In order to improve the performance degradation prediction accuracy of proton exchange membrane fuel cell(PEMFC),a fusion prediction method(CKDG)based on adaptive noise complete ensemble empirical mode decomposition(C...In order to improve the performance degradation prediction accuracy of proton exchange membrane fuel cell(PEMFC),a fusion prediction method(CKDG)based on adaptive noise complete ensemble empirical mode decomposition(CEEMDAN),kernel principal component analysis(KPCA)and dual attention mechanism gated recurrent unit neural network(DA-GRU)was proposed.CEEMDAN and KPCA were used to extract the input feature data sequence,reduce the influence of random factors,and capture essential feature components to reduce the model complexity.The DA-GRU network helps to learn the feature mapping relationship of data in long time series and predict the changing trend of performance degradation data more accurately.The actual aging experimental data verify the performance of the CKDG method.The results show that under the steady-state condition of 20%training data prediction,the CKDA method can reduce the root mean square error(RMSE)by 52.7%and 34.6%,respectively,compared with the traditional LSTM and GRU neural networks.Compared with the simple DA-GRU network,RMSE is reduced by 15%,and the degree of over-fitting is reduced,which has higher accuracy.It also shows excellent prediction performance under the dynamic condition data set and has good universality.展开更多
The Wireless Sensor Networks(WSNs)are widely utilized in various industrial and environmental monitoring applications.The process of data gathering within the WSN is significant in terms of reporting the environmental...The Wireless Sensor Networks(WSNs)are widely utilized in various industrial and environmental monitoring applications.The process of data gathering within the WSN is significant in terms of reporting the environmental data.However,it might occur that certain sensor node malfunctions due to the energy draining out or unexpected damage.Therefore,the collected data may become inaccurate or incomplete.Focusing on the spatiotemporal correlation among sensor nodes,this paper proposes a novel algorithm to predict the value of the missing or inaccurate data and predict the future data in replacement of certain nonfunctional sensor nodes.The Long-Short-Term-Memory Recurrent Neural Network(LSTM RNN)helps to more accurately derive the time-series data corresponding to the sets of past collected data,making the prediction results more reliable.It is observed from the simulation results that the proposed algorithm provides an outstanding data gathering efficiency while ensuring the data accuracy.展开更多
Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vect...Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vector machine,particle swarm optimization,etc.,lack accuracy,robustness and efficiency,in this study,the authors propose a new method for the prediction of NSS based on recurrent neural network(RNN)with gated recurrent unit.Design/methodology/approach-This method extracts internal and external information features from the original time-series network data for the first time.Then,the extracted features are applied to the deep RNN model for training and validation.After iteration and optimization,the accuracy of predictions of NSS will be obtained by the well-trained model,and the model is robust for the unstable network data.Findings-Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models.Although the deep RNN models need more time consumption for training,they guarantee the accuracy and robustness of prediction in return for validation.Originality/value-In the prediction of NSS time-series data,the proposed internal and external information features are well described the original data,and the employment of deep RNN model will outperform the state-of-the-arts models.展开更多
It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the process.The proliferation of industrial sensors and the availability of ...It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the process.The proliferation of industrial sensors and the availability of thickening-system data make this possible.However,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive models.To address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening systems.Using a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental results.The results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system trajectories.The proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.展开更多
基金funded by Shaanxi Province Key Industrial Chain Project(2023-ZDLGY-24)Industrialization Project of Shaanxi Provincial Education Department(21JC018)+1 种基金Shaanxi Province Key Research and Development Program(2021ZDLGY13-02)the Open Foundation of State Key Laboratory for Advanced Metals and Materials(2022-Z01).
文摘In order to improve the performance degradation prediction accuracy of proton exchange membrane fuel cell(PEMFC),a fusion prediction method(CKDG)based on adaptive noise complete ensemble empirical mode decomposition(CEEMDAN),kernel principal component analysis(KPCA)and dual attention mechanism gated recurrent unit neural network(DA-GRU)was proposed.CEEMDAN and KPCA were used to extract the input feature data sequence,reduce the influence of random factors,and capture essential feature components to reduce the model complexity.The DA-GRU network helps to learn the feature mapping relationship of data in long time series and predict the changing trend of performance degradation data more accurately.The actual aging experimental data verify the performance of the CKDG method.The results show that under the steady-state condition of 20%training data prediction,the CKDA method can reduce the root mean square error(RMSE)by 52.7%and 34.6%,respectively,compared with the traditional LSTM and GRU neural networks.Compared with the simple DA-GRU network,RMSE is reduced by 15%,and the degree of over-fitting is reduced,which has higher accuracy.It also shows excellent prediction performance under the dynamic condition data set and has good universality.
基金Funding for this research is provided by the Natural Sciences and Engineering Research Council of Canada
文摘The Wireless Sensor Networks(WSNs)are widely utilized in various industrial and environmental monitoring applications.The process of data gathering within the WSN is significant in terms of reporting the environmental data.However,it might occur that certain sensor node malfunctions due to the energy draining out or unexpected damage.Therefore,the collected data may become inaccurate or incomplete.Focusing on the spatiotemporal correlation among sensor nodes,this paper proposes a novel algorithm to predict the value of the missing or inaccurate data and predict the future data in replacement of certain nonfunctional sensor nodes.The Long-Short-Term-Memory Recurrent Neural Network(LSTM RNN)helps to more accurately derive the time-series data corresponding to the sets of past collected data,making the prediction results more reliable.It is observed from the simulation results that the proposed algorithm provides an outstanding data gathering efficiency while ensuring the data accuracy.
基金supported by the funds of Ningde Normal University Youth Teacher Research Program(2015Q15)The Education Science Project of the Junior Teacher in the Education Department of Fujian province(JAT160532).
文摘Purpose-The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations(NSS).Because the conventional methods for the prediction of NSS,such as support vector machine,particle swarm optimization,etc.,lack accuracy,robustness and efficiency,in this study,the authors propose a new method for the prediction of NSS based on recurrent neural network(RNN)with gated recurrent unit.Design/methodology/approach-This method extracts internal and external information features from the original time-series network data for the first time.Then,the extracted features are applied to the deep RNN model for training and validation.After iteration and optimization,the accuracy of predictions of NSS will be obtained by the well-trained model,and the model is robust for the unstable network data.Findings-Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models.Although the deep RNN models need more time consumption for training,they guarantee the accuracy and robustness of prediction in return for validation.Originality/value-In the prediction of NSS time-series data,the proposed internal and external information features are well described the original data,and the employment of deep RNN model will outperform the state-of-the-arts models.
基金supported by National Key Research and Development Program of China(2019YFC0605300)the National Natural Science Foundation of China(61873299,61902022,61972028)+2 种基金Scientific and Technological Innovation Foundation of Shunde Graduate School,University of Science and Technology Beijing(BK21BF002)Macao Science and Technology Development Fund under Macao Funding Scheme for Key R&D Projects(0025/2019/AKP)Macao Science and Technology Development Fund(0015/2020/AMJ)。
文摘It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the process.The proliferation of industrial sensors and the availability of thickening-system data make this possible.However,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive models.To address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening systems.Using a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental results.The results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system trajectories.The proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.
