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Journal articles on the topic 'Short-time features'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Martinez-Heath, M. R., and A. G. Deacon. "Engineering Risk Assessment in Manufacturing Products with Short Time-to-Market Windows." Journal of Engineering for Industry 117, no. 1 (February 1, 1995): 49–54. http://dx.doi.org/10.1115/1.2803277.

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This paper establishes the concept of manufacturing risk assessment based on design features. It also presents a methodology for assessing risks associated with producing feature-rich products with short time-to-market windows. Product design features are classified as inherent and value-added. Inherent features are functionalities of a product that provide a core benefit to the user. Without inherent features, a product cannot exist in the marketplace. Value-added design features augment the customer-perceived value of a product. A product with value-added features provides the manufacturer w

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Behzad, M., A. R. Bastami, and D. Mba. "Rolling bearing fault detection by short-time statistical features." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 226, no. 3 (October 19, 2011): 229–37. http://dx.doi.org/10.1177/0954408911422635.

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3

Akulenko, L. D., Yu G. Markov, V. V. Perepelkin, and L. V. Rykhlova. "Short-time-scale features of the Earth’s polar motion." Astronomy Reports 53, no. 11 (November 2009): 1070–77. http://dx.doi.org/10.1134/s1063772909110122.

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Heinen, Marco, Peter Holmqvist, Adolfo J. Banchio, and Gerhard Nägele. "Short-time diffusion of charge-stabilized colloidal particles: generic features." Journal of Applied Crystallography 43, no. 5 (August 19, 2010): 970–80. http://dx.doi.org/10.1107/s002188981002724x.

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Analytical theory and Stokesian dynamics simulations are used in conjunction with dynamic light scattering to investigate the role of hydrodynamic interactions in short-time diffusion in suspensions of charge-stabilized colloidal particles. The particles are modeled as solvent-impermeable charged spheres, repelling each otherviaa screened Coulomb potential. Numerical results for self-diffusion and sedimentation coefficients, as well as hydrodynamic and short-time diffusion functions, are compared with experimental data for a wide range of volume fractions. The theoretical predictions for the g

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Riest, Jonas, and Gerhard Nägele. "Short-time dynamics in dispersions with competing short-range attraction and long-range repulsion." Soft Matter 11, no. 48 (2015): 9273–80. http://dx.doi.org/10.1039/c5sm02099a.

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Sundararajan, Narasimman, A. Ebrahimi, and Nannappa Vasudha. "Two Dimensional Short Time Hartley Transforms." Sultan Qaboos University Journal for Science [SQUJS] 21, no. 1 (November 1, 2016): 41. http://dx.doi.org/10.24200/squjs.vol21iss1pp41-47.

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The Hartley transform, as in the case of the Fourier transform, is not suitably applicable to non-stationary representations of signals whose statistical properties change as a function of time. Hence, different versions of 2-D short time Hartley transforms (STHT) are given in comparison with the short time Fourier transform (STFT). Although the two different versions of STHT defined here with their inverses are equally applicable, one of them is mathematically incorrect/incompatible due to the incorrect definition of the 2-D Hartley transform in literature. These definitions of STHTs can easi

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Ramalingam, A., and S. Krishnan. "Gaussian Mixture Modeling of Short-Time Fourier Transform Features for Audio Fingerprinting." IEEE Transactions on Information Forensics and Security 1, no. 4 (December 2006): 457–63. http://dx.doi.org/10.1109/tifs.2006.885036.

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8

Rusnak, Yu. "SEMANTIC AND STRUCTURAL FEATURES OF TIME ADVERBS IN OLGA KOBYLYANSKA’S SHORT PROSE." International Humanitarian University Herald. Philology 2, no. 46 (2020): 104–7. http://dx.doi.org/10.32841/2409-1154.2020.46-2.25.

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Sun, Dechao, Jiali Wu, Hong Huang, Renfang Wang, Feng Liang, and Hong Xinhua. "Prediction of Short-Time Rainfall Based on Deep Learning." Mathematical Problems in Engineering 2021 (March 30, 2021): 1–8. http://dx.doi.org/10.1155/2021/6664413.

