The machinability tests were conducted by using various process parameters on a CA6164 lathe with a dynamometer. The metallurgical properties, machinability and mechanical properties of the developed alloy were compar...The machinability tests were conducted by using various process parameters on a CA6164 lathe with a dynamometer. The metallurgical properties, machinability and mechanical properties of the developed alloy were compared with those of an austenite stainless steel 1Cr18Ni9Ti. The results show that the machinability of the austenitic stainless steels with free cutting additives is much better than that of 1Cr18Ni9Ti. This is attributed to the existence of machinable additives. The inclusions might be composed of MnS. Sulfur and copper addition contributes to the improvement of the machinability of austenitic stainless steel. Bismuth is an important factor to improve the machinability of austenitic stainless steel, and it has a distinct advantage over lead. The mechanical properties of the free cutting austenitic stainless steel are similar to those of 1Cr18Ni9Ti. A new Pb-free austenitic stainless steel with high machinability as well as satisfactory mechanical properties has been developed.展开更多
Ti2AlC and Ti3AlC2 are the most light-weight and oxidation resistant layered ternary carbides belonging to the MAX phases.This review highlights recent achievements on the processing,microstructure,physical,mechanical...Ti2AlC and Ti3AlC2 are the most light-weight and oxidation resistant layered ternary carbides belonging to the MAX phases.This review highlights recent achievements on the processing,microstructure,physical,mechanical and chemical properties of these two machinable and electrically conductive carbides.Ti2AlC and Ti3AlC2 display superior properties such as fracture toughness,electrical and thermal conductivities,and oxidation resistance over their binary counterpart.This paper provides a comprehensive overview of the processing-microstructure-property correlations of these two carbides.Potential fields of applications for Ti2AlC and Ti3AlC2 are surveyed.In addition,we point out methods for further improving their properties in some specific applications through appropriate structural design and modification.展开更多
Tailored surface textures at the micro-or nanoscale dimensions are widely used to get required functional performances.Rotary ultrasonic texturing(RUT)technique has been proved to be capable of fabricating periodic mi...Tailored surface textures at the micro-or nanoscale dimensions are widely used to get required functional performances.Rotary ultrasonic texturing(RUT)technique has been proved to be capable of fabricating periodic micro-and nanostructures.In the present study,diamond tools with geometrically defined cutting edges were designed for fabricating different types of tailored surface textures using the RUT method.Surface generation mechanisms and machinable structures of the RUT process are analyzed and simulated with a 3D-CAD program.Textured surfaces generated by using a triangular pyramid cutting tip are constructed.Different textural patterns from several micrometers to several tens of micrometers with few burrs were successfully fabricated,which proved that tools with a proper two-rake-face design are capable of removing cutting chips efficiently along a sinusoidal cutting locus in the RUT process.Technical applications of the textured surfaces are also discussed.Wetting properties of textured aluminum surfaces were evaluated by combining the test of surface roughness features.The results show that the real surface area of the textured aluminum surfaces almost doubled by comparing with that of a flat surface,and anisotropic wetting properties were obtained due to the obvious directional textural features.展开更多
For pulsed power devices, surface flashover phenomena across solid insulators greatly restrict their overall performance. In recent decades, much attention has been paid on enhancing the surface electric withstanding ...For pulsed power devices, surface flashover phenomena across solid insulators greatly restrict their overall performance. In recent decades, much attention has been paid on enhancing the surface electric withstanding strength of insulators, and it is found that surface treatment of material is useful to improve the surface flashover voltage. The carburization treatment is employed to modify the surface components of newly-developed machinable ceramics （MC） materials. A series of MC samples with different glucose solution concentration （0%, 10%, 20%, 30% and 40%） are prepared by chemical reactions for surface carburization modification, and their surface fiashover characteristics are investigated under pulsed voltage in vacuum. It is found that the surface carburization treatment greatly modifies the surface resistivity of MCs and hence the flashover behaviors. Based on the reduction of surface resistivity and the secondary electron emission avalanche （SEEA） theory, the adjustment of flashover withstanding ability can be reasonably explained.展开更多
A new type of machinable bioactive glass-ceramics for bone substitution has been developed in the glass system SiO_2-MgO-K_2O-F^--CaO-P_2O_5, which contains Mg- muscovite [K_2Mg_5 (Si_8O_(20)) F_4] and fluorapatite as...A new type of machinable bioactive glass-ceramics for bone substitution has been developed in the glass system SiO_2-MgO-K_2O-F^--CaO-P_2O_5, which contains Mg- muscovite [K_2Mg_5 (Si_8O_(20)) F_4] and fluorapatite as the two main crystal phases. The phase separation and the crystallization of the glass have been studied. A series of tests have showed that the material is good at mechanical property and bioactivity. Espe- cially, by analysing the structure of the interface layer between the material and the bone of animal with scanning electron microscope, electron probe, etc., it has been found that the new bone hydroxya- patite is formed on the surface of the material so that the material is connected firmly with the bone.展开更多
