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
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 effectively extract multi-scale information from observation data and improve computational efficiency,a multi-scale second-order autoregressive recursive filter(MSRF)method is designed.The second-order autoregress...To effectively extract multi-scale information from observation data and improve computational efficiency,a multi-scale second-order autoregressive recursive filter(MSRF)method is designed.The second-order autoregressive filter used in this study has been attempted to replace the traditional first-order recursive filter used in spatial multi-scale recursive filter(SMRF)method.The experimental results indicate that the MSRF scheme successfully extracts various scale information resolved by observations.Moreover,compared with the SMRF scheme,the MSRF scheme improves computational accuracy and efficiency to some extent.The MSRF scheme can not only propagate to a longer distance without the attenuation of innovation,but also reduce the mean absolute deviation between the reconstructed sea ice concentration results and observations reduced by about 3.2%compared to the SMRF scheme.On the other hand,compared with traditional first-order recursive filters using in the SMRF scheme that multiple filters are executed,the MSRF scheme only needs to perform two filter processes in one iteration,greatly improving filtering efficiency.In the two-dimensional experiment of sea ice concentration,the calculation time of the MSRF scheme is only 1/7 of that of SMRF scheme.This means that the MSRF scheme can achieve better performance with less computational cost,which is of great significance for further application in real-time ocean or sea ice data assimilation systems in the future.展开更多
We prove that non-recursive base conversion can always be implemented by using a deterministic Markov process. Our paper discusses the pros and cons of recursive and non-recursive methods, in general. And we include a...We prove that non-recursive base conversion can always be implemented by using a deterministic Markov process. Our paper discusses the pros and cons of recursive and non-recursive methods, in general. And we include a comparison between non-recursion and a deterministic Markov process, proving that the Markov process is twice as efficient.展开更多
Presently the research based on the accurate seismic imaging methods for surface relief, complex structure, and complicated velocity distributions is of great significance. Reverse-time migration is considered to be o...Presently the research based on the accurate seismic imaging methods for surface relief, complex structure, and complicated velocity distributions is of great significance. Reverse-time migration is considered to be one of highly accurate methods. In this paper, we propose a new non-reflecting recursive algorithm for reverse-time migration by introducing the wave impedance function into the acoustic wave equation and the algorithm for the surface relief case is derived from the coordinate transformation principle. Using the exploding reflector principle and the zero-time imaging condition of poststack reverse- time migration, poststack numerical simulation and reverse-time migration with complex conditions can be realized. The results of synthetic and real data calculations show that the method effectively suppresses unwanted internal reflections and also deals with the seismic imaging problems resulting from surface relief. So, we prove that this method has strong adaptability and practicality.展开更多
In order to decrease the calculation complexity of connectivity reliability of road networks, an improved recursive decomposition arithmetic is proposed. First, the basic theory of recursive decomposition arithmetic i...In order to decrease the calculation complexity of connectivity reliability of road networks, an improved recursive decomposition arithmetic is proposed. First, the basic theory of recursive decomposition arithmetic is reviewed. Then the characteristics of road networks, which are different from general networks, are analyzed. Under this condition, an improved recursive decomposition arithmetic is put forward which fits road networks better. Furthermore, detailed calculation steps are presented which are convenient for the computer, and the advantage of the approximate arithmetic is analyzed based on this improved arithmetic. This improved recursive decomposition arithmetic directly produces disjoint minipaths and avoids the non-polynomial increasing problems. And because the characteristics of road networks are considered, this arithmetic is greatly simplified. Finally, an example is given to prove its validity.展开更多
