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
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
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
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
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
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
Nonlinear variations in the coordinate time series of global navigation satellite system(GNSS) reference stations are strongly correlated with surface displacements caused by environmental loading effects,including at...Nonlinear variations in the coordinate time series of global navigation satellite system(GNSS) reference stations are strongly correlated with surface displacements caused by environmental loading effects,including atmospheric, hydrological, and nontidal ocean loading. Continuous improvements in the accuracy of surface mass loading products, performance of Earth models, and precise data-processing technologies have significantly advanced research on the effects of environmental loading on nonlinear variations in GNSS coordinate time series. However, owing to theoretical limitations, the lack of high spatiotemporal resolution surface mass observations, and the coupling of GNSS technology-related systematic errors, environmental loading and nonlinear GNSS reference station displacements remain inconsistent. The applicability and capability of these loading products across different regions also require further evaluation. This paper outlines methods for modeling environmental loading, surface mass loading products, and service organizations. In addition, it summarizes recent advances in applying environmental loading to address nonlinear variations in global and regional GNSS coordinate time series. Moreover, the scientific questions of existing studies are summarized, and insights into future research directions are provided. The complex nonlinear motion of reference stations is a major factor limiting the accuracy of the current terrestrial reference frame. Further refining the environmental load modeling method, establishing a surface mass distribution model with high spatiotemporal resolution and reliability, exploring other environmental load factors such as ice sheet and artificial mass-change effects, and developing an optimal data-processing model and strategy for reprocessing global reference station data consistently could contribute to the development of a millimeter-level nonlinear motion model for GNSS reference stations with actual physical significance and provide theoretical support for establishing a terrestrial reference frame with 1 mm accuracy by 2050.展开更多
The El Pintado 1 Silurian section in Seville Province,Spain,described by Loydell et al.(2015),has been ratified by the IUGS as the replacement GSSP for the base of the Telychian Stage,to replace the Cefn Cerig quarry ...The El Pintado 1 Silurian section in Seville Province,Spain,described by Loydell et al.(2015),has been ratified by the IUGS as the replacement GSSP for the base of the Telychian Stage,to replace the Cefn Cerig quarry section in the Llandovery area of Wales,which was found to be within a sedimentary mélange and therefore not a continuous section.No section other than El Pintado 1 has been found to be continuously fossiliferous across the Aeronian/Telychian boundary.展开更多
Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational s...Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.展开更多
In the context of the Diagnosis Related Groups(DRGs)system,the orthopedic hospital implemented refined drug control to provide a pharmacological reference for promoting rational clinical drug use.A statistical analysi...In the context of the Diagnosis Related Groups(DRGs)system,the orthopedic hospital implemented refined drug control to provide a pharmacological reference for promoting rational clinical drug use.A statistical analysis was conducted on the hospital’s data from January to December 2021(prior to the implementation of control),focusing on the types of unreasonable prescriptions.A multi-dimensional analysis was also conducted to identify the underlying causes of inappropriate medication practices.Following this,refined drug control measures were introduced,and data from January to December 2022(post-control)were compared,examining factors such as the average drug cost,drug expenses for the IC29 diagnosis group,and the drug cost ratio.An interrupted time-series analysis was employed to evaluate the effects of these interventions.The results showed that after the implementation of refined drug control in the orthopedic department,significant reductions were observed in the average cost per patient,average drug cost per patient,drug cost ratio,cost consumption index,average length of hospital stay,and allocation ratio(P<0.05).In particular,the first month of control(January 2022)saw a marked decrease in average drug costs per patient by 1272.90 yuan(P<0.01),a reduction in the drug cost ratio by 0.98%,and a decline in drug costs for the IC29 diagnosis group by 616.79 yuan(P>0.05).Moreover,the rate of unreasonable inappropriate prescribing dropped dramatically from 40.48%in 2021 to 3.57%by December 2022.The refined control of drug use within the orthopedic hospital significantly improved the rationality of clinical prescribing practices,reduced the occurrence of adverse drug reactions,and enhanced patient adherence to prescribed treatments.These findings demonstrated considerable clinical value in promoting efficient and safe drug use.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
