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International Conference on Machine Intelligence and Digital Applications (MIDA)

作者： Han, Jing Maanshan Teachers Coll Sch Foreign Languages Maanshan 243041 Anhui Peoples R China

ISBN: (纸本)9798400718144

In response to the limited data coverage and lack of personalized learning among students in English education, vocabulary analysis was conducted utilizing the Long Short-Term Memory (lstm) algorithm. By improving the accuracy and efficiency of vocabulary analysis, deepening the understanding of student learning processes, and other methods, traditional research problems were solved, thereby improving the quality and effectiveness of English education. This paper analyzed and modeled the learning data of students, and combined the advantages of lstm algorithm to achieve personalized learning paths and guidance for different students, better meeting the learning needs and levels of different students;in order to better understand how students learn vocabulary and to offer more useful advice and support for teaching practices, the lstm algorithm was applied. The training loss obtained by using grid search method was 0.65, and the validation loss was 0.75;the training loss obtained by the random search method was 0.7, and the validation loss was 0.85;the training loss obtained by Bayesian optimization method was 0.8, and the validation loss was 0.9;the training loss obtained by the genetic algorithm method was 0.85, and the validation loss was 1.1. The model obtained by the grid search method performed well on both the training and validation sets, with good fitting and generalization abilities.

关键词： Vocabulary Analysis lstm algorithm English Education Word Learning Efficiency Learning Performance Evaluation

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Prediction of Egional Temperature Change Trend Based on lstm algorithm 4

Prediction of Egional Temperature Change Trend Based on LSTM...

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4th IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Wu, Tao Liu, Changchun He, Cheng Shanghai Polytech Univ Mech Intelligence Mfg Inst Shanghai Peoples R China 2360 Jinhai Rd Shanghai Peoples R China

ISBN: (纸本)9781728143903

In order to better predict the trend of temperature in future regions, a time recurrent neural network algorithm lstm is proposed to predict regional temperature trends. This paper obtains temperature changes in Alberta, Quebec, and Saskatchewan, Canada. Based on the average temperature timing characteristics of each province, lstm (long short-term memory) is used to analyze the provinces of Canada. Temperature and time trends of temperature and modelling, predicting temperature changes in future Canadian provinces;The results show that after the above model predicts the temperature change trend for the next three years, the predicted temperature change trend is almost consistent with the existing data, and the prediction accuracy is also relatively high. Therefore, the lstm algorithm based on this paper can be applied to the prediction of regional temperature trends, and the prediction results and accuracy are very good, which has certain value and significance for real life.

关键词： lstm algorithm predictive model temperature change

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Research on communication performance indexes in the key area based on stream processing and lstm algorithm 24

Research on communication performance indexes in the key are...

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1st International Conference on Image Processing Machine Learning and Pattern Recognition

作者： An, Xinzhou Sun, Yancheng Miao, Fengqing Wang, Zhe Kang, Zhiwen Zhang, Qingwei Gao, Zongbao China Mobile Commun Grp Design Inst Co Ltd Shandong Branch Jinan Peoples R China Xinjiang Univ Coll Math & Syst Sci Urumqi Peoples R China

ISBN: (纸本)9798400707032

In existing research, the analysis of network performance in the key area are is based on offline statistical data. This method has the disadvantages of slow speed in summarizing cell indexes, long time consumption for calculating and slow positioning of problematic cells. In addition, this method cannot predict the trend of index changes within a region and evaluate the optimization effect on communication cells. To solve the above problems, this study proposes using stream processing and lstm algorithm to analyze indexes of the key region. First, this study obtains the list information of all communication cells in the key area and downloads 15-minute granularity performance files required for six indexes calculations. The Java program completes parsing and sends them to Kafka. Next, Spark Steaming reads data from Kafka, calculates the index values of communication cell, and writes them to Click-House database. If value of the index is abnormal, information about the communication cell in poor quality will be displayed as a report. If the number of exceptions for cell exceeds the set value, a text message reminder will be sent for network optimization. In addition, using Spring Boot programs to summarize the indexes in the key area into daily granularity. Based on the historical data of daily granularity, an lstm model is established to predict the trend of indexes in the key area. The optimization effect of the communication cell is evaluated based on this method, and if the prediction index exceeds a set value, an SMS alert will be triggered.

关键词： Index Key area Stream processing lstm algorithm

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Construction of an English Resource Library Network Information Recommendation Model Based on lstm algorithm 1

Construction of an English Resource Library Network Informat...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Liu, Ying Zhang, Rongxing Hubei University of Automotive Technology School of Foreign Languages Shiyan China Hubei University of Automotive Technology School of Economics and Management Shiyan China

ISBN: (纸本)9798350352931

With the rapid development of information technology and the trend of digitization of educational resources, the library of English teaching resources is increasing, and how to efficiently utilize these resources to provide users with accurate recommendations has become a major challenge in the field of educational technology. First, collecting and organizing users' historical browsing data and English education content in the resource library;second, using these data to train the lstm model to capture the time-series features in users' behaviors, as well as the dynamic changes of the content;and then analyzing the users' potential needs through the model to achieve personalized recommendation. New users' satisfaction with the lstm system increased from 6 to 8, which suggests that the new system may have improved the new users' experience through more accurate recommendations. This research not only has a guiding significance for resource management in educational institutions, but also provides a theoretical basis and a practical guide for the development and optimization of educational resources in the future. © 2024 IEEE.

