Reinforced concrete(RC)columns are often subjected to off-central explosion due to the uncertainty of blast locations.However,few studies have focused on the dynamic response of RC columns under offcentral explosions....Reinforced concrete(RC)columns are often subjected to off-central explosion due to the uncertainty of blast locations.However,few studies have focused on the dynamic response of RC columns under offcentral explosions.A field blast experiment was conducted under close-in explosion with varying detonation offset distances(0 m,0.5 m,and 1 m),the overpressure load and dynamic responses of the full-scale RC columns were measured.Compared with the centrally detonated condition,a relative offset distance of 1.67 decreases the maximum and residual deflections of the RC column by 16.8%and 21.4%,respectively,while increasing the maximum and residual support rotations by 24.7%and 17.8%.Based on the experimental results,a theoretical model was proposed that considers the detonation location and charge mass,boundary conditions,axial compression ratio and material properties.The theoretical model exhibited good agreement with the experimental results,with prediction errors below 10%for both maximum and residual deflection.The effects of parameters were analyzed,and it indicated that an increase in offset distance results in decreased maximum and residual deflections but an increased support angle,thereby exacerbating damage.Higher axial load ratio,span-depth ratio,and longitudinal reinforcement ratio reduce both deflections and support angle.Additionally,a rapid method to predict the maximum and residual deflection of RC columns under off-central blast loading was also proposed based on the Generalized Regression Neural Network(GRNN).Eleven features which related to the RC column properties and the blast characteristics were used in the training process of GRNN,and accurate predictions were achieved with prediction errors within 20%.This study fills the gap in predicting the dynamic response of RC columns under off-central explosion,providing valuable references for blast-resistant design.展开更多
The liquid-only transfer dividing wall column(LDWC)offers a promising path for industrializing dividing wall columns by simplifying vapor split control.However,their energy efficiency is insufficient due to the additi...The liquid-only transfer dividing wall column(LDWC)offers a promising path for industrializing dividing wall columns by simplifying vapor split control.However,their energy efficiency is insufficient due to the addition of heat at the bottom and its removal at the top.Therefore,developing an effective strategy to enhance the energy efficiency of the entire LDWC system is crucial.This work investigates the intensification of LDWC based on the column grand composite curve(CGCC)and thermodynamic analysis,proposing a novel intensification strategy to improve energy efficiency effectively.An optimization model with four blocks is developed to minimize the total annual cost(TAC)of the intensified LDWC.Energy,exergy,economic,and environmental analyses are used to evaluate its performance.Ternary mixtures with different easy separation indexes(ESI)are selected as illustrative examples.For mixtures with ESI≤1,the optimal configuration involves partial feed preheating,compressors and intermediate reboilers on both side sections,along with optimized operating pressure.This setup leads to significant reductions in total energy consumption,TAC,and gas emissions by 43.80%,28.08%,and 42.85%for ESI=1,and by 46.17%,29.06%,and 45.35%for ESI<1,respectively,when compared to conventional distillation sequences(CDS).For mixtures with ESI>1,the best performance is achieved by implementing partial feed preheating and modifications only to the right section.This results in reductions of 21.64%in energy consumption,16.26%in TAC,and 21.51%in gas emissions when compared to CDS.In all cases,the optimal configurations show the lowest lost work and minimum work,indicating an improved thermodynamic performance.展开更多
This study proposes a new post-tensioned precast bridge column(PT-PBC)with a socket connection.Compared to conventional PBCs connected by PT tendons,the combination of the PT tendons with the socket connection can avo...This study proposes a new post-tensioned precast bridge column(PT-PBC)with a socket connection.Compared to conventional PBCs connected by PT tendons,the combination of the PT tendons with the socket connection can avoid tensioning the PT tendons on site,which further accelerates construction speed while improving construction quality and safety.In addition,compared to conventional PBCs with a socket connection,a rocking interface can avoid the formation of a plastic hinge in a column,which greatly alleviates seismic damage to that area.One specimen for quasi-static testing is used to validate the feasibility of this connection type.Subsequently,finite element models(FEM)are established to systematically predict the responses of the proposed columns under lateral cyclic loading.The accuracy of the FEM is verified through quasistatic testing.Next,the influences of the key design parameters of the PT-PBC,including the area ratio and prestress level of the PT tendons,the area ratio of energy dissipation(ED)steel rebars,and the total axial compression ratio on the seismic performances of PT-PBC are systematically investigated.The use of shape memory alloy(SMA)rods as energy dissipation devices and their performances also are investigated.The results show that increasing the area ratio and prestress level of PT tendons has an overall positive impact on the self-centering capacity of the column.The prestress level of PT tendons should be kept between 35%and 55%,depending on different conditions.The total compression axial ratio of the columns should be maintained between 0.3 and 0.4.Both ED steel rebars and SMA rods can boost the column’s energy dissipation capacity,while SMA rods can reduce residual deformation due to their inherent mechanical properties.展开更多
