In this paper,we study and characterize the volume estimates of geodesic balls on Finsler gradient Ricci solitons.We get the upper bounds on the volumes of geodesic balls of all three kinds of Finsler gradient Ricci s...In this paper,we study and characterize the volume estimates of geodesic balls on Finsler gradient Ricci solitons.We get the upper bounds on the volumes of geodesic balls of all three kinds of Finsler gradient Ricci solitons under certain condition about the Laplacian of thedistance function.展开更多
Background: Biological maturation refers to the progressive process through which individuals transition toward an adult state during growth and development. To address the challenges posed by differences in biologica...Background: Biological maturation refers to the progressive process through which individuals transition toward an adult state during growth and development. To address the challenges posed by differences in biological maturity and the limitations of existing testing methods, particularly in adolescent sports contexts, there is a pressing need for a non-invasive method that is convenient, accurate, and broadly applicable to monitor the biological maturity of adolescent athletes comprehensively. In response to this need, a maturity assessment method based on the smartphone application Maturo has been developed. This study evaluates the accuracy and validity of the Maturo software, an automated tool for estimating biological age and related maturation metrics.Methods: A sample of 103 actively training teenage athletes aged 9-17 years. The sample included 76 males(age = 11.74 ± 1.55 years, mean ±SD) and 27 females(age = 13.95 ± 1.40 years), all without medical conditions that might impact growth or development.Results: Compared to traditional expert evaluations, the intraclass correlation coefficients(ICCs) and Pearson correlation coefficients demonstrated reliable positive correlations and significant agreement between the Maturo software and expert methods across multiple metrics, such as biological age(ICC = 0.965, R = 0.97), corrected biological age(ICC = 0.973, R = 0.99), predicted adult height(ICC = 0.991, R = 0.99), and percentage of adult height achieved(ICC = 0.955, R = 0.97). The Bland-Altman plots provided additional evidence of the validity of the Maturo software estimations, showing low systematic error in most measures. The linear regression analysis produced excellent adjusted R2values: 0.95for biological age and 0.99 for anticipated adult height. The Maturo approach demonstrated a high level of dependability in classifying teenagers into groups based on their maturity status and timing. The κ coefficients of 0.93 for maturity status and 0.82 for maturity timing indicate a nearly perfect agreement with the expert technique.Conclusion: While the Maturo software's non-invasive nature, cost-effectiveness, and ease of use could make it a potential tool for regular monitoring of growth and maturation in young athletes, its promising results in assessing maturation should be interpreted with caution due to limitations such as sample size and demographic constraints. Further longitude research with larger and more diverse populations is needed to validate these preliminary findings and strengthen the evidence for its broader applicability.展开更多
Earthquakes can cause significant damage and loss of life,necessitating immediate assessment of the resulting fatalities.Rapid assessment and timely revision of fatality estimates are crucial for effective emergency d...Earthquakes can cause significant damage and loss of life,necessitating immediate assessment of the resulting fatalities.Rapid assessment and timely revision of fatality estimates are crucial for effective emergency decisionmaking.This study using the February 6,2023,M_(S)8.0 and M_(S)7.9 Kahramanmaras,Türkiye earthquakes as an example to estimate the ultimate number of fatalities.An early Quick Rough Estimate(QRE)based on the number of deaths reported by the Disaster and Emergency Management Presidency of Türkiye(AFAD)is conducted,and it dynamically adjusts these estimates as new data becomes available.The range of estimates of the final number of deaths can be calculated as 31384–56475 based on the"the QRE of the second day multiplied by 2–3" rule,which incorporates the reported final deaths 50500.The Quasi-Linear and Adaptive Estimation(QLAE)method adaptively adjusts the final fatality estimate within two days and predicts subsequent reported deaths.The correct order of magnitude of the final death toll can be estimated as early as 13 hr after the M_(S)8.0 earthquake.In addition,additional earthquakes such as May 12,2008,M_(S)8.1 Wenchuan earthquake(China),September 8,2023,M_(S)7.2 Al Haouz earthquake(Morocco),November 3,2023,M_(S)5.8 Mid-Western Nepal earthquake,December 18,2023,M_(S)6.1 Jishishan earthquake(China),January 1,2024,M_(S)7.2 Noto Peninsula earthquake(Japan)and August 8,2023,Maui,Hawaii,fires are added again to verified the correctness of the model.The fatalities from the Maui fires are found to be approximately equivalent to those resulting from an M_(S)7.4 earthquake.These methods complement existing frameworks such as Quake Loss Assessment for Response and Mitigation(QLARM)and Prompt Assessment of Global.展开更多
BACKGROUND Acute pancreatitis(AP),a severe pancreatic inflammatory condition,with a mor-tality rate reaching up to 40%.Recently,AP shows a steadily elevating prevalence,which causes the greater number of hospital admi...BACKGROUND Acute pancreatitis(AP),a severe pancreatic inflammatory condition,with a mor-tality rate reaching up to 40%.Recently,AP shows a steadily elevating prevalence,which causes the greater number of hospital admissions,imposing the substantial economic burden.Acute kidney injury(AKI)complicates take up approximately 15%of AP cases,with an associated mortality rate of 74.7%-81%.AIM To evaluate the efficacy of estimated plasma volume status(ePVS)in forecasting AKI in patients with AP.METHODS In this retrospective cohort study,AP cases were recruited from the First College of Clinical Medical Science of China Three Gorges University between January 2019 and October 2023.Electronic medical records were adopted for data extrac-tion,including demographic data and clinical characteristics.The association between ePVS and AKI was analyzed using multivariate logistic regression models,with potential