In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the l...In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control pattern.Interestingly,the instances driven by neural networks have the ability to outperform the original analytically driven scenarios.Three different control schemes,namely perfect,linear-quadratic,and generalized predictive controllers were used in the theoretical study.In addition,the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application.展开更多
Electric vehicles(EVs)are gradually being deployed in the transportation sector.Although they have a high impact on reducing greenhouse gas emissions,their penetration is challenged by their random energy demand and d...Electric vehicles(EVs)are gradually being deployed in the transportation sector.Although they have a high impact on reducing greenhouse gas emissions,their penetration is challenged by their random energy demand and difficult scheduling of their optimal charging.To cope with these problems,this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand forecasting.The present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’charging scheduling task.By using predictive algorithms for solar generation and load demand estimation,this approach aimed at ensuring dynamic and efficient energy flow between the solar energy source,the grid and the electric vehicles.The main contribution of this paper lies in developing an intelligent approach based on deep recurrent neural networks to forecast the energy demand using only its previous records.Therefore,various forecasters based on Long Short-term Memory,Gated Recurrent Unit,and their bi-directional and stacked variants were investigated using a real dataset collected from an EV charging station located at Trieste University(Italy).The developed forecasters have been evaluated and compared according to different metrics,including R,RMSE,MAE,and MAPE.We found that the obtained R values for both PV power generation and energy demand ranged between 97%and 98%.These study findings can be used for reliable and efficient decision-making on the management side of the optimal scheduling of the charging operations.展开更多
The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stag...The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stage.Effectively identifying and predicting the smelt-ing stage poses a significant challenge within industrial production.Traditional image-based methodologies,which rely on a single static flame image as input,demonstrate low recognition accuracy and inadequately extract the dynamic changes in smelting stage.To address this issue,the present study introduces an innovative recognition model that preprocesses flame video sequences from the furnace mouth and then employs a convolutional recurrent neural network(CRNN)to extract spatiotemporal features and derive recognition outputs.Ad-ditionally,we adopt feature layer visualization techniques to verify the model’s effectiveness and further enhance model performance by integrating the Bayesian optimization algorithm.The results indicate that the ResNet18 with convolutional block attention module(CBAM)in the convolutional layer demonstrates superior image feature extraction capabilities,achieving an accuracy of 90.70%and an area under the curve of 98.05%.The constructed Bayesian optimization-CRNN(BO-CRNN)model exhibits a significant improvement in comprehensive performance,with an accuracy of 97.01%and an area under the curve of 99.85%.Furthermore,statistics on the model’s average recognition time,computational complexity,and parameter quantity(Average recognition time:5.49 ms,floating-point opera-tions per second:18260.21 M(1 M=1×10^(6)),parameters:11.58 M)demonstrate superior performance.Through extensive repeated ex-periments on real-world datasets,the proposed CRNN model is capable of rapidly and accurately identifying smelting stages,offering a novel approach for converter smelting endpoint control.展开更多
In the pharmaceutical industry,model-based prediction is a crucial stage in process development that allows pharmaceutical companies to simulate different scenarios toward improving process efficiency,reducing costs,a...In the pharmaceutical industry,model-based prediction is a crucial stage in process development that allows pharmaceutical companies to simulate different scenarios toward improving process efficiency,reducing costs,and enhancing product quality.Nevertheless,ensuring the quality of formulated pharmaceutical products through the management of raw material variations has always been a challenging task.In this work,data-driven chance-constrained recurrent neural networks(CCRNNs)are developed to address the issue arising from raw material uncertainty.Our goal is to explore how,by proactively incorporating uncertainty into the model training process,more accurate predictions and enhanced robustness can be realized.The proposed approach is tested on a fluid bed dryer(FBD)from a continuous pharmaceutical manufacturing pilot plant.The results demonstrate that CCRNN models offer more robust and accurate predictions for the critical quality attribute(CQA)-in this case,moisture content-when material variations occur,compared with conventional recurrent neural network-based models.展开更多
