Malaria continues to be a major public health problem on the African continent, particularly in Sub-Saharan Africa despite the ongoing efforts and significant progress that has been made. In the case of Burundi, malar...Malaria continues to be a major public health problem on the African continent, particularly in Sub-Saharan Africa despite the ongoing efforts and significant progress that has been made. In the case of Burundi, malaria remains a major public health concern in the general population. In the literature, there are limited malaria prediction models for Burundi knowing that such tools are much needed for intervention design. In this study, deep-learning models are built to estimate malaria cases in Burundi. The forecast of malaria cases was carried out both at the provincial and national levels. Long short term memory (LSTM) model, a type of deep learning model, has been used to achieve best results using climate-change related factors such as temperature, rainfall, relative humidity, together with malaria historical data and human population. With this model, the results showed that different parameter tuning can be used to determine the minimum and maximum expected malaria cases. The univariate version of that model (LSTM), which learns from previous dynamics of malaria cases, gives more precise estimates, but both univariate and multivariate models have the same overall trends at the province level and country level.展开更多
In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LST...In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.展开更多
Purpose-To improve the accuracy of stock price trend prediction in the field of quantitative financial trading,this paper takes the prediction accuracy as the goal and avoid the enormous number of network structures a...Purpose-To improve the accuracy of stock price trend prediction in the field of quantitative financial trading,this paper takes the prediction accuracy as the goal and avoid the enormous number of network structures and hyperparameter adjustments of long-short-term memory(LSTM).Design/methodology/approach-In this paper,an adaptive genetic algorithm based on individual ordering is used to optimize the network structure and hyperparameters of the LSTM neural network automatically.Findings-The simulation results show that the accuracy of the rise and fall of the stock outperform than the model with LSTM only as well as other machine learning models.Furthermore,the efficiency of parameter adjustment is greatly higher than other hyperparameter optimization methods.Originality/value-(1)The AGA-LSTM algorithm is used to input various hyperparameter combinations into genetic algorithm to find the best hyperparameter combination.Compared with other models,it has higher accuracy in predicting the up and down trend of stock prices in the next day.(2)Adopting real coding,elitist preservation and self-adaptive adjustment of crossover and mutation probability based on individual ordering in the part of genetic algorithm,the algorithm is computationally efficient and the results are more likely to converge to the global optimum.展开更多
Real-time voltage stability assessment(VSA)has long been an extensively research topic.In recent years,rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algor...Real-time voltage stability assessment(VSA)has long been an extensively research topic.In recent years,rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data.Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states,or even unstable state.However,in practice,the data of insecure or unstable state is very rare,as the power system should be guaranteed to operate far away from voltage collapse.Under this circumstance,this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system.The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data,while it fails to rebuild data of insecure states.Thus,the residual of reconstruction is effective in indicating VSA.Besides,to develop a more accurate and robust algorithm,long short-term memory(LSTM)networks combined with fully-connected(FC)layers are used to build the autoencoder,and a moving strategy is introduced to bias the features of testing data toward the secure feature domain.Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method.展开更多
Ionosphere delay is one of the main sources of noise affecting global navigation satellite systems, operation of radio detection and ranging systems and very-long-baseline-interferometry. One of the most important and...Ionosphere delay is one of the main sources of noise affecting global navigation satellite systems, operation of radio detection and ranging systems and very-long-baseline-interferometry. One of the most important and common methods to reduce this phase delay is to establish accurate nowcasting and forecasting ionospheric total electron content models. For forecasting models, compared to mid-to-high latitudes, at low latitudes, an active ionosphere leads to extreme differences between long-term prediction models and the actual state of the ionosphere. To solve the problem of low accuracy for long-term prediction models at low latitudes, this article provides a low-latitude, long-term ionospheric prediction model based on a multi-input-multi-output, long-short-term memory neural network. To verify the feasibility of the model, we first made predictions of the vertical total electron content data 24 and 48 hours in advance for each day of July 2020 and then compared both the predictions corresponding to a given day, for all days. Furthermore, in the model modification part, we selected historical data from June 2020 for the validation set, determined a large offset from the results that were predicted to be active, and used the ratio of the mean absolute error of the detected results to that of the predicted results as a correction coefficient to modify our multi-input-multi-output long short-term memory model. The average root mean square error of the 24-hour-advance predictions of our modified model was 4.4 TECU, which was lower and better than5.1 TECU of the multi-input-multi-output, long short-term memory model and 5.9 TECU of the IRI-2016 model.展开更多