confounders being adjusted.Nonlinear relationship was examined with smooth curve fitting,and infection points were calculated.Further analyses were performed on stratified subgroups and interaction tests were conducted.RESULTS Among the 1508 AP patients,251(16.6%)developed AKI.ePVS was calculated using Duarte(D-ePVS)and Kaplan-Hakim(KH-ePVS)formulas.After adjusting for covariates,the AKI risk exhibited 46%[odds ratio(OR)=1.46,95%confidence interval(CI):0.96-2.24]and 11%(OR=1.11,95%CI:0.72-1.72)increases in the low tertile(T1)of D-ePVS and KH-ePVS,respectively,and 101%(OR=2.01,95%CI:1.31-3.05)and 51%(OR=1.51,95%CI:1.00-2.29)increases in the high tertile(T3)relative to the reference tertile(T2).Nonlinear curve fitting revealed a U-shaped association of D-ePVS with AKI and a J-shaped association for KH-ePVS,with inflection points at 4.3 dL/g and-2.8%,res-pectively.Significant interactions were not observed in age,gender,hypertension,diabetes mellitus,sequential organ failure assessment score,or AP severity(all P for interaction>0.05).CONCLUSION Our results indicated that ePVS demonstrated the nonlinear association with AKI incidence in AP patients.A U-shaped curve was observed with an inflection point at 4.3 dL/g for the Duarte formula,and a J-shaped curve at-2.8%for the Kaplan-Hakim formula.展开更多
The distribution of the sediment material storage quantity along the debris flow channels(SMSQ_DFC)can provide a foundation for runoffgenerated debris flow prediction or susceptibility assessment.Current models for es...The distribution of the sediment material storage quantity along the debris flow channels(SMSQ_DFC)can provide a foundation for runoffgenerated debris flow prediction or susceptibility assessment.Current models for estimating SMSQ_DFC do not consider the capacity of the channel cross-section to accommodate sediment materials.This accommodation condition serves as a limiting factor in determining whether the expected surplus of sediment materials can ultimately be stored.To address this issue,a mass-conservative index was used to represent the balance of deposit materials at any cross-section,considering the influx from upstream,outflux to downstream,and accommodation capacity.Based on this index,a new model for estimating SMSQ_DFC was developed and subsequently evaluated.The evaluation results show that the model meets the accuracy requirements with average error rates of 14.06%for self-validation and 14.81%for generalization ability validation.To assess its practical applications,the model was applied to the Yeniu Gully in Wenchuan County,Sichuan Province,an area with detailed field survey data.The results show that the model exhibits a commendable performance.Compared to traditional theoretical and semi-theoretical statistical models,our model is easier to use(input parameters can be obtained using Geographic Information Systems(GIS)).The modeling parameters chosen in this study have more theoretical significance than those used in existing purely statistical models,offering more effective technical support for estimating SMSQ_DFC.展开更多
Aging is an inevitable process that is usually measured by chronological age,with people aged 65 and over being defined as"older individuals".There is disagreement in the current scientific literature regard...Aging is an inevitable process that is usually measured by chronological age,with people aged 65 and over being defined as"older individuals".There is disagreement in the current scientific literature regarding the best methods to estimate glomerular filtration rate(eGFR)in older adults.Several studies suggest the use of an age-adjusted definition to improve accuracy and avoid overdiagnosis.In contrast,some researchers argue that such changes could complicate the classification of chronic kidney disease(CKD).Several formulas,including the Modification of Diet in Renal Disease,CKD-Epidemiology Collaboration,and Cockcroft-Gault equations,are used to estimate eGFR.However,each of these formulas has significant limitations when applied to older adults,primarily due to sarcopenia and malnutrition,which greatly affect both muscle mass and creatinine levels.Alternative formulas,such as the Berlin Initiative Study and the Full Age Spectrum equations,provide more accurate estimates of values for older adults by accounting for age-related physiological changes.In frail older adults,the use of cystatin C leads to better eGFR calculations to assess renal function.Accurate eGFR measurements improve the health of older patients by enabling better medication dosing.A thorough approach that includes multiple calibrated diagnostic methods and a detailed geriatric assessment is necessary for the effective management of kidney disease and other age-related conditions in older adults.展开更多
The Wenchuan Ms 8.0 earthquake on May 12, 2008 induced a huge number of landslides. The distribution and volume of the landslides are very important for assessing risks and understanding the landslide - debris flow - ...The Wenchuan Ms 8.0 earthquake on May 12, 2008 induced a huge number of landslides. The distribution and volume of the landslides are very important for assessing risks and understanding the landslide - debris flow - barrier lake - bursts flood disaster chain. The number and the area of landslides in a wide region can be easily obtained by remote sensing technique, while the volume is relatively difficult to obtain because it requires some detailed geometric information of slope failure surface and sub-surface. Different empirical models for estimating landslide volume were discussed based on the data of 107 landslides in the earthquake-stricken area. The volume data of these landslides were collected by field survey. Their areas were obtained by interpreting remote sensing images while their apparent friction coefficients and height were extracted from the images unifying DEM (digital elevation model). By analyzing the relationships between the volume and the area, apparent friction coefficients, and the height, two models were established, one for the adaptation of a magnitude scale landslide events in a wide range of region, another for the adaptation in a small scope. The correlation coefficients (R2) are 0.7977 and 0.8913, respectively. The results estimated by the two models agree well with the measurement data.展开更多
