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Статті в журналах з теми "Spatio temporal networks"
Williams, Matthew J., and Mirco Musolesi. "Spatio-temporal networks: reachability, centrality and robustness." Royal Society Open Science 3, no. 6 (June 2016): 160196. http://dx.doi.org/10.1098/rsos.160196.
Повний текст джерелаQIN, Chao, and Xiaoguang GAO. "Spatio-Temporal Generative Adversarial Networks." Chinese Journal of Electronics 29, no. 4 (July 1, 2020): 623–31. http://dx.doi.org/10.1049/cje.2020.04.001.
Повний текст джерелаChao, Qin, and Gao Xiaoguang. "Distributed spatio-temporal generative adversarial networks." Journal of Systems Engineering and Electronics 31, no. 3 (June 2020): 578–92. http://dx.doi.org/10.23919/jsee.2020.000026.
Повний текст джерелаPichardo-Corpus, J. A., H. A. Solano Lamphar, R. Lopez-Farias, and O. Delgadillo Ruiz. "Spatio-temporal networks of light pollution." Journal of Quantitative Spectroscopy and Radiative Transfer 253 (September 2020): 107068. http://dx.doi.org/10.1016/j.jqsrt.2020.107068.
Повний текст джерелаGao, Nan, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, and Flora D. Salim. "Generative Adversarial Networks for Spatio-temporal Data: A Survey." ACM Transactions on Intelligent Systems and Technology 13, no. 2 (April 30, 2022): 1–25. http://dx.doi.org/10.1145/3474838.
Повний текст джерелаYang, Zhaoqilin, Gaoyun An, and Ruichen Zhang. "STSM: Spatio-Temporal Shift Module for Efficient Action Recognition." Mathematics 10, no. 18 (September 10, 2022): 3290. http://dx.doi.org/10.3390/math10183290.
Повний текст джерелаSchutera, Mark, Stefan Elser, Jochen Abhau, Ralf Mikut, and Markus Reischl. "Strategies for supplementing recurrent neural network training for spatio-temporal prediction." at - Automatisierungstechnik 67, no. 7 (July 26, 2019): 545–56. http://dx.doi.org/10.1515/auto-2018-0124.
Повний текст джерелаTempelmeier, Nicolas, Udo Feuerhake, Oskar Wage, and Elena Demidova. "Mining Topological Dependencies of Recurrent Congestion in Road Networks." ISPRS International Journal of Geo-Information 10, no. 4 (April 8, 2021): 248. http://dx.doi.org/10.3390/ijgi10040248.
Повний текст джерелаZhao, Pengpeng, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng, and Xiaofang Zhou. "Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5877–84. http://dx.doi.org/10.1609/aaai.v33i01.33015877.
Повний текст джерелаLi, He, Xuejiao Li, Liangcai Su, Duo Jin, Jianbin Huang, and Deshuang Huang. "Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction." ACM Transactions on Intelligent Systems and Technology 13, no. 2 (April 30, 2022): 1–21. http://dx.doi.org/10.1145/3510829.
Повний текст джерелаДисертації з теми "Spatio temporal networks"
Moradi, Mohammad Mehdi. "Spatial and spatio-temporal point patterns on linear networks." Doctoral thesis, Universitat Jaume I, 2018. http://hdl.handle.net/10803/664140.
Повний текст джерелаThe last decade witnessed an extraordinary increase in interest in the analysis of network related data and trajectories. This pervasive interest is partly caused by a strongly expanded availability of such datasets. In the spatial statistics field, there are numerous real examples such as the locations of traffic accidents and geo-coded locations of crimes in the streets of cities that need to restrict the support of the underlying process over such linear networks to set and define a more realistic scenario. Examples of trajectories are the path taken by moving objects such as taxis, human beings, animals, etc. Intensity estimation on a network of lines, such as a road network, seems to be a surprisingly complicated task. Several techniques published in the literature, in geography and computer science, have turned out to be erroneous. We propose several adaptive and non-adaptive intensity estimators, based on kernel smoothing and Voronoi tessellation. Theoretical properties such as bias, variance, asymptotics, bandwidth selection, variance estimation, relative risk estimation, and adaptive smoothing are discussed. Moreover, their statistical performance is studied through simulation studies and is compared with existing methods. Adding the temporal component, we also consider spatio-temporal point patterns with spatial locations restricted to a linear network. We present a nonparametric kernel-based intensity estimator and develop second-order characteristics of spatio-temporal point processes on linear networks such as K-function and pair correlation function to analyse the type of interaction between points. In terms of trajectories, we introduce the R package trajectories that contains different classes and methods to handle, summarise and analyse trajectory data. Simulation and model fitting, intensity estimation, distance analysis, movement smoothing, Chi maps and second-order summary statistics are discussed. Moreover, we analyse different real datasets such as a crime data from Chicago (US), anti-social behaviour in Castell´on (Spain), traffic accidents in Medell´ın (Colombia), traffic accidents in Western Australia, motor vehicle traffic accidents in an area of Houston (US), locations of pine saplings in a Finnish forest, traffic accidents in Eastbourne (UK) and one week taxi movements in Beijing (China).
