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Journal articles on the topic 'Feature stationarity'

Author: Grafiati

Published: 10 March 2023

Last updated: 31 July 2025

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1

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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Fang, 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 (2020): 979–83. http://dx.doi.org/10.1166/jmihi.2020.3050.

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Epilepsy is a difficult problem that has puzzled the medical profession for a long time. The complexity, randomness, non-stationarity and nonlinearity of EEG signal of epilepsy bring great challenge to the detection of epilepsy. The study of epilepsy is an important subject of neutral system diseases. For automatic epilepsy detection system, the accuracy of identifying epilepsy and predicting epilepsy is of great significance to the treatment of doctors and the recovery of patients. This paper proposes the mixed feature extraction to extract the feature by mixture of time-domain method and non

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Hidalgo, Javier, and Pedro C. L. Souza. "A TEST FOR WEAK STATIONARITY IN THE SPECTRAL DOMAIN." Econometric Theory 35, no. 03 (2018): 547–600. http://dx.doi.org/10.1017/s0266466618000191.

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We examine a test for weak stationarity against alternatives that covers both local-stationarity and break point models. A key feature of the test is that its asymptotic distribution is a functional of the standard Brownian bridge sheet in [0,1]2, so that it does not depend on any unknown quantity. The test has nontrivial power against local alternatives converging to the null hypothesis at a T−1/2 rate, where T is the sample size. We also examine an easy-to-implement bootstrap analogue and present the finite sample performance in a Monte Carlo experiment. Finally, we implement the methodology

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Li, Diyang, and Bin Gu. "When Online Learning Meets ODE: Learning without Forgetting on Variable Feature Space." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8545–53. http://dx.doi.org/10.1609/aaai.v37i7.26029.

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Machine learning systems that built upon varying feature space are ubiquitous across the world. When the set of practical or virtual features changes, the online learning approach can adjust the learned model accordingly rather than re-training from scratch and has been an attractive area of research. Despite its importance, most studies for algorithms that are capable of handling online features have no ensurance of stationarity point convergence, while the accuracy guaranteed methods are still limited to some simple cases such as L_1 or L_2 norms with square loss. To address this challenging

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Chi, Haodong, and Huiyuan Chen. "Research on Rolling Bearing Fault Diagnosis Method Based on MPE and Multi-Strategy Improved Sparrow Search Algorithm Under Local Mean Decomposition." Machines 13, no. 4 (2025): 336. https://doi.org/10.3390/machines13040336.

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To address the issues of non-stationarity, noise interference, and insufficient discriminative power of traditional fault feature extraction methods in rolling bearing vibration signals, this paper proposes a fault diagnosis method based on multi-scale permutation entropy (MPE) and a multi-strategy improved sparrow search algorithm (MSSA) under local mean decomposition (LMD). First, LMD is employed to adaptively decompose the original signal. Effective product functions (PFs) are then selected using the Pearson correlation coefficient, enabling signal reconstruction that suppresses noise inter

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N, Raji Gopinathan, and Elizabeth Sherly. "A NON-FEEDBACK EEG-BASED BRAIN COMPUTER INTERFACE (BCI) FOR COGNITIVE STATE CLASSIFICATION IN TIME DOMAIN." ICTACT Journal on Soft Computing 15, no. 4 (2025): 3729–36. https://doi.org/10.21917/ijsc.2025.0517.

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This paper introduces an innovative EEG-based Brain-Computer Interface (BCI), aiming to discern two cognitive states experienced by students during learning sessions. Focusing on “Relaxation” and “Engagement in learning tasks”, the study identifies attentive students and students exhibiting disengagement. Utilizing single EEG channel and signals from the fronto-polar region, it aims to develop a real-time engagement detection system compatible with portable devices. Employing a basic machine learning pipeline, the research focuses on time-domain feature extraction and capturing heterogeneous h

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Pang, Yuxin, and Dehui Wang. "A New Random Coefficient Autoregressive Model Driven by an Unobservable State Variable." Mathematics 12, no. 24 (2024): 3890. https://doi.org/10.3390/math12243890.