Volume is an important attribute used in many forest management decisions.Data from 83 fixed-area plots located in central New Brunswick,Canada,are used to examine how different measures of stand-level diameter and he...Volume is an important attribute used in many forest management decisions.Data from 83 fixed-area plots located in central New Brunswick,Canada,are used to examine how different measures of stand-level diameter and height influence volume prediction using a stand-level variant of Honer's(1967)volume equation.When density was included in the models(Volume=f(Diameter,Height,Density))choice of diameter measure was more important than choice of height measure.When density was not included(Volume=f(Diameter,Height)),the opposite was true.For models with density included,moment-based estimators of stand diameter and height performed better than all other measures.For models without density,largest tree estimators of stand diameter and height performed better than other measures.The overall best equation used quadratic mean diameter,Lorey's height,and density(root mean square error=5.26 m^3·ha^(-1);1.9%relative error).The best equation without density used mean diameter of the largest trees needed to calculate a stand density index of 400 and the mean height of the tallest 400 trees per ha(root mean square error=32.08 m^(3)·ha^(-1);11.8%relative error).The results of this study have some important implications for height subsampling and LiDAR-derived forest inventory analyses.展开更多
Holevo bound plays an important role in quantum metrology as it sets the ultimate limit for multi-parameter estimations,which can be asymptotically achieved.Except for some trivial cases,the Holevo bound is implicitly...Holevo bound plays an important role in quantum metrology as it sets the ultimate limit for multi-parameter estimations,which can be asymptotically achieved.Except for some trivial cases,the Holevo bound is implicitly defined and formulated with the help of weight matrices.Here we report the first instance of an intrinsic Holevo bound,namely,without any reference to weight matrices,in a nontrivial case.Specifically,we prove that the Holevo bound for estimating two parameters of a qubit is equivalent to the joint constraint imposed by two quantum Cramér–Rao bounds corresponding to symmetric and right logarithmic derivatives.This weightless form of Holevo bound enables us to determine the precise range of independent entries of the mean-square error matrix,i.e.,two variances and one covariance that quantify the precisions of the estimation,as illustrated by different estimation models.Our result sheds some new light on the relations between the Holevo bound and quantum Cramer–Rao bounds.Possible generalizations are discussed.展开更多
The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering s...The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis.Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms.The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index.ETVI3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike emergence.Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,respectively.These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY.This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.展开更多
In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and gangue.Recently,separation methods have become more intelligent and efficient,using new...In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and gangue.Recently,separation methods have become more intelligent and efficient,using newtechnologies and applying different features for recognition.One such method exploits the difference in substancedensity,leading to excellent coal/gangue recognition.Therefore,this study uses density differences to distinguishcoal from gangue by performing volume prediction on the samples.Our training samples maintain a record of3-side images as input,volume,and weight as the ground truth for the classification.The prediction process relieson a Convolutional neural network(CGVP-CNN)model that receives an input of a 3-side image and then extractsthe needed features to estimate an approximation for the volume.The classification was comparatively performedvia ten different classifiers,namely,K-Nearest Neighbors(KNN),Linear Support Vector Machines(Linear SVM),Radial Basis Function(RBF)SVM,Gaussian Process,Decision Tree,Random Forest,Multi-Layer Perceptron(MLP),Adaptive Boosting(AdaBosst),Naive Bayes,and Quadratic Discriminant Analysis(QDA).After severalexperiments on testing and training data,results yield a classification accuracy of 100%,92%,95%,96%,100%,100%,100%,96%,81%,and 92%,respectively.The test reveals the best timing with KNN,which maintained anaccuracy level of 100%.Assessing themodel generalization capability to newdata is essential to ensure the efficiencyof the model,so by applying a cross-validation experiment,the model generalization was measured.The useddataset was isolated based on the volume values to ensure the model generalization not only on new images of thesame volume but with a volume outside the trained range.Then,the predicted volume values were passed to theclassifiers group,where classification reported accuracy was found to be(100%,100%,100%,98%,88%,87%,100%,87%,97%,100%),respectively.Although obtaining a classification with high accuracy is the main motive,this workhas a remarkable reduction in the data preprocessing time compared to related works.The CGVP-CNN modelmanaged to reduce the data preprocessing time of previous works to 0.017 s while maintaining high classificationaccuracy using the estimated volume value.展开更多
