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Real-time decision support for bolter recovery safety:Long short-term memory network-driven aircraft sequencing 认领 引用
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作者 Wei Han Changjiu Li +4 位作者 Xichao Su Yong Zhang Fang Guo Tongtong Yu Xuan Li 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第2期184-205,共22页
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th... The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations. 展开更多
关键词 Carrier-based aircraft Recovery scheduling Deep reinforcement learning Long short-term memory networks Dynamic real-time decision-making
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Deep learning-enhanced prediction of small intestinal bleeding points using long short-term memory networks 认领 引用
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作者 Hsin-Yu Kuo Kun-Hua Lee +6 位作者 Chu-Kuang Chou Arvind Mukundan Riya Karmakar Tsung-Hsien Chen Thong-Lin Wang Ping-Hung Liu Hsiang-Chen Wang 《World Journal of Gastroenterology》 SCIE CAS 2026年第15期88-101,共14页
BACKGROUND Small intestinal bleeding(SIB)remains a significant challenge in the diagnosis of obscure gastrointestinal bleeding.While capsule endoscopy(CE)is the gold standard for visualization,manual interpretation of... BACKGROUND Small intestinal bleeding(SIB)remains a significant challenge in the diagnosis of obscure gastrointestinal bleeding.While capsule endoscopy(CE)is the gold standard for visualization,manual interpretation of the extensive video footage is labor-intensive and subject to inter-observer variability.Although convolutional neural networks(CNNs)have improved lesion detection,standard models often fail to account for temporal continuity,limiting their ability to accurately predict the specific location of bleeding points within the small bowel.AIM To develop and validate a deep learning framework integrating CNNs with long short-term memory(LSTM)networks to enhance the automated detection and precise localization of SIB.METHODS This study employed two datasets for automated bleeding detection:One from Cheng Kung University,consisting of white light imaging images from 100 patients obtained via PillCamTMSB 3 CE,and the Kvasir-Capsule Image dataset,which includes 47238 labeled images across 14 pathological categories.Nineteen continuous picture sequences were recovered,comprising 3806 bleeding photos and 3275 non-bleeding images.RESULTS Data augmentation was implemented,utilizing CNNs for feature extraction,succeeded by long short-term memory networks for prediction.The CNN model attained an accuracy of 98.6%for 10 categories and 96.7%for 2 categories.Findings demonstrate that CNN-LSTM models exhibit superior performance with expanded category sets.CONCLUSION These findings underscore the capability of deep learning models to enhance the accuracy and efficiency of CEbased gastrointestinal bleeding diagnosis,hence facilitating improved clinical decision-making. 展开更多
关键词 Capsule endoscopy Small intestinal bleeding Convolutional neural networks Long short-term memory networks Temporal modeling
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Load-measurement method for floating offshore wind turbines based on a long short-term memory (LSTM) neural network 认领 引用 被引量：1
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作者 Yonggang LIN Xiangheng FENG +1 位作者 Hongwei LIU Yong SUN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第5期456-470,共15页
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
关键词 Floating offshore wind turbine(FOWT) Long short-term memory(LSTM)neural network Machine learning technique Load measurement Hybrid-scale model test
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Road pavement performance prediction using a time series long short-term memory (LSTM) model 认领 引用 被引量：3
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作者 Chuanchuan HOU Huan WANG +1 位作者 Wei GUAN Jun CHEN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第5期424-437,共14页
Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict... Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict key performance indicators(PIs)of pavement,namely the international roughness index(IRI)and rutting depth(RD).Subsequently,we propose a comprehensive performance indicator for the pavement quality index(PQI),which leverages the highway performance assessment standard method,entropy weight method,and fuzzy comprehensive evaluation method.This indicator can evaluate the overall performance condition of the pavement.The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks,called MnRoad and RIOHTrack.Six variables are used as predictors,including temperature,precipitation,total traffic volume,asphalt surface layer thickness,pavement age,and maintenance condition.Furthermore,wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy.In comparison to a traditional autoregressive integrated moving average(ARIMAX)model,the proposed LSTM model performs better in terms of PI prediction and resiliency to noise.Finally,the overall prediction accuracy of our proposed performance indicator PQI is 93.8%. 展开更多
关键词 Asphalt pavement performance model International roughness index(IRI) Rutting depth(RD) Long short-term memory(LSTM)model Pavement management system
