Littérature scientifique sur le sujet « Feature stationarity »
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Articles de revues sur le sujet "Feature stationarity"
Conni, Michele, et Hilda Deborah. « Texture Stationarity Evaluation with Local Wavelet Spectrum ». London Imaging Meeting 2020, no 1 (29 septembre 2020) : 24–27. http://dx.doi.org/10.2352/issn.2694-118x.2020.lim-20.
Texte intégralNing, Jing, Mingkuan Fang, Wei Ran, Chunjun Chen et 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 (18 juin 2020) : 3457. http://dx.doi.org/10.3390/s20123457.
Texte intégralNi, Sihan, Zhongyi Wang, Yuanyuan Wang, Minghao Wang, Shuqi Li et 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 (13 décembre 2022) : 620. http://dx.doi.org/10.3390/ijgi11120620.
Texte intégralGao, Yuqing, Khalid M. Mosalam, Yueshi Chen, Wei Wang et Yiyi Chen. « Auto-Regressive Integrated Moving-Average Machine Learning for Damage Identification of Steel Frames ». Applied Sciences 11, no 13 (30 juin 2021) : 6084. http://dx.doi.org/10.3390/app11136084.
Texte intégralEntezami, Alireza, et 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 (30 janvier 2018) : 347–75. http://dx.doi.org/10.1177/1475921718754372.
Texte intégralFang, Yan, TaiSheng Zeng et 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 (1 mai 2020) : 979–83. http://dx.doi.org/10.1166/jmihi.2020.3050.
Texte intégralFRANK, T. D., et 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 (décembre 2013) : 1350022. http://dx.doi.org/10.1142/s0219477513500223.
Texte intégralHidalgo, Javier, et Pedro C. L. Souza. « A TEST FOR WEAK STATIONARITY IN THE SPECTRAL DOMAIN ». Econometric Theory 35, no 03 (20 juillet 2018) : 547–600. http://dx.doi.org/10.1017/s0266466618000191.
Texte intégralvan Doorn, Erik A., et 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 (octobre 1995) : 121–44. http://dx.doi.org/10.1017/s0334270000007621.
Texte intégralCai, Jianhua. « Feature extraction of rolling bearing fault signal based on local mean decomposition and Teager energy operator ». Industrial Lubrication and Tribology 69, no 6 (13 novembre 2017) : 872–80. http://dx.doi.org/10.1108/ilt-12-2015-0200.
Texte intégralThèses sur le sujet "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.
Texte intégralSchwalbe, Karsten, et 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.
Texte intégralYaseen, 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.
Texte intégralBruni, Matteo. « Incremental Learning of Stationary Representations ». Doctoral thesis, 2021. http://hdl.handle.net/2158/1237986.
Texte intégralVinson, Robert G. « Rotating machine diagnosis using smart feature selection under non-stationary operating conditions ». Diss., 2015. http://hdl.handle.net/2263/43764.
Texte intégralDissertation (MEng)--University of Pretoria, 2015.
Mechanical and Aeronautical Engineering
Unrestricted
Su, Shun-Chi, et 蘇順吉. « Studies on underwater acoustic stationary and transient signals spectrum features ». Thesis, 1998. http://ndltd.ncl.edu.tw/handle/20487262396994551309.
Texte intégral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, et 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.
Texte intégral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.
Livres sur le sujet "Feature stationarity"
Prohorov, Viktor. Semiconductor converters of electrical energy. ru : INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1019082.
Texte intégralPrasad, Girijesh. Brain–machine interfaces. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0049.
Texte intégralChapitres de livres sur le sujet "Feature stationarity"
Eitzinger, Christian, et Stefan Thumfart. « Optimizing Feature Calculation in Adaptive Machine Vision Systems ». Dans 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.
Texte intégralFtoutou, Ezzeddine, Mnaouar Chouchane et Noureddine Besbès. « Feature Selection for Diesel Engine Fault Classification ». Dans 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.
Texte intégralEntezami, Alireza. « Feature Extraction in Time Domain for Stationary Data ». Dans 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.
Texte intégralThaler, Tilen, Primož Potočnik, Peter Mužič, Ivan Bric, Rudi Bric et Edvard Govekar. « Chatter Recognition in Band Sawing Based on Feature Extraction and Discriminant Analysis ». Dans 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.
