Готові списки джерел за темами / Feature stationarity

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Добірка наукової літератури з теми "Feature stationarity"

Автор: Grafiati

Опубліковано: 10 березня 2023

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Статті в журналах з теми "Feature stationarity"

Conni, Michele, and Hilda Deborah. "Texture Stationarity Evaluation with Local Wavelet Spectrum." London Imaging Meeting 2020, no. 1 (September 29, 2020): 24–27. http://dx.doi.org/10.2352/issn.2694-118x.2020.lim-20.

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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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Більше джерел

Дисертації з теми "Feature stationarity"

Wood, Mark. "Discriminant analysis using wavelet derived features." Thesis, University of Aberdeen, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.252149.

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This thesis examines the ability of the wavelet transform to form features which may be used successfully in a discriminant analysis. We apply our methods to two different data sets and consider the problem of selecting the 'best' features for discrimination. In the first data set, our interest is in automatically recognising the variety of a carrot from an image. After necessary image preprocessing we examine the usefulness of shape descriptors and texture features for discrimination. We show that it is better to use the different 'types' of features separately, and that the wavelet coefficients of the outline coordinates are more useful. In the second data set we consider the task of automatically identifying individual haddock from the sounds they produce. We use the smoothing property of wavelets to automatically isolate individual haddock sounds, and use the stationary wavelet transform to overcome the shift dependence of the standard wavelet transform. Again we calculate different 'types' of wavelet features and compare their usefulness in classification and show that including information on the source of the previous sound can substantially increase the correct classification rate. We also apply our techniques to recognise different species of fish which is also highly successful. In each analysis, we explore different allocation rules via regularised discriminant analysis and show that the highest classification rates obtained are only slightly better than linear discriminant analysis. We also consider the problem of selecting the best subset of features for discrimination. We propose two new measures for selecting good subsets and using a genetic algorithm we search for the 'best' subsets. We investigate the relationship between out measures and classification rates showing that our method is better than selection based on F-ratios and we also discover that our two measures are closely related.

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Schwalbe, Karsten, and Karl Heinz Hoffmann. "Performance Features of a Stationary Stochastic Novikov Engine." Universitätsbibliothek Chemnitz, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-232585.

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In this article a Novikov engine with fluctuating hot heat bath temperature is presented. Based on this model, the performance measure maximum expected power as well as the corresponding efficiency and entropy production rate is investigated for four different stationary distributions: continuous uniform, normal, triangle, quadratic, and Pareto. It is found that the performance measures increase monotonously with increasing expectation value and increasing standard deviation of the distributions. Additionally, we show that the distribution has only little influence on the performance measures for small standard deviations. For larger values of the standard deviation, the performance measures in the case of the Pareto distribution are significantly different compared to the other distributions. These observations are explained by a comparison of the Taylor expansions in terms of the distributions’ standard deviations. For the considered symmetric distributions, an extension of the well known Curzon–Ahlborn efficiency to a stochastic Novikov engine is given.

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Yaseen, Muhammad Usman. "Identification of cause of impairment in spiral drawings, using non-stationary feature extraction approach." Thesis, Högskolan Dalarna, Datateknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:du-6473.

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Parkinson’s disease is a clinical syndrome manifesting with slowness and instability. As it is a progressive disease with varying symptoms, repeated assessments are necessary to determine the outcome of treatment changes in the patient. In the recent past, a computer-based method was developed to rate impairment in spiral drawings. The downside of this method is that it cannot separate the bradykinetic and dyskinetic spiral drawings. This work intends to construct the computer method which can overcome this weakness by using the Hilbert-Huang Transform (HHT) of tangential velocity. The work is done under supervised learning, so a target class is used which is acquired from a neurologist using a web interface. After reducing the dimension of HHT features by using PCA, classification is performed. C4.5 classifier is used to perform the classification. Results of the classification are close to random guessing which shows that the computer method is unsuccessful in assessing the cause of drawing impairment in spirals when evaluated against human ratings. One promising reason is that there is no difference between the two classes of spiral drawings. Displaying patients self ratings along with the spirals in the web application is another possible reason for this, as the neurologist may have relied too much on this in his own ratings.

