This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic modelling.The Methodology of this study is categorized into three p...This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic modelling.The Methodology of this study is categorized into three phases:the Text Classification Approach(TCA),the Proposed Algorithms Interpretation(PAI),andfinally,Information Retrieval Approach(IRA).The TCA reflects the text preprocessing pipeline called a clean corpus.The Global Vec-tors for Word Representation(Glove)pre-trained model,FastText,Term Frequency-Inverse Document Fre-quency(TF-IDF),and Bag-of-Words(BOW)for extracting the features have been interpreted in this research.The PAI manifests the Bidirectional Long Short-Term Memory(Bi-LSTM)and Convolutional Neural Network(CNN)to classify the COVID-19 news.Again,the IRA explains the mathematical interpretation of Latent Dirich-let Allocation(LDA),obtained for modelling the topic of Information Retrieval(IR).In this study,99%accuracy was obtained by performing K-fold cross-validation on Bi-LSTM with Glove.A comparative analysis between Deep Learning and Machine Learning based on feature extraction and computational complexity exploration has been performed in this research.Furthermore,some text analyses and the most influential aspects of each document have been explored in this study.We have utilized Bidirectional Encoder Representations from Trans-formers(BERT)as a Deep Learning mechanism in our model training,but the result has not been uncovered satisfactory.However,the proposed system can be adjustable in the real-time news classification of COVID-19.展开更多
An effective and reliable prediction of the remaining useful life(RUL)of a tool is important to a metal forming process because it can significantly reduce unexpected maintenance,avoid machine shutdowns and increase s...An effective and reliable prediction of the remaining useful life(RUL)of a tool is important to a metal forming process because it can significantly reduce unexpected maintenance,avoid machine shutdowns and increase system stability.This study proposes a new data-driven approach to the RUL prediction for metal forming processes under multiple contact sliding conditions.The data-driven approach took advantage of bidirectional long short-term memory(BLSTM)and convolutional neural networks(CNN).A pre-trained lightweight CNN-based network,WearNet,was re-trained to classify the wear states of workpiece surfaces with a high accuracy,then the classification results were passed into a BLSTM-based regression model as inputs for RUL estimation.The experimental results demonstrated that this approach was able to predict the RUL values with a small error(below 5%)and a low root mean square error(RMSE)(around 1.5),which was more superior and robust than the other state-of-the-art methods.展开更多
Numerous influence factors will lead to the inaccurate prediction of fatigue crack propagation(FCP)life of the metal structure based on the existing FCP model,while the prediction method based on machine learning(ML)a...Numerous influence factors will lead to the inaccurate prediction of fatigue crack propagation(FCP)life of the metal structure based on the existing FCP model,while the prediction method based on machine learning(ML)and data-driven approach can provide a new idea for accurately predicting the FCP life of the metal structure.In response to the inconvenience of the online prediction method and the inaccu-racy of the offline prediction method,an improved offline prediction method based on data feedback is presented in this paper.FCP tests of reduced scale models of balcony opening corners in a cruise ship are conducted to obtain experimental data with respect to the a-N curves.The crack length corresponding to the cycle is trained using a support vector regression(SVR)and back propagation neural network(BP NN)algorithms.FCP prediction lives of test specimens are performed according to the online,offline,and improved offline prediction methods.Effects of the number of feedback data,the sequence length(SL)in the input set,and the cycle interval on prediction accuracy are discussed.The generalization ability of the proposed method is validated by comparing the prediction results with the experimental data in the literature.The larger the number of feedback data,the higher the prediction accuracy.The results show that 1/5 and 1/2 feedback data are needed in the SVR and BP NN algorithm with SL is 5,respectively.Furthermore,the SVR algorithm and SL=5 are recommended for FCP life prediction using the improved offline prediction method.展开更多
Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance ...Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.展开更多
A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis ca...A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.展开更多
In a smart grid,state estimation(SE)is a very important component of energy management system.Its main functions include system SE and detection of cyber anomalies.Recently,it has been shown that conventional SE techn...In a smart grid,state estimation(SE)is a very important component of energy management system.Its main functions include system SE and detection of cyber anomalies.Recently,it has been shown that conventional SE techniques are vulnerable to false data injection(FDI)attack,which is a sophisticated new class of attacks on data integrity in smart grid.The main contribution of this paper is to propose a new FDI attack detection technique using a new data-driven SE model,which is different from the traditional weighted least square based SE model.This SE model has a number of unique advantages compared with traditional SE models.First,the prediction technique can better maintain the inherent temporal correlations among consecutive measurement vectors.Second,the proposed SE model can learn the actual power system states.Finally,this paper shows that this SE model can be effectively used to detect FDI attacks that otherwise remain stealthy to traditional SE-based bad data detectors.The proposed FDI attack detection technique is evaluated on a number of standard bus systems.The performance of state prediction and the accuracy of FDI attack detection are benchmarked against the state-ofthe-art techniques.Experimental results show that the proposed FDI attack detection technique has a higher detection rate compared with the existing techniques while reducing the false alarms significantly.展开更多