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Short-time heavy rainfall is a kind of sudden strong and heavy precipitation weather, which seriously threatens people’s life and property safety. Accurate precipitation nowcasting is of great significance for the government to make disaster prevention and mitigation decisions in time. In order to make high-resolution forecasts of regional rainfall, this paper proposes a convolutional 3D GRU (Conv3D-GRU) model to predict the future rainfall intensity over a relatively short period of time from the machine learning perspective. Firstly, the spatial features of radar echo maps with different hei

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Qiao, Mu, and Zixuan Cheng. "A Novel Long- and Short-Term Memory Network with Time Series Data Analysis Capabilities." Mathematical Problems in Engineering 2020 (October 13, 2020): 1–9. http://dx.doi.org/10.1155/2020/8885625.

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Time series data are an extremely important type of data in the real world. Time series data gradually accumulate over time. Due to the dynamic growth in time series data, they tend to have higher dimensions and large data scales. When performing cluster analysis on this type of data, there are shortcomings in using traditional feature extraction methods for processing. To improve the clustering performance on time series data, this study uses a recurrent neural network (RNN) to train the input data. First, an RNN called the long short-term memory (LSTM) network is used to extract the features

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Itaya, Satoko, Naoki Yoshinaga, Peter Davis, Rie Tanaka, Taku Konishi, Shinichi Doi, and Keiji Yamada. "Common features of short-time dynamics of e-mail communication in work groups." Nonlinear Theory and Its Applications, IEICE 5, no. 1 (2014): 89–99. http://dx.doi.org/10.1587/nolta.5.89.

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Abdelbaky, Amany, and Saleh Aly. "Human action recognition using short-time motion energy template images and PCANet features." Neural Computing and Applications 32, no. 16 (January 19, 2020): 12561–74. http://dx.doi.org/10.1007/s00521-020-04712-1.

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13

Zakharov, S. M. "VARIABILITY OF ARTERIAL PRESSURE AT SHORT-TERM TIME INTERVALS." Issues of radio electronics, no. 1 (January 20, 2019): 72–77. http://dx.doi.org/10.21778/2218-5453-2019-1-72-77.

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The time and spectral analysis of blood pressure signals (BP of systolic, diastolic, pulse) obtained in real time and reflecting the work of the heart at short time intervals is presented. As a time interval, a sequence of one hundred cardiac cycles was chosen. The main parameters of variability are determined. The proposed method of analysis is an analogue of heart rate variability (HRV), based on the study of RR cardiointervals. Spectral analysis of blood pressure signals shows differences in the degree of orderliness or disorder of individual frequencies or the spectrum as a whole. The pres

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Lee, Myung-Jun, Ji-Eun Kim, Bo-Hyun Ryu, and Kyung-Tae Kim. "Robust Maritime Target Detector in Short Dwell Time." Remote Sensing 13, no. 7 (March 30, 2021): 1319. http://dx.doi.org/10.3390/rs13071319.

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Detection of small-sized maritime targets is an important task for a marine surveillance radar. Recently, with the emergence of a marine surveillance radar system that has a narrow azimuth beamwidth and rapidly rotating antennas, the available dwell time for detecting a maritime target is usually very short. This short dwell time considerably degrades the performance of conventional detectors, especially those focusing on small-sized targets. In this paper, we propose an efficient detector for small-sized maritime targets to provide a reliable detection performance, even in short dwell times.

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Ouzounov, A. "Mean-Delta Features for Telephone Speech Endpoint Detection." Information Technologies and Control 12, no. 3-4 (December 1, 2014): 36–44. http://dx.doi.org/10.1515/itc-2016-0005.

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Abstract In this paper, a brief summary of the author’s research in the field of the contour-based telephone speech Endpoint Detection (ED) is presented. This research includes: development of new robust features for ED – the Mean-Delta feature and the Group Delay Mean-Delta feature and estimation of the effect of the analyzed ED features and two additional features in the Dynamic Time Warping fixed-text speaker verification task with short noisy telephone phrases in Bulgarian language.

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Wang, Baoxiang, Hongxia Pan, and Wei Yang. "A complementary approach for fault diagnosis of rolling bearing using canonical variate analysis based short-time energy feature." Journal of Vibration and Control 24, no. 18 (July 18, 2017): 4195–210. http://dx.doi.org/10.1177/1077546317721844.