The current research on emotional classification uses many methods that combine the attention mechanism with neural networks.However,the effect is unsatisfactory when dealing with complex text.An emotional classificat...The current research on emotional classification uses many methods that combine the attention mechanism with neural networks.However,the effect is unsatisfactory when dealing with complex text.An emotional classification model is proposed,which combines multi-head attention(MHA)with improved structured-self attention(SSA).The model makes several different linear transformations of input by introducing MHA mechanism and can extract more comprehensive high-level phrase representation features from the word embedded vector.Meanwhile,it can realize the parallelization calculation and ensure the training speed of the model.The improved SSA structure uses matrices to represent different parts of a sentence to extract local key information,to ensure that the degree of dependence between words is not affected by time and sentence length,and generate the overall semantics of the sentence.Experiment results show that the current model effectively obtains global structural information and improves classification accuracy.展开更多
由于太阳能和天气变量的随机性和不稳定性,光伏发电具有很高的不确定性,准确的光伏发电功率预测对于光伏电站的短期调度和发电计划的运行至关重要。提出一种基于多模式分解、多通道输入、并联卷积神经网络(parallel convolutional neura...由于太阳能和天气变量的随机性和不稳定性,光伏发电具有很高的不确定性,准确的光伏发电功率预测对于光伏电站的短期调度和发电计划的运行至关重要。提出一种基于多模式分解、多通道输入、并联卷积神经网络(parallel convolutional neural network,PCNN)和双向长短期记忆网络(bi-directional long short term memory,BiLSTM)的组合预测方法,用于不同天气类型的超短期光伏发电功率预测。首先,由相关性分析算法确定辐照度和温度是对光伏发电贡献最大的2个环境变量,并根据环境因素与光伏功率波动特征的关联性将全年数据划分为4类;其次,使用完全集合经验模态分解、奇异谱分解和变分模态分解对辐照度、温度和光伏发电功率进行分解,以降低原始数据的复杂度和非平稳性,实现不同模式模态分量规律互补;最后,建立基于PCNN和BiLSTM的组合预测模型,使用PCNN提取不同的深度特征,并将PCNN输出的特征融合后输入到BiLSTM中,使用BiLSTM建立历史数据之间的时间特征关系,学习历史数据间的正、反向规律,在时空相关性分析的基础上得到最终光伏发电功率预测结果。实验结果表明,提出的组合预测方法在超短期光伏发电功率预测中具有较高的准确性和稳定性,并优于其他深度学习方法。展开更多
文摘Malaria continues to be a major public health problem on the African continent, particularly in Sub-Saharan Africa despite the ongoing efforts and significant progress that has been made. In the case of Burundi, malaria remains a major public health concern in the general population. In the literature, there are limited malaria prediction models for Burundi knowing that such tools are much needed for intervention design. In this study, deep-learning models are built to estimate malaria cases in Burundi. The forecast of malaria cases was carried out both at the provincial and national levels. Long short term memory (LSTM) model, a type of deep learning model, has been used to achieve best results using climate-change related factors such as temperature, rainfall, relative humidity, together with malaria historical data and human population. With this model, the results showed that different parameter tuning can be used to determine the minimum and maximum expected malaria cases. The univariate version of that model (LSTM), which learns from previous dynamics of malaria cases, gives more precise estimates, but both univariate and multivariate models have the same overall trends at the province level and country level.
文摘In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.
基金This work was supported by the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China(No.2018AAA0101301)the Key Projects of Artificial Intelligence of High School in Guangdong Province(No.2019KZDZX1011)The High School innovation Project(No.2018KTSCX222).
文摘Purpose-To improve the accuracy of stock price trend prediction in the field of quantitative financial trading,this paper takes the prediction accuracy as the goal and avoid the enormous number of network structures and hyperparameter adjustments of long-short-term memory(LSTM).Design/methodology/approach-In this paper,an adaptive genetic algorithm based on individual ordering is used to optimize the network structure and hyperparameters of the LSTM neural network automatically.Findings-The simulation results show that the accuracy of the rise and fall of the stock outperform than the model with LSTM only as well as other machine learning models.Furthermore,the efficiency of parameter adjustment is greatly higher than other hyperparameter optimization methods.Originality/value-(1)The AGA-LSTM algorithm is used to input various hyperparameter combinations into genetic algorithm to find the best hyperparameter combination.Compared with other models,it has higher accuracy in predicting the up and down trend of stock prices in the next day.(2)Adopting real coding,elitist preservation and self-adaptive adjustment of crossover and mutation probability based on individual ordering in the part of genetic algorithm,the algorithm is computationally efficient and the results are more likely to converge to the global optimum.
