Journal articles on the topic 'Feature stationarity'
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Conni, Michele, and Hilda Deborah. "Texture Stationarity Evaluation with Local Wavelet Spectrum." London Imaging Meeting 2020, no. 1 (September 29, 2020): 24–27. http://dx.doi.org/10.2352/issn.2694-118x.2020.lim-20.
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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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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 (June 18, 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 high-speed trains are typically non-stationary, it is necessary to utilize a rapid feature fusion method to identify the evolutionary trends of hunting motions quickly before the phenomenon is fully manifested. In this paper, the proposed method is applied to identify the evolutionary trend of hunting motions quickly and accurately. Results verify that the accuracy of this method is much higher than that of the EEMD-JADE and EEMD-SVD-LTSA methods. This method can also be used to fuse multi-sensor features of non-stationary data rapidly.
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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 (December 13, 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-attribute proximities deep neural network to aggregate data from the spatial feature and attribute feature, so that one unified distance metric can be used to express the spatial and attribute relationships between sample points at the same time. Based on GNNWR, we designed a spatial and attribute neural network weighted regression (SANNWR) model to adapt to this new unified distance metric. We developed one case study to examine the effectiveness of SANNWR. We used PM2.5 concentration data in China as the research object and compared the prediction accuracy between GWR, GNNWR and SANNWR. The results showed that the “spatial-attribute” unified distance metric is useful, and that the SANNWR model showed the best performance.
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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 (June 30, 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 framework, namely auto-regressive integrated moving-average machine learning (ARIMA-ML) with modules for pre-processing, model parameter determination, feature extraction, and classification. Based on shaking table tests of a space steel frame, floor acceleration data were collected and labeled according to experimental observations and records. Subsequently, we designed three damage classification tasks for: (1) global damage detection, (2) local damage detection, and (3) local damage pattern recognition. The results from these three tasks indicated the robustness and accuracy of the proposed framework where 97%, 98%, and 80% average segment accuracy were achieved, respectively. The confusion matrix results showed the unbiased model performance even under an imbalanced-class distribution. In summary, the presented study revealed the high potential of the proposed ARIMA-ML framework in vibration-based SHM.
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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 (January 30, 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 feature extraction under ambient vibration and non-stationary signals. The improved complete ensemble empirical mode decomposition with adaptive noise method is an improvement on the well-known ensemble empirical mode decomposition technique by removing redundant intrinsic mode functions. In addition, a novel automatic approach is presented to select the most relevant intrinsic mode functions to damage based on the intrinsic mode function energy level. Fitting an autoregressive moving average model to each selected intrinsic mode function, the model residuals are extracted as the damage-sensitive features. The main limitation is that such features are high-dimensional multivariate time series data, which may make a difficult and time-consuming decision-making process for damage localization. Multivariate distance correlation methods are introduced to cope with this drawback and locate structural damage using the multivariate residual sets of the normal and damaged conditions. The accuracy and robustness of the proposed methods are validated by a numerical shear-building model and an experimental benchmark structure. The effects of sampling frequency and time duration are evaluated as well. Results demonstrate the effectiveness and capability of the proposed methods to extract sufficient and reliable features, identify damage location, and quantify damage severity under ambient excitations and non-stationary signals.
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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 (May 1, 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 nonlinear analysis method, and the extracted feature is optimized using evolutionary optimization algorithm, and finally train the epilepsy classifier by utilizing the optimized features through the Random forest algorithm. In the experiment, the accuracies of two-classification problems and three-classification problems respectively reach 99.2% and 98.1%. The results of cross-over experiment for many times show that, the method is of effectiveness in the classified feature extraction aiming at epilepsy brain wave.
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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 (December 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 in terms of the autocorrelation function are discussed. It is shown that due to the stochastic feedback via the process mean, transient and stationary responses may exhibit qualitatively different temporal patterns. That is, the model offers a time-discrete description of many-body systems that in certain parameter domains feature qualitatively different transient and stationary response dynamics.
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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 (July 20, 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 to assess the stability of inflation dynamics in the United States and on a set of neuroscience tremor data.
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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 (October 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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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 (November 13, 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-stationary signal was processed to verify the effect of the new method. Meanwhile, an analysis was conducted by using the recorded vibration signals which include inner race, out race and bearing ball fault signal. Findings The results show that the proposed method is more suitable for the non-stationary fault signal because the LMD–Teager transform method breaks through the difficulty of the Fourier transform method that can process only the stationary signal. The new method can extract more useful information and can provide better analysis accuracy and resolution compared with the traditional Fourier method. Originality/value Combining the advantage of the local mean decomposition and the Teager energy operator, the LMD–Teager method suits the nature of the fault signal. A marginal spectrum obtained from the LMD–Teager method minimizes the estimation bias brought about by the non-stationarity of the fault signal. So, the LMD–Teager transform has better analysis accuracy and resolution than the traditional Fourier method, which provides a good alternative for fault diagnosis of the rolling bearing.
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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 is time-varying. Thus only the time-domain or frequency-domain analysis is not sufficient to describe the characteristics of non-stationary signal. The time-frequency analysis, which can provide the information about that the spectrum of the signal varies with the time, is a useful tool for non-stationary signal analysis. In this paper, combined time-frequency analysis with blind source separation, a blind source separation method for the non-stationary signal of the mechanical equipment based on time-frequency analysis is proposed and studied. The simulation and experimental results show that the proposed approach is feasible and effective.
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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 (June 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 EEMD and correlation coefficients are extracted by using the EEMD Algorithm, which is commonly used in the field of natural sequence decomposition. Finally, the Savitzky-Golay filter is designed to smooth the reconstructed sequence in order to remove the noise introduced by EEMD, the final usable characteristic component is obtained. Compared with EEMD Algorithm, this method is more reasonable in the pre-processing process, and the power distribution in frequency is more concentrated.
