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Journal articles on the topic 'Spatiotemporal identification'

Author: Grafiati

Published: 14 April 2023

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1

Lakumarapu, Srikanth, and Rashmi Agarwal. "Cramming Identification through Spatiotemporal Data." International Journal of Computer Sciences and Engineering 6, no. 6 (2018): 693–701. http://dx.doi.org/10.26438/ijcse/v6i6.693701.

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2

Voss, H., M. Bünner, and M. Abel. "Identification of continuous, spatiotemporal systems." Physical Review E 57, no. 3 (1998): 2820–23. http://dx.doi.org/10.1103/physreve.57.2820.

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3

PAN, Y., and S. A. BILLINGS. "THE IDENTIFICATION OF COMPLEX SPATIOTEMPORAL PATTERNS USING COUPLED MAP LATTICE MODELS." International Journal of Bifurcation and Chaos 18, no. 04 (2008): 997–1013. http://dx.doi.org/10.1142/s021812740802080x.

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Many complex and interesting spatiotemporal patterns have been observed in a wide range of scientific areas. In this paper, two kinds of spatiotemporal patterns including spot replication and Turing systems are investigated and new identification methods are proposed to obtain Coupled Map Lattice (CML) models for this class of systems. Initially, a new correlation analysis method is introduced to determine an appropriate temporal and spatial data sampling procedure for the identification of spatiotemporal systems. A new combined Orthogonal Forward Regression and Bayesian Learning algorithm wit

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4

Pan, J. B., S. C. Hu, H. Wang, Q. Zou, and Z. L. Ji. "PaGeFinder: quantitative identification of spatiotemporal pattern genes." Bioinformatics 28, no. 11 (2012): 1544–45. http://dx.doi.org/10.1093/bioinformatics/bts169.

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5

Conkling, Tara J., James A. Martin, Jerrold L. Belant, and Travis L. DeVault. "Spatiotemporal Dynamics in Identification of Aircraft–Bird Strikes." Transportation Research Record: Journal of the Transportation Research Board 2471, no. 1 (2015): 19–25. http://dx.doi.org/10.3141/2471-03.

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6

Pan, Y., and S. A. Billings. "Neighborhood Detection for the Identification of Spatiotemporal Systems." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 3 (2008): 846–54. http://dx.doi.org/10.1109/tsmcb.2008.918571.

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7

Ning, Hanwen, Xingjian Jing, and Li Cheng. "Identification of non-linear stochastic spatiotemporal dynamical systems." IET Control Theory & Applications 7, no. 17 (2013): 2069–83. http://dx.doi.org/10.1049/iet-cta.2013.0150.

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8

Krakover, Shaul. "Identification of Spatiotemporal Paths of Spread and Backwash." Geographical Analysis 15, no. 4 (2010): 318–29. http://dx.doi.org/10.1111/j.1538-4632.1983.tb00790.x.

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9

Ellison, Adrian B., Richard B. Ellison, Asif Ahmed, Dean Rance, and Stephen P. Greaves. "Spatiotemporal Identification of Trip Stops from Smartphone Data." Applied Spatial Analysis and Policy 12, no. 1 (2016): 27–43. http://dx.doi.org/10.1007/s12061-016-9188-0.

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10

Dong, Xunde, and Cong Wang. "Identification of the Gray–Scott Model via Deterministic Learning." International Journal of Bifurcation and Chaos 31, no. 04 (2021): 2150051. http://dx.doi.org/10.1142/s0218127421500516.

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Gray–Scott model is one of the most well-known reaction–diffusion models which has a wealth of spatiotemporal chaos behavior. It is commonly used to study spatiotemporal chaos. In the paper, a novel method is proposed for the identification of the Gray–Scott model via deterministic learning and interpolation. The method mainly consists of two phases: the local identification phase and the global identification phase. Local identification is achieved using the finite difference method and deterministic learning. Based on the local identification results, the interpolation method is employed to

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11

PAN, Y., S. A. BILLINGS, and Y. ZHAO. "THE IDENTIFICATION OF COUPLED MAP LATTICE MODELS FOR AUTONOMOUS CELLULAR NEURAL NETWORK PATTERNS." International Journal of Bifurcation and Chaos 18, no. 04 (2008): 985–96. http://dx.doi.org/10.1142/s0218127408020793.