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Real-time yield prediction in microchannel gas-liquid sulfonation via augmented convolutional long short-term memory-based soft measurement 认领 引用
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作者 Yingjin Wang Yingxin Mu +3 位作者 Shaokui Fu Muxuan Qin Wenjin Zhou Wei Zhang 《ENGINEERING Chemical Engineering》 SCIE EI CAS CSCD 2026年第3期1-12,共12页
Real-time monitoring of gas-liquid sulfonation in microchannel reactors is challenging due to complex internal spatiotemporal dynamics and limited data availability,despite the reactors’excellent heat and mass transf... Real-time monitoring of gas-liquid sulfonation in microchannel reactors is challenging due to complex internal spatiotemporal dynamics and limited data availability,despite the reactors’excellent heat and mass transfer properties.Therefore,this study proposes a deep learning-based measurement method that directly extracts key spatiotemporal information from reaction image sequences within microchannels,enabling accurate prediction of the yield level of sodiumα-olefin sulfonate products.The core of the framework is a convolutional long short-term memory network and combines a TimeDistributed module to efficiently capture and analyze dynamic visual features.To address the issue of data sparsity in experimental studies,we developed a novel frame sampling temporal image augmentation strategy that significantly improves the temporal learning efficiency of the model by mining microscopic dynamic changes under macroscopic stable conditions.On the experimental data set,the augmented convolutional long short-term memory network model achieved an average accuracy of up to 97.44%,outperforming the model without augmentation by 19.66%and a traditional convolutional neural network by 9.94%.These results demonstrate that the proposed method is a robust and effective tool for monitoring microchannel gas-liquid sulfonation,paving the way for intelligent,data-driven control of complex micro-chemical processes. 展开更多
关键词 convolutional long short-term memory network microchannel reactor soft measurement
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Data-Driven Method for Predicting Remaining Useful Life of Bearings Based on Multi-Layer Perception Neural Network and Bidirectional Long Short-Term Memory Network 认领 引用
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作者 Yongfeng Tai Xingyu Yan +3 位作者 Xiangyi Geng Lin Mu Mingshun Jiang Faye Zhang 《Structural Durability & Health Monitoring》 EI 2025年第2期365-383,共19页
The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through acceler... The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through accelerated life testing.In the absence of lifetime data,the hidden long-term correlation between performance degradation data is challenging to mine effectively,which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method.To address this problem,a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed.Firstly,a nonlinear health indicator(HI)calculation method based on kernel principal component analysis(KPCA)and exponential weighted moving average(EWMA)is designed.Then,using the raw vibration data and HI,a multi-layer perceptron(MLP)neural network is trained to further calculate the HI of the online bearing in real time.Furthermore,The bidirectional long short-term memory model(BiLSTM)optimized by particle swarm optimization(PSO)is used to mine the time series features of HI and predict the remaining service life.Performance verification experiments and comparative experiments are carried out on the XJTU-SY bearing open dataset.The research results indicate that this method has an excellent ability to predict future HI and remaining life. 展开更多
关键词 Remaining useful life prediction rolling bearing health indicator construction multilayer perceptron bidirectional long short-term memory network
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Optimizing Stock Market Prediction Using Long Short-Term Memory Networks 认领 引用
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作者 Nadia Afrin Ritu Samsun Nahar Khandakar +1 位作者 Md. Masum Bhuiyan Md. Imdadul Islam 《Journal of Computer and Communications》 2025年第2期207-222,共16页
Deep learning plays a vital role in real-life applications, for example object identification, human face recognition, speech recognition, biometrics identification, and short and long-term forecasting of data. The ma... Deep learning plays a vital role in real-life applications, for example object identification, human face recognition, speech recognition, biometrics identification, and short and long-term forecasting of data. The main objective of our work is to predict the market performance of the Dhaka Stock Exchange (DSE) on day closing price using different Deep Learning techniques. In this study, we have used the LSTM (Long Short-Term Memory) network to forecast the data of DSE for the convenience of shareholders. We have enforced LSTM networks to train data as well as forecast the future time series that has differentiated with test data. We have computed the Root Mean Square Error (RMSE) value to scrutinize the error between the forecasted value and test data that diminished the error by updating the LSTM networks. As a consequence of the renovation of the network, the LSTM network provides tremendous performance which outperformed the existing works to predict stock market prices. 展开更多