Information on the population distribution at the building scale can help governments make supplemental decisions to address complex urban management issues.However,the discontinuity and strong spatial heterogeneity o...Information on the population distribution at the building scale can help governments make supplemental decisions to address complex urban management issues.However,the discontinuity and strong spatial heterogeneity of research units at the building scale make it challenging to fuse multi-source geographic data,which causes significant errors in population estimation.To address this problem,this study proposes a method for population estimation at the building scale based on Dual-Environment Feature Fusion(DEFF).The dual environments of buildings were constructed by splitting the physical boundaries and extracting features suitable for the dual-environment scale from multi-source geographic data to describe the complex environmental features of buildings.Meanwhile,Data Quality Weighting based Technique for Order of Preference by Similarity to Ideal Solution(DQW-TOPSIS)method was proposed to assign appropriate weights to the features of the external environment for better feature fusion.Finally,a regression model was established using dual-environment features for building-scale population estimation.The experimental areas chosen for this study were Jianghan and Wuchang Districts,both located in Wuhan City,China.The estimated results of the DEFF were compared with those of the ablation experiments,as well as three publicly accessible population datasets,specifically LandScan,WorldPop,and GHS-POP,at the community scale.The evaluation results showed that DEFF had an R2 of approximately 0.8,Mean Absolute Error(MAE)of approximately 1200,Root Mean Square Error(RMSE)of approximately 1700,and both Mean Absolute Percentage Error(MAPE)and Symmetric Mean Absolute Percentage Error(SMAPE)of approximately 26%,indicating an improved performance and verifying the validity of the proposed method for fine-scale population estimation.展开更多
The study focuses on estimating the input power of a power plant from available data, using the theoretical inverter efficiency as the key parameter. The paper addresses the problem of missing data in power generation...The study focuses on estimating the input power of a power plant from available data, using the theoretical inverter efficiency as the key parameter. The paper addresses the problem of missing data in power generation systems and proposes an approach based on the efficiency formula widely documented in the literature. In the absence of input data, this method makes it possible to estimate the plant’s input power using data extracted from the site, in particular that provided by the Ministry of the Environment. The importance of this study lies in the need to accurately determine the input power in order to assess the overall performance of the energy system.展开更多
Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This st...Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.展开更多
The establishment of crop yield estimating model based on microwave and optical satellite images can conduct the mutual verification of the accuracy of the reported crop yield and the precision of the estimating model...The establishment of crop yield estimating model based on microwave and optical satellite images can conduct the mutual verification of the accuracy of the reported crop yield and the precision of the estimating model. With Shou County and Huaiyuan County of Anhui Province as the experimental fields of winter wheat producing areas, the linear winter wheat yield estimating models were established by adopting backscattering coefficient and Normalized Difference Vegetation Index（NDVI） based on images from the synthetic aperture radar（SAR）—RDARSAT-2 and HJ satellite photographed in mid-April and early May, 2014, and then comparisons were conducted on the accuracy of the yield estimating models. The accuracies of the yield estimating models established using co-polarized（HH） and cross-polarized（HV） modes of SAR in Jiangou Town, Shou County were 68.37% and 74.01%, respectively, while the accuracies in Longkang Town, Huaiyuan County were 63.10%and 69.10%, respectively. Accuracies of yield estimating models established by HJ satellite data were 69.52% and 66.43% in Shou County and Huaiyuan County, respectively. Accuracies of winter yield estimating model based on HJ satellite data and that based on SAR were closed, and the yield difference of winter wheat in the lodging region was analyzed in detail. The model results laid the foundation and accumulated experience for the verification, parameters correction and promotion of the winter wheat yield estimating model.展开更多
An innovative approach based on water environmental capacity for non-point source NPS pollution removal rate estimation was discussed by using both univariate and multivariate data analysis.Taking Shenzhen city as the...An innovative approach based on water environmental capacity for non-point source NPS pollution removal rate estimation was discussed by using both univariate and multivariate data analysis.Taking Shenzhen city as the study case a 67% to 74% NPS pollutant load removal rate can lead to meeting the chemical oxygen demand COD pollution control target for most watersheds.In contrast it is hardly to achieve the ammonia nitrogen NH4-N total phosphorus TP and biological oxygen demand BOD5 pollution control target by simply removing NPS pollutants. This highlights that the pollution control strategies should be taken according to different pollutant species and sources in different watersheds rather than one-size-fits-all .展开更多
The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport ...The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport industry must innovate in key technologies to ensure high-quality transmissions for passengers and railway operations.These systems must function effectively under high mobility conditions while prioritizing safety,ecofriendliness,comfort,transparency,predictability,and reliability.On the other hand,the proposal of 6 G wireless technology introduces new possibilities for innovation in communication technologies,which may truly realize the current vision of HSR.Therefore,this article gives a review of the current advanced 6 G wireless communication technologies for HSR,including random access and switching,channel estimation and beamforming,integrated sensing and communication,and edge computing.The main application scenarios of these technologies are reviewed,as well as their current research status and challenges,followed by an outlook on future development directions.展开更多
The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational per...The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.展开更多
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
基金Supported by NSFC(Nos.12371051,12141101,11871126)。
文摘In this paper,we study and characterize the volume estimates of geodesic balls on Finsler gradient Ricci solitons.We get the upper bounds on the volumes of geodesic balls of all three kinds of Finsler gradient Ricci solitons under certain condition about the Laplacian of thedistance function.