Both the global exponential stability and the existence of periodic solutions for a class of recurrent neural networks with continuously distributed delays （RNNs） are studied. By employing the inequality α∏k=1^m ...Both the global exponential stability and the existence of periodic solutions for a class of recurrent neural networks with continuously distributed delays （RNNs） are studied. By employing the inequality α∏k=1^m bk^qk≤1/r ∑qkbk^r＋1/rα^r（α≥0,bk≥0,qk〉0,with ∑k=1^m qk=r-1,r≥1, constructing suitable Lyapunov r k=l k=l functions and applying the homeomorphism theory, a family of simple and new sufficient conditions are given ensuring the global exponential stability and the existence of periodic solutions of RNNs. The results extend and improve the results of earlier publications.展开更多
Recurrent neural networks （RNN） have been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN learning is a difficult task, partly because there are many comp...Recurrent neural networks （RNN） have been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN learning is a difficult task, partly because there are many competing and complex hidden units, such as the long short-term memory （LSTM） and the gated recurrent unit （GRU）. We propose a gated unit for RNN, named as minimal gated unit （MCU）, since it only contains one gate, which is a minimal design among all gated hidden units. The design of MCU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MCU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN＇s applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU＇s properties theoretically and empirically.展开更多
Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicabilit...Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy.展开更多
This paper proposes a co-evolutionary recurrent neural network （CERNN） for the multi-step-prediction of chaotic time series, it estimates the proper parameters of phase space reconstruction and optimizes the structu...This paper proposes a co-evolutionary recurrent neural network （CERNN） for the multi-step-prediction of chaotic time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent neural networks by coevolutionary strategy. The searching space was separated into two subspaces and the individuals are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability of recurrent neural network to incorporate past experience due to internal recurrence. The effectiveness of CERNN is evaluated by using three benchmark chaotic time series data sets： the Lorenz series, Mackey-Glass series and real-world sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic time series.展开更多
In view of the failure of GNSS signals,this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network(RNN).This proposed method utilizes the calculation principle of INS and the mem...In view of the failure of GNSS signals,this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network(RNN).This proposed method utilizes the calculation principle of INS and the memory function of the RNN to estimate the errors of the INS,thereby obtaining a continuous,reliable and high-precision navigation solution.The performance of the proposed method is firstly demonstrated using an INS/GNSS simulation environment.Subsequently,an experimental test on boat is also conducted to validate the performance of the method.The results show a promising application prospect for RNN in the field of positioning for INS/GNSS integrated navigation in the absence of GNSS signal,as it outperforms extreme learning machine(ELM)and EKF by approximately 30%and 60%,respectively.展开更多
To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and app...To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and application effect analysis were carried out. Since the traditional Fully Connected Neural Network(FCNN) is incapable of preserving spatial dependency, the Long Short-Term Memory(LSTM) network, which is a kind of Recurrent Neural Network(RNN), was utilized to establish a method for log reconstruction. By this method, synthetic logs can be generated from series of input log data with consideration of variation trend and context information with depth. Besides, a cascaded LSTM was proposed by combining the standard LSTM with a cascade system. Testing through real well log data shows that: the results from the LSTM are of higher accuracy than the traditional FCNN; the cascaded LSTM is more suitable for the problem with multiple series data; the machine learning method proposed provides an accurate and cost effective way for synthetic well log generation.展开更多
The Global Positioning System(GPS)has become a foundation for most location-based services and navigation systems,such as autonomous vehicles,drones,ships,and wearable devices.However,it is a challenge to verify if th...The Global Positioning System(GPS)has become a foundation for most location-based services and navigation systems,such as autonomous vehicles,drones,ships,and wearable devices.However,it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools.Pervasive tools,such as Fake GPS,Lockito,and software-defined radio,enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected.Furthermore,it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors.To this end,we propose DeepPOSE,a deep learning model,to address the noise introduced in sensor readings and detect GPS spoofing attacks on mobile platforms.Our design uses a convolutional and recurrent neural network to reduce the noise,to recover a vehicle's real-time trajectory from multiple sensor inputs.We further propose a novel scheme to map the constructed trajectory from sensor readings onto the Google map,to smartly eliminate the accumulation of errors on the trajectory estimation.The reconstructed trajectory from sensors is then used to detect the GPS spoofing attack.Compared with the existing method,the proposed approach demonstrates a significantly higher degree of accuracy for detecting GPS spoofing attacks.展开更多
