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Optimal quasi-periodic maintenance policies for two-unit series system 被引量:2
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作者 高文科 张志胜 +1 位作者 周一帆 甘淑媛 《Journal of Southeast University(English Edition)》 EI CAS 2013年第4期450-455,共6页
To investigate the effects of various random factors on the preventive maintenance (PM) decision-making of one type of two-unit series system, an optimal quasi-periodic PM policy is introduced. Assume that PM is per... To investigate the effects of various random factors on the preventive maintenance (PM) decision-making of one type of two-unit series system, an optimal quasi-periodic PM policy is introduced. Assume that PM is perfect for unit 1 and only mechanical service for unit 2 in the model. PM activity is randomly performed according to a dynamic PM plan distributed in each implementation period. A replacement is determined based on the competing results of unplanned and planned replacements. The unplanned replacement is trigged by a catastrophic failure of unit 2, and the planned replacement is executed when the PM number reaches the threshold N. Through modeling and analysis, a solution algorithm for an optimal implementation period and the PM number is given, and optimal process and parametric sensitivity are provided by a numerical example. Results show that the implementation period should be decreased as soon as possible under the condition of meeting the needs of practice, which can increase mean operating time and decrease the long-run cost rate. 展开更多
关键词 maintenance policy optimization quasi-periodic preventive maintenance two-unit series system
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Bayesian and Multiple Bayesian Analysis of the Reliability Performances for Series System with Cold Standby Units 被引量:2
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作者 许勇 康会光 师义民 《Chinese Quarterly Journal of Mathematics》 CSCD 2002年第2期26-30,共5页
By using Bayesian and multiple Bayesian method, the failure probability, reliability and mean time to failure(MTTF) of series system with cold standby units are estimated. At last, we compare the two estimators by mea... By using Bayesian and multiple Bayesian method, the failure probability, reliability and mean time to failure(MTTF) of series system with cold standby units are estimated. At last, we compare the two estimators by means of Monte_Carlo simulation. 展开更多
关键词 ESTIMATION multiple Bayes reliability performance series system cold standby
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Equivalent series system to model a multiple friction pendulum system with numerous sliding interfaces for seismic analyses 被引量:7
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作者 C.S.Tsai H.C.Su T.C.Chiang 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2014年第1期85-99,共15页
Current structural analysis software programs offer few if any applicable device-specifi c hysteresis rules or nonlinear elements to simulate the precise mechanical behavior of a multiple friction pendulum system(MFPS... Current structural analysis software programs offer few if any applicable device-specifi c hysteresis rules or nonlinear elements to simulate the precise mechanical behavior of a multiple friction pendulum system(MFPS) with numerous sliding interfaces.Based on the concept of subsystems,an equivalent series system that adopts existing nonlinear elements with parameters systematically calculated and mathematically proven through rigorous derivations is proposed.The aim is to simulate the characteristics of sliding motions for an MFPS isolation system with numerous concave sliding interfaces without prior knowledge of detailed information on the mobilized forces at various sliding stages.An MFPS with numerous concave sliding interfaces and one articulated or rigid slider located between these interfaces is divided into two subsystems: the fi rst represents the concave sliding interfaces above the slider,and the second represents those below the slider.The equivalent series system for the entire system is then obtained by connecting those for each subsystem in series.The equivalent series system is validated by comparing numerical results for an MFPS with four sliding interfaces obtained from the proposed method with those from a previous study by Fenz and Constantinou.Furthermore,these numerical results demonstrate that an MFPS isolator with numerous concave sliding interfaces,which may have any number of sliding interfaces,is a good isolation device to protect structures from earthquake damage through appropriate designs with controllable mechanisms. 展开更多
关键词 seismic isolation base isolation earthquake engineering multiple friction pendulum system structural control mathematical modeling equivalent series system
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Well-Posedness of an N-Unit Series System with Finite Number of Vacations 被引量:1
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作者 Abdugeni Osman Abdukerim Haji 《Journal of Applied Mathematics and Physics》 2016年第8期1592-1599,共9页