Vehicle mass is an important parameter in vehicle dynamics control systems. Although many algorithms have been developed for the estimation of mass, none of them have yet taken into account the different types of resi...Vehicle mass is an important parameter in vehicle dynamics control systems. Although many algorithms have been developed for the estimation of mass, none of them have yet taken into account the different types of resistance that occur under different conditions. This paper proposes a vehicle mass estimator. The estimator incorporates road gradient information in the longitudinal accelerometer signal, and it removes the road grade from the longitudinal dynamics of the vehicle. Then, two different recursive least square method （RLSM） schemes are proposed to estimate the driving resistance and the mass independently based on the acceleration partition under different conditions. A 6 DOF dynamic model of four In-wheel Motor Vehicle is built to assist in the design of the algorithm and in the setting of the parameters. The acceleration limits are determined to not only reduce the estimated error but also ensure enough data for the resistance estimation and mass estimation in some critical situations. The modification of the algorithm is also discussed to improve the result of the mass estimation. Experiment data on asphalt road, plastic runway, and gravel road and on sloping roads are used to validate the estimation algorithm. The adaptability of the algorithm is improved by using data collected under several critical operating conditions. The experimental results show the error of the estimation process to be within 2.6%, which indicates that the algorithm can estimate mass with great accuracy regardless of the road surface and gradient changes and that it may be valuable in engineering applications. This paper proposes a recursive least square vehicle mass estimation method based on acceleration partition.展开更多
Based on the least-square minimization a computationally efficient learning algorithm for the Principal Component Analysis(PCA) is derived. The dual learning rate parameters are adaptively introduced to make the propo...Based on the least-square minimization a computationally efficient learning algorithm for the Principal Component Analysis(PCA) is derived. The dual learning rate parameters are adaptively introduced to make the proposed algorithm providing the capability of the fast convergence and high accuracy for extracting all the principal components. It is shown that all the information needed for PCA can be completely represented by the unnormalized weight vector which is updated based only on the corresponding neuron input-output product. The convergence performance of the proposed algorithm is briefly analyzed.The relation between Oja’s rule and the least squares learning rule is also established. Finally, a simulation example is given to illustrate the effectiveness of this algorithm for PCA.展开更多
From the viewpoint of energy saving and improving transmission efficiency, the ZL50E wheel loader is taken as the study object. And the system model is analyzed based on the transmission system of the construction veh...From the viewpoint of energy saving and improving transmission efficiency, the ZL50E wheel loader is taken as the study object. And the system model is analyzed based on the transmission system of the construction vehicle. A new four-parameter shift schedule is presented, which can keep the torque converter working in the high efficiency area. The control algorithm based on the Elman recursive neural network is applied, and four-parameter control system is developed which is based on industrial computer. The system is used to collect data accurately and control 4D180 power-shift gearbox of ZL50E wheel loader shift timely. An experiment is done on automatic transmission test-bed, and the result indicates that the control system could reliably and safely work and improve the efficiency of hydraulic torque converter. Four-parameter shift strategy that takes into account the power consuming of the working pump has important operating significance and reflects the actual working status of construction vehicle.展开更多
To reduce the computational complexity of matrix inversion, which is the majority of processing in many practical applications, two numerically efficient recursive algorithms （called algorithms I and II, respectively...To reduce the computational complexity of matrix inversion, which is the majority of processing in many practical applications, two numerically efficient recursive algorithms （called algorithms I and II, respectively） are presented. Algorithm I is used to calculate the inverse of such a matrix, whose leading principal minors are all nonzero. Algorithm II, whereby, the inverse of an arbitrary nonsingular matrix can be evaluated is derived via improving the algorithm I. The implementation, for algorithm II or I, involves matrix-vector multiplications and vector outer products. These operations are computationally fast and highly parallelizable. MATLAB simulations show that both recursive algorithms are valid.展开更多