Early fault warning for nuclear power machinery is conducive to timely troubleshooting and reductions in safety risks and unnecessary costs. This paper presents a novel intelligent fault prediction method, integrated ...Early fault warning for nuclear power machinery is conducive to timely troubleshooting and reductions in safety risks and unnecessary costs. This paper presents a novel intelligent fault prediction method, integrated probabilistic principal component analysis(PPCA), multi-resolution wavelet analysis, Bayesian inference, and RNN model for nuclear power machinery that consider data uncertainty and chaotic time series. After denoising the source data, the Bayesian PPCA method is employed for dimensional reduction to obtain a refined data group. A recurrent neural network(RNN) prediction model is constructed, and a Bayesian statistical inference approach is developed to quantitatively assess the prediction reliability of the model. By modeling and analyzing the data collected on the steam turbine and components of a nuclear power plant, the results of the goodness of fit, mean square error distribution, and Bayesian confidence indicate that the proposed RNN model can implement early warning in the fault creep period. The accuracy and reliability of the proposed model are quantitatively verified.展开更多
In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was...In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.展开更多
For vehicle integrated navigation systems, real-time estimating states of the dead reckoning （DR） unit is much more difficult than that of the other measuring sensors under indefinite noises and nonlinear characteri...For vehicle integrated navigation systems, real-time estimating states of the dead reckoning （DR） unit is much more difficult than that of the other measuring sensors under indefinite noises and nonlinear characteristics. Compared with the well known, extended Kalman filter （EKF）, a recurrent neural network is proposed for the solution, which not only improves the location precision and the adaptive ability of resisting disturbances, but also avoids calculating the analytic derivation and Jacobian matrices of the nonlinear system model. To test the performances of the recurrent neural network, these two methods are used to estimate the state of the vehicle＇s DR navigation system. Simulation results show that the recurrent neural network is superior to the EKF and is a more ideal filtering method for vehicle DR navigation.展开更多
This paper deals with the stability of static recurrent neural networks （RNNs） with a time-varying delay. An augmented Lyapunov-Krasovskii functional is employed, in which some useful terms are included. Furthermore...This paper deals with the stability of static recurrent neural networks （RNNs） with a time-varying delay. An augmented Lyapunov-Krasovskii functional is employed, in which some useful terms are included. Furthermore, the relationship among the timevarying delay, its upper bound and their difierence, is taken into account, and novel bounding techniques for 1- τ（t） are employed. As a result, without ignoring any useful term in the derivative of the Lyapunov-Krasovskii functional, the resulting delay-dependent criteria show less conservative than the existing ones. Finally, a numerical example is given to demonstrate the effectiveness of the proposed methods.展开更多
Accurate estimation of the remaining useful life(RUL)and health state for rollers is of great significance to hot rolling production.It can provide decision support for roller management so as to improve the productiv...Accurate estimation of the remaining useful life(RUL)and health state for rollers is of great significance to hot rolling production.It can provide decision support for roller management so as to improve the productivity of the hot rolling process.In addition,the RUL prediction for rollers is helpful in transitioning from the current regular maintenance strategy to conditional-based maintenance.Therefore,a new method that can extract coarse-grained and fine-grained features from batch data to predict the RUL of the rollers is proposed in this paper.Firstly,a new deep learning network architecture based on recurrent neural networks that can make full use of the extracted coarsegrained fine-grained features to estimate the heath indicator(HI)is developed,where the HI is able to indicate the health state of the roller.Following that,a state-space model is constructed to describe the HI,and the probabilistic distribution of RUL can be estimated by extrapolating the HI degradation model to a predefined failure threshold.Finally,application to a hot strip mill is given to verify the effectiveness of the proposed methods using data collected from an industrial site,and the relatively low RMSE and MAE values demonstrate its advantages compared with some other popular deep learning methods.展开更多
