Minglu Xie
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Minglu Xie has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Man Xu
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213000, China
1 shared publication - Pan Wang
Sichuan Academy of Agricultural Machinery Sciences, 5 Niusha Road, Chengdu 610066, China
1 shared publication - Xiaoting Wang
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213000, China
1 shared publication - Xiaoyan Ling
Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
1 shared publication
Latest Publications
- Open Access
Performance Analysis and Evaluation of Vegetable Cold-Chain Drying Equipment
This study proposes a systematic performance analysis and evaluation framework for vegetable cold-chain drying equipment, aiming to enhance its energy efficiency, automation level, and standardization. First, common drying technologies are compared to assess their effects on energy consumption, drying efficiency, and product quality. Subsequently, key energy consumption metrics are established for this equipment. Furthermore, a comprehensive performance evaluation index system is developed using a fusion approach based on machine learning techniques that combine Boosting algorithms with multiple kernel functions.
Open AccessPerformance Analysis and Evaluation of Vegetable Cold-Chain Drying Equipment
This study proposes a systematic performance analysis and evaluation framework for vegetable cold-chain drying equipment, aiming to enhance its energy efficiency, automation level, and standardization. First, common drying technologies are compared to assess their effects on energy consumption, drying efficiency, and product quality. Subsequently, key energy consumption metrics are established for this equipment. Furthermore, a comprehensive performance evaluation index system is developed using a fusion approach based on machine learning techniques that combine Boosting algorithms with multiple kernel functions.