Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficient...Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.展开更多
This paper examines the effectiveness of the Differential autoregressive integrated moving average (ARIMA) model in comparison to the Long Short Term Memory (LSTM) neural network model for predicting Wordle user-repor...This paper examines the effectiveness of the Differential autoregressive integrated moving average (ARIMA) model in comparison to the Long Short Term Memory (LSTM) neural network model for predicting Wordle user-reported scores. The ARIMA and LSTM models were trained using Wordle data from Twitter between 7th January 2022 and 31st December 2022. User-reported scores were predicted using evaluation metrics such as MSE, RMSE, R2, and MAE. Various regression models, including XG-Boost and Random Forest, were used to conduct comparison experiments. The MSE, RMSE, R2, and MAE values for the ARIMA(0,1,1) and LSTM models are 0.000, 0.010, 0.998, and 0.006, and 0.000, 0.024, 0.987, and 0.013, respectively. The results indicate that the ARIMA model is more suitable for predicting Wordle user scores than the LSTM model.展开更多
Nitrogen(N)and potassium(K)are two key mineral nutrient elements involved in rice growth.Accurate diagnosis of N and K status is very important for the rational application of fertilizers at a specific rice growth sta...Nitrogen(N)and potassium(K)are two key mineral nutrient elements involved in rice growth.Accurate diagnosis of N and K status is very important for the rational application of fertilizers at a specific rice growth stage.Therefore,we propose a hybrid model for diagnosing rice nutrient levels at the early panicle initiation stage(EPIS),which combines a convolutional neural network(CNN)with an attention mechanism and a long short-term memory network(LSTM).The model was validated on a large set of sequential images collected by an unmanned aerial vehicle(UAV)from rice canopies at different growth stages during a two-year experiment.Compared with VGG16,AlexNet,GoogleNet,DenseNet,and inceptionV3,ResNet101 combined with LSTM obtained the highest average accuracy of 83.81%on the dataset of Huanghuazhan(HHZ,an indica cultivar).When tested on the datasets of HHZ and Xiushui 134(XS134,a japonica rice variety)in 2021,the ResNet101-LSTM model enhanced with the squeeze-and-excitation(SE)block achieved the highest accuracies of 85.38 and 88.38%,respectively.Through the cross-dataset method,the average accuracies on the HHZ and XS134 datasets tested in 2022 were 81.25 and 82.50%,respectively,showing a good generalization.Our proposed model works with the dynamic information of different rice growth stages and can efficiently diagnose different rice nutrient status levels at EPIS,which are helpful for making practical decisions regarding rational fertilization treatments at the panicle initiation stage.展开更多
Predicting traffic flow is a crucial component of an intelligent transportation system.Precisely monitoring and predicting traffic flow remains a challenging endeavor.However,existingmethods for predicting traffic flo...Predicting traffic flow is a crucial component of an intelligent transportation system.Precisely monitoring and predicting traffic flow remains a challenging endeavor.However,existingmethods for predicting traffic flow do not incorporate various external factors or consider the spatiotemporal correlation between spatially adjacent nodes,resulting in the loss of essential information and lower forecast performance.On the other hand,the availability of spatiotemporal data is limited.This research offers alternative spatiotemporal data with three specific features as input,vehicle type(5 types),holidays(3 types),and weather(10 conditions).In this study,the proposed model combines the advantages of the capability of convolutional(CNN)layers to extract valuable information and learn the internal representation of time-series data that can be interpreted as an image,as well as the efficiency of long short-term memory(LSTM)layers for identifying short-term and long-term dependencies.Our approach may utilize the heterogeneous spatiotemporal correlation features of the traffic flowdataset to deliver better performance traffic flow prediction than existing deep learning models.The research findings show that adding spatiotemporal feature data increases the forecast’s performance;weather by 25.85%,vehicle type by 23.70%,and holiday by 14.02%.展开更多
Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems(ITS).According to previous studies,it is found that the prediction effect of a single model i...Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems(ITS).According to previous studies,it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy.In order to provide persuasive passenger flow forecast data for ITS,a deep learning model considering the influencing factors is proposed in this paper.In view of the lack of objective analysis on the selection of influencing factors by predecessors,this paper uses analytic hierarchy processes(AHP)and one-way ANOVA analysis to scientifically select the factor of time characteristics,which classifies and gives weight to the hourly passenger flow through Duncan test.Then,combining the time weight,BILSTM based model considering the hourly travel characteristics factors is proposed.The model performance is verified through the inbound passenger flow of Ningbo rail transit.The proposed model is compared with many current mainstream deep learning algorithms,the effectiveness of the BILSTM model considering influencing factors is validated.Through comparison and analysis with various evaluation indicators and other deep learning models,the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968,and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%.展开更多