文摘Background: Biological maturation refers to the progressive process through which individuals transition toward an adult state during growth and development. To address the challenges posed by differences in biological maturity and the limitations of existing testing methods, particularly in adolescent sports contexts, there is a pressing need for a non-invasive method that is convenient, accurate, and broadly applicable to monitor the biological maturity of adolescent athletes comprehensively. In response to this need, a maturity assessment method based on the smartphone application Maturo has been developed. This study evaluates the accuracy and validity of the Maturo software, an automated tool for estimating biological age and related maturation metrics.Methods: A sample of 103 actively training teenage athletes aged 9-17 years. The sample included 76 males(age = 11.74 ± 1.55 years, mean ±SD) and 27 females(age = 13.95 ± 1.40 years), all without medical conditions that might impact growth or development.Results: Compared to traditional expert evaluations, the intraclass correlation coefficients(ICCs) and Pearson correlation coefficients demonstrated reliable positive correlations and significant agreement between the Maturo software and expert methods across multiple metrics, such as biological age(ICC = 0.965, R = 0.97), corrected biological age(ICC = 0.973, R = 0.99), predicted adult height(ICC = 0.991, R = 0.99), and percentage of adult height achieved(ICC = 0.955, R = 0.97). The Bland-Altman plots provided additional evidence of the validity of the Maturo software estimations, showing low systematic error in most measures. The linear regression analysis produced excellent adjusted R2values: 0.95for biological age and 0.99 for anticipated adult height. The Maturo approach demonstrated a high level of dependability in classifying teenagers into groups based on their maturity status and timing. The κ coefficients of 0.93 for maturity status and 0.82 for maturity timing indicate a nearly perfect agreement with the expert technique.Conclusion: While the Maturo software's non-invasive nature, cost-effectiveness, and ease of use could make it a potential tool for regular monitoring of growth and maturation in young athletes, its promising results in assessing maturation should be interpreted with caution due to limitations such as sample size and demographic constraints. Further longitude research with larger and more diverse populations is needed to validate these preliminary findings and strengthen the evidence for its broader applicability.
基金supported by the National Natural Science Foundation of China(NSFC,grant number U2039207).
文摘Earthquakes can cause significant damage and loss of life,necessitating immediate assessment of the resulting fatalities.Rapid assessment and timely revision of fatality estimates are crucial for effective emergency decisionmaking.This study using the February 6,2023,M_(S)8.0 and M_(S)7.9 Kahramanmaras,Türkiye earthquakes as an example to estimate the ultimate number of fatalities.An early Quick Rough Estimate(QRE)based on the number of deaths reported by the Disaster and Emergency Management Presidency of Türkiye(AFAD)is conducted,and it dynamically adjusts these estimates as new data becomes available.The range of estimates of the final number of deaths can be calculated as 31384–56475 based on the"the QRE of the second day multiplied by 2–3" rule,which incorporates the reported final deaths 50500.The Quasi-Linear and Adaptive Estimation(QLAE)method adaptively adjusts the final fatality estimate within two days and predicts subsequent reported deaths.The correct order of magnitude of the final death toll can be estimated as early as 13 hr after the M_(S)8.0 earthquake.In addition,additional earthquakes such as May 12,2008,M_(S)8.1 Wenchuan earthquake(China),September 8,2023,M_(S)7.2 Al Haouz earthquake(Morocco),November 3,2023,M_(S)5.8 Mid-Western Nepal earthquake,December 18,2023,M_(S)6.1 Jishishan earthquake(China),January 1,2024,M_(S)7.2 Noto Peninsula earthquake(Japan)and August 8,2023,Maui,Hawaii,fires are added again to verified the correctness of the model.The fatalities from the Maui fires are found to be approximately equivalent to those resulting from an M_(S)7.4 earthquake.These methods complement existing frameworks such as Quake Loss Assessment for Response and Mitigation(QLARM)and Prompt Assessment of Global.
文摘BACKGROUND Acute pancreatitis(AP),a severe pancreatic inflammatory condition,with a mor-tality rate reaching up to 40%.Recently,AP shows a steadily elevating prevalence,which causes the greater number of hospital admissions,imposing the substantial economic burden.Acute kidney injury(AKI)complicates take up approximately 15%of AP cases,with an associated mortality rate of 74.7%-81%.AIM To evaluate the efficacy of estimated plasma volume status(ePVS)in forecasting AKI in patients with AP.METHODS In this retrospective cohort study,AP cases were recruited from the First College of Clinical Medical Science of China Three Gorges University between January 2019 and October 2023.Electronic medical records were adopted for data extrac-tion,including demographic data and clinical characteristics.The association between ePVS and AKI was analyzed using multivariate logistic regression models,with potential confounders being adjusted.Nonlinear relationship was examined with smooth curve fitting,and infection points were calculated.Further analyses were performed on stratified subgroups and interaction tests were conducted.RESULTS Among the 1508 AP patients,251(16.6%)developed AKI.ePVS was calculated using Duarte(D-ePVS)and Kaplan-Hakim(KH-ePVS)formulas.After adjusting for covariates,the AKI risk exhibited 46%[odds ratio(OR)=1.46,95%confidence interval(CI):0.96-2.24]and 11%(OR=1.11,95%CI:0.72-1.72)increases in the low tertile(T1)of D-ePVS and KH-ePVS,respectively,and 101%(OR=2.01,95%CI:1.31-3.05)and 51%(OR=1.51,95%CI:1.00-2.29)increases in the high tertile(T3)relative to the reference tertile(T2).Nonlinear curve fitting revealed a U-shaped association of D-ePVS with AKI and a J-shaped association for KH-ePVS,with inflection points at 4.3 dL/g and-2.8%,res-pectively.Significant interactions were not observed in age,gender,hypertension,diabetes mellitus,sequential organ failure assessment score,or AP severity(all P for interaction>0.05).CONCLUSION Our results indicated that ePVS demonstrated the nonlinear association with AKI incidence in AP patients.A U-shaped curve was observed with an inflection point at 4.3 dL/g for the Duarte formula,and a J-shaped curve at-2.8%for the Kaplan-Hakim formula.