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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 the faults of machine are classified.Practical experimental data is used to verify this method,and the diagnosis results and comparative tests fully validate its effectiveness and generalization abilities.
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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 (IMFs) from the original wind speed time series to eliminate the non-stationarity in the time series. FS and SVR are combined to predict the high-frequency IMF obtained by EMD. LassoCV is used to complete the prediction of low-frequency IMF and trend. Results Data collected from two wind stations in Michigan, USA are adopted to test the proposed combined model. Experimental results show that in multi-step wind speed forecasting, compared with the classic individual and traditional EMD-based combined models, the proposed model has better prediction performance. Conclusions Through the proposed combined model, the wind speed forecast can be effectively improved.
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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 (October 8, 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 element of the proposed feature extraction approach is to establish a two-stage offline and online learning algorithms for extracting the residuals of AutoRegressive model as the main damage-sensitive features. This technique brings the great benefit of reducing the computational time and storage space for feature extraction in long-term monitoring conditions. The major contribution of Partition-based Kullback–Leibler Divergence method is to exploit a partitioning strategy for dividing random features into individual partitions and utilize numerical information of partitioning in distance calculation rather than directly applying random samples. Dealing with the major challenging issue of using the high-dimensional features in decision making and applicability to both correlated and uncorrelated random datasets are the main advantages of Partition-based Kullback–Leibler Divergence method. The accuracy and reliability of the proposed approaches are experimentally validated by two well-known benchmark structures. The stationarity and linearity of measured vibration responses for using in AutoRegressive modeling are evaluated by two hypothesis tests. Comparative studies are also conducted to demonstrate the superiority of the proposed methods over some exciting state-of-the-art techniques. Results show that the methods presented here succeed in detecting and locating damage and make time-saving and efficient tools for feature extraction and damage diagnosis.
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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 (October 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 common average reference) and the independent component analysis (ICA) were applied on each datasets, to eliminate the impact of artifact. Three sets of features where extracted from artifact free ICA components, from which more relevant features were selected. Moreover, the selected feature subsets were incorporated into three classifiers, NB, Regression Tree and K-NN to predict four MI and hybrid tasks. K-NN classifier outperformed the other two classifies in each dataset. The highest classification accuracy is obtained in hybrid task EEG dataset. Moreover, accurately predicted EEG classes were applied to a robotic arm control.
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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 (September 19, 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-crafted feature extraction and feature selection. However, the raw vibration signal is rarely taken as a direct input. This study combines a convolutional neural network with automatic hyper-parametric optimization and proposes two deep learning models for time-series pattern recognition to achieve “end-to-end” bearing fault diagnosis: a one-dimensional-convolutional neural network and a dilated convolutional neural network. The architecture of the two models are tweaked by automatic optimization rather than manual trial or grid search. Further, we try to figure out the inner operating mechanism of the proposed methods by visualizing the automatically learned features. The proposed methods are applied to diagnose roller bearing faults on a benchmark experiment and a prototype experiment. The results verify that our methods can achieve better performance than other intelligent methods via a Gaussian-noise test.
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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 (August 4, 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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Miles, William. "International Real Estate Review." International Real Estate Review 23, no. 3 (September 30, 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 prices relative to first time buyer income for the UK and its twelve constituent regions, which gives us a direct measure of affordability. We test for the stationarity of the home price/first time buyer income ratio with linear tests, and, as in past studies, fail to find a long run relationship. However, we then employ a nonlinear test, and find a stationary relationship for the UK and seven of the twelve regions. In particular, the regions closest to London appear most clearly to have a stationary relationship between home prices and income.
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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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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 (January 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 of spikes from the given pattern with a threshold of 25%.Then non parametric statistical analysis using Wilcoxon signed rank test is introduced to evaluate the impact of statistical measures such as mean, standard deviation, p-value and box-plot analysis under various conditions .The statistical test shows significant difference between wake and sleep with p<0.005 for the applied feature, thus demonstrating the efficiency of simple thresholding in distinguishing sleep and wake stage .
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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 the signal characteristic of cyclo-stationarity under the SaS impulse noisy. The algorithm takes full advantage of the cycle frequency information of the signal, and can be equipped with the capacity of suppression interference. The simulation results show that the algorithm proposed in this paper can work very well in the presence of the impulse noise and interference, the superior to that algorithm based on the second order cycle fuzzy function, and is a robust joint estimation algorithm with time delay and Doppler shift.
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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 (March 10, 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 methods and global-scale datasets, including 210 viper species with phylogenetic and climatic data. We find that climatic niche evolution in Viperidae shows an overall pattern of phylogenetic conservatism, but with different dynamics depending on the niche feature (niche breadth or niche position) and the evolutionary history of particular lineages within the family, thus resulting in phylogenetic non-stationarity. Indeed, we find several shifts in niche breadth evolution that were probably influenced by the main geological and environmental changes experienced during the evolutionary history of the family. These results highlight the importance of considering complex patterns of climatic niche evolution and their role in shaping patterns of diversity and distribution.
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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 (June 20, 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 recognition, it will lead a failure of recognition if the background scene contains very similar features with the object of concern. To solve this problem, we introduce a scheme that robots share the relation between the landmarks and the object of interest where landmark information is generated autonomously. The experimental results show the effectiveness of the proposed multi-robot cognitive sharing.
