AIMC Topic: Spatial Analysis
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LSTMED: An uneven dynamic process monitoring method based on LSTM and Autoencoder neural network.
Neural networks : the official journal of the International Neural Network Society
Nov 16, 2022
Due to the complicated production mechanism in multivariate industrial processes, different dynamic features of variables raise challenges to traditional data-driven process monitoring methods which assume the process data is static or dynamically co...
Uncertainty and spatial analysis in wheat yield prediction based on robust inclusive multiple models.
Environmental science and pollution research international
Oct 20, 2022
Reliable prediction of wheat yield ahead of harvest is a critical challenge for decision-makers along the supply chain. Predicting wheat yield is a real challenge for better agriculture and food security management. Modeling wheat yield is complex an...
Design and optimization of wall-climbing robot impeller by genetic algorithm based on computational fluid dynamics and kriging model.
Scientific reports
Jun 10, 2022
In recent years, wall-climbing robots have begun to replace manual work at heights to reduce economic losses and casualties caused by working at heights. This paper designs a negative pressure adsorption type wall-climbing robot and analyzes the inte...
Predicting Social Events with Multimodal Fusion of Spatial and Temporal Dynamic Graph Representations.
Big data
May 6, 2022
Big data has been satisfactorily used to solve social issues in several parts of the word. Social event prediction is related to social stability and sustainable development. However, current research rarely takes into account the dynamic connections...
Prediction of MODIS land surface temperature using new hybrid models based on spatial interpolation techniques and deep learning models.
Environmental science and pollution research international
May 6, 2022
Land surface temperature (LST) prediction is of great importance for climate change, ecology, environmental and industrial studies. These studies require accurate LST map predictions considering both spatial and temporal dynamics. In this study, mult...
Spatial analysis of tumor-infiltrating lymphocytes in histological sections using deep learning techniques predicts survival in colorectal carcinoma.
The journal of pathology. Clinical research
Apr 28, 2022
This study aimed to explore the prognostic impact of spatial distribution of tumor-infiltrating lymphocytes (TILs) quantified by deep learning (DL) approaches based on digitalized whole-slide images stained with hematoxylin and eosin in patients with...
Application of a semivariogram based on a deep neural network to Ordinary Kriging interpolation of elevation data.
PloS one
Apr 22, 2022
The Ordinary Kriging method is a common spatial interpolation algorithm in geostatistics. Because the semivariogram required for kriging interpolation greatly influences this process, optimal fitting of the semivariogram is of major significance for ...
Spatial-Temporal Convolutional Transformer Network for Multivariate Time Series Forecasting.
Sensors (Basel, Switzerland)
Jan 22, 2022
Multivariate time series forecasting has long been a research hotspot because of its wide range of application scenarios. However, the dynamics and multiple patterns of spatiotemporal dependencies make this problem challenging. Most existing methods ...
Using machine learning to model nontraditional spatial dependence in occupancy data.
Ecology
Dec 22, 2021
Spatial models for occupancy data are used to estimate and map the true presence of a species, which may depend on biotic and abiotic factors as well as spatial autocorrelation. Traditionally researchers have accounted for spatial autocorrelation in ...
Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction.
Neural networks : the official journal of the International Neural Network Society
Oct 28, 2021
The prediction of crowd flows is an important urban computing issue whose purpose is to predict the future number of incoming and outgoing people in regions. Measuring the complicated spatial-temporal dependencies with external factors, such as weath...
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