The piecewise linear recursive convolution （PLRC） finite-different time-domain （FDTD） method greatly improves accuracy over the original recursive convolution （RC） FDTD approach but retains its speed and efficie...The piecewise linear recursive convolution （PLRC） finite-different time-domain （FDTD） method greatly improves accuracy over the original recursive convolution （RC） FDTD approach but retains its speed and efficiency advantages. A PLRC-FDTD formulation for magnetized plasma which incorporates both anisotropy and frequency dispersion at the same time is presented, enabled the transient analysis of magnetized plasma media. The technique is illustrated by numerical simulations the reflection and transmission coefficients through a magnetized plasma layer. The results show that the PLRC-FDTD method has significantly improved the accuracy over the original RC method.展开更多
Objective：To assess prognostic factors and validate the effectiveness of recursive partitioning analysis （RPA） classes and graded prognostic assessment （GPA） in 290 non-small cell lung cancer （NSCLC） patients w...Objective：To assess prognostic factors and validate the effectiveness of recursive partitioning analysis （RPA） classes and graded prognostic assessment （GPA） in 290 non-small cell lung cancer （NSCLC） patients with brain metastasis （BM）.Methods：From Jan 2008 to Dec 2009,the clinical data of 290 NSCLC cases with BM treated with multiple modalities including brain irradiation,systemic chemotherapy and tyrosine kinase inhibitors （TKIs） in two institutes were analyzed.Survival was estimated by Kaplan-Meier method.The differences of survival rates in subgroups were assayed using log-rank test.Multivariate Cox＇s regression method was used to analyze the impact of prognostic factors on survival.Two prognostic indexes models （RPA and GPA） were validated respectively.Results：All patients were followed up for 1-44 months,the median survival time after brain irradiation and its corresponding 95% confidence interval （95% CI） was 14 （12.3-15.8） months.1-,2-and 3-year survival rates in the whole group were 56.0%,28.3%,and 12.0%,respectively.The survival curves of subgroups,stratified by both RPA and GPA,were significantly different （P0.001）.In the multivariate analysis as RPA and GPA entered Cox＇s regression model,Karnofsky performance status （KPS） ≥ 70,adenocarcinoma subtype,longer administration of TKIs remained their prognostic significance,RPA classes and GPA also appeared in the prognostic model.Conclusion：KPS ≥70,adenocarcinoma subtype,longer treatment of molecular targeted drug,and RPA classes and GPA are the independent prognostic factors affecting the survival rates of NSCLC patients with BM.展开更多
In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are co...In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition(SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method.展开更多
Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obst...Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O（L2） from O（L4）. The effectiveness of the proposed algorithm is illustrated by experimental results.展开更多
The strap-down inertial navigation system (SINS) error of ballistic missile is generated by the mutual influence of gyroscope and accelerometer, and the recursive model is completely different from that of gimbaled IN...The strap-down inertial navigation system (SINS) error of ballistic missile is generated by the mutual influence of gyroscope and accelerometer, and the recursive model is completely different from that of gimbaled INS. In the paper, a discrete error recursive model was obtained by studying the applied SINS error model of ballistic missile, and the discrete Kalman filtering simulation based on the model was carried out. The simulated results show that the model can depict the SINS error exactly and provide the advantages for research on integrated guidance and improved hit accuracy.展开更多
Here presented is a matrix representation of recursive number sequences of order 3 defined by an = pa（n-1） ＋ qa（n-2） ＋ ra（n-3） with arbitrary initial conditions a0, a1 = 0, and a2 and their special cases of Pa...Here presented is a matrix representation of recursive number sequences of order 3 defined by an = pa（n-1） ＋ qa（n-2） ＋ ra（n-3） with arbitrary initial conditions a0, a1 = 0, and a2 and their special cases of Padovan number sequence and Perrin number sequence with initial conditions a0 = a1 = 0 and a2 = 1 and a0 = 3, a1 = 0, and a2 = 2, respectively. The matrix representation is used to construct many well known and new identities of recursive number sequences as well as Pavodan and Perrin sequences.展开更多
