The quantitative rules of the transfer and variation of errors,when the Gaussian integral functions F.(z) are evaluated sequentially by recurring,have been expounded.The traditional viewpoint to negate the applicabili...The quantitative rules of the transfer and variation of errors,when the Gaussian integral functions F.(z) are evaluated sequentially by recurring,have been expounded.The traditional viewpoint to negate the applicability and reliability of upward recursive formula in principle is amended.An optimal scheme of upward-and downward-joint recursions has been developed for the sequential F(z) computations.No additional accuracy is needed with the fundamental term of recursion because the absolute error of Fn(z) always decreases with the recursive approach.The scheme can be employed in modifying any of existent subprograms for Fn<z> computations.In the case of p-d-f-and g-type Gaussians,combining this method with Schaad's formulas can reduce,at least,the additive operations by a factor 40%;the multiplicative and exponential operations by a factor 60%.展开更多
The present work is much motivated by finding an explicit way in the construction of the Jack symmetric function,which is the spectrum generating function for the Calogero-Sutherland (CS) model.To accomplish this work...The present work is much motivated by finding an explicit way in the construction of the Jack symmetric function,which is the spectrum generating function for the Calogero-Sutherland (CS) model.To accomplish this work,the hidden Virasoro structure in the CS model is much explored.In particular,we found that the Virasoro singular vectors form a skew hierarchy in the CS model.Literally,skew is analogous to coset,but here specifically refer to the operation on the Young tableaux.In fact,based on the construction of the Virasoro singular vectors,this hierarchical structure can be used to give a complete construction of the CS states,i.e.the Jack symmetric functions,recursively.The construction is given both in operator formalism as well as in integral representation.This new integral representation for the Jack symmetric functions may shed some insights on the spectrum constructions for the other integrable systems.展开更多
In this paper we make a study of the use of the iniiai values of a recursion skilfully, so that the count for finding a solution of any recursion will be more simplified. Finally, we give out the "moving forth pr...In this paper we make a study of the use of the iniiai values of a recursion skilfully, so that the count for finding a solution of any recursion will be more simplified. Finally, we give out the "moving forth prineiple" of initial values of a recursion.展开更多
New approach to systems of polynomial recursions is developed based on the Carleman linearization procedure. The article is divided into two main sections: firstly, we focus on the case of uni-variable depth-one polyn...New approach to systems of polynomial recursions is developed based on the Carleman linearization procedure. The article is divided into two main sections: firstly, we focus on the case of uni-variable depth-one polynomial recurrences. Subsequently, the systems of depth-one polynomial recurrence relations are discussed. The corresponding transition matrix is constructed and upper triangularized. Furthermore, the powers of the transition matrix are calculated using the back substitution procedure. The explicit expression for a solution to a broad family of recurrence relations is obtained. We investigate to which recurrences the framework can be applied and construct sufficient conditions for the method to work. It is shown how introduction of auxiliary variables can be used to reduce arbitrary depth systems to the depth-one system of recurrences dealt with earlier. Finally, the limitations of the method are discussed, outlining possible directions for future research.展开更多
In the actuarial literature, several exact and approximative recursive methods have been proposed for calculating the distribution of a sum of mutually independent compound Bernoulli distributed random variables. In t...In the actuarial literature, several exact and approximative recursive methods have been proposed for calculating the distribution of a sum of mutually independent compound Bernoulli distributed random variables. In this paper, we give an overview of these methods. We compare their performance with the straight- forward convolution technique by counting the number of dot operations involved in each method. It turns out that in many practicle situations, the recursive methods outperform the convolution method.展开更多
This paper focuses on the problem of leaderfollowing consensus for nonlinear cascaded multi-agent systems.The control strategies for these systems are transformed into successive control problem schemes for lower-orde...This paper focuses on the problem of leaderfollowing consensus for nonlinear cascaded multi-agent systems.The control strategies for these systems are transformed into successive control problem schemes for lower-order error subsystems.A distributed consensus analysis for the corresponding error systems is conducted by employing recursive methods and virtual controllers,accompanied by a series of Lyapunov functions devised throughout the iterative process,which solves the leaderfollowing consensus problem of a class of nonlinear cascaded multi-agent systems.Specific simulation examples illustrate the effectiveness of the proposed control algorithm.展开更多