BACKGROUND Dislocation rates after hemiarthroplasty reportedly vary from 1%to 17%.This serious complication is associated with increased morbidity and mortality rates.Approaches to this surgery are still debated,with ...BACKGROUND Dislocation rates after hemiarthroplasty reportedly vary from 1%to 17%.This serious complication is associated with increased morbidity and mortality rates.Approaches to this surgery are still debated,with no consensus regarding the superiority of any single approach.AIM To compare early postoperative complications after implementing the direct anterior and posterior approaches(PL)for hip hemiarthroplasty after femoral neck fractures.METHODS This is a comparative,retrospective,single-center cohort study conducted at a university hospital.Between March 2008 and December 2018,273 patients(a total of 280 hips)underwent bipolar hemiarthroplasties(n=280)for displaced femoral neck fractures using either the PL(n=171)or the minimally invasive direct anterior approach(DAA)(n=109).The choice of approach was related to the surgeons’practices;the implant types were similar and unrelated to the approach.Dislocation rates and other complications were reviewed after a minimum followup of 6 mo.RESULTS Both treatment groups had similarly aged patients(mean age:82 years),sex ratios,patient body mass indexes,and patient comorbidities.Surgical data(surgery delay time,operative time,and blood loss volume)did not differ significantly between the groups.The 30 d mortality rate was higher in the PL group(9.9%)than in the DAA group(3.7%),but the difference was not statistically significant(P=0.052).Among the one-month survivors,a significantly higher rate of dislocation was observed in the PL group(14/154;9.1%)than in the DAA group(0/105;0%)(P=0.002).Of the 14 patients with dislocation,8 underwent revision surgery for recurrent instability(posterior group),and one of them had 2 additional procedures due to a deep infection.The rate of other complications(e.g.,perioperative and early postoperative periprosthetic fractures and infection-related complications)did not differ significantly between the groups.CONCLUSION These findings suggest that the DAA to bipolar hemiarthroplasty for patients with femoral neck fractures is associated with a lower dislocation rate(<1%)than the PL.展开更多
With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair compar...With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.展开更多
The reliable operation of high-speed wire rod finishing mills is crucial in the steel production enterprise.As complex system-level equipment,it is difficult for high-speed wire rod finishing mills to realize fault lo...The reliable operation of high-speed wire rod finishing mills is crucial in the steel production enterprise.As complex system-level equipment,it is difficult for high-speed wire rod finishing mills to realize fault location and real-time monitoring.To solve the above problems,an expert experience and data-driven-based hybrid fault diagnosis method for high-speed wire rod finishing mills is proposed in this paper.First,based on its mechanical structure,time and frequency domain analysis are improved in fault feature extraction.The approach of combining virtual value,peak value with kurtosis value index,is adopted in time domain analysis.Speed adjustment and side frequency analysis are proposed in frequency domain analysis to obtain accurate component characteristic frequency and its corresponding sideband.Then,according to time and frequency domain characteristics,fault location based on expert experience is proposed to get an accurate fault result.Finally,the proposed method is implemented in the equipment intelligent diagnosis system.By taking an equipment fault on site,for example,the effectiveness of the proposed method is illustrated in the system.展开更多
The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was p...The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.展开更多
Consider a typical situation where an investor is considering acquiring an unexplored oilfield.The oilfield has undergone a preliminary geological and geophysical study in which pre-discovery data such as lithology,de...Consider a typical situation where an investor is considering acquiring an unexplored oilfield.The oilfield has undergone a preliminary geological and geophysical study in which pre-discovery data such as lithology,depth,depositional system,diagenetic overprint,structural compartmentalization,and trap type are available.In this situation,investors usually estimate production rates using a volumetric approach.A more accurate estimation of production rates can be obtained using analytical methods,which require additional data such as net pay,porosity,oil formation volume factor,permeability,viscosity,and pressure.We call these data post-discovery parameters because they are only available after discovery through exploration drilling.A data-driven approach to estimating post-discovery parameters of an unexplored oilfield is developed based on its pre-discovery data by learning from proven reservoir data.Using the Gaussian mixture model,and a data-driven reservoir typology based on the joint probability distribution of post-discovery parameters is established.We came up with 12 reservoir types.Subsequently,an artificial neural network classification model with the resilient backpropagation algorithm is used to find relationships between pre-discovery data and reservoir types.Based on k-fold crossvalidation with k?10,the accuracy of the classification model is stable with an average of 87.9%.With our approach,an investor considering acquiring an unexplored oilfield can classify the oilfield's reservoir into a particular type and estimate its post-discovery parameters'joint probability distribution.The investor can incorporate this information into a valuation model to calculate the production rates more accurately,estimate the oilfield's value and risk,and make an informed acquisition decision accordingly.展开更多