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Signal decomposition is a meaningful and effective methodology which is widely used for fault diagnosis. Mode/feature selection is an inevitable topic for fault diagnosis of rolling bearing due to over-decomposition. In practical application, the selection of sensitive modes is a challenging task, so many valuable works have been performed to cope with it. However, the published works lack an effective approach to acquire few meaningful modes by avoiding the complicated mode selection procedures, prior to feature extraction. Moreover, selection of the modes of interest fails to take the residu

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Mahyudi, Johan, Djoko Saryono, Wahyudi Siswanto, and Yuni Pratiwi. "Construction of Visual Features of Indonesian Digital Poetry." International Journal of Linguistics, Literature and Culture 3, no. 5 (September 3, 2017): 1. http://dx.doi.org/10.21744/ijllc.v3i5.526.

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In short time, Indonesian digital poetry attracts its audience through a series of visualization features of the digital art. This research uses a short segment analysis on Indonesian videography digital poetry to demonstrate the existence of visual conglomeration practices through the creation of objects, features, a feature of space, measuring distance in feature space, and dimension reduction. These five approaches are proposed by Manovich (2014) in grouping millions of visual artworks based on simple criteria. Of the three common objects are found, Indonesian animators, prefer individual

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18

Ran, Xiangdong, Zhiguang Shan, Yong Shi, and Chuang Lin. "Short-Term Travel Time Prediction: A Spatiotemporal Deep Learning Approach." International Journal of Information Technology & Decision Making 18, no. 04 (July 2019): 1087–111. http://dx.doi.org/10.1142/s0219622019500202.

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Traffic prediction is a complex, nonlinear spatiotemporal relationship modeling task with the randomness of traffic demand, the spatial and temporal dependency between traffic flows, and other recurrent and nonrecurrent factors. Based on the ability to learn generic features from history information, deep learning approaches have been recently applied to traffic prediction. Convolutional neural network (CNN) methods that learn traffic as images can improve the predictive accuracy by leveraging the implicit correlations among nearby links. Traffic prediction based on CNN is still in its initial

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19

Sherstiuk, N. "Space-time features of the short story “The Purloined Letter” by Edgar Allan Poe." International Humanitarian University Herald. Philology 40, no. 3 (2019): 46–49. http://dx.doi.org/10.32841/2409-1154.2019.40.3.11.

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He, Aixiang, Jun Yu, Guangfen Wei, Yi Chen, Hao Wu, and Zhenan Tang. "Short-Time Fourier Transform and Decision Tree-Based Pattern Recognition for Gas Identification Using Temperature Modulated Microhotplate Gas Sensors." Journal of Sensors 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/7603931.

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Because the sensor response is dependent on its operating temperature, modulated temperature operation is usually applied in gas sensors for the identification of different gases. In this paper, the modulated operating temperature of microhotplate gas sensors combined with a feature extraction method based on Short-Time Fourier Transform (STFT) is introduced. Because the gas concentration in the ambient air usually has high fluctuation, STFT is applied to extract transient features from time-frequency domain, and the relationship between the STFT spectrum and sensor response is further explore

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Chen, Shuang, Zengcai Wang, and Wenxin Chen. "Driver Drowsiness Estimation Based on Factorized Bilinear Feature Fusion and a Long-Short-Term Recurrent Convolutional Network." Information 12, no. 1 (December 22, 2020): 3. http://dx.doi.org/10.3390/info12010003.

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The effective detection of driver drowsiness is an important measure to prevent traffic accidents. Most existing drowsiness detection methods only use a single facial feature to identify fatigue status, ignoring the complex correlation between fatigue features and the time information of fatigue features, and this reduces the recognition accuracy. To solve these problems, we propose a driver sleepiness estimation model based on factorized bilinear feature fusion and a long- short-term recurrent convolutional network to detect driver drowsiness efficiently and accurately. The proposed framework

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22

Paton, Steven. "Time- Lessness, simultaneity and successivity: repetition in Beckett’s short prose." Language and Literature: International Journal of Stylistics 18, no. 4 (October 27, 2009): 357–66. http://dx.doi.org/10.1177/0963947009343953.

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This article focuses on the ways in which Samuel Beckett’s short prose work Lessness constructs the idea of timelessness through formal means. It shows how stylistic features such as the exceptionally high levels of repetition and parallelism, omission of tensed verbs, and omission of connectives and subordinate clauses, work to remove time from the form of the text. In Jakobsonian terms these formal features are seen to replace the forces of successivity — movement in time and narrative progression — with a radical simultaneity. The article then deals with the problematic form of successivity

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23

Park, Dajeong, Miran Lee, Sunghee Park, Joon-Kyung Seong, and Inchan Youn. "Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor." Sensors 18, no. 7 (July 23, 2018): 2387. http://dx.doi.org/10.3390/s18072387.