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The identification problem for spatiotemporal patterns which are generated by autonomous Cellular Neural Networks (CNN) is investigated in this paper. The application of traditional identification algorithms to these special spatiotemporal systems can produce poor models due to the inherent piecewise nonlinear structure of CNN. To solve this problem, a new type of Coupled Map Lattice model with output constraints and corresponding identification algorithms are proposed in the present study. Numerical examples show that the identified CML models have good prediction capabilities even over the l

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12

Reich, Brian J., and Michael D. Porter. "Partially supervised spatiotemporal clustering for burglary crime series identification." Journal of the Royal Statistical Society: Series A (Statistics in Society) 178, no. 2 (2014): 465–80. http://dx.doi.org/10.1111/rssa.12076.

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13

Chang Hyun Roh, Hyun Sop Chang, Han Gon Kim, and Soon Heung Chang. "Identification of reactor vessel failures using spatiotemporal neural networks." IEEE Transactions on Nuclear Science 43, no. 6 (1996): 3223–29. http://dx.doi.org/10.1109/23.552722.

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14

Arcolin, M. Godi, M. Giardini, and S. Corna. "Identification of key spatiotemporal gait variables in elderly subjects." Gait & Posture 74 (September 2019): 3. http://dx.doi.org/10.1016/j.gaitpost.2019.07.445.

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15

Wang, Min, Ping Li, Hao Wang, Lina Dong, Changxin Wu, and Zhonghua Zhao. "Identification and spatiotemporal expression of gpr161 genes in zebrafish." Gene 730 (March 2020): 144303. http://dx.doi.org/10.1016/j.gene.2019.144303.

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16

Lin, Yu-Pin, Johnathen Anthony, Wei-Chih Lin, Wan-Yu Lien, Joy R. Petway, and Te-En Lin. "Spatiotemporal identification of roadkill probability and systematic conservation planning." Landscape Ecology 34, no. 4 (2019): 717–35. http://dx.doi.org/10.1007/s10980-019-00807-w.

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17

Sui, Tengfei, Xiaofeng Tao, and Jin Xu. "Random Matrix Theory-Based ROI Identification for Wireless Networks." Wireless Communications and Mobile Computing 2022 (June 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/3644592.

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The identification of region of interests (ROIs) in wireless networks holds the potential to resolve the challenging problems of resource allocation and network traffic prediction for large scale traffic data generated by mobile applications. The rationale is that ROIs are capable of gathering single regions that share similar network characteristics, which promotes better network traffic prediction performance. Previous studies show that spatiotemporal information in network traffic data, such as user behaviors and network status, is nontrivial to ROI identification. However, the modeling bet

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18

Li, Feng, Yun Xiao, Fei Huang, et al. "Spatiotemporal-specific lncRNAs in the brain, colon, liver and lung of macaque during development." Molecular BioSystems 11, no. 12 (2015): 3253–63. http://dx.doi.org/10.1039/c5mb00474h.

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19

Herrero, Ana, David Matallanas, and Walter Kolch. "The spatiotemporal regulation of RAS signalling." Biochemical Society Transactions 44, no. 5 (2016): 1517–22. http://dx.doi.org/10.1042/bst20160127.

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Nearly 30% of human tumours harbour mutations in RAS family members. Post-translational modifications and the localisation of RAS within subcellular compartments affect RAS interactions with regulator, effector and scaffolding proteins. New insights into the control of spatiotemporal RAS signalling reveal that activation kinetics and subcellular compartmentalisation are tightly coupled to the generation of specific biological outcomes. Computational modelling can help utilising these insights for the identification of new targets and design of new therapeutic approaches.