Soil tensile strength is a critical parameter governing the initiation and propagation of tensile cracking.This study proposes an eco-friendly approach to improve the tensile behavior and crack resistance of clayey so...Soil tensile strength is a critical parameter governing the initiation and propagation of tensile cracking.This study proposes an eco-friendly approach to improve the tensile behavior and crack resistance of clayey soils.To validate the feasibility and efficacy of the proposed approach,direct tensile tests were employed to determine the tensile strength of the compacted soil with different W-OH treatment concentrations and water contents.Desiccation tests were also performed to evaluate the effectiveness of W-OH treatment in enhancing soil tensile cracking resistance.During this period,the effects of W-OH treatment concentration and water content on tensile properties,soil suction and microstructure were investigated.The tensile tests reveal that W-OH treatment has a significant impact on the tensile strength and failure mode of the soil,which not only effectively enhances the tensile strength and failure displacement,but also changes the brittle failure behavior into a more ductile quasi-brittle failure behavior.The suction measurements and mercury intrusion porosimetry(MIP)tests show that W-OH treatment can slightly reduce soil suction by affecting skeleton structure and increasing macropores.Combined with the microstructural analysis,it becomes evident that the significant improvement in soil tensile behavior through W-OH treatment is mainly attributed to the W-OH gel's ability to provide additional binding force for bridging and encapsulating the soil particles.Moreover,desiccation tests demonstrate that W-OH treatment can significantly reduce or even inhibit the formation of soil tensile cracking.With the increase of W-OH treatment concentration,the surface crack ratio and total crack length are significantly reduced.This study enhances a fundamental understanding of eco-polymer impacts on soil mechanical properties and provides valuable insight into their potential application for improving soil crack resistance.展开更多
BACKGROUND Advancements in laparoscopic technology and a deeper understanding of intra-hepatic anatomy have led to the establishment of more precise laparoscopic hepatectomy(LH)techniques.The indocyanine green(ICG)flu...BACKGROUND Advancements in laparoscopic technology and a deeper understanding of intra-hepatic anatomy have led to the establishment of more precise laparoscopic hepatectomy(LH)techniques.The indocyanine green(ICG)fluorescence navi-gation technique has emerged as the most effective method for identifying hepatic regions,potentially overcoming the limitations of LH.While laparoscopic left hemihepatectomy(LLH)is a standardized procedure,there is a need for innova-tive strategies to enhance its outcomes.important anatomical markers,surgical skills,and ICG staining methods.METHODS Thirty-seven patients who underwent ICG fluorescence-guided LLH at Qujing Second People's Hospital between January 2019 and February 2022 were retrospectively analyzed.The cranial-dorsal approach was performed which involves dissecting the left hepatic vein cephalad,isolating the Arantius ligament,exposing the middle hepatic vein,and dissecting the parenchyma from the dorsal to the foot in order to complete the anatomical LLH.The surgical methods,as well as intra-and post-surgical data,were recorded and analyzed.Our hospital’s Medical Ethics Committee approved this study(Ethical review:2022-019-01).RESULTS Intraoperative blood loss during LLH was 335.68±99.869 mL and the rates of transfusion and conversion to laparotomy were 13.5%and 0%,respectively.The overall incidence of complications throughout the follow-up(median of 18 months;range 1-36 months)was 21.6%.No mortality or severe complications(level IV)were reported.CONCLUSION LLH has the potential to become a novel,standardized approach that can effectively,safely,and simply expose the middle hepatic vein and meet the requirements of precision surgery.展开更多
BACKGROUND Total hip arthroplasty is as an effective intervention to relieve pain and improve hip function.Approaches of the hip have been exhaustively explored about pros and cons.The efficacy and the complications o...BACKGROUND Total hip arthroplasty is as an effective intervention to relieve pain and improve hip function.Approaches of the hip have been exhaustively explored about pros and cons.The efficacy and the complications of hip approaches remains inconclusive.This study conducted an umbrella review to systematically appraise previous meta-analysis(MAs)including conventional posterior approach(PA),and minimally invasive surgeries as the lateral approach(LA),direct anterior approach(DAA),2-incisions method,mini-lateral approach and the newest technique direct superior approach(DSA)or supercapsular percutaneouslyassisted total hip(SuperPath).AIM To compare the efficacy and complications of hip approaches that have been published in all MAs and randomized controlled trials(RCTs).METHODS MAs were identified from MEDLINE and Scopus from inception until 2023.RCTs were then updated from the latest MA to September 2023.This study included studies which compared hip approaches and reported at least one outcome such as Harris Hip Score(HHS),dislocation,intra-operative fracture,wound compliData were independently selected,extracted and assessed by two reviewers.Network MA and cluster rank and surface under the cumulative ranking curve(SUCRA)were estimated for treatment efficacy and safety.RESULTS Finally,twenty-eight MAs(40 RCTs),and 13 RCTs were retrieved.In total 47 RCTs were included for reanalysis.The results of corrected covered area showed high degree(13.80%).Among 47 RCTs,most of the studies were low risk of bias in part of random process and outcome reporting,while other domains were medium to high risk of bias.DAA significantly provided higher HHS at three months than PA[pooled unstandardized mean difference(USMD):3.49,95%confidence interval(CI):0.98,6.00 with SUCRA:85.9],followed by DSA/SuperPath(USMD:1.57,95%CI:-1.55,4.69 with SUCRA:57.6).All approaches had indifferent dislocation and intraoperative fracture rates.SUCRA comparing early functional outcome and composite complications(dislocation,intra-operative fracture,wound complication,and nerve injury)found DAA was the best approach followed by DSA/SuperPath.CONCLUSION DSA/SuperPath had better earlier functional outcome than PA,but still could not overcome the result of DAA.This technique might be the other preferred option with acceptable complications.展开更多
