NAND flash memory requires a garbage collection strategy due to its physical constraints,which significantly degrades system efficiency.Precise hot and cold data separation is critical.In addition,NAND flash memory can only endure a limited number of erase operations.Therefore,improving wear leveling is crucial for extending its lifespan.Existing methods fail to learn data access patterns online and cannot accurately identify hot and cold data under dynamic workloads.To address these issues,a Reinforcement Learning-based Garbage Collection algorithm(RLGC)is proposed.It realizes accurate classification of hot and cold data through online Adaptive Learning and integrating wear leveling mechanism.Experimental results show that compared with the existing best performance of the baseline algorithm,the proposed algorithm reduces the block erase times by 16.4%,the effective page copy times by 50.0%,and improves the wear leveling degree by 33.3%,which significantly improves the garbage collection efficiency and prolongs the lifetime of flash memory.
This paper explores effective methods for predicting gold prices,and proposes three modeling strategies including standalone Long Short-Term Memory(LSTM)networks,Attention-based models,and LSTM-Attention combination model.Through empirical analysis,it systematically compares the performance of these three models in gold price prediction,with a focus on evaluating their predictive accuracy.The experimental results indicate that the LSTM-Attention combination model significantly outperforms the standalone LSTM and Attention models in terms of prediction accuracy,demonstrating a more comprehensive ability to capture the dynamic features of price fluctuations.This model not only showcases the potential of effective integration of LSTM and Attention Mechanism in time series prediction,but also provides a practical tool for financial decision-making,offering valuable references for investors in a volatile market environment.
Aiming at the problem that the prediction effect of the model is poor due to the large prediction space of the sequence annotation label,a multi-aspect extraction and sentiment analysis model based on Bert_Bilstm_Fusion is proposed.The method of span prediction is used for joint training of aspect words extraction and aspect words sentiment prediction and classification.The text embedding representation is obtained by using the Bert pre-trained language model.It uses the dependency relationship on the Bilstm learning observation sequence to enhance the learning position information to improve the model extraction effect.It conducts the feature fusion of output features of each layer for the Bert pre-trained language model,improves the sentiment polarity classification effect of the model,and designs comparison experiments and ablation experiments on three public data sets Laptop,Restaurant and Twitter.The experimental results show that the F1 values of the Bert_Bilstm_Fusion model reach 66.72%,78.44%and 62.10%,respectively,which are higher than those of the comparison model,indicating the effectiveness of the proposed model.
The Open University of China requires to improve the digitalization of teaching process.Grassroots open universities cannot achieve reform and innovation in the process of teaching support service without the support of information technology.This paper takes serving students as the starting point,based on standardizing the teaching and educational administration process and providing schools with decision-making analysis,and constructs a student-centered teaching and educational administration management information system of grassroots open university.The system realizes the"three integrations"of teaching management process and results,student management and service,refinement and informatization of educational administration,and has an important reference role for grassroots open universities to carry out teaching and educational administration management informatization construction.
Taking the steel reinforcement construction model of Zhangzhou"Hualong No.1"nuclear island civil engineering as the data interactive carrier,the two-dimensional design drawing information is transformed into a standardized steel reinforcement bill of materials through three-dimensional BIM software,which improves the problems of inconsistent and diverse forms of drawings'annotation in the traditional steel reinforcement in-depth design,integrates the automatic steel reinforcement processing equipment for BIM data integration,reduces manual input operations.In the distribution process of semi-finished steel reinforcement,develop a steel reinforcement positioning system that matches the steel reinforcement management process,and optimize the steel reinforcement distribution into a supervision mode with online as the main line and offline as the auxiliary,reducing the loss rate of steel reinforcement in the complex environment of nuclear island construction.At the same time,optimize the process,and connect the three key business processes of BIM cutting,automatic processing equipment,and positioning and distribution in the steel reinforcement management process with data.Using visualization technology to improve the efficiency of steel reinforcement transportation and achieve precise cost control of steel reinforcement main materials.