Stock market has a profound impact on the market economy,Hence,the prediction of future movement of stocks is of great significance to investors.Therefore,an efficient prediction system can solve this problem to a gre...Stock market has a profound impact on the market economy,Hence,the prediction of future movement of stocks is of great significance to investors.Therefore,an efficient prediction system can solve this problem to a great extent.In this paper,we used the stock price of Google Inc.as a prediction object,selected 3810 adjusted closing prices,and used long short-term memory(LSTM)method to predict the future price trend of the stock.We built a three-layer LSTM model and divided the entire data into a test set and a training set according to the ratio of 8 to 2.The final results show that while the LSTM model can predict the stock trend of Google Inc.very well,it cannot predict the specific price accurately.展开更多
A long short-term memory(LSTM)neural network has excellent learning ability applicable to time series of nuclear pulse signals.It can accurately estimate parameters associated with amplitude,time,and so on,in digitall...A long short-term memory(LSTM)neural network has excellent learning ability applicable to time series of nuclear pulse signals.It can accurately estimate parameters associated with amplitude,time,and so on,in digitally shaped nuclear pulse signals—especially signals from overlapping pulses.By learning the mapping relationship between Gaussian overlapping pulses after digital shaping and exponential pulses before shaping,the shaping parameters of the overlapping exponential nuclear pulses can be estimated using the LSTM model.Firstly,the Gaussian overlapping nuclear pulse(ONP)parameters which need to be estimated received Gaussian digital shaping treatment,after superposition by multiple exponential nuclear pulses.Secondly,a dataset containing multiple samples was produced,each containing a sequence of sample values from Gaussian ONP,after digital shaping,and a set of shaping parameters from exponential pulses before digital shaping.Thirdly,the Training Set in the dataset was used to train the LSTM model.From these datasets,the values sampled from the Gaussian ONP were used as the input data for the LSTM model,and the pulse parameters estimated by the current LSTM model were calculated by forward propagation.Next,the loss function was used to calculate the loss value between the network-estimated pulse parameters and the actual pulse parameters.Then,a gradient-based optimization algorithm was applied,to feedback the loss value and the gradient of the loss function to the neural network,to update the weight of the LSTM model,thereby achieving the purpose of training the network.Finally,the sampled value of the Gaussian ONP for which the shaping parameters needed to be estimated was used as the input data for the LSTM model.After this,the LSTM model produced the required nuclear pulse parameter set.In summary,experimental results showed that the proposed method overcame the defect of local convergence encountered in traditional methods and could accurately extract parameters from multiple,severely overlapping Gaussian pulses,to achieve optimal estimation of nuclear pulse parameters in the global sense.These results support the conclusion that this is a good method for estimating nuclear pulse parameters.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
The development of multimedia content has resulted in a massiveincrease in network traffic for video streaming. It demands such types ofsolutions that can be addressed to obtain the user’s Quality-of-Experience(QoE)....The development of multimedia content has resulted in a massiveincrease in network traffic for video streaming. It demands such types ofsolutions that can be addressed to obtain the user’s Quality-of-Experience(QoE). 360-degree videos have already taken up the user’s behavior by storm.However, the users only focus on the part of 360-degree videos, known as aviewport. Despite the immense hype, 360-degree videos convey a loathsomeside effect about viewport prediction, making viewers feel uncomfortablebecause user viewport needs to be pre-fetched in advance. Ideally, we canminimize the bandwidth consumption if we know what the user motionin advance. Looking into the problem definition, we propose an EncoderDecoder based Long-Short Term Memory (LSTM) model to more accuratelycapture the non-linear relationship between past and future viewport positions. This model takes the transforming data instead of taking the direct inputto predict the future user movement. Then, this prediction model is combinedwith a rate adaptation approach that assigns the bitrates to various tiles for360-degree video frames under a given network capacity. Hence, our proposedwork aims to facilitate improved system performance when QoE parametersare jointly optimized. Some experiments were carried out and compared withexisting work to prove the performance of the proposed model. Last but notleast, the experiments implementation of our proposed work provides highuser’s QoE than its competitors.展开更多
