Papers
1
Total Citations
3
H-Index
1
About
Ze Ren is a researcher in mining engineering and intelligent excavation, with a primary focus on improving the efficiency and precision of roadheader cutting operations. His most notable contribution is the development of an enhanced cutting trajectory planning method using an improved particle swarm optimization algorithm, published in 2019. This work addresses a critical challenge in automated mining—optimizing the path of cutting heads to reduce energy consumption, minimize wear, and increase productivity in underground environments. While his citation count is modest, with his leading paper garnering 3 citations, Ren’s research is foundational for advancing autonomous mining technologies. His work is particularly relevant for researchers and engineers seeking to integrate swarm intelligence into heavy machinery control, offering a practical framework for real-time trajectory optimization. Ren’s contributions highlight the potential of computational methods to transform traditional mining practices, making him a valuable voice in the field of intelligent excavation systems.
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Top Papers
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