The present paper discusses the development of the first and second order model for predicting the chemical etching variables, namely, etching rate, surface roughness and accuracy of advanced ceramics. The first and s...The present paper discusses the development of the first and second order model for predicting the chemical etching variables, namely, etching rate, surface roughness and accuracy of advanced ceramics. The first and second order etching rate, surface roughness and accuracy equations were developed using the Response Surface Method (RSM). The etching variables included etching temperature, etching duration, solution and solution concentration. The predictive models’ analyses were supported with the aid of the statistical software package – Design Expert (DE 7). The effects of the individual etching variables and interaction between these variables were also investigated. The study showed that predictive models successfully predicted the etching rate, surface roughness and accuracy readings recorded experimentally with 95% confident interval. The results obtained from the predictive models were also compared with Multilayer Perceptron Artificial Neural Network (ANN). Chemical Etching variables predictive by ANN were in good agreement with those with those obtained by RSM. This observation indicated the potential of ANN in predicting chemical etching variables thus eliminating the need for exhaustive chemical etching in optimization.展开更多
A machinable Y TZP/LaPO 4 composite ceramic was prepared by infiltrating LaPO 4 liquid precursor into Y TZP porous ceramic. Sintered Y TZP ceramic preformed with 35% (volume fraction) open pore volume was made by...A machinable Y TZP/LaPO 4 composite ceramic was prepared by infiltrating LaPO 4 liquid precursor into Y TZP porous ceramic. Sintered Y TZP ceramic preformed with 35% (volume fraction) open pore volume was made by adding graphite (30%, volume fraction). The Y TZP/LaPO 4 composite ceramics containing different LaPO 4 contents were obtained by infiltration and pyrolysis cycles. The machinability and mechanical properties of materials were investigated. The results show that the machinable Y TZP/LaPO 4 composite ceramics containing 2 3% to 7.5% (volume fraction) LaPO 4 has good machinability as well as outstanding mechanical properties.展开更多
The purpose of this research was to prepare machinable bioactive glass-ceramics by sol-gel method. A multi-component composite sol with great uniformity and stability was first prepared by a 2-step method. The compos...The purpose of this research was to prepare machinable bioactive glass-ceramics by sol-gel method. A multi-component composite sol with great uniformity and stability was first prepared by a 2-step method. The composite sol was then transformed into gel by aging under different temperatures. The gel was dried finally by super critically drying method and sintered to obtain the machinable bioactive glass-ceramics. Effect of thermal treatment on crystallization of the glass-ceramics was investigated by X-ray diffraction （ XRD ） analysis. Microstructure of the glass- ceramics was observed by Scanning Electron Microscopy （SEM） and the mechanism of machinability was discussed. Phlogopite and hydroxylapatite were identified as main crystal phases by XRD analysis under thermal treatment at 750℃ and 950℃ for 1.5 h separately. The relative bulk density could achieve 99% under 1050℃ for 4 h. Microstructure of the glass-ceramics showed that the randomly distributed phlogopite and hydroxylapatite phases were favorable to the machinability of the glass-ceramics. A mean bending strength of about 160- 180 MPa and a fracture toughness parameter KIC of aboat 2.1-2.3 were determined for the glass-ceramics.展开更多
Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face...Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face challenges,including high metal usage,high process costs,and low cyclohexene yield.This study utilizes existing literature data combined with machine learning methods to analyze the factors influencing benzene conversion,cyclohexene selectivity,and yield in the benzene hydrogenation to cyclohexene reaction.It constructs predictive models based on XGBoost and Random Forest algorithms.After analysis,it was found that reaction time,Ru content,and space velocity are key factors influencing cyclohexene yield,selectivity,and benzene conversion.Shapley Additive Explanations(SHAP)analysis and feature importance analysis further revealed the contribution of each variable to the reaction outcomes.Additionally,we randomly generated one million variable combinations using the Dirichlet distribution to attempt to predict high-yield catalyst formulations.This paper provides new insights into the application of machine learning in heterogeneous catalysis and offers some reference for further research.展开更多
Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the m...Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.展开更多
