Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,w...Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.展开更多
Neural regeneration, or neuroregeneration, is a brain mechanism essential for rescuing cognitive functions pharmacologically against memory disorders and aging-related memory abnormality. In this short Perspective art...Neural regeneration, or neuroregeneration, is a brain mechanism essential for rescuing cognitive functions pharmacologically against memory disorders and aging-related memory abnormality. In this short Perspective article, we intend to briefly present the essential roles of neuroregeneration and neural plasticity in learning and memory, memory disorders, and critical involvement in an effective treatment of memory disorders.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
Aiming at the problem of insufficient consideration of the correlation between components in the prediction of the remaining life of mechanical equipment,the method of remaining life prediction that combines the self-...Aiming at the problem of insufficient consideration of the correlation between components in the prediction of the remaining life of mechanical equipment,the method of remaining life prediction that combines the self-attention mechanism with the long short-term memory neural network(LSTM-NN)is proposed,called Self-Attention-LSTM.First,the auto-encoder is used to obtain the component-level state information;second,the state information of each component is input into the self-attention mechanism to learn the correlation between components;then,the multi-component correlation matrix is added to the LSTM input gate,and the LSTM-NN is used for life prediction.Finally,combined with the commercial modular aero-propulsion system simulation data set(C-MAPSS),the experiment was carried out and compared with the existing methods.Research results show that the proposed method can achieve better prediction accuracy and verify the feasibility of the method.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
Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables...Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.展开更多
The combustion characteristic parameters of mining conveyor belts represent a crucial index for measuring the fire performance and hazard posed by combustible materials.An accurate prediction of its value provides imp...The combustion characteristic parameters of mining conveyor belts represent a crucial index for measuring the fire performance and hazard posed by combustible materials.An accurate prediction of its value provides important guidance on preventing conveyor belt fires.The critical parameters of a flame-retardant polyvinyl chloride gum elastic conveyor belt were measured under different radiative heat fluxes,including mass loss rate,heat release rate,effective heat of combustion and gas production rates for CO and CO_(2).The prediction method for the combustion characteristics of conveyor belts was proposed by combining a convolutional neural network with long short-term memory.Results indicated that the peak values of the mass loss,heat release,smoke production and gas production rates of CO and CO_(2) were positively correlated with radiative heat flux,whilst the time required to reach the peak value was negatively correlated with it.The peak time of the effective heat of combustion occurred earlier.Through deep learning modelling,mean absolute error,root mean square error and coefficient of determination were determined as 2.09,3.45 and 9.93×10^(-1),respectively.Compared with convolutional neural network,long short-term memory and multilayer perceptron,mean absolute error decreased by 26.92%,24.82%and 25.09%,root mean square error declined by 27.82%,29.59%and 29.59%and coefficient of determination increased by 0.05×10^(-1),0.06×10^(-1) and 0.06×10^(-1),respectively.The findings provide a quantitative reference benchmark for the development of conveyor belt fires and offer new technical support for the construction of early warning systems for conveyor belt fires in coal mines.展开更多
There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement an...There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.展开更多
This paper is concerned with multidirectional associative memory neural network with distributed delays on almost-periodic time scales.Some sufficient conditions on the existence,uniqueness and the global exponential ...This paper is concerned with multidirectional associative memory neural network with distributed delays on almost-periodic time scales.Some sufficient conditions on the existence,uniqueness and the global exponential stability of almost-periodic solutions are established.An example is presented to illustrate the feasibility and effectiveness of the obtained results.展开更多