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20

Zhang, Ronghui, and Xiaojun Jing. "Device-Free Human Identification Using Behavior Signatures in WiFi Sensing." Sensors 21, no. 17 (2021): 5921. http://dx.doi.org/10.3390/s21175921.

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Wireless sensing can be used for human identification by mining and quantifying individual behavior effects on wireless signal propagation. This work proposes a novel device-free biometric (DFB) system, WirelessID, that explores the joint human fine-grained behavior and body physical signatures embedded in channel state information (CSI) by extracting spatiotemporal features. In addition, the signal fluctuations corresponding to different parts of the body contribute differently to the identification performance. Inspired by the success of the attention mechanism in computer vision (CV), thus,

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21

Zhang, Xuan, Hesheng Tang, Deyuan Zhou, Shanshan Chen, Taotao Zhao, and Songtao Xue. "Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect Identification in a Precast Structure." Sensors 20, no. 11 (2020): 3264. http://dx.doi.org/10.3390/s20113264.

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Due to the increased service life, environmental corrosion, unreasonable construction, and other issues, local defects inevitably exist in civil structures, which affect the structural performance and can lead to structural failure. However, research on grout defect identification of precast reinforced concrete frame structures with rebars spliced by sleeves faces great challenges owing to the complexity of the problem. This study presents a multiple-variable spatiotemporal regression model algorithm to identify local defects based on structural vibration responses collected using a sensor net

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22

Mohseni, Hamid Reza, Foad Ghaderi, Edward L. Wilding, and Saeid Sanei. "Variational Bayes for Spatiotemporal Identification of Event-Related Potential Subcomponents." IEEE Transactions on Biomedical Engineering 57, no. 10 (2010): 2413–28. http://dx.doi.org/10.1109/tbme.2010.2050318.

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23

Hinsley, Gerard N., Cameron M. Kewish, and Grant A. van Riessen. "Dynamic coherent diffractive imaging using unsupervised identification of spatiotemporal constraints." Optics Express 28, no. 24 (2020): 36862. http://dx.doi.org/10.1364/oe.408530.

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24

Hanwen Ning, Xingjian Jing, and Li Cheng. "Online Identification of Nonlinear Spatiotemporal Systems Using Kernel Learning Approach." IEEE Transactions on Neural Networks 22, no. 9 (2011): 1381–94. http://dx.doi.org/10.1109/tnn.2011.2161331.

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25

Aram, Parham, Visakan Kadirkamanathan, and Sean R. Anderson. "Spatiotemporal System Identification With Continuous Spatial Maps and Sparse Estimation." IEEE Transactions on Neural Networks and Learning Systems 26, no. 11 (2015): 2978–83. http://dx.doi.org/10.1109/tnnls.2015.2392563.

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26

Morrill, Richard. "Identification of Spatiotemporal Paths of Spread and Backwash: A Comment." Geographical Analysis 17, no. 3 (2010): 247–50. http://dx.doi.org/10.1111/j.1538-4632.1985.tb00845.x.

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27

Gao, Changxin, Yang Chen, Jin-Gang Yu, and Nong Sang. "Pose-guided spatiotemporal alignment for video-based person Re-identification." Information Sciences 527 (July 2020): 176–90. http://dx.doi.org/10.1016/j.ins.2020.04.007.

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28

Niazazari, Iman, Reza Jalilzadeh Hamidi, Hanif Livani, and Reza Arghandeh. "Cause identification of electromagnetic transient events using spatiotemporal feature learning." International Journal of Electrical Power & Energy Systems 123 (December 2020): 106255. http://dx.doi.org/10.1016/j.ijepes.2020.106255.

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29

Yan, He, Liyuan Chen, Quansheng Ge, Chengming Tian, and Jixia Huang. "Spatiotemporal Pattern and Aggregation Effects of Poplar Canker in Northeast China." Forests 11, no. 4 (2020): 454. http://dx.doi.org/10.3390/f11040454.

