Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast ele...Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and complex.This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)network.Leveraging a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear relationships.The bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal direction.This design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and robustness.Compared to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these benchmarks.These results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive cities.By integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.展开更多
Nowadays,Internet has become an indispensable part of daily life and is used in many fields.Due to the large amount of Internet traffic,computers are subject to various security threats,which may cause serious economi...Nowadays,Internet has become an indispensable part of daily life and is used in many fields.Due to the large amount of Internet traffic,computers are subject to various security threats,which may cause serious economic losses and even endanger national security.It is hoped that an effective security method can systematically classify intrusion data in order to avoid leakage of important data or misuse of data.As machine learning technology matures,deep learning is widely used in various industries.Combining deep learning with network security and intrusion detection is the current trend.In this paper,the problem of data classification in intrusion detection system is studied.We propose an intrusion detection model based on stack bidirectional long short-term memory(LSTM),introduce stack bidirectional LSTM into the field of intrusion detection and apply it to the intrusion detection.In order to determine the appropriate parameters and structure of stack bidirectional LSTM network,we have carried out experiments on various network structures and parameters and analyzed the experimental results.The classic KDD Cup’1999 dataset was selected for experiments so that we can obtain convincing and comparable results.Experimental results derived from the KDD Cup’1999 dataset show that the network with three hidden layers containing 80 LSTM cells is superior to other algorithms in computational cost and detection performance due to stack bidirectional LSTM model’s ability to review time and correlate with connected records continuously.The experiment shows the effectiveness of stack bidirectional LSTM network in intrusion detection.展开更多
Accurate precipitation forecasting is crucial for mitigating the impacts of ex-treme weather events and enhancing disaster preparedness.This study evalu-ates the performance of Long Short-Term Memory and Bidirectional...Accurate precipitation forecasting is crucial for mitigating the impacts of ex-treme weather events and enhancing disaster preparedness.This study evalu-ates the performance of Long Short-Term Memory and Bidirectional LSTM models in predicting hourly precipitation in Dar es Salaam using a multivariate time-series approach.The dataset consists of temperature,pressure,U-wind,V-wind,and precipitation,preprocessed to handle missing values and normal-ized to improve model performance.Performance metrics indicate that BiLSTM outperforms LSTM,achieving lower Mean Absolute Error and Root Mean Squared Error by 6.4%and 6.5%,respectively along with improved threshold scores.It demonstrated better overall prediction accuracy.It also im-proves moderate precipitation detection(TS3.0)by 16.9%compared to LSTM.These results highlight the advantage of bidirectional processing in capturing complex atmospheric patterns,making BiLSTM a more effective approach for precipitation forecasting.The findings contribute to the development of im-proved deep learning models for early warning systems and climate risk man-agement.展开更多
The increasing fluency of advanced language models,such as GPT-3.5,GPT-4,and the recently introduced DeepSeek,challenges the ability to distinguish between human-authored and AI-generated academic writing.This situati...The increasing fluency of advanced language models,such as GPT-3.5,GPT-4,and the recently introduced DeepSeek,challenges the ability to distinguish between human-authored and AI-generated academic writing.This situation is raising significant concerns regarding the integrity and authenticity of academic work.In light of the above,the current research evaluates the effectiveness of Bidirectional Long Short-TermMemory(BiLSTM)networks enhanced with pre-trained GloVe(Global Vectors for Word Representation)embeddings to detect AIgenerated scientific Abstracts drawn from the AI-GA(Artificial Intelligence Generated Abstracts)dataset.Two core BiLSTM variants were assessed:a single-layer approach and a dual-layer design,each tested under static or adaptive embeddings.The single-layer model achieved nearly 97%accuracy with trainable GloVe,occasionally surpassing the deeper model.Despite these gains,neither configuration fully matched the 98.7%benchmark set by an earlier LSTMWord2Vec pipeline.Some runs were over-fitted when embeddings were fine-tuned,whereas static embeddings offered a slightly lower yet stable accuracy of around 96%.This lingering gap reinforces a key ethical and procedural concern:relying solely on automated tools,such as Turnitin’s AI-detection features,to penalize individuals’risks and unjust outcomes.Misclassifications,whether legitimate work is misread as AI-generated or engineered text,evade detection,demonstrating that these classifiers should not stand as the sole arbiters of authenticity.Amore comprehensive approach is warranted,one which weaves model outputs into a systematic process supported by expert judgment and institutional guidelines designed to protect originality.展开更多
In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mo...In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.展开更多
