Complex-valued neural networks: A comprehensive survey
Complex-valued neural networks (CVNNs) have shown their excellent efficiency compared
to their real counter-parts in speech enhancement, image and signal processing …
to their real counter-parts in speech enhancement, image and signal processing …
Electric vehicle scheduling: State of the art, critical challenges, and future research opportunities
Electric vehicles can be perceived as a means to achieve carbon reduction, energy
efficiency, and sustainable development of the transportation industry. Electric vehicle sales …
efficiency, and sustainable development of the transportation industry. Electric vehicle sales …
Control framework for cooperative robots in smart home using bio-inspired neural network
In this paper, we present a model-free tracking controller for a cooperative mobile-
manipulators, which are the cornerstone for future smart homes. The mobile-manipulators …
manipulators, which are the cornerstone for future smart homes. The mobile-manipulators …
Human–robot collaborative disassembly line balancing problem with stochastic operation time and a solution via multi-objective shuffled frog lea** algorithm
Product disassembly is critically important in recycling end-of-life products, reducing their
negative impact on environmental pollution and minimizing resource waste. Disassembly …
negative impact on environmental pollution and minimizing resource waste. Disassembly …
Hierarchical particle swarm optimization-incorporated latent factor analysis for large-scale incomplete matrices
A Stochastic Gradient Descent (SGD)-based Latent Factor Analysis (LFA) model is highly
efficient in representative learning on a High-Dimensional and Sparse (HiDS) matrix, where …
efficient in representative learning on a High-Dimensional and Sparse (HiDS) matrix, where …
Short-term load forecasting and associated weather variables prediction using ResNet-LSTM based deep learning
Short-term load forecasting is mainly utilized in control centers to explore the changing
patterns of consumer loads and predict the load value at a certain time in the future. It is one …
patterns of consumer loads and predict the load value at a certain time in the future. It is one …
A graph neural network-based node classification model on class-imbalanced graph data
Z Huang, Y Tang, Y Chen - Knowledge-Based Systems, 2022 - Elsevier
Node classification for highly imbalanced graph data is challenging, with existing graph
neural networks (GNNs) typically utilizing a balanced class distribution to learn node …
neural networks (GNNs) typically utilizing a balanced class distribution to learn node …
Multi-objective optimization of energy-efficient remanufacturing system scheduling problem with lot-streaming production mode
Most previous studies on the scheduling problem in remanufacturing systems have focused
on single or two production stages and economic criteria such as makespan or tardiness …
on single or two production stages and economic criteria such as makespan or tardiness …
A bi-population cooperative optimization algorithm assisted by an autoencoder for medium-scale expensive problems
This study presents an autoencoder-embedded optimization (AEO) algorithm which involves
a bi-population cooperative strategy for medium-scale expensive problems (MEPs). A huge …
a bi-population cooperative strategy for medium-scale expensive problems (MEPs). A huge …
Cryptocurrency transaction network embedding from static and dynamic perspectives: An overview
Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests
from both industrial and academic communities. With its rapid development, the …
from both industrial and academic communities. With its rapid development, the …