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Arthroscopic M-shaped suture fixation for tibia avulsion fracture of posterior cruciate ligament:A modified technique and case series
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作者 Xiao-Hui Zhang Jian Yu +3 位作者 Meng-Yao Zhao Jin-Hui Cao Bing Wu Dan-Feng Xu 《World Journal of Orthopedics》 2024年第7期642-649,共8页
BACKGROUND Tibial avulsion fractures of the posterior cruciate ligament(PCL)are challenging to treat and compromise knee stability and function.Traditional open surgery often requires extensive soft tissue dissection,... BACKGROUND Tibial avulsion fractures of the posterior cruciate ligament(PCL)are challenging to treat and compromise knee stability and function.Traditional open surgery often requires extensive soft tissue dissection,which may increase the risk of morbidity.In response to these concerns,arthroscopic techniques have been evolving.The aim of this study was to introduce a modified arthroscopic tech-nique utilizing an M-shaped suture fixation method for the treatment of tibial avulsion fractures of the PCL and to evaluate its outcomes through a case series.AIM To evaluate the effects of arthroscopic M-shaped suture fixation on treating tibia avulsion fractures of the PCL.METHODS We developed a modified arthroscopic M-shaped suture fixation technique for tibia avulsion fractures of the PCL.This case series included 18 patients who underwent the procedure between January 2021 and December 2022.The patients were assessed for range of motion(ROM),Lysholm score and International knee documentation committee(IKDC)score.Postoperative complications were also recorded.RESULTS The patients were followed for a mean of 13.83±2.33 months.All patients showed radiographic union.At the final follow-up,all patients had full ROM and a negative posterior drawer test.The mean Lysholm score significantly improved from 45.28±8.92 preoperatively to 91.83±4.18 at the final follow-up(P<0.001),and the mean IKDC score improved from 41.98±6.06 preoperatively to 90.89±5.32 at the final follow-up(P<0.001).CONCLUSION The modified arthroscopic M-shaped suture fixation technique is a reliable and effective treatment for tibia avulsion fractures of the PCL,with excellent fracture healing and functional recovery. 展开更多
关键词 Posterior cruciate ligament Avulsion fracture ARTHROSCOPIC Case series Suture fixation
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Classification and Source Materials of Continental Crust Transformation Series Granitoids in South China 被引量:4
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作者 Liu Changshi Zhu Jinchu +1 位作者 Shen Weizhou Xu Shijin Department of Earth Sciences, Nanjing University, Nanjing, Jiangsu Jiang Minxi 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 1990年第3期287-298,共12页
The granitoids of the continental crust transformation series in South China may be divided into threetypes: (1) synorogenic migmatic and magmatic type. (2) anorogenic continental crust anatexis type, and (3)syncollis... The granitoids of the continental crust transformation series in South China may be divided into threetypes: (1) synorogenic migmatic and magmatic type. (2) anorogenic continental crust anatexis type, and (3)syncollision type. Based on the results of Sr and Nd isotopic determinations, the source material compositionof the three types of granitoids is calculated with crust-mantle binary mixing simulation. The calculations indi-cate that the granitoids of the first type consist of 78.6-89.7% upper crust endmember materials and15.0-10.3% depleted mantle endmember materials, the granitoids of the second type are composed of 63.7%upper crust endmember materials and 36.3% depleted mantle endmember materials, and those of the third type100% upper crust endmember materials. Hence. the source material composition of the granitoids of all thethree types is dominated by upper crust endmembers. 展开更多
关键词 Classification and Source Materials of Continental crust Transformation series Granitoids in South China SOURCE
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Research on Regional Crustal Deformation Characteristics Using Displacement Time Series Data of GNSS Reference Stations in Xinjiang
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作者 Li Guirong Wang Xiaoqiang +3 位作者 Liu Daiqin Ailixiati Yushan Chen Li Li Rui 《Earthquake Research in China》 CSCD 2018年第3期388-399,共12页
Using GNSS data from the Crustal Movement Observation Network of China( CMONOC),and PODAP software which was developed by the Satellite Navigation Institute of Wuhan University,the authors calculated data from 31 GNSS... Using GNSS data from the Crustal Movement Observation Network of China( CMONOC),and PODAP software which was developed by the Satellite Navigation Institute of Wuhan University,the authors calculated data from 31 GNSS stations from July 1,2011 to December 31,2014,sampling at 30 seconds,and studied regional crustal deformation characteristics. Analysis results showed that in southwestern Xinjiang,the NS movement rate was influenced by Indian plate pushing. Under the blocking effect of the Tarim Basin,the EW movement rate was slightly smaller. In the north Tianshan area,the vertical dimension movement rate was quite different,which shows as a high gradient zone in the combination area between the basin and the mountain. With regards to regional overall characteristics,the authors considered that the intersection region from south Tianshan and Kunlun Mountains was prone to strong earthquakes, especially moderate-strong earthquakes,even more than M ≥ 7. 0 earthquakes. Middle of North Tianshan was the turning point of the vertical movement rate around Bayanbulak,and was also the high gradient zone of vertical movement. The area is also prone to strong earthquakes in the future. 展开更多
