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Found 6 papers, 0 papers with code
Spatial-temporal associations representation and application for process monitoring using graph convolution neural network
Thank you very much for the attention and concern of colleagues and scholars in this work.
Deep Domain Adaptation for Pavement Crack Detection
In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns domain invariant features by taking advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available.
Graph neural network-based fault diagnosis: a review
Graph neural network (GNN)-based fault diagnosis (FD) has received increasing attention in recent years, due to the fact that data coming from several application domains can be advantageously represented as graphs.
An ensemble learning framework based on group decision making
The classification problem is a significant topic in machine learning which aims to teach machines how to group together data by particular criteria.
A Discrete State Transition Algorithm for Generalized Traveling Salesman Problem
In this paper, an efficient discrete state transition algorithm (DSTA) for GTSP is proposed, where a new local search operator named \textit{K-circle}, directed by neighborhood information in space, has been introduced to DSTA to shrink search space and strengthen search ability.
State Transition Algorithm
In terms of the concepts of state and state transition, a new heuristic random search algorithm named state transition algorithm is proposed.