In this paper we propose a novel model ＂recursive directed graph＂ based on feature structure, and apply it to represent the semantic relations of postpositive attributive structures in biomedical texts. The usages o...In this paper we propose a novel model ＂recursive directed graph＂ based on feature structure, and apply it to represent the semantic relations of postpositive attributive structures in biomedical texts. The usages of postpositive attributive are complex and variable, especially three categories： present participle phrase, past participle phrase, and preposition phrase as postpositire attributive, which always bring the difficulties of automatic parsing. We summarize these categories and annotate the semantic information. Compared with dependency structure, feature structure, being recursive directed graph, enhances semantic information extraction in biomedical field. The annotation results show that recursive directed graph is more suitable to extract complex semantic relations for biomedical text mining.展开更多
Based on Recursive Radial Basis Function(RRBF)neural network,the Reduced Order Model(ROM)of compressor cascade was established to meet the urgent demand of highly efficient prediction of unsteady aerodynamics performa...Based on Recursive Radial Basis Function(RRBF)neural network,the Reduced Order Model(ROM)of compressor cascade was established to meet the urgent demand of highly efficient prediction of unsteady aerodynamics performance of turbomachinery.One novel ROM called ASA-RRBF model based on Adaptive Simulated Annealing(ASA)algorithm was developed to enhance the generalization ability of the unsteady ROM.The ROM was verified by predicting the unsteady aerodynamics performance of a highly-loaded compressor cascade.The results show that the RRBF model has higher accuracy in identification of the dimensionless total pressure and dimensionless static pressure of compressor cascade under nonlinear and unsteady conditions,and the model behaves higher stability and computational efficiency.However,for the strong nonlinear characteristics of aerodynamic parameters,the RRBF model presents lower accuracy.Additionally,the RRBF model predicts with a large error in the identification of aerodynamic parameters under linear and unsteady conditions.For ASA-RRBF,by introducing a small-amplitude and highfrequency sinusoidal signal as validation sample,the width of the basis function of the RRBF model is optimized to improve the generalization ability of the ROM under linear unsteady conditions.Besides,this model improves the predicting accuracy of dimensionless static pressure which has strong nonlinear characteristics.The ASA-RRBF model has higher prediction accuracy than RRBF model without significantly increasing the total time consumption.This novel model can predict the linear hysteresis of dimensionless static pressure happened in the harmonic condition,but it cannot accurately predict the beat frequency of dimensionless total pressure.展开更多
A new recursive vertex-deleting formula for the computation of the chromatic polynomial of a graph is obtained in this paper. This algorithm is not only a good tool for further studying chromatic polynomials but also ...A new recursive vertex-deleting formula for the computation of the chromatic polynomial of a graph is obtained in this paper. This algorithm is not only a good tool for further studying chromatic polynomials but also the fastest among all the algorithms for the computation of chromatic polynomials.展开更多
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The National Key Research and Development Program of China under contract No.2023YFC3107701the National Natural Science Foundation of China under contract No.42375143.
文摘To effectively extract multi-scale information from observation data and improve computational efficiency,a multi-scale second-order autoregressive recursive filter(MSRF)method is designed.The second-order autoregressive filter used in this study has been attempted to replace the traditional first-order recursive filter used in spatial multi-scale recursive filter(SMRF)method.The experimental results indicate that the MSRF scheme successfully extracts various scale information resolved by observations.Moreover,compared with the SMRF scheme,the MSRF scheme improves computational accuracy and efficiency to some extent.The MSRF scheme can not only propagate to a longer distance without the attenuation of innovation,but also reduce the mean absolute deviation between the reconstructed sea ice concentration results and observations reduced by about 3.2%compared to the SMRF scheme.On the other hand,compared with traditional first-order recursive filters using in the SMRF scheme that multiple filters are executed,the MSRF scheme only needs to perform two filter processes in one iteration,greatly improving filtering efficiency.In the two-dimensional experiment of sea ice concentration,the calculation time of the MSRF scheme is only 1/7 of that of SMRF scheme.This means that the MSRF scheme can achieve better performance with less computational cost,which is of great significance for further application in real-time ocean or sea ice data assimilation systems in the future.