For nonlinear state estimation driven by non-Gaussian noise,the estimator is required to be updated iteratively.Since the iterative update approximates a linear process,it fails to capture the nonlinearity of observat...For nonlinear state estimation driven by non-Gaussian noise,the estimator is required to be updated iteratively.Since the iterative update approximates a linear process,it fails to capture the nonlinearity of observation models,and this further degrades filtering accuracy and consistency.Given the flaws of nonlinear iteration,this work incorporates a recursive strategy into generalized M-estimation rather than the iterative strategy.The proposed algorithm extends nonlinear recursion to nonlinear systems using the statistical linear regression method.The recursion allows for the gradual release of observation information and consequently enables the update to proceed along the nonlinear direction.Considering the correlated state and observation noise induced by recursions,a separately reweighting strategy is adopted to build a robust nonlinear system.Analogous to the nonlinear recursion,a robust nonlinear recursive update strategy is proposed,where the associated covariances and the observation noise statistics are updated recursively to ensure the consistency of observation noise statistics,thereby completing the nonlinear solution of the robust system.Compared with the iterative update strategies under non-Gaussian observation noise,the recursive update strategy can facilitate the estimator to achieve higher filtering accuracy,stronger robustness,and better consistency.Therefore,the proposed strategy is more suitable for the robust nonlinear filtering framework.展开更多
To obtain new integrable nonlinear differential equations there are some well-known methods such as Lax equations with different Lax representations.There are also some other methods that are based on integrable scala...To obtain new integrable nonlinear differential equations there are some well-known methods such as Lax equations with different Lax representations.There are also some other methods that are based on integrable scalar nonlinear partial differential equations.We show that some systems of integrable equations published recently are the M_(2)-extension of integrable such scalar equations.For illustration,we give Korteweg-de Vries,Kaup-Kupershmidt,and SawadaKotera equations as examples.By the use of such an extension of integrable scalar equations,we obtain some new integrable systems with recursion operators.We also give the soliton solutions of the systems and integrable standard nonlocal and shifted nonlocal reductions of these systems.展开更多
Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusio...Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature space.Then,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior features.Finally,a support vector machine(SVM)classifier is used to classify intrusion behaviors.The proposed model is verified by experiments on the NSL-KDD dataset.The results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion classification.The proposed method can provide new insight into network intrusion detection.展开更多
Software defect prediction aims to use measurement data of code and historical defects to predict potential problems,optimize testing resources and defect management.However,current methods face challenges:(1)Coarse-g...Software defect prediction aims to use measurement data of code and historical defects to predict potential problems,optimize testing resources and defect management.However,current methods face challenges:(1)Coarse-grained file level detection cannot accurately locate specific defects.(2)Fine-grained line-level defect prediction methods rely solely on local information of a single line of code,failing to deeply analyze the semantic context of the code line and ignoring the heuristic impact of line-level context on the code line,making it difficult to capture the interaction between global and local information.Therefore,this paper proposes a telecontext-enhanced recursive interactive attention fusion method for line-level defect prediction(TRIA-LineDP).Firstly,using a bidirectional hierarchical attention network to extract semantic features and contextual information from the original code lines as the basis.Then,the extracted contextual information is forwarded to the telecontext capture module to aggregate the global context,thereby enhancing the understanding of broader code dynamics.Finally,a recursive interaction model is used to simulate the interaction between code lines and line-level context,passing information layer by layer to enhance local and global information exchange,thereby achieving accurate defect localization.Experimental results from within-project defect prediction(WPDP)and cross-project defect prediction(CPDP)conducted on nine different projects(encompassing a total of 32 versions)demonstrated that,within the same project,the proposed methods will respectively recall at top 20%of lines of code(Recall@Top20%LOC)and effort at top 20%recall(Effort@Top20%Recall)has increased by 11%–52%and 23%–77%.In different projects,improvements of 9%–60%and 18%–77%have been achieved,which are superior to existing advanced methods and have good detection performance.展开更多