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A novel random coefficient autoregressive model is proposed, and a feature of the model is the non-stationarity of the state equation. The autoregressive coefficient is an unknown function with an unobservable state variable, which can be estimated by the local linear regression method. The iterative algorithm is constructed to estimate the parameters based on the ordinary least squares method. The ordinary least squares residuals are used to estimate the variances of the errors. The Kalman-smoothed estimation method is used to estimate the unobservable state variable because of its ability to

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Cai, Jianhua. "Feature extraction of rolling bearing fault signal based on local mean decomposition and Teager energy operator." Industrial Lubrication and Tribology 69, no. 6 (2017): 872–80. http://dx.doi.org/10.1108/ilt-12-2015-0200.

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Purpose This paper aims to explore a new way to extract the fault feature of a rolling bearing signal on the basis of a combinatorial method. Design/methodology/approach By combining local mean decomposition (LMD) with Teager energy operator, a new feature-extraction method of a rolling bearing fault signal was proposed, called the LMD–Teager transform method. The principles and steps of method are presented, and the physical meaning of the time–frequency power spectrum and marginal spectrum is discussed. On the basis of comparison with the fast Fourier transform method, a simulated non-statio

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Zhang, Zhengzhu, Haining Chai, Liyan Wu, Ning Zhang, and Fenghe Wu. "Automobile-Demand Forecasting Based on Trend Extrapolation and Causality Analysis." Electronics 13, no. 16 (2024): 3294. http://dx.doi.org/10.3390/electronics13163294.

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Accurate automobile-demand forecasting can provide effective guidance for automobile-manufacturing enterprises in terms of production planning and supply planning. However, automobile sales volume is affected by historical sales volume and other external factors, and it shows strong non-stationarity, nonlinearity, autocorrelation and other complex characteristics. It is difficult to accurately forecast sales volume using traditional models. To solve this problem, a forecasting model combining trend extrapolation and causality analysis is proposed and derived from the historical predictors of s

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Li, Zhi Nong, Fen Zhang, Xu Ping He, and Yao Xian Xiao. "Application of the Blind Source Separation Based on Time-Frequency Analysis in Mechanical Fault Diagnosis." Advanced Materials Research 945-949 (June 2014): 1054–62. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1054.

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Blind source separation provides a new method for the separation of mechanical sources under high level background noise, as well as the diagnosis of the compound fault. At present, the blind source separation has been successfully applied to the mecanical fault diagnosis. But the traditional mechanical source separation methods are restricted to non-gauss, stationary and mutually independent source signals. However, the mechanical fault signals do not suffice to these conditions, and generally exhibit non-stationarity and non-independence. For the non-stationary signal, its spectral feature i

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van 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 (1995): 121–44. http://dx.doi.org/10.1017/s0334270000007621.

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AbstractWe study two aspects of discrete-time birth-death processes, the common feature of which is the central role played by the decay parameter of the process. First, conditions for geometric ergodicity and bounds for the decay parameter are obtained. Then the existence and structure of quasi-stationary distributions are discussed. The analyses are based on the spectral representation for the n-step transition probabilities of a birth-death process developed by Karlin and McGregor.

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Smelik, Viktor, Ayrat Valiev, Aleksandr Dobrinov, and Aleksandr Perekopskiy. "TO THE METHOD OF FORMING A DIGITAL TWIN OF THE TECHNOLOGICAL PROCESS OF DRYING ORGANIC TIMOTHY SEEDS." Vestnik of Kazan State Agrarian University 19, no. 3 (2024): 75–83. http://dx.doi.org/10.12737/2073-0462-2024-75-83.

