scholarly journals Spatio-Temporal Behavior Analysis and Pheromone-Based Fusion Model for Big Trace Data

2017 ◽  
Vol 6 (5) ◽  
pp. 151 ◽  
Author(s):  
Luliang Tang ◽  
Qianqian Zou ◽  
Xia Zhang ◽  
Chang Ren ◽  
Qingquan Li
Keyword(s):  
Behavior Analysis ◽  
Temporal Behavior ◽  
Fusion Model ◽  
Trace Data ◽  
Spatio Temporal

Insights into Attacks’ Progression: Prediction of Spatio-Temporal Behavior of DDoS Attacks

2020 ◽  
pp. 362-374
Author(s):  
Ahmed Abusnaina ◽  
Mohammed Abuhamad ◽  
DaeHun Nyang ◽  
Songqing Chen ◽  
An Wang ◽  
...  
Keyword(s):  
Temporal Behavior ◽  
Ddos Attacks ◽  
Spatio Temporal ◽  
Progression Prediction

Visitors’ spatio-temporal behavior at a zoo in China

2020 ◽  
Vol 25 (9) ◽  
pp. 931-947
Author(s):  
Ding Xu ◽  
Li Cong ◽  
Geoffrey Wall
Keyword(s):  
Temporal Behavior ◽  
Spatio Temporal

scholarly journals Differences in Spatio-Temporal Behavior of Zebrafish in the Open Tank Paradigm after a Short-Period Confinement into Dark and Bright Environments

PLoS ONE ◽  
2011 ◽  
Vol 6 (5) ◽  
pp. e19397 ◽  
Author(s):  
Denis B. Rosemberg ◽  
Eduardo P. Rico ◽  
Ben Hur M. Mussulini ◽  
Ângelo L. Piato ◽  
Maria E. Calcagnotto ◽  
...  
Keyword(s):  
Temporal Behavior ◽  
Short Period ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Clustering of Time-Dependent Origin-Destination Electronic Trace Data

2018 ◽  
Vol 130 ◽  
pp. 359-367
Author(s):  
Daphne van Leeuwen ◽  
Joost Bosman ◽  
Elenna Dugundji
Keyword(s):  
Time Dependent ◽  
Trace Data ◽  
Temporal Clustering ◽  
Spatio Temporal

Evidence of Chaotic Hierarchy in a Semiconductor Experiment

1989 ◽  
Vol 44 (11) ◽  
pp. 1046-1050 ◽  
Author(s):  
J. Parisi ◽  
J. Peinke ◽  
R. P. Huebener
Keyword(s):  
Fractal Dimensions ◽  
Physical Situation ◽  
Temporal Behavior ◽  
Nonlinear Coupling ◽  
Characterization Methods ◽  
Self Consistent ◽  
Semiconductor System ◽  
Consistent Picture ◽  
Spatio Temporal ◽  
Takens Theorem

We study the cooperative spatio-temporal behavior of semiconductor breakdown via both probabilistic and dynamical characterization methods (fractal dimensions, entropies, Lyapunov exponents, and the corresponding scaling functions). Agreement between the results obtained from the different numerical concepts (e.g., verification of the Kaplan-Yorke conjecture and the Newhouse- Ruelle-Takens theorem) gives a self-consistent picture of the physical situation investigated. As a consequence, the affirmed chaotic hierarchy of generalized horseshoe-type strange attractors may be ascribed to weak nonlinear coupling between competing localized oscillation centers intrinsic to the present semiconductor system

scholarly journals Spatio-Temporal Data Fusion for Satellite Images Using Hopfield Neural Network

2019 ◽  
Vol 11 (18) ◽  
pp. 2077 ◽  
Author(s):  
Fung ◽  
Wong ◽  
Chan
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Satellite Images ◽  
Hopfield Neural Network ◽  
Temporal Data ◽  
Fusion Model ◽  
Resolution Image ◽  
Coarse Resolution ◽  
Spatio Temporal ◽  
Fine Resolution

Spatio-temporal data fusion refers to the technique of combining high temporal resolution from coarse satellite images and high spatial resolution from fine satellite images. However, data availability remains a major limitation in algorithm development. Existing spatio-temporal data fusion algorithms require at least one known image pair between the fine and coarse resolution image. However, data which come from two different satellite platforms do not necessarily have an overlap in their overpass times, hence restricting the application of spatio-temporal data fusion. In this paper, a new algorithm named Hopfield Neural Network SPatio-tempOral daTa fusion model (HNN-SPOT) is developed by utilizing the optimization concept in the Hopfield neural network (HNN) for spatio-temporal image fusion. The algorithm derives a synthesized fine resolution image from a coarse spatial resolution satellite image (similar to downscaling), with the use of one fine resolution image taken on an arbitrary date and one coarse image taken on a predicted date. The HNN-SPOT particularly addresses the problem when the fine resolution and coarse resolution images are acquired from different satellite overpass times over the same geographic extent. Both simulated datasets and real datasets over Hong Kong and Australia have been used in the evaluation of HNN-SPOT. Results showed that HNN-SPOT was comparable with an existing fusion algorithm, the spatial and temporal adaptive reflectance fusion model (STARFM). HNN-SPOT assumes consistent spatial structure for the target area between the date of data acquisition and the prediction date. Therefore, it is more applicable to geographical areas with little or no land cover change. It is shown that HNN-SPOT can produce accurate fusion results with >90% of correlation coefficient over consistent land covers. For areas that have undergone land cover changes, HNN-SPOT can still produce a prediction about the outlines and the tone of the features, if they are large enough to be recorded in the coarse resolution image at the prediction date. HNN-SPOT provides a relatively new approach in spatio-temporal data fusion, and further improvements can be made by modifying or adding new goals and constraints in its HNN architecture. Owing to its lower demand for data prerequisites, HNN-SPOT is expected to increase the applicability of fine-scale applications in remote sensing, such as environmental modeling and monitoring.

scholarly journals Capturing the spatio-temporal behavior of real traffic data

2002 ◽  
Vol 49 (1-4) ◽  
pp. 147-163 ◽  
Author(s):  
Mengzhi Wang ◽  
Anastassia Ailamaki ◽  
Christos Faloutsos
Keyword(s):  
Temporal Behavior ◽  
Traffic Data ◽  
Spatio Temporal ◽  
Real Traffic
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