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

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Humans and animals, during their life, continuously acquire new knowledge over time while making new experiences. They learn new concepts without forgetting what already learned, they typically use a few training examples (i.e. a child could recognize a giraffe after seeing a single picture) and they are able to discern what is known from what is unknown (i.e. unknown faces). In contrast, current supervised learning systems, work under the assumption that all data is known and available during learning, training is performed offline and a test dataset is typically required. What is missing in current research is a way to bridge the human learning capabilities in an artificial learning system where learning is performed incrementally from a data stream of infinite length (i.e. lifelong learning). This is a challenging task that is not sufficiently studied in the literature. According to this, in this thesis, we investigated different aspects of Deep Neural Network models (DNNs) to obtain stationary representations. Similar to fixed representations these representations can remain compatible between learning steps and are therefore well suited for incremental learning. Specifically, in the first part of the thesis, we propose a memory-based approach that collects and preserves all the past visual information observed so far, building a comprehensive and cumulative representation. We exploit a pre-trained fixed representation for the task of learning the appearance of face identities from unconstrained video streams leveraging temporal-coherence as a form of self-supervision. In this task, the representation allows us to learn from a few images and to detect unknown subjects similar to how humans learn. As the proposed approach makes use of a pre-trained fixed representation, learning is somewhat limited. This is due to the fact that the features stored in the memory bank remain fixed (i.e. they are not undergoing learning) and only the memory bank is learned. To address this issue, in the second part of the thesis, we propose a representation learning approach that can be exploited to learn both the feature and the memory without considering their joint learning (computationally prohibitive). The intuition is that every time the internal feature representation changes the memory bank must be relearned from scratch. The proposed method can mitigate the need of feature relearning by keeping the compatibility of features between learning steps thanks to feature stationarity. We show that the stationarity of the internal representation can be achieved with a fixed classifier by setting the classifier weights according to values taken from the coordinate vertices of the regular polytopes in high dimensional space. In the last part of the thesis, we apply the previously stationary representation method in the task of class incremental learning. We show that the method is as effective as the standard approaches while exhibiting novel stationarity properties of the internal feature representation that are otherwise non-existent. The approach exploits future unseen classes as negative examples and learns features that do not change their geometric configuration as novel classes are incorporated in the learning model. We show that a large number of classes can be learned with no loss of accuracy allowing the method to meet the underlying assumption of incremental lifelong learning.

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

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This dissertation investigates the effectiveness of a two stage fault identification methodology for rotating machines operating under non-stationary conditions with the use of a single vibration transducer. The proposed methodology transforms the machine vibration signal into a discrepancy signal by means of smart feature selection and statistical models. The discrepancy signal indicates the angular position and relative magnitude of irregular signal patterns which are assumed to be indicative of gear faults. The discrepancy signal is also independent of healthy vibration components, such as the meshing frequency, and effects of fluctuating operating conditions. The use of the discrepancy signal significantly reduces the complexity of fault detection and diagnosis. The first stage of the methodology involves extracting smart instantaneous operating condition specific features, while the second stage requires extracting smart instantaneous fault sensitive features. The instantaneous operating condition features are extracted from the coefficients of the low frequency region of the STFT of the vibration signal, since they are sensitive to operating condition changes and robust to the presence of faults. Then the sequence of operating conditions are classified using a hidden Markov model (HMM). The instantaneous fault features are then extracted from the coefficients in the wavelet packet transform (WPT) around the natural frequencies of the gearbox. These features are the converse to the operating condition features,since they are sensitive to the presence of faults and robust to the fluctuating operating conditions. The instantaneous fault features are sent to a set of Gaussian mixture models (GMMs), one GMM for each identified operating condition which enables the instantaneous fault features to be evaluated with respect to their operating condition. The GMMs generate a discrepancy signal, in the angular domain, from which gear faults may be detected and diagnosed by means of simple analysis techniques. The proposed methodology is validated using experimental data from an accelerated life test of a gearbox operated under fluctuating load and speed conditions.
Dissertation (MEng)--University of Pretoria, 2015.
Mechanical and Aeronautical Engineering
Unrestricted

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Su, Shun-Chi, and 蘇順吉. "Studies on underwater acoustic stationary and transient signals spectrum features." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/20487262396994551309.

