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
Glucose molecules are of great significance being one of the most important molecules in metabolic chain.However,due to the small Raman scattering cross-section and weak/non-adsorption on bare metals,accurately obtain...Glucose molecules are of great significance being one of the most important molecules in metabolic chain.However,due to the small Raman scattering cross-section and weak/non-adsorption on bare metals,accurately obtaining their"fingerprint information"remains a huge obstacle.Herein,we developed a tip-enhanced Raman scattering(TERS)technique to address this challenge.Adopting an optical fiber radial vector mode internally illuminates the plasmonic fiber tip to effectively suppress the background noise while generating a strong electric-field enhanced tip hotspot.Furthermore,the tip hotspot approaching the glucose molecules was manipulated via the shear-force feedback to provide more freedom for selecting substrates.Consequently,our TERS technique achieves the visualization of all Raman modes of glucose molecules within spectral window of 400-3200 cm^(-1),which is not achievable through the far-field/surface-enhanced Raman,or the existing TERS techniques.Our TERS technique offers a powerful tool for accurately identifying Raman scattering of molecules,paving the way for biomolecular analysis.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
Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advanc...Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advancements in machine learning,achieving both high accuracy and low computational cost for arrhythmia classification remains a critical issue.Computer-aided diagnosis systems can play a key role in early detection,reducing mortality rates associated with cardiac disorders.This study proposes a fully automated approach for ECG arrhythmia classification using deep learning and machine learning techniques to improve diagnostic accuracy while minimizing processing time.The methodology consists of three stages:1)preprocessing,where ECG signals undergo noise reduction and feature extraction;2)feature Identification,where deep convolutional neural network(CNN)blocks,combined with data augmentation and transfer learning,extract key parameters;3)classification,where a hybrid CNN-SVM model is employed for arrhythmia recognition.CNN-extracted features were fed into a binary support vector machine(SVM)classifier,and model performance was assessed using five-fold cross-validation.Experimental findings demonstrated that the CNN2 model achieved 85.52%accuracy,while the hybrid CNN2-SVM approach significantly improved accuracy to 97.33%,outperforming conventional methods.This model enhances classification efficiency while reducing computational complexity.The proposed approach bridges the gap between accuracy and processing speed in ECG arrhythmia classification,offering a promising solution for real-time clinical applications.Its superior performance compared to nonlinear classifiers highlights its potential for improving automated cardiac diagnosis.展开更多
The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limite...The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.展开更多
In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical...In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical span graph must be updated to meet more stringent engineering requirements.Given this,this study introduces the support vector machine(SVM),along with multiple ensemble(bagging,adaptive boosting,and stacking)and optimization(Harris hawks optimization(HHO),cuckoo search(CS))techniques,to overcome the limitations of the traditional methods.The analysis indicates that the hybrid model combining SVM,bagging,and CS strategies has a good prediction performance,and its test accuracy reaches 0.86.Furthermore,the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases.Compared with previous empirical or semi-empirical methods,the new model overcomes the interference of subjective factors and possesses higher interpretability.Since relying solely on one technology cannot ensure prediction credibility,this study further introduces genetic programming(GP)and kriging interpolation techniques.The explicit expressions derived through GP can offer the stability probability value,and the kriging technique can provide interpolated definitions for two new subclasses.Finally,a prediction platform is developed based on the above three approaches,which can rapidly provide engineering feedback.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