The present study was designed to determine microtubule-associated protein-2 and synaptophysin expression in the hippocampal CA3 region in a rat model of middle cerebral artery occlusion. The rats were treated with ac...The present study was designed to determine microtubule-associated protein-2 and synaptophysin expression in the hippocampal CA3 region in a rat model of middle cerebral artery occlusion. The rats were treated with acupuncture at Baihui （GV 20）, Qubin （GB 7）, and Qianding （GV 21） points, in addition to exercise training. Results were compared with rats undergoing exercise training only. The Y-maze method and immunohistochemistry revealed decreased error frequency of passing through Y-maze, as well as significantly increased microtubule-associated protein-2 and synaptophysin expression, in the acupuncture with exercise training group compared with the model and exercise training groups after 5 weeks. Microtubule-associated protein-2 and synaptophysin expressions negatively correlated with error frequency of passing through the Y-maze. These results suggested that acupuncture combined with exercise training improved learning and memory functions in a rat model of cerebral infarction. The mechanisms of action were hypothesized to be associated with dendritic or synaptic plasticity in the ipsilateral hippocampal CA3 region.展开更多
Abundant evidence indicates that propofol profoundly affects memory processes, although its specific effects on memory retrieval have not been clarified. A recent study has indicated that hippocampal glycogen synthase...Abundant evidence indicates that propofol profoundly affects memory processes, although its specific effects on memory retrieval have not been clarified. A recent study has indicated that hippocampal glycogen synthase kinase-3β（GSK-3β） activity affects memory. Constitutively active GSK-3β is required for memory retrieval, and propofol has been shown to inhibit GSK-3β. Thus, the present study examined whether propofol affects memory retrieval, and, if so, whether that effect is mediated through altered GSK-3β activity. Adult Sprague-Dawley rats were trained on a Morris water maze task（eight acquisition trials in one session） and subjected under the influence of a subhypnotic dose of propofol to a 24-hour probe trial memory retrieval test. The results showed that rats receiving pretest propofol（25 mg/kg） spent significantly less time in the target quadrant but showed no change in locomotor activity compared with those in the control group. Memory retrieval was accompanied by reduced phosphorylation of the serine-9 residue of GSK-3β in the hippocampus, whereas phosphorylation of the tyrosine-216 residue was unaffected. However, propofol blocked this retrieval-associated serine-9 phosphorylation. These findings suggest that subhypnotic propofol administration impairs memory retrieval and that the amnestic effects of propofol may be mediated by attenuated GSK-3β signaling in the hippocampus.展开更多
Studies on the stability of the equilibrium points of continuous bidirectional associative memory (BAM) neural network have yielded many useful results. A novel neural network model called standard neural network mode...Studies on the stability of the equilibrium points of continuous bidirectional associative memory (BAM) neural network have yielded many useful results. A novel neural network model called standard neural network model (SNNM) is ad- vanced. By using state affine transformation, the BAM neural networks were converted to SNNMs. Some sufficient conditions for the global asymptotic stability of continuous BAM neural networks were derived from studies on the SNNMs’ stability. These conditions were formulated as easily verifiable linear matrix inequalities (LMIs), whose conservativeness is relatively low. The approach proposed extends the known stability results, and can also be applied to other forms of recurrent neural networks (RNNs).展开更多
Several novel stability conditions for BAM neural networks with time-varying delays are studied.Based on Lyapunov-Krasovskii functional combined with linear matrix inequality approach,the delay-dependent linear matrix...Several novel stability conditions for BAM neural networks with time-varying delays are studied.Based on Lyapunov-Krasovskii functional combined with linear matrix inequality approach,the delay-dependent linear matrix inequality（LMI） conditions are established to guarantee robust asymptotic stability for given delayed BAM neural networks.These criteria can be easily verified by utilizing the recently developed algorithms for solving LMIs.A numerical example is provided to demonstrate the effectiveness and less conservatism of the main results.展开更多
By employing the Lyapunov stability theory and linear matrix inequality（LMI）technique,delay-dependent stability criterion is derived to ensure the exponential stability of bi-directional associative memory（BAM）neu...By employing the Lyapunov stability theory and linear matrix inequality（LMI）technique,delay-dependent stability criterion is derived to ensure the exponential stability of bi-directional associative memory（BAM）neural networks with time-varying delays.The proposed condition can be checked easily by LMI control toolbox in Matlab.A numerical example is given to demonstrate the effectiveness of our results.展开更多