BACKGROUND:Hemiplegia,a prevalent stroke-related condition,is often studied for motor dysfunction;however,spasticity remains under-researched.Abnormal muscle tone significantly hinders hemiplegic patients’walking rec...BACKGROUND:Hemiplegia,a prevalent stroke-related condition,is often studied for motor dysfunction;however,spasticity remains under-researched.Abnormal muscle tone significantly hinders hemiplegic patients’walking recovery.OBJECTIVE:To determine whether early suspension-protected training with a personal assistant machine for stroke patients enhances walking ability and prevents muscle spasms.METHODS:Thirty-two early-stage stroke patients from Shenzhen University General Hospital and the China Rehabilitation Research Center were randomly assigned to the experimental group(n=16)and the control group(n=16).Both groups underwent 4 weeks of gait training under the suspension protection system for 30 minutes daily,5 days a week.The experimental group used the personal assistant machine during training.Three-dimensional gait analysis(using the Cortex motion capture system),Brunnstrom staging,Fugl-Meyer Assessment for lower limb motor function,Fugl-Meyer balance function,and the modified Ashworth Scale were evaluated within 1 week before the intervention and after 4 weeks of intervention.RESULTS AND CONCLUSION:After the 4-week intervention,all outcome measures showed significant changes in each group.The experimental group had a small but significant increase in the modified Ashworth Scale score(P<0.05,d=|0.15|),while the control group had a large significant increase(P<0.05,d=|1.48|).The experimental group demonstrated greater improvements in walking speed(16.5 to 38.44 cm/s,P<0.05,d=|4.01|),step frequency(46.44 to 64.94 steps/min,P<0.05,d=|2.32|),stride length(15.50 to 29.81 cm,P<0.05,d=|3.44|),and peak hip and knee flexion(d=|1.82|to|2.17|).After treatment,the experimental group showed significantly greater improvements than the control group in walking speed(38.44 vs.26.63 cm/s,P<0.05,d=|2.75|),stride length,peak hip and knee flexion(d=|1.31|to|1.45|),step frequency(64.94 vs.59.38 steps/min,P<0.05,d=|0.85|),and a reduced support phase(bilateral:24.31%vs.28.38%,P<0.05,d=|0.88|;non-paretic:66.19%vs.70.13%,P<0.05,d=|0.94|).For early hemiplegia,personal assistant machine-assisted gait training under the suspension protection system helps establish a correct gait pattern,prevents muscle spasms,and improves motor function.展开更多
In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative...In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative spam detection method utilizing the Horse Herd Optimization Algorithm(HHOA),designed for binary classification within multi⁃objective framework.The method proficiently identifies essential features,minimizing redundancy and improving classification precision.The suggested HHOA attained an impressive accuracy of 97.21%on the Kaggle email dataset,with precision of 94.30%,recall of 90.50%,and F1⁃score of 92.80%.Compared to conventional techniques,such as Support Vector Machine(93.89%accuracy),Random Forest(96.14%accuracy),and K⁃Nearest Neighbours(92.08%accuracy),HHOA exhibited enhanced performance with reduced computing complexity.The suggested method demonstrated enhanced feature selection efficiency,decreasing the number of selected features while maintaining high classification accuracy.The results underscore the efficacy of HHOA in spam identification and indicate its potential for further applications in practical email filtering systems.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
Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders,traumatic i...Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders,traumatic injuries,and neurological diseases.Neural machine interface technology establishes direct connections with the brain or peripheral nervous system to restore impaired motor,sensory,and cognitive functions,significantly improving patients'quality of life.This review analyzes the chronological development and integration of various neural machine interface technologies,including regenerative peripheral nerve interfaces,targeted muscle and sensory reinnervation,agonist–antagonist myoneural interfaces,and brain–machine interfaces.Recent advancements in flexible electronics and bioengineering have led to the development of more biocompatible and highresolution electrodes,which enhance the performance and longevity of neural machine interface technology.However,significant challenges remain,such as signal interference,fibrous tissue encapsulation,and the need for precise anatomical localization and reconstruction.The integration of advanced signal processing algorithms,particularly those utilizing artificial intelligence and machine learning,has the potential to improve the accuracy and reliability of neural signal interpretation,which will make neural machine interface technologies more intuitive and effective.These technologies have broad,impactful clinical applications,ranging from motor restoration and sensory feedback in prosthetics to neurological disorder treatment and neurorehabilitation.This review suggests that multidisciplinary collaboration will play a critical role in advancing neural machine interface technologies by combining insights from biomedical engineering,clinical surgery,and neuroengineering to develop more sophisticated and reliable interfaces.By addressing existing limitations and exploring new technological frontiers,neural machine interface technologies have the potential to revolutionize neuroprosthetics and neurorehabilitation,promising enhanced mobility,independence,and quality of life for individuals with neurological impairments.By leveraging detailed anatomical knowledge and integrating cutting-edge neuroengineering principles,researchers and clinicians can push the boundaries of what is possible and create increasingly sophisticated and long-lasting prosthetic devices that provide sustained benefits for users.展开更多
Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.展开更多
Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to ev...Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to evaluate debris flow susceptibility and associated hazards across Tajikistan.A dataset comprising 405 documented debris flow points and 14 influencing factors,encompassing geological,climatic-hydrological,and anthropogenic variables,was established.Three machine learning algorithms—Random Forest,Support Vector Machine(SVM),and Multi-layer Perceptron—were applied to generate susceptibility maps and delineate debris flow risk zones.The results indicate that the areas of higher and high susceptibility accounted for 20.43%and 4.41%of the national area,respectively,and were predominantly concentrated along the Zeravshan and Vakhsh river basins.Among the evaluated models,SVM model demonstrated the highest predictive performance.Beyond conventional topographic and environmental controls,drought conditions were identified as a critical factor influencing debris flow occurrence within the arid and semi-arid mountainous regions of Tajikistan.These findings provide a scientific basis for regional debris flow risk management and disaster mitigation planning,and offer practical guidance for selecting conditioning factors in machine-learning-based susceptibility assessments in other dry mountainous environments.展开更多
Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy...Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.展开更多
The complex interactions and conflicting performance demands in multi-component composites pose significant challenges for achieving balanced multi-property optimization through conventional trial-and-error approaches...The complex interactions and conflicting performance demands in multi-component composites pose significant challenges for achieving balanced multi-property optimization through conventional trial-and-error approaches.Machine learning(ML)offers a promising solution,markedly improving materials discovery efficiency.However,the high dimensionality of feature spaces in such systems has long impeded effective ML-driven feature representation and inverse design.To overcome this,we present an Intelligent Screening System(ISS)framework to accelerate the discovery of optimal formulations balancing four key properties in 15-component PTFE-based copper-clad laminate composites(PTFE-CCLCs).ISS adopts modular descriptors based on the physical information of component volume fractions,thereby simplifying the feature representation.By leveraging the inverse prediction capability of ML models and constructing a performance-driven virtual candidate database,ISS significantly reduced the computational complexity associated with high-dimensional spaces.Experimental validation confirmed that ISSoptimized formulations exhibited superior synergy,notably resolving the trade-off between thermal conductivity and peel strength,and outperform many commercial counterparts.Despite limited data and inherent process variability,ISS achieved an average prediction accuracy of 76.5%,with thermal conductivity predictions exceeding 90%,demonstrating robust reliability.This work provides an innovative,efficient strategy for multifunctional optimization and accelerated discovery in ultra-complex composite systems,highlighting the integration of ML and advanced materials design.展开更多
The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on e...The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on empirical models with limited predictive capabilities.This study focuses on the influence of optical basicity on viscosity in CaO-Al_(2)O_(3)-based refining slags,leveraging machine learning to address data scarcity and improve prediction accuracy.An automated framework for algorithm integration,parameter tuning,and evaluation ranking framework(Auto-APE)is employed to develop customized data-driven models for various slag systems,including CaO-Al_(2)O_(3)-SiO_(2),CaO-Al_(2)O_(3)-CaF_(2),CaO-Al_(2)O_(3)-SiO_(2)-MgO,and CaO-Al_(2)O_(3)-SiO_(2)-MgO-CaF_(2).By incorporating optical basicity as a key feature,the models achieve an average validation error of 8.0%to 15.1%,significantly outperforming traditional empirical models.Additionally,symbolic regression is introduced to rapidly construct domain-specific features,such as optical basicity-like descriptors,offering a potential breakthrough in performance prediction for small datasets.This work highlights the critical role of domain-specific knowledge in understanding and predicting viscosity,providing a robust machine learning-based approach for optimizing refining slag properties.展开更多
Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and stru...Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.展开更多
基金Item Sponsored by National Natural Science Foundation of China (50334010)
文摘The machinability tests were conducted by using various process parameters on a CA6164 lathe with a dynamometer. The metallurgical properties, machinability and mechanical properties of the developed alloy were compared with those of an austenite stainless steel 1Cr18Ni9Ti. The results show that the machinability of the austenitic stainless steels with free cutting additives is much better than that of 1Cr18Ni9Ti. This is attributed to the existence of machinable additives. The inclusions might be composed of MnS. Sulfur and copper addition contributes to the improvement of the machinability of austenitic stainless steel. Bismuth is an important factor to improve the machinability of austenitic stainless steel, and it has a distinct advantage over lead. The mechanical properties of the free cutting austenitic stainless steel are similar to those of 1Cr18Ni9Ti. A new Pb-free austenitic stainless steel with high machinability as well as satisfactory mechanical properties has been developed.
