A hybrid spatio-temporal model for radio propagation in urban environment

2007 ◽  
Author(s):  
Yu-Jiun Ren Yu-Jiun Ren ◽  
Jenn-Hwan Tarng Jenn-Hwan Tarng
Keyword(s):  
Urban Environment ◽  
Radio Propagation ◽  
Temporal Model ◽  
Spatio Temporal

scholarly journals A comparison of multiple neighborhood matrix specifications for spatio-temporal model fitting: a case study on COVID-19 data

2021 ◽  
Author(s):  
Álvaro Briz-Redón ◽  
Adina Iftimi ◽  
Juan Francisco Correcher ◽  
Jose De Andrés ◽  
Manuel Lozano ◽  
...  
Keyword(s):  
Model Fitting ◽  
Temporal Model ◽  
Spatio Temporal

scholarly journals Theory and Experimental Validation of a Spatio-temporal Model of Chemotherapy Transport to Enhance Tumor Cell Kill

2016 ◽  
Vol 12 (6) ◽  
pp. e1004969 ◽  
Author(s):  
Zhihui Wang ◽  
Romica Kerketta ◽  
Yao-Li Chuang ◽  
Prashant Dogra ◽  
Joseph D. Butner ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Experimental Validation ◽  
Cell Kill ◽  
Temporal Model ◽  
Spatio Temporal ◽  
Enhance Tumor Cell

Based on spatio-temporal graph convolution networks with residual connection for intelligence behavior recognition

2021 ◽  
pp. 002072092199660
Author(s):  
Yinong Zhang ◽  
Shanshan Guan ◽  
Cheng Xu ◽  
Hongzhe Liu
Keyword(s):  
Human Behavior ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Skeleton Model ◽  
Basic Technology ◽  
Temporal Model ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Human Behavior Recognition ◽  
Behavior Characteristics

In the era of intelligent education, human behavior recognition based on computer vision is an important branch of pattern recognition. Human behavior recognition is a basic technology in the fields of intelligent monitoring and human-computer interaction in education. The dynamic changes of human skeleton provide important information for the recognition of educational behavior. Traditional methods usually use manual information to label or traverse rules only, resulting in limited representation capabilities and poor generalization performance of the model. In this paper, a kind of dynamic skeleton model with residual is adopted—a spatio-temporal graph convolutional network based on residual connections, which not only overcomes the limitations of previous methods, but also can learn the spatio-temporal model from the skeleton data. In the big bone NTU-RGB + D dataset, the network model not only improved the representation ability of human behavior characteristics, but also improved the generalization ability, and achieved better recognition effect than the existing model. In addition, this paper also compares the results of behavior recognition on subsets of different joint points, and finds that spatial structure division have better effects.

scholarly journals Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production

MATEMATIKA ◽  
2018 ◽  
Vol 34 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Suhartono Suhartono ◽  
Dedy Dwi Prastyo ◽  
Heri Kuswanto ◽  
Muhammad Hisyam Lee
Keyword(s):  
Forecast Accuracy ◽  
Oil Production ◽  
Training Data ◽  
Temporal Data ◽  
Monthly Data ◽  
Vector Autoregressive ◽  
Temporal Models ◽  
Accurate Forecast ◽  
Temporal Model ◽  
Spatio Temporal

Monthly data about oil production at several drilling wells is an example of spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal model, i.e. Feedforward Neural Network - Vector Autoregressive (FFNN-VAR) and FFNN - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast accuracy to linear spatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal models are proposed and applied for forecasting monthly oil production data at three drilling wells in East Java, Indonesia. There are 60 observations that be divided to two parts, i.e. the first 50 observations for training data and the last 10 observations for testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11) as linear spatio-temporal models. Moreover, further research about nonlinear spatio-temporal models based on neural networks and GSTAR is needed for developing new hybrid models that could improve the forecast accuracy.

scholarly journals Impact of Settlement Growth on Yenagoa’s Urban Environment

2021 ◽  
Vol 2 (1) ◽  
pp. 24-29
Author(s):  
E. E. Imaitor-Uku ◽  
O. B. Owei ◽  
L. Hart ◽  
A. Ayotamuno
Keyword(s):  
Land Use ◽  
Urban Environment ◽  
Sustainable Land Use ◽  
Mapping Technique ◽  
Citizen Education ◽  
Management Framework ◽  
Urban Planning And Development ◽  
Spatio Temporal ◽  
The City ◽  
Settlement Growth

This research is a study on the assessment of settlement growth and its impact on the urban environment in Yenagoa Metropolis. Landsat imageries of 1988, 1996, 2004, 2012 and 2020 were acquired from the United Sates Geographical Survey. Supervised image classifications using level 1 classification scheme was adopted to extract LandUse/LandCover. The five Epochs of images were used to extract the built-up areas, water bodies and vegetation areas. The area (ha) of land use for each epoch was determined using clipping images. The spatio-temporal changes were determined as a percentage of LULC per epoch and which was done following standard methods. Mapping technique was used to compare satellite imageries. Findings showed that built up areas was 1,279.81 in 1988, 2,497.87 in 1996, 4,554.73 in 2009, 7,804.30 in 2012 and 10,447.50 in 2020. While the percentage change of built-up are in 1988 was 6.12%, 1996 was 11.94%, 2004 was 21.78%, 2012 was 34.31% and 2020 was 30.09%, respectively. It is therefore recommended here that government should empower urban planning and development agencies, legally and financially to carry out their responsibility to implement existing master plan of the city. Engage in citizen education on how to contribute in managing the environment and sustainable land use management framework in the study area for proper management of the urban environment to enhance sustained settlement growth in the city.  

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