关键词： english language repository lstm algorithm usage experience web information recommendation model

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Daily milk yield prediction of dairy cows based on the GA-lstm algorithm 15

Daily milk yield prediction of dairy cows based on the GA-LS...

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15th IEEE International Conference on Signal Processing (ICSP)

作者： Zhang, Wenli Yang, Kun Yu, Naigong Cheng, Tingting Liu, Jichao Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Beijing Sanyuan Food Co LTD Qual Dept Beijing Peoples R China

ISBN: (纸本)9781728144801

Currently, the prediction on milk yield can help pasture managers coordinate production and transportation planning for a farm on time. However, the input of each current daily milk yield prediction research is simple and the current researches are not considering the time sequence characteristic of daily milk yield of dairy cows. In this study, we analyzed the correlation analysis on the input variables. After analysis, we selected parity, early lactation, peak lactation, mid-lactation, late lactation, weight, and the total amount of feed as input. we propose a prediction algorithm, GA-lstm. The algorithm introduces the genetic algorithm (GA) into the long short-term memory (lstm) algorithm structure, which considers the time sequence and correlations between the above input variables. The experiment demonstrates that the GA-lstm algorithm is more accurate and stable than the traditional lstm algorithm in predicting daily milk yields.

关键词： daily milk yield prediction lstm algorithm GA algorithm

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Prediction of Mine Subsidence Based on InSAR Technology and the lstm algorithm: A Case Study of the Shigouyi Coalfield, Ningxia (China)

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REMOTE SENSING 2023年第11期15卷

作者： Ma, Fei Sui, Lichun Lian, Wei Changzhi Univ Dept Comp Sci Changzhi 046011 Peoples R China Changan Univ Coll Geol Engn & Geomatics Xian 710054 Peoples R China

The accurate prediction of surface subsidence induced by coal mining is critical to safeguarding the environment and resources. However, the precision of current prediction models is often restricted by the lack of pertinent data or imprecise model parameters. To overcome these limitations, this study proposes an approach to predicting mine subsidence that leverages Interferometric Synthetic Aperture Radar (InSAR) technology and the long short-term memory network (lstm). The proposed approach utilizes small baseline multiple-master high-coherent target (SBMHCT) interferometric synthetic aperture radar technology to monitor the mine surface and applies the long short-term memory (lstm) algorithm to construct the prediction model. The Shigouyi coalfield in Ningxia Province, China was chosen as a study area, and time series ground subsidence data were obtained based on Sentinel-1A data from 9 March 2015 to 7 June 2016. To evaluate the proposed approach, the prediction accuracies of lstm and Support Vector Regression (SVR) were compared. The results show that the proposed approach could accurately predict mine subsidence, with maximum absolute errors of less than 2 cm and maximum relative errors of less than 6%. The findings demonstrate that combining InSAR technology with the lstm algorithm is an effective and robust approach for predicting mine subsidence.

关键词： InSAR technology mines time series subsidence monitoring deformation prediction deep learning lstm algorithm

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Wind Profile Retrieval Based on lstm algorithm and Mobile Observation of Brightness Temperature over the Tibetan Plateau

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REMOTE SENSING 2024年第6期16卷

作者： Chen, Bing Cheng, Xinghong Su, Debin Xu, Xiangde Ma, Siying Hu, Zhiqun Chengdu Univ Informat Technol Coll Elect Engn Chengdu 610225 Peoples R China Chinese Acad Meteorol Sci State Key Lab Severe Weather Beijing 100081 Peoples R China

Stationary or mobile microwave radiometers (MRs) can measure atmospheric temperature, relative humidity, and water vapor density profiles with high spatio-temporal resolution, but cannot obtain the vertical variations of wind field. Based on a dataset of brightness temperatures (TBs) measured with a mobile MR over the Three-River-Source Region of the Tibetan Plateau from 18 to 30 July 2021, we develop a direct retrieval method for the wind profile (WP) based on the Long Short-Term Memory (lstm) network technique, and obtain the reliable dynamic variation characteristics of the WP in the region. Furthermore, the ground-based radiative transfer model for TOVS (RTTOV-gb) was employed to validate the reliability of the TB observation, and we analyzed the impact of weather conditions, altitude, observational mode, and TB diurnal variation on the accuracy of the TB measurement and the retrieval of the WP. Results show that the TB from the mobile observation (MOTB) on clear and cloudy days are close to those of the simulated TB with the RTTOV-gb model, while TB measurements on rainy days are far larger than the modeled TBs. When compared with radiosonde observations, the WPs retrieved with the lstm algorithm are better than the ERA5 reanalysis data, especially below 350 hPa, where the root mean square errors for both wind speed and wind direction are smaller than those of ERA5. The major factors influencing WP retrieval include the weather conditions, altitude, observational mode, and TB diurnal variation. Under clear-sky and cloudy conditions, the lstm retrieval method can reproduce the spatio-temporal evolution of wind field and vertical wind shear characteristics. The findings of this study help to improve our understanding of meso-scale atmospheric dynamic structures, characteristics of vertical wind shear, atmospheric boundary layer turbulence, and enhance the assessment and forecasting accuracy of wind energy resources.