Our study describes the reported rate of the Institutional Review Board(IRB)approval,declaration of Helsinki(DoH),and informed consent in the case reports and case series and investigates factors associated with the e...Our study describes the reported rate of the Institutional Review Board(IRB)approval,declaration of Helsinki(DoH),and informed consent in the case reports and case series and investigates factors associated with the ethical approval report.We searched PubMed for case reports and case series from 2006 to 2017.Annually,we obtained the first 20 articles of a case report cluster from 20 distinct publications.This analysis initially contained at least 2400 papers,with 100 papers each study design and year.Only 26(5.4%)of 480 included studies reported IRB approval,DoH approval,and participant informed consent;58(12.1%)reported two out of three ethical statements(DoH,informed consent,IRB);and 151(31.5%)reported only one,leading to nearly 245 studies(51.0%)did not report any ethical approval item.Both clusters mentioned the DoH the least.Only years,ages,ethical item types,and cluster types were associated with ethical reporting practices.This study found the serious under‐reporting of ethical practices in both case reports and case series.展开更多
Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferom...Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferometric synthetic aperture radar(InSAR)stands out as an efficient and prevalent tool for monitoring landslide deformation and offers new prospects for displacement prediction.However,challenges such as inherent limitation of satellite viewing geometry,long revisit cycles,and limited data volume hinder its application in displacement forecasting,notably for landslides with near-north-south deformation less detectable by InSAR.To address these issues,we propose a novel strategy for predicting three-dimensional(3D)landslide displacement,integrating InSAR and global navigation satellite system(GNSS)measurements with machine learning(ML).This framework first synergizes InSAR line-of-sight(LOS)results with GNSS horizontal data to reconstruct 3D displacement time series.It then employs ML models to capture complex nonlinear relationships between external triggers,landslide evolutionary states,and 3D displacements,thus enabling accurate future deformation predictions.Utilizing four advanced ML algorithms,i.e.random forest(RF),support vector machine(SVM),long short-term memory(LSTM),and gated recurrent unit(GRU),with Bayesian optimization(BO)for hyperparameter tuning,we applied this innovative approach to the north-facing,slow-moving Xinpu landslide in the Three Gorges Reservoir Area(TGRA)of China.Leveraging over 6.5 years of Sentinel-1 satellite data and GNSS measurements,our framework demonstrates satisfactory and robust prediction performance,with an average root mean square deviation(RMSD)of 9.62 mm and a correlation coefficient(CC)of 0.996.This study presents a promising strategy for 3D displacement prediction,illustrating the efficacy of integrating InSAR monitoring with ML forecasting in enhancing landslide early warning capabilities.展开更多
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
Time series anomaly detection is crucial in finance,healthcare,and industrial monitoring.However,traditional methods often face challenges when handling time series data,such as limited feature extraction capability,p...Time series anomaly detection is crucial in finance,healthcare,and industrial monitoring.However,traditional methods often face challenges when handling time series data,such as limited feature extraction capability,poor temporal dependency handling,and suboptimal real-time performance,sometimes even neglecting the temporal relationships between data.To address these issues and improve anomaly detection performance by better capturing temporal dependencies,we propose an unsupervised time series anomaly detection method,VLT-Anomaly.First,we enhance the Variational Autoencoder(VAE)module by redesigning its network structure to better suit anomaly detection through data reconstruction.We introduce hyperparameters to control the weight of the Kullback-Leibler(KL)divergence term in the Evidence Lower Bound(ELBO),thereby improving the encoder module’s decoupling and expressive power in the latent space,which yields more effective latent representations of the data.Next,we incorporate transformer and Long Short-Term Memory(LSTM)modules to estimate the long-term dependencies of the latent representations,capturing both forward and backward temporal relationships and performing time series forecasting.Finally,we compute the reconstruction error by averaging the predicted results and decoder reconstruction and detect anomalies through grid search for optimal threshold values.Experimental results demonstrate that the proposed method performs superior anomaly detection on multiple public time series datasets,effectively extracting complex time-related features and enabling efficient computation and real-time anomaly detection.It improves detection accuracy and robustness while reducing false positives and false negatives.展开更多
Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids.It offers substantial benefits across social,environmental,and economic dimensions.To effectively r...Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids.It offers substantial benefits across social,environmental,and economic dimensions.To effectively realize these advantages,a fine-grained collection and analysis of smart meter data is essential.However,the high dimensionality and volume of such time-series present significant challenges,including increased computational load,data transmission overhead,latency,and complexity in real-time analysis.This study proposes a novel,computationally efficient framework for feature extraction and selection tailored to smart meter time-series data.The approach begins with an extensive offline analysis,where features are derived from multiple domains—time,frequency,and statistical—to capture diverse signal characteristics.Various feature sets are fused and evaluated using robust machine learning classifiers to identify the most informative combinations for automated appliance categorization.The bestperforming fused features set undergoes further refinement using Analysis of Variance(ANOVA)to identify the most discriminative features.The mathematical models,used to compute the selected features,are optimized to extract them with computational efficiency during online processing.Moreover,a notable dimension reduction is secured which facilitates data storage,transmission,and post processing.Onward,a specifically designed LogitBoost(LB)based ensemble of Random Forest base learners is used for an automated classification.The proposed solution demonstrates a high classification accuracy(97.93%)for the case of nine-class problem and dimension reduction(17.33-fold)with minimal front-end computational requirements,making it well-suited for real-world applications in smart grid environments.展开更多
Accurate prediction of cloud resource utilization is critical.It helps improve service quality while avoiding resource waste and shortages.However,the time series of resource usage in cloud computing systems often exh...Accurate prediction of cloud resource utilization is critical.It helps improve service quality while avoiding resource waste and shortages.However,the time series of resource usage in cloud computing systems often exhibit multidimensionality,nonlinearity,and high volatility,making the high-precision prediction of resource utilization a complex and challenging task.At present,cloud computing resource prediction methods include traditional statistical models,hybrid approaches combining machine learning and classical models,and deep learning techniques.Traditional statistical methods struggle with nonlinear predictions,hybrid methods face challenges in feature extraction and long-term dependencies,and deep learning methods incur high computational costs.The above methods are insufficient to achieve high-precision resource prediction in cloud computing systems.Therefore,we propose a new time series prediction model,called SDVformer,which is based on the Informer model by integrating the Savitzky-Golay(SG)filters,a novel Discrete-Variation Self-Attention(DVSA)mechanism,and a type-aware mixture of experts(T-MOE)framework.The SG filter is designed to reduce noise and enhance the feature representation of input data.The DVSA mechanism is proposed to optimize the selection of critical features to reduce computational complexity.The T-MOE framework is designed to adjust the model structure based on different resource characteristics,thereby improving prediction accuracy and adaptability.Experimental results show that our proposed SDVformer significantly outperforms baseline models,including Recurrent Neural Network(RNN),Long Short-Term Memory(LSTM),and Informer in terms of prediction precision,on both the Alibaba public dataset and the dataset collected by Beijing Jiaotong University(BJTU).Particularly compared with the Informer model,the average Mean Squared Error(MSE)of SDVformer decreases by about 80%,fully demonstrating its advantages in complex time series prediction tasks in cloud computing systems.展开更多
Time series forecasting is important in the fields of finance,energy,and meteorology,but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data.In this paper,we prop...Time series forecasting is important in the fields of finance,energy,and meteorology,but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data.In this paper,we propose the FractalNet-LSTM model,which combines fractal convolutional units with recurrent long short-term memory(LSTM)layers to model time series efficiently.To test the effectiveness of the model,data with complex structures and patterns,in particular,with seasonal and cyclical effects,were used.To better demonstrate the obtained results and the formed conclusions,the model performance was shown on the datasets of electricity consumption,sunspot activity,and Spotify stock price.The result showed that the proposed model outperforms traditional approaches at medium forecasting horizons and demonstrates high accuracy for data with long-term and cyclical dependencies.However,for financial data with high volatility,the model’s efficiency decreases at long forecasting horizons,indicating the need for further adaptation.The findings suggest further adaptation.The findings suggest that integrating fractal properties into neural network architecture improves the accuracy of time series forecasting and can be useful for developing more accurate and reliable forecasting systems in various industries.展开更多