We investigate the solution of an N-unit series system with finite number of vacations. By using C0-semigroup theory of linear operators, we prove well-posedness and the existence of the unique positive dynamic soluti... We investigate the solution of an N-unit series system with finite number of vacations. By using C0-semigroup theory of linear operators, we prove well-posedness and the existence of the unique positive dynamic solution of the system. 展开更多
关键词 N-Unit series system C_0-Semigroup Dynamic Solution WELL-POSEDNESS
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Derivation of Reliability Index Vector Formula for Series System and Its Application
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作者 康海贵 张晶 +1 位作者 孙英伟 郭伟 《China Ocean Engineering》 SCIE EI CSCD 2013年第2期159-168,共10页
In this study, a reliability index vector formula is proposed for series system with two failure modes in term of the concept of reliability index vector and equivalent failure modes. Firstly, the reliability index ve... In this study, a reliability index vector formula is proposed for series system with two failure modes in term of the concept of reliability index vector and equivalent failure modes. Firstly, the reliability index vector is introduced to determine the correlation coefficient between two failure modes, and then, the reliability index vector of a series system can be obtained. Several numerical cases and an analysis on offshore platform are performed, and the results show that this scheme provided here has better computational accuracy, and its calculation process is simpler for the series systems reliability calculations compared with the other methods. Also this scheme is more convenient for the engineering applications. 展开更多
关键词 reliability index vector series system equivalent failure mode correlation coefficient
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Asymptotic Stability of the Dynamic Solution of an N-Unit Series System with Finite Number of Vacations 被引量:1
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作者 Abdugeni Osman Abdukerim Haji Askar Ablimit 《Journal of Applied Mathematics and Physics》 2018年第11期2202-2218,共17页
We investigate an N-unit series system with finite number of vacations. By analyzing the spectral distribution of the system operator and taking into account the irreducibility of the semigroup generated by the system... We investigate an N-unit series system with finite number of vacations. By analyzing the spectral distribution of the system operator and taking into account the irreducibility of the semigroup generated by the system operator we prove that the dynamic solution converges strongly to the steady state solution. Thus we obtain asymptotic stability of the dynamic solution of the system. 展开更多
关键词 N-Unit series system C0-SEMIGROUP IRREDUCIBILITY ASYMPTOTIC Stability
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Combined hybrid energy storage system and transmission grid model for peak shaving based on time series operation simulation 被引量:1
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作者 Mingkui Wei Yiyu Wen +3 位作者 Qiu Meng Shunwei Zheng Yuyang Luo Kai Liao 《Global Energy Interconnection》 EI CAS CSCD 2023年第2期154-165,共12页
This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure o... This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy. First, a linear model for the optimal operation of the HESS is established, which considers the different power-efficiency characteristics of the pumped storage system, electrochemical storage system, and a new type of liquid compressed air energy storage. Second, a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS. Based on the proposed model, this study considers the transmission capacity of a TG. By adding the power-flow constraints of the TG, a TSOS-based HESS and TG combination model for peak shaving is established. Finally, the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model. 展开更多
关键词 Peak shaving Hybrid energy storage system Combined energy storage and transmission grid model Time series operation simulation
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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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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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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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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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A Wire-Driven Series Elastic Mechanism Based on Ultrasonic Motor for Walking Assistive System
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作者 Weihao Ren Hiroki Yoshioka +1 位作者 Lin Yang Takeshi Morita 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2023年第3期179-190,共12页