Accurately predicting motion responses is a crucial component of the design process for floating offshore structures.This study introduces a hybrid model that integrates a convolutional neural network(CNN),a bidirecti...Accurately predicting motion responses is a crucial component of the design process for floating offshore structures.This study introduces a hybrid model that integrates a convolutional neural network(CNN),a bidirectional long short-term memory(BiLSTM)neural network,and an attention mechanism for forecasting the short-term motion responses of a semisubmersible.First,the motions are processed through the CNN for feature extraction.The extracted features are subsequently utilized by the BiLSTM network to forecast future motions.To enhance the predictive capability of the neural networks,an attention mechanism is integrated.In addition to the hybrid model,the BiLSTM is independently employed to forecast the motion responses of the semi-submersible,serving as benchmark results for comparison.Furthermore,both the 1D and 2D convolutions are conducted to check the influence of the convolutional dimensionality on the predicted results.The results demonstrate that the hybrid 1D CNN-BiLSTM network with an attention mechanism outperforms all other models in accurately predicting motion responses.展开更多
Nowadays,with the rapid development of industrial Internet technology,on the one hand,advanced industrial control systems(ICS)have improved industrial production efficiency.However,there are more and more cyber-attack...Nowadays,with the rapid development of industrial Internet technology,on the one hand,advanced industrial control systems(ICS)have improved industrial production efficiency.However,there are more and more cyber-attacks targeting industrial control systems.To ensure the security of industrial networks,intrusion detection systems have been widely used in industrial control systems,and deep neural networks have always been an effective method for identifying cyber attacks.Current intrusion detection methods still suffer from low accuracy and a high false alarm rate.Therefore,it is important to build a more efficient intrusion detection model.This paper proposes a hybrid deep learning intrusion detection method based on convolutional neural networks and bidirectional long short-term memory neural networks(CNN-BiLSTM).To address the issue of imbalanced data within the dataset and improve the model’s detection capabilities,the Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors(SMOTE-ENN)algorithm is applied in the preprocessing phase.This algorithm is employed to generate synthetic instances for the minority class,simultaneously mitigating the impact of noise in the majority class.This approach aims to create a more equitable distribution of classes,thereby enhancing the model’s ability to effectively identify patterns in both minority and majority classes.In the experimental phase,the detection performance of the method is verified using two data sets.Experimental results show that the accuracy rate on the CICIDS-2017 data set reaches 97.7%.On the natural gas pipeline dataset collected by Lan Turnipseed from Mississippi State University in the United States,the accuracy rate also reaches 85.5%.展开更多
Traditional biological neural networks cannot simulate the real situation of the abrupt synaptic connections between neurons while modeling associative memory of human brains.In this paper,the memristive multidirectio...Traditional biological neural networks cannot simulate the real situation of the abrupt synaptic connections between neurons while modeling associative memory of human brains.In this paper,the memristive multidirectional associative memory neural networks(MAMNNs)with mixed time-varying delays are investigated in the sense of Filippov solution.First,three steps are given to prove the existence of the almost periodic solution.Two new lemmas are proposed to prove the boundness of the solution and the asymptotical almost periodicity of the solution by constructing Lyapunov function.Second,the uniqueness and global exponential stability of the almost periodic solution of memristive MAMNNs are investigated by a new Lyapunov function.The sufficient conditions guaranteeing the properties of almost periodic solution are derived based on the relevant definitions,Halanay inequality and Lyapunov function.The investigation is an extension of the research on the periodic solution and almost periodic solution of bidirectional associative memory neural networks.Finally,numerical examples with simulations are presented to show the validity of the main results.展开更多