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An important specific feature of the functioning of machines and equipment in the technologies of post-harvest processing of grain and seeds is the probabilistic variability of external conditions that negatively affect the efficiency of their work. The drying process is also characterized by non-stationarity in its probabilistic and statistical characteristics. The methods of forming digital twins of stationary processes are quite well mastered, but the construction of digital twins of non-stationary processes is a much more complex task, the solution of which requires taking into account the

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Zhang, Zhenyu, and Ziyu Pan. "Improved Pre-Processing Process of Climate Series Based on Chebyshev Filter." Journal of Physics: Conference Series 2289, no. 1 (2022): 012014. http://dx.doi.org/10.1088/1742-6596/2289/1/012014.

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Abstract In the process of data mining for time series of environmental factors of dendrobium origin, it is difficult to separate the available features because the feature components are too rich and the de-noising method is not feasible, in this paper, we try to improve the pre-processing process of sequence. In this method, we first de centralize the sequence and check the unit root to evaluate the stationarity of the sequence, and then design a band-pass filter to extract the feature components from the sequence in the range of monthly frequency to annual frequency, then, the features of E

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Ni, Junjie, Gangjin Huang, Jing Yang, Nan Wang, and Junheng Fu. "Early-Fault Feature Extraction for Rolling Bearings Based on Parameter-Optimized Variation Mode Decomposition." Machines 13, no. 3 (2025): 210. https://doi.org/10.3390/machines13030210.

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Bearing-vibration signals, characterized by strong non-stationarity, typically consist of multiple components. The periodic pulses related to bearing faults are frequently obscured by surrounding noise, and early bearing-fault vibrations are feeble, which complicates the extraction of inherent fault characteristics. The aim of this research is to develop an effective method for extracting early-fault characteristic frequencies in rolling bearings. VMD, short for variational mode decomposition, is an innovative technique rooted in the classical Wiener filter for analyzing signals that include m

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Obaidan, Hanan Bin, Muhammad Hussain, and Reham AlMajed. "EEG_DMNet: A Deep Multi-Scale Convolutional Neural Network for Electroencephalography-Based Driver Drowsiness Detection." Electronics 13, no. 11 (2024): 2084. http://dx.doi.org/10.3390/electronics13112084.

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Drowsy driving is one of the major causes of traffic accidents, injuries, and deaths on roads worldwide. One of the best physiological signals that are useful in detecting a driver’s drowsiness is electroencephalography (EEG), a kind of brain signal that directly measures neurophysiological activities in the brain and is widely utilized for brain–computer interfaces (BCIs). However, designing a drowsiness detection method using EEG signals is still challenging because of their non-stationary nature. Deep learning, specifically convolutional neural networks (CNNs), has recently shown promising

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Liu, Wei Dong, and Hu Sheng Wu. "Study on Mechanical Fault Diagnosis Based on IMF Complexity Feature and Support Vector Machine." Applied Mechanics and Materials 246-247 (December 2012): 37–42. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.37.

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According to the non-stationarity characteristics of the vibration signals from reciprocating machinery,a fault diagnosis method based on empirical mode decomposition,Lempel-Ziv complexity and support vector machine(SVM) is proposed.Firstly,the vibration signals were decomposed into a finite number of intrinsic mode functions(IMF), then choosed some IMF components with the criteria of mutual correlation coefficient between IMF components and denoised signal.Thirdly the complexity feature of each IMF component was calculated as faulty eigenvector and served as input of SVM classifier so that th

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Wang, Tao. "A combined model for short-term wind speed forecasting based on empirical mode decomposition, feature selection, support vector regression and cross-validated lasso." PeerJ Computer Science 7 (September 24, 2021): e732. http://dx.doi.org/10.7717/peerj-cs.732.

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Background The planning and control of wind power production rely heavily on short-term wind speed forecasting. Due to the non-linearity and non-stationarity of wind, it is difficult to carry out accurate modeling and prediction through traditional wind speed forecasting models. Methods In the paper, we combine empirical mode decomposition (EMD), feature selection (FS), support vector regression (SVR) and cross-validated lasso (LassoCV) to develop a new wind speed forecasting model, aiming to improve the prediction performance of wind speed. EMD is used to extract the intrinsic mode functions

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Entezami, Alireza, Hashem Shariatmadar, and Abbas Karamodin. "Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods." Structural Health Monitoring 18, no. 5-6 (2018): 1416–43. http://dx.doi.org/10.1177/1475921718800306.