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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 (August 23, 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 collected from thirty-five young and healthy male participants while performing an attention inhibition task and a visuospatial short-term memory-retention task. The sub-frequency components, theta, alpha, beta and gamma bands of EEG were extracted using Discrete wavelet transform (DWT). The intra and inter-hemispheric coherence in EEG sub-frequency bands were assessed as a feature for the analysis. Results: Theta, alpha, beta and gamma coherence has shown a significant difference (p<0.05) between AVGPs and non-AVGPs in both the visuo-spatial tasks in intra and inter-hemispheric functionality. More than 90% classification accuracies are achieved with ANFIS algorithm. Results also indicate that frontoparietal connectivity is significantly improved in AVGPs in both the visual sensory tasks considered. Conclusion: These EEG based analysis reports enhanced neural communication with improved attention inhibition and short-term memory retention in AVGPs. Result also established the Wavelet coherence as an effective tool in understanding the neural communication among different brain locations.
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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 (January 14, 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 method is also proposed. Secondly, the intrinsic mode functions (IMF), which are sensitive to the signal characteristics, are selected for signal reconstruction based on a comprehensive evaluation factor method. As well, the reconstructed signal is transformed into a two-dimensional snowflake image through using the symmetrical polar coordinate method. Finally, the image features are extracted by the gray level co-occurrence matrix to form the state feature vector, which is input into the fuzzy neural network to realize the rotor fault diagnosis. Through the analysis of measured signals, the experimental results show that the proposed method can reach a higher recognition rate of 98% and the k-cross-validation experiment is used to demonstrate the robustness of the fuzzy neural network, and the average recognition accuracy of this experiment is 99.2%. Compared with some similar methods, the proposed method still has the highest fault recognition precision 98.4%, and the smallest standard deviation 0.5477.
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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 (February 11, 2020): 1221. http://dx.doi.org/10.3390/app10041221.
Full textAbstract:
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 proposes a method for testing the Wide-Sense Stationary (WSS) part of the WSSUS feature of a UAC channel on the basis of the complex envelope of a received probe Pseudo-Random Binary Sequence (PRBS) signal. Two correlation coefficients are calculated that can be interpreted, together, as a measure that determines whether the channel is WSS or not. A similar wide-sense stationarity assessment can be performed on the basis of the Time-Varying Impulse Response (TVIR) of a UAC channel. However, the method proposed in this paper requires fewer computational operations in the receiver of a UAC system. PRBS signal transmission tests were conducted in the UAC channel simulator and in real conditions during an inland water experiment. The correlation coefficient values obtained using the method based on the envelope of a probe signal and the method of analysing the TVIR estimates are compared. The results are similar, and thus, it is possible to assess if the UAC channel can be modelled as a WSS stochastic process without the need for TVIR estimation.
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Chen, Yinsheng, Tinghao Zhang, Zhongming Luo, and Kun Sun. "A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method." Applied Sciences 9, no. 11 (June 8, 2019): 2356. http://dx.doi.org/10.3390/app9112356.
Full textAbstract:
To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. A fast sample entropy calculation method based on a kd tree is adopted to improve the real-time performance of fault detection in this paper. In view of the non-linearity and non-stationarity of the vibration signals, the vibration signal of the rolling bearing is decomposed into several sub-signals containing fault information by using a wavelet packet. Then, the energy entropy values of the sub-signals decomposed by the wavelet packet are calculated to generate the feature vectors for describing different fault types and severity levels of rolling bearings. The multiclass relevance vector machine modeled by the feature vectors of different fault types and severity levels is used to realize fault type identification and a fault severity analysis of the bearings. The proposed fault diagnosis and severity analysis method is fully evaluated by experiments. The experimental results demonstrate that the fault detection method based on the sample entropy can effectively detect rolling bearing failure. The fault feature extraction method based on the wavelet packet energy entropy can effectively extract the fault features of vibration signals and a multiclass relevance vector machine can identify the fault type and severity by means of the fault features contained in these signals. Compared with some existing bearing rolling fault diagnosis methods, the proposed method is excellent for fault diagnosis and severity analysis and improves the fault identification rate reaching as high as 99.47%.
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Xue, Liang, Diao Li, Cheng Dai, and Tongchao Nan. "Characterization of Aquifer Multiscale Properties by Generating Random Fractal Field with Truncated Power Variogram Model Using Karhunen–Loève Expansion." Geofluids 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/1361289.
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The traditional geostatistics to describe the spatial variation of hydrogeological properties is based on the assumption of stationarity or statistical homogeneity. However, growing evidences show and it has been widely recognized that the spatial distribution of many hydrogeological properties can be characterized as random fractals with multiscale feature, and spatial variation can be described by power variogram model. It is difficult to generate a multiscale random fractal field by directly using nonstationary power variogram model due to the lack of explicit covariance function. Here we adopt the stationary truncated power variogram model to avoid this difficulty and generate the multiscale random fractal field using Karhunen–Loève (KL) expansion. The results show that either the unconditional or conditional (on measurements) multiscale random fractal field can be generated by using truncated power variogram model and KL expansion when the upper limit of the integral scale is sufficiently large, and the main structure of the spatial variation can be described by using only the first few dominant KL expansion terms associated with large eigenvalues. The latter provides a foundation to perform dimensionality reduction and saves computational effort when analyzing the stochastic flow and transport problems.
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Chen, Xiao, and Haiying Liu. "Basic Unit Layer Rate Control Algorithm for H.264 Based on Human Visual System." Mathematical Problems in Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/270692.
Full textAbstract:
In the process of the video coding, special attention should be paid to the subjective quality of the image. In the JVT-G012 algorithm for H.264, the influence of the human visual characteristic in basic unit layer rate control was not taken into account. This paper takes the influence of the human visual characteristic into the full consideration and offers ways to improve the subjective quality of the image. The visual characteristic factor, which is constituted by the motion feature and edge feature, is used to reasonably allocate the target bits, and then its quantization parameter is adjusted by encoded frame information. The experimental results show that, in comparison to the original algorithm, the proposed algorithm can not only control the bit rate more accurately but also make the peak signal to noise ratio (PSNR) stable, so as to improve the stationarity of the video image. The subjective quality of the reconstructed video is more satisfying.
