Jian Yang

Xilin Chen

Weishi Zheng

Josef Kittler

Jingyi Yu

Jiwen Lu

Weiyao Lin

Hongkai Xiong

This 18-volume set constitutes the refereed proceedings of the 8th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2025, held in Shanghai, China, during October 15-18, 2025. The 692 full papers included in this book, comprising 66 oral presentations and 626 posters, were carefully reviewed and selected from 2370 submissions. PRCV's mission is to serve as a comprehensive platform for dialogues among researchers from both the academic field and the industry. While its primary focus is to encourage academic exchange, it also places emphasis on fostering ties between academia and industry, keeping abreast of leading academic innovations and to showcase the most recent research breakthroughs, pioneering thoughts, and advanced techniques in pattern recognition and computer vision.

8th Chinese Conference, PRCV 2025, Shanghai, China, October 15–18, 2025, Proceedings, Part XV

Pattern Recognition and Computer Vision

8th Chinese Conference, PRCV 2025, Shanghai, China, October 15–18, 2025, Proceedings, Part VI

8th Chinese Conference, PRCV 2025, Shanghai, China, October 15–18, 2025, Proceedings, Part VII

In this research, we study the new cache method LRU-SAI to boost the performance of file system access. Various cache algorithms have been developed to improve the I/O performance, but most of them are focusing on minimizing the replacement cost and improving the cache hit ratio. Our new algorithm, LRU-SAI, is to make separation of active and inactive data in the cache, then write pages into the SSD block according to the activity level of these pages. Active pages will be written to active blocks and inactive one to inactive block. As a result, SSD blocks will have a bimodal distribution. This distribution can significantly reduce the cleaning overhead on SSD. We find that LRU-SAI can greatly improve the performance by approximately 20% when the cleaning efficiency in SSD is low. Even with high cleaning efficiency, LRU-SAI can work as well as CFLRU, improving the system by about 9% compared to the LRU.

The Use of LRU Algorithm with Separation of Active and Inactive Pages to Improve Solid State Storage Device Performance

LRU-SAI

Jingyi Yu

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