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Feature extraction by time-series analysis and decision making through distance-based methods are powerful and efficient statistical pattern recognition techniques for data-driven structural health monitoring. The motivation of this article is to propose an innovative residual-based feature extraction approach based on AutoRegressive modeling and a novel statistical distance method named as Partition-based Kullback–Leibler Divergence for damage detection and localization by using randomly high-dimensional damage-sensitive features under environmental and operational variability. The key novel

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Ai, Yandi, Dong Fang, Zhiping Tian, and Kaiyang Yan. "RUL prediction method based on cross-view hybrid network model." ITM Web of Conferences 77 (2025): 01041. https://doi.org/10.1051/itmconf/20257701041.

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Remaining Useful Life (RUL) prediction has become a core technology in the field of prognostics and health management (PHM). However, due to the non-stationarity, weak signal characteristics and concurrent multiple faults of original signals, the estimation of RUL in a single view tends to ignore the structural relationship of samples in different spaces. To this end, this paper designs a RUL prediction framework based on a cross-view hybrid network model (CVHNet). Firstly, a dual-channel feature extraction hybrid network (DCF-HybridNet) is constructed. The original features are decomposed int

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Maswanganyi, Clifford, Chungling Tu, Pius Owolawi, and Shengzhi Du. "Factors influencing low intension detection rate in a non-invasive EEG-based brain computer interface system." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 167. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp167-175.

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Motor imagery (MI) responses extracted from the brain in the form of EEG signals have been widely utilized for intention detection in brain computer interface (BCI) systems. However, due to the non-linearity and the non-stationarity of EEG signals, BCI systems suffer from low MI prediction rate with both known and unknown influncing factors. This paper investigates the impact of visual stimulus, feature dimensions and artifacts on MI task detection rate, towards improving MI prediction rate. Three EEG datasets were utilized to facilitate the investigation. Three filters (band-pass, notch and c

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Li, Heng, Qing Zhang, Xianrong Qin, and Sun Yuantao. "Raw vibration signal pattern recognition with automatic hyper-parameter-optimized convolutional neural network for bearing fault diagnosis." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 234, no. 1 (2019): 343–60. http://dx.doi.org/10.1177/0954406219875756.

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Bearing fault diagnosis is of great significance for evaluating the reliability of machines because bearings are the critical components in rotating machinery and are prone to failure. Because of non-stationarity and the low signal-noise rate of raw vibration signals, traditional fault diagnosis methods often construct representative fault features via the technologies of feature engineering. These methods rely heavily on expertise and are inadequate in actual applications. Recently, methods based on convolutional neural networks have been studied extensively to relieve the demands of hand-cra

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Huang, Xiangyu, Yan Cheng, Jing Jin, and Aiqing Kou. "Research on Dynamic Subsidy Based on Deep Reinforcement Learning for Non-Stationary Stochastic Demand in Ride-Hailing." Sustainability 16, no. 15 (2024): 6289. http://dx.doi.org/10.3390/su16156289.

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The ride-hailing market often experiences significant fluctuations in traffic demand, resulting in supply-demand imbalances. In this regard, the dynamic subsidy strategy is frequently employed by ride-hailing platforms to incentivize drivers to relocate to zones with high demand. However, determining the appropriate amount of subsidy at the appropriate time remains challenging. First, traffic demand exhibits high non-stationarity, characterized by multi-context patterns with time-varying statistical features. Second, high-dimensional state/action spaces contain multiple spatiotemporal dimensio

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Miles, William. "International Real Estate Review." International Real Estate Review 23, no. 3 (2020): 397–416. http://dx.doi.org/10.53383/100307.