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Анотація:

碩士
中正理工學院
電機工程研究所
86
Underwater acoustic signals are non-linear, time-varying, and with low signal-to-noise ratio. These properties make the signal analysis difficulty and complex. For resolving targets through the underwater acoustic signals, effective methods are proposed in this thesis to process underwater acoustic signals, Base on these methods, an signal acoustic recognition system is also designed. Traditionally, the Fourier transform (FT) and Morlet wavelet transform (MWT) are the main tool for stationary and transient signals spectrum analysis, respectively. Here in, a modify power spectrum density (PSD) function is used to extract the critical features for stationary underwater acoustic signals, A multi-scaling MWT kernel is also proposed which can depict the underwater transient spectrum successfully. To illustrate the effectiveness of these two novel design methods, some experiments are taken to perform by using simulation and recorded real underwater acoustic signals. Experimented results show that the proposed methods can detect and analyze both stationary and transient underwater acoustic signals successfully. An underwater acoustic signals analysis is also implemented on Matlab base personal computer to detect, analyze, and recognize targets by stationary signal features. It is hoped that an automatic underwater targets recognition system can be realized by methods discussed in this thesis in the future.

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Chang, Chia-Chi, and 張家齊. "The feature extraction and quantitative assessment of non-stationary medical signal based on Hilbert-Huang transform – Cardiovascular autoregulation for example." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/74009753049420916719.

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Анотація:

博士
國立交通大學
資訊科學與工程研究所
102
In 2008, world health organization estimated that there are 17.3 million people died from cardiovascular diseases (CVDs) and CVDs is one of the ten leading causes of death in Taiwan. CVDs is preventable compared to cancers and can be detected by cardiovascular monitoring. The health care in cardiovascular circulation becomes important now a day. The portable healthcare device becomes mature owing to the developments of several techniques, including wireless data transfer, system on chip, and wearable sensor network. The requirement of health care device becomes huge owing to aging population. Recent non-invasive cardiovascular monitoring system focuses on the development of electrocardiogram, including the specific process chip and the evaluation index of cardiac function, but the research of cardiovascular auto-regulation is relatively rare. Traditional studies investigated that the auto-regulation could be assessed by heart rate variability (HRV). Most of the HRV studies adopted Fourier transform or wavelet transform as spectral analysis method. These methods have good properties to extract and present the characteristics of stationary signal based on their own mathematical fundamental basis, such as sinusoid function or wavelet function, but the characteristics of medical signal are usually non-stationary. Recently, N.E. Huang proposed a novel adaptive method, called Hilbert-Huang transform (HHT). HHT has good capability for non-stationary characterization without information loss and extracts the intrinsic features in multiple scales. The extraction method of HHT, called empirical mode decomposition (EMD), extracts the intrinsic features of signal based on signal's fluctuations, such as the envelop of signal, and is adaptive for different target signals. The aim of this research is to 1) extract the intrinsic features of blood pulse signal by HHT; 2) quantitatively assess the non-stationary features in multiple time scales; 3) examine the usefulness of the assessment in clinic. The results showed that the blood pulse signal could be decomposed into four different intrinsic features in four physiological time scales, including noise, pulse wave morphology, short-term trend, long-term trend. By analysis of arterial blood pressure (ABP), the reflection wave could be enhanced by EMD and the results were consistent with traditional studies. The pulse wave could be extracted from ABP by EMD, and instantaneous pulse rate (iPR) was estimated by normalized Hilbert transform. The results showed that the iPR presents the characteristics of respiration and cardiovascular auto-regulation. In short-term ABP trend study, the individual ABP regulation was extracted adaptively by EMD. This method helps for the exploration of individual optimal frequency band in auto-regulation assessment. This research also designed and implemented the prototype of cardiovascular auto-regulation monitoring system based on embedded system development and network programming. The progress of this research contains several parts. Currently, there are several non-linear approach for cardiovascular auto-regulation analysis, such as detrend fluctuation analysis and multiscale entropy. Though, the iPR can be used as the estimator of cardiovascular auto-regulation function, the relationship between iPR and CVDs needs further investigation. Besides, the ABP signal is hard to get in daily life, the replacement of ABP signal by blood pulse sensing needs further examination and validation.

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Книги з теми "Feature stationarity"

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

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The textbook considers the need, principles and methods of mutual conversion of parameters of electric energy at DC and AC for stationary and Autonomous objects. Features of operation of power electronics elements in specific conditions of their continuous high-frequency switching are described. Low-current control systems that provide the necessary logic for the operation of Executive power devices of converters are considered. A large number of specific practical electrical diagrams of electric energy converters are given. It is intended for students studying in the direction of 13.03.02 "electric power and electrical engineering". It can be useful for graduate students and specialists involved in the development and operation of electric power converters.