In order to improve the elderly people's quality of life,supporting their walking behaviors is a promising technology.Therefore,based on one ultrasonic motor,a wire-driven series elastic mechanism for walking assi... In order to improve the elderly people's quality of life,supporting their walking behaviors is a promising technology.Therefore,based on one ultrasonic motor,a wire-driven series elastic mechanism for walking assistive system is proposed and investigated in this research.In contrast to tradition,it innovatively utilizes an ultrasonic motor and a wire-driven series elastic mechanism to achieve superior system performances in aspects of simple structure,high torque/weight ratio,quiet operation,quick response,favorable electromagnetic compatibility,strong shock resistance,better safety,and accurately stable force control.The proposed device is mainly composed of an ultrasonic motor,a linear spring,a steel wire,four pulleys and one rotating part.To overcome the ultrasonic motor's insufficient output torque,a steel wire and pulleys are smartly combined to directly magnify the torque instead of using a conventional gear reducer.Among the pulleys,there is one tailored pulley playing an important role to keep the reduction ratio as 4.5 constantly.Meanwhile,the prototype is manufactured and its actual performance is verified by experimental results.In a one-second operating cycle,it only takes 86 ms for this mechanism to output an assistive torque of 1.6 N·m.At this torque,the ultrasonic motor's speed is around 4.1 rad/s.Moreover,experiments with different operation periods have been conducted for different application scenarios.This study provides a useful idea for the application of ultrasonic motor in walking assistance system. 展开更多
关键词 Ultrasonic motor WIRE-DRIVEN series elastic mechanism Walking assistive system Pulley Reduction ratio
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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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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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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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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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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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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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The changes in soil organic carbon stock and quality across a subalpine forest successional series
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作者 Fei Li Zhihui Wang +3 位作者 Jianfeng Hou Xuqing Li Dan Wang Wanqin Yang 《Forest Ecosystems》 SCIE CSCD 2024年第4期423-433,共11页
Soil organic carbon(SOC)affects the function of terrestrial ecosystem and plays a vital role in global carbon cycle.Yet,large uncertainty still existed regarding the changes in SOC stock and quality with forest succes... Soil organic carbon(SOC)affects the function of terrestrial ecosystem and plays a vital role in global carbon cycle.Yet,large uncertainty still existed regarding the changes in SOC stock and quality with forest succession.Here,the stock and quality of SOC at 1-m soil profile were investigated across a subalpine forest series,including shrub,deciduous broad-leaved forest,broadleaf-conifer mixed forest,middle-age coniferous forest and mature coniferous forest,which located at southeast of Tibetan Plateau.The results showed that SOC stock ranged from 9.8 to29.9 kg·m^(-2),and exhibited a hump-shaped response pattern across the forest successional series.The highest and lowest SOC stock was observed in the mixed forest and shrub forest,respectively.The SOC stock had no significant relationships with soil temperature and litter stock,but was positively correlated with wood debris stock.Meanwhile,the average percentages of polysaccharides,lignins,aromatics and aliphatics based on FTIR spectroscopy were 79.89%,0.94%,18.87%and 0.29%,respectively.Furthermore,the percentage of polysaccharides exhibited an increasing pattern across the forest successional series except for the sudden decreasing in the mixed forest,while the proportions of lignins,aromatics and aliphatics exhibited a decreasing pattern across the forest successional series except for the sudden increasing in the mixed forest.Consequently,the humification indices(HIs)were highest in the mixed forest compared to the other four successional stages,which means that the SOC quality in mixed forest was worse than other successional stages.In addition,the SOC stock,recalcitrant fractions and HIs decreased with increasing soil depth,while the polysaccharides exhibited an increasing pattern.These findings demonstrate that the mixed forest had higher SOC stock and worse SOC quality than other successional stages.The high proportion of SOC stock(66%at depth of 20-100 cm)and better SOC quality(lower HIs)indicate that deep soil have tremendous potential to store SOC and needs more attention under global chan ge. 展开更多
关键词 Forest successional series Soil organic cubon stock Molecular composition Humification indices Soil organic carbon quality
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Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels
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作者 Weisi Chen Walayat Hussain +1 位作者 Francesco Cauteruccio Xu Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期187-224,共38页
Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep lear... Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions. 展开更多
关键词 Financial time series prediction convolutional neural network long short-term memory deep learning attention mechanism FINANCE
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