关键词 GNSS DISPLACEMENT TIME series VARIATION Characteristics
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Analysis of the causes of primary revision after unicompartmental knee arthroplasty: A case series 被引量:3
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作者 Jin-Long Zhao Xiao Jin +5 位作者 He-Tao Huang Wei-Yi Yang Jia-Hui Li Ming-Hui Luo Jun Liu Jian-Ke Pan 《World Journal of Clinical Cases》 SCIE 2024年第9期1560-1568,共9页
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
关键词 Unicompartmental knee arthroplasty Total knee arthroplasty CAUSES REVISION Case series
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Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties 被引量:2
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作者 Luqi Wang Lin Wang +3 位作者 Wengang Zhang Xuanyu Meng Songlin Liu Chun Zhu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第10期3951-3960,共10页
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
关键词 Machine learning(ML) Reservoir bank landslide Spatial variability Time series prediction Failure probability
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Defect Detection Model Using Time Series Data Augmentation and Transformation 被引量:1
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作者 Gyu-Il Kim Hyun Yoo +1 位作者 Han-Jin Cho Kyungyong Chung 《Computers, Materials & Continua》 SCIE EI 2024年第2期1713-1730,共18页
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
关键词 Defect detection time series deep learning data augmentation data transformation
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Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection 被引量:1
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作者 Rui Wang Yao Zhou +2 位作者 Guangchun Luo Peng Chen Dezhong Peng 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第6期3011-3027,共17页
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
关键词 Time series anomaly detection unsupervised feature learning feature fusion
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Reservoir characteristics and formation model of Upper Carboniferous bauxite series in eastern Ordos Basin,NW China 被引量:1
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作者 LI Yong WANG Zhuangsen +2 位作者 SHAO Longyi GONG Jiaxun WU Peng 《Petroleum Exploration and Development》 SCIE 2024年第1期44-53,共10页
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
关键词 North China Craton eastern Ordos Basin Upper Carboniferous bauxite series reservoir characteristics formation model gas accumulation
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Mathematical modeling on multi-stage series crushing ratio distribu- tion based on fuzzy physical programming
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作者 Yu-Long QI Chen-Chen CAI Ping-Zhen LANG 《Journal of Coal Science & Engineering(China)》 2013年第2期262-267,共6页
Double-layer, multi-roller plate crusher is a new device, that uses a multi-stage series crushing style to break particles, with the crushing ratio distribution directly influencing the machine's performance. Three c... Double-layer, multi-roller plate crusher is a new device, that uses a multi-stage series crushing style to break particles, with the crushing ratio distribution directly influencing the machine's performance. Three crushing ratios of 2.25, 2.15 and 2.01, used for fuzzy physical programming, were determined. The comparison of the optimized result between the double-layer multi-roller plate crusher and a high pressure roll grinder showed that the double-layer multi-roller plate crusher had a better performance, reducing crushing force and wear. 展开更多
关键词 fuzzy physical programming DOUBLE-LAYER multi-roller plate crushing multi-stage series crushing crushing ratio
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Modeling the Nigerian Bonny Light Crude Oil Price: The Power of Fuzzy Time Series
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作者 Desmond Chekwube Bartholomew Ukamaka Cynthia Orumie +2 位作者 Chukwudi Paul Obite Blessing Iheoma Duru Felix Chikereuba Akanno 《Open Journal of Modelling and Simulation》 2021年第4期370-3900,共21页