Finding the best model to predict the trend of stock prices is an issue that has always garnered attention,and it is also closely related to investors’investment dynamics.Even the commonly used autoregressive integra...Finding the best model to predict the trend of stock prices is an issue that has always garnered attention,and it is also closely related to investors’investment dynamics.Even the commonly used autoregressive integrated moving average(ARIMA),extreme gradient boosting(XGBoost),and long short-term memory(LSTM)have their own advantages and disadvantages.We use mean squared error(MSE)to judge the most suitable model for predicting Amazon’s stock price from many aspects and find that LSTM is the model with the best fitting effect and the closest to the real curve.However,the LSTM model still needs to improve in terms of performance so as to reduce the bias.We anticipate the discovery of more models that are apt for predicting stocks in the future.展开更多
Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales ...Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales data of a fresh food e-commerce enterprise as the logistics demand, analyzes the influence of time and meteorological factors on the demand, extracts the characteristic factors with greater influence, and proposes a logistics demand forecast scheme of fresh food e-commerce based on the Bi-LSTM model. The scheme is compared with other schemes based on the BP neural network and LSTM neural network models. The experimental results show that the Bi-LSTM model has good prediction performance on the problem of logistics demand prediction. This facilitates further research on some supply chain issues, such as business decision-making, inventory control, and logistics capacity planning.展开更多
Since the establishment of financial models for risk prediction,the measurement of volatility at risky market has improved,and its significance has also grown.For high-frequency financial data,the degree of investment...Since the establishment of financial models for risk prediction,the measurement of volatility at risky market has improved,and its significance has also grown.For high-frequency financial data,the degree of investment risk,which has always been the focus of attention,is measured by the variance of residual sequence obtained following model regression.By integrating the long short-term memory(LSTM)model with multiple generalized autoregressive conditional heteroscedasticity(GARCH)models,a new hybrid LSTM model is used to predict stock price volatility.In this paper,three GARCH models are used,and the model that can best fit the data is determined.展开更多
Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content.The extraction of encrypted traffic attributes and their...Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content.The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge.The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets,with the dataset’s imbalance significantly affecting the model’s performance.In the present study,a new model,referred to as UD-VLD(Unbalanced Dataset-VAE-LSTM-DRN),was proposed to address above problem.The proposed model is an encrypted traffic identification model for handling unbalanced datasets.The encoder of the variational autoencoder(VAE)is combined with the decoder and Long-short term Memory(LSTM)in UD-VLD model to realize the data enhancement processing of the original unbalanced datasets.The enhanced data is processed by transforming the deep residual network(DRN)to address neural network gradient-related issues.Subsequently,the data is classified and recognized.The UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique,thereby solving the processing problem for unbalanced datasets.The UD-VLD model was tested using the publicly available Tor dataset and VPN dataset.The UD-VLD model is evaluated against other comparative models in terms of accuracy,loss rate,precision,recall,F1-score,total time,and ROC curve.The results reveal that the UD-VLD model exhibits better performance in both binary and multi classification,being higher than other encrypted traffic recognition models that exist for unbalanced datasets.Furthermore,the evaluation performance indicates that the UD-VLD model effectivelymitigates the impact of unbalanced data on traffic classification.and can serve as a novel solution for encrypted traffic identification.展开更多
In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling struct...In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,theDensityPeakClustering(DPC)algorithmis used todetermine referential values of indicators forBRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.展开更多
密集蘑菇簇会严重影响蘑菇质量和自动采摘成功率。为避免形成超密集蘑菇簇,提出一种蘑菇生长状态时空预测算法,对蘑菇生长状态进行预测以指导提前疏蕾。该算法采用编码器-预测器框架,将历史序列图像转换为3D张量序列作为模型的输入;编...密集蘑菇簇会严重影响蘑菇质量和自动采摘成功率。为避免形成超密集蘑菇簇,提出一种蘑菇生长状态时空预测算法,对蘑菇生长状态进行预测以指导提前疏蕾。该算法采用编码器-预测器框架,将历史序列图像转换为3D张量序列作为模型的输入;编码器网络中将卷积和长短时记忆(Long short term memory, LSTM)网络融合实现对蘑菇生长的时空相关性特征的提取;在预测网络中加入扩散模型以解决预测图像的模糊问题;此外,在损失函数中增加了蘑菇面积差异损失函数来进一步减小预测蘑菇与实际蘑菇的形状和位置偏差。实验结果表明,本文算法峰值信噪比可达35.611 dB、多层级结构相似性为0.927、蘑菇预测准确性高达0.93,有效提高了蘑菇生长状态图像预测质量和精度,为食用菌生长预测提供了一种新思路。展开更多
基金supported by the Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology(Grant No.202202H)the National Key R&D Program of China(Grant No.2019YFB1600702)the National Natural Science Foundation of China(Grant Nos.51978600&51808336).