With the application of distributed power sources,the stability of the power system has been dramatically affected.Therefore,scholars have proposed the concept of a virtual synchronous generator(VSG).However,after the...With the application of distributed power sources,the stability of the power system has been dramatically affected.Therefore,scholars have proposed the concept of a virtual synchronous generator(VSG).However,after the system is disturbed,how to make it respond quickly and effectively to maintain the stability of the system becomes a complex problem.To address this problem,a frequency prediction component is incorporated into the control module of the VSG to enhance its performance.The Convolutional Neural NetworkLong Short-Term Memory(CNN-LSTM)model is used for frequency prediction,ensuring that the maximum energy capacity released by the storage system is maintained.Additionally,it guarantees that the inverter's output power does not exceed its rated capacity,based on the predicted frequency limit after the system experiences a disturbance.The advantage of real-time adjustment of inverter parameters is that the setting intervals for inertia and damping can be increased.The selection criteria for inertia and damping can be derived from the power angle oscillation curve of the synchronous generator.Consequently,an adaptive control strategy for VSG parameters is implemented to enhance the system's frequency restoration following disturbances.The validity and effectiveness of the model are verified through simulations in Matlab/Simulink.展开更多
This paper considers the automatic carrier landing problem of carrier-based aircrafts subjected to constraints,deck motion,measurement noises,and unknown disturbances.The iterative model predictive control(MPC)strateg...This paper considers the automatic carrier landing problem of carrier-based aircrafts subjected to constraints,deck motion,measurement noises,and unknown disturbances.The iterative model predictive control(MPC)strategy with constraints is proposed for automatic landing control of the aircraft.First,the long short-term memory(LSTM)neural network is used to calculate the adaptive reference trajectories of the aircraft.Then the Sage-Husa adaptive Kalman filter and the disturbance observer are introduced to design the composite compensator.Second,an iterative optimization algorithm is presented to fast solve the receding horizon optimal control problem of MPC based on the Lagrange’s theory.Moreover,some sufficient conditions are derived to guarantee the stability of the landing system in a closed loop with the MPC.Finally,the simulation results of F/A-18A aircraft show that compared with the conventional MPC,the presented MPC strategy improves the computational efficiency by nearly 56%and satisfies the control performance requirements of carrier landing.展开更多
The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms...The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.展开更多
基金This work is supported by the National Key Research and Development Program of China(No.2023YFB4203000)the National Natural Science Foundation of China(No.U22A20178)
文摘Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.
文摘Neural regeneration, or neuroregeneration, is a brain mechanism essential for rescuing cognitive functions pharmacologically against memory disorders and aging-related memory abnormality. In this short Perspective article, we intend to briefly present the essential roles of neuroregeneration and neural plasticity in learning and memory, memory disorders, and critical involvement in an effective treatment of memory disorders.
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 Natural Science Foundation of China(Nos.51875451 and 51834006)。
文摘Aiming at the problem of insufficient consideration of the correlation between components in the prediction of the remaining life of mechanical equipment,the method of remaining life prediction that combines the self-attention mechanism with the long short-term memory neural network(LSTM-NN)is proposed,called Self-Attention-LSTM.First,the auto-encoder is used to obtain the component-level state information;second,the state information of each component is input into the self-attention mechanism to learn the correlation between components;then,the multi-component correlation matrix is added to the LSTM input gate,and the LSTM-NN is used for life prediction.Finally,combined with the commercial modular aero-propulsion system simulation data set(C-MAPSS),the experiment was carried out and compared with the existing methods.Research results show that the proposed method can achieve better prediction accuracy and verify the feasibility of the method.
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supported by the National Natural Science Foundation of China(No.42061065)the Third Xinjiang Comprehensive Scientific Expedition,China(No.2022xjkk03010102).
文摘Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.
基金supported by the Natural Science Foundation of Shaanxi Province(No.2024JC-YBQN-0458)National Natural Science Foundation of China(No.5217-4204)+1 种基金Innovation Capacity Improve-ment Project for Small and Medium-Sized Scientific and Technological Enterprises of Shandong Province(No.2023TSGC0952)Luzhou Science and Technology Planning Project of China(No.2024JYJ057).