With the rapid development of the nuclear power industry on a global scale,the discharge of radioactive e uents from nuclear power plants and their impact on the environment have become important issues in radioactive...With the rapid development of the nuclear power industry on a global scale,the discharge of radioactive e uents from nuclear power plants and their impact on the environment have become important issues in radioactive waste management,radiation protection,and environmental impact assessments.-detection of nuclides requires tedious processes,such as waiting for the radioactive balance of the sample and pretreatment separation,and there is an urgent need for a method specifically designed for mixing rapid energy spectrum measurement method for nuclide samples.The analysis of hybrid-energy spectrum is proposed in this study as a new algorithm,which takes advantage of the spectral analysis of-logarithmic energy spectrum and fitting ability of Fourier series.The logarithmic energy spectrum is obtained by logarithmic conversion of the hybrid linear energy spectrum.The Fourier fitting interpolation method is used to fit the logarithmic energy spectrum numerically.Next,the interpolation points for the‘e ective high-energy window’and‘e ective low-energy window’corresponding to the highest E_(m)nuclide in the hybrid logarithmic fitted energy spectrum are set,and spline interpolation is performed three times to obtain the logarithmic fitted energy spectrum of the highest E_(m)nuclide.Finally,the logarithmic fitted spectrum of the highest E_(m)nuclide is subtracted from the hybrid logarithmic fitted spectrum to obtain a logarithmic fitted spectrum comprised of the remaining lower E_(m)nuclides.The aforementioned process is iterated in a loop to resolve the logarithmic spectra of each nuclide in the original hybrid logarithmic spectra.Then,the radioactivity of E_(m)nuclides to be measured is calculated.In the experimental tests,^(14)C,^(90)Sr,and ^(90)Y spectra,which are obtained using the Fourier fitting interpolation method are compared with the original simulated ^(14)C,^(90)Sr,and ^(90)Y spectra of GEANT4.The measured liquid scintillator data of ^(90)Sr∕^(90)Y sample source and simulated data from GEANT4 are then analyzed.Analysis of the experimental results indicates that the Fourier fitting interpolation method accurately solves ^(14)C,^(90)Sr,and ^(90)Y energy spectra,which is in good agreement with the original GEANT4 simulation.The error in ^(90)Y activity,calculated using the actual detection e ciency,is less than 10%and less than 5%when using the simulated full-spectrum detection e ciency,satisfying the experimental expectations.展开更多
Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on t...Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on time series spectral index data of the complete growth season.A recent development in maize phenology detection research is to use canopy height(CH)data instead of spectral indices,but its robustness in multiple treatments and stages has not been confirmed.Meanwhile,because data of a complete growth season are needed,the need for timely in-season TD identification remains unmet.This study proposed an approach to timely identify and forecast the maize TD.We obtained RGB and light detection and ranging(Li DAR)data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments.After CH estimation,the feature points(inflection point)from the Logistic curve of the CH time series were extracted as TD.We examined the impact of various independent variables(day of year vs.accumulated growing degree days(AGDD)),sensors(RGB and Li DAR),time series denoise methods,different feature points,and temporal resolution on TD identification.Lastly,we used early CH time series data to predict height growth and further forecast TD.The results showed that using the 99th percentile of plot scale digital surface model and the minimum digital terrain model from Li DAR to estimate maize CH was the most stable across treatments and stages(R~2:0.928 to0.943).For TD identification,the best performance was achieved by using Li DAR data with AGDD as the independent variable,combined with the knee point method,resulting in RMSE of 2.95 d.The high accuracy was maintained at temporal resolutions as coarse as 14 d.TD forecast got more accurate as the CH time series extended.The optimal timing for forecasting TD was when the CH exceeded half of its maximum.Using only Li DAR CH data below 1.6 m and empirical growth rate estimates,the forecasted TD showed an RMSE of 3.90 d.In conclusion,this study exploited the growth characteristics of maize height to provide a practical approach for the timely identification and forecast of maize TD.展开更多
基金The National Natural Science Foundation of China(No.51275090,71201025)the Program for Special Talent in Six Fields of Jiangsu Province(No.2008144)+1 种基金the Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1302)the Scientific Innovation Research of College Graduates in Jiangsu Province(No.CXLX12_0078)
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
文摘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.