基金funded by the National Natural Science Foundation of China (NSFC) under Grant Nos. 50802097,50832008the IMR Innovative Research Foundation
文摘Ti2AlC and Ti3AlC2 are the most light-weight and oxidation resistant layered ternary carbides belonging to the MAX phases.This review highlights recent achievements on the processing,microstructure,physical,mechanical and chemical properties of these two machinable and electrically conductive carbides.Ti2AlC and Ti3AlC2 display superior properties such as fracture toughness,electrical and thermal conductivities,and oxidation resistance over their binary counterpart.This paper provides a comprehensive overview of the processing-microstructure-property correlations of these two carbides.Potential fields of applications for Ti2AlC and Ti3AlC2 are surveyed.In addition,we point out methods for further improving their properties in some specific applications through appropriate structural design and modification.
基金Supported by Japan Society for the Promotion of Science(Grant Nos.14J04115,16K17990)
文摘Tailored surface textures at the micro-or nanoscale dimensions are widely used to get required functional performances.Rotary ultrasonic texturing(RUT)technique has been proved to be capable of fabricating periodic micro-and nanostructures.In the present study,diamond tools with geometrically defined cutting edges were designed for fabricating different types of tailored surface textures using the RUT method.Surface generation mechanisms and machinable structures of the RUT process are analyzed and simulated with a 3D-CAD program.Textured surfaces generated by using a triangular pyramid cutting tip are constructed.Different textural patterns from several micrometers to several tens of micrometers with few burrs were successfully fabricated,which proved that tools with a proper two-rake-face design are capable of removing cutting chips efficiently along a sinusoidal cutting locus in the RUT process.Technical applications of the textured surfaces are also discussed.Wetting properties of textured aluminum surfaces were evaluated by combining the test of surface roughness features.The results show that the real surface area of the textured aluminum surfaces almost doubled by comparing with that of a flat surface,and anisotropic wetting properties were obtained due to the obvious directional textural features.
基金supported in part by National Natural Science Foundation of China(Nos.50937004,50777051)NSFC-JSPS Joint Project(50911140103)
文摘For pulsed power devices, surface flashover phenomena across solid insulators greatly restrict their overall performance. In recent decades, much attention has been paid on enhancing the surface electric withstanding strength of insulators, and it is found that surface treatment of material is useful to improve the surface flashover voltage. The carburization treatment is employed to modify the surface components of newly-developed machinable ceramics （MC） materials. A series of MC samples with different glucose solution concentration （0%, 10%, 20%, 30% and 40%） are prepared by chemical reactions for surface carburization modification, and their surface fiashover characteristics are investigated under pulsed voltage in vacuum. It is found that the surface carburization treatment greatly modifies the surface resistivity of MCs and hence the flashover behaviors. Based on the reduction of surface resistivity and the secondary electron emission avalanche （SEEA） theory, the adjustment of flashover withstanding ability can be reasonably explained.
文摘A new type of machinable bioactive glass-ceramics for bone substitution has been developed in the glass system SiO_2-MgO-K_2O-F^--CaO-P_2O_5, which contains Mg- muscovite [K_2Mg_5 (Si_8O_(20)) F_4] and fluorapatite as the two main crystal phases. The phase separation and the crystallization of the glass have been studied. A series of tests have showed that the material is good at mechanical property and bioactivity. Espe- cially, by analysing the structure of the interface layer between the material and the bone of animal with scanning electron microscope, electron probe, etc., it has been found that the new bone hydroxya- patite is formed on the surface of the material so that the material is connected firmly with the bone.
文摘The present paper discusses the development of the first and second order model for predicting the chemical etching variables, namely, etching rate, surface roughness and accuracy of advanced ceramics. The first and second order etching rate, surface roughness and accuracy equations were developed using the Response Surface Method (RSM). The etching variables included etching temperature, etching duration, solution and solution concentration. The predictive models’ analyses were supported with the aid of the statistical software package – Design Expert (DE 7). The effects of the individual etching variables and interaction between these variables were also investigated. The study showed that predictive models successfully predicted the etching rate, surface roughness and accuracy readings recorded experimentally with 95% confident interval. The results obtained from the predictive models were also compared with Multilayer Perceptron Artificial Neural Network (ANN). Chemical Etching variables predictive by ANN were in good agreement with those with those obtained by RSM. This observation indicated the potential of ANN in predicting chemical etching variables thus eliminating the need for exhaustive chemical etching in optimization.
