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...
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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.
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...
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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.
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 fo...
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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.
In order to better predict the trend of temperature in future regions, a time recurrent neural network algorithmlstm is proposed to predict regional temperature trends. This paper obtains temperature changes in Alber...
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ISBN:
(纸本)9781728143903
In order to better predict the trend of temperature in future regions, a time recurrent neural network algorithmlstm 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.
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 p...
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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 pe...
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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.
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...
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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.
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 foreca...
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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 grea...
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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.
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 o...
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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.
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