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Journal of Electronic Systems  | Volume: 14, Issue: 4 (  December   2024) |
Editorial Message |
Research |
Electrical Control System of Harvesting Robotic Arm Based on PLC and Particle Swarm Algorithm Xibao Sun, Xiuli Zhai Page: 117-123 Abstract Full Text Download: 23 times https://doi.org/10.6025/jes/2024/14/4/117-123 Abstract: This paper aims to explore a new control system optimization algorithm that combines
PLC controller with computer image processing technology to enhance the control
performance of a harvesting robotic arm. The algorithm autonomously selects the
optimal picking sequence based on the collected images of fruits, significantly reducing
the end-effector’s operation time and greatly improving work efficiency. By applying
the particle swarm algorithm, we compared the distance of the robot’s moving tail
and the picking accuracy in an actual scenario of harvesting bell peppers. It was
found that the application of the particle swarm algorithm can greatly improve the
positioning and recognition accuracy of fruit trees and substantially reduce
transportation distances, thereby significantly enhancing work efficiency.
| | Analysis of Electrical Equipment Information Detection and Diagnosis Based on Multiple Information Integration Run Ma Page: 124-131 Abstract Full Text Download: 16 times https://doi.org/10.6025/jes/2024/14/4/124-131 Abstract: detection based on multi-information integration. By integrating and analyzing
information from multiple sources of electrical equipment, the accuracy and efficiency
of equipment fault detection and diagnosis can be effectively improved. In detecting
and diagnosing electrical equipment information, multiple sources of information
are usually involved, including equipment operation data, sensor data, historical
maintenance records, etc. This information has different forms and characteristics,
so it needs to be integrated and comprehensively analyzed to fully explore the useful
information within it. This article proposes a method for detecting, diagnosing and
analyzing information on electrical equipment based on multi-information integration.
This method first preprocesses and integrates information from different sources,
then utilises machine learning and data mining techniques to analyze and mine the
information. Among them, special attention is paid to the complementarity of
information and fusion methods to extract valuable features and patterns from
different information fully.
| | Optimization Model of Ship Engine Room Electrical Equipment Layout Based on Deep Learning Xiliang Nie, Hanxing Li Page: 132-138 Abstract Full Text Download: 6 times https://doi.org/10.6025/jes/2024/14/4/132-138 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 ship’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.
| | Factor Analysis of Railway Carrying Capacity Coordination Optimization Considering Energy Consumption Yi Feng, Zhijiang Lan Page: 139-145 Abstract Full Text Download: 4 times https://doi.org/10.6025/jes/2024/14/4/139-145 Abstract: High-speed railways, as crucial transportation tools, are characterized by high efficiency,
safety, and eco-friendliness, making them an ideal mode of transportation.
However, we face increasing energy consumption pressures to achieve better transport
services. This paper employs factor analysis to explore improving high-speed
railway transport services by adopting resource-saving principles. Railway carrying
capacity construction is investigated to better understand its structural features
and development trends. Various methods, such as data cleaning, filling, and normalization,
are used to obtain more accurate results. Through in-depth analysis, we
identify several factors closely related to energy utilization, including train operation
speed, urban rail planning, and energy-saving technologies. These measures improve
the economic benefits of high-speed railways and contribute to sustainable
social development. However, challenges arising from these efforts should be noticed.
By combining factor analysis with optimization techniques, reducing energy
consumption enhances the railway’s cargo-carrying capacity and ensures its longterm
sustainability. Future scientific and technological advancements will further
explore and apply this approach.
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