Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the origin...Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
This paper presents a comparative study of ARIMA and Neural Network AutoRegressive (NNAR) models for time series forecasting. The study focuses on simulated data generated using ARIMA(1, 1, 0) and applies both models ...This paper presents a comparative study of ARIMA and Neural Network AutoRegressive (NNAR) models for time series forecasting. The study focuses on simulated data generated using ARIMA(1, 1, 0) and applies both models for training and forecasting. Model performance is evaluated using MSE, AIC, and BIC. The models are further applied to neonatal mortality data from Saudi Arabia to assess their predictive capabilities. The results indicate that the NNAR model outperforms ARIMA in both training and forecasting.展开更多
Time series forecasting is essential for generating predictive insights across various domains, including healthcare, finance, and energy. This study focuses on forecasting patient health data by comparing the perform...Time series forecasting is essential for generating predictive insights across various domains, including healthcare, finance, and energy. This study focuses on forecasting patient health data by comparing the performance of traditional linear time series models, namely Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA, and Moving Average (MA) against neural network architectures. The primary goal is to evaluate the effectiveness of these models in predicting healthcare outcomes using patient records, specifically the Cancerpatient.xlsx dataset, which tracks variables such as patient age, symptoms, genetic risk factors, and environmental exposures over time. The proposed strategy involves training each model on historical patient data to predict age progression and other related health indicators, with performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. Our findings reveal that neural networks consistently outperform ARIMA and SARIMA by capturing non-linear patterns and complex temporal dependencies within the dataset, resulting in lower forecasting errors. This research highlights the potential of neural networks to enhance predictive accuracy in healthcare applications, supporting better resource allocation, patient monitoring, and long-term health outcome predictions.展开更多
Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread ap...Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread application, often encounter difficulties in handling the complexities of oil production data, which is characterized by non-linear patterns, skewed distributions, and the presence of outliers. To overcome these limitations, deep learning methods have emerged as more robust alternatives. However, while deep neural networks offer improved accuracy, they demand substantial amounts of data for effective training. Conversely, shallow networks with fewer layers lack the capacity to model complex data distributions adequately. To address these challenges, this study introduces a novel hybrid model called Transfer LSTM to GRU (TLTG), which combines the strengths of deep and shallow networks using transfer learning. The TLTG model integrates Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU) to enhance predictive accuracy while maintaining computational efficiency. Gaussian transformation is applied to the input data to reduce outliers and skewness, creating a more normal-like distribution. The proposed approach is validated on datasets from various wells in the Tahe oil field, China. Experimental results highlight the superior performance of the TLTG model, achieving 100% accuracy and faster prediction times (200 s) compared to eight other approaches, demonstrating its effectiveness and efficiency.展开更多
BACKGROUND Unicompartmental knee arthroplasty(UKA)has great advantages in the treatment of unicompartmental knee osteoarthritis,but its revision rate is higher than that of total knee arthroplasty.AIM To summarize and...BACKGROUND Unicompartmental knee arthroplasty(UKA)has great advantages in the treatment of unicompartmental knee osteoarthritis,but its revision rate is higher than that of total knee arthroplasty.AIM To summarize and analyse the causes of revision after UKA.METHODS This is a retrospective case series study in which the reasons for the first revision after UKA are summarized.We analysed the clinical symptoms,medical histories,laboratory test results,imaging examination results and treatment processes of the patients who underwent revision and summarized the reasons for primary revision after UKA.RESULTS A total of 13 patients,including 3 males and 10 females,underwent revision surgery after UKA.The average age of the included patients was 67.62 years.The prosthesis was used for 3 d to 72 months.The main reasons for revision after UKA were improper suturing of the surgical opening(1 patient),osteophytes(2 patients),intra-articular loose bodies(2 patients),tibial prosthesis loosening(2 patients),rheumatoid arthritis(1 patient),gasket dislocation(3 patients),anterior cruciate ligament injury(1 patient),and medial collateral ligament injury with residual bone cement(1 patient).CONCLUSION The causes of primary revision after UKA were gasket dislocation,osteophytes,intra-articular loose bodies and tibial prosthesis loosening.Avoidance of these factors may greatly reduce the rate of revision after UKA,improve patient satisfaction and reduce medical burden.展开更多
Historically,landslides have been the primary type of geological disaster worldwide.Generally,the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations.Moreover,the stab...Historically,landslides have been the primary type of geological disaster worldwide.Generally,the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations.Moreover,the stability of reservoir banks changes with the long-term dynamics of external disastercausing factors.Thus,assessing the time-varying reliability of reservoir landslides remains a challenge.In this paper,a machine learning(ML)based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction.This study systematically investigated the prediction performances of three ML algorithms,i.e.multilayer perceptron(MLP),convolutional neural network(CNN),and long short-term memory(LSTM).Additionally,the effects of the data quantity and data ratio on the predictive power of deep learning models are considered.The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides.The CNN model outperforms both the MLP and LSTM models in predicting the failure probability.Furthermore,selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.展开更多
Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal depende...Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.展开更多
To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)pre...To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention mechanism.Firstly,a traversal search is conducted through the traversal layer for finite parameters in the phase space.Then,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input layer.Finally,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction results.The model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space structure.Additionally,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction accuracy.The experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction accuracy.Also,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space parameters.The experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.展开更多
Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconst...Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly detection.However,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series.In this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as CAFFN.Specifically,a series and feature mixing block is introduced to learn representations in 1D space.Additionally,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature extraction.Finally,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly detection.Experimental results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.展开更多
Through core observation,thin section identification,X-ray diffraction analysis,scanning electron microscopy,and low-temperature nitrogen adsorption and isothermal adsorption experiments,the lithology and pore charact...Through core observation,thin section identification,X-ray diffraction analysis,scanning electron microscopy,and low-temperature nitrogen adsorption and isothermal adsorption experiments,the lithology and pore characteristics of the Upper Carboniferous bauxite series in eastern Ordos Basin were analyzed to reveal the formation and evolution process of the bauxite reservoirs.A petrological nomenclature and classification scheme for bauxitic rocks based on three units(aluminum hydroxides,iron minerals and clay minerals)is proposed.It is found that bauxitic mudstone is in the form of dense massive and clastic structures,while the(clayey)bauxite is of dense massive,pisolite,oolite,porous soil and clastic structures.Both bauxitic mudstone and bauxite reservoirs develop dissolution pores,intercrystalline pores,and microfractures as the dominant gas storage space,with the porosity less than 10% and mesopores in dominance.The bauxite series in the North China Craton can be divided into five sections,i.e.,ferrilite(Shanxi-style iron ore,section A),bauxitic mudstone(section B),bauxite(section C),bauxite mudstone(debris-containing,section D)and dark mudstone-coal section(section E).The burrow/funnel filling,lenticular,layered/massive bauxite deposits occur separately in the karst platforms,gentle slopes and low-lying areas.The karst platforms and gentle slopes are conducive to surface water leaching,with strong karstification,well-developed pores,large reservoir thickness and good physical properties,but poor strata continuity.The low-lying areas have poor physical properties but relatively continuous and stable reservoirs.The gas enrichment in bauxites is jointly controlled by source rock,reservoir rock and fractures.This recognition provides geological basis for the exploration and development of natural gas in the Upper Carboniferous in the study area and similar bauxite systems.展开更多
Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on t...Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on time series segmentation,most of them focus more on change point detection(CPD)methods and overlook the advances in boundary detection(BD)and state detection(SD)methods.In this paper,we categorize time series segmentation methods into CPD,BD,and SD methods,with a specific focus on recent advances in BD and SD methods.Within the scope of BD and SD,we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category.As a conclusion,we found that:(1)Existing methods failed to provide sufficient support for online working,with only a few methods supporting online deployment;(2)Most existing methods require the specification of parameters,which hinders their ability to work adaptively;(3)Existing SD methods do not attach importance to accurate detection of boundary points in evaluation,which may lead to limitations in boundary point detection.We highlight the ability to working online and adaptively as important attributes of segmentation methods,the boundary detection accuracy as a neglected metrics for SD methods.展开更多
In order to attain good quality transfer function estimates from magnetotelluric field data(i.e.,smooth behavior and small uncertainties across all frequencies),we compare time series data processing with and without ...In order to attain good quality transfer function estimates from magnetotelluric field data(i.e.,smooth behavior and small uncertainties across all frequencies),we compare time series data processing with and without a multitaper approach for spectral estimation.There are several common ways to increase the reliability of the Fourier spectral estimation from experimental(noisy)data;for example to subdivide the experimental time series into segments,taper these segments(using single taper),perform the Fourier transform of the individual segments,and average the resulting spectra.展开更多
In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.A...In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.展开更多
基金supported in part by the Interdisciplinary Project of Dalian University(DLUXK-2023-ZD-001).
文摘Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
文摘This paper presents a comparative study of ARIMA and Neural Network AutoRegressive (NNAR) models for time series forecasting. The study focuses on simulated data generated using ARIMA(1, 1, 0) and applies both models for training and forecasting. Model performance is evaluated using MSE, AIC, and BIC. The models are further applied to neonatal mortality data from Saudi Arabia to assess their predictive capabilities. The results indicate that the NNAR model outperforms ARIMA in both training and forecasting.