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

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A brain–machine interface (BMI) is a biohybrid system intended as an alternative communication channel for people suffering from severe motor impairments. A BMI can involve either invasively implanted electrodes or non-invasive imaging systems. The focus in this chapter is on non-invasive approaches; EEG-based BMI is the most widely investigated. Event-related de-synchronization/ synchronization (ERD/ERS) of sensorimotor rhythms (SMRs), P300, and steady-state visual evoked potential (SSVEP) are the three main cortical activation patterns used for designing an EEG-based BMI. A BMI involves multiple stages: brain data acquisition, pre-processing, feature extraction, and feature classification, along with a device to communicate or control with or without neurofeedback. Despite extensive research worldwide, there are still several challenges to be overcome in making BMI practical for daily use. One such is to account for non-stationary brainwaves dynamics. Also, some people may initially find it difficult to establish a reliable BMI with sufficient accuracy. BMI research, however, is progressing in two broad areas: replacing neuromuscular pathways and neurorehabilitation.

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Частини книг з теми "Feature stationarity"

Eitzinger, Christian, and Stefan Thumfart. "Optimizing Feature Calculation in Adaptive Machine Vision Systems." In Learning in Non-Stationary Environments, 349–74. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-8020-5_13.

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Ftoutou, Ezzeddine, Mnaouar Chouchane, and Noureddine Besbès. "Feature Selection for Diesel Engine Fault Classification." In Condition Monitoring of Machinery in Non-Stationary Operations, 309–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_33.

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Entezami, Alireza. "Feature Extraction in Time Domain for Stationary Data." In Structural Health Monitoring by Time Series Analysis and Statistical Distance Measures, 17–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66259-2_2.

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Thaler, Tilen, Primož Potočnik, Peter Mužič, Ivan Bric, Rudi Bric, and Edvard Govekar. "Chatter Recognition in Band Sawing Based on Feature Extraction and Discriminant Analysis." In Condition Monitoring of Machinery in Non-Stationary Operations, 607–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_63.

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Bhanu, Bir, and Ju Han. "Human Recognition on Combining Kinematic and Stationary Features." In Lecture Notes in Computer Science, 600–608. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44887-x_71.

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Schaffernicht, Erik, Volker Stephan, and Horst-Michael Gross. "Adaptive Feature Transformation for Image Data from Non-stationary Processes." In Artificial Neural Networks – ICANN 2009, 735–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_74.

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Entezami, Alireza. "Feature Extraction in Time-Frequency Domain for Non-Stationary Data." In Structural Health Monitoring by Time Series Analysis and Statistical Distance Measures, 47–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66259-2_3.

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Rustamova, D. F., and A. M. Mehdiyeva. "Features of Digital Processing of Non-stationary Processes in Measurement and Control." In Informatics and Cybernetics in Intelligent Systems, 592–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77448-6_58.

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Cardona-Morales, O., D. Alvarez-Marin, and G. Castellanos-Dominguez. "Condition Monitoring Under Non-Stationary Operating Conditions using Time–Frequency Representation-Based Dynamic Features." In Lecture Notes in Mechanical Engineering, 441–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39348-8_38.

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Biloborodova, Tetiana, Inna Skarga-Bandurova, Illia Skarha-Bandurov, Yelyzaveta Yevsieieva, and Oleh Biloborodov. "ECG Classification Using Combination of Linear and Non-Linear Features with Neural Network." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220388.

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In this paper, we present an approach to improve the accuracy and reliability of ECG classification. The proposed method combines features analysis of linear and non-linear ECG dynamics. Non-linear features are represented by complexity measures of assessment of ordinal network non-stationarity. We describe the basic concept of ECG partitioning and provide an experiment on PQRST complex data. The results demonstrate that the proposed technique effectively detects abnormalities via automatic feature extraction and improves the state-of-the-art detection performance on one of the standard collections of heartbeat signals, the ECG5000 dataset.

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Тези доповідей конференцій з теми "Feature stationarity"

Poulos, Marios. "Definition text's syntactic feature using stationarity control." In 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA). IEEE, 2017. http://dx.doi.org/10.1109/iisa.2017.8316418.

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Kawanabe, Motoaki. "Robust feature construction against non-stationarity for EEG brain-machine interface." In 2014 International Winter Workshop on Brain-Computer Interface (BCI). IEEE, 2014. http://dx.doi.org/10.1109/iww-bci.2014.6782557.

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Yu, Shujian, Xiaoyang Wang, and José C. Príncipe. "Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/421.