The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.Howev...The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.However,most scholars currently focus on modifying methods to enhance model accuracy,while overlooking the extent to which input parameters influence accuracy.To address this issue,in this study,a prediction model for the endpoint carbon content in the converter was developed using factor analysis(FA)and support vector machine(SVM)optimized by improved particle swarm optimization(IPSO).Analysis of the factors influencing the endpoint carbon content during the converter smelting process led to the identification of 21 input parameters.Subsequently,FA was used to reduce the dimensionality of the data and applied to the prediction model.The results demonstrate that the performance of the FA-IPSO-SVM model surpasses several existing methods,such as twin support vector regression and support vector machine.The model achieves hit rates of 89.59%,96.21%,and 98.74%within error ranges of±0.01%,±0.015%,and±0.02%,respectively.Finally,based on the prediction results obtained by sequentially removing input parameters,the parameters were classified into high influence(5%-7%),medium influence(2%-5%),and low influence(0-2%)categories according to their varying degrees of impact on prediction accuracy.This classi-fication provides a reference for selecting input parameters in future prediction models for endpoint carbon content.展开更多
Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain c...Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.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
In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot al...In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.展开更多
To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explo...To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.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
To enhance network security,this study employs a deep graph matching model for vulnerability similarity detection.The model utilizes a Word Embedding layer to vectorize data words,an Image Embedding layer to vectorize...To enhance network security,this study employs a deep graph matching model for vulnerability similarity detection.The model utilizes a Word Embedding layer to vectorize data words,an Image Embedding layer to vectorize data graphs,and an LSTM layer to extract the associations between word and graph vectors.A Dropout layer is applied to randomly deactivate neurons in the LSTM layer,while a Softmax layer maps the LSTM analysis results.Finally,a fully connected layer outputs the detection results with a dimension of 1.Experimental results demonstrate that the AUC of the deep graph matching vulnerability similarity detection model is 0.9721,indicating good stability.The similarity scores for vulnerabilities such as memory leaks,buffer overflows,and targeted attacks are close to 1,showing significant similarity.In contrast,the similarity scores for vulnerabilities like out-of-bounds memory access and logical design flaws are less than 0.4,indicating good similarity detection performance.The model’s evaluation metrics are all above 97%,with high detection accuracy,which is beneficial for improving network security.展开更多
The total nitrogen(TN)is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality.Accurate and rapid methods are crucial for determining the TN content in water.Herei...The total nitrogen(TN)is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality.Accurate and rapid methods are crucial for determining the TN content in water.Herein,a fast,highly sensitive,and pollution-free approach is proposed,which combines ultraviolet(UV)absorption spectroscopy with Bayesian optimized least squares support vector machine(LSSVM)for detecting TN content in water.Water samples collected from sampling points near the Yangtze River basin in Chongqing of China were analyzed using national standard methods to measure TN content as reference values.The prediction of TN content in water was achieved by integrating the UV absorption spectra of water samples with LSSVM.To make the model quickly and accurately select the optimal parameters to improve the accuracy of the prediction model,the Bayesian optimization(BO)algorithm was used to optimize the parameters of the LSSVM.Results show that the prediction model performs well in predicting TN concentration,with a high coefficient of prediction determination(R^(2)=0.9413)and a low root mean square error of prediction(RMSE=0.0779 mg/L).Comparative analysis with previous studies indicates that the model used in this paper achieves lower prediction errors and superior predictive performance.展开更多
To enhance direction of arrival(DOA)estimation accuracy,this paper proposes a low-cost method for calibrating farfield steering vectors of large aperture millimeter wave radar(mmWR).To this end,we first derive the ste...To enhance direction of arrival(DOA)estimation accuracy,this paper proposes a low-cost method for calibrating farfield steering vectors of large aperture millimeter wave radar(mmWR).To this end,we first derive the steering vectors with amplitude and phase errors,assuming that mmWR works in the time-sharing mode.Then,approximate relationship between the near-field calibration steering vector and the far-field calibration steering vector is analyzed,which is used to accomplish the mapping between the two of them.Finally,simulation results verify that the proposed method can effectively improve the angle measurement accuracy of mmWR with existing amplitude and phase errors.展开更多
In this paper,we obtain a vector bundle valued mixed hard Lefschetz theorem.The argument is mainly based on the works of Tien-Cuong Dinh and Viet-Anh Nguyen.