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Research Highlights: This study looks at poplar canker caused by Cytospora chrysosperma as a geographical phenomenon, and it applies spatial statistics to reveal the pattern and aggregation effects of the disease on a large scale in time and space. The incidence area of poplar canker in Northeast China has spatial (spatiotemporal) aggregation effects, which emphasize the importance of coordinated prevention. The results of spatial and spatiotemporal clusters can guide specific regional prevention and indicate the possible predisposing factors, respectively. Background and Objectives: Poplar ca

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30

Li, Tao, and Anming Bao. "Identification and Characteristics of Historical Extreme High-Temperature Events over the China–Pakistan Economic Corridor." Atmosphere 14, no. 3 (2023): 530. http://dx.doi.org/10.3390/atmos14030530.

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Recently, there has been an increase in the occurrence of extreme high-temperature events across the China–Pakistan Economic Corridor (CPEC). Regional spatiotemporal identification and evaluation of extreme high temperatures are essential for accurate forecasting of future climate changes. When such events generate a meteorological hazard, it is important to understand their temporal and spatial features, return period, and identification criteria. Accurately identifying extreme events can help assess risk and predict their spatial–temporal variation. While past studies have focused on individ

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31

MÜLLER, T. G., and J. TIMMER. "PARAMETER IDENTIFICATION TECHNIQUES FOR PARTIAL DIFFERENTIAL EQUATIONS." International Journal of Bifurcation and Chaos 14, no. 06 (2004): 2053–60. http://dx.doi.org/10.1142/s0218127404010424.

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Many physical systems exhibiting nonlinear spatiotemporal dynamics can be modeled by partial differential equations. Although information about the physical properties for many of these systems is available, normally not all dynamical parameters are known and, therefore, have to be estimated from experimental data. We analyze two prominent approaches to solve this problem and describe advantages and disadvantages of both methods. Specifically, we focus on the dependence of the quality of the parameter estimates with respect to noise and temporal and spatial resolution of the measurements.

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32

Hartfield, Molly I., and Richard F. Gunst. "Identification of model components for a class of continuous spatiotemporal models." Journal of Agricultural, Biological, and Environmental Statistics 8, no. 1 (2003): 105–21. http://dx.doi.org/10.1198/1085711031175.

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33

Huynh-The, Thien, Cam-Hao Hua, Nguyen Anh Tu, and Dong-Seong Kim. "Learning 3D spatiotemporal gait feature by convolutional network for person identification." Neurocomputing 397 (July 2020): 192–202. http://dx.doi.org/10.1016/j.neucom.2020.02.048.

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34

Chiu, Chih-Chou, Shin-Ying Hwang, Deborah F. Cook, and Yuan-Ping Luh. "Process disturbance identification through integration of spatiotemporal ICA and CART approach." Neural Computing and Applications 19, no. 5 (2009): 677–89. http://dx.doi.org/10.1007/s00521-009-0324-5.

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35

Lü, Ling, and Le Meng. "Parameter identification and synchronization of spatiotemporal chaos in uncertain complex network." Nonlinear Dynamics 66, no. 4 (2011): 489–95. http://dx.doi.org/10.1007/s11071-010-9927-8.

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36

Lyu Ling, Li Gang, Zhang Meng, Li Yu-Shan, Wei Lin-Ling, and Yu Miao. "Parameter identification and synchronization of spatiotemporal chaos in globally coupled network." Acta Physica Sinica 60, no. 9 (2011): 090505. http://dx.doi.org/10.7498/aps.60.090505.

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37

Dubey, Rahul, Subhransu Ranjan Samantaray, and Bijay Ketan Panigrahi. "An spatiotemporal information system based wide-area protection fault identification scheme." International Journal of Electrical Power & Energy Systems 89 (July 2017): 136–45. http://dx.doi.org/10.1016/j.ijepes.2017.02.001.