文摘A machinable Y TZP/LaPO 4 composite ceramic was prepared by infiltrating LaPO 4 liquid precursor into Y TZP porous ceramic. Sintered Y TZP ceramic preformed with 35% (volume fraction) open pore volume was made by adding graphite (30%, volume fraction). The Y TZP/LaPO 4 composite ceramics containing different LaPO 4 contents were obtained by infiltration and pyrolysis cycles. The machinability and mechanical properties of materials were investigated. The results show that the machinable Y TZP/LaPO 4 composite ceramics containing 2 3% to 7.5% (volume fraction) LaPO 4 has good machinability as well as outstanding mechanical properties.
文摘The purpose of this research was to prepare machinable bioactive glass-ceramics by sol-gel method. A multi-component composite sol with great uniformity and stability was first prepared by a 2-step method. The composite sol was then transformed into gel by aging under different temperatures. The gel was dried finally by super critically drying method and sintered to obtain the machinable bioactive glass-ceramics. Effect of thermal treatment on crystallization of the glass-ceramics was investigated by X-ray diffraction （ XRD ） analysis. Microstructure of the glass- ceramics was observed by Scanning Electron Microscopy （SEM） and the mechanism of machinability was discussed. Phlogopite and hydroxylapatite were identified as main crystal phases by XRD analysis under thermal treatment at 750℃ and 950℃ for 1.5 h separately. The relative bulk density could achieve 99% under 1050℃ for 4 h. Microstructure of the glass-ceramics showed that the randomly distributed phlogopite and hydroxylapatite phases were favorable to the machinability of the glass-ceramics. A mean bending strength of about 160- 180 MPa and a fracture toughness parameter KIC of aboat 2.1-2.3 were determined for the glass-ceramics.
基金Supported by CAS Basic and Interdisciplinary Frontier Scientific Research Pilot Project(XDB1190300,XDB1190302)Youth Innovation Promotion Association CAS(Y2021056)+1 种基金Joint Fund of the Yulin University and the Dalian National Laboratory for Clean Energy(YLU-DNL Fund 2022007)The special fund for Science and Technology Innovation Teams of Shanxi Province(202304051001007)。
文摘Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face challenges,including high metal usage,high process costs,and low cyclohexene yield.This study utilizes existing literature data combined with machine learning methods to analyze the factors influencing benzene conversion,cyclohexene selectivity,and yield in the benzene hydrogenation to cyclohexene reaction.It constructs predictive models based on XGBoost and Random Forest algorithms.After analysis,it was found that reaction time,Ru content,and space velocity are key factors influencing cyclohexene yield,selectivity,and benzene conversion.Shapley Additive Explanations(SHAP)analysis and feature importance analysis further revealed the contribution of each variable to the reaction outcomes.Additionally,we randomly generated one million variable combinations using the Dirichlet distribution to attempt to predict high-yield catalyst formulations.This paper provides new insights into the application of machine learning in heterogeneous catalysis and offers some reference for further research.
基金Supported by the National Basic Research Program of China(2012CB025904)Zhengzhou Shengda University of Economics,Business and Management(SD-YB2025085)。
文摘Gauss radial basis functions(GRBF)are frequently employed in data fitting and machine learning.Their linear independence property can theoretically guarantee the avoidance of data redundancy.In this paper,one of the main contributions is proving this property using linear algebra instead of profound knowledge.This makes it easy to read and understand this fundamental fact.The proof of linear independence of a set of Gauss functions relies on the constructing method for one-dimensional space and on the deducing method for higher dimensions.Additionally,under the condition of preserving the same moments between the original function and interpolating function,both the interpolating existence and uniqueness are proven for GRBF in one-dimensional space.The final work demonstrates the application of the GRBF method to locate lunar olivine.By combining preprocessed data using GRBF with the removing envelope curve method,a program is created to find the position of lunar olivine based on spectrum data,and the numerical experiment shows that it is an effective scheme.