After the outbreak of COVID-19,the global economy entered a deep freeze.This observation is supported by the Volatility Index(VIX),which reflects the market risk expected by investors.In the current study,we predicted...After the outbreak of COVID-19,the global economy entered a deep freeze.This observation is supported by the Volatility Index(VIX),which reflects the market risk expected by investors.In the current study,we predicted the VIX using variables obtained fromthe sentiment analysis of data on Twitter posts related to the keyword“COVID-19,”using a model integrating the bidirectional long-term memory(BiLSTM),autoregressive integrated moving average(ARIMA)algorithm,and generalized autoregressive conditional heteroskedasticity(GARCH)model.The Linguistic Inquiry and Word Count(LIWC)program and Valence Aware Dictionary for Sentiment Reasoning(VADER)model were utilized as sentiment analysis methods.The results revealed that during COVID-19,the proposed integrated model,which trained both the Twitter sentiment values and historical VIX values,presented better results in forecasting the VIX in time-series regression and direction prediction than those of the other existing models.展开更多
Botnets often use domain generation algorithms(DGA)to connect to a command and control(C2)server,which enables the compromised hosts connect to the C2 server for accessing many domains.The detection of DGA domains is ...Botnets often use domain generation algorithms(DGA)to connect to a command and control(C2)server,which enables the compromised hosts connect to the C2 server for accessing many domains.The detection of DGA domains is critical for blocking the C2 server,and for identifying the compromised hosts as well.However,the detection is difficult,because some DGA domain names look normal.Much of the previous work based on statistical analysis of machine learning relies on manual features and contextual information,which causes long response time and cannot be used for real-time detection.In addition,when a new family of DGA appears,the classifier has to be re-trained from the very beginning.This paper presents a deep learning approach based on bidirectional long short-term memory(Bi-LSTM)model for DGA domain detection.The classifier can extract features without the need for manual feature extraction,and the trainable model can effectively deal with new unknown DGA family members.In addition,the proposed model only needs the domain name without any additional context information.All domain names are preprocessed by bigram and the length of each processed domain name is set as a value longer than the most samples.Bidirectional LSTM model receives the encoded data and returns labels to check whether domain names are normal or not.Experiments show that our model outperforms state-of-the-art approaches and is able to detect new DGA families reliably.展开更多
Accurate prediction of transient nitrogen oxides(NOx)emissions from diesel vehicles is essential for precise emission inventories and effective pollution control but challenged by data nonlinearity and dynamic operati...Accurate prediction of transient nitrogen oxides(NOx)emissions from diesel vehicles is essential for precise emission inventories and effective pollution control but challenged by data nonlinearity and dynamic operating conditions.This study develops the Wavelet Decomposition(WD)-Parallel Spatiotemporal Attention-based Long Short-Term Memory(PSTALSTM)model,using real-world Portable Emission Measurement System(PEMS)and On-Board Diagnostics(OBD)data.WD preprocessing reduces emission data non-stationarity,generating more stable inputs.The PSTALSTM architecture,built upon Bidirectional Long Short-Term Memory(Bi-LSTM),incorporates a parallel attention mechanism to adaptively weight features and temporal segments,effectively capturing spatiotemporal correlations within the emission data.Validation with on-road test data demonstrates WD-PSTALSTM’s superior performance over existing models.It achieves reductions exceeding 20%in mean absolute error(MAE)and 15%in root mean square error(RMSE),significantly enhancing prediction accuracy.These results establish WD-PSTALSTM as an effective approach for forecasting transient diesel engine NOx emissions.The research provides valuable methodologies for emission prediction based on vehicle operational data,contributing to environmental pollution mitigation efforts.展开更多
为了减少视频异常事件检测过程中冗余帧对检测效果的影响,更好地利用视频中关键帧包含的有用信息,提出了一种结合双流膨胀卷积神经网络(Two-stream Inflated 3D ConvNets,I3D)模型和压缩-激励注意力机制多示例异常检测算法。首先,利用...为了减少视频异常事件检测过程中冗余帧对检测效果的影响,更好地利用视频中关键帧包含的有用信息,提出了一种结合双流膨胀卷积神经网络(Two-stream Inflated 3D ConvNets,I3D)模型和压缩-激励注意力机制多示例异常检测算法。首先,利用双流膨胀卷积神经网络提取视频时空特征;其次,通过双向长短期记忆(Bidirectional Long Short Term Memory,Bidirectional LSTM)神经网络获取视频特征长时序信息;再次,借助压缩-激励注意力机制分配特征权重;最后,通过多示例排序损失函数得到异常排序模型,并在排序损失函数中加入稀疏损失和平滑损失,更好地预测视频异常分数。实验表明,在公开数据集UCF-Crime上检测准确率达到了82.84%,高于基线模型7.43%。展开更多
As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the...As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the energy efficiency of this CPS.This paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two levels.The high-level establishes a parallel system first,which includes a real powertrain system and an artificial system.Then,the synthesized data from this parallel system is trained by a bidirectional LSTM network.The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)framework.PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules.Finally,real vehicle testing is implemented and relevant experiment data is collected and calibrated.Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.展开更多
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R195),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy demands.Within this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and complex.This study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)network.Leveraging a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear relationships.The bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal direction.This design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and robustness.Compared to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these benchmarks.These results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive cities.By integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.