Aeromagnetic compensation is one of the key issues in high-precision geomagnetic fl ight carrier navigation, directly determining the accuracy and reliability of real-time magnetic measurement data. The accurate model...Aeromagnetic compensation is one of the key issues in high-precision geomagnetic fl ight carrier navigation, directly determining the accuracy and reliability of real-time magnetic measurement data. The accurate modeling and compensation of interference magnetic measurements on carriers are of great signifi cance for the construction of reference and real-time maps for geomagnetic navigation. Current research on aeromagnetic compensation algorithms mainly focuses on accurately modeling interference magnetic fields from model- and data-driven perspectives based on measured aeromagnetic data. Challenges in obtaining aeromagnetic data and low information complexity adversely aff ect the generalization performance of a constructed model. To address these issues, a recursive least square algorithm based on elastic weight consolidation is proposed, which eff ectively suppresses the occurrence of catastrophic forgetting by controlling the direction of parameter updates. Experimental verifi cation with publicly available aeromagnetic datasets shows that the proposed algorithm can eff ectively circumvent historical information loss caused by interference magnetic field models during parameter updates and improve the stability, robustness, and accuracy of interference magnetic fi eld models.展开更多
Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used pho...Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used photovoltaic forecasting methods,which struggle to handle issues such as non-u-niform lengths of time series data for power generation and meteorological conditions,overlapping photovoltaic characteristics,and nonlinear correlations,an improved method that utilizes spectral clustering and dynamic time warping(DTW)for selecting similar days is proposed to optimize the dataset along the temporal dimension.Furthermore,XGBoost is employed for recursive feature selec-tion.On this basis,to address the issue that single forecasting models excel at capturing different data characteristics and tend to exhibit significant prediction errors under adverse meteorological con-ditions,an improved forecasting model based on Stacking and weighted fusion is proposed to reduce the independent bias and variance of individual models and enhance the predictive accuracy.Final-ly,experimental validation is carried out using real data from a photovoltaic power station in the Xi-aoshan District of Hangzhou,China,demonstrating that the proposed method can still achieve accu-rate and robust forecasting results even under conditions of significant meteorological fluctuations.展开更多
Spartina alterniflora is now listed among the world’s 100 most dangerous invasive species,severely affecting the ecological balance of coastal wetlands.Remote sensing technologies based on deep learning enable large-...Spartina alterniflora is now listed among the world’s 100 most dangerous invasive species,severely affecting the ecological balance of coastal wetlands.Remote sensing technologies based on deep learning enable large-scale monitoring of Spartina alterniflora,but they require large datasets and have poor interpretability.A new method is proposed to detect Spartina alterniflora from Sentinel-2 imagery.Firstly,to get the high canopy cover and dense community characteristics of Spartina alterniflora,multi-dimensional shallow features are extracted from the imagery.Secondly,to detect different objects from satellite imagery,index features are extracted,and the statistical features of the Gray-Level Co-occurrence Matrix(GLCM)are derived using principal component analysis.Then,ensemble learning methods,including random forest,extreme gradient boosting,and light gradient boosting machine models,are employed for image classification.Meanwhile,Recursive Feature Elimination with Cross-Validation(RFECV)is used to select the best feature subset.Finally,to enhance the interpretability of the models,the best features are utilized to classify multi-temporal images and SHapley Additive exPlanations(SHAP)is combined with these classifications to explain the model prediction process.The method is validated by using Sentinel-2 imageries and previous observations of Spartina alterniflora in Chongming Island,it is found that the model combining image texture features such as GLCM covariance can significantly improve the detection accuracy of Spartina alterniflora by about 8%compared with the model without image texture features.Through multiple model comparisons and feature selection via RFECV,the selected model and eight features demonstrated good classification accuracy when applied to data from different time periods,proving that feature reduction can effectively enhance model generalization.Additionally,visualizing model decisions using SHAP revealed that the image texture feature component_1_GLCMVariance is particularly important for identifying each land cover type.展开更多