Bolting steel angles at the bottom ends of columns provides a rapid and efficient method for repairing damaged structures,while also offering a viable approach to restore their potential bearing capacity.To validate t...Bolting steel angles at the bottom ends of columns provides a rapid and efficient method for repairing damaged structures,while also offering a viable approach to restore their potential bearing capacity.To validate the suitability of specific strengthening strategies,particularly the utilization of bolted steel angles,three reinforced concrete frame specimens were subjected to hysteresis testing.These specimens all featured RC columns strengthened with steel angle ends.Additionally,one control specimen without steel angle ends was included in the testing.The hysteresis effects of bolting steel angles were discussed in terms of typical failure mode,hysteresis and skeleton curves,stiffness degradation and energy dissipation.The experimental results revealed that the three specimens that had bolted steel angles exhibited ductile failure behavior.Through analysis of hysteresis and skeleton curves,it was observed that the frame demonstrated distinct plasticity,maintaining sufficient load-bearing capacity even after yielding and exhibiting superior displacement ductility performance.Considering equivalent viscous damping,the energy dissipation capacity of the RC frame increased linearly with drift and remained largely unaffected by structural damage.Therefore,bolting steel angles at specified cross-sections proved to be a viable technique for structural repair and restoration.展开更多
Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural netwo...Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.展开更多
The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates ...The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.展开更多
Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme ...Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme of co-directional secondary flow was designed based on a 30 kgf thrust turbojet engine,an equivalent rudder deflection control variable of Mass Flow Combination(MFC)was proposed,and a control model was established to form a FTV control system scheme,which was integrated with the flight control system of a 100 kg tailless flying wing with medium aspect ratio to achieve closed-loop control of the yaw attitude based on FTV.The heading stability augmentation and maneuvering control characteristics and time response characteristics of tailless flying wing by FTV were quantitatively studied through virtual flight test in a wind tunnel at a wind speed of 35 m/s.The results show that the control strategy based on MFC achieves bidirectional continuous and stable control of thrust vector angle in a range of±11°,and the thrust vector angle varies monotonically with MFC;the co-directional FTV realizes bidirectional continuous and stable control of the yaw attitude of tailless flying wing,without longitudinal/lateral coupling moment.The increment of the maximum yawing moment coefficient is 0.0029,the maximum yaw rate is 7.55(°)/s,and the response time of the yaw rate of the vectoring nozzle actuated by the secondary flow is about 0.06 s,which satisfies the heading stability augmentation and maneuvering control response requirements of the aircraft with statically unstable heading,and provides new control means for the heading rudderless attitude control of tailless flying wing.展开更多
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc...The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.展开更多
To enhance the deformation capacity of vertical support columns of underground structures and improve their overall seismic performance,a new truncated column connected by unbonded prestressed tendons is proposed,insp...To enhance the deformation capacity of vertical support columns of underground structures and improve their overall seismic performance,a new truncated column connected by unbonded prestressed tendons is proposed,inspired by the concepts of the toughness seismic resistance and rocking design.Although many experimental and numerical studies have focused on underground structures,research on the behavior of truncated columns remains limited.This paper develops threedimensional(3D)finite element(FE)models for various columns,including cast-in-place column(CIPC)and prestressed tendon truncated column(PTTC),to evaluate the effects of three parameters,including axial compression ratio(ACR),initial tendon stress,and the effect of hole diameter on mechanical performance—specifically deformation capacity,strength,residual deformation and gap width.The results indicate that the deformability and self-centering ability of the prestressed tendon truncated column is obviously superior to the cast-in-place column,but its strength was comparatively lower.The axial compression ratio has obvious effects on seismic performance,especially deformation and residual deformation,while initial tendon stress and hole diameter influence performance only in the case of a small axial compression ratio.This study systematically identifies the influence of various factors on seismic performance.Additionally,this study proposes a method to evaluate the self-centering capability of structures and establishes an empirical relationship between maximum recoverable deformation and the axial compression ratio.The developed numerical model can serve as a tool for future studies to predict the seismic responses of overall subway stations that feature truncated columns.展开更多