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Asset prices and fundamentals can move apart, as is the case during bubble episodes. However, they should exhibit a stable relationship in the long run. For UK housing, previous studies have investigated whether house prices share a long run relationship with income. Results thus far have not yet found such stability in the interaction of the two variables. These previous papers have imposed linear adjustment on the relationship. Nonlinear adjustment, however, has been shown to be a feature in a number of housing market relationships. In this study, we utilize a data set that consists of home

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Yamada, Makoto, and Masashi Sugiyama. "Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 549–54. http://dx.doi.org/10.1609/aaai.v25i1.7905.

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Methods for estimating the ratio of two probability density functions have been actively explored recently since they can be used for various data processing tasks such as non-stationarity adaptation, outlier detection, feature selection, and conditional probability estimation. In this paper, we propose a new density-ratio estimator which incorporates dimensionality reduction into the density-ratio estimation procedure. Through experiments, the proposed method is shown to compare favorably with existing density-ratio estimators in terms of both accuracy and computational costs.

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Wang, Yi, Feng Li, Mengge Lv, Tianzhen Wang, and Xiaohang Wang. "A Multi-Index Fusion Adaptive Cavitation Feature Extraction for Hydraulic Turbine Cavitation Detection." Entropy 27, no. 4 (2025): 443. https://doi.org/10.3390/e27040443.

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Under cavitation conditions, hydraulic turbines can suffer from mechanical damage, which will shorten their useful life and reduce power generation efficiency. Timely detection of cavitation phenomena in hydraulic turbines is critical for ensuring operational reliability and maintaining energy conversion efficiency. However, extracting cavitation features is challenging due to strong environmental noise interference and the inherent non-linearity and non-stationarity of a cavitation hydroacoustic signal. A multi-index fusion adaptive cavitation feature extraction and cavitation detection metho

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Benbouzid, Abdelmoheiman Zakaria, Mireille Turmine, and Vincent Vivier. "Inductive Behavior in Electrochemical Impedance Spectroscopy: Can We Discriminate between Adsorption and Non-Stationarity." ECS Meeting Abstracts MA2024-02, no. 27 (2024): 2119. https://doi.org/10.1149/ma2024-02272119mtgabs.

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Electrochemical impedance spectroscopy (EIS) is a widely used technique for studying electrochemical processes. EIS typically assumes a linear relationship between the applied signal (potential or current) and the measured response (current or potential). In other words, doubling the input perturbation must result in doubling the output, and the output signal must show no change in shape. However, by using “small input signals”, a linear response can be achieved. Similarly, stationarity is also a requirement for EIS measurements. In this case, several factors may be at the origin of the proble

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Triana-Martinez, Jenniffer Carolina, Andrés Marino Álvarez-Meza, Julian Gil-González, Tom De Swaef, and Jose A. Fernandez-Gallego. "Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot." Remote Sensing 16, no. 15 (2024): 2854. http://dx.doi.org/10.3390/rs16152854.

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To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we

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Rajabhushanam, C. "Clustering data analytics of urban land use for change detection." Computing and Artificial Intelligence 2, no. 2 (2024): 570. http://dx.doi.org/10.59400/cai.v2i2.570.

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In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized

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Rajabhushanam, C. "Clustering data analytics of urban land use for change detection." Computing and Artificial Intelligence 2, no. 1 (2024): 570. http://dx.doi.org/10.59400/cai.v2i1.570.

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In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized

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Purnima, B. R., N. Sriraam, U. Krishnaswamy, and K. Radhika. "A Measure to Detect Sleep Onset Using Statistical Analysis of Spike Rhythmicity." International Journal of Biomedical and Clinical Engineering 3, no. 1 (2014): 27–41. http://dx.doi.org/10.4018/ijbce.2014010103.

