Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory Model
Cultural heritage digitization is of great significance for the protection, restoration, and rejuvenation of cultural relics. In particular, the investigation of fading mechanisms is essential for virtual restoration to accurately recreate the original appearance of artifacts and facilitate humanist...
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2024-10-01
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| author | Zhen Liu An-Ran Zhao Si-Lu Liu |
| author_facet | Zhen Liu An-Ran Zhao Si-Lu Liu |
| author_sort | Zhen Liu |
| collection | DOAJ |
| description | Cultural heritage digitization is of great significance for the protection, restoration, and rejuvenation of cultural relics. In particular, the investigation of fading mechanisms is essential for virtual restoration to accurately recreate the original appearance of artifacts and facilitate humanistic and historical research. For the purpose of investigating the color fading mechanism of pigments, we propose a color fading time series model using a combined long short-term memory recurrent neural network modified by the gray wolf optimization algorithm (GWOAD-LSTM). First, the general gray wolf algorithm was scaled up to two dimensions and combined with an LSTM model for optimal parameter search. Second, six pigments commonly used in painted artifacts were subjected to simulated aging experiments. Third, by applying the experimental data to different predictors, the results of the Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), Long Short-Term Memory on Gray Wolf Optimizer (GWO-LSTM), and GWOAD-LSTM models were compared. The results showed that our proposed GWOAD-LSTM model outperformed other models in terms of accuracy and generalization ability, especially in predicting hLC color attributes. Our study aimed to provide a new application tool for the restoration and rejuvenation of painted artifacts. |
| format | Article |
| id | doaj-art-10ae71b092fa454bbaf6ac832edc6bcf |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-10ae71b092fa454bbaf6ac832edc6bcf2025-08-20T02:13:12ZengMDPI AGApplied Sciences2076-34172024-10-011421973510.3390/app14219735Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory ModelZhen Liu0An-Ran Zhao1Si-Lu Liu2College of Media, Qufu Normal University, Rizhao 276826, ChinaCollege of Engineering, Qufu Normal University, Rizhao 276826, ChinaCollege of Engineering, Qufu Normal University, Rizhao 276826, ChinaCultural heritage digitization is of great significance for the protection, restoration, and rejuvenation of cultural relics. In particular, the investigation of fading mechanisms is essential for virtual restoration to accurately recreate the original appearance of artifacts and facilitate humanistic and historical research. For the purpose of investigating the color fading mechanism of pigments, we propose a color fading time series model using a combined long short-term memory recurrent neural network modified by the gray wolf optimization algorithm (GWOAD-LSTM). First, the general gray wolf algorithm was scaled up to two dimensions and combined with an LSTM model for optimal parameter search. Second, six pigments commonly used in painted artifacts were subjected to simulated aging experiments. Third, by applying the experimental data to different predictors, the results of the Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), Long Short-Term Memory on Gray Wolf Optimizer (GWO-LSTM), and GWOAD-LSTM models were compared. The results showed that our proposed GWOAD-LSTM model outperformed other models in terms of accuracy and generalization ability, especially in predicting hLC color attributes. Our study aimed to provide a new application tool for the restoration and rejuvenation of painted artifacts.https://www.mdpi.com/2076-3417/14/21/9735GWOAD-LSTMpigment fadinghLC color attributeprediction accuracy |
| spellingShingle | Zhen Liu An-Ran Zhao Si-Lu Liu Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory Model Applied Sciences GWOAD-LSTM pigment fading hLC color attribute prediction accuracy |
| title | Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory Model |
| title_full | Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory Model |
| title_fullStr | Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory Model |
| title_full_unstemmed | Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory Model |
| title_short | Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory Model |
| title_sort | prediction of fading for painted cultural relics using the optimized gray wolf optimization long short term memory model |
| topic | GWOAD-LSTM pigment fading hLC color attribute prediction accuracy |
| url | https://www.mdpi.com/2076-3417/14/21/9735 |
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