O'Donnell, David. "Spatial prediction and spatio-temporal modelling on river networks." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3161/.
Повний текст джерелаSutherland, Connie. "Spatio-temporal feedback in stochastic neural networks." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27559.
Повний текст джерелаMitchell, Elaine Irwin. "Spatio-temporal modelling of gene regulatory networks." Thesis, University of Dundee, 2018. https://discovery.dundee.ac.uk/en/studentTheses/259d76f6-76cf-474d-a26a-2802808b126e.
Повний текст джерелаAkbarzadeh, Vahab. "Spatio-temporal coverage optimization of sensor networks." Doctoral thesis, Université Laval, 2016. http://hdl.handle.net/20.500.11794/27065.
Повний текст джерелаSensor networks consist in a set of devices able to individually capture information on a given environment and to exchange information in order to obtain a higher level representation on the activities going on in the area of interest. Such a distributed sensing with many devices close to the phenomena of interest is of great interest in domains such as surveillance, agriculture, environmental monitoring, industrial monitoring, etc. We are proposing in this thesis several approaches to achieve spatiotemporal optimization of the operations of these devices, by determining where to place them in the environment and how to control them over time in order to sense the moving targets of interest. The first novelty consists in a realistic sensing model representing the coverage of a sensor network in its environment. We are proposing for that a probabilistic 3D model of sensing capacity of a sensor over its surrounding area. This model also includes information on the environment through the evaluation of line-of-sight visibility. From this sensing model, spatial optimization is conducted by searching for the best location and direction of each sensor making a network. For that purpose, we are proposing a new algorithm based on gradient descent, which has been favourably compared to other generic black box optimization methods in term of performance, while being more effective when considering processing requirements. Once the sensors are placed in the environment, the temporal optimization consists in covering well a group of moving targets in the environment. That starts by predicting the future location of the mobile targets detected by the sensors. The prediction is done either by using the history of other targets who traversed the same environment (long term prediction), or only by using the previous displacements of the same target (short term prediction). We are proposing new algorithms under each category which outperformed or produced comparable results when compared to existing methods. Once future locations of targets are predicted, the parameters of the sensors are optimized so that targets are properly covered in some future time according to the predictions. For that purpose, we are proposing a heuristics for making such sensor control, which deals with both the probabilistic targets trajectory predictions and probabilistic coverage of sensors over the targets. In the final stage, both spatial and temporal optimization method have been successfully integrated and applied, demonstrating a complete and effective pipeline for spatiotemporal optimization of sensor networks.
Dondo, C. "Bayesian networks for spatio-temporal integrated catchment assessment." Doctoral thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/10327.
Повний текст джерелаIncludes bibliographical references (leaves 181-203).
In this thesis, a methodology for integrated catchment water resources assessment using Bayesian Networks was developed. A custom made software application that combines Bayesian Networks with GIS was used to facilitate data pre-processing and spatial modelling. Dynamic Bayesian Networks were implemented in the software for time-series modelling.
YEGHIKYAN, Gevorg. "Urban Structure and Mobility as Spatio-temporal complex Networks." Doctoral thesis, Scuola Normale Superiore, 2020. http://hdl.handle.net/11384/94477.
Повний текст джерелаSu, Jionglong. "Online predictions for spatio-temporal systems using time-varying RBF networks." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.578701.
Повний текст джерелаSturrock, Marc. "Spatio-temporal modelling of gene regulatory networks containing negative feedback loops." Thesis, University of Dundee, 2013. https://discovery.dundee.ac.uk/en/studentTheses/b824506e-d515-442a-b9dc-ff82568f3c09.
Повний текст джерелаHolm, Noah, and Emil Plynning. "Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks." Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952.
Повний текст джерелаКниги з теми "Spatio temporal networks"
George, Betsy, and Sangho Kim. Spatio-temporal Networks. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4918-8.