Disruption of host physiological processes,leading to symptom expression,is a common hallmark during plant virus infections.The concept of“symptoms as strategy”is rapidly reshaping our understanding of plant virolog...Disruption of host physiological processes,leading to symptom expression,is a common hallmark during plant virus infections.The concept of“symptoms as strategy”is rapidly reshaping our understanding of plant virology.An emerging theme is that symptom expressions—such as stunting,curling,and yellowing,which devastate yield—may themselves be evolved viral adaptation strategies rather than collateral damage.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
Thrust-vectoring capability has become a critical feature for propulsion systems as space missions move from static to dynamic.Thrust-vectoring is a well-developed area of rocket engine science.For electric propulsion...Thrust-vectoring capability has become a critical feature for propulsion systems as space missions move from static to dynamic.Thrust-vectoring is a well-developed area of rocket engine science.For electric propulsion,however,it is an evolving field that has taken a new leap forward in recent years.A review and analysis of thrust-vectoring schemes for electric propulsion systems have been conducted.The scope of this review includes thrust-vectoring schemes that can be implemented for electrostatic,electromagnetic,and beam-driven thrusters.A classification of electric propulsion schemes that provide thrust-vectoring capability is developed.More attention is given to schemes implemented in laboratory prototypes and flight models.The final part is devoted to a discussion on the suitability of different electric propulsion systems with thrust-vectoring capability for modern space mission operations.The thrust-vectoring capability of electric propulsion is necessary for inner and outer space satellites,which are at a disadvantage with conventional unidirectional propulsion systems due to their limited maneuverability.展开更多
Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the eva...Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.展开更多
Karst collapse columns typically appear unpredictably and without a uniform spatial arrangement,posing challenges for mining operations and water inrush risk assessment.As major structural pathways for mine water inru...Karst collapse columns typically appear unpredictably and without a uniform spatial arrangement,posing challenges for mining operations and water inrush risk assessment.As major structural pathways for mine water inrush,they are responsible for some of the most frequent and severe water-related disasters in coal mining.Understanding the mechanisms of water inrush in these collapse columns is therefore essential for effective disaster prevention and control,making it a key research priority.Additionally,investigating the developmental characteristics of collapse columns is crucial for analyzing seepage instability mechanisms.In such a context,this paper provides a comprehensive review of four critical aspects:(1)The development characteristics and hydrogeological properties of collapse columns;(2)Fluid-solid coupling mechanisms under mining-induced stress;(3)Non-Darcy seepage behavior in fractured rock masses;(4)Flow regime transitions and mass variation effects.Key findings highlight the role of flow-solid coupling in governing the seepage mechanisms of fractured rock masses within karst collapse columns.By synthesizing numerous studies on flow pattern transitions,this paper outlines the complete seepage process-from groundwater movement within the aquifer to its migration through the collapse column and eventual inflow into mine roadways or working faces-along with the associated transformations in flow patterns.Furthermore,the seepage characteristics and water inrush behaviors influenced by particle migration are examined through both experimental and numerical simulation approaches.展开更多
Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experie...Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.展开更多
Cement treatment,such as cement-mixing columns,is commonly used for deep soft soil improvement to increase the bearing capacity and reduce settlement.However,cement production entails high energy consumption and carbo...Cement treatment,such as cement-mixing columns,is commonly used for deep soft soil improvement to increase the bearing capacity and reduce settlement.However,cement production entails high energy consumption and carbon and pollutant emissions.CO_(2)capture and mineralization represent promising solutions to these issues.This study proposes a sustainable alternative:a novel CO_(2)-carbonated MgO-mixing column that integrates CO_(2)mineralization with soil reinforcement.This approach involves in situ mixing of MgO with deep soil to form columns,which are then carbonated and solidified by injecting captured CO_(2)through gas-permeable pipe piles,achieving both carbon reduction and soil improvement.In this study,CO_(2)-carbonated MgO-mixing columns were comprehensively evaluated to investigate variations in strength,deformation,pH,and CO_(2)sequestration with depth.Two rapid and cost-effective methods to assess its mechanical properties,uniformity,and CO_(2)sequestration capacity are proposed.The results show that the carbonated MgO-treated soil has good strength along the depth direction,with an average unconfined compressive strength(UCS)of 1.02 MPa and a lower pH than that of cement-mixing columns.It also achieves notable CO_(2)sequestration,ranging from 4.88%to 13.10%(average 8.31%),and exhibits good uniformity,as shown by electrical resistivity tests.Needle penetration and electrical resistivity tests could be used to effectively predict the UCS,deformation modulus,and CO_(2)sequestration.XRD,FTIR,SEM,and TG-DTG analyses reveal distinct microstructural differences at various depths,with unhydrated MgO,magnesite,and dypingite/hydromagnesite present in shallow columns,and brucite,nesquehonite,and dypingite/hydromagnesite present in deep columns.These products bind soil particles and fill pores,enhancing the strength of the MgO-mixing column.展开更多