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Electroencephalogram (EEG) signals derived from polysomnography recordings play an important role in assessing the physiological and behavioral changes during onset of sleep. This paper suggests a spike rhythmicity based feature for discriminating the wake and sleep state. The polysomnography recordings are segmented into 1 second EEG patterns to ensure stationarity of the signal and four windowing scheme overlaps (0%, 50%, 60% and 75%)of EEG pattern are introduced to study the influence of the pre-processing procedure. The application of spike rhythmicity feature helps to estimate the number

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Zheng, Jia Chun, Wen Xu, Jianwei Guo, and Wei Dong Xie. "Joint Estimation with Time Delay and Doppler Frequency Shift in the Multi-Carrier Acoustic Communication." Applied Mechanics and Materials 263-266 (December 2012): 994–99. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.994.

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Timing synchronization is very important in the multi-carrier acoustic communication system. With regard to the transmitted signal of the baseband OFDM in the acoustic communication system, which is in the complex environment over the acoustic channel gauss noisy and SαS impulse noisy interference,this paper proposes a joint time delay and Doppler frequency shift estimation algorithm based on the fractional lower order cyclic cross ambiguity function with multi-cycle frequency(FCCAF). This method combines the fractional lower order moment with the feature of cycle stationary, and can detect th

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Shaheen, Ehab M. "Blind FrFT-OFDM signal parameters estimation for underlay cognitive radio based on second-order cyclo-stationarity feature." International Journal of Vehicle Information and Communication Systems 3, no. 3 (2017): 230. http://dx.doi.org/10.1504/ijvics.2017.087609.

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Shaheen, Ehab M. "Blind FrFT-OFDM signal parameters estimation for underlay cognitive radio based on second-order cyclo-stationarity feature." International Journal of Vehicle Information and Communication Systems 3, no. 3 (2017): 230. http://dx.doi.org/10.1504/ijvics.2017.10008586.

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Kumar, Surinder, Sumika Chauhan, Govind Vashishtha, Sunil Kumar, and Rajesh Kumar. "Fault Feature Extraction Using L-Kurtosis and Minimum Entropy-Based Signal Demodulation." Applied Sciences 14, no. 18 (2024): 8342. http://dx.doi.org/10.3390/app14188342.

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The health of mechanical components can be assessed by analyzing the vibration and acoustic signals they produce. These signals contain valuable information about the component’s condition, often encoded within specific frequency bands. However, extracting this information is challenging due to noise contamination from various sources. Narrow-band amplitude demodulation presents a robust technique for isolating fault-related information within the signal. This work proposes a novel approach based on cluster-based segmentation for demodulating the signal and extracting the frequency band of int

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Esparza-Estrada, Citlalli Edith, Levi Carina Terribile, Octavio Rojas-Soto, Carlos Yáñez-Arenas, and Fabricio Villalobos. "Evolutionary dynamics of climatic niche influenced the current geographical distribution of Viperidae (Reptilia: Squamata) worldwide." Biological Journal of the Linnean Society 135, no. 4 (2022): 665–78. http://dx.doi.org/10.1093/biolinnean/blac012.

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Abstract An understanding of patterns of climatic niche evolution has important implications for ecological and evolutionary theory and conservation planning. However, despite considerable testing, niche evolution studies continue to focus on clade-wide, homogeneous patterns, without considering the potentially complex dynamics (i.e. phylogenetic non-stationarity) along the evolutionary history of a clade. Here, we examine the dynamics of climatic niche evolution in vipers and discuss its implication for their current patterns of diversity and distribution. We use comparative phylogenetic meth

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Umeda, Takayuki, Kosuke Sekiyama, and Toshio Fukuda. "Vision-Based Object Tracking by Multi-Robots." Journal of Robotics and Mechatronics 24, no. 3 (2012): 531–39. http://dx.doi.org/10.20965/jrm.2012.p0531.

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This paper proposes a cooperative visual object tracking by a multi-robot system, where robust cognitive sharing is essential between robots. Robots identify the object of interest by using various types of information in the image recognition field. However, the most effective type of information for recognizing an object accurately is the difference between the object and its surrounding environment. Therefore we propose two evaluation criteria, called ambiguity and stationarity, in order to select the best information. Although robots attempt to select the best available feature for recogni

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Zhong, Jiajun. "Dynamic Multi-Scale Feature Fusion for Robust Sleep Stage Classification Using Single-Channel EEG." Journal of Computing and Electronic Information Management 16, no. 1 (2025): 14–20. https://doi.org/10.54097/1swr9p34.