High-speed milling(HSM)is advantageous for machining high-quality complex-structure surface components with various materials.Identifying and estimating cutting force signals for characterizing HSM is of high signific...High-speed milling(HSM)is advantageous for machining high-quality complex-structure surface components with various materials.Identifying and estimating cutting force signals for characterizing HSM is of high significance.However,considering the tool runout and size effects,many proposed models focus on the material and mechanical characteristics.This study presents a novel approach for predicting micromilling cutting forces using a semianalytical multidimensional model that integrates experimental empirical data and a mechanical theoretical force model.A novel analytical optimization approach is provided to identify the cutting forces,classify the cutting states,and determine the tool runout using an adaptive algorithm that simplifies modeling and calculation.The instantaneous un-deformed chip thickness(IUCT)is determined from the trochoidal trajectories of each tool flute and optimized using the bisection method.Herein,the computational efficiency is improved,and the errors are clarified.The tool runout parameters are identified from the processed displacement signals and determined from the preprocessed vibration signals using an adaptive signal processing method.It is reliable and stable for determining tool runout and is an effective foundation for the force model.This approach is verified using HSM tests.Herein,the determination coefficients are stable above 0.9.It is convenient and efficient for achieving the key intermediate parameters(IUCT and tool runout),which can be generalized to various machining conditions and operations.展开更多
The aim of this work is to study the traveling wavefronts in a discrete-time integral recursion with a Gauss kernel in R2.We first establish the existence of traveling wavefronts as well as their precise asymptotic be...The aim of this work is to study the traveling wavefronts in a discrete-time integral recursion with a Gauss kernel in R2.We first establish the existence of traveling wavefronts as well as their precise asymptotic behavior.Then,by employing the comparison principle and upper and lower solutions technique,we prove the asymptotic stability and uniqueness of such monostable wavefronts in the sense of phase shift and circumnutation.We also obtain some similar results in R.展开更多
Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study intr...Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.展开更多
To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation,a control method involving flexible multistate switches(FMSs)is proposed in this st...To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation,a control method involving flexible multistate switches(FMSs)is proposed in this study.This approach is based on an improved double-loop recursive fuzzy neural network(DRFNN)sliding mode,which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults.First,an improved DRFNN sliding mode control(SMC)method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system.To improve the robustness of the system,an adaptive parameter-adjustment strategy for the DRFNN is designed,where its dynamic mapping capabilities are leveraged to improve the transient compensation control.Additionally,a quasi-continuous second-order sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability.The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem.A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink.The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis.展开更多
This paper proposes a novel approach for identifying distributed dynamic loads in the time domain.Using polynomial andmodal analysis,the load is transformed intomodal space for coefficient identification.This allows t...This paper proposes a novel approach for identifying distributed dynamic loads in the time domain.Using polynomial andmodal analysis,the load is transformed intomodal space for coefficient identification.This allows the distributed dynamic load with a two-dimensional form in terms of time and space to be simultaneously identified in the form of modal force,thereby achieving dimensionality reduction.The Impulse-based Force Estimation Algorithm is proposed to identify dynamic loads in the time domain.Firstly,the algorithm establishes a recursion scheme based on convolution integral,enabling it to identify loads with a long history and rapidly changing forms over time.Secondly,the algorithm introduces moving mean and polynomial fitting to detrend,enhancing its applicability in load estimation.The aforementioned methodology successfully accomplishes the reconstruction of distributed,instead of centralized,dynamic loads on the continuum in the time domain by utilizing acceleration response.To validate the effectiveness of the method,computational and experimental verification were conducted.展开更多
文摘The quantitative rules of the transfer and variation of errors,when the Gaussian integral functions F.(z) are evaluated sequentially by recurring,have been expounded.The traditional viewpoint to negate the applicability and reliability of upward recursive formula in principle is amended.An optimal scheme of upward-and downward-joint recursions has been developed for the sequential F(z) computations.No additional accuracy is needed with the fundamental term of recursion because the absolute error of Fn(z) always decreases with the recursive approach.The scheme can be employed in modifying any of existent subprograms for Fn<z> computations.In the case of p-d-f-and g-type Gaussians,combining this method with Schaad's formulas can reduce,at least,the additive operations by a factor 40%;the multiplicative and exponential operations by a factor 60%.