Повний текст джерелаGeorge, Betsy. Spatio-temporal Networks: Modeling and Algorithms. New York, NY: Springer New York, 2013.
Знайти повний текст джерелаSpatio-temporal narratives: Historical GIS and the study of global trading networks (1500-1800). Newcastle upon Tyne: Cambridge Scholars Publishing, 2014.
Знайти повний текст джерелаZhongguo chan ye ji qun shi kong fa zhan yan jiu: Spatio-temporal development study on industrial clusters in China. Beijing Shi: Jing ji guan li chu ban she, 2011.
Знайти повний текст джерелаSwain, Eric D. Spatial and temporal statistical analysis of a ground-water level network, Broward County, Florida. Tallahassee, Fla: U.S. Dept. of the Interior, U.S. Geological Survey, 1994.
Знайти повний текст джерелаSwain, Eric D. Spatial and temporal statistical analysis of a ground-water level network, Broward County, Florida. Tallahassee, Fla: U.S. Dept. of the Interior, U.S. Geological Survey, 1994.
Знайти повний текст джерелаSwain, Eric D. Spatial and temporal statistical analysis of a ground-water level network, Broward County, Florida. Tallahassee, Fla: U.S. Dept. of the Interior, U.S. Geological Survey, 1994.
Знайти повний текст джерелаBenchaib, Abdelkrim. Advanced Control of AC / DC Power Networks: System of Systems Approach Based on Spatio-Temporal Scales. Wiley & Sons, Incorporated, John, 2015.
Знайти повний текст джерелаBenchaib, Abdelkrim. Advanced Control of AC / DC Power Networks: System of Systems Approach Based on Spatio-Temporal Scales. Wiley & Sons, Incorporated, John, 2015.
Знайти повний текст джерелаBenchaib, Abdelkrim. Advanced Control of AC / DC Power Networks: System of Systems Approach Based on Spatio-Temporal Scales. Wiley & Sons, Incorporated, John, 2015.
Знайти повний текст джерелаЧастини книг з теми "Spatio temporal networks"
Manganaro, Gabriele, Paolo Arena, and Luigi Fortuna. "Spatio-temporal Phenomena." In Cellular Neural Networks, 105–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60044-9_5.
Повний текст джерелаHolan, Scott H., and Christopher K. Wikle. "Semiparametric Dynamic Design of Monitoring Networks for Non-Gaussian Spatio-Temporal Data." In Spatio-Temporal Design, 269–84. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118441862.ch12.
Повний текст джерелаGeorge, Betsy, and Sangho Kim. "Spatio-temporal Networks: An Introduction." In SpringerBriefs in Computer Science, 1–6. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4918-8_1.
Повний текст джерелаHuaien, Luo, and Sadasivan Puthusserypady. "Neural Networks for fMRI Spatio-temporal Analysis." In Neural Information Processing, 1292–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_201.
Повний текст джерелаBabloyantz, A., and J. A. Sepulchre. "Information Processing by Spatio-temporal Chaotic Networks." In ICANN ’93, 670–75. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-2063-6_183.
Повний текст джерелаCozza, Vittoria, Antonio Messina, Danilo Montesi, Luca Arietta, and Matteo Magnani. "Spatio-Temporal Keyword Queries in Social Networks." In Advances in Databases and Information Systems, 70–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40683-6_6.
Повний текст джерелаGunturi, Viswanath, Shashi Shekhar, and Arnab Bhattacharya. "Minimum Spanning Tree on Spatio-Temporal Networks." In Lecture Notes in Computer Science, 149–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15251-1_11.
Повний текст джерелаSteer, Kelly, and Joseph G. Vella. "Link Prediction Based on Spatio-Temporal Networks." In Information Systems and Management Science, 228–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86223-7_20.
Повний текст джерелаCho, Sangwoo, and Hassan Foroosh. "Spatio-Temporal Fusion Networks for Action Recognition." In Computer Vision – ACCV 2018, 347–64. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20887-5_22.
Повний текст джерелаMartirosyan, Anahit, and Azzedine Boukerche. "Spatio-temporal Context in Wireless Sensor Networks." In Monographs in Theoretical Computer Science, 293–318. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14849-1_10.
Повний текст джерелаТези доповідей конференцій з теми "Spatio temporal networks"
Khan, Shujaat, Jawwad Ahmad, Alishba Sadiq, Imran Naseem, and Muhammad Moinuddin. "Spatio-Temporal RBF Neural Networks." In 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST). IEEE, 2018. http://dx.doi.org/10.1109/iceest.2018.8643322.