基金supported by the Basic Science Center Project of the National Natural Science Foundation of China(42388102)the National Natural Science Foundation of China(42174030)+2 种基金the Special Fund of Hubei Luojia Laboratory(220100020)the Major Science and Technology Program for Hubei Province(2022AAA002)the Fundamental Research Funds for the Central Universities of China(2042022dx0001 and 2042023kfyq01)。
文摘Nonlinear variations in the coordinate time series of global navigation satellite system(GNSS) reference stations are strongly correlated with surface displacements caused by environmental loading effects,including atmospheric, hydrological, and nontidal ocean loading. Continuous improvements in the accuracy of surface mass loading products, performance of Earth models, and precise data-processing technologies have significantly advanced research on the effects of environmental loading on nonlinear variations in GNSS coordinate time series. However, owing to theoretical limitations, the lack of high spatiotemporal resolution surface mass observations, and the coupling of GNSS technology-related systematic errors, environmental loading and nonlinear GNSS reference station displacements remain inconsistent. The applicability and capability of these loading products across different regions also require further evaluation. This paper outlines methods for modeling environmental loading, surface mass loading products, and service organizations. In addition, it summarizes recent advances in applying environmental loading to address nonlinear variations in global and regional GNSS coordinate time series. Moreover, the scientific questions of existing studies are summarized, and insights into future research directions are provided. The complex nonlinear motion of reference stations is a major factor limiting the accuracy of the current terrestrial reference frame. Further refining the environmental load modeling method, establishing a surface mass distribution model with high spatiotemporal resolution and reliability, exploring other environmental load factors such as ice sheet and artificial mass-change effects, and developing an optimal data-processing model and strategy for reprocessing global reference station data consistently could contribute to the development of a millimeter-level nonlinear motion model for GNSS reference stations with actual physical significance and provide theoretical support for establishing a terrestrial reference frame with 1 mm accuracy by 2050.
基金funded by project PDI2021-125585NB-I00 of the Spanish Ministry of Science,Innovation and Universities‒Agencia Estatal de Investigacion.JF thanks the Grant Agency of the Czech Republic for support of his study(GA23-06198S).
文摘The El Pintado 1 Silurian section in Seville Province,Spain,described by Loydell et al.(2015),has been ratified by the IUGS as the replacement GSSP for the base of the Telychian Stage,to replace the Cefn Cerig quarry section in the Llandovery area of Wales,which was found to be within a sedimentary mélange and therefore not a continuous section.No section other than El Pintado 1 has been found to be continuously fossiliferous across the Aeronian/Telychian boundary.
基金The Key R&D Project of Jilin Province,Grant/Award Number:20230201067GX。
文摘Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.
基金Jiangxi Provincial Hospital Pharmacy Special Research Fund Project(Grant No.2024-ZXYJ02).
文摘In the context of the Diagnosis Related Groups(DRGs)system,the orthopedic hospital implemented refined drug control to provide a pharmacological reference for promoting rational clinical drug use.A statistical analysis was conducted on the hospital’s data from January to December 2021(prior to the implementation of control),focusing on the types of unreasonable prescriptions.A multi-dimensional analysis was also conducted to identify the underlying causes of inappropriate medication practices.Following this,refined drug control measures were introduced,and data from January to December 2022(post-control)were compared,examining factors such as the average drug cost,drug expenses for the IC29 diagnosis group,and the drug cost ratio.An interrupted time-series analysis was employed to evaluate the effects of these interventions.The results showed that after the implementation of refined drug control in the orthopedic department,significant reductions were observed in the average cost per patient,average drug cost per patient,drug cost ratio,cost consumption index,average length of hospital stay,and allocation ratio(P<0.05).In particular,the first month of control(January 2022)saw a marked decrease in average drug costs per patient by 1272.90 yuan(P<0.01),a reduction in the drug cost ratio by 0.98%,and a decline in drug costs for the IC29 diagnosis group by 616.79 yuan(P>0.05).Moreover,the rate of unreasonable inappropriate prescribing dropped dramatically from 40.48%in 2021 to 3.57%by December 2022.The refined control of drug use within the orthopedic hospital significantly improved the rationality of clinical prescribing practices,reduced the occurrence of adverse drug reactions,and enhanced patient adherence to prescribed treatments.These findings demonstrated considerable clinical value in promoting efficient and safe drug use.
