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... 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. Read More Read Less

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... 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. Read More Read Less

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... 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. Read More Read Less

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,... 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. Read More Read Less