Journal articles: 'Short-time features'

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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 with a competitive and lucrative edge in the market place. To quantify risks associated with making a product, manufacturing is divided into three subprocesses: reliability, inherent feature augmentation, and value-added tolerance capabilities. Reliability estimates the availability to manufacture the selected product consistently; feature augmentation quantifies risks associated with incorporating inherent design features; and value-added tolerance capabilities measures the consistency in manufacturing value-added design. An example of a leading-edge technology product with a short life cycle is provided. Because of a short time-to-market window coupled with constraints in environmental variables, the bootstrap method is used to estimate key statistical parameters. Once manufacturing risks have been identified and evaluated, the next logical step for future work will be to couple these estimates with uncertainties involved in financing and marketing the product in question.

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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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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 generic behavior of short-time properties obtained from this model are shown to be in full accord with experimental data. In addition, the effects of microion kinetics, nonzero particle porosity and residual attractive forces on the form of the hydrodynamic function are estimated. This serves to rule out possible causes for the strikingly small hydrodynamic function values determined in certain synchrotron radiation experiments.

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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 easily be extended to multi-dimensions. Computations of the STFT and the two versions of STHTs are illustrated based on 32 channels (traces) of synthetic seismic data consisting of 256 samples in each trace. Salient features of STHTs are incorporated.

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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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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 heights are extracted by 3D convolution, and then, the radar echo maps on time series are coded and decoded by using GRU. Finally, the trained model is used to predict the radar echo maps in the next 1-2 hours. The experimental results show that the algorithm can effectively extract the temporal and spatial features of radar echo maps, reduce the error between the predicted value and the real value of rainfall, and improve the accuracy of short-term rainfall prediction.

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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 of time series data. Second, pooling technology is used to reduce the dimensionality of the output features in the last layer of the LSTM network. Due to the long time series, the hidden layer in the LSTM network cannot remember the information at all times. As a result, it is difficult to obtain a compressed representation of the global information in the last layer. Therefore, it is necessary to combine the information from the previous hidden unit to supplement all of the data. By stacking all the hidden unit information and performing a pooling operation, a dimensionality reduction effect of the hidden unit information is achieved. In this way, the memory loss caused by an excessively long sequence is compensated. Finally, considering that many time series data are unbalanced data, the unbalanced K-means (UK-means) algorithm is used to cluster the features after dimensionality reduction. The experiments were conducted on multiple publicly available time series datasets. The experimental results show that LSTM-based feature extraction combined with the dimensionality reduction processing of the pooling technology and cluster processing for imbalanced data used in this study has a good effect on the processing of time series data.

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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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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 presented methodology will allow to reveal further features for use in the diagnosis of various pathologies.

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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. The proposed scheme is based on a new joint metric, which results from the product of the magnitude and difference features in the Doppler spectra. We discriminate the target bins from sea clutter bins using a statistical discriminator based on the joint metric, whose probability density function follows the product distribution of standard gamma distributions. Compared to conventional detectors, the proposed scheme can provide a robust performance in terms of the average signal-to-clutter ratio as well as the detection rate, especially in shorter 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 residual part into account, which makes the diagnosis result sensitive to the number of modes/features retained. This paper proposes a complementary approach to extract fault features and avoid the selection of single mode of interest, which employs canonical variate analysis to convert the original variable into two complementary spaces; canonical variate space; and residual space. Then the complementary statistical indicators Hotelling T2 statistic and Q statistic are used to provide important information about the conditions of the rolling bearing. Subsequently, a new feature index, complementary short-time energy extracted from the two statistics are used as fault features which are given as an input to a classifier such as a support vector machine. Two data sets collected from different test rigs are used for demonstration of the proposed work. The experimental result shows that the troublesome feature/mode selection issue is avoided, and the diagnosis result is not sensitive to the number of canonical variate retained. Besides, the proposed approach can identify various working conditions of rolling bearing accurately, which is simple and effective for fault diagnosis of rolling bearing, compared with the existing methods.