The remaining useful life(RUL)of a system is generally predicted by utilising the data collected from the sensors that continuously monitor different indicators.Recently,different deep learning(DL)techniques have been...The remaining useful life(RUL)of a system is generally predicted by utilising the data collected from the sensors that continuously monitor different indicators.Recently,different deep learning(DL)techniques have been used for RUL prediction and achieved great success.Because the data is often time-sequential,recurrent neural network(RNN)has attracted significant interests due to its efficiency in dealing with such data.This paper systematically reviews RNN and its variants for RUL prediction,with a specific focus on understanding how different components(e.g.,types of optimisers and activation functions)or parameters(e.g.,sequence length,neuron quantities)affect their performance.After that,a case study using the well-studied NASA’s C-MAPSS dataset is presented to quantitatively evaluate the influence of various state-of-the-art RNN structures on the RUL prediction performance.The result suggests that the variant methods usually perform better than the original RNN,and among which,Bi-directional Long Short-Term Memory generally has the best performance in terms of stability,precision and accuracy.Certain model structures may fail to produce valid RUL prediction result due to the gradient vanishing or gradient exploring problem if the parameters are not chosen appropriately.It is concluded that parameter tuning is a crucial step to achieve optimal prediction performance.展开更多
The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). I...The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.展开更多
After a recursive multi-step-ahead predictor for nonlinear systems based on local recurrent neural networks is introduced, an intelligent FID controller is adopted to correct the errors including identified model erro...After a recursive multi-step-ahead predictor for nonlinear systems based on local recurrent neural networks is introduced, an intelligent FID controller is adopted to correct the errors including identified model errors and accumulated errors produced in the recursive process. Characterized by predictive control, this method can achieve a good control accuracy and has good robustness. A simulation study shows that this control algorithm is very effective.展开更多
As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symboli...As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.展开更多
文摘In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control pattern.Interestingly,the instances driven by neural networks have the ability to outperform the original analytically driven scenarios.Three different control schemes,namely perfect,linear-quadratic,and generalized predictive controllers were used in the theoretical study.In addition,the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application.
基金University of Jeddah,Jeddah,Saudi Arabia,grant No.(UJ-23-SRP-10).
文摘Electric vehicles(EVs)are gradually being deployed in the transportation sector.Although they have a high impact on reducing greenhouse gas emissions,their penetration is challenged by their random energy demand and difficult scheduling of their optimal charging.To cope with these problems,this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand forecasting.The present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’charging scheduling task.By using predictive algorithms for solar generation and load demand estimation,this approach aimed at ensuring dynamic and efficient energy flow between the solar energy source,the grid and the electric vehicles.The main contribution of this paper lies in developing an intelligent approach based on deep recurrent neural networks to forecast the energy demand using only its previous records.Therefore,various forecasters based on Long Short-term Memory,Gated Recurrent Unit,and their bi-directional and stacked variants were investigated using a real dataset collected from an EV charging station located at Trieste University(Italy).The developed forecasters have been evaluated and compared according to different metrics,including R,RMSE,MAE,and MAPE.We found that the obtained R values for both PV power generation and energy demand ranged between 97%and 98%.These study findings can be used for reliable and efficient decision-making on the management side of the optimal scheduling of the charging operations.
基金financially supported by the National Natural Science Foundation of China(No.52374320).
文摘The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stage.Effectively identifying and predicting the smelt-ing stage poses a significant challenge within industrial production.Traditional image-based methodologies,which rely on a single static flame image as input,demonstrate low recognition accuracy and inadequately extract the dynamic changes in smelting stage.To address this issue,the present study introduces an innovative recognition model that preprocesses flame video sequences from the furnace mouth and then employs a convolutional recurrent neural network(CRNN)to extract spatiotemporal features and derive recognition outputs.Ad-ditionally,we adopt feature layer visualization techniques to verify the model’s effectiveness and further enhance model performance by integrating the Bayesian optimization algorithm.The results indicate that the ResNet18 with convolutional block attention module(CBAM)in the convolutional layer demonstrates superior image feature extraction capabilities,achieving an accuracy of 90.70%and an area under the curve of 98.05%.The constructed Bayesian optimization-CRNN(BO-CRNN)model exhibits a significant improvement in comprehensive performance,with an accuracy of 97.01%and an area under the curve of 99.85%.Furthermore,statistics on the model’s average recognition time,computational complexity,and parameter quantity(Average recognition time:5.49 ms,floating-point opera-tions per second:18260.21 M(1 M=1×10^(6)),parameters:11.58 M)demonstrate superior performance.Through extensive repeated ex-periments on real-world datasets,the proposed CRNN model is capable of rapidly and accurately identifying smelting stages,offering a novel approach for converter smelting endpoint control.