文摘BACKGROUND:Hemiplegia,a prevalent stroke-related condition,is often studied for motor dysfunction;however,spasticity remains under-researched.Abnormal muscle tone significantly hinders hemiplegic patients’walking recovery.OBJECTIVE:To determine whether early suspension-protected training with a personal assistant machine for stroke patients enhances walking ability and prevents muscle spasms.METHODS:Thirty-two early-stage stroke patients from Shenzhen University General Hospital and the China Rehabilitation Research Center were randomly assigned to the experimental group(n=16)and the control group(n=16).Both groups underwent 4 weeks of gait training under the suspension protection system for 30 minutes daily,5 days a week.The experimental group used the personal assistant machine during training.Three-dimensional gait analysis(using the Cortex motion capture system),Brunnstrom staging,Fugl-Meyer Assessment for lower limb motor function,Fugl-Meyer balance function,and the modified Ashworth Scale were evaluated within 1 week before the intervention and after 4 weeks of intervention.RESULTS AND CONCLUSION:After the 4-week intervention,all outcome measures showed significant changes in each group.The experimental group had a small but significant increase in the modified Ashworth Scale score(P<0.05,d=|0.15|),while the control group had a large significant increase(P<0.05,d=|1.48|).The experimental group demonstrated greater improvements in walking speed(16.5 to 38.44 cm/s,P<0.05,d=|4.01|),step frequency(46.44 to 64.94 steps/min,P<0.05,d=|2.32|),stride length(15.50 to 29.81 cm,P<0.05,d=|3.44|),and peak hip and knee flexion(d=|1.82|to|2.17|).After treatment,the experimental group showed significantly greater improvements than the control group in walking speed(38.44 vs.26.63 cm/s,P<0.05,d=|2.75|),stride length,peak hip and knee flexion(d=|1.31|to|1.45|),step frequency(64.94 vs.59.38 steps/min,P<0.05,d=|0.85|),and a reduced support phase(bilateral:24.31%vs.28.38%,P<0.05,d=|0.88|;non-paretic:66.19%vs.70.13%,P<0.05,d=|0.94|).For early hemiplegia,personal assistant machine-assisted gait training under the suspension protection system helps establish a correct gait pattern,prevents muscle spasms,and improves motor function.
文摘In recent decades,the proliferation of email communication has markedly escalated,resulting in a concomitant surge in spam emails that congest networks and presenting security risks.This study introduces an innovative spam detection method utilizing the Horse Herd Optimization Algorithm(HHOA),designed for binary classification within multi⁃objective framework.The method proficiently identifies essential features,minimizing redundancy and improving classification precision.The suggested HHOA attained an impressive accuracy of 97.21%on the Kaggle email dataset,with precision of 94.30%,recall of 90.50%,and F1⁃score of 92.80%.Compared to conventional techniques,such as Support Vector Machine(93.89%accuracy),Random Forest(96.14%accuracy),and K⁃Nearest Neighbours(92.08%accuracy),HHOA exhibited enhanced performance with reduced computing complexity.The suggested method demonstrated enhanced feature selection efficiency,decreasing the number of selected features while maintaining high classification accuracy.The results underscore the efficacy of HHOA in spam identification and indicate its potential for further applications in practical email filtering systems.
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supported in part by the National Natural Science Foundation of China,Nos.81927804(to GL),82260456(to LY),U21A20479(to LY)Science and Technology Planning Project of Shenzhen,No.JCYJ20230807140559047(to LY)+3 种基金Key-Area Research and Development Program of Guangdong Province,No.2020B0909020004(to GL)Guangdong Basic and Applied Research Foundation,No.2023A1515011478(to LY)the Science and Technology Program of Guangdong Province,No.2022A0505090007(to GL)Ministry of Science and Technology,Shenzhen,No.QN2022032013L(to LY)。
文摘Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders,traumatic injuries,and neurological diseases.Neural machine interface technology establishes direct connections with the brain or peripheral nervous system to restore impaired motor,sensory,and cognitive functions,significantly improving patients'quality of life.This review analyzes the chronological development and integration of various neural machine interface technologies,including regenerative peripheral nerve interfaces,targeted muscle and sensory reinnervation,agonist–antagonist myoneural interfaces,and brain–machine interfaces.Recent advancements in flexible electronics and bioengineering have led to the development of more biocompatible and highresolution electrodes,which enhance the performance and longevity of neural machine interface technology.However,significant challenges remain,such as signal interference,fibrous tissue encapsulation,and the need for precise anatomical localization and reconstruction.The integration of advanced signal processing algorithms,particularly those utilizing artificial intelligence and machine learning,has the potential to improve the accuracy and reliability of neural signal interpretation,which will make neural machine interface technologies more intuitive and effective.These technologies have broad,impactful clinical applications,ranging from motor restoration and sensory feedback in prosthetics to neurological disorder treatment and neurorehabilitation.This review suggests that multidisciplinary collaboration will play a critical role in advancing neural machine interface technologies by combining insights from biomedical engineering,clinical surgery,and neuroengineering to develop more sophisticated and reliable interfaces.By addressing existing limitations and exploring new technological frontiers,neural machine interface technologies have the potential to revolutionize neuroprosthetics and neurorehabilitation,promising enhanced mobility,independence,and quality of life for individuals with neurological impairments.By leveraging detailed anatomical knowledge and integrating cutting-edge neuroengineering principles,researchers and clinicians can push the boundaries of what is possible and create increasingly sophisticated and long-lasting prosthetic devices that provide sustained benefits for users.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
基金supported by the National Natural Science Foundation of China(42361144880)the Science and Technology Program of Xizang Autonomous Region,China(XZ202402ZD0001)the Qinghai Province Basic Research Program Project,China(2024-ZJ-904).