文摘The combustion characteristic parameters of mining conveyor belts represent a crucial index for measuring the fire performance and hazard posed by combustible materials.An accurate prediction of its value provides important guidance on preventing conveyor belt fires.The critical parameters of a flame-retardant polyvinyl chloride gum elastic conveyor belt were measured under different radiative heat fluxes,including mass loss rate,heat release rate,effective heat of combustion and gas production rates for CO and CO_(2).The prediction method for the combustion characteristics of conveyor belts was proposed by combining a convolutional neural network with long short-term memory.Results indicated that the peak values of the mass loss,heat release,smoke production and gas production rates of CO and CO_(2) were positively correlated with radiative heat flux,whilst the time required to reach the peak value was negatively correlated with it.The peak time of the effective heat of combustion occurred earlier.Through deep learning modelling,mean absolute error,root mean square error and coefficient of determination were determined as 2.09,3.45 and 9.93×10^(-1),respectively.Compared with convolutional neural network,long short-term memory and multilayer perceptron,mean absolute error decreased by 26.92%,24.82%and 25.09%,root mean square error declined by 27.82%,29.59%and 29.59%and coefficient of determination increased by 0.05×10^(-1),0.06×10^(-1) and 0.06×10^(-1),respectively.The findings provide a quantitative reference benchmark for the development of conveyor belt fires and offer new technical support for the construction of early warning systems for conveyor belt fires in coal mines.
文摘There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.
基金the National Natural Science Foundation of China(11671406,12071491)the Research Fund of Shenzhen Institute of Information Technology(QN201703).
文摘This paper is concerned with multidirectional associative memory neural network with distributed delays on almost-periodic time scales.Some sufficient conditions on the existence,uniqueness and the global exponential stability of almost-periodic solutions are established.An example is presented to illustrate the feasibility and effectiveness of the obtained results.
文摘The present study was designed to determine microtubule-associated protein-2 and synaptophysin expression in the hippocampal CA3 region in a rat model of middle cerebral artery occlusion. The rats were treated with acupuncture at Baihui （GV 20）, Qubin （GB 7）, and Qianding （GV 21） points, in addition to exercise training. Results were compared with rats undergoing exercise training only. The Y-maze method and immunohistochemistry revealed decreased error frequency of passing through Y-maze, as well as significantly increased microtubule-associated protein-2 and synaptophysin expression, in the acupuncture with exercise training group compared with the model and exercise training groups after 5 weeks. Microtubule-associated protein-2 and synaptophysin expressions negatively correlated with error frequency of passing through the Y-maze. These results suggested that acupuncture combined with exercise training improved learning and memory functions in a rat model of cerebral infarction. The mechanisms of action were hypothesized to be associated with dendritic or synaptic plasticity in the ipsilateral hippocampal CA3 region.
基金financially supported by the National Natural Science Foundation of China,No.81571039the Foundation for Fostering the National Natural Science Foundation of First Affiliated Hospital of Anhui Medical University in China,No.2015KJ12
文摘Abundant evidence indicates that propofol profoundly affects memory processes, although its specific effects on memory retrieval have not been clarified. A recent study has indicated that hippocampal glycogen synthase kinase-3β（GSK-3β） activity affects memory. Constitutively active GSK-3β is required for memory retrieval, and propofol has been shown to inhibit GSK-3β. Thus, the present study examined whether propofol affects memory retrieval, and, if so, whether that effect is mediated through altered GSK-3β activity. Adult Sprague-Dawley rats were trained on a Morris water maze task（eight acquisition trials in one session） and subjected under the influence of a subhypnotic dose of propofol to a 24-hour probe trial memory retrieval test. The results showed that rats receiving pretest propofol（25 mg/kg） spent significantly less time in the target quadrant but showed no change in locomotor activity compared with those in the control group. Memory retrieval was accompanied by reduced phosphorylation of the serine-9 residue of GSK-3β in the hippocampus, whereas phosphorylation of the tyrosine-216 residue was unaffected. However, propofol blocked this retrieval-associated serine-9 phosphorylation. These findings suggest that subhypnotic propofol administration impairs memory retrieval and that the amnestic effects of propofol may be mediated by attenuated GSK-3β signaling in the hippocampus.