基金Financial support from the Engineering and Physical Sciences Research Council grant EP/V034723/1(RiFTMaP)is gratefully acknowledged.
文摘In the pharmaceutical industry,model-based prediction is a crucial stage in process development that allows pharmaceutical companies to simulate different scenarios toward improving process efficiency,reducing costs,and enhancing product quality.Nevertheless,ensuring the quality of formulated pharmaceutical products through the management of raw material variations has always been a challenging task.In this work,data-driven chance-constrained recurrent neural networks(CCRNNs)are developed to address the issue arising from raw material uncertainty.Our goal is to explore how,by proactively incorporating uncertainty into the model training process,more accurate predictions and enhanced robustness can be realized.The proposed approach is tested on a fluid bed dryer(FBD)from a continuous pharmaceutical manufacturing pilot plant.The results demonstrate that CCRNN models offer more robust and accurate predictions for the critical quality attribute(CQA)-in this case,moisture content-when material variations occur,compared with conventional recurrent neural network-based models.
文摘Both the global exponential stability and the existence of periodic solutions for a class of recurrent neural networks with continuously distributed delays （RNNs） are studied. By employing the inequality α∏k=1^m bk^qk≤1/r ∑qkbk^r＋1/rα^r（α≥0,bk≥0,qk〉0,with ∑k=1^m qk=r-1,r≥1, constructing suitable Lyapunov r k=l k=l functions and applying the homeomorphism theory, a family of simple and new sufficient conditions are given ensuring the global exponential stability and the existence of periodic solutions of RNNs. The results extend and improve the results of earlier publications.
基金supported by National Natural Science Foundation of China(Nos.61422203 and 61333014)National Key Basic Research Program of China(No.2014CB340501)
文摘Recurrent neural networks （RNN） have been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN learning is a difficult task, partly because there are many competing and complex hidden units, such as the long short-term memory （LSTM） and the gated recurrent unit （GRU）. We propose a gated unit for RNN, named as minimal gated unit （MCU）, since it only contains one gate, which is a minimal design among all gated hidden units. The design of MCU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MCU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN＇s applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU＇s properties theoretically and empirically.
基金supported by the Natural Science Foundation of China(Grant Nos.51979158,51639008,51679135,and 51422905)the Program of Shanghai Academic Research Leader by Science and Technology Commission of Shanghai Municipality(Project No.19XD1421900)。
文摘Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy.
基金Project supported by the State Key Program of National Natural Science of China (Grant No 30230350)the Natural Science Foundation of Guangdong Province,China (Grant No 07006474)
文摘This paper proposes a co-evolutionary recurrent neural network （CERNN） for the multi-step-prediction of chaotic time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent neural networks by coevolutionary strategy. The searching space was separated into two subspaces and the individuals are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability of recurrent neural network to incorporate past experience due to internal recurrence. The effectiveness of CERNN is evaluated by using three benchmark chaotic time series data sets： the Lorenz series, Mackey-Glass series and real-world sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic time series.
基金supported in part by the National Natural Science Foundation of China(No.41876222)。
文摘In view of the failure of GNSS signals,this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network(RNN).This proposed method utilizes the calculation principle of INS and the memory function of the RNN to estimate the errors of the INS,thereby obtaining a continuous,reliable and high-precision navigation solution.The performance of the proposed method is firstly demonstrated using an INS/GNSS simulation environment.Subsequently,an experimental test on boat is also conducted to validate the performance of the method.The results show a promising application prospect for RNN in the field of positioning for INS/GNSS integrated navigation in the absence of GNSS signal,as it outperforms extreme learning machine(ELM)and EKF by approximately 30%and 60%,respectively.