The Macao Science Satellite-1(known as MSS-1)is the first scientific exploration satellite that was designed to measure the Earth's low latitude magnetic field at high resolution and with high precision by collect...The Macao Science Satellite-1(known as MSS-1)is the first scientific exploration satellite that was designed to measure the Earth's low latitude magnetic field at high resolution and with high precision by collecting data in a near-equatorial orbit.Magnetic field data from MSS-1's onboard Vector Fluxgate Magnetometer(VFM),collected at a sample rate of 50 Hz,allows us to detect and investigate sources of magnetic data contamination,from DC to relevant Nyquist frequency.Here we report two types of artificial disturbances in the VFM data.One is V-shaped events concentrated at night,with frequencies sweeping from the Nyquist frequency down to zero and back up.The other is 5-Hz events(ones that exhibit a distinct 5 Hz spectrum peak);these events are always accompanied by intervals of spiky signals,and are clearly related to the attitude control of the satellite.Our analyses show that VFM noise levels in daytime are systematically lower than in nighttime.The daily average noise levels exhibit a period of about 52 days.The V-shaped events are strongly correlated with higher VFM noise levels.展开更多
In the task of inspecting underwater suspended pipelines,multi-beam sonar(MBS)can provide two-dimensional water column images(WCIs).However,systematic interferences(e.g.,sidelobe effects)may induce misdetection in WCI...In the task of inspecting underwater suspended pipelines,multi-beam sonar(MBS)can provide two-dimensional water column images(WCIs).However,systematic interferences(e.g.,sidelobe effects)may induce misdetection in WCIs.To address this issue and improve the accuracy of detection,we developed a density-based clustering method for three-dimensional water column point clouds.During the processing of WCIs,sidelobe effects are mitigated using a bilateral filter and brightness transformation.The cross-sectional point cloud of the pipeline is then extracted by using the Canny operator.In the detection phase,the target is identified by using density-based spatial clustering of applications with noise(DBSCAN).However,the selection of appropriate DBSCAN parameters is obscured by the uneven distribution of the water column point cloud.To overcome this,we propose an improved DBSCAN based on a parameter interval estimation method(PIE-DBSCAN).First,kernel density estimation(KDE)is used to determine the candidate interval of parameters,after which the exact cluster number is determined via density peak clustering(DPC).Finally,the optimal parameters are selected by comparing the mean silhouette coefficients.To validate the performance of PIE-DBSCAN,we collected water column point clouds from an anechoic tank and the South China Sea.PIE-DBSCAN successfully detected both the target points of the suspended pipeline and non-target points on the seafloor surface.Compared to the K-Means and Mean-Shift algorithms,PIE-DBSCAN demonstrates superior clustering performance and shows feasibility in practical applications.展开更多
基金financially supported by the National Natural Science Foundation of China(Grants No.12472399)。
文摘Reinforced concrete(RC)columns are often subjected to off-central explosion due to the uncertainty of blast locations.However,few studies have focused on the dynamic response of RC columns under offcentral explosions.A field blast experiment was conducted under close-in explosion with varying detonation offset distances(0 m,0.5 m,and 1 m),the overpressure load and dynamic responses of the full-scale RC columns were measured.Compared with the centrally detonated condition,a relative offset distance of 1.67 decreases the maximum and residual deflections of the RC column by 16.8%and 21.4%,respectively,while increasing the maximum and residual support rotations by 24.7%and 17.8%.Based on the experimental results,a theoretical model was proposed that considers the detonation location and charge mass,boundary conditions,axial compression ratio and material properties.The theoretical model exhibited good agreement with the experimental results,with prediction errors below 10%for both maximum and residual deflection.The effects of parameters were analyzed,and it indicated that an increase in offset distance results in decreased maximum and residual deflections but an increased support angle,thereby exacerbating damage.Higher axial load ratio,span-depth ratio,and longitudinal reinforcement ratio reduce both deflections and support angle.Additionally,a rapid method to predict the maximum and residual deflection of RC columns under off-central blast loading was also proposed based on the Generalized Regression Neural Network(GRNN).Eleven features which related to the RC column properties and the blast characteristics were used in the training process of GRNN,and accurate predictions were achieved with prediction errors within 20%.This study fills the gap in predicting the dynamic response of RC columns under off-central explosion,providing valuable references for blast-resistant design.