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Sleep stage classification is pivotal in evaluating sleep quality and diagnosing sleep-related disorders. Recent advancements in automated single-channel electroencephalogram (EEG)--based classification have gained traction due to their cost-effectiveness and portability. However, the inherent non-stationarity of EEG signals and inter-class imbalance pose significant challenges for model design. This paper proposes MultiScaleSleepNet, an enhanced deep learning architecture that addresses these limitations through dynamic multi-scale feature fusion and residual structural optimizations. Our con

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Jiang, Wei, Yahui Shan, Xiaoming Xue, Jianpeng Ma, Zhong Chen, and Nan Zhang. "Fault Diagnosis for Rolling Bearing of Combine Harvester Based on Composite-Scale-Variable Dispersion Entropy and Self-Optimization Variational Mode Decomposition Algorithm." Entropy 25, no. 8 (2023): 1111. http://dx.doi.org/10.3390/e25081111.

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Because of the influence of harsh and variable working environments, the vibration signals of rolling bearings for combine harvesters usually show obvious characteristics of strong non-stationarity and nonlinearity. Accomplishing accurate fault diagnosis using these signals for rolling bearings is a challenging subject. In this paper, a novel fault diagnosis method based on composite-scale-variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is proposed, systematically combining the nonstationary signal analysis approach and machine learning technolo

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Hazarika, Jupitara, Piyush Kant, Rajdeep Dasgupta, and Shahedul Haque Laskar. "EEG Wavelet Coherence Based Analysis of Neural Connectivity in Action Video Game Players in Attention Inhibition and Short-term Memoryretention Task." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 12, no. 4 (2019): 324–38. http://dx.doi.org/10.2174/2352096511666180821111536.

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Background: The involvement in action video gaming alters the cognitive abilities and hence affects the neural functionality. Electroencephalogram (EEG) favorably provides the measure. Wavelet coherence, which is a wavelet transform based feature that provides useful information regarding synchronized activity between two signals. It does not depend on the stationarity of the signal and hence very much relevant for non-stationary EEG application. Methods: We aimed to examine how the task-related synchronization pattern of action video game players (AVGPs) differs from non-AVGPs. EEG data were

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Zhou, Xiaolong, Xiangkun Wang, Haotian Wang, Linlin Cao, Zhongyuan Xing, and Zhilun Yang. "Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network." Applied Sciences 13, no. 2 (2023): 1134. http://dx.doi.org/10.3390/app13021134.

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Rotor fault diagnosis has attracted much attention due to its difficulties such as non-stationarity of fault signals, difficulty in fault feature extraction and low diagnostic accuracy of small samples. In order to extract fault feature information of rotors more effectively and to improve fault diagnosis precision, this paper proposed a fault diagnosis method based on variational mode decomposition (VMD) symmetrical polar image and fuzzy neural network. Firstly, the original rotor vibration signal is decomposed by using the VMD method and the relevant parameter selection algorithm of the VMD

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Kochanska, Iwona. "Assessment of Wide-Sense Stationarity of an Underwater Acoustic Channel Based on a Pseudo-Random Binary Sequence Probe Signal." Applied Sciences 10, no. 4 (2020): 1221. http://dx.doi.org/10.3390/app10041221.

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The performances of Underwater Acoustic Communication (UAC) systems are strongly related to the specific propagation conditions of the underwater channel. Designing the physical layer of a reliable data transmission system requires a knowledge of channel characteristics in terms of the specific parameters of the stochastic model. The Wide-Sense Stationary Uncorrelated Scattering (WSSUS) assumption simplifies the stochastic description of the channel, and thus the estimation of its transmission parameters. However, shallow underwater channels may not meet the WSSUS assumption. This paper propos

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