Bibliografías temáticas / Feature stationarity

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Literatura académica sobre el tema "Feature stationarity"

Autor: Grafiati

Publicado: 10 de marzo de 2023

Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Feature stationarity"

Ma, Xiang, Xuemei Li, Lexin Fang, Tianlong Zhao, and Caiming Zhang. "U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14255–62. http://dx.doi.org/10.1609/aaai.v38i13.29337.

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Time series forecasting is a crucial task in various domains. Caused by factors such as trends, seasonality, or irregular fluctuations, time series often exhibits non-stationary. It obstructs stable feature propagation through deep layers, disrupts feature distributions, and complicates learning data distribution changes. As a result, many existing models struggle to capture the underlying patterns, leading to degraded forecasting performance. In this study, we tackle the challenge of non-stationarity in time series forecasting with our proposed framework called U-Mixer. By combining Unet and

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Conni, Michele, and Hilda Deborah. "Texture Stationarity Evaluation with Local Wavelet Spectrum." London Imaging Meeting 2020, no. 1 (2020): 24–27. http://dx.doi.org/10.2352/issn.2694-118x.2020.lim-20.

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In texture analysis, stationarity is a fundamental property. There are various ways to evaluate if a texture image is stationary or not. One of the most recent and effective of these is a standard test based on non-decimated stationary wavelet transform. This method permits to evaluate how stationary is an image depending on the scale considered. We propose to use this feature to characterize an image and we discuss the implication of such approach.

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Chen, Liyuan, Qingkang Cui, and Yixin Cheng. "Fault feature extraction of variable speed bearings based on order analysis." Journal of Computing and Electronic Information Management 11, no. 3 (2023): 39–41. http://dx.doi.org/10.54097/jceim.v11i3.09.

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In the case of variable speed, the vibration signal of the measured bearing will contain the bearing itself information and the speed change information at the same time, and the signal is very non-periodic and non-stationary. In this paper, the order analysis method is adopted, the bearing vibration signal and the speed pulse signal are analyzed, and the signal is reconstructed by angle resampling, which reduces the non-stationarity of the signal, enhances the usability of the subsequent time-frequency analysis method, and can better extract the bearing fault characteristics in the variable s

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Ning, 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 (2020): 3457. http://dx.doi.org/10.3390/s20123457.

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Joint Approximate Diagonalization of Eigen-matrices (JADE) cannot deal with non-stationary data. Therefore, in this paper, a method called Non-stationary Kernel JADE (NKJADE) is proposed, which can extract non-stationary features and fuse multi-sensor features precisely and rapidly. In this method, the non-stationarity of the data is considered and the data from multi-sensor are used to fuse the features efficiently. The method is compared with EEMD-SVD-LTSA and EEMD-JADE using the bearing fault data of CWRU, and the validity of the method is verified. Considering that the vibration signals of

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Fu, Dong, Qifu Lu, Longhua Tang, et al. "Complementary Ensemble Empirical Mode Decomposition and Maximum Correlated Kurtosis Deconvolution for Wind Turbine Bearing Fault Feature Extraction." Journal of Physics: Conference Series 2659, no. 1 (2023): 012010. http://dx.doi.org/10.1088/1742-6596/2659/1/012010.

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Abstract Wind turbine bearing fault signals exhibit characteristics such as nonlinearity, non-stationarity, and susceptibility to external noise interference, making it challenging to extract fault features and identify them accurately. In light of these issues, this paper proposes a fault signal feature extraction method that combines complementary ensemble empirical mode decomposition (CEEMD) and maximum correlated kurtosis deconvolution (MCKD). CEEMD is utilized to decompose the signals, reducing mode mixing and eliminating residual auxiliary noise in the decomposition process, thereby obta

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Dong, Yunlong, Jifeng Wei, Hao Ding, Ningbo Liu, Zheng Cao, and Hengli Yu. "A Dynamic False Alarm Rate Control Method for Small Target Detection in Non-Stationary Sea Clutter." Journal of Marine Science and Engineering 12, no. 10 (2024): 1770. http://dx.doi.org/10.3390/jmse12101770.