文摘Time series forecasting is essential for generating predictive insights across various domains, including healthcare, finance, and energy. This study focuses on forecasting patient health data by comparing the performance of traditional linear time series models, namely Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA, and Moving Average (MA) against neural network architectures. The primary goal is to evaluate the effectiveness of these models in predicting healthcare outcomes using patient records, specifically the Cancerpatient.xlsx dataset, which tracks variables such as patient age, symptoms, genetic risk factors, and environmental exposures over time. The proposed strategy involves training each model on historical patient data to predict age progression and other related health indicators, with performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) metrics. Our findings reveal that neural networks consistently outperform ARIMA and SARIMA by capturing non-linear patterns and complex temporal dependencies within the dataset, resulting in lower forecasting errors. This research highlights the potential of neural networks to enhance predictive accuracy in healthcare applications, supporting better resource allocation, patient monitoring, and long-term health outcome predictions.
文摘Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread application, often encounter difficulties in handling the complexities of oil production data, which is characterized by non-linear patterns, skewed distributions, and the presence of outliers. To overcome these limitations, deep learning methods have emerged as more robust alternatives. However, while deep neural networks offer improved accuracy, they demand substantial amounts of data for effective training. Conversely, shallow networks with fewer layers lack the capacity to model complex data distributions adequately. To address these challenges, this study introduces a novel hybrid model called Transfer LSTM to GRU (TLTG), which combines the strengths of deep and shallow networks using transfer learning. The TLTG model integrates Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU) to enhance predictive accuracy while maintaining computational efficiency. Gaussian transformation is applied to the input data to reduce outliers and skewness, creating a more normal-like distribution. The proposed approach is validated on datasets from various wells in the Tahe oil field, China. Experimental results highlight the superior performance of the TLTG model, achieving 100% accuracy and faster prediction times (200 s) compared to eight other approaches, demonstrating its effectiveness and efficiency.
基金Supported by National Natural Science Foundation of China,No.82004386and Guangdong Basic and Applied Basic Research Foundation,No.2022A1515011700.
文摘BACKGROUND Unicompartmental knee arthroplasty(UKA)has great advantages in the treatment of unicompartmental knee osteoarthritis,but its revision rate is higher than that of total knee arthroplasty.AIM To summarize and analyse the causes of revision after UKA.METHODS This is a retrospective case series study in which the reasons for the first revision after UKA are summarized.We analysed the clinical symptoms,medical histories,laboratory test results,imaging examination results and treatment processes of the patients who underwent revision and summarized the reasons for primary revision after UKA.RESULTS A total of 13 patients,including 3 males and 10 females,underwent revision surgery after UKA.The average age of the included patients was 67.62 years.The prosthesis was used for 3 d to 72 months.The main reasons for revision after UKA were improper suturing of the surgical opening(1 patient),osteophytes(2 patients),intra-articular loose bodies(2 patients),tibial prosthesis loosening(2 patients),rheumatoid arthritis(1 patient),gasket dislocation(3 patients),anterior cruciate ligament injury(1 patient),and medial collateral ligament injury with residual bone cement(1 patient).CONCLUSION The causes of primary revision after UKA were gasket dislocation,osteophytes,intra-articular loose bodies and tibial prosthesis loosening.Avoidance of these factors may greatly reduce the rate of revision after UKA,improve patient satisfaction and reduce medical burden.
基金supported by the National Natural Science Foundation of China(Grant No.52308340)the Innovative Projects of Universities in Guangdong(Grant No.2022KTSCX208)Sichuan Transportation Science and Technology Project(Grant No.2018-ZL-01).
文摘Historically,landslides have been the primary type of geological disaster worldwide.Generally,the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations.Moreover,the stability of reservoir banks changes with the long-term dynamics of external disastercausing factors.Thus,assessing the time-varying reliability of reservoir landslides remains a challenge.In this paper,a machine learning(ML)based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction.This study systematically investigated the prediction performances of three ML algorithms,i.e.multilayer perceptron(MLP),convolutional neural network(CNN),and long short-term memory(LSTM).Additionally,the effects of the data quantity and data ratio on the predictive power of deep learning models are considered.The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides.The CNN model outperforms both the MLP and LSTM models in predicting the failure probability.Furthermore,selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.
基金This research was financially supported by the Ministry of Trade,Industry,and Energy(MOTIE),Korea,under the“Project for Research and Development with Middle Markets Enterprises and DNA(Data,Network,AI)Universities”(AI-based Safety Assessment and Management System for Concrete Structures)(ReferenceNumber P0024559)supervised by theKorea Institute for Advancement of Technology(KIAT).