The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively...The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively evaluate the relative importance of model parameters on the production forecasting performance,sensitivity analysis of parameters is required.The parameters are ranked according to the sensitivity coefficients for the subsequent optimization scheme design.A data-driven global sensitivity analysis(GSA)method using convolutional neural networks(CNN)is proposed to identify the influencing parameters in shale gas production.The CNN is trained on a large dataset,validated against numerical simulations,and utilized as a surrogate model for efficient sensitivity analysis.Our approach integrates CNN with the Sobol'global sensitivity analysis method,presenting three key scenarios for sensitivity analysis:analysis of the production stage as a whole,analysis by fixed time intervals,and analysis by declining rate.The findings underscore the predominant influence of reservoir thickness and well length on shale gas production.Furthermore,the temporal sensitivity analysis reveals the dynamic shifts in parameter importance across the distinct production stages.展开更多
For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to in...For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.展开更多
OBJECTIVE To assess the feasibility and safety of the minimalistic approach to left atrial appendage occlusion(LAAO) guided by cardiac computed tomography angiography(CCTA).METHODS Ninety consecutive patients who unde...OBJECTIVE To assess the feasibility and safety of the minimalistic approach to left atrial appendage occlusion(LAAO) guided by cardiac computed tomography angiography(CCTA).METHODS Ninety consecutive patients who underwent LAAO, with or without CCTA-guided, were matched(1:2). Each step of the LAAO procedure in the computed tomography(CT) guidance group(CT group) was directed by preprocedural CT planning. In the control group, LAAO was performed using the standard method. All patients were followed up for 12 months, and device surveillance was conducted using CCTA.RESULTS A total of 90 patients were included in the analysis, with 30 patients in the CT group and 60 matched patients in the control group. All patients were successfully implanted with Watchman devices. The mean ages for the CT group and the control group were 70.0 ± 9.4 years and 68.4 ± 11.9 years(P = 0.52), respectively. The procedure duration(45.6 ± 10.7 min vs. 58.8 ± 13.0 min,P < 0.001) and hospital stay(7.5 ± 2.4 day vs. 9.6 ± 2.8 day, P = 0.001) in the CT group was significantly shorter compared to the control group. However, the total radiation dose was higher in the CT group compared to the control group(904.9 ± 348.0 m Gy vs.711.9 ± 211.2 m Gy, P = 0.002). There were no significant differences in periprocedural pericardial effusion(3.3% vs. 6.3%, P = 0.8) between the two groups. The rate of postprocedural adverse events(13.3% vs. 18.3%, P = 0.55) were comparable between both groups at 12 months follow-up.CONCLUSIONS CCTA is capable of detailed LAAO procedure planning. Minimalistic LAAO with preprocedural CCTA planning was feasible and safe, with shortened procedure time and acceptable increased radiation and contras consumption. For patients with contraindications to general anesthesia and/or transesophageal echocardiography, this promising method may be an alternative to conventional LAAO.展开更多
This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod ...This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.展开更多
High-energy gamma-ray radiography has exceptional penetration ability and has become an indispensable nondestructive testing(NDT)tool in various fields.For high-energy photons,point projection radiography is almost th...High-energy gamma-ray radiography has exceptional penetration ability and has become an indispensable nondestructive testing(NDT)tool in various fields.For high-energy photons,point projection radiography is almost the only feasible imaging method,and its spatial resolution is primarily constrained by the size of the gamma-ray source.In conventional industrial applications,gamma-ray sources are commonly based on electron beams driven by accelerators,utilizing the process of bremsstrahlung radiation.The size of the gamma-ray source is dependent on the dimensional characteristics of the electron beam.Extensive research has been conducted on various advanced accelerator technologies that have the potential to greatly improve spatial resolution in NDT.In our investigation of laser-driven gamma-ray sources,a spatial resolution of about 90μm is achieved when the areal density of the penetrated object is 120 g/cm^(2).A virtual source approach is proposed to optimize the size of the gamma-ray source used for imaging,with the aim of maximizing spatial resolution.In this virtual source approach,the gamma ray can be considered as being emitted from a virtual source within the convertor,where the equivalent gamma-ray source size in imaging is much smaller than the actual emission area.On the basis of Monte Carlo simulations,we derive a set of evaluation formulas for virtual source scale and gamma-ray emission angle.Under optimal conditions,the virtual source size can be as small as 15μm,which can significantly improve the spatial resolution of high-penetration imaging to less than 50μm.展开更多
Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a cr...Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.展开更多
文摘This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic modelling.The Methodology of this study is categorized into three phases:the Text Classification Approach(TCA),the Proposed Algorithms Interpretation(PAI),andfinally,Information Retrieval Approach(IRA).The TCA reflects the text preprocessing pipeline called a clean corpus.The Global Vec-tors for Word Representation(Glove)pre-trained model,FastText,Term Frequency-Inverse Document Fre-quency(TF-IDF),and Bag-of-Words(BOW)for extracting the features have been interpreted in this research.The PAI manifests the Bidirectional Long Short-Term Memory(Bi-LSTM)and Convolutional Neural Network(CNN)to classify the COVID-19 news.Again,the IRA explains the mathematical interpretation of Latent Dirich-let Allocation(LDA),obtained for modelling the topic of Information Retrieval(IR).In this study,99%accuracy was obtained by performing K-fold cross-validation on Bi-LSTM with Glove.A comparative analysis between Deep Learning and Machine Learning based on feature extraction and computational complexity exploration has been performed in this research.Furthermore,some text analyses and the most influential aspects of each document have been explored in this study.We have utilized Bidirectional Encoder Representations from Trans-formers(BERT)as a Deep Learning mechanism in our model training,but the result has not been uncovered satisfactory.However,the proposed system can be adjustable in the real-time news classification of COVID-19.