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Sea surface non-stationarity poses significant challenges to sea-surface small target detection, particularly in maintaining a stable false alarm rate (FAR). In dynamic maritime scenarios with non-stationary characteristics, the non-stationarity of sea clutter can easily cause significant changes in the clutter feature space, leading to a notable deviation between the preset FAR and the measured FAR. By analyzing the temporal and spatial variations in sea clutter, we model the relationship between the preset FAR and the measured FAR as a two-parameter linear function. To address the impact of

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Ni, 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 (2022): 620. http://dx.doi.org/10.3390/ijgi11120620.

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Geographically neural network weighted regression is an improved model of GWR combined with a neural network. It has a stronger ability to fit nonlinear functions, and complex geographical processes can be modeled more fully. GNNWR uses the distance metric of Euclidean space to express the relationship between sample points. However, except for spatial location features, geographic entities also have many diverse attribute features. Incorporating attribute features into the modeling process can make the model more suitable for the real geographical process. Therefore, we proposed a spatial-att

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Gao, 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 (2021): 6084. http://dx.doi.org/10.3390/app11136084.

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Auto-regressive (AR) time series (TS) models are useful for structural damage detection in vibration-based structural health monitoring (SHM). However, certain limitations, e.g., non-stationarity and subjective feature selection, have reduced its wide-spread use. With increasing trends in machine learning (ML) technologies, automated structural damage recognition is becoming popular and attracting many researchers. In this paper, we combined TS modeling and ML classification to automatically extract damage features and overcome the limitation of non-stationarity. We propose a two-stage framewo

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Entezami, 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 (2018): 347–75. http://dx.doi.org/10.1177/1475921718754372.

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Ambient excitations applied to structures may lead to non-stationary vibration responses. In such circumstances, it may be difficult or improper to extract meaningful and significant damage features through methods that mainly rely on the stationarity of data. This article proposes a new hybrid algorithm for feature extraction as a combination of a new adaptive signal decomposition method called improved complete ensemble empirical mode decomposition with adaptive noise and autoregressive moving average model. The major contribution of this algorithm is to address the important issue of featur

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FRANK, 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 (2013): 1350022. http://dx.doi.org/10.1142/s0219477513500223.

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Two widely used concepts in physics and the life sciences are combined: mean field theory and time-discrete time series modeling. They are merged within the framework of strongly nonlinear stochastic processes, which are processes whose stochastic evolution equations depend self-consistently on process expectation values. Explicitly, a generalized autoregressive (AR) model is presented for an AR process that depends on its process mean value. Criteria for stationarity are derived. The transient dynamics in terms of the relaxation of the first moment and the stationary response to fluctuations

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Tesis sobre el tema "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.

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This thesis examines the ability of the wavelet transform to form features which may be used successfully in a discriminant analysis. We apply our methods to two different data sets and consider the problem of selecting the 'best' features for discrimination. In the first data set, our interest is in automatically recognising the variety of a carrot from an image. After necessary image preprocessing we examine the usefulness of shape descriptors and texture features for discrimination. We show that it is better to use the different 'types' of features separately, and that the wavelet coefficie

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Schwalbe, 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.

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In this article a Novikov engine with fluctuating hot heat bath temperature is presented. Based on this model, the performance measure maximum expected power as well as the corresponding efficiency and entropy production rate is investigated for four different stationary distributions: continuous uniform, normal, triangle, quadratic, and Pareto. It is found that the performance measures increase monotonously with increasing expectation value and increasing standard deviation of the distributions. Additionally, we show that the distribution has only little influence on the performance measures

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Yaseen, 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.

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Parkinson’s disease is a clinical syndrome manifesting with slowness and instability. As it is a progressive disease with varying symptoms, repeated assessments are necessary to determine the outcome of treatment changes in the patient. In the recent past, a computer-based method was developed to rate impairment in spiral drawings. The downside of this method is that it cannot separate the bradykinetic and dyskinetic spiral drawings. This work intends to construct the computer method which can overcome this weakness by using the Hilbert-Huang Transform (HHT) of tangential velocity. The work is

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RANDAZZO, VINCENZO. "Novel neural approaches to data topology analysis and telemedicine." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850610.

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Bruni, Matteo. "Incremental Learning of Stationary Representations." Doctoral thesis, 2021. http://hdl.handle.net/2158/1237986.