基金supported by Geological Disaster Patterns and Mitigation Strategies Under River-Reservoir Hydrodynamics in the Three Gorges Reservoir Fluctuation Zone(5000002024CC20004)the National Key Research and Development Program of China(2023YFC3007205)+1 种基金the National Natural Science Foundation of China(No.42271013)the West Light Foundation of the Chinese Academy of Sciences.
文摘The distribution of the sediment material storage quantity along the debris flow channels(SMSQ_DFC)can provide a foundation for runoffgenerated debris flow prediction or susceptibility assessment.Current models for estimating SMSQ_DFC do not consider the capacity of the channel cross-section to accommodate sediment materials.This accommodation condition serves as a limiting factor in determining whether the expected surplus of sediment materials can ultimately be stored.To address this issue,a mass-conservative index was used to represent the balance of deposit materials at any cross-section,considering the influx from upstream,outflux to downstream,and accommodation capacity.Based on this index,a new model for estimating SMSQ_DFC was developed and subsequently evaluated.The evaluation results show that the model meets the accuracy requirements with average error rates of 14.06%for self-validation and 14.81%for generalization ability validation.To assess its practical applications,the model was applied to the Yeniu Gully in Wenchuan County,Sichuan Province,an area with detailed field survey data.The results show that the model exhibits a commendable performance.Compared to traditional theoretical and semi-theoretical statistical models,our model is easier to use(input parameters can be obtained using Geographic Information Systems(GIS)).The modeling parameters chosen in this study have more theoretical significance than those used in existing purely statistical models,offering more effective technical support for estimating SMSQ_DFC.
文摘Aging is an inevitable process that is usually measured by chronological age,with people aged 65 and over being defined as"older individuals".There is disagreement in the current scientific literature regarding the best methods to estimate glomerular filtration rate(eGFR)in older adults.Several studies suggest the use of an age-adjusted definition to improve accuracy and avoid overdiagnosis.In contrast,some researchers argue that such changes could complicate the classification of chronic kidney disease(CKD).Several formulas,including the Modification of Diet in Renal Disease,CKD-Epidemiology Collaboration,and Cockcroft-Gault equations,are used to estimate eGFR.However,each of these formulas has significant limitations when applied to older adults,primarily due to sarcopenia and malnutrition,which greatly affect both muscle mass and creatinine levels.Alternative formulas,such as the Berlin Initiative Study and the Full Age Spectrum equations,provide more accurate estimates of values for older adults by accounting for age-related physiological changes.In frail older adults,the use of cystatin C leads to better eGFR calculations to assess renal function.Accurate eGFR measurements improve the health of older patients by enabling better medication dosing.A thorough approach that includes multiple calibrated diagnostic methods and a detailed geriatric assessment is necessary for the effective management of kidney disease and other age-related conditions in older adults.
基金supported financially by the Knowledge Innovation Project of Chinese Academy of Sciences (KZCX2-YW-Q03-5)the National Science and Technology Support Plan Project (2009BAK56B05)the National Natural Science Foundation of China (40802072)
文摘The Wenchuan Ms 8.0 earthquake on May 12, 2008 induced a huge number of landslides. The distribution and volume of the landslides are very important for assessing risks and understanding the landslide - debris flow - barrier lake - bursts flood disaster chain. The number and the area of landslides in a wide region can be easily obtained by remote sensing technique, while the volume is relatively difficult to obtain because it requires some detailed geometric information of slope failure surface and sub-surface. Different empirical models for estimating landslide volume were discussed based on the data of 107 landslides in the earthquake-stricken area. The volume data of these landslides were collected by field survey. Their areas were obtained by interpreting remote sensing images while their apparent friction coefficients and height were extracted from the images unifying DEM (digital elevation model). By analyzing the relationships between the volume and the area, apparent friction coefficients, and the height, two models were established, one for the adaptation of a magnitude scale landslide events in a wide range of region, another for the adaptation in a small scope. The correlation coefficients (R2) are 0.7977 and 0.8913, respectively. The results estimated by the two models agree well with the measurement data.
