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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 (June 30, 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 (March 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 (April 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 with Laplace priors is introduced to identify sparse and robust CML models for complex spatiotemporal patterns. The final identified CML models are validated using correlation-based model validation tests for spatiotemporal systems. Numerical results illustrate the identification procedure and demonstrate the validity of the identified models.

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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 (April 6, 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 (January 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 (June 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 (November 21, 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 (September 3, 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 (May 4, 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 (March 30, 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 obtain global identification. Numerical experiments show the feasibility and effectiveness of the proposed method.

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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 (April 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 long term, and the main dynamics of the original patterns appears to be well represented.

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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 (September 3, 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 (April 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 between these clues regarding spatiotemporal information is not yet fully explored. To this end, we propose a random matrix theory-based ROI identification (RRI) approach. By observing the intensification or diminution of network characteristic differences, i.e., divergence, between adjacent single regions, the ROIs can be identified. Firstly, we leverage the spatiotemporal information of area network traffic data with a spike model which can be described as a zero mean random matrix with a deterministic perturbation matrix. Then, we put forward an average divergence capacity model for ROI identification by estimating the divergent degree of adjacent regions. Case studies on three real-world network traffic datasets demonstrate the effectiveness of our proposed RRI method. The ROI identification greatly improves the network traffic prediction performance, yielding a decrease of root mean square error and mean absolute error by 36.87 % and 52.26 % , respectively.

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18

Li, Feng, Yun Xiao, Fei Huang, Wei Deng, Hongying Zhao, Xinrui Shi, Shuyuan Wang, 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 (October 15, 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 (September 3, 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, to extract more robust features, we introduce the spatiotemporal attention function into our system. To evaluate the performance, commercial WiFi devices are used for prototyping WirelessID in a real laboratory environment with an average accuracy of 93.14% and a best accuracy of 97.72% for five individuals.

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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 (June 8, 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 network. First, numerical simulations were carried out on precast beam–column connection models by comparing the identification results based on a single-variable regression model, two-variable spatial regression model, and two-variable spatiotemporal regression model; furthermore, a multiple-variable spatiotemporal regression model was proposed and robustness analysis of the damage indicator was carried out. Then, to explore the validity of the proposed method, a nondestructive vibration experiment was considered on a half-scaled, two-floor, precast concrete frame structure with column rebars spliced by defective grout sleeves. The results show that local defects were successfully identified based on a multiple-variable spatiotemporal regression model.

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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 (October 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 (November 19, 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 (September 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 (November 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 (September 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 (April 17, 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 canker, a harmful forest biological disease that is widespread throughout Northeast China, brings enormous ecological and economic losses. The limited cognition of its spatiotemporal pattern and aggregation effects restricts the decision-making for regional prevention and the identification of disease-inducing conditions. This study aims to explore the spatiotemporal pattern and to detect the aggregation effects of the disease, trying to provide references for prevention. Materials and Methods: According to the incidence data of poplar canker reported by each county in Northeast China from 2002 to 2015, we mapped the distribution of the incidence rate in ArcGIS and performed retrospective scan statistics in SaTScan to detect the spatial and spatiotemporal aggregation effects of the incidence area. Results: The spatiotemporal pattern of poplar canker’s incidence rate presents the characteristic of “outbreak-aggregation-spread-stability.” The incidence area of the disease when we performed spatial aggregation scan statistics showed the primary cluster covering Liaoning province (LLR = 86469.86, p < 0.001). The annual spatial scan statistics detected a total of 14 primary clusters and 37 secondary clusters, indicating three phases of aggregation. The incidence area of disease also shows spatiotemporal aggregation effects with the primary cluster located around Liaoning province, appearing from 2009 to 2015 (LLR = 64182.00, p < 0.001). Conclusions: The incidence area of poplar canker presents significant characteristics of spatial and spatiotemporal aggregation, and we suggest attaching importance to the clues provided by the aggregation effects in disease prevention and identification of predisposing factors.

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Li, Tao, and Anming Bao. "Identification and Characteristics of Historical Extreme High-Temperature Events over the China–Pakistan Economic Corridor." Atmosphere 14, no. 3 (March 9, 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 individual sites, extreme heat events generally manifest as spatially and temporally continuous regional events. In this study, we propose an objective identification technique based on gridded data and spatiotemporal continuity to reveal the spatiotemporal characteristics of intensity, frequency, and duration events of extreme heat events in the CPEC from May to October between 1961 and 2015. Furthermore, we estimate the return period of extreme heat in the study region using the generalized Pareto distribution (GPD). Our findings indicate that the historical extreme temperature events (intensity, frequency, and duration) in the CPEC have significantly increased. Areas with a high incidence of extreme heat events are concentrated in eastern Balochistan, northern Sindh, and southeastern Punjab. These findings suggest that disaster prevention and mitigation plans should be targeted towards areas with a high frequency of extreme heat events in the CPEC, allowing policy makers to better prepare for and respond to future events.