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Humans and animals, during their life, continuously acquire new knowledge over time while making new experiences. They learn new concepts without forgetting what already learned, they typically use a few training examples (i.e. a child could recognize a giraffe after seeing a single picture) and they are able to discern what is known from what is unknown (i.e. unknown faces). In contrast, current supervised learning systems, work under the assumption that all data is known and available during learning, training is performed offline and a test dataset is typically required. What is missin

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Vinson, Robert G. "Rotating machine diagnosis using smart feature selection under non-stationary operating conditions." Diss., 2015. http://hdl.handle.net/2263/43764.

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This dissertation investigates the effectiveness of a two stage fault identification methodology for rotating machines operating under non-stationary conditions with the use of a single vibration transducer. The proposed methodology transforms the machine vibration signal into a discrepancy signal by means of smart feature selection and statistical models. The discrepancy signal indicates the angular position and relative magnitude of irregular signal patterns which are assumed to be indicative of gear faults. The discrepancy signal is also independent of healthy vibration components, such as

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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.

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碩士 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. He

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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.

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博士 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 bec

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Sharma, Neeraj Kumar. "Information-rich Sampling of Time-varying Signals." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/4126.

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Confrontation with signal non-stationarity is a rule rather than an exception in the analysis of natural signals, such as speech, animal vocalization, music, bio-medical, atmospheric, ans seismic signals. Interestingly, our auditory system analyzes signal non-stationarity to trigger our perception. It does this with a performance which is unparalleled when compared to any man-made sound analyzer. Non-stationary signal analysis is a fairly challenging problem in the expanse of signal processing. Conventional approaches to analyze non-stationary signals are based on short-time quasi- stationary

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Libros sobre el tema "Feature stationarity"

Prohorov, Viktor. Semiconductor converters of electrical energy. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1019082.

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The textbook considers the need, principles and methods of mutual conversion of parameters of electric energy at DC and AC for stationary and Autonomous objects. Features of operation of power electronics elements in specific conditions of their continuous high-frequency switching are described. Low-current control systems that provide the necessary logic for the operation of Executive power devices of converters are considered. A large number of specific practical electrical diagrams of electric energy converters are given. It is intended for students studying in the direction of 13.03.

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Prasad, Girijesh. Brain–machine interfaces. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0049.

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A brain–machine interface (BMI) is a biohybrid system intended as an alternative communication channel for people suffering from severe motor impairments. A BMI can involve either invasively implanted electrodes or non-invasive imaging systems. The focus in this chapter is on non-invasive approaches; EEG-based BMI is the most widely investigated. Event-related de-synchronization/ synchronization (ERD/ERS) of sensorimotor rhythms (SMRs), P300, and steady-state visual evoked potential (SSVEP) are the three main cortical activation patterns used for designing an EEG-based BMI. A BMI involves mult

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Capítulos de libros sobre el tema "Feature stationarity"

Eitzinger, Christian, and Stefan Thumfart. "Optimizing Feature Calculation in Adaptive Machine Vision Systems." In Learning in Non-Stationary Environments. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-8020-5_13.

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Ftoutou, Ezzeddine, Mnaouar Chouchane, and Noureddine Besbès. "Feature Selection for Diesel Engine Fault Classification." In Condition Monitoring of Machinery in Non-Stationary Operations. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_33.

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Entezami, Alireza. "Feature Extraction in Time Domain for Stationary Data." In Structural Health Monitoring by Time Series Analysis and Statistical Distance Measures. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66259-2_2.

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Thaler, 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. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_63.

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Bhanu, Bir, and Ju Han. "Human Recognition on Combining Kinematic and Stationary Features." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44887-x_71.

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Schaffernicht, Erik, Volker Stephan, and Horst-Michael Gross. "Adaptive Feature Transformation for Image Data from Non-stationary Processes." In Artificial Neural Networks – ICANN 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_74.

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Entezami, Alireza. "Feature Extraction in Time-Frequency Domain for Non-Stationary Data." In Structural Health Monitoring by Time Series Analysis and Statistical Distance Measures. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66259-2_3.

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Rustamova, 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. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77448-6_58.

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Chang, Chia-Chi Joseph. "Non-stationary Intrinsic Feature Assessment of Health/Medical Data Representation – Blood Pulse Signal for Example." In Current and Future Trends in Health and Medical Informatics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42112-9_12.

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Dadzie, Benjamin Mensah, and Piotr Porwik. "Feature-Based Drift Detection in Non-stationary Data Streams Using Multiple Classifiers: A Comprehensive Analysis." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-6005-6_20.