基金the Natural Sciences and Engineering Research Council of Canada(Discovery Grant RGPIN-2023-05879)the New Brunswick Innovation Foundation(Emerging Projects Grant EP-0000000033)。
文摘Volume is an important attribute used in many forest management decisions.Data from 83 fixed-area plots located in central New Brunswick,Canada,are used to examine how different measures of stand-level diameter and height influence volume prediction using a stand-level variant of Honer's(1967)volume equation.When density was included in the models(Volume=f(Diameter,Height,Density))choice of diameter measure was more important than choice of height measure.When density was not included(Volume=f(Diameter,Height)),the opposite was true.For models with density included,moment-based estimators of stand diameter and height performed better than all other measures.For models without density,largest tree estimators of stand diameter and height performed better than other measures.The overall best equation used quadratic mean diameter,Lorey's height,and density(root mean square error=5.26 m^3·ha^(-1);1.9%relative error).The best equation without density used mean diameter of the largest trees needed to calculate a stand density index of 400 and the mean height of the tallest 400 trees per ha(root mean square error=32.08 m^(3)·ha^(-1);11.8%relative error).The results of this study have some important implications for height subsampling and LiDAR-derived forest inventory analyses.
基金Project supported by the Key-Area Research and Development Program of Guangdong Province of China(Grant Nos.2020B0303010001 and SIQSE202104).
文摘Holevo bound plays an important role in quantum metrology as it sets the ultimate limit for multi-parameter estimations,which can be asymptotically achieved.Except for some trivial cases,the Holevo bound is implicitly defined and formulated with the help of weight matrices.Here we report the first instance of an intrinsic Holevo bound,namely,without any reference to weight matrices,in a nontrivial case.Specifically,we prove that the Holevo bound for estimating two parameters of a qubit is equivalent to the joint constraint imposed by two quantum Cramér–Rao bounds corresponding to symmetric and right logarithmic derivatives.This weightless form of Holevo bound enables us to determine the precise range of independent entries of the mean-square error matrix,i.e.,two variances and one covariance that quantify the precisions of the estimation,as illustrated by different estimation models.Our result sheds some new light on the relations between the Holevo bound and quantum Cramer–Rao bounds.Possible generalizations are discussed.
基金supported by the National Natural Science Foundation of China(32371990,31971784)the Earmarked Fund for Jiangsu Agricultural Industry Technology System(JATS(2022)168,JATS(2022)468)+1 种基金the Jiangsu Provincial Cooperative Promotion Plan of Major Agricultural Technologies(2021-ZYXT-01-1)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX23_0783)。
文摘The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis.Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms.The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index.ETVI3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike emergence.Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,respectively.These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY.This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.
基金the National Natural Science Foundation of China under Grant No.52274159 received by E.Hu,https://www.nsfc.gov.cn/Grant No.52374165 received by E.Hu,https://www.nsfc.gov.cn/the China National Coal Group Key Technology Project Grant No.(20221CY001)received by Z.Guan,and E.Hu,https://www.chinacoal.com/.
文摘In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and gangue.Recently,separation methods have become more intelligent and efficient,using newtechnologies and applying different features for recognition.One such method exploits the difference in substancedensity,leading to excellent coal/gangue recognition.Therefore,this study uses density differences to distinguishcoal from gangue by performing volume prediction on the samples.Our training samples maintain a record of3-side images as input,volume,and weight as the ground truth for the classification.The prediction process relieson a Convolutional neural network(CGVP-CNN)model that receives an input of a 3-side image and then extractsthe needed features to estimate an approximation for the volume.The classification was comparatively performedvia ten different classifiers,namely,K-Nearest Neighbors(KNN),Linear Support Vector Machines(Linear SVM),Radial Basis Function(RBF)SVM,Gaussian Process,Decision Tree,Random Forest,Multi-Layer Perceptron(MLP),Adaptive Boosting(AdaBosst),Naive Bayes,and Quadratic Discriminant Analysis(QDA).After severalexperiments on testing and training data,results yield a classification accuracy of 100%,92%,95%,96%,100%,100%,100%,96%,81%,and 92%,respectively.The test reveals the best timing with KNN,which maintained anaccuracy level of 100%.Assessing themodel generalization capability to newdata is essential to ensure the efficiencyof the model,so by applying a cross-validation experiment,the model generalization was measured.The useddataset was isolated based on the volume values to ensure the model generalization not only on new images of thesame volume but with a volume outside the trained range.Then,the predicted volume values were passed to theclassifiers group,where classification reported accuracy was found to be(100%,100%,100%,98%,88%,87%,100%,87%,97%,100%),respectively.Although obtaining a classification with high accuracy is the main motive,this workhas a remarkable reduction in the data preprocessing time compared to related works.The CGVP-CNN modelmanaged to reduce the data preprocessing time of previous works to 0.017 s while maintaining high classificationaccuracy using the estimated volume value.