基金Supported by the National Natural Science Foundation of China(U1663208,51520105005)the National Science and Technology Major Project of China(2017ZX05009-005,2016ZX05037-003)
文摘To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and application effect analysis were carried out. Since the traditional Fully Connected Neural Network(FCNN) is incapable of preserving spatial dependency, the Long Short-Term Memory(LSTM) network, which is a kind of Recurrent Neural Network(RNN), was utilized to establish a method for log reconstruction. By this method, synthetic logs can be generated from series of input log data with consideration of variation trend and context information with depth. Besides, a cascaded LSTM was proposed by combining the standard LSTM with a cascade system. Testing through real well log data shows that: the results from the LSTM are of higher accuracy than the traditional FCNN; the cascaded LSTM is more suitable for the problem with multiple series data; the machine learning method proposed provides an accurate and cost effective way for synthetic well log generation.
基金This work was supported in part by NSF under Grants CNS-1950704,CNS-1828593,and OAC-1829771,ONR under Grant N00014-20-1-2065,NSA under Grant H98230-21-1-0278,and the Commonwealth Cyber Initiative.
文摘The Global Positioning System(GPS)has become a foundation for most location-based services and navigation systems,such as autonomous vehicles,drones,ships,and wearable devices.However,it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools.Pervasive tools,such as Fake GPS,Lockito,and software-defined radio,enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected.Furthermore,it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors.To this end,we propose DeepPOSE,a deep learning model,to address the noise introduced in sensor readings and detect GPS spoofing attacks on mobile platforms.Our design uses a convolutional and recurrent neural network to reduce the noise,to recover a vehicle's real-time trajectory from multiple sensor inputs.We further propose a novel scheme to map the constructed trajectory from sensor readings onto the Google map,to smartly eliminate the accumulation of errors on the trajectory estimation.The reconstructed trajectory from sensors is then used to detect the GPS spoofing attack.Compared with the existing method,the proposed approach demonstrates a significantly higher degree of accuracy for detecting GPS spoofing attacks.
基金the National Natural Science Foundation of China(No.51875209)the Guangdong Basic and Applied Basic Research Foundation(No.2019B1515120060)the Open Funds of State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment。
文摘Early fault warning for nuclear power machinery is conducive to timely troubleshooting and reductions in safety risks and unnecessary costs. This paper presents a novel intelligent fault prediction method, integrated probabilistic principal component analysis(PPCA), multi-resolution wavelet analysis, Bayesian inference, and RNN model for nuclear power machinery that consider data uncertainty and chaotic time series. After denoising the source data, the Bayesian PPCA method is employed for dimensional reduction to obtain a refined data group. A recurrent neural network(RNN) prediction model is constructed, and a Bayesian statistical inference approach is developed to quantitatively assess the prediction reliability of the model. By modeling and analyzing the data collected on the steam turbine and components of a nuclear power plant, the results of the goodness of fit, mean square error distribution, and Bayesian confidence indicate that the proposed RNN model can implement early warning in the fault creep period. The accuracy and reliability of the proposed model are quantitatively verified.
基金Projects(70572090, 70373017) supported by the National Natural Science Foundation of China
文摘In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.
文摘For vehicle integrated navigation systems, real-time estimating states of the dead reckoning （DR） unit is much more difficult than that of the other measuring sensors under indefinite noises and nonlinear characteristics. Compared with the well known, extended Kalman filter （EKF）, a recurrent neural network is proposed for the solution, which not only improves the location precision and the adaptive ability of resisting disturbances, but also avoids calculating the analytic derivation and Jacobian matrices of the nonlinear system model. To test the performances of the recurrent neural network, these two methods are used to estimate the state of the vehicle＇s DR navigation system. Simulation results show that the recurrent neural network is superior to the EKF and is a more ideal filtering method for vehicle DR navigation.