基金Supported by the Chinese Academy of Sciences Program "Frontier Topics in Mathematical Physics" (KJCX3-SYW-S03)Supported Partially by the National Natural Science Foundation of China under Grant No.11035008
文摘The present work is much motivated by finding an explicit way in the construction of the Jack symmetric function,which is the spectrum generating function for the Calogero-Sutherland (CS) model.To accomplish this work,the hidden Virasoro structure in the CS model is much explored.In particular,we found that the Virasoro singular vectors form a skew hierarchy in the CS model.Literally,skew is analogous to coset,but here specifically refer to the operation on the Young tableaux.In fact,based on the construction of the Virasoro singular vectors,this hierarchical structure can be used to give a complete construction of the CS states,i.e.the Jack symmetric functions,recursively.The construction is given both in operator formalism as well as in integral representation.This new integral representation for the Jack symmetric functions may shed some insights on the spectrum constructions for the other integrable systems.
文摘In this paper we make a study of the use of the iniiai values of a recursion skilfully, so that the count for finding a solution of any recursion will be more simplified. Finally, we give out the "moving forth prineiple" of initial values of a recursion.
文摘New approach to systems of polynomial recursions is developed based on the Carleman linearization procedure. The article is divided into two main sections: firstly, we focus on the case of uni-variable depth-one polynomial recurrences. Subsequently, the systems of depth-one polynomial recurrence relations are discussed. The corresponding transition matrix is constructed and upper triangularized. Furthermore, the powers of the transition matrix are calculated using the back substitution procedure. The explicit expression for a solution to a broad family of recurrence relations is obtained. We investigate to which recurrences the framework can be applied and construct sufficient conditions for the method to work. It is shown how introduction of auxiliary variables can be used to reduce arbitrary depth systems to the depth-one system of recurrences dealt with earlier. Finally, the limitations of the method are discussed, outlining possible directions for future research.
基金Support by the Onderzoeksfonds K.U.Leuven(GOA/02:Actuarile,financile en statistische aspecten van afhankelijkheden in vcrzekerings-en financile portefeuilles)Support by the Dutch Organization for Scientific Research(No.NWO 048.031.2003.001)
文摘In the actuarial literature, several exact and approximative recursive methods have been proposed for calculating the distribution of a sum of mutually independent compound Bernoulli distributed random variables. In this paper, we give an overview of these methods. We compare their performance with the straight- forward convolution technique by counting the number of dot operations involved in each method. It turns out that in many practicle situations, the recursive methods outperform the convolution method.
基金National Natural Science Foundation of China(No.12071370)。
文摘This paper focuses on the problem of leaderfollowing consensus for nonlinear cascaded multi-agent systems.The control strategies for these systems are transformed into successive control problem schemes for lower-order error subsystems.A distributed consensus analysis for the corresponding error systems is conducted by employing recursive methods and virtual controllers,accompanied by a series of Lyapunov functions devised throughout the iterative process,which solves the leaderfollowing consensus problem of a class of nonlinear cascaded multi-agent systems.Specific simulation examples illustrate the effectiveness of the proposed control algorithm.