基金supported by the National Natural Science Foundation of China[Grant numbers U20A2091,41930107]。
文摘Information on the population distribution at the building scale can help governments make supplemental decisions to address complex urban management issues.However,the discontinuity and strong spatial heterogeneity of research units at the building scale make it challenging to fuse multi-source geographic data,which causes significant errors in population estimation.To address this problem,this study proposes a method for population estimation at the building scale based on Dual-Environment Feature Fusion(DEFF).The dual environments of buildings were constructed by splitting the physical boundaries and extracting features suitable for the dual-environment scale from multi-source geographic data to describe the complex environmental features of buildings.Meanwhile,Data Quality Weighting based Technique for Order of Preference by Similarity to Ideal Solution(DQW-TOPSIS)method was proposed to assign appropriate weights to the features of the external environment for better feature fusion.Finally,a regression model was established using dual-environment features for building-scale population estimation.The experimental areas chosen for this study were Jianghan and Wuchang Districts,both located in Wuhan City,China.The estimated results of the DEFF were compared with those of the ablation experiments,as well as three publicly accessible population datasets,specifically LandScan,WorldPop,and GHS-POP,at the community scale.The evaluation results showed that DEFF had an R2 of approximately 0.8,Mean Absolute Error(MAE)of approximately 1200,Root Mean Square Error(RMSE)of approximately 1700,and both Mean Absolute Percentage Error(MAPE)and Symmetric Mean Absolute Percentage Error(SMAPE)of approximately 26%,indicating an improved performance and verifying the validity of the proposed method for fine-scale population estimation.
文摘The study focuses on estimating the input power of a power plant from available data, using the theoretical inverter efficiency as the key parameter. The paper addresses the problem of missing data in power generation systems and proposes an approach based on the efficiency formula widely documented in the literature. In the absence of input data, this method makes it possible to estimate the plant’s input power using data extracted from the site, in particular that provided by the Ministry of the Environment. The importance of this study lies in the need to accurately determine the input power in order to assess the overall performance of the energy system.
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004)Supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155885,Artificial Intelligence Convergence Innovation Human Resources Development(Hanyang University ERICA)).
文摘Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical robots.This study proposes a novel end-to-end disparity estimation model to address these challenges.Our approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting interferences.This study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and efficiency.The model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video data.Experimental results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing parameters.Moreover,the model exhibited faster convergence during training,contributing to overall performance enhancement.This study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.
基金Supported by the National Natural Science Foundation of China(41205126)the Discipline Construction and Macroscopic Agricultural Research Project of Anhui Academy of Agricultural Sciences(13A1424)+2 种基金the Fund for Youth Innovation of Anhui Academy of Agricultural Sciences(14B1460)the Innovative Research Team for Agricultural Disaster Risk Analysis in Anhui ProvinceAnhui Academy of Agricultural Sciences(14C1409)~~
文摘The establishment of crop yield estimating model based on microwave and optical satellite images can conduct the mutual verification of the accuracy of the reported crop yield and the precision of the estimating model. With Shou County and Huaiyuan County of Anhui Province as the experimental fields of winter wheat producing areas, the linear winter wheat yield estimating models were established by adopting backscattering coefficient and Normalized Difference Vegetation Index（NDVI） based on images from the synthetic aperture radar（SAR）—RDARSAT-2 and HJ satellite photographed in mid-April and early May, 2014, and then comparisons were conducted on the accuracy of the yield estimating models. The accuracies of the yield estimating models established using co-polarized（HH） and cross-polarized（HV） modes of SAR in Jiangou Town, Shou County were 68.37% and 74.01%, respectively, while the accuracies in Longkang Town, Huaiyuan County were 63.10%and 69.10%, respectively. Accuracies of yield estimating models established by HJ satellite data were 69.52% and 66.43% in Shou County and Huaiyuan County, respectively. Accuracies of winter yield estimating model based on HJ satellite data and that based on SAR were closed, and the yield difference of winter wheat in the lodging region was analyzed in detail. The model results laid the foundation and accumulated experience for the verification, parameters correction and promotion of the winter wheat yield estimating model.
基金The National Science and Technology Major Project of China(No.2012ZX07301-001)the Shenzhen Environmental Research Project,China Postdoctoral Science Foundation(No.2013M530642)
文摘An innovative approach based on water environmental capacity for non-point source NPS pollution removal rate estimation was discussed by using both univariate and multivariate data analysis.Taking Shenzhen city as the study case a 67% to 74% NPS pollutant load removal rate can lead to meeting the chemical oxygen demand COD pollution control target for most watersheds.In contrast it is hardly to achieve the ammonia nitrogen NH4-N total phosphorus TP and biological oxygen demand BOD5 pollution control target by simply removing NPS pollutants. This highlights that the pollution control strategies should be taken according to different pollutant species and sources in different watersheds rather than one-size-fits-all .
基金National Natural Science Foundation of China(U2468201,62122012,62221001).
文摘The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport industry must innovate in key technologies to ensure high-quality transmissions for passengers and railway operations.These systems must function effectively under high mobility conditions while prioritizing safety,ecofriendliness,comfort,transparency,predictability,and reliability.On the other hand,the proposal of 6 G wireless technology introduces new possibilities for innovation in communication technologies,which may truly realize the current vision of HSR.Therefore,this article gives a review of the current advanced 6 G wireless communication technologies for HSR,including random access and switching,channel estimation and beamforming,integrated sensing and communication,and edge computing.The main application scenarios of these technologies are reviewed,as well as their current research status and challenges,followed by an outlook on future development directions.
基金National Natural Science Foundation of China (52075420)Fundamental Research Funds for the Central Universities (xzy022023049)National Key Research and Development Program of China (2023YFB3408600)。
文摘The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