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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 (June 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 (March 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 (December 29, 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 (January 18, 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 (March 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 (December 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 behavioral studies based on RFID technology. It allows discovering and comparing stable patterns in an RFID signal and is suitable for continuous learning.

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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 (November 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 simulation results show the feasibility of the theory.

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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 (December 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 COVID-19 cases, and we found that a neighborhood was 1.90 times more likely to become a COVID-19 hotspot if it had been an influenza hotspot in 2018 to 2019. Conclusions. The use of readily available hospital data allows the real-time identification of spatiotemporal trends and hotspots of microepidemics. Public Health Implications. As local governments move to reopen the economy and ease physical distancing, the use of historic influenza hotspots could guide early prevention interventions, while the real-time identification of hotspots would enable the implementation of interventions that focus on small-area containment and mitigation.

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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 (April 6, 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 signals are employed as inputs to the branch of the CNN for spatial representation learning, and the temporal spectral features of audio signals are fed into the branch of the RD-TCN network for temporal representation learning. This achieves simultaneous learning of long-term and short-term features to obtain an accurate representation of device-related information. In the spatiotemporal feature fusion stage, three attention mechanisms—temporal, spatial, and branch attention mechanisms—are designed to capture spatiotemporal weights and achieve effective deep feature fusion. The proposed framework achieves state-of-the-art performance on the benchmark CCNU_Mobile dataset, reaching an accuracy of 97.6% for the identification of 45 recording devices, with a significant reduction in training time compared to other models.

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44

Lyle, Mark A., Jake C. Jensen, Jennifer L. Hunnicutt, Jonathan J. Brown, Cynthia P. Chambliss, Michael A. Newsome, John W. Xerogeanes, and Liang-Ching Tsai. "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 (July 30, 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 ± 17.2 months post-ACLR) participated. Main Outcome Measures: Peak knee flexion and adduction moments were measured while walking at self-selected speeds. Spatiotemporal gait parameters were recorded with a pressure walkway, and peak isokinetic knee extensor strength (60°/s) was recorded on a dynamometer. Pearson coefficients were used to examine the association of peak knee moments with strength and gait parameters. Variables correlated with peak knee flexion and adduction moments were entered into a stepwise regression model. Results: Step width and knee extensor strength were the strongest predictors of knee flexion moment accounting for 44% of data variance, whereas stance phase time and step width were the strongest predictors of knee adduction moment explaining 62% of data variance. Conclusions: The spatiotemporal variables that were identified could be clinically feasible targets for biofeedback to improve gait after ACLR.

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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 (December 8, 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 due to viewpoint variance and occlusion of body parts in the 2D images. In the study, we conducted a comparative study of two different approaches: feature-based and spatiotemporal-based viewpoint invariant person re-identification using gait patterns. The first method is to use gait features extracted from time-series 3D joint positions to identify an individual. The second method uses a neural network, a Siamese Long Short Term Memory (LSTM) network with the 3D spatiotemporal changes of key joint positions in a gait cycle to classify an individual without extracting gait features. To validate and compare these two methods, we conducted experiments with two open datasets of the MARS and CASIA-A datasets. The results show that the Siamese LSTM outperforms the gait feature-based approaches on the MARS dataset by 20% and 55% on the CASIA-A dataset. The results show that feature-based gait analysis using 2D and 3D pose estimators is premature. As a future study, we suggest developing large-scale human gait datasets and designing accurate 2D and 3D joint position estimators specifically for gait patterns. We expect that the current comparative study and the future work could contribute to rehabilitation study, forensic gait analysis and early detection of neurological disorders.

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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 four moving objects. After the motion has stopped, they had to mark the initial four targets. Our results have shown that participants pay more attention to the featural properties than to spatiotemporal characteristics. Since our task was more difficult than the tasks typically reported in the literature, the results might be interpreted as if the subjects relied mostly on attentional processes. The task in Experiment 2 was made even more difficult: the subjects were asked to direct attention on identity of every target. Consequently, the task demanded more complex cognitive processes and emphasizing effects of featural properties. Results suggest that color and shape does not have the same influences on multiple object tracking, but that color has more significant effect.

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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 interchangeably TA-QS-SVM) results in a reduced node density and gives the sameevent detectionperformance as more denseSpatiotemporal-Attribute Quarter-Sphere SVM(STA-QS-SVM) formulation which exploits both spatiotemporal and attribute correlations. Moreover, this paper develops theoretical bounds on the internode distance, the optimal number of sensors, and the sensing range with SensGru so that the performance difference with SensGru and STA-QS-SVM is negligibly small. Both schemes achieve event detection rates as high as 100% and an extremely low false positive rate.

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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 (August 8, 2022): 1305–25. http://dx.doi.org/10.1080/17538947.2022.2107099.

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Zhang, Zhenyu, Yong Li, Jing Duan, Yilong Duan, Yixiu Guo, Yijia Cao, and Christian Rehtanz. "A non‐intrusive load state identification method considering non‐local spatiotemporal feature." IET Generation, Transmission & Distribution 16, no. 4 (October 25, 2021): 792–803. http://dx.doi.org/10.1049/gtd2.12330.

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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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