基金co-supported by the National Natural Science Foundation of China(No.62303246,No.62103204)the China Postdoctoral Science Foundation(No.2023M731788)。
文摘For nonlinear state estimation driven by non-Gaussian noise,the estimator is required to be updated iteratively.Since the iterative update approximates a linear process,it fails to capture the nonlinearity of observation models,and this further degrades filtering accuracy and consistency.Given the flaws of nonlinear iteration,this work incorporates a recursive strategy into generalized M-estimation rather than the iterative strategy.The proposed algorithm extends nonlinear recursion to nonlinear systems using the statistical linear regression method.The recursion allows for the gradual release of observation information and consequently enables the update to proceed along the nonlinear direction.Considering the correlated state and observation noise induced by recursions,a separately reweighting strategy is adopted to build a robust nonlinear system.Analogous to the nonlinear recursion,a robust nonlinear recursive update strategy is proposed,where the associated covariances and the observation noise statistics are updated recursively to ensure the consistency of observation noise statistics,thereby completing the nonlinear solution of the robust system.Compared with the iterative update strategies under non-Gaussian observation noise,the recursive update strategy can facilitate the estimator to achieve higher filtering accuracy,stronger robustness,and better consistency.Therefore,the proposed strategy is more suitable for the robust nonlinear filtering framework.
基金partially supported by the Scientific and Technological Research Council of Turkey(TüBITAK)。
文摘To obtain new integrable nonlinear differential equations there are some well-known methods such as Lax equations with different Lax representations.There are also some other methods that are based on integrable scalar nonlinear partial differential equations.We show that some systems of integrable equations published recently are the M_(2)-extension of integrable such scalar equations.For illustration,we give Korteweg-de Vries,Kaup-Kupershmidt,and SawadaKotera equations as examples.By the use of such an extension of integrable scalar equations,we obtain some new integrable systems with recursion operators.We also give the soliton solutions of the systems and integrable standard nonlocal and shifted nonlocal reductions of these systems.
基金supported in part by the National Natural Science Foundation of China(No.62001333)the Scientific Research Project of Education Department of Hubei Province(No.D20221702).
文摘Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature space.Then,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior features.Finally,a support vector machine(SVM)classifier is used to classify intrusion behaviors.The proposed model is verified by experiments on the NSL-KDD dataset.The results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion classification.The proposed method can provide new insight into network intrusion detection.
基金supported by National Natural Science Foundation of China(no.62376240).
文摘Software defect prediction aims to use measurement data of code and historical defects to predict potential problems,optimize testing resources and defect management.However,current methods face challenges:(1)Coarse-grained file level detection cannot accurately locate specific defects.(2)Fine-grained line-level defect prediction methods rely solely on local information of a single line of code,failing to deeply analyze the semantic context of the code line and ignoring the heuristic impact of line-level context on the code line,making it difficult to capture the interaction between global and local information.Therefore,this paper proposes a telecontext-enhanced recursive interactive attention fusion method for line-level defect prediction(TRIA-LineDP).Firstly,using a bidirectional hierarchical attention network to extract semantic features and contextual information from the original code lines as the basis.Then,the extracted contextual information is forwarded to the telecontext capture module to aggregate the global context,thereby enhancing the understanding of broader code dynamics.Finally,a recursive interaction model is used to simulate the interaction between code lines and line-level context,passing information layer by layer to enhance local and global information exchange,thereby achieving accurate defect localization.Experimental results from within-project defect prediction(WPDP)and cross-project defect prediction(CPDP)conducted on nine different projects(encompassing a total of 32 versions)demonstrated that,within the same project,the proposed methods will respectively recall at top 20%of lines of code(Recall@Top20%LOC)and effort at top 20%recall(Effort@Top20%Recall)has increased by 11%–52%and 23%–77%.In different projects,improvements of 9%–60%and 18%–77%have been achieved,which are superior to existing advanced methods and have good detection performance.