基金support provided by the National Natural Science Foundation of China(U24B6016)the Higher Education Institution Academic Discipline Innovation and Talent Introduction Plan(“111 Plan”)(No.B23025)are gratefully acknowledged.
文摘The liquid-only transfer dividing wall column(LDWC)offers a promising path for industrializing dividing wall columns by simplifying vapor split control.However,their energy efficiency is insufficient due to the addition of heat at the bottom and its removal at the top.Therefore,developing an effective strategy to enhance the energy efficiency of the entire LDWC system is crucial.This work investigates the intensification of LDWC based on the column grand composite curve(CGCC)and thermodynamic analysis,proposing a novel intensification strategy to improve energy efficiency effectively.An optimization model with four blocks is developed to minimize the total annual cost(TAC)of the intensified LDWC.Energy,exergy,economic,and environmental analyses are used to evaluate its performance.Ternary mixtures with different easy separation indexes(ESI)are selected as illustrative examples.For mixtures with ESI≤1,the optimal configuration involves partial feed preheating,compressors and intermediate reboilers on both side sections,along with optimized operating pressure.This setup leads to significant reductions in total energy consumption,TAC,and gas emissions by 43.80%,28.08%,and 42.85%for ESI=1,and by 46.17%,29.06%,and 45.35%for ESI<1,respectively,when compared to conventional distillation sequences(CDS).For mixtures with ESI>1,the best performance is achieved by implementing partial feed preheating and modifications only to the right section.This results in reductions of 21.64%in energy consumption,16.26%in TAC,and 21.51%in gas emissions when compared to CDS.In all cases,the optimal configurations show the lowest lost work and minimum work,indicating an improved thermodynamic performance.
基金Natural Science Foundation of China under Grant No.52178449,the Beijing Natural Science Foundation under Grant No.8234060the Innovation Center of Beijing Association for Science and Technology。
文摘This study proposes a new post-tensioned precast bridge column(PT-PBC)with a socket connection.Compared to conventional PBCs connected by PT tendons,the combination of the PT tendons with the socket connection can avoid tensioning the PT tendons on site,which further accelerates construction speed while improving construction quality and safety.In addition,compared to conventional PBCs with a socket connection,a rocking interface can avoid the formation of a plastic hinge in a column,which greatly alleviates seismic damage to that area.One specimen for quasi-static testing is used to validate the feasibility of this connection type.Subsequently,finite element models(FEM)are established to systematically predict the responses of the proposed columns under lateral cyclic loading.The accuracy of the FEM is verified through quasistatic testing.Next,the influences of the key design parameters of the PT-PBC,including the area ratio and prestress level of the PT tendons,the area ratio of energy dissipation(ED)steel rebars,and the total axial compression ratio on the seismic performances of PT-PBC are systematically investigated.The use of shape memory alloy(SMA)rods as energy dissipation devices and their performances also are investigated.The results show that increasing the area ratio and prestress level of PT tendons has an overall positive impact on the self-centering capacity of the column.The prestress level of PT tendons should be kept between 35%and 55%,depending on different conditions.The total compression axial ratio of the columns should be maintained between 0.3 and 0.4.Both ED steel rebars and SMA rods can boost the column’s energy dissipation capacity,while SMA rods can reduce residual deformation due to their inherent mechanical properties.