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Our study describes the reported rate of the Institutional Review Board(IRB)approval,declaration of Helsinki(DoH),and informed consent in the case reports and case series and investigates factors associated with the ethical approval report.We searched PubMed for case reports and case series from 2006 to 2017.Annually,we obtained the first 20 articles of a case report cluster from 20 distinct publications.This analysis initially contained at least 2400 papers,with 100 papers each study design and year.Only 26(5.4%)of 480 included studies reported IRB approval,DoH approval,and participant informed consent;58(12.1%)reported two out of three ethical statements(DoH,informed consent,IRB);and 151(31.5%)reported only one,leading to nearly 245 studies(51.0%)did not report any ethical approval item.Both clusters mentioned the DoH the least.Only years,ages,ethical item types,and cluster types were associated with ethical reporting practices.This study found the serious under‐reporting of ethical practices in both case reports and case series.
基金jointly supported by the International Research Center of Big Data for Sustainable Development Goals(Grant No.CBAS2022GSP02)the National Natural Science Foundation of China(Grant Nos.42072320 and 42372264).
文摘Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferometric synthetic aperture radar(InSAR)stands out as an efficient and prevalent tool for monitoring landslide deformation and offers new prospects for displacement prediction.However,challenges such as inherent limitation of satellite viewing geometry,long revisit cycles,and limited data volume hinder its application in displacement forecasting,notably for landslides with near-north-south deformation less detectable by InSAR.To address these issues,we propose a novel strategy for predicting three-dimensional(3D)landslide displacement,integrating InSAR and global navigation satellite system(GNSS)measurements with machine learning(ML).This framework first synergizes InSAR line-of-sight(LOS)results with GNSS horizontal data to reconstruct 3D displacement time series.It then employs ML models to capture complex nonlinear relationships between external triggers,landslide evolutionary states,and 3D displacements,thus enabling accurate future deformation predictions.Utilizing four advanced ML algorithms,i.e.random forest(RF),support vector machine(SVM),long short-term memory(LSTM),and gated recurrent unit(GRU),with Bayesian optimization(BO)for hyperparameter tuning,we applied this innovative approach to the north-facing,slow-moving Xinpu landslide in the Three Gorges Reservoir Area(TGRA)of China.Leveraging over 6.5 years of Sentinel-1 satellite data and GNSS measurements,our framework demonstrates satisfactory and robust prediction performance,with an average root mean square deviation(RMSD)of 9.62 mm and a correlation coefficient(CC)of 0.996.This study presents a promising strategy for 3D displacement prediction,illustrating the efficacy of integrating InSAR monitoring with ML forecasting in enhancing landslide early warning capabilities.
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support from the Fundamental Research Funds for Central Public Welfare Research Institutes(SK202324)the Central Guidance on Local Science and Technology Development Fund of Hebei Province(236Z0104G)+1 种基金the National Natural Science Foundation of China(62476078)the Geological Survey Project of China Geological Survey(G202304-2).
文摘Time series anomaly detection is crucial in finance,healthcare,and industrial monitoring.However,traditional methods often face challenges when handling time series data,such as limited feature extraction capability,poor temporal dependency handling,and suboptimal real-time performance,sometimes even neglecting the temporal relationships between data.To address these issues and improve anomaly detection performance by better capturing temporal dependencies,we propose an unsupervised time series anomaly detection method,VLT-Anomaly.First,we enhance the Variational Autoencoder(VAE)module by redesigning its network structure to better suit anomaly detection through data reconstruction.We introduce hyperparameters to control the weight of the Kullback-Leibler(KL)divergence term in the Evidence Lower Bound(ELBO),thereby improving the encoder module’s decoupling and expressive power in the latent space,which yields more effective latent representations of the data.Next,we incorporate transformer and Long Short-Term Memory(LSTM)modules to estimate the long-term dependencies of the latent representations,capturing both forward and backward temporal relationships and performing time series forecasting.Finally,we compute the reconstruction error by averaging the predicted results and decoder reconstruction and detect anomalies through grid search for optimal threshold values.Experimental results demonstrate that the proposed method performs superior anomaly detection on multiple public time series datasets,effectively extracting complex time-related features and enabling efficient computation and real-time anomaly detection.It improves detection accuracy and robustness while reducing false positives and false negatives.