基金supported by the Baosteel Australia Research and Development Centre(BAJC)Portfolio(Grant No.BA17001)the ARC Hub for Computational Particle Technology(Grant No.IH140100035)+1 种基金the Chinese Guangdong Specific Discipline Project(Grant No.2020ZDZX2006)the Shenzhen Key Laboratory Project of Cross-scale Manufacturing Mechanics(Grant No.ZDSYS20200810171201007).
文摘An effective and reliable prediction of the remaining useful life(RUL)of a tool is important to a metal forming process because it can significantly reduce unexpected maintenance,avoid machine shutdowns and increase system stability.This study proposes a new data-driven approach to the RUL prediction for metal forming processes under multiple contact sliding conditions.The data-driven approach took advantage of bidirectional long short-term memory(BLSTM)and convolutional neural networks(CNN).A pre-trained lightweight CNN-based network,WearNet,was re-trained to classify the wear states of workpiece surfaces with a high accuracy,then the classification results were passed into a BLSTM-based regression model as inputs for RUL estimation.The experimental results demonstrated that this approach was able to predict the RUL values with a small error(below 5%)and a low root mean square error(RMSE)(around 1.5),which was more superior and robust than the other state-of-the-art methods.
文摘Numerous influence factors will lead to the inaccurate prediction of fatigue crack propagation(FCP)life of the metal structure based on the existing FCP model,while the prediction method based on machine learning(ML)and data-driven approach can provide a new idea for accurately predicting the FCP life of the metal structure.In response to the inconvenience of the online prediction method and the inaccu-racy of the offline prediction method,an improved offline prediction method based on data feedback is presented in this paper.FCP tests of reduced scale models of balcony opening corners in a cruise ship are conducted to obtain experimental data with respect to the a-N curves.The crack length corresponding to the cycle is trained using a support vector regression(SVR)and back propagation neural network(BP NN)algorithms.FCP prediction lives of test specimens are performed according to the online,offline,and improved offline prediction methods.Effects of the number of feedback data,the sequence length(SL)in the input set,and the cycle interval on prediction accuracy are discussed.The generalization ability of the proposed method is validated by comparing the prediction results with the experimental data in the literature.The larger the number of feedback data,the higher the prediction accuracy.The results show that 1/5 and 1/2 feedback data are needed in the SVR and BP NN algorithm with SL is 5,respectively.Furthermore,the SVR algorithm and SL=5 are recommended for FCP life prediction using the improved offline prediction method.
基金supported by the National Natural Science Foundation of China (No.62173281,52377217,U23A20651)Sichuan Science and Technology Program (No.24NSFSC0024,23ZDYF0734,23NSFSC1436)+2 种基金Dazhou City School Cooperation Project (No.DZXQHZ006)Technopole Talent Summit Project (No.KJCRCFH08)Robert Gordon University。
文摘Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.
基金This work was supported by the National Natural Science Foundation of China(Grant No.42050104)the Science Foundation of SINOPEC Group(Grant No.P20030).
文摘A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.
文摘In a smart grid,state estimation(SE)is a very important component of energy management system.Its main functions include system SE and detection of cyber anomalies.Recently,it has been shown that conventional SE techniques are vulnerable to false data injection(FDI)attack,which is a sophisticated new class of attacks on data integrity in smart grid.The main contribution of this paper is to propose a new FDI attack detection technique using a new data-driven SE model,which is different from the traditional weighted least square based SE model.This SE model has a number of unique advantages compared with traditional SE models.First,the prediction technique can better maintain the inherent temporal correlations among consecutive measurement vectors.Second,the proposed SE model can learn the actual power system states.Finally,this paper shows that this SE model can be effectively used to detect FDI attacks that otherwise remain stealthy to traditional SE-based bad data detectors.The proposed FDI attack detection technique is evaluated on a number of standard bus systems.The performance of state prediction and the accuracy of FDI attack detection are benchmarked against the state-ofthe-art techniques.Experimental results show that the proposed FDI attack detection technique has a higher detection rate compared with the existing techniques while reducing the false alarms significantly.
基金This study was reviewed and approved by the Ethics Committee of the HUB-Hospital Erasme.