基金supported by National Natural Science Foundation of China (No. 60874025)Natural Science Foundation of Hunan Province of China (No. 10JJ6098)
文摘This paper deals with the stability of static recurrent neural networks （RNNs） with a time-varying delay. An augmented Lyapunov-Krasovskii functional is employed, in which some useful terms are included. Furthermore, the relationship among the timevarying delay, its upper bound and their difierence, is taken into account, and novel bounding techniques for 1- τ（t） are employed. As a result, without ignoring any useful term in the derivative of the Lyapunov-Krasovskii functional, the resulting delay-dependent criteria show less conservative than the existing ones. Finally, a numerical example is given to demonstrate the effectiveness of the proposed methods.
基金the Natural Science Foundation of China(NSFC)(61873024,61773053)the China Central Universities of USTB(FRF-TP-19-049A1Z)the National Key RD Program of China(2017YFB0306403)。
文摘Accurate estimation of the remaining useful life(RUL)and health state for rollers is of great significance to hot rolling production.It can provide decision support for roller management so as to improve the productivity of the hot rolling process.In addition,the RUL prediction for rollers is helpful in transitioning from the current regular maintenance strategy to conditional-based maintenance.Therefore,a new method that can extract coarse-grained and fine-grained features from batch data to predict the RUL of the rollers is proposed in this paper.Firstly,a new deep learning network architecture based on recurrent neural networks that can make full use of the extracted coarsegrained fine-grained features to estimate the heath indicator(HI)is developed,where the HI is able to indicate the health state of the roller.Following that,a state-space model is constructed to describe the HI,and the probabilistic distribution of RUL can be estimated by extrapolating the HI degradation model to a predefined failure threshold.Finally,application to a hot strip mill is given to verify the effectiveness of the proposed methods using data collected from an industrial site,and the relatively low RMSE and MAE values demonstrate its advantages compared with some other popular deep learning methods.
基金Supported by U.K.EPSRC Platform Grant(Grant No.EP/P027121/1).
文摘The remaining useful life(RUL)of a system is generally predicted by utilising the data collected from the sensors that continuously monitor different indicators.Recently,different deep learning(DL)techniques have been used for RUL prediction and achieved great success.Because the data is often time-sequential,recurrent neural network(RNN)has attracted significant interests due to its efficiency in dealing with such data.This paper systematically reviews RNN and its variants for RUL prediction,with a specific focus on understanding how different components(e.g.,types of optimisers and activation functions)or parameters(e.g.,sequence length,neuron quantities)affect their performance.After that,a case study using the well-studied NASA’s C-MAPSS dataset is presented to quantitatively evaluate the influence of various state-of-the-art RNN structures on the RUL prediction performance.The result suggests that the variant methods usually perform better than the original RNN,and among which,Bi-directional Long Short-Term Memory generally has the best performance in terms of stability,precision and accuracy.Certain model structures may fail to produce valid RUL prediction result due to the gradient vanishing or gradient exploring problem if the parameters are not chosen appropriately.It is concluded that parameter tuning is a crucial step to achieve optimal prediction performance.
基金jointly supported by the National Science Foundation of China (Grant Nos. 42275007 and 41865003)Jiangxi Provincial Department of science and technology project (Grant No. 20171BBG70004)。
文摘The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.
基金This project was supported by the National Natural Science Foundation of China(60174021)Natural Science Foundation Key Project of Tianjin(013800711).
文摘After a recursive multi-step-ahead predictor for nonlinear systems based on local recurrent neural networks is introduced, an intelligent FID controller is adopted to correct the errors including identified model errors and accumulated errors produced in the recursive process. Characterized by predictive control, this method can achieve a good control accuracy and has good robustness. A simulation study shows that this control algorithm is very effective.
基金Supported in part by Natural Science Foundation of China(Grant Nos.51835009,51705398)Shaanxi Province 2020 Natural Science Basic Research Plan(Grant No.2020JQ-042)Aeronautical Science Foundation(Grant No.2019ZB070001).
文摘As an integrated application of modern information technologies and artificial intelligence,Prognostic and Health Management(PHM)is important for machine health monitoring.Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry.In this paper,a multi-scale Convolutional Gated Recurrent Unit network(MCGRU)is proposed to address raw sensory data for tool wear prediction.At the bottom of MCGRU,six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network,which augments the adaptability to features of different time scales.These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations.At the top of the MCGRU,a fully connected layer and a regression layer are built for cutting tool wear prediction.Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.