文摘We prove that non-recursive base conversion can always be implemented by using a deterministic Markov process. Our paper discusses the pros and cons of recursive and non-recursive methods, in general. And we include a comparison between non-recursion and a deterministic Markov process, proving that the Markov process is twice as efficient.
基金supported by the National Natural Science Foundation of China (Grant No. 40974073)the National 863 Program (Grant No.2007AA060504)the National 973 Program (Grant No. 2007CB209605) and CNPC Geophysical Laboratories
文摘Presently the research based on the accurate seismic imaging methods for surface relief, complex structure, and complicated velocity distributions is of great significance. Reverse-time migration is considered to be one of highly accurate methods. In this paper, we propose a new non-reflecting recursive algorithm for reverse-time migration by introducing the wave impedance function into the acoustic wave equation and the algorithm for the surface relief case is derived from the coordinate transformation principle. Using the exploding reflector principle and the zero-time imaging condition of poststack reverse- time migration, poststack numerical simulation and reverse-time migration with complex conditions can be realized. The results of synthetic and real data calculations show that the method effectively suppresses unwanted internal reflections and also deals with the seismic imaging problems resulting from surface relief. So, we prove that this method has strong adaptability and practicality.
基金The National Key Technology R& D Program of Chinaduring the 11th Five-Year Plan Period (No.2006BAJ18B03).
文摘In order to decrease the calculation complexity of connectivity reliability of road networks, an improved recursive decomposition arithmetic is proposed. First, the basic theory of recursive decomposition arithmetic is reviewed. Then the characteristics of road networks, which are different from general networks, are analyzed. Under this condition, an improved recursive decomposition arithmetic is put forward which fits road networks better. Furthermore, detailed calculation steps are presented which are convenient for the computer, and the advantage of the approximate arithmetic is analyzed based on this improved arithmetic. This improved recursive decomposition arithmetic directly produces disjoint minipaths and avoids the non-polynomial increasing problems. And because the characteristics of road networks are considered, this arithmetic is greatly simplified. Finally, an example is given to prove its validity.
基金Supported by National Basic Research Program of China(Grant No.2011CB711200)
文摘Vehicle mass is an important parameter in vehicle dynamics control systems. Although many algorithms have been developed for the estimation of mass, none of them have yet taken into account the different types of resistance that occur under different conditions. This paper proposes a vehicle mass estimator. The estimator incorporates road gradient information in the longitudinal accelerometer signal, and it removes the road grade from the longitudinal dynamics of the vehicle. Then, two different recursive least square method （RLSM） schemes are proposed to estimate the driving resistance and the mass independently based on the acceleration partition under different conditions. A 6 DOF dynamic model of four In-wheel Motor Vehicle is built to assist in the design of the algorithm and in the setting of the parameters. The acceleration limits are determined to not only reduce the estimated error but also ensure enough data for the resistance estimation and mass estimation in some critical situations. The modification of the algorithm is also discussed to improve the result of the mass estimation. Experiment data on asphalt road, plastic runway, and gravel road and on sloping roads are used to validate the estimation algorithm. The adaptability of the algorithm is improved by using data collected under several critical operating conditions. The experimental results show the error of the estimation process to be within 2.6%, which indicates that the algorithm can estimate mass with great accuracy regardless of the road surface and gradient changes and that it may be valuable in engineering applications. This paper proposes a recursive least square vehicle mass estimation method based on acceleration partition.
基金Supported by the National Natural Science Foundation of Chinathe Science foundation of Guangxi Educational Administration
文摘Based on the least-square minimization a computationally efficient learning algorithm for the Principal Component Analysis(PCA) is derived. The dual learning rate parameters are adaptively introduced to make the proposed algorithm providing the capability of the fast convergence and high accuracy for extracting all the principal components. It is shown that all the information needed for PCA can be completely represented by the unnormalized weight vector which is updated based only on the corresponding neuron input-output product. The convergence performance of the proposed algorithm is briefly analyzed.The relation between Oja’s rule and the least squares learning rule is also established. Finally, a simulation example is given to illustrate the effectiveness of this algorithm for PCA.