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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 individuals and texts as the main impression. The movement features are found in cinematic poetry and its rely depend on kinetic texts. Meanwhile, non-movement features can be found in the form of human imitation or part of them, portraits, silhouettes, and comics. Indonesian digital poetry of space features in form of textual space is prioritizing on the kinetics text, the space of time is prioritizing the presentation of objects association of words are spoken, the neutral space is prioritizing the use of computer technology application. The grouping of visual art composition is based on two criteria: the technique of creating and artistic impressions. The dimensional reducing is prominently practiced by Afrizal Malna.

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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 stage without making full use of spatiotemporal traffic information. In this paper, we improve the predictive accuracy by directly capturing the relationship between the input sequence and the predicted value. We propose the new local receptive fields for spatiotemporal traffic information to provide the constraints in the task domain for CNN which is different from traditionally learning traffic as images. We explore a max-pooled CNN followed by a fully connected layer with a nonlinear activation function to convolute the new local receptive fields. The higher global-level features are fed into a predictor to generate the predicted output. Based on the dataset provided by Highways England, we validate the assumption that there exists direct relationship between the input sequence and the predicted value. We train the proposed method by using the backpropagation approach, and we employ the AdaGrad method to update the parameters of the proposed method. The experimental results show that the proposed method can improve the predictive accuracy.

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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 explored. Because of the low thermal time constant, the sufficient discriminatory information of different gases is preserved in the envelope of the response curve. Feature information tends to be contained in the lower frequencies, but not at higher frequencies. Therefore, features are extracted from the STFT amplitude values at the frequencies ranging from 0 Hz to the fundamental frequency to accomplish the identification task. These lower frequency features are extracted and further processed by decision tree-based pattern recognition. The proposed method shows high classification capability by the analysis of different concentration of carbon monoxide, methane, and ethanol.

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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 includes three models: fatigue feature extraction, fatigue feature fusion, and driver drowsiness detection. First, we used a convolutional neural network (CNN) to effectively extract the deep representation of eye and mouth-related fatigue features from the face area detected in each video frame. Then, based on the factorized bilinear feature fusion model, we performed a nonlinear fusion of the deep feature representations of the eyes and mouth. Finally, we input a series of fused frame-level features into a long-short-term memory (LSTM) unit to obtain the time information of the features and used the softmax classifier to detect sleepiness. The proposed framework was evaluated with the National Tsing Hua University drowsy driver detection (NTHU-DDD) video dataset. The experimental results showed that this method had better stability and robustness compared with other methods.

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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 created by the repetition which structures Lessness (in which the first 60 sentences are repeated in a different random permutation to create the text’s second half). Employing Genette’s (1980) terminology, the article shows that although the second half of Lessness does result in an increase in ‘narrative time’ (reader time), it does not result in any parallel increase in ‘story time’ (time within the fiction), and this is because the second set of sentences necessarily conveys the same timelessness as the first. Whereas Esslin (1986) reads this repetition as an economical way of showing that the text’s situation will last into infinity (i.e. in time), this article suggests that the repetition is necessary to show that the fictional situation is timeless and cannot possibly progress. Furthermore, the article ends by showing that the final sentence of Lessness closes off the text’s capacity for infinite regeneration by creating a strong sense of closure in form and content.

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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). Experimental results indicate that the HRV time domain features generally decrease under long-term stress, and the HRV frequency domain features have substantially significant differences under short-term stress. Further, an SVM classifier with a radial basis function kernel proved most accurate (93.11%) when using an optimal HRV feature set comprising the mean of R-R intervals (mRR), the standard deviation of R-R intervals (SDRR), and the coefficient of variance of R-R intervals (CVRR) as time domain features, and the normalized low frequency (nLF) and the normalized high frequency (nHF) as frequency domain features. Our findings indicate that the optimal HRV features identified in this study can effectively and efficiently detect stress. This knowledge facilitates development of in-facility and mobile healthcare system designs to support stress monitoring in daily life.

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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 coefficient. Then, the proposed method is used to evaluate the optimal feature subset from the original feature set. To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2), binary particle swarm optimization (BPSO), and genetic algorithm (GA). The experimental results show the superiority of CBGWO not only in classification performance, but also feature reduction. In addition, CBGWO has a very low computational cost, which is more suitable for real world application.

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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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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 frequency spectrum. Selected spectrum features are then provided to a number of intelligent classifiers. The classifiers’ scores were evaluated using the F1-score metric. Most classifiers proved to be reliable as their performance score was above 90%.