文摘Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.
文摘This paper examines the effectiveness of the Differential autoregressive integrated moving average (ARIMA) model in comparison to the Long Short Term Memory (LSTM) neural network model for predicting Wordle user-reported scores. The ARIMA and LSTM models were trained using Wordle data from Twitter between 7th January 2022 and 31st December 2022. User-reported scores were predicted using evaluation metrics such as MSE, RMSE, R2, and MAE. Various regression models, including XG-Boost and Random Forest, were used to conduct comparison experiments. The MSE, RMSE, R2, and MAE values for the ARIMA(0,1,1) and LSTM models are 0.000, 0.010, 0.998, and 0.006, and 0.000, 0.024, 0.987, and 0.013, respectively. The results indicate that the ARIMA model is more suitable for predicting Wordle user scores than the LSTM model.
基金supported by the National Key Research and Development Program of China(2022YFD2300700)the Open Project Program of State Key Laboratory of Rice Biology,China National Rice Research Institute(20210403)the Zhejiang“Ten Thousand Talents”Plan Science and Technology Innovation Leading Talent Project,China(2020R52035)。
文摘Nitrogen(N)and potassium(K)are two key mineral nutrient elements involved in rice growth.Accurate diagnosis of N and K status is very important for the rational application of fertilizers at a specific rice growth stage.Therefore,we propose a hybrid model for diagnosing rice nutrient levels at the early panicle initiation stage(EPIS),which combines a convolutional neural network(CNN)with an attention mechanism and a long short-term memory network(LSTM).The model was validated on a large set of sequential images collected by an unmanned aerial vehicle(UAV)from rice canopies at different growth stages during a two-year experiment.Compared with VGG16,AlexNet,GoogleNet,DenseNet,and inceptionV3,ResNet101 combined with LSTM obtained the highest average accuracy of 83.81%on the dataset of Huanghuazhan(HHZ,an indica cultivar).When tested on the datasets of HHZ and Xiushui 134(XS134,a japonica rice variety)in 2021,the ResNet101-LSTM model enhanced with the squeeze-and-excitation(SE)block achieved the highest accuracies of 85.38 and 88.38%,respectively.Through the cross-dataset method,the average accuracies on the HHZ and XS134 datasets tested in 2022 were 81.25 and 82.50%,respectively,showing a good generalization.Our proposed model works with the dynamic information of different rice growth stages and can efficiently diagnose different rice nutrient status levels at EPIS,which are helpful for making practical decisions regarding rational fertilization treatments at the panicle initiation stage.
基金Supported by Universitas Muhammadiyah Yogyakarta,Indonesia and Asia University,Taiwan.
文摘Predicting traffic flow is a crucial component of an intelligent transportation system.Precisely monitoring and predicting traffic flow remains a challenging endeavor.However,existingmethods for predicting traffic flow do not incorporate various external factors or consider the spatiotemporal correlation between spatially adjacent nodes,resulting in the loss of essential information and lower forecast performance.On the other hand,the availability of spatiotemporal data is limited.This research offers alternative spatiotemporal data with three specific features as input,vehicle type(5 types),holidays(3 types),and weather(10 conditions).In this study,the proposed model combines the advantages of the capability of convolutional(CNN)layers to extract valuable information and learn the internal representation of time-series data that can be interpreted as an image,as well as the efficiency of long short-term memory(LSTM)layers for identifying short-term and long-term dependencies.Our approach may utilize the heterogeneous spatiotemporal correlation features of the traffic flowdataset to deliver better performance traffic flow prediction than existing deep learning models.The research findings show that adding spatiotemporal feature data increases the forecast’s performance;weather by 25.85%,vehicle type by 23.70%,and holiday by 14.02%.
基金supported by the Program of Humanities and Social Science of Education Ministry of China(Grant No.20YJA630008)the Ningbo Natural Science Foundation of China(Grant No.202003N4142)+1 种基金the Natural Science Foundation of Zhejiang Province,China(Grant No.LY20G010004)the K.C.Wong Magna Fund in Ningbo University,China.