基金supported by Research Fund for Doctoral Program of Higher Education of China (No.20020183003)
文摘From the viewpoint of energy saving and improving transmission efficiency, the ZL50E wheel loader is taken as the study object. And the system model is analyzed based on the transmission system of the construction vehicle. A new four-parameter shift schedule is presented, which can keep the torque converter working in the high efficiency area. The control algorithm based on the Elman recursive neural network is applied, and four-parameter control system is developed which is based on industrial computer. The system is used to collect data accurately and control 4D180 power-shift gearbox of ZL50E wheel loader shift timely. An experiment is done on automatic transmission test-bed, and the result indicates that the control system could reliably and safely work and improve the efficiency of hydraulic torque converter. Four-parameter shift strategy that takes into account the power consuming of the working pump has important operating significance and reflects the actual working status of construction vehicle.
文摘To reduce the computational complexity of matrix inversion, which is the majority of processing in many practical applications, two numerically efficient recursive algorithms （called algorithms I and II, respectively） are presented. Algorithm I is used to calculate the inverse of such a matrix, whose leading principal minors are all nonzero. Algorithm II, whereby, the inverse of an arbitrary nonsingular matrix can be evaluated is derived via improving the algorithm I. The implementation, for algorithm II or I, involves matrix-vector multiplications and vector outer products. These operations are computationally fast and highly parallelizable. MATLAB simulations show that both recursive algorithms are valid.
基金The project was supported by the National Natural Science Foundation of China (60471002) and the Jiangxi ProvincialNatural Science Foundation (0412014)
文摘The piecewise linear recursive convolution （PLRC） finite-different time-domain （FDTD） method greatly improves accuracy over the original recursive convolution （RC） FDTD approach but retains its speed and efficiency advantages. A PLRC-FDTD formulation for magnetized plasma which incorporates both anisotropy and frequency dispersion at the same time is presented, enabled the transient analysis of magnetized plasma media. The technique is illustrated by numerical simulations the reflection and transmission coefficients through a magnetized plasma layer. The results show that the PLRC-FDTD method has significantly improved the accuracy over the original RC method.
文摘Objective：To assess prognostic factors and validate the effectiveness of recursive partitioning analysis （RPA） classes and graded prognostic assessment （GPA） in 290 non-small cell lung cancer （NSCLC） patients with brain metastasis （BM）.Methods：From Jan 2008 to Dec 2009,the clinical data of 290 NSCLC cases with BM treated with multiple modalities including brain irradiation,systemic chemotherapy and tyrosine kinase inhibitors （TKIs） in two institutes were analyzed.Survival was estimated by Kaplan-Meier method.The differences of survival rates in subgroups were assayed using log-rank test.Multivariate Cox＇s regression method was used to analyze the impact of prognostic factors on survival.Two prognostic indexes models （RPA and GPA） were validated respectively.Results：All patients were followed up for 1-44 months,the median survival time after brain irradiation and its corresponding 95% confidence interval （95% CI） was 14 （12.3-15.8） months.1-,2-and 3-year survival rates in the whole group were 56.0%,28.3%,and 12.0%,respectively.The survival curves of subgroups,stratified by both RPA and GPA,were significantly different （P0.001）.In the multivariate analysis as RPA and GPA entered Cox＇s regression model,Karnofsky performance status （KPS） ≥ 70,adenocarcinoma subtype,longer administration of TKIs remained their prognostic significance,RPA classes and GPA also appeared in the prognostic model.Conclusion：KPS ≥70,adenocarcinoma subtype,longer treatment of molecular targeted drug,and RPA classes and GPA are the independent prognostic factors affecting the survival rates of NSCLC patients with BM.
基金Supported in part by the National Thousand Talents Program of Chinathe National Natural Science Foundation of China(61473054)the Fundamental Research Funds for the Central Universities of China
文摘In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition(SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method.