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Zhang, Xun, Guanghua Xu, Jiachen Kuang, Lin Suo, Sicong Zhang, and Umair Khalique. "A Three-Phase Current Tacholess Envelope Order Analysis Method for Feature Extraction of Planetary Gearbox under Variable Speed Conditions." Sensors 21, no. 17 (August 25, 2021): 5714. http://dx.doi.org/10.3390/s21175714.
Full textAbstract:
Planetary gearboxes are the key components of large equipment, such as wind turbines, shield machines, etc. The operating state of the planetary gearbox is related to the safety of the equipment as a whole, and its feature extraction technology is essential. In assessing the problem of the non-stationarity of the current signal under variable speed conditions and the difficulty of evaluating the operating state of the planetary gearbox under a tacholess condition, a three-phase current, variable-speed tacholess envelope order analysis method is proposed. Firstly, a tacholess rotation speed estimation is completed by extracting the trend term of the instantaneous frequency of the asynchronous motor’s three-phase currents. The motor slip rate is assumed to be constant. Then, the envelope order analysis signal is obtained by re-sampling in the angular domain. Finally, the features of the envelope order signal are extracted, and a linear discriminant analysis (LDA) algorithm is used to fuse multiple indexes to generate a comprehensive feature reflecting the operating status of the planetary gearbox. The results of the simulation analysis and experimental verification show that the proposed method is effective in evaluating the operating state of the planetary gearbox under variable speed conditions. Compared with the traditional time–frequency ridge extraction method, the tacholess speed estimation method can improve the instantaneous speed estimation accuracy. The comprehensive index of envelope order completes the planetary gearbox state identification process, and a 95% classification accuracy rate is achieved.
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Fakhry, Mahmoud, and Abeer FathAllah Brery. "Comparison of window shapes and lengths in short-time feature extraction for classification of heart sound signals." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6090. http://dx.doi.org/10.11591/ijece.v12i6.pp6090-6102.
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<span lang="EN-US">Heart sound signals, phonocardiography (PCG) signals, allow for the automatic diagnosis of potential cardiovascular pathology. Such classification task can be tackled using the bidirectional long short-term memory (biLSTM) network, trained on features extracted from labeled PCG signals. Regarding the non-stationarity of PCG signals, it is recommended to extract the features from multiple short-length segments of the signals using a sliding window of certain shape and length. However, some window contains unfavorable spectral side lobes, which distort the features. Accordingly, it is preferable to adapt the window shape and length in terms of classification performance. We propose an experimental evaluation for three window shapes, each with three window lengths. The biLSTM network is trained and tested on statistical features extracted, and the performance is reported in terms of the window shapes and lengths. Results show that the best performance is obtained when the Gaussian window is used for splitting the signals, and the triangular window competes with the Gaussian window for a length of 75 ms. Although the rectangular window is a commonly offered option, it is the worst choice for splitting the signals. Moreover, the classification performance obtained with a 75 ms Gaussian window outperforms that of a baseline method.</span>
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Tayeb, Zied, Juri Fedjaev, Nejla Ghaboosi, Christoph Richter, Lukas Everding, Xingwei Qu, Yingyu Wu, Gordon Cheng, and Jörg Conradt. "Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals." Sensors 19, no. 1 (January 8, 2019): 210. http://dx.doi.org/10.3390/s19010210.
Full textAbstract:
Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.
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Wang, Dongyu, Xiwen Cui, and Dongxiao Niu. "Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF." Sustainability 14, no. 12 (June 15, 2022): 7307. http://dx.doi.org/10.3390/su14127307.
Full textAbstract:
Improving the accuracy of wind power forecasting can guarantee the stable dispatch and safe operation of the grid system. Here, we propose an EMD-PCA-RF-LSTM wind power forecasting model to solve problems in traditional wind power forecasting such as incomplete consideration of influencing factors, inaccurate feature identification, and complex space–time relationships between variables. The proposed model incorporates Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA), Random Forest (RF), and Long Short-Term Memory (LSTM) neural networks, And environmental factors are filtered by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm when pre-processing the data. First, the environmental factors are extended by the EMD algorithm to reduce the non-stationarity of the series. Second, the key influence series are extracted by the PCA algorithm in order to remove noisy information, which can seriously interfere with the data regression analysis. The data are then subjected to further feature extraction by calculating feature importance through the RF algorithm. Finally, the LSTM algorithm is used to perform dynamic time modeling of multivariate feature series for wind power forecasting. The above combined model is beneficial for analyzing the effects of different environmental factors on wind power and for obtaining more accurate prediction results. In a case study, the proposed combined forecasting model was verified using actual measured data from a power station. The results indicate that the proposed model provides the most accurate results when compared to benchmark models: MSE 7.26711 MW, RMSE 2.69576 MW, MAE 1.73981 MW, and adj-R2 0.9699203s.
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Prosdocimi, I., T. R. Kjeldsen, and C. Svensson. "Non-stationarity in annual and seasonal series of peak flow and precipitation in the UK." Natural Hazards and Earth System Sciences Discussions 1, no. 5 (October 14, 2013): 5499–544. http://dx.doi.org/10.5194/nhessd-1-5499-2013.