基金National Key R&D Program of China under Grant No.2023YFC3805100Technologies R&D Project of China Construction First Group Corporation Limited under Grant No.PT-2022-09National Natural Science Foundation of China under Grant No.52178126。
文摘Bolting steel angles at the bottom ends of columns provides a rapid and efficient method for repairing damaged structures,while also offering a viable approach to restore their potential bearing capacity.To validate the suitability of specific strengthening strategies,particularly the utilization of bolted steel angles,three reinforced concrete frame specimens were subjected to hysteresis testing.These specimens all featured RC columns strengthened with steel angle ends.Additionally,one control specimen without steel angle ends was included in the testing.The hysteresis effects of bolting steel angles were discussed in terms of typical failure mode,hysteresis and skeleton curves,stiffness degradation and energy dissipation.The experimental results revealed that the three specimens that had bolted steel angles exhibited ductile failure behavior.Through analysis of hysteresis and skeleton curves,it was observed that the frame demonstrated distinct plasticity,maintaining sufficient load-bearing capacity even after yielding and exhibiting superior displacement ductility performance.Considering equivalent viscous damping,the energy dissipation capacity of the RC frame increased linearly with drift and remained largely unaffected by structural damage.Therefore,bolting steel angles at specified cross-sections proved to be a viable technique for structural repair and restoration.
基金supported by the Gansu Provincial Natural Science Foundation(grant number 25JRRA074)the Gansu Provincial Key R&D Science and Technology Program(grant number 24YFGA060)the National Natural Science Foundation of China(grant number 62161019).
文摘Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.
基金supported by grants PID2020-120308RB-I00 and PID2023-147802OB-I00 funded by MICIU/AEI/10.13039/501100011033FEDER,UE,by Aligning Science Across Parkinson’s(ref.ASAP-020505)through the Michael J.Fox Foundation for Parkinson’s Research+1 种基金by CiberNed Intramural Collaborative Projects(ref.PI2020/09)by the Spanish Fundación Mutua Madrile?a de Investigación Médica(to JLL)。
文摘The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.
文摘Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme of co-directional secondary flow was designed based on a 30 kgf thrust turbojet engine,an equivalent rudder deflection control variable of Mass Flow Combination(MFC)was proposed,and a control model was established to form a FTV control system scheme,which was integrated with the flight control system of a 100 kg tailless flying wing with medium aspect ratio to achieve closed-loop control of the yaw attitude based on FTV.The heading stability augmentation and maneuvering control characteristics and time response characteristics of tailless flying wing by FTV were quantitatively studied through virtual flight test in a wind tunnel at a wind speed of 35 m/s.The results show that the control strategy based on MFC achieves bidirectional continuous and stable control of thrust vector angle in a range of±11°,and the thrust vector angle varies monotonically with MFC;the co-directional FTV realizes bidirectional continuous and stable control of the yaw attitude of tailless flying wing,without longitudinal/lateral coupling moment.The increment of the maximum yawing moment coefficient is 0.0029,the maximum yaw rate is 7.55(°)/s,and the response time of the yaw rate of the vectoring nozzle actuated by the secondary flow is about 0.06 s,which satisfies the heading stability augmentation and maneuvering control response requirements of the aircraft with statically unstable heading,and provides new control means for the heading rudderless attitude control of tailless flying wing.
基金supported by the China Agriculture Research System of MOF and MARAthe National Natural Science Foundation of China (31872337 and 31501919)the Agricultural Science and Technology Innovation Project,China (ASTIP-IAS02)。
文摘The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.
基金National Natural Science Foundation of China under Grant Nos.52478488 and 51908013the National Key Basic Research and Development Program of China under Grant No.2018YFC1504305。
文摘To enhance the deformation capacity of vertical support columns of underground structures and improve their overall seismic performance,a new truncated column connected by unbonded prestressed tendons is proposed,inspired by the concepts of the toughness seismic resistance and rocking design.Although many experimental and numerical studies have focused on underground structures,research on the behavior of truncated columns remains limited.This paper develops threedimensional(3D)finite element(FE)models for various columns,including cast-in-place column(CIPC)and prestressed tendon truncated column(PTTC),to evaluate the effects of three parameters,including axial compression ratio(ACR),initial tendon stress,and the effect of hole diameter on mechanical performance—specifically deformation capacity,strength,residual deformation and gap width.The results indicate that the deformability and self-centering ability of the prestressed tendon truncated column is obviously superior to the cast-in-place column,but its strength was comparatively lower.The axial compression ratio has obvious effects on seismic performance,especially deformation and residual deformation,while initial tendon stress and hole diameter influence performance only in the case of a small axial compression ratio.This study systematically identifies the influence of various factors on seismic performance.Additionally,this study proposes a method to evaluate the self-centering capability of structures and establishes an empirical relationship between maximum recoverable deformation and the axial compression ratio.The developed numerical model can serve as a tool for future studies to predict the seismic responses of overall subway stations that feature truncated columns.