关键词 Long Short-Term Memory (LSTM) Stock Market Prediction Time Series Analysis Deep Learning
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Recognition and classification of microseismic signals based on Bayesian-optimized CNN-LSTM neural network 认领 引用
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作者 WU Yang LIU Jian-feng +4 位作者 WANG Chun-ping LIU Jun-jie JI Zhen-xing TIAN Cheng-yu XUE Fu-jun 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第6期2762-2787,共26页
Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering.This study developed a"S... Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering.This study developed a"Surface+Underground"microseismic intelligent monitoring system to evaluate dynamic disaster risks during construction at the Beishan High-level Radioactive Waste Geological Disposal Laboratory in China.The datasets of four typical one-dimensional time-domain microseismic signals,including rock fracture,blasting,TBM tunneling,and drilling,were constructed,and the BO-CNN-LSTM model is developed to identify and classify these signals..Based on the classification results,the typical time-frequency domain characteristics of the four types of signals are analyzed.The classification results of BO-CNN-LSTM,CNN,LSTM and CNN-LSTM models show that the recognition accuracy of the four models is 98.0%,85.7%,66.7%and 91.0%,respectively.Among all types,the four models demonstrate the highest effectiveness in identifying rock fracture and borehole signals.The findings further confirm that the BO-CNN-LSTM model efficiently recognizes and extracts microseismic signal features,demonstrating superior performance and stability in the classification task.Finally,the study suggests several future research directions,particularly in the areas of raw signal denoising,and automation of feature extraction. 展开更多
关键词 deep ground engineering microseismic monitoring signal classification neural network Bayesian optimization convolutional neural network(CNN) long short-term memory network(LSTM) BO-CNN-LSTM model
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Model predictive control based on LSTM neural network for maglev vehicle’suspension system 认领 引用
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作者 Mengjuan Liu Han Wu +3 位作者 Xin Liang Jiali Liu Xiaohui Zeng Kaixuan Hu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期504-517,共14页
To improve the suspension performance of high-speed maglev vehicles under complex external disturbance,a composite model predictive control(MPC)algorithm based on a neural network is proposed.Firstly,the nonlinear dyn... To improve the suspension performance of high-speed maglev vehicles under complex external disturbance,a composite model predictive control(MPC)algorithm based on a neural network is proposed.Firstly,the nonlinear dynamic response prediction model is constructed utilizing the long short-term memory(LSTM)neural network,and this model is trained by machine learning.Subsequently,a rolling optimization controller of the MPC algorithm is designed according to the vehicle suspension system’s prediction model and the suspension target.To compensate for the error of the prediction model resulting from changes in the control algorithm,a composite MPC algorithm is devised by combining both the proportionalintegral-derivative(PID)algorithm and the MPC algorithm.This composite approach enables the suspension system to switch the selection of control algorithms in the suspension system according to the prediction error.Finally,the effectiveness of the composite MPC algorithm is verified by simulation and experiment.The results show that the prediction model based on the LSTM neural network can effectively predict the future dynamic response of the vehicle.Moreover,the proposed MPC algorithm can effectively suppress the suspension gap fluctuation in the high-speed maglev vehicle,thereby fostering improved stability in the suspension system. 展开更多
关键词 Maglev vehicle Suspension system Model predictive control Long short-term memory neural network
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Coal burst spatio‑temporal prediction method based on bidirectional long short‑term memory network 认领 引用 被引量：1
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作者 Xu Yang Yapeng Liu +4 位作者 Anye Cao Yaoqi Liu Changbin Wang Weiwei Zhao Qiang Niu 《International Journal of Coal Science & Technology》 SCIE EI CAS CSCD 2025年第1期228-245,共18页
The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production,and it has become a challenging task to enhance the accuracy of coal burst disaster predic... The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production,and it has become a challenging task to enhance the accuracy of coal burst disaster prediction.To address the issue of insufficient exploration of the spatio-temporal characteristic of microseismic data and the challenging selection of the optimal time window size in spatio-temporal prediction,this paper integrates deep learning methods and theory to propose a novel coal burst spatio-temporal prediction method based on Bidirectional Long Short-Term Memory(Bi-LSTM)network.The method involves three main modules,including microseismic spatio-temporal characteristic indicators construction,temporal prediction model,and spatial prediction model.To validate the effectiveness of the proposed method,engineering application tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia,focusing on 13 high-energy microseismic events with energy levels greater than 105 J.In terms of temporal prediction,the analysis indicates that the temporal prediction results consist of 10 strong predictions and 3 medium predictions,and there is no false alarm detected throughout the entire testing period.Moreover,compared to the traditional threshold-based coal burst temporal prediction method,the accuracy of the proposed method is increased by 38.5%.In terms of spatial prediction,the distribution of spatial prediction results for high-energy events comprises 6 strong hazard predictions,3 medium hazard predictions,and 4 weak hazard predictions. 展开更多