文摘Debris flow events are frequent in Tajikistan,yet comprehensive investigations at the regional scale are limited.This study integrates remote sensing,Geographic Information System,and machine learning techniques to evaluate debris flow susceptibility and associated hazards across Tajikistan.A dataset comprising 405 documented debris flow points and 14 influencing factors,encompassing geological,climatic-hydrological,and anthropogenic variables,was established.Three machine learning algorithms—Random Forest,Support Vector Machine(SVM),and Multi-layer Perceptron—were applied to generate susceptibility maps and delineate debris flow risk zones.The results indicate that the areas of higher and high susceptibility accounted for 20.43%and 4.41%of the national area,respectively,and were predominantly concentrated along the Zeravshan and Vakhsh river basins.Among the evaluated models,SVM model demonstrated the highest predictive performance.Beyond conventional topographic and environmental controls,drought conditions were identified as a critical factor influencing debris flow occurrence within the arid and semi-arid mountainous regions of Tajikistan.These findings provide a scientific basis for regional debris flow risk management and disaster mitigation planning,and offer practical guidance for selecting conditioning factors in machine-learning-based susceptibility assessments in other dry mountainous environments.
基金financially supported by the National Key Research and Development Program of China (No. 2023YFB3812601)the National Natural Science Foundation of China (No. 51925401)the Young Elite Scientists Sponsorship Program by CAST, China (No. 2022QNRC001)。
文摘Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.
基金financially supported by the National Key Research and Development Project of China(No.2022YFB3806900)。
文摘The complex interactions and conflicting performance demands in multi-component composites pose significant challenges for achieving balanced multi-property optimization through conventional trial-and-error approaches.Machine learning(ML)offers a promising solution,markedly improving materials discovery efficiency.However,the high dimensionality of feature spaces in such systems has long impeded effective ML-driven feature representation and inverse design.To overcome this,we present an Intelligent Screening System(ISS)framework to accelerate the discovery of optimal formulations balancing four key properties in 15-component PTFE-based copper-clad laminate composites(PTFE-CCLCs).ISS adopts modular descriptors based on the physical information of component volume fractions,thereby simplifying the feature representation.By leveraging the inverse prediction capability of ML models and constructing a performance-driven virtual candidate database,ISS significantly reduced the computational complexity associated with high-dimensional spaces.Experimental validation confirmed that ISSoptimized formulations exhibited superior synergy,notably resolving the trade-off between thermal conductivity and peel strength,and outperform many commercial counterparts.Despite limited data and inherent process variability,ISS achieved an average prediction accuracy of 76.5%,with thermal conductivity predictions exceeding 90%,demonstrating robust reliability.This work provides an innovative,efficient strategy for multifunctional optimization and accelerated discovery in ultra-complex composite systems,highlighting the integration of ML and advanced materials design.
基金supported by the National Key Research and Development Program of China(No.2023YFB3712401),the National Natural Science Foundation of China(No.52274301)the Aeronautical Science Foundation of China(No.2023Z0530S6005)the Ningbo Yongjiang Talent-Introduction Programme(No.2022A-023-C).
文摘The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on empirical models with limited predictive capabilities.This study focuses on the influence of optical basicity on viscosity in CaO-Al_(2)O_(3)-based refining slags,leveraging machine learning to address data scarcity and improve prediction accuracy.An automated framework for algorithm integration,parameter tuning,and evaluation ranking framework(Auto-APE)is employed to develop customized data-driven models for various slag systems,including CaO-Al_(2)O_(3)-SiO_(2),CaO-Al_(2)O_(3)-CaF_(2),CaO-Al_(2)O_(3)-SiO_(2)-MgO,and CaO-Al_(2)O_(3)-SiO_(2)-MgO-CaF_(2).By incorporating optical basicity as a key feature,the models achieve an average validation error of 8.0%to 15.1%,significantly outperforming traditional empirical models.Additionally,symbolic regression is introduced to rapidly construct domain-specific features,such as optical basicity-like descriptors,offering a potential breakthrough in performance prediction for small datasets.This work highlights the critical role of domain-specific knowledge in understanding and predicting viscosity,providing a robust machine learning-based approach for optimizing refining slag properties.
文摘Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.