Texte intégralBhanu, Bir, et Ju Han. « Human Recognition on Combining Kinematic and Stationary Features ». Dans Lecture Notes in Computer Science, 600–608. Berlin, Heidelberg : Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44887-x_71.
Texte intégralSchaffernicht, Erik, Volker Stephan et Horst-Michael Gross. « Adaptive Feature Transformation for Image Data from Non-stationary Processes ». Dans 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.
Texte intégralEntezami, Alireza. « Feature Extraction in Time-Frequency Domain for Non-Stationary Data ». Dans 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.
Texte intégralRustamova, D. F., et A. M. Mehdiyeva. « Features of Digital Processing of Non-stationary Processes in Measurement and Control ». Dans 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.
Texte intégralCardona-Morales, O., D. Alvarez-Marin et G. Castellanos-Dominguez. « Condition Monitoring Under Non-Stationary Operating Conditions using Time–Frequency Representation-Based Dynamic Features ». Dans 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.
Texte intégralBiloborodova, Tetiana, Inna Skarga-Bandurova, Illia Skarha-Bandurov, Yelyzaveta Yevsieieva et Oleh Biloborodov. « ECG Classification Using Combination of Linear and Non-Linear Features with Neural Network ». Dans Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220388.
Texte intégralActes de conférences sur le sujet "Feature stationarity"
Poulos, Marios. « Definition text's syntactic feature using stationarity control ». Dans 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA). IEEE, 2017. http://dx.doi.org/10.1109/iisa.2017.8316418.
Texte intégralKawanabe, Motoaki. « Robust feature construction against non-stationarity for EEG brain-machine interface ». Dans 2014 International Winter Workshop on Brain-Computer Interface (BCI). IEEE, 2014. http://dx.doi.org/10.1109/iww-bci.2014.6782557.
Texte intégralYu, Shujian, Xiaoyang Wang et José C. Príncipe. « Request-and-Reverify : Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels ». Dans 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.
Texte intégralMarple, S. Lawrence, Phillip M. Corbell et Muralidhar Rangaswamy. « New Non-Stationary Target Feature Detection Techniques ». Dans 2006 Fortieth Asilomar Conference on Signals, Systems and Computers. IEEE, 2006. http://dx.doi.org/10.1109/acssc.2006.354808.
Texte intégralTuske, Zoltan, Pavel Golik, Ralf Schluter et Friedhelm R. Drepper. « Non-stationary feature extraction for automatic speech recognition ». Dans ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947530.
Texte intégralWang, Yonghui, et Suxia Cui. « Hyperspectral image feature classification using stationary wavelet transform ». Dans 2014 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2014. http://dx.doi.org/10.1109/icwapr.2014.6961299.
Texte intégralNi, Bingbing, Shuicheng Yan et Ashraf Kassim. « Directed Markov Stationary Features for visual classification ». Dans ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4959711.
Texte intégralZhang, Yudong, Zhengchao Dong, Lenan Wu, Shuihua Wang et Zhenyu Zhou. « Feature Extraction of Brain MRI by Stationary Wavelet Transform ». Dans 2010 International Conference on Biomedical Engineering and Computer Science (ICBECS). IEEE, 2010. http://dx.doi.org/10.1109/icbecs.2010.5462491.
Texte intégralTakyu, Osamu, Hiroyoshi Yano, Takeo Fujii et Tomoaki Ohtsuki. « Double stage and combining detection for cyclo-stationary feature ». Dans 2012 IEEE Radio and Wireless Symposium (RWS). IEEE, 2012. http://dx.doi.org/10.1109/rws.2012.6175367.
Texte intégralOrtego, Diego, et Juan C. SanMiguel. « Multi-feature stationary foreground detection for crowded video-surveillance ». Dans 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025486.
Texte intégralRapports d'organisations sur le sujet "Feature stationarity"
ZOTOVA, V. A., E. G. SKACHKOVA et 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, avril 2022. http://dx.doi.org/10.12731/2227-930x-2022-12-1-2-43-53.
Texte intégralSymonenko, Svitlana V., Nataliia V. Zaitseva, Viacheslav V. Osadchyi, Kateryna P. Osadcha et Ekaterina O. Shmeltser. Virtual reality in foreign language training at higher educational institutions. [б. в.], février 2020. http://dx.doi.org/10.31812/123456789/3759.
Texte intégral