文摘BACKGROUND Dislocation rates after hemiarthroplasty reportedly vary from 1%to 17%.This serious complication is associated with increased morbidity and mortality rates.Approaches to this surgery are still debated,with no consensus regarding the superiority of any single approach.AIM To compare early postoperative complications after implementing the direct anterior and posterior approaches(PL)for hip hemiarthroplasty after femoral neck fractures.METHODS This is a comparative,retrospective,single-center cohort study conducted at a university hospital.Between March 2008 and December 2018,273 patients(a total of 280 hips)underwent bipolar hemiarthroplasties(n=280)for displaced femoral neck fractures using either the PL(n=171)or the minimally invasive direct anterior approach(DAA)(n=109).The choice of approach was related to the surgeons’practices;the implant types were similar and unrelated to the approach.Dislocation rates and other complications were reviewed after a minimum followup of 6 mo.RESULTS Both treatment groups had similarly aged patients(mean age:82 years),sex ratios,patient body mass indexes,and patient comorbidities.Surgical data(surgery delay time,operative time,and blood loss volume)did not differ significantly between the groups.The 30 d mortality rate was higher in the PL group(9.9%)than in the DAA group(3.7%),but the difference was not statistically significant(P=0.052).Among the one-month survivors,a significantly higher rate of dislocation was observed in the PL group(14/154;9.1%)than in the DAA group(0/105;0%)(P=0.002).Of the 14 patients with dislocation,8 underwent revision surgery for recurrent instability(posterior group),and one of them had 2 additional procedures due to a deep infection.The rate of other complications(e.g.,perioperative and early postoperative periprosthetic fractures and infection-related complications)did not differ significantly between the groups.CONCLUSION These findings suggest that the DAA to bipolar hemiarthroplasty for patients with femoral neck fractures is associated with a lower dislocation rate(<1%)than the PL.
基金supported by the National Natural Science Foundation of China (52075420)the National Key Research and Development Program of China (2020YFB1708400)。
文摘With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.
基金the National Key Research and Development Program of China under Grant 2021YFB3301300the National Natural Science Foundation of China under Grant 62203213+1 种基金the Natural Science Foundation of Jiangsu Province under Grant BK20220332the Open Project Program of Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System under Grant 2022A0004.
文摘The reliable operation of high-speed wire rod finishing mills is crucial in the steel production enterprise.As complex system-level equipment,it is difficult for high-speed wire rod finishing mills to realize fault location and real-time monitoring.To solve the above problems,an expert experience and data-driven-based hybrid fault diagnosis method for high-speed wire rod finishing mills is proposed in this paper.First,based on its mechanical structure,time and frequency domain analysis are improved in fault feature extraction.The approach of combining virtual value,peak value with kurtosis value index,is adopted in time domain analysis.Speed adjustment and side frequency analysis are proposed in frequency domain analysis to obtain accurate component characteristic frequency and its corresponding sideband.Then,according to time and frequency domain characteristics,fault location based on expert experience is proposed to get an accurate fault result.Finally,the proposed method is implemented in the equipment intelligent diagnosis system.By taking an equipment fault on site,for example,the effectiveness of the proposed method is illustrated in the system.
基金financially supported by the National Key Research and Development Program of China(2022YFB3706800,2020YFB1710100)the National Natural Science Foundation of China(51821001,52090042,52074183)。
文摘The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.
基金This research is supported and partially funded by Parahyangan Catholic University in Bandung,Indonesia.
文摘Consider a typical situation where an investor is considering acquiring an unexplored oilfield.The oilfield has undergone a preliminary geological and geophysical study in which pre-discovery data such as lithology,depth,depositional system,diagenetic overprint,structural compartmentalization,and trap type are available.In this situation,investors usually estimate production rates using a volumetric approach.A more accurate estimation of production rates can be obtained using analytical methods,which require additional data such as net pay,porosity,oil formation volume factor,permeability,viscosity,and pressure.We call these data post-discovery parameters because they are only available after discovery through exploration drilling.A data-driven approach to estimating post-discovery parameters of an unexplored oilfield is developed based on its pre-discovery data by learning from proven reservoir data.Using the Gaussian mixture model,and a data-driven reservoir typology based on the joint probability distribution of post-discovery parameters is established.We came up with 12 reservoir types.Subsequently,an artificial neural network classification model with the resilient backpropagation algorithm is used to find relationships between pre-discovery data and reservoir types.Based on k-fold crossvalidation with k?10,the accuracy of the classification model is stable with an average of 87.9%.With our approach,an investor considering acquiring an unexplored oilfield can classify the oilfield's reservoir into a particular type and estimate its post-discovery parameters'joint probability distribution.The investor can incorporate this information into a valuation model to calculate the production rates more accurately,estimate the oilfield's value and risk,and make an informed acquisition decision accordingly.
基金supported by the National Natural Science Foundation of China(Grant Nos.41925012,42230710)Key Laboratory Cooperation Special Project of Western Cross Team of Western Light,Chinese Academy of Sciences(Grant No.xbzg-zdsys-202107).
