scholarly journals Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network

2021 ◽  
Author(s):  
Amit Roy ◽  
Kashob Kumar Roy ◽  
Amin Ahsan Ali ◽  
M Ashraful Amin ◽  
A K M Mahbubur Rahman
Keyword(s):  
Neural Network ◽  
Temporal Modeling ◽  
Traffic Forecasting ◽  
Spatio Temporal

GANNSTER: Graph-Augmented Neural Network Spatio-Temporal Reasoner for Traffic Forecasting

2020 ◽  
pp. 63-76
Author(s):  
Carlos Salort Sánchez ◽  
Alexander Wieder ◽  
Paolo Sottovia ◽  
Stefano Bortoli ◽  
Jan Baumbach ◽  
...  
Keyword(s):  
Neural Network ◽  
Traffic Forecasting ◽  
Spatio Temporal

scholarly journals Neural Network-Based Learning from Demonstration of an Autonomous Ground Robot

Machines ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 24 ◽  
Author(s):  
Yiwei Fu ◽  
Devesh K. Jha ◽  
Zeyu Zhang ◽  
Zhenyuan Yuan ◽  
Asok Ray
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Short Term Memory ◽  
Autonomous Systems ◽  
Imitation Learning ◽  
Machine Learning Techniques ◽  
Convolutional Network ◽  
Temporal Modeling ◽  
Imitation Model ◽  
Spatio Temporal

This paper presents and experimentally validates a concept of end-to-end imitation learning for autonomous systems by using a composite architecture of convolutional neural network (ConvNet) and Long Short Term Memory (LSTM) neural network. In particular, a spatio-temporal deep neural network is developed, which learns to imitate the policy used by a human supervisor to drive a car-like robot in a maze environment. The spatial and temporal components of the imitation model are learned by using deep convolutional network and recurrent neural network architectures, respectively. The imitation model learns the policy of a human supervisor as a function of laser light detection and ranging (LIDAR) data, which is then used in real time to drive a robot in an autonomous fashion in a laboratory setting. The performance of the proposed model for imitation learning is compared with that of several other state-of-the-art methods, reported in the machine learning literature, for spatial and temporal modeling. The learned policy is implemented on a robot using a Nvidia Jetson TX2 board which, in turn, is validated on test tracks. The proposed spatio-temporal model outperforms several other off-the-shelf machine learning techniques to learn the policy.

scholarly journals Pyramidal Framework: Guidance for the Next Generation of GIS Spatial-Temporal Models

2021 ◽  
Vol 10 (3) ◽  
pp. 188
Author(s):  
Cyril Carré ◽  
Younes Hamdani
Keyword(s):  
Conceptual Models ◽  
Future Generations ◽  
Temporal Data ◽  
Temporal Modeling ◽  
Temporal Models ◽  
Gis Data ◽  
Spatio Temporal ◽  
New Interpretation ◽  
Conventional Literature

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.

scholarly journals Machine learning estimation of tissue optical properties

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Brett H. Hokr ◽  
Joel N. Bixler
Keyword(s):  
Neural Network ◽  
Optical Properties ◽  
Biological Tissues ◽  
Homogeneous Layer ◽  
In Vivo Measurement ◽  
Tissue Optical Properties ◽  
The Neural Network ◽  
Spatio Temporal ◽  
Fully Connected

AbstractDynamic, in vivo measurement of the optical properties of biological tissues is still an elusive and critically important problem. Here we develop a technique for inverting a Monte Carlo simulation to extract tissue optical properties from the statistical moments of the spatio-temporal response of the tissue by training a 5-layer fully connected neural network. We demonstrate the accuracy of the method across a very wide parameter space on a single homogeneous layer tissue model and demonstrate that the method is insensitive to parameter selection of the neural network model itself. Finally, we propose an experimental setup capable of measuring the required information in real time in an in vivo environment and demonstrate proof-of-concept level experimental results.

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