关键词： wind profile retrieval lstm algorithm microwave radiometer mobile observation brightness temperatures

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The Macroeconomic Prediction Model based on lstm Improved algorithm 2

The Macroeconomic Prediction Model based on LSTM Improved Al...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Zhao, Jiayao Jilin Agricultural Science and Technology University Jilin China

ISBN: (纸本)9798350317558

In order to solve the overfitting problem caused by large sample size and small sample size of market indicators in macroeconomic forecasting, the lstm algorithm is adopted to improve the efficiency of economic forecasting. At the same time, the prediction results of the unbiased grey model and the improved grey model are weighted and averaged to be taken into the improved lstm algorithm model. Through the comparison of prediction effects of various models, it is found that under lstm model, the difference between the real values and the predicted values of CPI prediction results is 0.3. The introduction of the grey model into the model can make a better scientific prediction of macro economy. And the improved lstm model has shown better accuracy in predicting GDP. The prediction results of GDP and CPI show that the improved lstm model can be applied to macroeconomic forecasting. © 2024 IEEE.

关键词： lstm algorithm macro economy prediction model sample characteristics unbiased grey model

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lstm Model-Based Rapid Prediction Method of Urban Inundation with Rainfall Time Series

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WATER RESOURCES MANAGEMENT 2025年第2期39卷 661-688页

作者： Pan, Xinxin Hou, Jingming Gao, Xujun Chen, Guangzhao Li, Donglai Imran, Muhammad Li, Xinyi Yang, Nan Ma, Menghua Zhou, Xiaoping Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg China Xian 710048 Shaanxi Peoples R China Northwest Engn Corp Ltd China Power Construction Grp Co LTD Xian 710065 Peoples R China Gansu Res Inst Water Conservancy Lanzhou 730000 Peoples R China Xian Univ Technol 5 Jinhua Rd Xian 710048 Peoples R China

In recent years, with the increasing frequency of extreme rainfall events, the resulting urban inundation disasters have become increasingly severe. Rapid and accurate urban flood simulation and prediction are of great significance for disaster prevention and mitigation. However, physically-based numerical models require substantial computation time for simulating urban flood processes. In this study, we introduce the lstm algorithm to replace physically-based numerical models for the rapid prediction of flood processes at urban inundation points. First, a hydrological-hydrodynamic numerical model for the study area is constructed to simulate flood processes under different rainfall scenarios, forming a result database. Next, the lstm algorithm is used to train and learn from the simulated flood data, and the reliability of this learning method is verified. Finally, a rapid prediction model for flood processes at inundation points in the study area is developed. The results indicate that the prediction model achieves high accuracy, with R-2 values above 0.90 for predicting flood processes and peak flood characteristics at single inundation points. The MAE is no greater than 0.069, and the RMSE is no greater than 0.077. The error in the inundation process ranges between - 0.5% and 0.5%. In terms of efficiency, the average time taken to predict a single rainfall event is only 0.193 s, compared to 4625.92 s for the hydrodynamic model, representing a speedup of approximately 23,968 times relative to the physically-based numerical model. These findings demonstrate that this method meets the needs of daily urban early warning and forecasting work, enhances the city's disaster prevention and mitigation capabilities, and effectively reduces the loss of life and property.

关键词： Urban Inundation lstm algorithm Rainfall Time Series Rapid Forecasting Physically-based Numerical Model

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Converter Capacitor Temperature Estimation Based on Continued Training lstm under Variable Load Conditions

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SENSORS 2024年第13期24卷 4304页

作者： Dai, Xiaoteng Chen, Yiqiang Chen, Jie Qiu, Ruichang Beijing Jiaotong Univ Sch Elect Engn Beijing 100044 Peoples R China China Elect Prod Reliabil & Environm Testing Res I Guangzhou 510610 Peoples R China

Capacitors are crucial components in power electronic converters, responsible for harmonic elimination, energy buffering, and voltage stabilization. However, they are also the most susceptible to damage due to their operational environment. Accurate temperature estimation of capacitors is essential for monitoring their condition and ensuring the reliability of the converter system. This paper presents a novel method for estimating the core temperature of capacitors using a long short-term memory (lstm) algorithm. The approach incorporates a continued training mechanism to adapt to variable load conditions in converters. Experimental results demonstrate the proposed method's high accuracy and robustness, making it suitable for real-time capacitor temperature monitoring in practical applications.

关键词： capacitor temperature estimation lstm algorithm continued training

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