Mosquitoes(Diptera:Culicidae)are vectors of various pathogens of public health concern,but replacing conventional insecticides remains a challenge.In this regard,natural products represent valuable sources of potentia...Mosquitoes(Diptera:Culicidae)are vectors of various pathogens of public health concern,but replacing conventional insecticides remains a challenge.In this regard,natural products represent valuable sources of potential insecticidal compounds,thus increasingly attracting research interest.Commiphora myrrha(T.Nees)Engl.(Burseraceae)is a medicinal plant whose oleo-gum resin is used in food,cosmetics,fragrances,and pharmaceuticals.Herein,the larvicidal potential of its essential oil(EO)was assessed on four mosquito species(Aedes albopictus Skuse,Ae.aegypti L.,Anopheles gambiae Giles and An.stephensi Liston),with LC_(50) values ranging from 4.42 to 16.80 μg/mL.The bio-guided EO fractionation identified furanosesquiterpenes as the main larvicidal compounds.A GC-MS-driven untargeted metabolomic analysis revealed 32 affected metabolic pathways in treated larvae.The EO non-target toxicity on Daphnia magna Straus(LC_(50)=4.51 μL/L)and its cytotoxicity on a human kidney cell line(HEK293)(IC50 of 14.38 μg/mL)were also assessed.This study shows the potential of plant products as innovative insecticidal agents and lays the ground-work for the possible exploitation of C.myrrha EO in the sustainable approaches for mosquito management.展开更多
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supported by National Natural Science Foundation of China(12374358,91950207)Guangdong Basic and Applied Basic Research Foundation(2024A1515010420).
文摘Glucose molecules are of great significance being one of the most important molecules in metabolic chain.However,due to the small Raman scattering cross-section and weak/non-adsorption on bare metals,accurately obtaining their"fingerprint information"remains a huge obstacle.Herein,we developed a tip-enhanced Raman scattering(TERS)technique to address this challenge.Adopting an optical fiber radial vector mode internally illuminates the plasmonic fiber tip to effectively suppress the background noise while generating a strong electric-field enhanced tip hotspot.Furthermore,the tip hotspot approaching the glucose molecules was manipulated via the shear-force feedback to provide more freedom for selecting substrates.Consequently,our TERS technique achieves the visualization of all Raman modes of glucose molecules within spectral window of 400-3200 cm^(-1),which is not achievable through the far-field/surface-enhanced Raman,or the existing TERS techniques.Our TERS technique offers a powerful tool for accurately identifying Raman scattering of molecules,paving the way for biomolecular analysis.
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.
文摘Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advancements in machine learning,achieving both high accuracy and low computational cost for arrhythmia classification remains a critical issue.Computer-aided diagnosis systems can play a key role in early detection,reducing mortality rates associated with cardiac disorders.This study proposes a fully automated approach for ECG arrhythmia classification using deep learning and machine learning techniques to improve diagnostic accuracy while minimizing processing time.The methodology consists of three stages:1)preprocessing,where ECG signals undergo noise reduction and feature extraction;2)feature Identification,where deep convolutional neural network(CNN)blocks,combined with data augmentation and transfer learning,extract key parameters;3)classification,where a hybrid CNN-SVM model is employed for arrhythmia recognition.CNN-extracted features were fed into a binary support vector machine(SVM)classifier,and model performance was assessed using five-fold cross-validation.Experimental findings demonstrated that the CNN2 model achieved 85.52%accuracy,while the hybrid CNN2-SVM approach significantly improved accuracy to 97.33%,outperforming conventional methods.This model enhances classification efficiency while reducing computational complexity.The proposed approach bridges the gap between accuracy and processing speed in ECG arrhythmia classification,offering a promising solution for real-time clinical applications.Its superior performance compared to nonlinear classifiers highlights its potential for improving automated cardiac diagnosis.
基金the National Key Research and Development Program of China(2021YFC2900300)the Natural Science Foundation of Guangdong Province(2024A1515030216)+2 种基金MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(GPMR202437)the Guangdong Province Introduced of Innovative R&D Team(2021ZT09H399)the Third Xinjiang Scientific Expedition Program(2022xjkk1301).
文摘The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.
基金supported by the National Natural Science Foundation of China(Grant No.42177164)the Distinguished Youth Science Foundation of Hunan Province of China(Grant No.2022JJ10073)the Outstanding Youth Project of Hunan Provincial Department of Education,China(Grant No.23B0008).