Full textAbstract:
Abstract. When designing or maintaining an hydraulic structure, an estimate of the frequency and magnitude of extreme events is required. The most common methods to obtain such estimates rely on the assumption of stationarity, i.e. the assumption that the process under study is not changing. The public perception and worry of a changing climate have led to a wide debate on the validity of this assumption. In this work trends for annual and seasonal maxima in peak river flow and catchment-average daily rainfall are explored. Assuming a 2-parameters log-normal distribution, a linear regression model is applied, allowing the mean of the distribution to vary with time. For the river flow data, the linear model is extended to include an additional variable, the 99th percentile of the daily rainfall for a year. From the fitted models, dimensionless magnification factors are estimated and plotted on a map, shedding light on whether or not geographical coherence can be found in the significant changes. The implications of the identified trends from a decision making perspective are then discussed, in particular with regard to the Type I and Type II error probabilities. One striking feature of the estimated trends is that the high variability found in the data leads to very inconclusive test results. Indeed, for most stations it is impossible to make a statement regarding whether or not the current design standards for the 2085 horizon can be considered precautionary. The power of tests on trends is further discussed in the light of statistical power analysis and sample size calculations.
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TODD, M. D., and S. T. VOHRA. "AN ALTERNATIVE APPROACH TO POINCARÉ SECTIONING IN WEAKLY NONLINEAR SYSTEMS." International Journal of Bifurcation and Chaos 09, no. 05 (May 1999): 953–62. http://dx.doi.org/10.1142/s0218127499000687.
Full textAbstract:
A large array of dynamical systems can be broadly classified as weakly nonlinear, where a common feature is the presence of dual time-scale dynamics. Often it is desirable to isolate the dynamics at one time scale (typically the slower time scale), and this paper presents a generic phase-sensitive detection method that successfully isolates the slow-time dynamics, stripping off the less-relevant and often dominant fast-time dynamics. We shall also show for the first time, under certain phase considerations, the equivalence of the present method and the traditional Poincaré section of the original full response. The method is first presented in theory and then applied to data from a numerical simulation of the forced spherical pendulum followed by data from a similar spherical pendulum experiment. The method is successful provided certain conditions apply for phase stationarity.
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Volkova, Natalya P., and Viktor N. Krylov. "HYBRID TEXTURE IDENTIFICATION METHOD." Herald of Advanced Information Technology 4, no. 2 (June 30, 2021): 123–34. http://dx.doi.org/10.15276/hait.02.2021.2.
Full textAbstract:
The importance of the modeling mode in systems of computer visual pattern recognition is shown. The purpose of the mode is to determine the types of textures that are present on the images processed in intelligent diagnostic systems. Images processed in technical diagnostic systems contain texture regions, which can be represented by different types of textures - spectral, statistical and spectral-statistical. Texture identification methods, such as, statistical, spectral, expert, multifractal, which are used to identify and analyze texture images, have been analyzed. To determine texture regions on images that are of a combined spectral-statistical nature, a hybrid texture identification method has been developed which makes it possible to take into account the local characteristics of the texture based on multifractal indicators characterizing the non-stationarity and impulsite of the data and the sign of the spectral texture. The stages of the developed hybrid texture identification method are: preprocessing; formation of the primary features vector; formation of the secondary features vector. The formation of the primary features vector is performed for the selected rectangular fragment of the image, in which the multifractal features and the spectral texture feature are calculated. To reduce the feature space at the stage of formation of the secondary identification vector, the principal component method was used. An experimental study of the developed hybrid texture identification method textures on model images of spectral, statistical, spectralstatistical textures has been carried out. The results of the study showed that the developed method made it possible to increase the probability of correct determination of the region of the combined spectral-statistical texture. The developed identification method was tested on images from Brodatz album of textures and images of wear zones of cutting tools, which are processed in intelligent systems of technical diagnostics. The probability of correctly identifying areas of spectral-statistical texture in the images of wear zones of cutting tools averaged 0.9, which is sufficient for the needs of practice
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Prosdocimi, I., T. R. Kjeldsen, and C. Svensson. "Non-stationarity in annual and seasonal series of peak flow and precipitation in the UK." Natural Hazards and Earth System Sciences 14, no. 5 (May 16, 2014): 1125–44. http://dx.doi.org/10.5194/nhess-14-1125-2014.
Full textAbstract:
Abstract. When designing or maintaining an hydraulic structure, an estimate of the frequency and magnitude of extreme events is required. The most common methods to obtain such estimates rely on the assumption of stationarity, i.e. the assumption that the stochastic process under study is not changing. The public perception and worry of a changing climate have led to a wide debate on the validity of this assumption. In this work trends for annual and seasonal maxima in peak river flow and catchment-average daily rainfall are explored. Assuming a two-parameter log-normal distribution, a linear regression model is applied, allowing the mean of the distribution to vary with time. For the river flow data, the linear model is extended to include an additional variable, the 99th percentile of the daily rainfall for a year. From the fitted models, dimensionless magnification factors are estimated and plotted on a map, shedding light on whether or not geographical coherence can be found in the significant changes. The implications of the identified trends from a decision-making perspective are then discussed, in particular with regard to the Type I and Type II error probabilities. One striking feature of the estimated trends is that the high variability found in the data leads to very inconclusive test results. Indeed, for most stations it is impossible to make a statement regarding whether or not the current design standards for the 2085 horizon can be considered precautionary. The power of tests on trends is further discussed in the light of statistical power analysis and sample size calculations. Given the observed variability in the data, sample sizes of some hundreds of years would be needed to confirm or negate the current safety margins when using at-site analysis.
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Avarucci, Marco, Eric Beutner, and Paolo Zaffaroni. "ON MOMENT CONDITIONS FOR QUASI-MAXIMUM LIKELIHOOD ESTIMATION OF MULTIVARIATE ARCH MODELS." Econometric Theory 29, no. 3 (November 12, 2012): 545–66. http://dx.doi.org/10.1017/s0266466612000473.