文摘Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids.It offers substantial benefits across social,environmental,and economic dimensions.To effectively realize these advantages,a fine-grained collection and analysis of smart meter data is essential.However,the high dimensionality and volume of such time-series present significant challenges,including increased computational load,data transmission overhead,latency,and complexity in real-time analysis.This study proposes a novel,computationally efficient framework for feature extraction and selection tailored to smart meter time-series data.The approach begins with an extensive offline analysis,where features are derived from multiple domains—time,frequency,and statistical—to capture diverse signal characteristics.Various feature sets are fused and evaluated using robust machine learning classifiers to identify the most informative combinations for automated appliance categorization.The bestperforming fused features set undergoes further refinement using Analysis of Variance(ANOVA)to identify the most discriminative features.The mathematical models,used to compute the selected features,are optimized to extract them with computational efficiency during online processing.Moreover,a notable dimension reduction is secured which facilitates data storage,transmission,and post processing.Onward,a specifically designed LogitBoost(LB)based ensemble of Random Forest base learners is used for an automated classification.The proposed solution demonstrates a high classification accuracy(97.93%)for the case of nine-class problem and dimension reduction(17.33-fold)with minimal front-end computational requirements,making it well-suited for real-world applications in smart grid environments.
文摘Accurate prediction of cloud resource utilization is critical.It helps improve service quality while avoiding resource waste and shortages.However,the time series of resource usage in cloud computing systems often exhibit multidimensionality,nonlinearity,and high volatility,making the high-precision prediction of resource utilization a complex and challenging task.At present,cloud computing resource prediction methods include traditional statistical models,hybrid approaches combining machine learning and classical models,and deep learning techniques.Traditional statistical methods struggle with nonlinear predictions,hybrid methods face challenges in feature extraction and long-term dependencies,and deep learning methods incur high computational costs.The above methods are insufficient to achieve high-precision resource prediction in cloud computing systems.Therefore,we propose a new time series prediction model,called SDVformer,which is based on the Informer model by integrating the Savitzky-Golay(SG)filters,a novel Discrete-Variation Self-Attention(DVSA)mechanism,and a type-aware mixture of experts(T-MOE)framework.The SG filter is designed to reduce noise and enhance the feature representation of input data.The DVSA mechanism is proposed to optimize the selection of critical features to reduce computational complexity.The T-MOE framework is designed to adjust the model structure based on different resource characteristics,thereby improving prediction accuracy and adaptability.Experimental results show that our proposed SDVformer significantly outperforms baseline models,including Recurrent Neural Network(RNN),Long Short-Term Memory(LSTM),and Informer in terms of prediction precision,on both the Alibaba public dataset and the dataset collected by Beijing Jiaotong University(BJTU).Particularly compared with the Informer model,the average Mean Squared Error(MSE)of SDVformer decreases by about 80%,fully demonstrating its advantages in complex time series prediction tasks in cloud computing systems.
文摘Time series forecasting is important in the fields of finance,energy,and meteorology,but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data.In this paper,we propose the FractalNet-LSTM model,which combines fractal convolutional units with recurrent long short-term memory(LSTM)layers to model time series efficiently.To test the effectiveness of the model,data with complex structures and patterns,in particular,with seasonal and cyclical effects,were used.To better demonstrate the obtained results and the formed conclusions,the model performance was shown on the datasets of electricity consumption,sunspot activity,and Spotify stock price.The result showed that the proposed model outperforms traditional approaches at medium forecasting horizons and demonstrates high accuracy for data with long-term and cyclical dependencies.However,for financial data with high volatility,the model’s efficiency decreases at long forecasting horizons,indicating the need for further adaptation.The findings suggest further adaptation.The findings suggest that integrating fractal properties into neural network architecture improves the accuracy of time series forecasting and can be useful for developing more accurate and reliable forecasting systems in various industries.