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Routine stress monitoring in daily life can predict potentially serious health impacts. Effective stress monitoring in medical and healthcare fields is dependent upon accurate determination of stress-related features. In this study, we determined the optimal stress-related features for effective monitoring of cumulative stress. We first investigated the effects of short- and long-term stress on various heart rate variability (HRV) features using a rodent model. Subsequently, we determined an optimal HRV feature set using support vector machine-recursive feature elimination (SVM-RFE). Experimen

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Too, Jingwei, Abdul Abdullah, Norhashimah Mohd Saad, Nursabillilah Mohd Ali, and Weihown Tee. "A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification." Computers 7, no. 4 (November 5, 2018): 58. http://dx.doi.org/10.3390/computers7040058.

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Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short-time Fourier transform (STFT) transforms the EMG signal into time-frequency representation. Ten time-frequency features are extracted from the STFT c

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WANG, L., J. B. ZHANG, H. P. YING, and D. R. JI. "SHORT-TIME CRITICAL DYNAMICS OF MULTISPIN INTERACTION ISING MODEL IN TWO DIMENSIONS." Modern Physics Letters B 13, no. 28 (December 10, 1999): 1011–18. http://dx.doi.org/10.1142/s021798499900124x.

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We investigated the short-time dynamics of a multispin model in two dimensions. A dynamical Monte Carlo simulation which avoids the critical slowing down is performed at critical temperature and the short-time dynamic scaling behavior is found. By using the universal power-law scaling features, the critical exponents θ, z and 2β/ν are estimated in our calculations.

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David, Edwin F., and Richard M. Stratt. "The anharmonic features of the short-time dynamics of fluids: The time evolution and mixing of instantaneous normal modes." Journal of Chemical Physics 109, no. 4 (July 22, 1998): 1375–90. http://dx.doi.org/10.1063/1.476690.

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27

Grcić, Ivan, Hrvoje Pandžić, and Damir Novosel. "Fault Detection in DC Microgrids Using Short-Time Fourier Transform." Energies 14, no. 2 (January 6, 2021): 277. http://dx.doi.org/10.3390/en14020277.

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Fault detection in microgrids presents a strong technical challenge due to the dynamic operating conditions. Changing the power generation and load impacts the current magnitude and direction, which has an adverse effect on the microgrid protection scheme. To address this problem, this paper addresses a field-transform-based fault detection method immune to the microgrid conditions. The faults are simulated via a Matlab/Simulink model of the grid-connected photovoltaics-based DC microgrid with battery energy storage. Short-time Fourier transform is applied to the fault time signal to obtain a

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Hao, Yaping, and Qiang Gao. "Predicting the Trend of Stock Market Index Using the Hybrid Neural Network Based on Multiple Time Scale Feature Learning." Applied Sciences 10, no. 11 (June 7, 2020): 3961. http://dx.doi.org/10.3390/app10113961.

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In the stock market, predicting the trend of price series is one of the most widely investigated and challenging problems for investors and researchers. There are multiple time scale features in financial time series due to different durations of impact factors and traders’ trading behaviors. In this paper, we propose a novel end-to-end hybrid neural network, a model based on multiple time scale feature learning to predict the price trend of the stock market index. Firstly, the hybrid neural network extracts two types of features on different time scales through the first and second layers of

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Jiang, Meihui, Xiangyun Gao, Haizhong An, Xiaoliang Jia, and Xiaoqi Sun. "Multiscale Fluctuation Features of the Dynamic Correlation between Bivariate Time Series." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/4742060.

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The fluctuation of the dynamic correlation between bivariate time series has some special features on the time-frequency domain. In order to study these fluctuation features, this paper built the dynamic correlation network models using two kinds of time series as sample data. After studying the dynamic correlation networks at different time-scales, we found that the correlation between time series is a dynamic process. The correlation is strong and stable in the long term, but it is weak and unstable in the short and medium term. There are key correlation modes which can effectively indicate

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Telesca, Luciano, Vincenzo Lapenna, Filippos Vallianatos, John Makris, and Vassilios Saltas. "Multifractal features in short-term time dynamics of ULF geomagnetic field measured in Crete, Greece." Chaos, Solitons & Fractals 21, no. 2 (July 2004): 273–82. http://dx.doi.org/10.1016/j.chaos.2003.10.020.

