Classification of spatio-temporal data represented as multiset for tactical profiling in badminton

Author(s):  
Kokum Weeratunga ◽  
Anuja Dharmaratne ◽  
Khoo Boon How
2021 ◽  
Vol 10 (3) ◽  
pp. 188
Author(s):  
Cyril Carré ◽  
Younes Hamdani

Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a demand to further develop spatio-temporal conceptual models to comprehensively represent the nature of the evolution of geographic objects. The latter involves a set of considerations like those related to managing changes and object identities, modeling possible causal relations, and integrating multiple interpretations. While conventional literature generally presents these concepts separately and rarely approaches them from a holistic perspective, they are in fact interrelated. Therefore, we believe that the semantics of modeling would be improved by considering these concepts jointly. In this work, we propose to represent these interrelationships in the form of a hierarchical pyramidal framework and to further explore this set of concepts. The objective of this framework is to provide a guideline to orient the design of future generations of GIS data models, enabling them to achieve a better representation of available spatio-temporal data. In addition, this framework aims at providing keys for a new interpretation and classification of spatio-temporal conceptual models. This work can be beneficial for researchers, students, and developers interested in advanced spatio-temporal modeling.


2021 ◽  
Vol 26 (2) ◽  
pp. 363-374
Author(s):  
Marta Karaliutė ◽  
Kęstutis Dučinskas

The novel approach to classification of spatio-temporal data based on Bayes discriminant functions is developed. We focus on the problem of supervised classifying of the spatiotemporal Gaussian random field (GRF) observation into one of two classes specified by different drift parameters, separable nonlinear covariance functions and nonstationary label field. The performance of proposed classification rule is validated by the values of local Bayes and empirical error rates realized by leave one out procedure. A simulation study for spatial covariance functions belonging to powered-exponential family and temporal covariance functions of AR(1) models is carried out. The influence of the values of spatial and temporal covariance parameters to error rates for several label field models are studied. The results showed that the proposed classification methodology can be applied successfully in  practice with small error rates and can be a useful tool for discriminant analysis of spatio-temporal data.


2011 ◽  
Vol 38 (9) ◽  
pp. 866-871 ◽  
Author(s):  
Zhi-Hua HUANG ◽  
Ming-Hong LI ◽  
Yuan-Ye MA ◽  
Chang-Le ZHOU

2019 ◽  
Vol 942 (12) ◽  
pp. 22-28
Author(s):  
A.V. Materuhin ◽  
V.V. Shakhov ◽  
O.D. Sokolova

Optimization of energy consumption in geosensor networks is a very important factor in ensuring stability, since geosensors used for environmental monitoring have limited possibilities for recharging batteries. The article is a concise presentation of the research results in the area of increasing the energy consumption efficiency for the process of collecting spatio-temporal data with wireless geosensor networks. It is shown that in the currently used configurations of geosensor networks there is a predominant direction of the transmitted traffic, which leads to the fact that through the routing nodes that are close to the sinks, a much more traffic passes than through other network nodes. Thus, an imbalance of energy consumption arises in the network, which leads to a decrease in the autonomous operation time of the entire wireless geosensor networks. It is proposed to use the possible mobility of sinks as an optimization resource. A mathematical model for the analysis of the lifetime of a wireless geosensor network using mobile sinks is proposed. The model is analyzed from the point of view of optimization energy consumption by sensors. The proposed approach allows increasing the lifetime of wireless geosensor networks by optimizing the relocation of mobile sinks.


Author(s):  
Didier A. Vega-Oliveros ◽  
Moshé Cotacallapa ◽  
Leonardo N. Ferreira ◽  
Marcos G. Quiles ◽  
Liang Zhao ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document