基金This work was supported by Scientific Research Starting Project of SWPU[Zheng,D.,No.0202002131604]Major Science and Technology Project of Sichuan Province[Zheng,D.,No.8ZDZX0143]+1 种基金Ministry of Education Collaborative Education Project of China[Zheng,D.,No.952]Fundamental Research Project[Zheng,D.,Nos.549,550].
文摘Nowadays,Internet has become an indispensable part of daily life and is used in many fields.Due to the large amount of Internet traffic,computers are subject to various security threats,which may cause serious economic losses and even endanger national security.It is hoped that an effective security method can systematically classify intrusion data in order to avoid leakage of important data or misuse of data.As machine learning technology matures,deep learning is widely used in various industries.Combining deep learning with network security and intrusion detection is the current trend.In this paper,the problem of data classification in intrusion detection system is studied.We propose an intrusion detection model based on stack bidirectional long short-term memory(LSTM),introduce stack bidirectional LSTM into the field of intrusion detection and apply it to the intrusion detection.In order to determine the appropriate parameters and structure of stack bidirectional LSTM network,we have carried out experiments on various network structures and parameters and analyzed the experimental results.The classic KDD Cup’1999 dataset was selected for experiments so that we can obtain convincing and comparable results.Experimental results derived from the KDD Cup’1999 dataset show that the network with three hidden layers containing 80 LSTM cells is superior to other algorithms in computational cost and detection performance due to stack bidirectional LSTM model’s ability to review time and correlate with connected records continuously.The experiment shows the effectiveness of stack bidirectional LSTM network in intrusion detection.
文摘Accurate precipitation forecasting is crucial for mitigating the impacts of ex-treme weather events and enhancing disaster preparedness.This study evalu-ates the performance of Long Short-Term Memory and Bidirectional LSTM models in predicting hourly precipitation in Dar es Salaam using a multivariate time-series approach.The dataset consists of temperature,pressure,U-wind,V-wind,and precipitation,preprocessed to handle missing values and normal-ized to improve model performance.Performance metrics indicate that BiLSTM outperforms LSTM,achieving lower Mean Absolute Error and Root Mean Squared Error by 6.4%and 6.5%,respectively along with improved threshold scores.It demonstrated better overall prediction accuracy.It also im-proves moderate precipitation detection(TS3.0)by 16.9%compared to LSTM.These results highlight the advantage of bidirectional processing in capturing complex atmospheric patterns,making BiLSTM a more effective approach for precipitation forecasting.The findings contribute to the development of im-proved deep learning models for early warning systems and climate risk man-agement.
文摘The increasing fluency of advanced language models,such as GPT-3.5,GPT-4,and the recently introduced DeepSeek,challenges the ability to distinguish between human-authored and AI-generated academic writing.This situation is raising significant concerns regarding the integrity and authenticity of academic work.In light of the above,the current research evaluates the effectiveness of Bidirectional Long Short-TermMemory(BiLSTM)networks enhanced with pre-trained GloVe(Global Vectors for Word Representation)embeddings to detect AIgenerated scientific Abstracts drawn from the AI-GA(Artificial Intelligence Generated Abstracts)dataset.Two core BiLSTM variants were assessed:a single-layer approach and a dual-layer design,each tested under static or adaptive embeddings.The single-layer model achieved nearly 97%accuracy with trainable GloVe,occasionally surpassing the deeper model.Despite these gains,neither configuration fully matched the 98.7%benchmark set by an earlier LSTMWord2Vec pipeline.Some runs were over-fitted when embeddings were fine-tuned,whereas static embeddings offered a slightly lower yet stable accuracy of around 96%.This lingering gap reinforces a key ethical and procedural concern:relying solely on automated tools,such as Turnitin’s AI-detection features,to penalize individuals’risks and unjust outcomes.Misclassifications,whether legitimate work is misread as AI-generated or engineered text,evade detection,demonstrating that these classifiers should not stand as the sole arbiters of authenticity.Amore comprehensive approach is warranted,one which weaves model outputs into a systematic process supported by expert judgment and institutional guidelines designed to protect originality.
文摘In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.
基金This work was supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(NRF-2020R1A2C1014957).