基金supported by the National Natural Science Foundation of China under Grant 61673017in part by the Science and Technology Department of Shaanxi Province under Grant 2024JC-YBQN-0657。
文摘Aeromagnetic compensation is one of the key issues in high-precision geomagnetic fl ight carrier navigation, directly determining the accuracy and reliability of real-time magnetic measurement data. The accurate modeling and compensation of interference magnetic measurements on carriers are of great signifi cance for the construction of reference and real-time maps for geomagnetic navigation. Current research on aeromagnetic compensation algorithms mainly focuses on accurately modeling interference magnetic fields from model- and data-driven perspectives based on measured aeromagnetic data. Challenges in obtaining aeromagnetic data and low information complexity adversely aff ect the generalization performance of a constructed model. To address these issues, a recursive least square algorithm based on elastic weight consolidation is proposed, which eff ectively suppresses the occurrence of catastrophic forgetting by controlling the direction of parameter updates. Experimental verifi cation with publicly available aeromagnetic datasets shows that the proposed algorithm can eff ectively circumvent historical information loss caused by interference magnetic field models during parameter updates and improve the stability, robustness, and accuracy of interference magnetic fi eld models.
基金Supported by the National Natural Science Foundation of China(No.52005442)the Technology Project of Zhejiang Huayun Information Technology Co.,Ltd.(No.HYJT/JS-2020-004).
文摘Accurate short-term photovoltaic(PV)output forecasting is beneficial for increasing grid stabil-ity and enhancing the capacity for photovoltaic power absorption.In response to the challenges faced by commonly used photovoltaic forecasting methods,which struggle to handle issues such as non-u-niform lengths of time series data for power generation and meteorological conditions,overlapping photovoltaic characteristics,and nonlinear correlations,an improved method that utilizes spectral clustering and dynamic time warping(DTW)for selecting similar days is proposed to optimize the dataset along the temporal dimension.Furthermore,XGBoost is employed for recursive feature selec-tion.On this basis,to address the issue that single forecasting models excel at capturing different data characteristics and tend to exhibit significant prediction errors under adverse meteorological con-ditions,an improved forecasting model based on Stacking and weighted fusion is proposed to reduce the independent bias and variance of individual models and enhance the predictive accuracy.Final-ly,experimental validation is carried out using real data from a photovoltaic power station in the Xi-aoshan District of Hangzhou,China,demonstrating that the proposed method can still achieve accu-rate and robust forecasting results even under conditions of significant meteorological fluctuations.
基金The National Key Research and Development Program of China under contract No.2023YFC3008204the National Natural Science Foundation of China under contract Nos 41977302 and 42476217.
文摘Spartina alterniflora is now listed among the world’s 100 most dangerous invasive species,severely affecting the ecological balance of coastal wetlands.Remote sensing technologies based on deep learning enable large-scale monitoring of Spartina alterniflora,but they require large datasets and have poor interpretability.A new method is proposed to detect Spartina alterniflora from Sentinel-2 imagery.Firstly,to get the high canopy cover and dense community characteristics of Spartina alterniflora,multi-dimensional shallow features are extracted from the imagery.Secondly,to detect different objects from satellite imagery,index features are extracted,and the statistical features of the Gray-Level Co-occurrence Matrix(GLCM)are derived using principal component analysis.Then,ensemble learning methods,including random forest,extreme gradient boosting,and light gradient boosting machine models,are employed for image classification.Meanwhile,Recursive Feature Elimination with Cross-Validation(RFECV)is used to select the best feature subset.Finally,to enhance the interpretability of the models,the best features are utilized to classify multi-temporal images and SHapley Additive exPlanations(SHAP)is combined with these classifications to explain the model prediction process.The method is validated by using Sentinel-2 imageries and previous observations of Spartina alterniflora in Chongming Island,it is found that the model combining image texture features such as GLCM covariance can significantly improve the detection accuracy of Spartina alterniflora by about 8%compared with the model without image texture features.Through multiple model comparisons and feature selection via RFECV,the selected model and eight features demonstrated good classification accuracy when applied to data from different time periods,proving that feature reduction can effectively enhance model generalization.Additionally,visualizing model decisions using SHAP revealed that the image texture feature component_1_GLCMVariance is particularly important for identifying each land cover type.