文摘In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical span graph must be updated to meet more stringent engineering requirements.Given this,this study introduces the support vector machine(SVM),along with multiple ensemble(bagging,adaptive boosting,and stacking)and optimization(Harris hawks optimization(HHO),cuckoo search(CS))techniques,to overcome the limitations of the traditional methods.The analysis indicates that the hybrid model combining SVM,bagging,and CS strategies has a good prediction performance,and its test accuracy reaches 0.86.Furthermore,the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases.Compared with previous empirical or semi-empirical methods,the new model overcomes the interference of subjective factors and possesses higher interpretability.Since relying solely on one technology cannot ensure prediction credibility,this study further introduces genetic programming(GP)and kriging interpolation techniques.The explicit expressions derived through GP can offer the stability probability value,and the kriging technique can provide interpolated definitions for two new subclasses.Finally,a prediction platform is developed based on the above three approaches,which can rapidly provide engineering feedback.
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financially supported by the National Natural Science Foundation of China(No.52174297).
文摘The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.However,most scholars currently focus on modifying methods to enhance model accuracy,while overlooking the extent to which input parameters influence accuracy.To address this issue,in this study,a prediction model for the endpoint carbon content in the converter was developed using factor analysis(FA)and support vector machine(SVM)optimized by improved particle swarm optimization(IPSO).Analysis of the factors influencing the endpoint carbon content during the converter smelting process led to the identification of 21 input parameters.Subsequently,FA was used to reduce the dimensionality of the data and applied to the prediction model.The results demonstrate that the performance of the FA-IPSO-SVM model surpasses several existing methods,such as twin support vector regression and support vector machine.The model achieves hit rates of 89.59%,96.21%,and 98.74%within error ranges of±0.01%,±0.015%,and±0.02%,respectively.Finally,based on the prediction results obtained by sequentially removing input parameters,the parameters were classified into high influence(5%-7%),medium influence(2%-5%),and low influence(0-2%)categories according to their varying degrees of impact on prediction accuracy.This classi-fication provides a reference for selecting input parameters in future prediction models for endpoint carbon content.
基金funded by the Natural Science Foundation of China(Grant Nos.42377164 and 41972280)the Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202305).
文摘Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.
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supported by the SP2024/089 Project by the Faculty of Materials Science and Technology,VˇSB-Technical University of Ostrava.
文摘In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.
基金Supported by the National Key R&D Program of China(2023YFD2101001)National Natural Science Foundation of China(32202144,61807001)。
文摘To address the issues of peak overlap caused by complex matrices in agricultural product terahertz(THz)spectral signals and the dynamic,nonlinear interference induced by environmental and system noise,this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality.THz time-domain spectroscopy(THz-TDS)combined with an attenuated total reflection(ATR)accessory was used to collect THz absorbance spectra from 48 peanut samples.Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example,wavelet transform(WT),empirical mode decomposition(EMD),local mean decomposition(LMD),and its improved methods-segmented local mean decomposition(SLMD)and piecewise mirror extension local mean decomposition(PME-LMD)-were employed for spectral denoising.The applicability of different denoising methods was evaluated using a support vector regression(SVR)model.Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance,with a root mean square error(RMSE),coefficient of determination(R^(2)),and mean absolute percentage error(MAPE)of 0.010,0.912,and 0.040,respectively.Compared with traditional methods,PME-LMD significantly improved spectral quality and model prediction performance.The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals,providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products.This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.
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Special Project Funded by Tsinghua University Press:“Engineering Drawing and CAD”Course Construction and Textbook Development。
文摘To enhance network security,this study employs a deep graph matching model for vulnerability similarity detection.The model utilizes a Word Embedding layer to vectorize data words,an Image Embedding layer to vectorize data graphs,and an LSTM layer to extract the associations between word and graph vectors.A Dropout layer is applied to randomly deactivate neurons in the LSTM layer,while a Softmax layer maps the LSTM analysis results.Finally,a fully connected layer outputs the detection results with a dimension of 1.Experimental results demonstrate that the AUC of the deep graph matching vulnerability similarity detection model is 0.9721,indicating good stability.The similarity scores for vulnerabilities such as memory leaks,buffer overflows,and targeted attacks are close to 1,showing significant similarity.In contrast,the similarity scores for vulnerabilities like out-of-bounds memory access and logical design flaws are less than 0.4,indicating good similarity detection performance.The model’s evaluation metrics are all above 97%,with high detection accuracy,which is beneficial for improving network security.