文摘Soil tensile strength is a critical parameter governing the initiation and propagation of tensile cracking.This study proposes an eco-friendly approach to improve the tensile behavior and crack resistance of clayey soils.To validate the feasibility and efficacy of the proposed approach,direct tensile tests were employed to determine the tensile strength of the compacted soil with different W-OH treatment concentrations and water contents.Desiccation tests were also performed to evaluate the effectiveness of W-OH treatment in enhancing soil tensile cracking resistance.During this period,the effects of W-OH treatment concentration and water content on tensile properties,soil suction and microstructure were investigated.The tensile tests reveal that W-OH treatment has a significant impact on the tensile strength and failure mode of the soil,which not only effectively enhances the tensile strength and failure displacement,but also changes the brittle failure behavior into a more ductile quasi-brittle failure behavior.The suction measurements and mercury intrusion porosimetry(MIP)tests show that W-OH treatment can slightly reduce soil suction by affecting skeleton structure and increasing macropores.Combined with the microstructural analysis,it becomes evident that the significant improvement in soil tensile behavior through W-OH treatment is mainly attributed to the W-OH gel's ability to provide additional binding force for bridging and encapsulating the soil particles.Moreover,desiccation tests demonstrate that W-OH treatment can significantly reduce or even inhibit the formation of soil tensile cracking.With the increase of W-OH treatment concentration,the surface crack ratio and total crack length are significantly reduced.This study enhances a fundamental understanding of eco-polymer impacts on soil mechanical properties and provides valuable insight into their potential application for improving soil crack resistance.
基金Supported by The High-level Talent Training Support Project of Yunnan Province,No.YNWR-MY-2020-053and the Key Project of the Second People's Hospital of Qujing in 2022,No.2022ynkt04。
文摘BACKGROUND Advancements in laparoscopic technology and a deeper understanding of intra-hepatic anatomy have led to the establishment of more precise laparoscopic hepatectomy(LH)techniques.The indocyanine green(ICG)fluorescence navi-gation technique has emerged as the most effective method for identifying hepatic regions,potentially overcoming the limitations of LH.While laparoscopic left hemihepatectomy(LLH)is a standardized procedure,there is a need for innova-tive strategies to enhance its outcomes.important anatomical markers,surgical skills,and ICG staining methods.METHODS Thirty-seven patients who underwent ICG fluorescence-guided LLH at Qujing Second People's Hospital between January 2019 and February 2022 were retrospectively analyzed.The cranial-dorsal approach was performed which involves dissecting the left hepatic vein cephalad,isolating the Arantius ligament,exposing the middle hepatic vein,and dissecting the parenchyma from the dorsal to the foot in order to complete the anatomical LLH.The surgical methods,as well as intra-and post-surgical data,were recorded and analyzed.Our hospital’s Medical Ethics Committee approved this study(Ethical review:2022-019-01).RESULTS Intraoperative blood loss during LLH was 335.68±99.869 mL and the rates of transfusion and conversion to laparotomy were 13.5%and 0%,respectively.The overall incidence of complications throughout the follow-up(median of 18 months;range 1-36 months)was 21.6%.No mortality or severe complications(level IV)were reported.CONCLUSION LLH has the potential to become a novel,standardized approach that can effectively,safely,and simply expose the middle hepatic vein and meet the requirements of precision surgery.
文摘BACKGROUND Total hip arthroplasty is as an effective intervention to relieve pain and improve hip function.Approaches of the hip have been exhaustively explored about pros and cons.The efficacy and the complications of hip approaches remains inconclusive.This study conducted an umbrella review to systematically appraise previous meta-analysis(MAs)including conventional posterior approach(PA),and minimally invasive surgeries as the lateral approach(LA),direct anterior approach(DAA),2-incisions method,mini-lateral approach and the newest technique direct superior approach(DSA)or supercapsular percutaneouslyassisted total hip(SuperPath).AIM To compare the efficacy and complications of hip approaches that have been published in all MAs and randomized controlled trials(RCTs).METHODS MAs were identified from MEDLINE and Scopus from inception until 2023.RCTs were then updated from the latest MA to September 2023.This study included studies which compared hip approaches and reported at least one outcome such as Harris Hip Score(HHS),dislocation,intra-operative fracture,wound compliData were independently selected,extracted and assessed by two reviewers.Network MA and cluster rank and surface under the cumulative ranking curve(SUCRA)were estimated for treatment efficacy and safety.RESULTS Finally,twenty-eight MAs(40 RCTs),and 13 RCTs were retrieved.In total 47 RCTs were included for reanalysis.The results of corrected covered area showed high degree(13.80%).Among 47 RCTs,most of the studies were low risk of bias in part of random process and outcome reporting,while other domains were medium to high risk of bias.DAA significantly provided higher HHS at three months than PA[pooled unstandardized mean difference(USMD):3.49,95%confidence interval(CI):0.98,6.00 with SUCRA:85.9],followed by DSA/SuperPath(USMD:1.57,95%CI:-1.55,4.69 with SUCRA:57.6).All approaches had indifferent dislocation and intraoperative fracture rates.SUCRA comparing early functional outcome and composite complications(dislocation,intra-operative fracture,wound complication,and nerve injury)found DAA was the best approach followed by DSA/SuperPath.CONCLUSION DSA/SuperPath had better earlier functional outcome than PA,but still could not overcome the result of DAA.This technique might be the other preferred option with acceptable complications.