Повний текст джерелаRao, Aniruddha Rajendra, Qiyao Wang, Haiyan Wang, Hamed Khorasgani, and Chetan Gupta. "Spatio-Temporal Functional Neural Networks." In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2020. http://dx.doi.org/10.1109/dsaa49011.2020.00020.
Повний текст джерелаHerzig, Roei, Elad Levi, Huijuan Xu, Hang Gao, Eli Brosh, Xiaolong Wang, Amir Globerson, and Trevor Darrell. "Spatio-Temporal Action Graph Networks." In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019. http://dx.doi.org/10.1109/iccvw.2019.00288.
Повний текст джерелаMaiti, Rajib, Arobinda Gupta, and Niloy Ganguly. "Delay tolerant networks as spatio-temporal networks." In 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2013. http://dx.doi.org/10.1109/infcomw.2013.6970737.
Повний текст джерелаChanhyun Kang, A. Pugliese, J. Grant, and V. S. Subrahmanian. "STUN: Spatio-Temporal Uncertain (Social) Networks." In 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012). IEEE, 2012. http://dx.doi.org/10.1109/asonam.2012.93.
Повний текст джерелаLe Van Quyen, M., J. Martinerie, and F. J. Varela. "SPATIO-TEMPORAL DYNAMICS OF EPILEPTOGENIC NETWORKS." In Proceedings of the Workshop. WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789812793782_0008.
Повний текст джерелаCheng, Zida, Siheng Chen, and Ya Zhang. "Spatio-Temporal Graph Complementary Scattering Networks." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747790.
Повний текст джерелаKim, Joongheon, Seunghoon Park, Soyi Jung, and Seehwan Yoo. "Spatio-Temporal Split Learning." In 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S). IEEE, 2021. http://dx.doi.org/10.1109/dsn-s52858.2021.00016.
Повний текст джерелаMachireddy, Amrutha, Prayag Gowgi, and Shayan Srinivasa Garani. "Extracting Temporal Correlations Using Hierarchical Spatio-Temporal Feature Maps." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534337.
Повний текст джерелаWu, Haoze, Jiawei Liu, Zheng-Jun Zha, Zhenzhong Chen, and Xiaoyan Sun. "Mutually Reinforced Spatio-Temporal Convolutional Tube for Human Action Recognition." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/136.
Повний текст джерелаЗвіти організацій з теми "Spatio temporal networks"
Shastri, Lokendra. Spatio-Temporal Neural Networks for Vision, Reasoning and Rapid Decision Making. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada299746.
Повний текст джерелаBurns, Robert T., Nir Keren, Ross Muhlbauer, Hongwei Xin, Steven J. Hoff, and Randy Swestka. Development of a Wireless Sensor Network to Quantify Spatial and Temporal H2S Concentrations in Swine Houses (A Progress Report). Ames (Iowa): Iowa State University, January 2009. http://dx.doi.org/10.31274/ans_air-180814-279.
Повний текст джерелаLeis, Sherry. Network scale fire atlas supports land management in national parks. Edited by Tani Hubbard. National Park Service, November 2022. http://dx.doi.org/10.36967/2295133.
Повний текст джерелаLeis, Sherry, Mike DeBacker, Lloyd Morrison, Gareth Rowell, and Jennifer Haack. Vegetation community monitoring protocol for the Heartland Inventory and Monitoring Network: Narrative, Version 4.0. Edited by Tani Hubbard. National Park Service, November 2022. http://dx.doi.org/10.36967/2294948.
Повний текст джерелаPstuty, Norbert, Mark Duffy, Dennis Skidds, Tanya Silveira, Andrea Habeck, Katherine Ames, and Glenn Liu. Northeast Coastal and Barrier Network Geomorphological Monitoring Protocol: Part I—Ocean Shoreline Position, Version 2. National Park Service, June 2022. http://dx.doi.org/10.36967/2293713.
Повний текст джерелаFait, Aaron, Grant Cramer, and Avichai Perl. Towards improved grape nutrition and defense: The regulation of stilbene metabolism under drought. United States Department of Agriculture, May 2014. http://dx.doi.org/10.32747/2014.7594398.bard.
Повний текст джерелаSpatial and temporal statistical analysis of a ground-water level network, Broward County, Florida. US Geological Survey, 1994. http://dx.doi.org/10.3133/wri944076.
Повний текст джерела