关键词 Coal burst Spatio-temporal prediction Microseismic spatio-temporal characteristic indicators Bidirectional long short-term memory network
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Hybrid deep learning model with VMD-BiLSTM-GRU networks for short-term traffic flow prediction 认领 引用 被引量：1
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作者 Changxi Ma Yanming Hu Xuecai Xu 《Data Science and Management》 EI CSCD 2025年第3期257-269,共13页
Accelerating urbanization and the rapid development of intelligent transportation systems have rendered shortterm traffic flow prediction an important research field.Accurate prediction of traffic flow is beneficial f... Accelerating urbanization and the rapid development of intelligent transportation systems have rendered shortterm traffic flow prediction an important research field.Accurate prediction of traffic flow is beneficial for the optimization of traffic planning,improvement of road utilization,reduction of traffic congestion,and reduction in the incidence of traffic accidents.However,data pertaining to traffic flow are typically influenced by a multitude of factors,resulting in data that exhibit a considerable degree of nonlinearity and complexity.To address the issue of noise in raw traffic flow data,this study proposes a hybrid model that combines variational mode decomposition(VMD),a bidirectional long short-term memory network(BiLSTM),and a gated recurrent unit(GRU)for short-term traffic flow prediction.To validate the effectiveness of the model,an experimental validation was conducted based on traffic flow data from UK highways,and the performance of the model was compared with common benchmark models.The experimental results demonstrate that the proposed method yields superior prediction results in terms of mean absolute error,coefficient of determination,and root-mean-square error compared to existing prediction techniques,thereby substantiating its efficacy in short-term traffic flow prediction. 展开更多
关键词 Deep learning Traffic flow prediction Variational mode decomposition Bi-directional long short-term memory networks Gated recurrent units
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Research on Ultra-Short-Term Photovoltaic Power Forecasting Based on Parallel Architecture TCN-BiLSTM with Temporal-Spatial Attention Mechanism 认领 引用
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作者 Hongbo Sun Xingyu Jiang +4 位作者 Wenyao Sun Yi Zhao Jifeng Cheng Xiaoyi Qian Guo Wang 《Energy Engineering》 EI 2026年第4期303-320,共18页
The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback ... The accuracy of photovoltaic(PV)power prediction is significantly influenced by meteorological and environmental factors.To enhance ultra-short-term forecasting precision,this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory(TCN-BiLSTM)architecture incorporating a spatiotemporal attention mechanism.Firstly,during data preprocessing,the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients.Subsequently,a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term periodic patterns,with spatiotemporal attention dynamically weighting spatiotemporal dependencies.Finally,Shapley Additive explanations(SHAP)additive analysis quantifies feature contribution rates and provides optimization feedback to the model.Validation using operational data from a PV power station in Northeast China demonstrates that compared to conventional deep learning models,the proposed method achieves a 17.6%reduction in root mean square error(RMSE),a 5.4%decrease in training time consumption,and a 4.78%improvement in continuous ranked probability score(CRPS),exhibiting significant advantages in both prediction accuracy and generalization capability.This approach enhances the application effectiveness of ultra-short-term PV power forecasting while simultaneously improving prediction accuracy and computational efficiency. 展开更多
关键词 Ultra-short-term forecasting temporal convolutional network bidirectional long short-term memory parallel dual-stream architecture temporal-spatial attention SHAP contribution analysis
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Long Short-Term Memory Recurrent Neural Network-Based Acoustic Model Using Connectionist Temporal Classification on a Large-Scale Training Corpus 认领 引用 被引量：10
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作者 Donghyun Lee Minkyu Lim +4 位作者 Hosung Park Yoseb Kang Jeong-Sik Park Gil-Jin Jang Ji-Hwan Kim 《China Communications》 SCIE CSCD 2017年第9期23-31,共9页