文摘Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems(ITS).According to previous studies,it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy.In order to provide persuasive passenger flow forecast data for ITS,a deep learning model considering the influencing factors is proposed in this paper.In view of the lack of objective analysis on the selection of influencing factors by predecessors,this paper uses analytic hierarchy processes(AHP)and one-way ANOVA analysis to scientifically select the factor of time characteristics,which classifies and gives weight to the hourly passenger flow through Duncan test.Then,combining the time weight,BILSTM based model considering the hourly travel characteristics factors is proposed.The model performance is verified through the inbound passenger flow of Ningbo rail transit.The proposed model is compared with many current mainstream deep learning algorithms,the effectiveness of the BILSTM model considering influencing factors is validated.Through comparison and analysis with various evaluation indicators and other deep learning models,the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968,and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%.
文摘Stock market has a profound impact on the market economy,Hence,the prediction of future movement of stocks is of great significance to investors.Therefore,an efficient prediction system can solve this problem to a great extent.In this paper,we used the stock price of Google Inc.as a prediction object,selected 3810 adjusted closing prices,and used long short-term memory(LSTM)method to predict the future price trend of the stock.We built a three-layer LSTM model and divided the entire data into a test set and a training set according to the ratio of 8 to 2.The final results show that while the LSTM model can predict the stock trend of Google Inc.very well,it cannot predict the specific price accurately.
基金supported by the National Natural Science Foundation of China(Nos.41774140 and 11675028)the Scientific Research Fund of Sichuan Provincial Education Department(No.18ZA0050)the Scientific Research Innovation Team of Chengdu University of Technology(No.10912-KYTD201701)
文摘A long short-term memory(LSTM)neural network has excellent learning ability applicable to time series of nuclear pulse signals.It can accurately estimate parameters associated with amplitude,time,and so on,in digitally shaped nuclear pulse signals—especially signals from overlapping pulses.By learning the mapping relationship between Gaussian overlapping pulses after digital shaping and exponential pulses before shaping,the shaping parameters of the overlapping exponential nuclear pulses can be estimated using the LSTM model.Firstly,the Gaussian overlapping nuclear pulse(ONP)parameters which need to be estimated received Gaussian digital shaping treatment,after superposition by multiple exponential nuclear pulses.Secondly,a dataset containing multiple samples was produced,each containing a sequence of sample values from Gaussian ONP,after digital shaping,and a set of shaping parameters from exponential pulses before digital shaping.Thirdly,the Training Set in the dataset was used to train the LSTM model.From these datasets,the values sampled from the Gaussian ONP were used as the input data for the LSTM model,and the pulse parameters estimated by the current LSTM model were calculated by forward propagation.Next,the loss function was used to calculate the loss value between the network-estimated pulse parameters and the actual pulse parameters.Then,a gradient-based optimization algorithm was applied,to feedback the loss value and the gradient of the loss function to the neural network,to update the weight of the LSTM model,thereby achieving the purpose of training the network.Finally,the sampled value of the Gaussian ONP for which the shaping parameters needed to be estimated was used as the input data for the LSTM model.After this,the LSTM model produced the required nuclear pulse parameter set.In summary,experimental results showed that the proposed method overcame the defect of local convergence encountered in traditional methods and could accurately extract parameters from multiple,severely overlapping Gaussian pulses,to achieve optimal estimation of nuclear pulse parameters in the global sense.These results support the conclusion that this is a good method for estimating nuclear pulse parameters.
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The development of multimedia content has resulted in a massiveincrease in network traffic for video streaming. It demands such types ofsolutions that can be addressed to obtain the user’s Quality-of-Experience(QoE). 360-degree videos have already taken up the user’s behavior by storm.However, the users only focus on the part of 360-degree videos, known as aviewport. Despite the immense hype, 360-degree videos convey a loathsomeside effect about viewport prediction, making viewers feel uncomfortablebecause user viewport needs to be pre-fetched in advance. Ideally, we canminimize the bandwidth consumption if we know what the user motionin advance. Looking into the problem definition, we propose an EncoderDecoder based Long-Short Term Memory (LSTM) model to more accuratelycapture the non-linear relationship between past and future viewport positions. This model takes the transforming data instead of taking the direct inputto predict the future user movement. Then, this prediction model is combinedwith a rate adaptation approach that assigns the bitrates to various tiles for360-degree video frames under a given network capacity. Hence, our proposedwork aims to facilitate improved system performance when QoE parametersare jointly optimized. Some experiments were carried out and compared withexisting work to prove the performance of the proposed model. Last but notleast, the experiments implementation of our proposed work provides highuser’s QoE than its competitors.
