AIMC Topic: Time Factors

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Predicting the monthly consumption and production of natural gas in the USA by using a new hybrid forecasting model based on two-layer decomposition.

Environmental science and pollution research international
As an efficient, economical, and clean energy, natural gas plays an important role in the development of the new energy revolution. Accurate prediction of natural gas consumption and production can adjust energy deployment in advance, which can ensur...

Coexistence and local stability of multiple equilibrium points for fractional-order state-dependent switched competitive neural networks with time-varying delays.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the coexistence and local stability of multiple equilibrium points for a class of competitive neural networks with sigmoidal activation functions and time-varying delays, in which fractional-order derivative and state-dependen...

Fixed-time and prescribed-time synchronization of quaternion-valued neural networks: A control strategy involving Lyapunov functions.

Neural networks : the official journal of the International Neural Network Society
A control strategy containing Lyapunov functions is proposed in this paper. Based on this strategy, the fixed-time synchronization of a time-delay quaternion-valued neural network (QVNN) is analyzed. This strategy is extended to the prescribed-time s...

Predefined-time synchronization of coupled neural networks with switching parameters and disturbed by Brownian motion.

Neural networks : the official journal of the International Neural Network Society
This article focuses on predefined time synchronization problem for a class of signal switching neural networks with time-varying delays. In the network models, we not only consider the coupling characteristics in the following networks, but also con...

Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets.

Sensors (Basel, Switzerland)
Smart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. ...

A Study on Structural Health Monitoring of a Large Space Antenna via Distributed Sensors and Deep Learning.

Sensors (Basel, Switzerland)
Most modern Earth and Universe observation spacecraft are now equipped with large lightweight and flexible structures, such as antennas, telescopes, and extendable elements. The trend of hosting more complex and bigger appendages, essential for high-...

Observer-based dynamical pattern recognition via deterministic learning.

Neural networks : the official journal of the International Neural Network Society
In this paper, based on the sampled-data observer and the deterministic learning theory, a rapid dynamical pattern recognition approach is proposed for univariate time series composed of the output signals of the dynamical systems. Specifically, loca...

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning.

International journal of environmental research and public health
The immense growth of the population generates a polluted environment that must be managed to ensure environmental sustainability, versatility and efficiency in our everyday lives. Particularly, the municipality is unable to cope with the increase in...

A Heterogeneous Ensemble Approach for Travel Time Prediction Using Hybridized Feature Spaces and Support Vector Regression.

Sensors (Basel, Switzerland)
Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. Th...

Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation.

IEEE/ACM transactions on computational biology and bioinformatics
Approximate Bayesian Computation is widely used in systems biology for inferring parameters in stochastic gene regulatory network models. Its performance hinges critically on the ability to summarize high-dimensional system responses such as time ser...