In earthmoving equipment, overloading and underloading transport vehicles reduce the
production efficiency. This paper presents a novel online loader payload prediction method …

The training phase of a deep learning neural network (DLNN) is a computationally
demanding process, particularly for models comprising multiple layers of intermediate …

Gas–liquid two-phase flows are widely encountered in many industrial applications, and the
evolutionary dynamics of the flow patterns are fundamental knowledge for the establishment …

In this work, the short-term electric load demand forecasting problem is addressed,
proposing a method inspired by the use of anchors in object detection methods. Specifically …

This paper establishes statistical properties of deep neural network (DNN) estimators under
dependent data. Two general results for nonparametric sieve estimators directly applicable …

Optimization research often confronts the challenge of developing time consuming
processes. This article introduces an innovative approach that leverages the computational …

Modeling complex data, eg time series as well as network-based data, is a prominent area
of research. In this paper, we focus on a combination of both, analyzing network-based …

This paper introduces an innovative methodology for conducting demand analysis within
various domains across multiple countries, presenting insights derived from a …

In this paper, we present a new fuzzy symbolization technique for energy load forecasting
with neural networks, FPLS-Sym. Symbolization techniques transform a numerical time …

Time series are ubiquitous in numerous science and engineering domains, eg, signal
processing, bioinformatics, and astronomy. Previous work has verified the efficacy of …