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This paper questions whether it is possible to derive consistency and asymptotic normality of the Gaussian quasi-maximum likelihood estimator (QMLE) for possibly the simplest multivariate GARCH model, namely, the multivariate ARCH(1) model of the Baba, Engle, Kraft, and Kroner form, under weak moment conditions similar to the univariate case. In contrast to the univariate specification, we show that the expectation of the log-likelihood function is unbounded, away from the true parameter value, if (and only if) the observable has unbounded second moment. Despite this nonstandard feature, consistency of the Gaussian QMLE is still warranted. The same moment condition proves to be necessary and sufficient for the stationarity of the score when evaluated at the true parameter value. This explains why high moment conditions, typically bounded sixth moment and above, have been used hitherto in the literature to establish the asymptotic normality of the QMLE in the multivariate framework.
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HAVLIN, S., S. V. BULDYREV, A. L. GOLDBERGER, R. N. MANTEGNA, C. K. PENG, M. SIMONS, and H. E. STANLEY. "STATISTICAL AND LINGUISTIC FEATURES OF DNA SEQUENCES." Fractals 03, no. 02 (June 1995): 269–84. http://dx.doi.org/10.1142/s0218348x95000229.
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We present evidence supporting the idea that the DNA sequence in genes containing noncoding regions is correlated, and that the correlation is remarkably long range—indeed, base pairs thousands of base pairs distant are correlated. We do not find such a long-range correlation in the coding regions of the gene. We resolve the problem of the “non-stationarity” feature of the sequence of base pairs by applying a new algorithm called Detrended Fluctuation Analysis (DFA). We address the claim of Voss that there is no difference in the statistical properties of coding and noncoding regions of DNA by systematically applying the DFA algorithm, as well as standard FFT analysis, to all eukaryotic DNA sequences (33 301 coding and 29 453 noncoding) in the entire GenBank database. We describe a simple model to account for the presence of long-range power-law correlations which is based upon a generalization of the classic Lévy walk. Finally, we describe briefly some recent work showing that the noncoding sequences have certain statistical features in common with natural languages. Specifically, we adapt to DNA the Zipf approach to analyzing linguistic texts, and the Shannon approach to quantifying the “redundancy” of a linguistic text in terms of a measurable entropy function. We suggest that noncoding regions in plants and invertebrates may display a smaller entropy and larger redundancy than coding regions, further supporting the possibility that noncoding regions of DNA may carry biological information.
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Hazer-Rau, Dilana, Lin Zhang, and Harald C. Traue. "A Workflow for Affective Computing and Stress Recognition from Biosignals." Engineering Proceedings 2, no. 1 (November 14, 2020): 85. http://dx.doi.org/10.3390/ecsa-7-08227.
Full textAbstract:
Affective computing and stress recognition from biosignals have a high potential in various medical applications such as early intervention, stress management and risk prevention, as well as monitoring individuals’ mental health. This paper presents an automated processing workflow for the psychophysiological recognition of emotion and stress states. Our proposed workflow allows the processing of biosignals in their raw state as obtained from wearable sensors. It consists of five stages: (1) Biosignal Preprocessing—raw data conversion and physiological data triggering, relevant information selection, artifact and noise filtering; (2) Feature Extraction—using different mathematical groups including amplitude, frequency, linearity, stationarity, entropy and variability, as well as cardiovascular-specific characteristics; (3) Feature Selection—dimension reduction and computation optimization using Forward Selection, Backward Elimination and Brute Force methods; (4) Affect Classification—machine learning using Support Vector Machine, Random Forest and k-Nearest Neighbor algorithms; (5) Model Validation—performance matrix computation using k-Cross, Leave-One-Subject-Out and Split Validations. All workflow stages are integrated into embedded functions and operators, allowing an automated execution of the recognition process. The next steps include further development of the algorithms and the integration of the developed tools into an easy-to-use system, thereby satisfying the needs of medical and psychological staff. Our automated workflow was evaluated using our uulmMAC database, previously developed for affective computing and machine learning applications in human–computer interaction.
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Liang, Hu, Na Li, and Shengrong Zhao. "Salt and Pepper Noise Removal Method Based on a Detail-Aware Filter." Symmetry 13, no. 3 (March 21, 2021): 515. http://dx.doi.org/10.3390/sym13030515.
Full textAbstract:
The median-type filter is an effective technique to remove salt and pepper (SAP) noise; however, such a mechanism cannot always effectively remove noise and preserve details due to the local diversity singularity and local non-stationarity. In this paper, a two-step SAP removal method was proposed based on the analysis of the median-type filter errors. In the first step, a median-type filter was used to process the image corrupted by SAP noise. Then, in the second step, a novel-designed adaptive nonlocal bilateral filter is used to weaken the error of the median-type filter. By building histograms of median-type filter errors, we found that the error almost obeys Gaussian–Laplacian mixture distribution statistically. Following this, an improved bilateral filter was proposed to utilize the nonlocal feature and bilateral filter to weaken the median-type filter errors. In the proposed filter, (1) the nonlocal strategy is introduced to improve the bilateral filter, and the intensity similarity is measured between image patches instead pixels; (2) a novel norm based on half-quadratic estimation is used to measure the image patch- spatial proximity and intensity similarity, instead of fixed L1 and L2 norms; (3) besides, the scale parameters, which were used to control the behavior of the half-quadratic norm, were updated based on the local image feature. Experimental results showed that the proposed method performed better compared with the state-of-the-art methods.
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Miglietta, Mario Marcello, and Richard Rotunno. "Numerical Simulations of Sheared Conditionally Unstable Flows over a Mountain Ridge." Journal of the Atmospheric Sciences 71, no. 5 (April 28, 2014): 1747–62. http://dx.doi.org/10.1175/jas-d-13-0297.1.