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Babalola, Oluwaseyi P., Ayinde M. Usman, Olayinka O. Ogundile, and Daniel J. J. Versfeld. "Detection of Bryde's whale short pulse calls using time domain features with hidden Markov models." SAIEE Africa Research Journal 112, no. 1 (March 2021): 15–23. http://dx.doi.org/10.23919/saiee.2021.9340533.

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32

Eyal Salman, Hamzeh, Abdelhak-Djamel Seriai, and Christophe Dony. "Feature-Level Change Impact Analysis Using Formal Concept Analysis." International Journal of Software Engineering and Knowledge Engineering 25, no. 01 (February 2015): 69–92. http://dx.doi.org/10.1142/s0218194015400045.

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Software Product Line Engineering (SPLE) is a systematic reuse approach to develop a short time-to-market and quality products, called Software Product Line (SPL). Usually, a SPL is not developed from scratch but it is developed by reusing features (resp. their implementing source code elements) of existing similar systems previously developed by ad-hoc reuse techniques. The features implementations that are reused may be changed for developing new products (SPL) using SPLE. Any code element can be a part of (shared by) different features implementations; modifying one feature's implementation

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33

Chatterjee, Anindya. "The Short-Time Impulse Response of Euler-Bernoulli Beams." Journal of Applied Mechanics 71, no. 2 (March 1, 2004): 208–18. http://dx.doi.org/10.1115/1.1667531.

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We study an undamped, simply supported, Euler-Bernoulli beam given an instantaneous impulse at a point G, far from its ends. The standard modal solution obscures interesting mathematical features of the initial response, which are studied here using dimensional analysis, an averaging procedure of Zener, a similarity solution for an infinite beam, asymptotics, heuristics, and numerics. Results obtained include short-time asymptotic estimates for various dynamic quantities, as well as a numerical demonstration of fractal behavior in the response. The leading order displacement of G is proportion

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Su, Xinyue, Tiejian Li, Chenge An, and Guangqian Wang. "Prediction of Short-Time Cloud Motion Using a Deep-Learning Model." Atmosphere 11, no. 11 (October 26, 2020): 1151. http://dx.doi.org/10.3390/atmos11111151.

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A cloud image can provide significant information, such as precipitation and solar irradiation. Predicting short-time cloud motion from images is the primary means of making intra-hour irradiation forecasts for solar-energy production and is also important for precipitation forecasts. However, it is very challenging to predict cloud motion (especially nonlinear motion) accurately. Traditional methods of cloud-motion prediction are based on block matching and the linear extrapolation of cloud features; they largely ignore nonstationary processes, such as inversion and deformation, and the bound

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Guo, Weicheng, Beizhi Li, and Qinzhi Zhou. "An intelligent monitoring system of grinding wheel wear based on two-stage feature selection and Long Short-Term Memory network." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 233, no. 13 (April 5, 2019): 2436–46. http://dx.doi.org/10.1177/0954405419840556.

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Grinding wheel condition is considered as the key factor affecting grinding performance, and therefore, accurate monitoring of wheel wear is necessary to prevent the deterioration of part quality. An intelligent wheel wear monitoring system is introduced in this article to realize processing of grinding signal, extraction of signal features, selection of optimal feature subset, and prediction of wheel wear. Physical information generated during the grinding of C-250 maraging steel is collected by a dynamometer, accelerometer, and acoustic emission sensor, and a large quantity of features in ti

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Monaco, Alfonso, Nicola Amoroso, Loredana Bellantuono, Ester Pantaleo, Sabina Tangaro, and Roberto Bellotti. "Multi-Time-Scale Features for Accurate Respiratory Sound Classification." Applied Sciences 10, no. 23 (December 1, 2020): 8606. http://dx.doi.org/10.3390/app10238606.

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The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of fe

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Wu, Shuang, Li He, Zhaolong Zhang, and Yu Du. "Forecast of Short-Term Electricity Price Based on Data Analysis." Mathematical Problems in Engineering 2021 (February 16, 2021): 1–14. http://dx.doi.org/10.1155/2021/6637183.

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The decision-making of power generation enterprises, power supply enterprises, and power consumers can be affected by forecasting the price of electricity. There are many irrelevant samples and features in big data, which often lead to low forecasting accuracy and high time-cost. Therefore, this paper proposes a forecasting framework based on big data processing, which selects a small quantity of data to achieve accurate forecasting while reducing the time-cost. First, the sample selection based on grey correlation analysis (GCA) is established to eliminate useless samples from the periodicity

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Kang, Leilei, Guojing Hu, Hao Huang, Weike Lu, and Lan Liu. "Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction." Journal of Advanced Transportation 2020 (August 14, 2020): 1–16. http://dx.doi.org/10.1155/2020/3247847.