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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 the convolutional neural network (CNN), together with the raw daily price series, reflect relatively short-, medium- and long-term features in the price sequence. Secondly, considering time dependencies existing in the three kinds of features, the proposed hybrid neural network leverages three long short-term memory (LSTM) recurrent neural networks to capture such dependencies, respectively. Finally, fully connected layers are used to learn joint representations for predicting the price trend. The proposed hybrid neural network demonstrates its effectiveness by outperforming benchmark models on the real dataset.

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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 the trend of the correlation. The transmission characteristics of correlation modes show that it is easier to judge the trend of the fluctuation of the correlation between time series from the short term to long term. The evolution of media capability of the correlation modes shows that the transmission media in the long term have higher value to predict the trend of correlation. This work does not only propose a new perspective to analyze the correlation between time series but also provide important information for investors and decision makers.

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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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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 can thus impact others. Therefore, feature-level Change Impact Analysis (CIA) is important to predict affected features for change management purpose. In this paper, we propose a feature-level CIA approach using Formal Concept Analysis (FCA) applied to SPL evolution. In our experimental evaluation using three case studies of different domains and sizes, we show the effectiveness of our technique in terms of the most commonly used metrics on the subject.

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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 proportional to t. The first correction involves small amplitudes and fast oscillations: something like t3/2 cost−1. The initial displacement of points away from G is something like t cost−1. For small t, the deformed shape at points x far from G is oscillatory with decreasing amplitude, something like x−2 cosx2. The impulse at G does not cause impulsive support reactions, but support forces immediately afterwards have large amplitudes and fast oscillations that depend on inner details of the impulse: for an impulse applied over a time period ε, the ensuing support forces are of Oε−1/2. Finally, the displacement of G as a function of time shows structure at all scales, and is nondifferentiable at infinitely many points.

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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 boundary conditions of the prediction region. In this paper, the prediction of cloud motion is regarded as a spatiotemporal sequence-forecasting problem, for which an end-to-end deep-learning model is established; both the input and output are spatiotemporal sequences. The model is based on gated recurrent unit (GRU)- recurrent convolutional network (RCN), a variant of the gated recurrent unit (GRU), which has convolutional structures to deal with spatiotemporal features. We further introduce surrounding context into the prediction task. We apply our proposed Multi-GRU-RCN model to FengYun-2G satellite infrared data and compare the results to those of the state-of-the-art method of cloud-motion prediction, the variational optical flow (VOF) method, and two well-known deep-learning models, namely, the convolutional long short-term memory (ConvLSTM) and GRU. The Multi-GRU-RCN model predicts intra-hour cloud motion better than the other methods, with the largest peak signal-to-noise ratio and structural similarity index. The results prove the applicability of the GRU-RCN method for solving the spatiotemporal data prediction problem and indicate the advantages of our model for further applications.

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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 time domain and frequency domain are extracted from the processed grinding signals. To reduce feature redundancy and increase relevancy of feature to wheel wear, a two-stage feature selection approach combining filter and wrapper framework is proposed. The filter preselects individual features by minimum Redundancy Maximum Relevance method, while the wrapper evaluates different feature subsets by the model performance. A deep learning network structure named Long Short-Term Memory network is adopted to develop the wheel wear monitoring model and is compared with a conventional machine learning algorithm, Random Forest. The results have shown that the two-stage feature selection method is able to provide the globally optimal feature subset for the model. Long Short-Term Memory model achieves an R2 of 0.994 and a root-mean-square error of 0.240 with four features, while Random Forest model obtains an R2 of 0.980 and a root-mean-square error of 0.463 with seven features, which indicates that Long Short-Term Memory model is capable of predicting wheel wear more accurately even with less features.

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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 features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85%±3% and an precision of 80%±8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation.

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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. Second, the feature selection based on GCA is established considering the feature classification and the temporal correlation features to further eliminate useless features. Third, principal component analysis is applied to reduce the noise among the data. Then, combined with a differential evolution algorithm (DE), a support-vector machine (SVM) is applied to forecast the price. Finally, the proposed framework is applied to the New England electricity market to forecast the short-term electricity price. The results show that, compared with DE-SVM without data processing, the forecasting accuracy is improved from 81.68% to 91.44%, and the time-cost is decreased from 35,074 s to 1,809 s which shows that the proposed method and model can provide a valuable tool for data processing and forecasting.