<span style="font-family:Verdana;">Several authors have used different classical statistical models to fit the Nigerian Bonny Light crude oil price but the application of machine learning models and Fu... <span style="font-family:Verdana;">Several authors have used different classical statistical models to fit the Nigerian Bonny Light crude oil price but the application of machine learning models and Fuzzy Time Series model on the crude oil price has been grossly understudied. Therefore, in this study, a classical statistical model</span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">—</span><span style="font-family:Verdana;">Autoregressive Integrated Moving Average (ARIMA), two machine learning models</span><span style="font-family:Verdana;">—</span><span style="font-family:Verdana;">Artificial Neural Network (ANN) and Random Forest (RF) and Fuzzy Time Series (FTS) Model were compared in modeling the Nigerian Bonny Light crude oil price data for the periods </span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">from</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> January, 2006 to December, 2020. The monthly secondary data were collected from the Nigerian National Petroleum Corporation (NNPC) and Reuters website and divided into train (70%) and test (30%) sets. The train set was used in building the models and the models were validated using the test set. The performance measures used for the comparison include: The modified Diebold-Mariano test, the Root Mean Square Error (RMSE), the Mean Absolute Percentage Error (MAPE) and Nash-Sutcliffe Efficiency (NSE) values. Based on the performance measures, ANN (4, 1, 1) and RF performed better than ARIMA (1, 1, 0) model but FTS model using Chen’s algorithm outperformed every other model. The results recommend the use of FTS model for forecasting future values of the Nigerian Bonny Light Crude oil. However, a hybrid model of ARIMA-ANN or ARIMA-RF should be built and compared with Chen’s algorithm FTS model for the same data set to further verify the power of FTS model using Chen’s algorithm.</span></span></span> 展开更多
关键词 ARIMA Artificial Neural Network Chen’s Algorithm Fuzzy Time series Random Forest
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Unsupervised Time Series Segmentation: A Survey on Recent Advances
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作者 Chengyu Wang Xionglve Li +1 位作者 Tongqing Zhou Zhiping Cai 《Computers, Materials & Continua》 SCIE EI 2024年第8期2657-2673,共17页
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
关键词 Time series segmentation time series state detection boundary detection change point detection
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Improved Responses with Multitaper Spectral Analysis for Magnetotelluric Time Series Data Processing:Examples from Field Data
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作者 Matthew J.COMEAU Rafael RIGAUD +2 位作者 Johanna PLETT Michael BECKEN Alexey KUVSHINOV 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 2024年第S01期14-17,共4页
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
关键词 MAGNETOTELLURICS electrical resistivity time series PROCESSING Fourier analysis multitaper
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Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things
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作者 Mengmeng Zhao Haipeng Peng +1 位作者 Lixiang Li Yeqing Ren 《Computers, Materials & Continua》 SCIE EI 2024年第8期2815-2837,共23页
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
关键词 Multivariate time series anomaly detection spatial-temporal network TRANSFORMER
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An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data
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作者 Hong Sun Fangquan Yang +2 位作者 Peiwen Zhang Yang Jiao Yunxiang Zhao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第3期2549-2569,共21页
With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk manageme... With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability. 展开更多
关键词 Safety engineering risk assessment time series data autoencoder LSTM
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Periodic signal extraction of GNSS height time series based on adaptive singular spectrum analysis
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作者 Chenfeng Li Peibing Yang +1 位作者 Tengxu Zhang Jiachun Guo 《Geodesy and Geodynamics》 EI CSCD 2024年第1期50-60,共11页
Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection... Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient.To improve the efficiency and accuracy of singular spectrum analysis,this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix.The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series,and the extraction efficiency of a single time series is six times that of singular spectrum analysis.The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites. 展开更多
关键词 GNSS Time series Singular spectrum analysis Trace matrix Periodic signal
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A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment
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作者 Haibo Li Yongbo Yu +1 位作者 Zhenbo Zhao Xiaokang Tang 《Computers, Materials & Continua》 SCIE EI 2024年第1期653-676,共24页
Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same g... Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method. 展开更多
关键词 Time series short-term prediction multi-granularity event ALIGNMENT event matching
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On power series statistical convergence and new uniform integrability of double sequences
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作者 Sevda Y■ld■z Kamil Demirci 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2024年第3期519-532,共14页