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In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. Due to the highly nonlinear and dynamic nature of travel time series, it is necessary to consider time dependence and the spatial reliance of travel time series for predicting the travel time of road networks. The dynamic feature of the travel time series can be

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Murray, Gabriel. "Graph-Based Prediction of Meeting Participation." Multimodal Technologies and Interaction 3, no. 3 (July 12, 2019): 54. http://dx.doi.org/10.3390/mti3030054.

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Given a meeting participant’s turn-taking dynamics during one segment of a meeting, and their contribution to the group discussion up to that point, our aim is to automatically predict their activity level at a later point of the meeting. The predictive models use verbal and nonverbal features derived from social network representations of each small group interaction. The best automatic prediction models consistently outperform two baseline models at multiple time-lags. We analyze which interaction features are most predictive of later meeting activity levels, and investigate the efficacy of

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40

Chen, Xu, and Jun Tang. "Research on Piano Music Signal Recognition Based on Short-Time Fourier Analysis." Advanced Materials Research 853 (December 2013): 680–85. http://dx.doi.org/10.4028/www.scientific.net/amr.853.680.

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This paper starts with the basic process of music recognition to complete the study on extraction and realization of seven musical characteristics of the music features characterization, at the same time, the paper in-depth studies the pitch value duration, tonality characteristic extraction unit. Fourier analysis method based on short-time uses the computer programming for audio signal automatic analysis and processing, implements the characteristics recognition of the piano music playing, Experimental data show that the average recognition rate of algorithm is above 95% with the strong recog

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Ouzounov, Atanas. "Telephone Speech Endpoint Detection using Mean-Delta Feature." Cybernetics and Information Technologies 14, no. 2 (July 15, 2014): 127–39. http://dx.doi.org/10.2478/cait-2014-0025.

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Abstract In the study the efficiency of three features for trajectory-based endpoint detection is experimentally evaluated in the fixed-text Dynamic Time Warping (DTW) - a based speaker verification task with short phrases of telephone speech. The employed features are Modified Teager Energy (MTE), Energy-Entropy (EE) feature and Mean-Delta (MD) feature. The utterance boundaries in the endpoint detector are provided by means of state automaton and a set of thresholds based only on trajectory characteristics. The training and testing have been done with noisy telephone speech (short phrases in

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Wen, Mofei, and Yuwei Wang. "Multimodal Sensor Motion Intention Recognition Based on Three-Dimensional Convolutional Neural Network Algorithm." Computational Intelligence and Neuroscience 2021 (June 3, 2021): 1–11. http://dx.doi.org/10.1155/2021/5690868.

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With the development of microelectronic technology and computer systems, the research of motion intention recognition based on multimodal sensors has attracted the attention of the academic community. Deep learning and other nonlinear neural network models have a wide range of applications in big data sets. We propose a motion intention recognition algorithm based on multimodal long-term and short-term spatiotemporal feature fusion. We divide the target data into multiple segments and use a three-dimensional convolutional neural network to extract the short-term spatiotemporal features. The th

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Yuo, Kuo-Hwei, and Hsiao-Chuan Wang. "Robust features for noisy speech recognition based on temporal trajectory filtering of short-time autocorrelation sequences." Speech Communication 28, no. 1 (May 1999): 13–24. http://dx.doi.org/10.1016/s0167-6393(99)00004-7.

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Mingaleev, V. Z., I. A. Ionova, K. S. Chirko, G. R. Mingaleeva, D. R. Sagitov, and A. G. Yaparova. "Kinetic features of short-time polymerization of isoprene in the presence of supported titanium–magnesium catalyst." Polymer Science, Series B 59, no. 4 (July 2017): 397–404. http://dx.doi.org/10.1134/s1560090417040078.

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Zhang, Chuyue, Xiaofan Zhao, Manchun Cai, Dawei Wang, and Luzhe Cao. "A new model for predicting the attributes of suspects." Computer Science and Information Systems 17, no. 3 (2020): 705–15. http://dx.doi.org/10.2298/csis200107016z.