文摘Finding the best model to predict the trend of stock prices is an issue that has always garnered attention,and it is also closely related to investors’investment dynamics.Even the commonly used autoregressive integrated moving average(ARIMA),extreme gradient boosting(XGBoost),and long short-term memory(LSTM)have their own advantages and disadvantages.We use mean squared error(MSE)to judge the most suitable model for predicting Amazon’s stock price from many aspects and find that LSTM is the model with the best fitting effect and the closest to the real curve.However,the LSTM model still needs to improve in terms of performance so as to reduce the bias.We anticipate the discovery of more models that are apt for predicting stocks in the future.
文摘Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales data of a fresh food e-commerce enterprise as the logistics demand, analyzes the influence of time and meteorological factors on the demand, extracts the characteristic factors with greater influence, and proposes a logistics demand forecast scheme of fresh food e-commerce based on the Bi-LSTM model. The scheme is compared with other schemes based on the BP neural network and LSTM neural network models. The experimental results show that the Bi-LSTM model has good prediction performance on the problem of logistics demand prediction. This facilitates further research on some supply chain issues, such as business decision-making, inventory control, and logistics capacity planning.
文摘Since the establishment of financial models for risk prediction,the measurement of volatility at risky market has improved,and its significance has also grown.For high-frequency financial data,the degree of investment risk,which has always been the focus of attention,is measured by the variance of residual sequence obtained following model regression.By integrating the long short-term memory(LSTM)model with multiple generalized autoregressive conditional heteroscedasticity(GARCH)models,a new hybrid LSTM model is used to predict stock price volatility.In this paper,three GARCH models are used,and the model that can best fit the data is determined.
基金supported by the Fundamental Research Funds for Higher Education Institutions of Heilongjiang Province(145209126)the Heilongjiang Province Higher Education Teaching Reform Project under Grant No.SJGY20200770.
文摘Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content.The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge.The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets,with the dataset’s imbalance significantly affecting the model’s performance.In the present study,a new model,referred to as UD-VLD(Unbalanced Dataset-VAE-LSTM-DRN),was proposed to address above problem.The proposed model is an encrypted traffic identification model for handling unbalanced datasets.The encoder of the variational autoencoder(VAE)is combined with the decoder and Long-short term Memory(LSTM)in UD-VLD model to realize the data enhancement processing of the original unbalanced datasets.The enhanced data is processed by transforming the deep residual network(DRN)to address neural network gradient-related issues.Subsequently,the data is classified and recognized.The UD-VLD model integrates the related techniques of deep learning into the encrypted traffic recognition technique,thereby solving the processing problem for unbalanced datasets.The UD-VLD model was tested using the publicly available Tor dataset and VPN dataset.The UD-VLD model is evaluated against other comparative models in terms of accuracy,loss rate,precision,recall,F1-score,total time,and ROC curve.The results reveal that the UD-VLD model exhibits better performance in both binary and multi classification,being higher than other encrypted traffic recognition models that exist for unbalanced datasets.Furthermore,the evaluation performance indicates that the UD-VLD model effectivelymitigates the impact of unbalanced data on traffic classification.and can serve as a novel solution for encrypted traffic identification.
基金supported by the Natural Science Foundation of China underGrant 61833016 and 61873293the Shaanxi OutstandingYouth Science Foundation underGrant 2020JC-34the Shaanxi Science and Technology Innovation Team under Grant 2022TD-24.
文摘In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,theDensityPeakClustering(DPC)algorithmis used todetermine referential values of indicators forBRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.
文摘密集蘑菇簇会严重影响蘑菇质量和自动采摘成功率。为避免形成超密集蘑菇簇,提出一种蘑菇生长状态时空预测算法,对蘑菇生长状态进行预测以指导提前疏蕾。该算法采用编码器-预测器框架,将历史序列图像转换为3D张量序列作为模型的输入;编码器网络中将卷积和长短时记忆(Long short term memory, LSTM)网络融合实现对蘑菇生长的时空相关性特征的提取;在预测网络中加入扩散模型以解决预测图像的模糊问题;此外,在损失函数中增加了蘑菇面积差异损失函数来进一步减小预测蘑菇与实际蘑菇的形状和位置偏差。实验结果表明,本文算法峰值信噪比可达35.611 dB、多层级结构相似性为0.927、蘑菇预测准确性高达0.93,有效提高了蘑菇生长状态图像预测质量和精度,为食用菌生长预测提供了一种新思路。
