulse sensing needs further examination and validation.
Books on the topic "Feature stationarity"
Prohorov, Viktor. Semiconductor converters of electrical energy. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1019082.
Full textPrasad, Girijesh. Brain–machine interfaces. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0049.
Full textBook chapters on the topic "Feature stationarity"
Eitzinger, Christian, and Stefan Thumfart. "Optimizing Feature Calculation in Adaptive Machine Vision Systems." In Learning in Non-Stationary Environments, 349–74. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-8020-5_13.
Full textFtoutou, Ezzeddine, Mnaouar Chouchane, and Noureddine Besbès. "Feature Selection for Diesel Engine Fault Classification." In Condition Monitoring of Machinery in Non-Stationary Operations, 309–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_33.
Full textEntezami, Alireza. "Feature Extraction in Time Domain for Stationary Data." In Structural Health Monitoring by Time Series Analysis and Statistical Distance Measures, 17–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66259-2_2.
Full textThaler, Tilen, Primož Potočnik, Peter Mužič, Ivan Bric, Rudi Bric, and Edvard Govekar. "Chatter Recognition in Band Sawing Based on Feature Extraction and Discriminant Analysis." In Condition Monitoring of Machinery in Non-Stationary Operations, 607–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_63.
Full textBhanu, Bir, and Ju Han. "Human Recognition on Combining Kinematic and Stationary Features." In Lecture Notes in Computer Science, 600–608. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44887-x_71.
Full textSchaffernicht, Erik, Volker Stephan, and Horst-Michael Gross. "Adaptive Feature Transformation for Image Data from Non-stationary Processes." In Artificial Neural Networks – ICANN 2009, 735–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_74.
Full textEntezami, Alireza. "Feature Extraction in Time-Frequency Domain for Non-Stationary Data." In Structural Health Monitoring by Time Series Analysis and Statistical Distance Measures, 47–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66259-2_3.
Full textRustamova, D. F., and A. M. Mehdiyeva. "Features of Digital Processing of Non-stationary Processes in Measurement and Control." In Informatics and Cybernetics in Intelligent Systems, 592–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77448-6_58.
Full textCardona-Morales, O., D. Alvarez-Marin, and G. Castellanos-Dominguez. "Condition Monitoring Under Non-Stationary Operating Conditions using Time–Frequency Representation-Based Dynamic Features." In Lecture Notes in Mechanical Engineering, 441–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39348-8_38.
Full textBiloborodova, Tetiana, Inna Skarga-Bandurova, Illia Skarha-Bandurov, Yelyzaveta Yevsieieva, and Oleh Biloborodov. "ECG Classification Using Combination of Linear and Non-Linear Features with Neural Network." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220388.
Full textConference papers on the topic "Feature stationarity"
Poulos, Marios. "Definition text's syntactic feature using stationarity control." In 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA). IEEE, 2017. http://dx.doi.org/10.1109/iisa.2017.8316418.
Full textKawanabe, Motoaki. "Robust feature construction against non-stationarity for EEG brain-machine interface." In 2014 International Winter Workshop on Brain-Computer Interface (BCI). IEEE, 2014. http://dx.doi.org/10.1109/iww-bci.2014.6782557.
Full textYu, Shujian, Xiaoyang Wang, and José C. Príncipe. "Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/421.
Full textMarple, S. Lawrence, Phillip M. Corbell, and Muralidhar Rangaswamy. "New Non-Stationary Target Feature Detection Techniques." In 2006 Fortieth Asilomar Conference on Signals, Systems and Computers. IEEE, 2006. http://dx.doi.org/10.1109/acssc.2006.354808.
Full textTuske, Zoltan, Pavel Golik, Ralf Schluter, and Friedhelm R. Drepper. "Non-stationary feature extraction for automatic speech recognition." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947530.
Full textWang, Yonghui, and Suxia Cui. "Hyperspectral image feature classification using stationary wavelet transform." In 2014 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2014. http://dx.doi.org/10.1109/icwapr.2014.6961299.
Full textNi, Bingbing, Shuicheng Yan, and Ashraf Kassim. "Directed Markov Stationary Features for visual classification." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4959711.
Full textZhang, Yudong, Zhengchao Dong, Lenan Wu, Shuihua Wang, and Zhenyu Zhou. "Feature Extraction of Brain MRI by Stationary Wavelet Transform." In 2010 International Conference on Biomedical Engineering and Computer Science (ICBECS). IEEE, 2010. http://dx.doi.org/10.1109/icbecs.2010.5462491.
Full textTakyu, Osamu, Hiroyoshi Yano, Takeo Fujii, and Tomoaki Ohtsuki. "Double stage and combining detection for cyclo-stationary feature." In 2012 IEEE Radio and Wireless Symposium (RWS). IEEE, 2012. http://dx.doi.org/10.1109/rws.2012.6175367.
Full textOrtego, Diego, and Juan C. SanMiguel. "Multi-feature stationary foreground detection for crowded video-surveillance." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025486.
Full textReports on the topic "Feature stationarity"
ZOTOVA, V. A., E. G. SKACHKOVA, and T. D. FEOFANOVA. METHODOLOGICAL FEATURES OF APPLICATION OF SIMILARITY THEORY IN THE CALCULATION OF NON-STATIONARY ONE-DIMENSIONAL LINEAR THERMAL CONDUCTIVITY OF A ROD. Science and Innovation Center Publishing House, April 2022. http://dx.doi.org/10.12731/2227-930x-2022-12-1-2-43-53.
Full textSymonenko, Svitlana V., Nataliia V. Zaitseva, Viacheslav V. Osadchyi, Kateryna P. Osadcha, and Ekaterina O. Shmeltser. Virtual reality in foreign language training at higher educational institutions. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3759.
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