基金Project (No. 60074008) supported by the National Natural Science Foundation of China
文摘Studies on the stability of the equilibrium points of continuous bidirectional associative memory (BAM) neural network have yielded many useful results. A novel neural network model called standard neural network model (SNNM) is ad- vanced. By using state affine transformation, the BAM neural networks were converted to SNNMs. Some sufficient conditions for the global asymptotic stability of continuous BAM neural networks were derived from studies on the SNNMs’ stability. These conditions were formulated as easily verifiable linear matrix inequalities (LMIs), whose conservativeness is relatively low. The approach proposed extends the known stability results, and can also be applied to other forms of recurrent neural networks (RNNs).
基金Supported by the National Natural Science Foundation of China (6067402760875039)+1 种基金Specialized Research Fund for the Doctoral Program of Higher Education (20050446001)Scientific Research Foundation of Qufu Normal University
文摘Several novel stability conditions for BAM neural networks with time-varying delays are studied.Based on Lyapunov-Krasovskii functional combined with linear matrix inequality approach,the delay-dependent linear matrix inequality（LMI） conditions are established to guarantee robust asymptotic stability for given delayed BAM neural networks.These criteria can be easily verified by utilizing the recently developed algorithms for solving LMIs.A numerical example is provided to demonstrate the effectiveness and less conservatism of the main results.
基金supported by Natural Science Foundation of Hebei Province under Grant No.E2007000381
文摘By employing the Lyapunov stability theory and linear matrix inequality（LMI）technique,delay-dependent stability criterion is derived to ensure the exponential stability of bi-directional associative memory（BAM）neural networks with time-varying delays.The proposed condition can be checked easily by LMI control toolbox in Matlab.A numerical example is given to demonstrate the effectiveness of our results.
基金the National Natural Science Foundation of China (Grant No. 52301322)the Jiangsu Provincial Natural Science Foundation (Grant No. BK20220653)+1 种基金the National Science Fund for Distinguished Young Scholars (Grant No. 52025112)the Key Projects of the National Natural Science Foundation of China (Grant No. 52331011)
文摘Accurately predicting motion responses is a crucial component of the design process for floating offshore structures.This study introduces a hybrid model that integrates a convolutional neural network(CNN),a bidirectional long short-term memory(BiLSTM)neural network,and an attention mechanism for forecasting the short-term motion responses of a semisubmersible.First,the motions are processed through the CNN for feature extraction.The extracted features are subsequently utilized by the BiLSTM network to forecast future motions.To enhance the predictive capability of the neural networks,an attention mechanism is integrated.In addition to the hybrid model,the BiLSTM is independently employed to forecast the motion responses of the semi-submersible,serving as benchmark results for comparison.Furthermore,both the 1D and 2D convolutions are conducted to check the influence of the convolutional dimensionality on the predicted results.The results demonstrate that the hybrid 1D CNN-BiLSTM network with an attention mechanism outperforms all other models in accurately predicting motion responses.
基金support from the Liaoning Province Nature Fund Project(No.2022-MS-291)the Scientific Research Project of Liaoning Province Education Department(LJKMZ20220781,LJKMZ20220783,LJKQZ20222457,JYTMS20231488).