文摘After the outbreak of COVID-19,the global economy entered a deep freeze.This observation is supported by the Volatility Index(VIX),which reflects the market risk expected by investors.In the current study,we predicted the VIX using variables obtained fromthe sentiment analysis of data on Twitter posts related to the keyword“COVID-19,”using a model integrating the bidirectional long-term memory(BiLSTM),autoregressive integrated moving average(ARIMA)algorithm,and generalized autoregressive conditional heteroskedasticity(GARCH)model.The Linguistic Inquiry and Word Count(LIWC)program and Valence Aware Dictionary for Sentiment Reasoning(VADER)model were utilized as sentiment analysis methods.The results revealed that during COVID-19,the proposed integrated model,which trained both the Twitter sentiment values and historical VIX values,presented better results in forecasting the VIX in time-series regression and direction prediction than those of the other existing models.
基金This work was supported by the National Natural Science Foundation of China(NSFC)under the grant(No.U1836102).
文摘Botnets often use domain generation algorithms(DGA)to connect to a command and control(C2)server,which enables the compromised hosts connect to the C2 server for accessing many domains.The detection of DGA domains is critical for blocking the C2 server,and for identifying the compromised hosts as well.However,the detection is difficult,because some DGA domain names look normal.Much of the previous work based on statistical analysis of machine learning relies on manual features and contextual information,which causes long response time and cannot be used for real-time detection.In addition,when a new family of DGA appears,the classifier has to be re-trained from the very beginning.This paper presents a deep learning approach based on bidirectional long short-term memory(Bi-LSTM)model for DGA domain detection.The classifier can extract features without the need for manual feature extraction,and the trainable model can effectively deal with new unknown DGA family members.In addition,the proposed model only needs the domain name without any additional context information.All domain names are preprocessed by bigram and the length of each processed domain name is set as a value longer than the most samples.Bidirectional LSTM model receives the encoded data and returns labels to check whether domain names are normal or not.Experiments show that our model outperforms state-of-the-art approaches and is able to detect new DGA families reliably.
基金supported by the Hainan Provincial Natural Science Foundation of China[125RC793].
文摘Accurate prediction of transient nitrogen oxides(NOx)emissions from diesel vehicles is essential for precise emission inventories and effective pollution control but challenged by data nonlinearity and dynamic operating conditions.This study develops the Wavelet Decomposition(WD)-Parallel Spatiotemporal Attention-based Long Short-Term Memory(PSTALSTM)model,using real-world Portable Emission Measurement System(PEMS)and On-Board Diagnostics(OBD)data.WD preprocessing reduces emission data non-stationarity,generating more stable inputs.The PSTALSTM architecture,built upon Bidirectional Long Short-Term Memory(Bi-LSTM),incorporates a parallel attention mechanism to adaptively weight features and temporal segments,effectively capturing spatiotemporal correlations within the emission data.Validation with on-road test data demonstrates WD-PSTALSTM’s superior performance over existing models.It achieves reductions exceeding 20%in mean absolute error(MAE)and 15%in root mean square error(RMSE),significantly enhancing prediction accuracy.These results establish WD-PSTALSTM as an effective approach for forecasting transient diesel engine NOx emissions.The research provides valuable methodologies for emission prediction based on vehicle operational data,contributing to environmental pollution mitigation efforts.
文摘为了减少视频异常事件检测过程中冗余帧对检测效果的影响,更好地利用视频中关键帧包含的有用信息,提出了一种结合双流膨胀卷积神经网络(Two-stream Inflated 3D ConvNets,I3D)模型和压缩-激励注意力机制多示例异常检测算法。首先,利用双流膨胀卷积神经网络提取视频时空特征;其次,通过双向长短期记忆(Bidirectional Long Short Term Memory,Bidirectional LSTM)神经网络获取视频特征长时序信息;再次,借助压缩-激励注意力机制分配特征权重;最后,通过多示例排序损失函数得到异常排序模型,并在排序损失函数中加入稀疏损失和平滑损失,更好地预测视频异常分数。实验表明,在公开数据集UCF-Crime上检测准确率达到了82.84%,高于基线模型7.43%。
基金supported in part by the National Natural Science Foundation of China(61533019,91720000)Beijing Municipal Science and Technology Commission(Z181100008918007)the Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles(pICRI-IACVq)
文摘As a complex and critical cyber-physical system(CPS),the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy.Energy management strategy(EMS)is playing a key role to improve the energy efficiency of this CPS.This paper presents a novel bidirectional long shortterm memory(LSTM)network based parallel reinforcement learning(PRL)approach to construct EMS for a hybrid tracked vehicle(HTV).This method contains two levels.The high-level establishes a parallel system first,which includes a real powertrain system and an artificial system.Then,the synthesized data from this parallel system is trained by a bidirectional LSTM network.The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning(RL)framework.PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules.Finally,real vehicle testing is implemented and relevant experiment data is collected and calibrated.Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.