文摘Real-time voltage stability assessment(VSA)has long been an extensively research topic.In recent years,rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data.Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states,or even unstable state.However,in practice,the data of insecure or unstable state is very rare,as the power system should be guaranteed to operate far away from voltage collapse.Under this circumstance,this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system.The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data,while it fails to rebuild data of insecure states.Thus,the residual of reconstruction is effective in indicating VSA.Besides,to develop a more accurate and robust algorithm,long short-term memory(LSTM)networks combined with fully-connected(FC)layers are used to build the autoencoder,and a moving strategy is introduced to bias the features of testing data toward the secure feature domain.Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method.
基金Project supported by the National Key Research and Development Program of China(Grant No.2016YFA0302101)the Initiative Program of State Key Laboratory of Precision Measurement Technology and Instrument。
文摘Ionosphere delay is one of the main sources of noise affecting global navigation satellite systems, operation of radio detection and ranging systems and very-long-baseline-interferometry. One of the most important and common methods to reduce this phase delay is to establish accurate nowcasting and forecasting ionospheric total electron content models. For forecasting models, compared to mid-to-high latitudes, at low latitudes, an active ionosphere leads to extreme differences between long-term prediction models and the actual state of the ionosphere. To solve the problem of low accuracy for long-term prediction models at low latitudes, this article provides a low-latitude, long-term ionospheric prediction model based on a multi-input-multi-output, long-short-term memory neural network. To verify the feasibility of the model, we first made predictions of the vertical total electron content data 24 and 48 hours in advance for each day of July 2020 and then compared both the predictions corresponding to a given day, for all days. Furthermore, in the model modification part, we selected historical data from June 2020 for the validation set, determined a large offset from the results that were predicted to be active, and used the ratio of the mean absolute error of the detected results to that of the predicted results as a correction coefficient to modify our multi-input-multi-output long short-term memory model. The average root mean square error of the 24-hour-advance predictions of our modified model was 4.4 TECU, which was lower and better than5.1 TECU of the multi-input-multi-output, long short-term memory model and 5.9 TECU of the IRI-2016 model.
基金the National Key Research and Development Program of China(No.2018YFB1702601)the Science and Technology Commission of Shanghai Municipality(No.19511105103)。
文摘The current research on emotional classification uses many methods that combine the attention mechanism with neural networks.However,the effect is unsatisfactory when dealing with complex text.An emotional classification model is proposed,which combines multi-head attention(MHA)with improved structured-self attention(SSA).The model makes several different linear transformations of input by introducing MHA mechanism and can extract more comprehensive high-level phrase representation features from the word embedded vector.Meanwhile,it can realize the parallelization calculation and ensure the training speed of the model.The improved SSA structure uses matrices to represent different parts of a sentence to extract local key information,to ensure that the degree of dependence between words is not affected by time and sentence length,and generate the overall semantics of the sentence.Experiment results show that the current model effectively obtains global structural information and improves classification accuracy.
文摘由于太阳能和天气变量的随机性和不稳定性,光伏发电具有很高的不确定性,准确的光伏发电功率预测对于光伏电站的短期调度和发电计划的运行至关重要。提出一种基于多模式分解、多通道输入、并联卷积神经网络(parallel convolutional neural network,PCNN)和双向长短期记忆网络(bi-directional long short term memory,BiLSTM)的组合预测方法,用于不同天气类型的超短期光伏发电功率预测。首先,由相关性分析算法确定辐照度和温度是对光伏发电贡献最大的2个环境变量,并根据环境因素与光伏功率波动特征的关联性将全年数据划分为4类;其次,使用完全集合经验模态分解、奇异谱分解和变分模态分解对辐照度、温度和光伏发电功率进行分解,以降低原始数据的复杂度和非平稳性,实现不同模式模态分量规律互补;最后,建立基于PCNN和BiLSTM的组合预测模型,使用PCNN提取不同的深度特征,并将PCNN输出的特征融合后输入到BiLSTM中,使用BiLSTM建立历史数据之间的时间特征关系,学习历史数据间的正、反向规律,在时空相关性分析的基础上得到最终光伏发电功率预测结果。实验结果表明,提出的组合预测方法在超短期光伏发电功率预测中具有较高的准确性和稳定性,并优于其他深度学习方法。