In the present paper,we mostly focus on P_(p)^(2)-statistical convergence.We will look into the uniform integrability via the power series method and its characterizations for double sequences.Also,the notions of P_(p... In the present paper,we mostly focus on P_(p)^(2)-statistical convergence.We will look into the uniform integrability via the power series method and its characterizations for double sequences.Also,the notions of P_(p)^(2)-statistically Cauchy sequence,P_(p)^(2)-statistical boundedness and core for double sequences will be described in addition to these findings. 展开更多
关键词 power series methods statistical convergence uniform integrability double sequences
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TSCND:Temporal Subsequence-Based Convolutional Network with Difference for Time Series Forecasting
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作者 Haoran Huang Weiting Chen Zheming Fan 《Computers, Materials & Continua》 SCIE EI 2024年第3期3665-3681,共17页
Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in t... Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address the first problem, we propose a subsequence-based dilated convolution method (SDC). By using multiple convolutional filters to convolve elements of neighboring subsequences, the method extracts temporal features from a growing receptive field via a growing subsequence rather than a single element. Ultimately, the receptive field of each output element can cover the whole input sequence. To address the second problem, we propose a difference and compensation method (DCM). The method reduces the discrepancies between and within the input sequences by difference operations and then compensates the outputs for the information lost due to difference operations. Based on SDC and DCM, we further construct a temporal subsequence-based convolutional network with difference (TSCND) for time series forecasting. The experimental results show that TSCND can reduce prediction mean squared error by 7.3% and save runtime, compared with state-of-the-art models and vanilla TCN. 展开更多
关键词 DIFFERENCE data prediction time series temporal convolutional network dilated convolution
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A novel framework for predicting non-stationary production time series of shale gas based on BiLSTM-RF-MPA deep fusion model
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作者 Bin Liang Jiang Liu +4 位作者 Li-Xia Kang Ke Jiang Jun-Yu You Hoonyoung Jeong Zhan Meng 《Petroleum Science》 SCIE EI CAS CSCD 2024年第5期3326-3339,共14页
Shale gas, as an environmentally friendly fossil energy resource, has gained significant commercial development and shows immense potential. However, accurately predicting shale gas production faces substantial challe... Shale gas, as an environmentally friendly fossil energy resource, has gained significant commercial development and shows immense potential. However, accurately predicting shale gas production faces substantial challenges due to the complex law of decline, nonlinear and non-stationary features in production data, which greatly repair the robustness of current models in predicting shale gas production time series. To address these challenges and improve accuracy in production forecasting, this paper introduces a novel and innovative approach: a hybrid proxy model that combines the bidirectional long short-term memory(BiLSTM) neural network and random forest(RF) through deep learning. The BiLSTM neural network is adept at capturing long-term dependencies, making it suitable for understanding the intricate relationships between input and output variables in shale gas production.On the other hand, RF serves a dual purpose: reducing model variance and addressing the concept drift problem that arises in non-stationary time series predictions made by BiLSTM. By integrating these two models, the hybrid approach effectively captures the inherent dependencies present in long and nonstationary production time series, thereby reducing model uncertainty. Furthermore, the combination of BiLSTM and RF is optimized using the recently-proposed marine predators algorithm(MPA) to fine-tune hyperparameters and enhance the overall performance of the proxy model. The results demonstrate that the proposed BiLSTM-RF-MPA model achieves higher prediction accuracy and demonstrates stronger generalization capabilities by effectively handling the complex nonlinear and non-stationary characteristics of shale gas production time series. Compared to other models such as LSTM, BiLSTM, and RF, the proposed model exhibits superior fitting and prediction performance, with an average improvement in performance indicators exceeding 20%. This innovative framework provides valuable insights for forecasting the complex production performance of unconventional oil and gas reservoirs, which sheds light on the development of data-driven proxy models in the field of subsurface energy utilization. 展开更多
关键词 Production forecasting Shale gas BiLSTM-RF-MPA model Nonstationary production time series Deep learning
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CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction
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作者 Xiao-Qian Lu Jun Tian +2 位作者 Qiang Liao Zheng-Wu Xu Lu Gan 《Journal of Electronic Science and Technology》 EI CAS CSCD 2024年第2期77-90,共14页
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
关键词 Chaotic time series Incremental attention mechanism Phase-space reconstruction
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