基金supported by the National Natural Science Foundation of China(32272482)the Innovation Research 2035 Pilot Plan of Southwest University(SWU-XDZD22002).
文摘Disruption of host physiological processes,leading to symptom expression,is a common hallmark during plant virus infections.The concept of“symptoms as strategy”is rapidly reshaping our understanding of plant virology.An emerging theme is that symptom expressions—such as stunting,curling,and yellowing,which devastate yield—may themselves be evolved viral adaptation strategies rather than collateral damage.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
基金performed at large-scale research facility"Beam-M"of Bauman Moscow State Technical University following the government task by the Ministry of Science and Higher Education of the Russian Federation(No.FSFN-2024-0007).
文摘Thrust-vectoring capability has become a critical feature for propulsion systems as space missions move from static to dynamic.Thrust-vectoring is a well-developed area of rocket engine science.For electric propulsion,however,it is an evolving field that has taken a new leap forward in recent years.A review and analysis of thrust-vectoring schemes for electric propulsion systems have been conducted.The scope of this review includes thrust-vectoring schemes that can be implemented for electrostatic,electromagnetic,and beam-driven thrusters.A classification of electric propulsion schemes that provide thrust-vectoring capability is developed.More attention is given to schemes implemented in laboratory prototypes and flight models.The final part is devoted to a discussion on the suitability of different electric propulsion systems with thrust-vectoring capability for modern space mission operations.The thrust-vectoring capability of electric propulsion is necessary for inner and outer space satellites,which are at a disadvantage with conventional unidirectional propulsion systems due to their limited maneuverability.
基金primarily supported by the National Key R&D Program of China[grant number 2021YFC3000904]the Jiangsu Provincial Key Technology R&D Program[grant number BE2022851]National Natural Science Foundation of China[grant number 42405035]。
文摘Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.
基金supported by the Natural Science Foundation of Henan Province(242300421246,222300420007,232300421134)the National Natural Science Foundation of China(52004082,52174073,52274079,42402255)+4 种基金the Science and Technology Project of Henan Province(232102321098)Zhongyuan Science and Technology Innovation Leading Talent Program(244200510005)the Program for Science&Technology Innovation Talents in Universities of Henan Province(24HASTIT021)the Program for the Scientific and Technological Innovation Team in Universities of Henan Province(23IRTSTHN005)the National Postdoctoral Researchers Program Foundation of China(GZC20230709)。
文摘Karst collapse columns typically appear unpredictably and without a uniform spatial arrangement,posing challenges for mining operations and water inrush risk assessment.As major structural pathways for mine water inrush,they are responsible for some of the most frequent and severe water-related disasters in coal mining.Understanding the mechanisms of water inrush in these collapse columns is therefore essential for effective disaster prevention and control,making it a key research priority.Additionally,investigating the developmental characteristics of collapse columns is crucial for analyzing seepage instability mechanisms.In such a context,this paper provides a comprehensive review of four critical aspects:(1)The development characteristics and hydrogeological properties of collapse columns;(2)Fluid-solid coupling mechanisms under mining-induced stress;(3)Non-Darcy seepage behavior in fractured rock masses;(4)Flow regime transitions and mass variation effects.Key findings highlight the role of flow-solid coupling in governing the seepage mechanisms of fractured rock masses within karst collapse columns.By synthesizing numerous studies on flow pattern transitions,this paper outlines the complete seepage process-from groundwater movement within the aquifer to its migration through the collapse column and eventual inflow into mine roadways or working faces-along with the associated transformations in flow patterns.Furthermore,the seepage characteristics and water inrush behaviors influenced by particle migration are examined through both experimental and numerical simulation approaches.
基金supported by the Deanship of Graduate Studies and Scientific Research at University of Bisha for funding this research through the promising program under grant number(UB-Promising-33-1445).
文摘Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.
基金funded by the National Natural Science Foundation of China(Grant No.42277146)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX22_0273)the Transportation Science and Technology Project of Jiangsu Province of China(Grant No.HTSQ(B)2021-249).