A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a force... A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method. 展开更多
关键词 acoustic model connectionisttemporal classification large-scale trainingcorpus long short-term memory recurrentneural network
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Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis 认领 引用 被引量：14
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作者 Shanwei Xiong Li Zhou +1 位作者 Yiyang Dai Xu Ji 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2023年第4期1-14,共14页
A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively ... A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years, methods based on deep neural networks have made significant breakthroughs, and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore, we propose a fusion deeplearning algorithm based on a fully convolutional neural network(FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory(LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model, aimed at highlighting the importance of features, which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process, our proposed model exhibits impressive performance, demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis. 展开更多
关键词 Safety Fault diagnosis Process systems Long short-term memory Attention mechanism Neural networks
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A forecasting model for wave heights based on a long short-term memory neural network 认领 引用 被引量：11
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作者 Song Gao Juan Huang +3 位作者 Yaru Li Guiyan Liu Fan Bi Zhipeng Bai 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2021年第1期62-69,共8页
To explore new operational forecasting methods of waves,a forecasting model for wave heights at three stations in the Bohai Sea has been developed.This model is based on long short-term memory(LSTM)neural network with... To explore new operational forecasting methods of waves,a forecasting model for wave heights at three stations in the Bohai Sea has been developed.This model is based on long short-term memory(LSTM)neural network with sea surface wind and wave heights as training samples.The prediction performance of the model is evaluated,and the error analysis shows that when using the same set of numerically predicted sea surface wind as input,the prediction error produced by the proposed LSTM model at Sta.N01 is 20%,18%and 23%lower than the conventional numerical wave models in terms of the total root mean square error(RMSE),scatter index(SI)and mean absolute error(MAE),respectively.Particularly,for significant wave height in the range of 3–5 m,the prediction accuracy of the LSTM model is improved the most remarkably,with RMSE,SI and MAE all decreasing by 24%.It is also evident that the numbers of hidden neurons,the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy.However,the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used.The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training.Overall,long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting. 展开更多
关键词 long short-term memory marine forecast neural network significant wave height
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Landslide displacement prediction based on optimized empirical mode decomposition and deep bidirectional long short-term memory network 认领 引用 被引量：7
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作者 ZHANG Ming-yue HAN Yang +1 位作者 YANG Ping WANG Cong-ling 《Journal of Mountain Science》 SCIE CSCD 2023年第3期637-656,共20页
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
关键词 Landslide displacement Empirical mode decomposition Soft screening stop criteria Deep bidirectional long short-term memory neural network Xintan landslide Bazimen landslide
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Dynamic prediction of landslide displacement using singular spectrum analysis and stack long short-term memory network 认领 引用 被引量：4
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作者 LI Li-min Zhang Ming-yue WEN Zong-zhou 《Journal of Mountain Science》 SCIE CSCD 2021年第10期2597-2611,共15页
An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models... An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models, this paper proposes a dynamic prediction model of landslide displacement based on singular spectrum analysis(SSA) and stack long short-term memory(SLSTM) network. The SSA is used to decompose the landslide accumulated displacement time series data into trend term and periodic term displacement subsequences. A cubic polynomial function is used to predict the trend term displacement subsequence, and the SLSTM neural network is used to predict the periodic term displacement subsequence. At the same time, the Bayesian optimization algorithm is used to determine that the SLSTM network input sequence length is 12 and the number of hidden layer nodes is 18. The SLSTM network is updated by adding predicted values to the training set to achieve dynamic displacement prediction. Finally, the accumulated landslide displacement is obtained by superimposing the predicted value of each displacement subsequence. The proposed model was verified on the Xintan landslide in Hubei Province, China. The results show that when predicting the displacement of the periodic term, the SLSTM network has higher prediction accuracy than the support vector machine(SVM) and auto regressive integrated moving average(ARIMA). The mean relative error(MRE) is reduced by 4.099% and 3.548% respectively, while the root mean square error(RMSE) is reduced by 5.830 mm and 3.854 mm respectively. It is concluded that the SLSTM network model can better simulate the dynamic characteristics of landslides. 展开更多