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In this paper, we propose a new model to predict the age and number of suspects through the feature modeling of historical data. We discrete the case information into values of 20 dimensions. After feature selection, we use 9 machine learning algorithms and Deep Neural Networks to extract the numerical features. In addition, we use Convolutional Neural Networks and Long Short- Term Memory to extract the text features of case description. These two types of features are fused and fed into fully connected layer and softmax layer. This work is an extension of our short conference proceeding paper

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Meagher, David, Dimitrios Adamis, Paula Trzepacz, and Maeve Leonard. "Features of subsyndromal and persistent delirium." British Journal of Psychiatry 200, no. 1 (January 2012): 37–44. http://dx.doi.org/10.1192/bjp.bp.111.095273.

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BackgroundLongitudinal studies of delirium phenomenology are lacking.AimsWe studied features that characterise subsyndromal delirium and persistent delirium over time.MethodTwice-weekly evaluations of 100 adults with DSM-IV delirium using the Delirium Rating Scale – Revised-98 (DRS-R98) and Cognitive Test for Delirium (CTD). The generalised estimating equation method identified symptom patterns distinguishing full syndromal from subsyndromal delirium and resolving from persistent delirium.ResultsParticipants (mean age 70.2 years (s.d. = 10.5)) underwent 323 assessments (range 2–9). Full syndro

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Rheuban, Karen S., Donna Cregan-Lambert, and Howard P. Gutgesell. "Prognostic features in childhood idiopathic dilated cardiomyopathy." Cardiology in the Young 7, no. 2 (April 1997): 183–87. http://dx.doi.org/10.1017/s1047951100009446.

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AbstractThe clinical course, noninvasive findings and outcome of 25 infants and children with idiopathic dilated cardiomyopathy were reviewed retrospectively to identify factors predictive of outcome both at diagnosis and at short-term follow-up. Patients, stratified by clinical status at last visit, were assigned to groups encompassing those who were asymptomatic and receiving on medication, those patients who were symptomatic or needed medication to control symptoms, and those who were dead, awaiting heart transplantation, or had undergone transplantation. Older age at diagnosis was strongly

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Gu, Xiaohua, Tian Wang, Jun Peng, Hongjin Wang, Qinfeng Xia, and Du Zhang. "Event Detection and Classification for Fiber Optic Perimeter Intrusion Detection System." International Journal of Cognitive Informatics and Natural Intelligence 13, no. 4 (October 2019): 39–55. http://dx.doi.org/10.4018/ijcini.2019100102.

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A perimeter intrusion detection system (PIDS) is critical for the security of a shale gas field. Among many technologies, the fiber optic sensor-based method is the most widely used, due to its passive, low-cost, long-life, and strong anti-interference ability and strong environmental adaptability. This article proposes an event detection and classification method for a fiber optic PIDS. In general, three types of features are extracted for an improved double-threshold method to improve the probability of detection. Also, the detected intrusion events are distinguished by a support vector mach

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Sui, Linfeng, Xuyang Zhao, Qibin Zhao, Toshihisa Tanaka, and Jianting Cao. "Hybrid Convolutional Neural Network for Localization of Epileptic Focus Based on iEEG." Neural Plasticity 2021 (April 27, 2021): 1–9. http://dx.doi.org/10.1155/2021/6644365.

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Epileptic focus localization by analysing intracranial electroencephalogram (iEEG) plays a critical role in successful surgical therapy of resection of the epileptogenic lesion. However, manual analysis and classification of the iEEG signal by clinicians are arduous and time-consuming and excessively depend on the experience. Due to individual differences of patients, the iEEG signal from different patients usually shows very diverse features even if the features belong to the same class. Accordingly, automatic detection of epileptic focus is required to improve the accuracy and to shorten the

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Chashei, I. V., S. А. Tyul’bashev, and Yu V. Pisanko. "Monitoring of Interplanetary Scintillation and Potential of Short-time Space Weather Forecasting." Meteorologiya i Gidrologiya 3 (2021): 28–37. http://dx.doi.org/10.52002/0130-2906-2021-3-28-37.

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Observations and initial analysis of interplanetary scintillation data are briefly described in the framework of the program for the solar wind monitoring with the modernized LPI LPA radio telescope that started in 2014. The examples of detecting interplanetary coronal mass injections (ICME) and co-rotating interaction regions (СIR) of different-speed flows are presented. It is shown that in the first case, enhancements in the scintillation level in extended sounded regions of solar wind are observed 20–30 hours before the arrival of the disturbances to the Earth; in the second case, the eveni

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