文摘Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.
文摘To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention mechanism.Firstly,a traversal search is conducted through the traversal layer for finite parameters in the phase space.Then,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input layer.Finally,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction results.The model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space structure.Additionally,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction accuracy.The experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction accuracy.Also,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space parameters.The experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.
基金supported in part by the National Natural Science Foundation of China(Grants 62376172,62006163,62376043)in part by the National Postdoctoral Program for Innovative Talents(Grant BX20200226)in part by Sichuan Science and Technology Planning Project(Grants 2022YFSY0047,2022YFQ0014,2023ZYD0143,2022YFH0021,2023YFQ0020,24QYCX0354,24NSFTD0025).
文摘Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly detection.However,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series.In this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as CAFFN.Specifically,a series and feature mixing block is introduced to learn representations in 1D space.Additionally,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature extraction.Finally,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly detection.Experimental results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.
基金Supported by the PetroChina Science and Technology Innovation Fund Project(2021DQ02-1003)Basic Research Project for Central Universities(2022JCCXDC02).
文摘Through core observation,thin section identification,X-ray diffraction analysis,scanning electron microscopy,and low-temperature nitrogen adsorption and isothermal adsorption experiments,the lithology and pore characteristics of the Upper Carboniferous bauxite series in eastern Ordos Basin were analyzed to reveal the formation and evolution process of the bauxite reservoirs.A petrological nomenclature and classification scheme for bauxitic rocks based on three units(aluminum hydroxides,iron minerals and clay minerals)is proposed.It is found that bauxitic mudstone is in the form of dense massive and clastic structures,while the(clayey)bauxite is of dense massive,pisolite,oolite,porous soil and clastic structures.Both bauxitic mudstone and bauxite reservoirs develop dissolution pores,intercrystalline pores,and microfractures as the dominant gas storage space,with the porosity less than 10% and mesopores in dominance.The bauxite series in the North China Craton can be divided into five sections,i.e.,ferrilite(Shanxi-style iron ore,section A),bauxitic mudstone(section B),bauxite(section C),bauxite mudstone(debris-containing,section D)and dark mudstone-coal section(section E).The burrow/funnel filling,lenticular,layered/massive bauxite deposits occur separately in the karst platforms,gentle slopes and low-lying areas.The karst platforms and gentle slopes are conducive to surface water leaching,with strong karstification,well-developed pores,large reservoir thickness and good physical properties,but poor strata continuity.The low-lying areas have poor physical properties but relatively continuous and stable reservoirs.The gas enrichment in bauxites is jointly controlled by source rock,reservoir rock and fractures.This recognition provides geological basis for the exploration and development of natural gas in the Upper Carboniferous in the study area and similar bauxite systems.
基金This work is supported by the National Key Research and Development Program of China(2022YFF1203001)National Natural Science Foundation of China(Nos.62072465,62102425)the Science and Technology Innovation Program of Hunan Province(Nos.2022RC3061,2023RC3027).
文摘Time series segmentation has attracted more interests in recent years,which aims to segment time series into different segments,each reflects a state of the monitored objects.Although there have been many surveys on time series segmentation,most of them focus more on change point detection(CPD)methods and overlook the advances in boundary detection(BD)and state detection(SD)methods.In this paper,we categorize time series segmentation methods into CPD,BD,and SD methods,with a specific focus on recent advances in BD and SD methods.Within the scope of BD and SD,we subdivide the methods based on their underlying models/techniques and focus on the milestones that have shaped the development trajectory of each category.As a conclusion,we found that:(1)Existing methods failed to provide sufficient support for online working,with only a few methods supporting online deployment;(2)Most existing methods require the specification of parameters,which hinders their ability to work adaptively;(3)Existing SD methods do not attach importance to accurate detection of boundary points in evaluation,which may lead to limitations in boundary point detection.We highlight the ability to working online and adaptively as important attributes of segmentation methods,the boundary detection accuracy as a neglected metrics for SD methods.
文摘In order to attain good quality transfer function estimates from magnetotelluric field data(i.e.,smooth behavior and small uncertainties across all frequencies),we compare time series data processing with and without a multitaper approach for spectral estimation.There are several common ways to increase the reliability of the Fourier spectral estimation from experimental(noisy)data;for example to subdivide the experimental time series into segments,taper these segments(using single taper),perform the Fourier transform of the individual segments,and average the resulting spectra.
基金This work is partly supported by the National Key Research and Development Program of China(Grant No.2020YFB1805403)the National Natural Science Foundation of China(Grant No.62032002)the 111 Project(Grant No.B21049).
文摘In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.