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38

YUAN, Z., A. LAU, H. ZHANG, J. YU, P. LOUIE, and J. FUNG. "Identification and spatiotemporal variations of dominant PM10 sources over Hong Kong." Atmospheric Environment 40, no. 10 (2006): 1803–15. http://dx.doi.org/10.1016/j.atmosenv.2005.11.030.

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39

Lishaev, P. N., A. A. Aleskerova, A. A. Kubryakov, N. V. Vasilenko, and S. V. Stanichny. "Spatiotemporal Variability of Cyanobacteria Blooms from Their MODIS-Based Automatic Identification." Izvestiya, Atmospheric and Oceanic Physics 58, no. 9 (2022): 981–92. http://dx.doi.org/10.1134/s0001433822090134.

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40

Cabanes, Guénaël, and Younès Bennani. "Unsupervised Topographic Learning for Spatiotemporal Data Mining." Advances in Artificial Intelligence 2010 (November 28, 2010): 1–12. http://dx.doi.org/10.1155/2010/832542.

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In recent years, the size and complexity of datasets have shown an exponential growth. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we propose a new unsupervised algorithm, suitable for the analysis of noisy spatiotemporal Radio Frequency IDentification (RFID) data. Two real applications show that this algorithm is an efficient data-mining tool for behavior

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41

Wang, HaiFeng, and BiGang Xu. "Discrete spatiotemporal network synchronization based on adaptive control." Journal of Physics: Conference Series 2365, no. 1 (2022): 012055. http://dx.doi.org/10.1088/1742-6596/2365/1/012055.

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Abstract This paper first introduces the basic concept of network synchronization and several common synchronization types. Secondly, aiming at the synchronization control problem of a class of discrete spatiotemporal networks, a standard synchronization control strategy and a synchronization controller are proposed based on Lyapunov stability theory. In order to further verify the effectiveness of the synchronization theory, the spatiotemporal network model is selected, the coupling matrix and the identification rate of unknown parameters are designed for numerical simulation. Finally, the si

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42

Mylona, Evangelia K., Fadi Shehadeh, Markos Kalligeros, Gregorio Benitez, Philip A. Chan, and Eleftherios Mylonakis. "Real-Time Spatiotemporal Analysis of Microepidemics of Influenza and COVID-19 Based on Hospital Network Data: Colocalization of Neighborhood-Level Hotspots." American Journal of Public Health 110, no. 12 (2020): 1817–24. http://dx.doi.org/10.2105/ajph.2020.305911.

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Objectives. To identify spatiotemporal patterns of epidemic spread at the community level. Methods. We extracted influenza cases reported between 2016 and 2019 and COVID-19 cases reported in March and April 2020 from a hospital network in Rhode Island. We performed a spatiotemporal hotspot analysis to simulate a real-time surveillance scenario. Results. We analyzed 6527 laboratory-confirmed influenza cases and identified microepidemics in more than 1100 neighborhoods, and more than half of the neighborhoods that had hotspots in a season became hotspots in the next season. We used data from 731

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43

Zeng, Chunyan, Shixiong Feng, Dongliang Zhu, and Zhifeng Wang. "Source Acquisition Device Identification from Recorded Audio Based on Spatiotemporal Representation Learning with Multi-Attention Mechanisms." Entropy 25, no. 4 (2023): 626. http://dx.doi.org/10.3390/e25040626.

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Source acquisition device identification from recorded audio aims to identify the source recording device by analyzing the intrinsic characteristics of audio, which is a challenging problem in audio forensics. In this paper, we propose a spatiotemporal representation learning framework with multi-attention mechanisms to tackle this problem. In the deep feature extraction stage of recording devices, a two-branch network based on residual dense temporal convolution networks (RD-TCNs) and convolutional neural networks (CNNs) is constructed. The spatial probability distribution features of audio s

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44

Lyle, Mark A., Jake C. Jensen, Jennifer L. Hunnicutt, et al. "Identification of strength and spatiotemporal gait parameters associated with knee loading during gait in persons after anterior cruciate ligament reconstruction." Journal of Athletic Training 2021, preprint (2021): 0000. http://dx.doi.org/10.4085/1062-6050-0186.21.