基金supported by the National Natural Science Foundation of China(No.12005026)。
文摘With the rapid development of the nuclear power industry on a global scale,the discharge of radioactive e uents from nuclear power plants and their impact on the environment have become important issues in radioactive waste management,radiation protection,and environmental impact assessments.-detection of nuclides requires tedious processes,such as waiting for the radioactive balance of the sample and pretreatment separation,and there is an urgent need for a method specifically designed for mixing rapid energy spectrum measurement method for nuclide samples.The analysis of hybrid-energy spectrum is proposed in this study as a new algorithm,which takes advantage of the spectral analysis of-logarithmic energy spectrum and fitting ability of Fourier series.The logarithmic energy spectrum is obtained by logarithmic conversion of the hybrid linear energy spectrum.The Fourier fitting interpolation method is used to fit the logarithmic energy spectrum numerically.Next,the interpolation points for the‘e ective high-energy window’and‘e ective low-energy window’corresponding to the highest E_(m)nuclide in the hybrid logarithmic fitted energy spectrum are set,and spline interpolation is performed three times to obtain the logarithmic fitted energy spectrum of the highest E_(m)nuclide.Finally,the logarithmic fitted spectrum of the highest E_(m)nuclide is subtracted from the hybrid logarithmic fitted spectrum to obtain a logarithmic fitted spectrum comprised of the remaining lower E_(m)nuclides.The aforementioned process is iterated in a loop to resolve the logarithmic spectra of each nuclide in the original hybrid logarithmic spectra.Then,the radioactivity of E_(m)nuclides to be measured is calculated.In the experimental tests,^(14)C,^(90)Sr,and ^(90)Y spectra,which are obtained using the Fourier fitting interpolation method are compared with the original simulated ^(14)C,^(90)Sr,and ^(90)Y spectra of GEANT4.The measured liquid scintillator data of ^(90)Sr∕^(90)Y sample source and simulated data from GEANT4 are then analyzed.Analysis of the experimental results indicates that the Fourier fitting interpolation method accurately solves ^(14)C,^(90)Sr,and ^(90)Y energy spectra,which is in good agreement with the original GEANT4 simulation.The error in ^(90)Y activity,calculated using the actual detection e ciency,is less than 10%and less than 5%when using the simulated full-spectrum detection e ciency,satisfying the experimental expectations.
基金supported by National Science and Technology Major Project(2022ZD0115701)Nanfan Special Project,CAAS(YBXM2305,YBXM2401,YBXM2402,PTXM2402)+1 种基金National Natural Science Foundation of China(42071426,42301427)the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences。
文摘Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on time series spectral index data of the complete growth season.A recent development in maize phenology detection research is to use canopy height(CH)data instead of spectral indices,but its robustness in multiple treatments and stages has not been confirmed.Meanwhile,because data of a complete growth season are needed,the need for timely in-season TD identification remains unmet.This study proposed an approach to timely identify and forecast the maize TD.We obtained RGB and light detection and ranging(Li DAR)data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments.After CH estimation,the feature points(inflection point)from the Logistic curve of the CH time series were extracted as TD.We examined the impact of various independent variables(day of year vs.accumulated growing degree days(AGDD)),sensors(RGB and Li DAR),time series denoise methods,different feature points,and temporal resolution on TD identification.Lastly,we used early CH time series data to predict height growth and further forecast TD.The results showed that using the 99th percentile of plot scale digital surface model and the minimum digital terrain model from Li DAR to estimate maize CH was the most stable across treatments and stages(R~2:0.928 to0.943).For TD identification,the best performance was achieved by using Li DAR data with AGDD as the independent variable,combined with the knee point method,resulting in RMSE of 2.95 d.The high accuracy was maintained at temporal resolutions as coarse as 14 d.TD forecast got more accurate as the CH time series extended.The optimal timing for forecasting TD was when the CH exceeded half of its maximum.Using only Li DAR CH data below 1.6 m and empirical growth rate estimates,the forecasted TD showed an RMSE of 3.90 d.In conclusion,this study exploited the growth characteristics of maize height to provide a practical approach for the timely identification and forecast of maize TD.