基金supported by the National Natural Science Foundation of China (Nos.52274048 and 52374017)Beijing Natural Science Foundation (No.3222037)the CNPC 14th five-year perspective fundamental research project (No.2021DJ2104)。
文摘The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively evaluate the relative importance of model parameters on the production forecasting performance,sensitivity analysis of parameters is required.The parameters are ranked according to the sensitivity coefficients for the subsequent optimization scheme design.A data-driven global sensitivity analysis(GSA)method using convolutional neural networks(CNN)is proposed to identify the influencing parameters in shale gas production.The CNN is trained on a large dataset,validated against numerical simulations,and utilized as a surrogate model for efficient sensitivity analysis.Our approach integrates CNN with the Sobol'global sensitivity analysis method,presenting three key scenarios for sensitivity analysis:analysis of the production stage as a whole,analysis by fixed time intervals,and analysis by declining rate.The findings underscore the predominant influence of reservoir thickness and well length on shale gas production.Furthermore,the temporal sensitivity analysis reveals the dynamic shifts in parameter importance across the distinct production stages.
基金supported by the National Natural Science Foundation of China (62173333, 12271522)Beijing Natural Science Foundation (Z210002)the Research Fund of Renmin University of China (2021030187)。
文摘For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.
基金supported by the Logistics Support Ministry of China (No.22BJZ41)the Capital's Funds for Health Improvement and Research (No.CFH2024-2-5071)。
文摘OBJECTIVE To assess the feasibility and safety of the minimalistic approach to left atrial appendage occlusion(LAAO) guided by cardiac computed tomography angiography(CCTA).METHODS Ninety consecutive patients who underwent LAAO, with or without CCTA-guided, were matched(1:2). Each step of the LAAO procedure in the computed tomography(CT) guidance group(CT group) was directed by preprocedural CT planning. In the control group, LAAO was performed using the standard method. All patients were followed up for 12 months, and device surveillance was conducted using CCTA.RESULTS A total of 90 patients were included in the analysis, with 30 patients in the CT group and 60 matched patients in the control group. All patients were successfully implanted with Watchman devices. The mean ages for the CT group and the control group were 70.0 ± 9.4 years and 68.4 ± 11.9 years(P = 0.52), respectively. The procedure duration(45.6 ± 10.7 min vs. 58.8 ± 13.0 min,P < 0.001) and hospital stay(7.5 ± 2.4 day vs. 9.6 ± 2.8 day, P = 0.001) in the CT group was significantly shorter compared to the control group. However, the total radiation dose was higher in the CT group compared to the control group(904.9 ± 348.0 m Gy vs.711.9 ± 211.2 m Gy, P = 0.002). There were no significant differences in periprocedural pericardial effusion(3.3% vs. 6.3%, P = 0.8) between the two groups. The rate of postprocedural adverse events(13.3% vs. 18.3%, P = 0.55) were comparable between both groups at 12 months follow-up.CONCLUSIONS CCTA is capable of detailed LAAO procedure planning. Minimalistic LAAO with preprocedural CCTA planning was feasible and safe, with shortened procedure time and acceptable increased radiation and contras consumption. For patients with contraindications to general anesthesia and/or transesophageal echocardiography, this promising method may be an alternative to conventional LAAO.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272257,12102292,12032006)the special fund for Science and Technology Innovation Teams of Shanxi Province(Nos.202204051002006).
文摘This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.12175212,11991071,12004353,11975214,and 11905202)the National Key R&D Program of China(Grant No.2022YFA1603300)+1 种基金the Science Challenge Project(Project No.TZ2018005)the Sciences and Technology on Plasma Physics Laboratory at CAEP(Grant No.6142A04200103).
文摘High-energy gamma-ray radiography has exceptional penetration ability and has become an indispensable nondestructive testing(NDT)tool in various fields.For high-energy photons,point projection radiography is almost the only feasible imaging method,and its spatial resolution is primarily constrained by the size of the gamma-ray source.In conventional industrial applications,gamma-ray sources are commonly based on electron beams driven by accelerators,utilizing the process of bremsstrahlung radiation.The size of the gamma-ray source is dependent on the dimensional characteristics of the electron beam.Extensive research has been conducted on various advanced accelerator technologies that have the potential to greatly improve spatial resolution in NDT.In our investigation of laser-driven gamma-ray sources,a spatial resolution of about 90μm is achieved when the areal density of the penetrated object is 120 g/cm^(2).A virtual source approach is proposed to optimize the size of the gamma-ray source used for imaging,with the aim of maximizing spatial resolution.In this virtual source approach,the gamma ray can be considered as being emitted from a virtual source within the convertor,where the equivalent gamma-ray source size in imaging is much smaller than the actual emission area.On the basis of Monte Carlo simulations,we derive a set of evaluation formulas for virtual source scale and gamma-ray emission angle.Under optimal conditions,the virtual source size can be as small as 15μm,which can significantly improve the spatial resolution of high-penetration imaging to less than 50μm.
基金supported by the Chinese Scholarship Council(Nos.202208320055 and 202108320111)the support from the energy department of Aalborg University was acknowledged.
文摘Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.