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars(60525303)Doctoral Foundation of Yanshan University(B243).
文摘Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O（L2） from O（L4）. The effectiveness of the proposed algorithm is illustrated by experimental results.
文摘The strap-down inertial navigation system (SINS) error of ballistic missile is generated by the mutual influence of gyroscope and accelerometer, and the recursive model is completely different from that of gimbaled INS. In the paper, a discrete error recursive model was obtained by studying the applied SINS error model of ballistic missile, and the discrete Kalman filtering simulation based on the model was carried out. The simulated results show that the model can depict the SINS error exactly and provide the advantages for research on integrated guidance and improved hit accuracy.
文摘Here presented is a matrix representation of recursive number sequences of order 3 defined by an = pa（n-1） ＋ qa（n-2） ＋ ra（n-3） with arbitrary initial conditions a0, a1 = 0, and a2 and their special cases of Padovan number sequence and Perrin number sequence with initial conditions a0 = a1 = 0 and a2 = 1 and a0 = 3, a1 = 0, and a2 = 2, respectively. The matrix representation is used to construct many well known and new identities of recursive number sequences as well as Pavodan and Perrin sequences.
基金Supported by the National Natural Science Foundation of China(61202193,61202304)the Major Projects of Chinese National Social Science Foundation(11&ZD189)the Chinese Postdoctoral Science Foundation(2013M540593,2014T70722)
文摘In this paper we propose a novel model ＂recursive directed graph＂ based on feature structure, and apply it to represent the semantic relations of postpositive attributive structures in biomedical texts. The usages of postpositive attributive are complex and variable, especially three categories： present participle phrase, past participle phrase, and preposition phrase as postpositire attributive, which always bring the difficulties of automatic parsing. We summarize these categories and annotate the semantic information. Compared with dependency structure, feature structure, being recursive directed graph, enhances semantic information extraction in biomedical field. The annotation results show that recursive directed graph is more suitable to extract complex semantic relations for biomedical text mining.
基金co-National Science and Technology Major Project(No.2017-II-0009-0023)Innovation Guidance Support Project for Taicang Top Research Institutes(No.TC2019DYDS09)。
文摘Based on Recursive Radial Basis Function(RRBF)neural network,the Reduced Order Model(ROM)of compressor cascade was established to meet the urgent demand of highly efficient prediction of unsteady aerodynamics performance of turbomachinery.One novel ROM called ASA-RRBF model based on Adaptive Simulated Annealing(ASA)algorithm was developed to enhance the generalization ability of the unsteady ROM.The ROM was verified by predicting the unsteady aerodynamics performance of a highly-loaded compressor cascade.The results show that the RRBF model has higher accuracy in identification of the dimensionless total pressure and dimensionless static pressure of compressor cascade under nonlinear and unsteady conditions,and the model behaves higher stability and computational efficiency.However,for the strong nonlinear characteristics of aerodynamic parameters,the RRBF model presents lower accuracy.Additionally,the RRBF model predicts with a large error in the identification of aerodynamic parameters under linear and unsteady conditions.For ASA-RRBF,by introducing a small-amplitude and highfrequency sinusoidal signal as validation sample,the width of the basis function of the RRBF model is optimized to improve the generalization ability of the ROM under linear unsteady conditions.Besides,this model improves the predicting accuracy of dimensionless static pressure which has strong nonlinear characteristics.The ASA-RRBF model has higher prediction accuracy than RRBF model without significantly increasing the total time consumption.This novel model can predict the linear hysteresis of dimensionless static pressure happened in the harmonic condition,but it cannot accurately predict the beat frequency of dimensionless total pressure.
基金This research is partially supported by NNSF of China.
文摘A new recursive vertex-deleting formula for the computation of the chromatic polynomial of a graph is obtained in this paper. This algorithm is not only a good tool for further studying chromatic polynomials but also the fastest among all the algorithms for the computation of chromatic polynomials.
