Optimization Model of Ship Engine Room Electrical Equipment Layout Based on Deep Learning
Xiliang Nie, Hanxing Li
Journal of Electronic Systems, Vol. 14, No. 4, 2024.
DOI: https://doi.org/10.6025/jes/2024/14/4/132-138
Link: https://www.dline.info/jes/fulltext/v14n4/jesv14n4_3.pdf
Abstract:
With the rapid development of computer technology and big data, algorithms such
as deep learning and neural networks have been widely applied. Among them, deep
learning has gradually become a hot topic and mainstream direction in intelligence.
This computer model constructed using multiple processing layers has shown
significant effects in aerospace, electrical engineering, automotive, and urban
transportation industries. This paper uses genetic algorithms and particle swarm
optimization algorithms in deep learning to study and optimize the layout of ship
engine room electrical equipment. Firstly, we analyze the application of genetic
algorithms to solve the layout planning of ship engine room electrical equipment.
The arrangement of equipment and the utilization of shipâ€™s electrical wires are
recombined through data analysis to understand the correlation of various factors
affecting the layout fully. Secondly, we use particle swarm quantum algorithm to
optimize the layout structure of genetic algorithms, establishing constraints to obtain
the final objective function path. Finally, attention should be paid to equipment
anomalies before the layout of ship engine room electrical equipment. Deep learning
establishes a spectrogram anomaly detection database to explore the location of
abnormal states through detection algorithms. The research results show that the
optimization of ship engine room electrical equipment layout based on deep learning
using particle swarm algorithms has achieved good results in terms of usage.