文摘Nowadays,with the rapid development of industrial Internet technology,on the one hand,advanced industrial control systems(ICS)have improved industrial production efficiency.However,there are more and more cyber-attacks targeting industrial control systems.To ensure the security of industrial networks,intrusion detection systems have been widely used in industrial control systems,and deep neural networks have always been an effective method for identifying cyber attacks.Current intrusion detection methods still suffer from low accuracy and a high false alarm rate.Therefore,it is important to build a more efficient intrusion detection model.This paper proposes a hybrid deep learning intrusion detection method based on convolutional neural networks and bidirectional long short-term memory neural networks(CNN-BiLSTM).To address the issue of imbalanced data within the dataset and improve the model’s detection capabilities,the Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors(SMOTE-ENN)algorithm is applied in the preprocessing phase.This algorithm is employed to generate synthetic instances for the minority class,simultaneously mitigating the impact of noise in the majority class.This approach aims to create a more equitable distribution of classes,thereby enhancing the model’s ability to effectively identify patterns in both minority and majority classes.In the experimental phase,the detection performance of the method is verified using two data sets.Experimental results show that the accuracy rate on the CICIDS-2017 data set reaches 97.7%.On the natural gas pipeline dataset collected by Lan Turnipseed from Mississippi State University in the United States,the accuracy rate also reaches 85.5%.
基金supported by the Beijing Municipal Natural Science Foundation(No.4202025)partially sponsored by the National Natural Science Foundation of China(No.61672070)the Beijing Municipal Education Commission(No.KZ201910005008).
文摘Traditional biological neural networks cannot simulate the real situation of the abrupt synaptic connections between neurons while modeling associative memory of human brains.In this paper,the memristive multidirectional associative memory neural networks(MAMNNs)with mixed time-varying delays are investigated in the sense of Filippov solution.First,three steps are given to prove the existence of the almost periodic solution.Two new lemmas are proposed to prove the boundness of the solution and the asymptotical almost periodicity of the solution by constructing Lyapunov function.Second,the uniqueness and global exponential stability of the almost periodic solution of memristive MAMNNs are investigated by a new Lyapunov function.The sufficient conditions guaranteeing the properties of almost periodic solution are derived based on the relevant definitions,Halanay inequality and Lyapunov function.The investigation is an extension of the research on the periodic solution and almost periodic solution of bidirectional associative memory neural networks.Finally,numerical examples with simulations are presented to show the validity of the main results.
文摘With the application of distributed power sources,the stability of the power system has been dramatically affected.Therefore,scholars have proposed the concept of a virtual synchronous generator(VSG).However,after the system is disturbed,how to make it respond quickly and effectively to maintain the stability of the system becomes a complex problem.To address this problem,a frequency prediction component is incorporated into the control module of the VSG to enhance its performance.The Convolutional Neural NetworkLong Short-Term Memory(CNN-LSTM)model is used for frequency prediction,ensuring that the maximum energy capacity released by the storage system is maintained.Additionally,it guarantees that the inverter's output power does not exceed its rated capacity,based on the predicted frequency limit after the system experiences a disturbance.The advantage of real-time adjustment of inverter parameters is that the setting intervals for inertia and damping can be increased.The selection criteria for inertia and damping can be derived from the power angle oscillation curve of the synchronous generator.Consequently,an adaptive control strategy for VSG parameters is implemented to enhance the system's frequency restoration following disturbances.The validity and effectiveness of the model are verified through simulations in Matlab/Simulink.
基金National Defense Science and Technology Innovation Project(No.2022-4b5s-wwht-0041)。
文摘This paper considers the automatic carrier landing problem of carrier-based aircrafts subjected to constraints,deck motion,measurement noises,and unknown disturbances.The iterative model predictive control(MPC)strategy with constraints is proposed for automatic landing control of the aircraft.First,the long short-term memory(LSTM)neural network is used to calculate the adaptive reference trajectories of the aircraft.Then the Sage-Husa adaptive Kalman filter and the disturbance observer are introduced to design the composite compensator.Second,an iterative optimization algorithm is presented to fast solve the receding horizon optimal control problem of MPC based on the Lagrange’s theory.Moreover,some sufficient conditions are derived to guarantee the stability of the landing system in a closed loop with the MPC.Finally,the simulation results of F/A-18A aircraft show that compared with the conventional MPC,the presented MPC strategy improves the computational efficiency by nearly 56%and satisfies the control performance requirements of carrier landing.
文摘The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.