文摘Cement treatment,such as cement-mixing columns,is commonly used for deep soft soil improvement to increase the bearing capacity and reduce settlement.However,cement production entails high energy consumption and carbon and pollutant emissions.CO_(2)capture and mineralization represent promising solutions to these issues.This study proposes a sustainable alternative:a novel CO_(2)-carbonated MgO-mixing column that integrates CO_(2)mineralization with soil reinforcement.This approach involves in situ mixing of MgO with deep soil to form columns,which are then carbonated and solidified by injecting captured CO_(2)through gas-permeable pipe piles,achieving both carbon reduction and soil improvement.In this study,CO_(2)-carbonated MgO-mixing columns were comprehensively evaluated to investigate variations in strength,deformation,pH,and CO_(2)sequestration with depth.Two rapid and cost-effective methods to assess its mechanical properties,uniformity,and CO_(2)sequestration capacity are proposed.The results show that the carbonated MgO-treated soil has good strength along the depth direction,with an average unconfined compressive strength(UCS)of 1.02 MPa and a lower pH than that of cement-mixing columns.It also achieves notable CO_(2)sequestration,ranging from 4.88%to 13.10%(average 8.31%),and exhibits good uniformity,as shown by electrical resistivity tests.Needle penetration and electrical resistivity tests could be used to effectively predict the UCS,deformation modulus,and CO_(2)sequestration.XRD,FTIR,SEM,and TG-DTG analyses reveal distinct microstructural differences at various depths,with unhydrated MgO,magnesite,and dypingite/hydromagnesite present in shallow columns,and brucite,nesquehonite,and dypingite/hydromagnesite present in deep columns.These products bind soil particles and fill pores,enhancing the strength of the MgO-mixing column.
基金supported by the National Key R&D Program of China(Grant2022YFF0503700)the National Natural Science Foundation of China(42474200 and 42174186)。
文摘The Macao Science Satellite-1(known as MSS-1)is the first scientific exploration satellite that was designed to measure the Earth's low latitude magnetic field at high resolution and with high precision by collecting data in a near-equatorial orbit.Magnetic field data from MSS-1's onboard Vector Fluxgate Magnetometer(VFM),collected at a sample rate of 50 Hz,allows us to detect and investigate sources of magnetic data contamination,from DC to relevant Nyquist frequency.Here we report two types of artificial disturbances in the VFM data.One is V-shaped events concentrated at night,with frequencies sweeping from the Nyquist frequency down to zero and back up.The other is 5-Hz events(ones that exhibit a distinct 5 Hz spectrum peak);these events are always accompanied by intervals of spiky signals,and are clearly related to the attitude control of the satellite.Our analyses show that VFM noise levels in daytime are systematically lower than in nighttime.The daily average noise levels exhibit a period of about 52 days.The V-shaped events are strongly correlated with higher VFM noise levels.
基金the National Natural Science Foundation of China(Nos.42176188,42176192)the Hainan Provincial Natural Science Foundation of China(No.421CXTD442)+2 种基金the Stable Supporting Fund of Acoustic Science and Technology Laboratory(No.JCKYS2024604SSJS007)the Fundamental Research Funds for the Central Universities(No.3072024CFJ0504)the Harbin Engineering University Doctoral Research and Innovation Fund(No.XK2050021034)。
文摘In the task of inspecting underwater suspended pipelines,multi-beam sonar(MBS)can provide two-dimensional water column images(WCIs).However,systematic interferences(e.g.,sidelobe effects)may induce misdetection in WCIs.To address this issue and improve the accuracy of detection,we developed a density-based clustering method for three-dimensional water column point clouds.During the processing of WCIs,sidelobe effects are mitigated using a bilateral filter and brightness transformation.The cross-sectional point cloud of the pipeline is then extracted by using the Canny operator.In the detection phase,the target is identified by using density-based spatial clustering of applications with noise(DBSCAN).However,the selection of appropriate DBSCAN parameters is obscured by the uneven distribution of the water column point cloud.To overcome this,we propose an improved DBSCAN based on a parameter interval estimation method(PIE-DBSCAN).First,kernel density estimation(KDE)is used to determine the candidate interval of parameters,after which the exact cluster number is determined via density peak clustering(DPC).Finally,the optimal parameters are selected by comparing the mean silhouette coefficients.To validate the performance of PIE-DBSCAN,we collected water column point clouds from an anechoic tank and the South China Sea.PIE-DBSCAN successfully detected both the target points of the suspended pipeline and non-target points on the seafloor surface.Compared to the K-Means and Mean-Shift algorithms,PIE-DBSCAN demonstrates superior clustering performance and shows feasibility in practical applications.