基金Supported by National Natural Science Foundation of China(Grant No.52175528).
文摘High-speed milling(HSM)is advantageous for machining high-quality complex-structure surface components with various materials.Identifying and estimating cutting force signals for characterizing HSM is of high significance.However,considering the tool runout and size effects,many proposed models focus on the material and mechanical characteristics.This study presents a novel approach for predicting micromilling cutting forces using a semianalytical multidimensional model that integrates experimental empirical data and a mechanical theoretical force model.A novel analytical optimization approach is provided to identify the cutting forces,classify the cutting states,and determine the tool runout using an adaptive algorithm that simplifies modeling and calculation.The instantaneous un-deformed chip thickness(IUCT)is determined from the trochoidal trajectories of each tool flute and optimized using the bisection method.Herein,the computational efficiency is improved,and the errors are clarified.The tool runout parameters are identified from the processed displacement signals and determined from the preprocessed vibration signals using an adaptive signal processing method.It is reliable and stable for determining tool runout and is an effective foundation for the force model.This approach is verified using HSM tests.Herein,the determination coefficients are stable above 0.9.It is convenient and efficient for achieving the key intermediate parameters(IUCT and tool runout),which can be generalized to various machining conditions and operations.
基金supported by National Natural Science Foundation of China(Grant No.10871085)US National Science Foundation (Grant Nos.DMS-0412047,DMS-0715772)
文摘The aim of this work is to study the traveling wavefronts in a discrete-time integral recursion with a Gauss kernel in R2.We first establish the existence of traveling wavefronts as well as their precise asymptotic behavior.Then,by employing the comparison principle and upper and lower solutions technique,we prove the asymptotic stability and uniqueness of such monostable wavefronts in the sense of phase shift and circumnutation.We also obtain some similar results in R.
基金funded by the National Science and Technology Council,Taiwan(Grant No.NSTC 112-2121-M-039-001)by China Medical University(Grant No.CMU112-MF-79).
文摘Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.
基金the Natural Science Foundation of Fujian,China(No.2021J01633).
文摘To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation,a control method involving flexible multistate switches(FMSs)is proposed in this study.This approach is based on an improved double-loop recursive fuzzy neural network(DRFNN)sliding mode,which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults.First,an improved DRFNN sliding mode control(SMC)method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system.To improve the robustness of the system,an adaptive parameter-adjustment strategy for the DRFNN is designed,where its dynamic mapping capabilities are leveraged to improve the transient compensation control.Additionally,a quasi-continuous second-order sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability.The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem.A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink.The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis.
文摘This paper proposes a novel approach for identifying distributed dynamic loads in the time domain.Using polynomial andmodal analysis,the load is transformed intomodal space for coefficient identification.This allows the distributed dynamic load with a two-dimensional form in terms of time and space to be simultaneously identified in the form of modal force,thereby achieving dimensionality reduction.The Impulse-based Force Estimation Algorithm is proposed to identify dynamic loads in the time domain.Firstly,the algorithm establishes a recursion scheme based on convolution integral,enabling it to identify loads with a long history and rapidly changing forms over time.Secondly,the algorithm introduces moving mean and polynomial fitting to detrend,enhancing its applicability in load estimation.The aforementioned methodology successfully accomplishes the reconstruction of distributed,instead of centralized,dynamic loads on the continuum in the time domain by utilizing acceleration response.To validate the effectiveness of the method,computational and experimental verification were conducted.