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ABSTRACT Context: Altered knee moments are common during gait in patients following anterior cruciate ligament reconstruction (ACLR). Modifiable factors that influence knee moments and are feasible to record in clinical settings such as strength and spatiotemporal parameters (e.g. step length, step width) have not been identified in persons after ACLR. Objective: The objective was to identify strength and spatiotemporal gait parameters that can predict knee moments in persons after ACLR. Design: Cross-Sectional Study Setting: Laboratory Patients: Twenty-three participants with ACLR (14.4 ± 17.

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45

Kwon, Jaerock, Yunju Lee, and Jehyung Lee. "Comparative Study of Markerless Vision-Based Gait Analyses for Person Re-Identification." Sensors 21, no. 24 (2021): 8208. http://dx.doi.org/10.3390/s21248208.

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The model-based gait analysis of kinematic characteristics of the human body has been used to identify individuals. To extract gait features, spatiotemporal changes of anatomical landmarks of the human body in 3D were preferable. Without special lab settings, 2D images were easily acquired by monocular video cameras in real-world settings. The 2D and 3D locations of key joint positions were estimated by the 2D and 3D pose estimators. Then, the 3D joint positions can be estimated from the 2D image sequences in human gait. Yet, it has been challenging to have the exact gait features of a person

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46

Vidakovic, Vesna, and Suncica Zdravkovic. "Color influences identification of the moving objects more than shape." Psihologija 42, no. 1 (2009): 79–93. http://dx.doi.org/10.2298/psi0901079v.

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When people track moving objects, they concentrate on different characteristics. Recent results show that people more often concentrate on spatiotemporal than featural properties of the objects. In other words, location and direction of motion seem to be more informative properties than the stable featural characteristics. This finding contradicts some of our knowledge about cognitive system. Current research was done in attempt to specify the effect of featural characteristics, especially color and shape. In Experiment 1, subjects were asked to track four mobile targets presented with another

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47

Shahid, Nauman, Ijaz Haider Naqvi, and Saad Bin Qaisar. "SVM Based Event Detection and Identification: Exploiting Temporal Attribute Correlations Using SensGru." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/259508.

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In the context of anomaly detection in cyber physical systems (CPS), spatiotemporal correlations are crucial for high detection rate. This work presents a new quarter sphere support vector machine (QS-SVM) formulation based on the novel concept ofattribute correlations. Our event detection approach, SensGru, groups multiple sensors on a single node and thus eliminates communication between sensor nodes without compromising the advantages of spatial correlation. It makes use of temporal-attribute (TA) correlations and is thus a TA-QS-SVM formulation. We show analytically that SensGru (or interc

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48

Mawuenyegah, Aleta, Songnian Li, and Shishuo Xu. "Exploring spatiotemporal patterns of geosocial media data for urban functional zone identification." International Journal of Digital Earth 15, no. 1 (2022): 1305–25. http://dx.doi.org/10.1080/17538947.2022.2107099.

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49

Zhang, Zhenyu, Yong Li, Jing Duan, et al. "A non‐intrusive load state identification method considering non‐local spatiotemporal feature." IET Generation, Transmission & Distribution 16, no. 4 (2021): 792–803. http://dx.doi.org/10.1049/gtd2.12330.

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50

Yang, Yizhou, Chao Liu, and Dongxiang Jiang. "Vibration propagation identification of rotor-bearing-casing system using spatiotemporal graphical modeling." Mechanism and Machine Theory 134 (April 2019): 24–38. http://dx.doi.org/10.1016/j.mechmachtheory.2018.12.018.

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