关键词 Landslide Singular spectrum analysis Stack long short-term memory network Dynamic displacement prediction
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Modeling injection-induced fault slip using long short-term memory networks 认领 引用 被引量：2
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作者 Utkarsh Mital Mengsu Hu +2 位作者 Yves Guglielmi James Brown Jonny Rutqvist 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第11期4354-4368,共15页
Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections an... Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections and geologic disposal of nuclear waste.Such activities are expected to rise in the future making it necessary to assess their short-and long-term safety.Here,a new machine learning(ML)approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed.The focus is on fault behavior near the injection borehole.To capture the temporal dependencies in the data,long short-term memory(LSTM)networks are utilized.To prevent error accumulation within the forecast window,four critical measures to train a robust LSTM model for predicting fault response are highlighted:(i)setting an appropriate value of LSTM lag,(ii)calibrating the LSTM cell dimension,(iii)learning rate reduction during weight optimization,and(iv)not adopting an independent injection cycle as a validation set.Several numerical experiments were conducted,which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection.The model also captured the decay in pressure and displacement during the injection shut-in period.Further,the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated,which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections. 展开更多
关键词 Machine learning Long short-term memory networks Fault Fluid injection
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Navigation jamming signal recognition based on long short-term memory neural networks 认领 引用 被引量：3
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作者 FU Dong LI Xiangjun +2 位作者 MOU Weihua MA Ming OU Gang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2022年第4期835-844,共10页
This paper introduces the time-frequency analyzed long short-term memory(TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System(GNSS) receiver. The method introduces ... This paper introduces the time-frequency analyzed long short-term memory(TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System(GNSS) receiver. The method introduces the long shortterm memory(LSTM) neural network into the recognition algorithm and combines the time-frequency(TF) analysis for signal preprocessing. Five kinds of navigation jamming signals including white Gaussian noise(WGN), pulse jamming, sweep jamming, audio jamming, and spread spectrum jamming are used as input for training and recognition. Since the signal parameters and quantity are unknown in the actual scenario, this work builds a data set containing multiple kinds and parameters jamming to train the TF-LSTM. The performance of this method is evaluated by simulations and experiments. The method has higher recognition accuracy and better robustness than the existing methods, such as LSTM and the convolutional neural network(CNN). 展开更多
关键词 satellite navigation jamming recognition time-frequency(TF)analysis long short-term memory(LSTM)
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Ventilation System Heating Demand Forecasting Based on Long Short-Term Memory Network 认领 引用 被引量：2
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作者 ZHANG Zhanluo ZHANG Zhinan +1 位作者 EIKEVIK Trygve Magne SMITT Silje Marie 《Journal of Shanghai Jiaotong university(Science)》 EI 2021年第2期129-137,共9页
Load forecasting can increase the efficiency of modern energy systems with built-in measuring systerms by providing a more accurate peak power shaving performance and thus more reliable control.An analysis of an integ... Load forecasting can increase the efficiency of modern energy systems with built-in measuring systerms by providing a more accurate peak power shaving performance and thus more reliable control.An analysis of an integrated CO2 heat pump and chiller system with a hot water storage system is presented in this paper.Drastic power fluctuations,which can be reduced with load forecasting,are found in historical operation records.A model that aims to forecast the ventilation system heating demand is thus established on the basis of a long short-term memory(LSTM)network.The model can successfully forecast the one hour ahead power using records of the past 48h of the system operation data and the ambient temperature.The mean absolute percentage error(MAPE)of the forecast results of the LSTM-based model is 10.70%,which is respectively 2.2%and 7.25%better than the MAPEs of the forecast results of the support vector regression based and persistence method based models. 展开更多
关键词 ventilation syster load forecasting long short-term memory(LSTM) walk-forward forecasting
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