An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source Data
Due to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/17/6391 |
| _version_ | 1797493227387879424 |
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| author | Lilan Liu Xiang Wan Jiaying Li Wenxi Wang Zenggui Gao |
| author_facet | Lilan Liu Xiang Wan Jiaying Li Wenxi Wang Zenggui Gao |
| author_sort | Lilan Liu |
| collection | DOAJ |
| description | Due to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system. It adds a fusion evaluation system based on the decision-level fusion algorithm and proposes a dynamic fusion strategy. The fusion evaluation system effectively solves the problem of data scale inconsistency among multi-source data, which leads to difficulties in fusing models and low fusion accuracy, and obtains optimal fusion results. The paper then verifies the effectiveness of the fusion evaluation system through experiments on the multilayer feature fusion of single-source data and the decision-level fusion of multi-source data, respectively. The results of this paper can be used in intelligent production and assembly plants in the discrete industry and provide the corresponding management and decision support with a certain practical value. |
| first_indexed | 2024-03-10T01:16:59Z |
| format | Article |
| id | doaj.art-4955f948542b47ef95e1a326100b83ca |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:16:59Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4955f948542b47ef95e1a326100b83ca2023-11-23T14:07:41ZengMDPI AGSensors1424-82202022-08-012217639110.3390/s22176391An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source DataLilan Liu0Xiang Wan1Jiaying Li2Wenxi Wang3Zenggui Gao4School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaAerospace System Engineering Shanghai, Shanghai 201108, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaDue to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system. It adds a fusion evaluation system based on the decision-level fusion algorithm and proposes a dynamic fusion strategy. The fusion evaluation system effectively solves the problem of data scale inconsistency among multi-source data, which leads to difficulties in fusing models and low fusion accuracy, and obtains optimal fusion results. The paper then verifies the effectiveness of the fusion evaluation system through experiments on the multilayer feature fusion of single-source data and the decision-level fusion of multi-source data, respectively. The results of this paper can be used in intelligent production and assembly plants in the discrete industry and provide the corresponding management and decision support with a certain practical value.https://www.mdpi.com/1424-8220/22/17/6391multi-source datadecision-level fusionfusion evaluation system |
| spellingShingle | Lilan Liu Xiang Wan Jiaying Li Wenxi Wang Zenggui Gao An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source Data Sensors multi-source data decision-level fusion fusion evaluation system |
| title | An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source Data |
| title_full | An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source Data |
| title_fullStr | An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source Data |
| title_full_unstemmed | An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source Data |
| title_short | An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source Data |
| title_sort | improved entropy weighted topsis method for decision level fusion evaluation system of multi source data |
| topic | multi-source data decision-level fusion fusion evaluation system |
| url | https://www.mdpi.com/1424-8220/22/17/6391 |
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