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One important assumption underlying common classification models is the stationarity of the data. However, in real-world streaming applications, the data concept indicated by the joint distribution of feature and label is not stationary but drifting over time. Concept drift detection aims to detect such drifts and adapt the model so as to mitigate any deterioration in the model's predictive performance. Unfortunately, most existing concept drift detection methods rely on a strong and over-optimistic condition that the true labels are available immediately for all already classified instances. In this paper, a novel Hierarchical Hypothesis Testing framework with Request-and-Reverify strategy is developed to detect concept drifts by requesting labels only when necessary. Two methods, namely Hierarchical Hypothesis Testing with Classification Uncertainty (HHT-CU) and Hierarchical Hypothesis Testing with Attribute-wise "Goodness-of-fit" (HHT-AG), are proposed respectively under the novel framework. In experiments with benchmark datasets, our methods demonstrate overwhelming advantages over state-of-the-art unsupervised drift detectors. More importantly, our methods even outperform DDM (the widely used supervised drift detector) when we use significantly fewer labels.

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Marple, S. Lawrence, Phillip M. Corbell, and Muralidhar Rangaswamy. "New Non-Stationary Target Feature Detection Techniques." In 2006 Fortieth Asilomar Conference on Signals, Systems and Computers. IEEE, 2006. http://dx.doi.org/10.1109/acssc.2006.354808.

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Tuske, Zoltan, Pavel Golik, Ralf Schluter, and Friedhelm R. Drepper. "Non-stationary feature extraction for automatic speech recognition." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947530.

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Wang, Yonghui, and Suxia Cui. "Hyperspectral image feature classification using stationary wavelet transform." In 2014 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2014. http://dx.doi.org/10.1109/icwapr.2014.6961299.

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Ni, Bingbing, Shuicheng Yan, and Ashraf Kassim. "Directed Markov Stationary Features for visual classification." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4959711.

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Zhang, Yudong, Zhengchao Dong, Lenan Wu, Shuihua Wang, and Zhenyu Zhou. "Feature Extraction of Brain MRI by Stationary Wavelet Transform." In 2010 International Conference on Biomedical Engineering and Computer Science (ICBECS). IEEE, 2010. http://dx.doi.org/10.1109/icbecs.2010.5462491.

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Takyu, Osamu, Hiroyoshi Yano, Takeo Fujii, and Tomoaki Ohtsuki. "Double stage and combining detection for cyclo-stationary feature." In 2012 IEEE Radio and Wireless Symposium (RWS). IEEE, 2012. http://dx.doi.org/10.1109/rws.2012.6175367.

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Ortego, Diego, and Juan C. SanMiguel. "Multi-feature stationary foreground detection for crowded video-surveillance." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025486.

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Звіти організацій з теми "Feature stationarity"

ZOTOVA, V. A., E. G. SKACHKOVA, and T. D. FEOFANOVA. METHODOLOGICAL FEATURES OF APPLICATION OF SIMILARITY THEORY IN THE CALCULATION OF NON-STATIONARY ONE-DIMENSIONAL LINEAR THERMAL CONDUCTIVITY OF A ROD. Science and Innovation Center Publishing House, April 2022. http://dx.doi.org/10.12731/2227-930x-2022-12-1-2-43-53.

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

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Symonenko, Svitlana V., Nataliia V. Zaitseva, Viacheslav V. Osadchyi, Kateryna P. Osadcha, and Ekaterina O. Shmeltser. Virtual reality in foreign language training at higher educational institutions. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3759.

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The paper deals with the urgent problem of application of virtual reality in foreign language training. Statistical data confirms that the number of smartphone users, Internet users, including wireless Internet users, has been increasing for recent years in Ukraine and tends to grow. The coherence of quick mobile Internet access and presence of supplementary equipment enables to get trained or to self-dependently advance due to usage of virtual reality possibilities for education in the stationary classrooms, at home and in motion. Several important features of virtual reality, its advantages for education are discussed. It is noted that virtual reality is remaining a relatively new technology in language learning. Benefits from virtual reality implementation into foreign language learning and teaching are given. The aspects of immersion and gamification in foreign language learning are considered. It is emphasized that virtual reality creates necessary preconditions for motivation increasing. The results of the survey at two higher education institution as to personal experience in using VR applications for learning foreign languages are presented. Most students at both universities have indicated quite a low virtual reality application usage. Six popular virtual reality applications for foreign language learning (Mondly, VRSpeech, VR Learn English, Gold Lotus, AltSpaceVR and VirtualSpeech) are analyzed. It is stated that the most preferred VR application for foreign language learning includes detailed virtual environment for maximal immersion, high- level visual effects similar to video games, simple avatar control, thorough material selection and complete complicity level accordance of every element and aspect, affordability, helpful and unobtrusive following up.

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