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Actas de conferencias sobre el tema "Feature stationarity"

Raj, Vandana Akshath, Subramanya G. Nayak, and Ananthakrishna Thalengala. "Feature-based Stationary Wavelet Transform for Removal of EEG Ocular Artifacts." In 2024 4th International Conference on Intelligent Technologies (CONIT). IEEE, 2024. http://dx.doi.org/10.1109/conit61985.2024.10627608.

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Wakayama, Takuya, Taiki Inoue, Jun Ogata, Makoto Iida, and Tetsuji Ogawa. "Normal with Occasional Anomalies: Feature Extraction for Detecting Non-Stationary Abnormal Events in Wind Turbines." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715327.

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Bhosekar, Shailesh, Prabhishek Singh, and Deepak Garg. "MIF-SWT-AD: Medical Image Fusion Using Stationary Wavelet Transform, Adaptive Feature Selection and Anisotropic Diffusion." In 2025 3rd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT). IEEE, 2025. https://doi.org/10.1109/dicct64131.2025.10986424.

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Bhosekar, Shailesh, Prabhishek Singh, and Deepak Garg. "A Feature Similarity and Directive Contrast Based Multi-Modal Medical Image Fusion Technique Using Stationary Wavelet Transform." In 2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2024. https://doi.org/10.1109/pdgc64653.2024.10983951.

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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.

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Kawanabe, 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.

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Yu, 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}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/421.

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One important assumption underlying common classification models is the stationarity of the data. However, in real-world streaming applications, the data concept indicated by the joint distribution of feature and label is not stationary but drifting over time. Concept drift detection aims to detect such drifts and adapt the model so as to mitigate any deterioration in the model's predictive performance. Unfortunately, most existing concept drift detection methods rely on a strong and over-optimistic condition that the true labels are available immediately for all already classified instances.

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Marple, 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.

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Tuske, 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.

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Wang, 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.

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Informes sobre el tema "Feature stationarity"

Vereecken, Evy, Martin Prignon, Antoine Tilmans, and Timo De Mets. HAMSTER Test Facility – Features and future Potential of a unique bi-climatic Chamber. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541620389.

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The HAMSTER project (2016-2022) aimed at designing, building, and validating a bi-climatic chamber. The test facility developed within the project, called the HAMSTER test facility, is a recent versatile bi-climatic chamber that is made to study the dynamic heat, air and moisture performance of building components of realistic size. The hot chamber is furthermore thoughtfully designed to conduct accurate stationary thermal transmittance tests according to the standards. Realistic climatic conditions, like rain, sun, and pressure differences, can be reproduced in the cold chamber so that many d

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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, 2022. http://dx.doi.org/10.12731/2227-930x-2022-12-1-2-43-53.

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The article describes the methodological features of the analytical solution of the problem of non-stationary one-dimensional linear thermal conductivity of the rod. The authors propose to obtain a solution to such problems by the method of finite differences using the Fourier similarity criterion. This approach is especially attractive because the similarity theory in the vast majority of cases makes it possible to do without expensive experiments and obtain simple solutions for a wide range of problems.

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Crump, Richard K., Stefano Eusepi, and Emanuel Moench. Is There Hope for the Expectations Hypothesis? Federal Reserve Bank of New York, 2024. http://dx.doi.org/10.59576/sr.1098.

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Most macroeconomic models impose a tight link between expected future short rates and the term structure of interest rates via the expectations hypothesis (EH). While the EH has been systematically rejected in the data, existing work evaluating the EH generally assumes either full-information rational expectations or stationarity of beliefs, or both. As such, these analyses are ill-equipped to refute the EH when these assumptions fail to hold, fueling hopes for a “resurrection” of the EH. We introduce a model of expectations formation which features time-varying means and accommodates deviatio

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Symonenko, 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.], 2020. http://dx.doi.org/10.31812/123456789/3759.

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The paper deals with the urgent problem of application of virtual reality in foreign language training. Statistical data confirms that the number of smartphone users, Internet users, including wireless Internet users, has been increasing for recent years in Ukraine and tends to grow. The coherence of quick mobile Internet access and presence of supplementary equipment enables to get trained or to self-dependently advance due to usage of virtual reality possibilities for education in the stationary classrooms, at home and in motion. Several important features of virtual reality, its advantages

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