基金supported by the National Natural Science Foundation of China(Nos.32171627 and 62105252)the Science and Technology Research Program of Chongqing Municipal Education Commission(No.KJZD-M202200602)the Hangzhou Science and Technology Development Project(No.202204T04).
文摘The total nitrogen(TN)is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality.Accurate and rapid methods are crucial for determining the TN content in water.Herein,a fast,highly sensitive,and pollution-free approach is proposed,which combines ultraviolet(UV)absorption spectroscopy with Bayesian optimized least squares support vector machine(LSSVM)for detecting TN content in water.Water samples collected from sampling points near the Yangtze River basin in Chongqing of China were analyzed using national standard methods to measure TN content as reference values.The prediction of TN content in water was achieved by integrating the UV absorption spectra of water samples with LSSVM.To make the model quickly and accurately select the optimal parameters to improve the accuracy of the prediction model,the Bayesian optimization(BO)algorithm was used to optimize the parameters of the LSSVM.Results show that the prediction model performs well in predicting TN concentration,with a high coefficient of prediction determination(R^(2)=0.9413)and a low root mean square error of prediction(RMSE=0.0779 mg/L).Comparative analysis with previous studies indicates that the model used in this paper achieves lower prediction errors and superior predictive performance.
文摘To enhance direction of arrival(DOA)estimation accuracy,this paper proposes a low-cost method for calibrating farfield steering vectors of large aperture millimeter wave radar(mmWR).To this end,we first derive the steering vectors with amplitude and phase errors,assuming that mmWR works in the time-sharing mode.Then,approximate relationship between the near-field calibration steering vector and the far-field calibration steering vector is analyzed,which is used to accomplish the mapping between the two of them.Finally,simulation results verify that the proposed method can effectively improve the angle measurement accuracy of mmWR with existing amplitude and phase errors.
基金supported by the National key R and D Program of China 2020YFA0713100the NSFC(12141104,12371062 and 12431004).
文摘In this paper,we obtain a vector bundle valued mixed hard Lefschetz theorem.The argument is mainly based on the works of Tien-Cuong Dinh and Viet-Anh Nguyen.
基金granted by the project PRIN 2022“Bioformulations for controlled release of botanical pesticides for sustainable agriculture”(prot.202274BK9L)supported by the Italian Ministry of University and Research(MUR)the Technology agency of the Czech Republic for its financial support concerning botanical pesticide(Project No.FW06010376).
文摘Mosquitoes(Diptera:Culicidae)are vectors of various pathogens of public health concern,but replacing conventional insecticides remains a challenge.In this regard,natural products represent valuable sources of potential insecticidal compounds,thus increasingly attracting research interest.Commiphora myrrha(T.Nees)Engl.(Burseraceae)is a medicinal plant whose oleo-gum resin is used in food,cosmetics,fragrances,and pharmaceuticals.Herein,the larvicidal potential of its essential oil(EO)was assessed on four mosquito species(Aedes albopictus Skuse,Ae.aegypti L.,Anopheles gambiae Giles and An.stephensi Liston),with LC_(50) values ranging from 4.42 to 16.80 μg/mL.The bio-guided EO fractionation identified furanosesquiterpenes as the main larvicidal compounds.A GC-MS-driven untargeted metabolomic analysis revealed 32 affected metabolic pathways in treated larvae.The EO non-target toxicity on Daphnia magna Straus(LC_(50)=4.51 μL/L)and its cytotoxicity on a human kidney cell line(HEK293)(IC50 of 14.38 μg/mL)were also assessed.This study shows the potential of plant products as innovative insecticidal agents and lays the ground-work for the possible exploitation of C.myrrha EO in the sustainable approaches for mosquito management.