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Abstract In two recent papers, the authors performed numerical simulations with a three-dimensional, explicitly cloud-resolving model for a uniform wind flowing past a bell-shaped ridge and using an idealized unstable (Weisman–Klemp) sounding with prescribed values of the relevant parameters. More recently, some observed cases of orographically forced wind profiles were analyzed, showing that, in order to reproduce larger rainfall rates, it was necessary to initialize the sounding with low-level flow toward the mountain with weak flow aloft (as observed). Additional experiments using the Weisman–Klemp sounding, but with nonuniform wind profiles, are performed here to identify the conditions in which the presence of a low-level cross-mountain flow together with calm flow aloft may increase the rain rates in conditionally unstable flows over the orography. The sensitivity of the solutions to the wind speed at the bottom and the top of a shear layer and the effect of different mountain widths and heights are systematically analyzed herein. Large rainfall rates are obtained when the cold pool, caused by the evaporative cooling of rain from precipitating convective clouds, remains quasi stationary upstream of the mountain peak. This condition occurs when the cold-pool propagation is approximately countered by the environmental wind. The large precipitation amounts can be attributed to weak upper-level flow, which favors stronger updrafts and upright convective cells, and to the ground-relative stationarity of the cells. This solution feature is produced with ambient wind shear within a narrow region of the parameter space explored here and does not occur in the numerical solutions obtained in the authors’ previous studies with uniform wind profiles.
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Pokalyuk, Vladimir, Igor Lomakin, Valentyn Verkhovtsev, and Vladimir Kochelab. "Tectonolinament framework of the Black Sea region and the surrounding areas." Ukrainian journal of remote sensing 8, no. 1 (February 9, 2021): 26–44. http://dx.doi.org/10.36023/ujrs.2021.8.1.189.
Full textAbstract:
Modern high-precision global digital 3-d models of the relief of the continents and the ocean floor (SRTM, GEBCO) are the objective basis to clarify the structure and features of the organization of the planetary fault network of of the Black Sea region and adjacent areas of the Mediterranean mobile belt and surrounding platform areas, to find out the location of the main transregional supermegalineaments forming the deep structural-tectonic framework of the territory. A complete consistency of the structural plan of faults and fault zones within the sea areas and continental surroundings is established. The structural position of the Black Sea basin as a whole is determined by its location at the intersection area (superposition, interference) of the diagonal (subdiagonal) transcontinental tectonolinament belts: the north-west – Elba-Zagros, Caucasus-Kopetdag, and the north-east – Atlas- Black Sea. The absence of large-scale lateral displacements at the intersection nodes of differently oriented supermegalineament systems indicates the relative autonomous stationarity and inheritance of the formation of the lyneament framework during the entire Mezozoic-Cenozoic and relatively low-shear nature of its implementation. This feature of the Black Sea region structural pattern significantly limits the possibility of using neomobilistic geodynamic models to explain the history of the geological development of the region. The strict consistency and orderliness of the lineament framework can be ensured only by global planetary factors associated with the influence of the rotational regime of the Earth's shells on the stress distribution in the lithosphere.
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Rowe, Shellie M., and Matthew H. Hitchman. "Rapid Destruction of a Stratospheric Potential Vorticity Anomaly by Convectively Induced Inertial Instability during the Southern Wisconsin Extreme Flooding Event of 20 August 2018." Monthly Weather Review 148, no. 11 (November 2020): 4397–414. http://dx.doi.org/10.1175/mwr-d-19-0213.1.
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AbstractThe stalling and rapid destruction of a potential vorticity (PV) anomaly in the upper troposphere–lower stratosphere (UTLS) by convectively detrained inertially unstable air is described. On 20 August 2018, 10–15 in. (~0.3–0.4 m) of rain fell on western Dane County, Wisconsin, primarily during 0100–0300 UTC 21 August (1900–2100 CDT 20 August), leading to extreme local flooding. Dynamical aspects are investigated using the University of Wisconsin Nonhydrostratic Modeling System (UWNMS). Results are compared with available radiosonde, radar, total rainfall estimates, satellite infrared, and high-resolution European Centre for Medium-Range Weather Forecasts (ECMWF) operational analyses. Using ECMWF analyses, the formation of the UTLS PV anomaly is traced to its origin a week earlier in a PV streamer over the west coast of North America. The rainfall maximum over southern Wisconsin was associated with this PV anomaly, whereby convection forming in the warm-upglide sector rotated cyclonically into the region. The quasi-stationarity of this rainfall feature was aided by a broad northeastward surge of inertially unstable convective outflow air into southeastern Wisconsin, which coincided with stalling of the eastward progression of the PV anomaly and its diversion into southern Wisconsin, extending heavy rainfall for several hours. Cessation of rainfall coincided with dilution of the PV maximum in less than an hour (2100–2200 CDT), associated with the arrival of negative PV in the upper troposphere. The region of negative PV was created when convection over Illinois transported air with low wind speed into northeastward shear. This feature is diagnosed using the convective momentum transport hypothesis.
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Ren, Hao, Jianfeng Qu, Yi Chai, Lei Huang, and Qiu Tang. "Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals." Applied Sciences 8, no. 9 (September 1, 2018): 1528. http://dx.doi.org/10.3390/app8091528.
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This study analyzes bioelectrical signals to achieve automatic epileptic seizure detection. Electroencephalographic (EEG) signals were recorded with electrodes on healthy, epileptic seizure-free, and epileptic seizure patients. The challenges in this field are generally regarded to be the impacts of non-stationarity and nonlinearity in EEG signals. To address these challenges, this study attempts to recognize different brain statuses. The idea originated from a novel hypothesis that considers EEG signals as convolution signals and regards itself as the generation mechanism of EEG signals, to some extent. Based on this hypothesis, the nonlinear problem can be viewed as a deconvolution procedure. As such, the method can be simplified into three parts: eliminating non-stationary is used to catch high-frequency to low-frequency signals, which is followed by a local mean decomposition (LMD) algorithm; these signals are deconvoluted to form ultra-high-dimensional feature sets, which is completely terminated by the mel-frequency cepstrum coefficients (MFCC) algorithm; and several classifiers are combined to achieve highly accurate recognition results and to verify the superiority and reasonableness of this method. The publicly available EEG database from the University of Bonn, Germany is employed to demonstrate the effectiveness and outstanding performance of this method. According to the results, the method has the ability to attain a higher average classification accuracy than other methods in all of the four following cases: healthy (datasets A and B) versus epileptic seizure (dataset E), epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), healthy (datasets A and B) versus epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), and healthy (dataset A) versus healthy (dataset B) versus epileptic seizure-free (dataset C) versus epileptic seizure-free (dataset D) versus epileptic seizure (dataset E).