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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 revealed by the EDM method, a nonlinear approach based on Chaos theory. Further, the spatial characteristic of urban traffic topology can be reflected from the perspective of complex networks. To fully guarantee the reasonability and validity of spatio-temporal features, which are dug by empirical dynamic modeling and complex networks (EDMCN), for urban traffic travel time prediction, an XGBoost prediction model is established for those characteristics. Through the in-depth exploration of the travel time and topology of a particular road network in Guiyang, the EDMCN-XGBoost prediction model’s performance is verified. The results show that, compared with the single XGBoost, autoregressive moving average, artificial neural network, support vector machine, and other models, the proposed EDMCN-XGBoost prediction model presents a better performance in forecasting.

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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 the verbal vs. nonverbal feature classes for this prediction task. At long time-lags, linguistic features become more crucial, but performance degrades compared with prediction at short time-lags.

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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 recognition ability, which provides the core technology support for developing the evaluation system of piano performance.

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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 Bulgarian language with length of about 2 s) selected from BG-SRDat corpus. The results of the experiments have shown that the MD feature demonstrates the best performance in the endpoint detection tests in terms of the verification rate.

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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 three types of features of the same segment are fused together and input into the LSTM network for time-series modeling to further fuse the features to obtain multimodal long-term spatiotemporal features with higher discrimination. According to the lower limb movement pattern recognition model, the minimum number of muscles and EMG signal characteristics required to accurately recognize the movement state of the lower limbs are determined. This minimizes the redundant calculation cost of the model and ensures the real-time output of the system results.

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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. The experimental results show that the new model improved accuracy by 3% in predicting the number of suspects and improved accuracy by 12% in predicting the number of suspects. To the best of our knowledge, it is the first time to combine machine learning and deep learning in crime prediction.

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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 syndromal delirium was significantly more severe than subsyndromal delirium for DRS-R98 thought process abnormalities, delusions, hallucinations, agitation, retardation, orientation, attention, and short- and long-term memory items, and CTD attention, vigilance, orientation and memory. Persistent full syndromal delirium had greater disturbance of DRS-R98 thought process abnormalities, delusions, agitation, orientation, attention, and short- and long-term memory items, and CTD attention, vigilance and orientation.ConclusionsFull syndromal delirium differs from subsyndromal delirium over time by greater severity of many cognitive and non-cognitive symptoms. Persistent delirium involves increasing prominence of recognised core diagnostic features and cognitive impairment.

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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 associated with poor clinical outcome (p=0.005), as was the presence of arrhythmias at the time of diagnosis (p=0.008) and at short-term follow-up-(p=0.003). Echocardiographic studies at diagnosis failed to predict eventual clinical outcome, although patients who ultimately recovered and did not need medications tended to demonstrate improvement in or resolution of echocardiographic abnormalities within 6 months of diagnosis.

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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 machine with wavelet features to reduce the nuisance alarm rate. Experiments on the PIDS in Chongqing Fuling's shale gas field show that detection algorithms based on the feature of short-time energy and short-time wavelet coefficient energy are much better, and the performance of event classification is satisfactory.

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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 time for treatment. In this paper, we propose a novel feature fusion-based iEEG classification method, a deep learning model termed Time-Frequency Hybrid Network (TF-HybridNet), in which short-time Fourier transform (STFT) and 1d convolution layers are performed on the input iEEG in parallel to extract features of the time-frequency domain and feature maps. And then, the time-frequency features and feature maps are fused and fed to a 2d convolutional neural network (CNN). We used the Bern-Barcelona iEEG dataset for evaluating the performance of TF-HybridNet, and the experimental results show that our approach is able to differentiate the focal from nonfocal iEEG signal with an average classification accuracy of 94.3% and demonstrates an improved accuracy rate compared to the model using only STFT or one-dimensional convolutional layers as feature extraction.

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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 evening and night scintillation level decrease is observed several days before the compressed region of disturbances comes to the Earth. These features are considered as a base of using interplanetary scintillation monitoring data for short-time space weather forecasting.

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Journal articles on the topic 'Short-time features'

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