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Lozychenko, Oleksandr. "NON-STATIONARY ECONOMY: ESSENCE AND FEATURES OF FORMATION." Economic discourse, no. 1-2 (June 30, 2022): 44–51. http://dx.doi.org/10.36742/2410-0919-2022-1-5.
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Introduction. The development of the national economy is determined by a large number of different factors. By its nature, the national economy is a complex system that develops in accordance with the basic provisions of the systems theory, synergy cyclically, going through both periods of prosperity and periods of decline with further restoration and transformation of the functioning. Scientists, studying the patterns and prerequisites for the development of economic systems, focus on the transition of this system from one state to the better state. However, there is a lack of scientific work, which analyses both theoretical and applied aspects of the national economy in difficult to predict, dissipative conditions. Methods. Within the study, a range of different in nature class and special methods of cognition is used. These include the following: content analysis, synthesis, generalization and comparative analysis, abstraction. An interdisciplinary approach, within which considerable attention is paid to methodological provisions of the systems theory and synergy, is also actively used. Results. Within the article, theoretical provisions of the non-stationary economy were considered. The analysis of scientific papers allowed us to conclude that scientists often use a range of synonymous words when considering non-stationary processes, namely: «instability», «unpredictability», «fluctuation», «stochasticity», «variability», «randomness». Taking this into account, the content of the non-stationary economy is determined. Getting acquainted with scientific papers in this direction allowed to single out its basic features and state a totally comprehensive nature of non-stationary processes when they occur in the national economy. Discussion. The obtained results of the study confirm the relevance of understanding the preconditions of the emergence of the signs of the non-stationarity within economic systems, but they also confirm the speed of its spread in the national economy. This requires new research in this area to find effective systems for detecting signs of non-stationarity, modelling the impact and consequences of its manifestation, finding new mechanisms to ensure the resilience of the national economy to new internal and external disturbances. Keywords: non-stationarity, national economy, non-stationary economy, dissipation, turbulence.
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Zhang, Yi, Yong Lv, and Mao Ge. "A Rolling Bearing Fault Classification Scheme Based on k-Optimized Adaptive Local Iterative Filtering and Improved Multiscale Permutation Entropy." Entropy 23, no. 2 (February 5, 2021): 191. http://dx.doi.org/10.3390/e23020191.
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The health condition of the rolling bearing seriously affects the operation of the whole mechanical system. When the rolling bearing parts fail, the time series collected in the field generally shows strong nonlinearity and non-stationarity. To obtain the faulty characteristics of mechanical equipment accurately, a rolling bearing fault detection technique based on k-optimized adaptive local iterative filtering (ALIF), improved multiscale permutation entropy (improved MPE), and BP neural network was proposed. In the ALIF algorithm, a k-optimized ALIF method based on permutation entropy (PE) is presented to select the number of ALIF decomposition layers adaptively. The completely average coarse-graining method was proposed to excavate more hidden information. The performance analysis of the simulation signal shows that the improved MPE can more accurately dig out the depth information of the time series, and the entropy value obtained is more consistent and stable. In the research application, rolling bearing time series are decomposed by k-optimized ALIF to obtain a certain number of intrinsic mode functions (IMFs). Then the improved MPE value of effective IMF is calculated and input into backpropagation (BP) neural network as the feature vector for automatic fault identification. The comparative analysis of simulation signals shows that this method can extract fault information effectively. At the same time, the experimental part shows that this scheme not only effectively extracts the fault features, but also realizes the classification and identification of different fault modes and faults of different degrees, which has a certain application prospect in the research and application direction of rolling bearing fault identification.
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Xu, Jing, François Anctil, and Marie-Amélie Boucher. "Exploring hydrologic post-processing of ensemble streamflow forecasts based on affine kernel dressing and non-dominated sorting genetic algorithm II." Hydrology and Earth System Sciences 26, no. 4 (February 22, 2022): 1001–17. http://dx.doi.org/10.5194/hess-26-1001-2022.
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Abstract. Forecast uncertainties are unfortunately inevitable when conducting a deterministic analysis of a dynamical system. The cascade of uncertainty originates from different components of the forecasting chain, such as the chaotic nature of the atmosphere, various initial conditions and boundaries, inappropriate conceptual hydrologic modeling, and the inconsistent stationarity assumption in a changing environment. Ensemble forecasting proves to be a powerful tool to represent error growth in the dynamical system and to capture the uncertainties associated with different sources. In practice, the proper interpretation of the predictive uncertainties and model outputs will also have a crucial impact on risk-based decisions. In this study, the performance of evolutionary multi-objective optimization (i.e., non-dominated sorting genetic algorithm II – NSGA-II) as a hydrological ensemble post-processor was tested and compared with a conventional state-of-the-art post-processor, the affine kernel dressing (AKD). Those two methods are theoretically/technically distinct, yet share the same feature in that both of them relax the parametric assumption of the underlying distribution of the data (the streamflow ensemble forecast). Both NSGA-II and AKD post-processors showed efficiency and effectiveness in eliminating forecast biases and maintaining a proper dispersion with increasing forecasting horizons. In addition, the NSGA-II method demonstrated superiority in communicating trade-offs with end-users on which performance aspects to improve.