The numerical dispersion phenomenon in the finite-difference forward modeling simulations of the wave equation significantly affects the imaging accuracy in acoustic reflection logging.This issue is particularly prono...The numerical dispersion phenomenon in the finite-difference forward modeling simulations of the wave equation significantly affects the imaging accuracy in acoustic reflection logging.This issue is particularly pronounced in the reverse time migration(RTM)method used for shear-wave(S-wave)logging imaging.This not only affects imaging accuracy but also introduces ambiguities in the interpretation of logging results.To address this challenge,this study proposes the use of a least-squares difference coefficient optimization algorithm aiming to suppress the numerical dispersion phenomenon in the RTM of S-wave reflection imaging logging.By optimizing the difference coefficients,the high-precision finite-difference algorithm serves as an effective operator for both forward and backward RTM processes.This approach is instrumental in eliminating migration illusions,which are often caused by numerical dispersion.The effectiveness of this optimized algorithm is demonstrated through numerical results,which indicate that it can achieve more accurate forward imaging results across various conditions,including high-and low-velocity strata,and is effective in both large and small spatial grids.The results of processing real data demonstrate that numerical dispersion optimization effectively reduces migration artifacts and diminishes ambiguities in logging interpretations.This optimization offers crucial technical support to the RTM method,enhancing its capability for accurately modeling and imaging S-wave reflections.展开更多
This paper presents a new highly parallel algorithm for computing the minimum-norm least-squares solution of inconsistent linear equations Ax = b(A∈Rm×n,b∈R (A)). By this algorithm the solution x = A + b is obt...This paper presents a new highly parallel algorithm for computing the minimum-norm least-squares solution of inconsistent linear equations Ax = b(A∈Rm×n,b∈R (A)). By this algorithm the solution x = A + b is obtained in T = n(log2m + log2(n - r + 1) + 5) + log2m + 1 steps with P=mn processors when m × 2(n - 1) and with P = 2n(n - 1) processors otherwise.展开更多
Order-recursive least-squares(ORLS)algorithms are applied to the prob-lems of estimation and identification of FIR or ARMA system parameters where a fixedset of input signal samples is available and the desired order ...Order-recursive least-squares(ORLS)algorithms are applied to the prob-lems of estimation and identification of FIR or ARMA system parameters where a fixedset of input signal samples is available and the desired order of the underlying model isunknown.On the basis of several universal formulae for updating nonsymmetric projec-tion operators,this paper presents three kinds of LS algorithms,called nonsymmetric,symmetric and square root normalized fast ORLS algorithms,respectively.As to the au-thors’ knowledge,the first and the third have not been so far provided,and the second isone of those which have the lowest computational requirement.Several simplified versionsof the algorithms are also considered.展开更多
Effective development and utilization of wood resources is critical.Wood modification research has become an integral dimension of wood science research,however,the similarities between modified wood and original wood...Effective development and utilization of wood resources is critical.Wood modification research has become an integral dimension of wood science research,however,the similarities between modified wood and original wood render it challenging for accurate identification and classification using conventional image classification techniques.So,the development of efficient and accurate wood classification techniques is inevitable.This paper presents a one-dimensional,convolutional neural network(i.e.,BACNN)that combines near-infrared spectroscopy and deep learning techniques to classify poplar,tung,and balsa woods,and PVA,nano-silica-sol and PVA-nano silica sol modified woods of poplar.The results show that BACNN achieves an accuracy of 99.3%on the test set,higher than the 52.9%of the BP neural network and 98.7%of Support Vector Machine compared with traditional machine learning methods and deep learning based methods;it is also higher than the 97.6%of LeNet,98.7%of AlexNet and 99.1%of VGGNet-11.Therefore,the classification method proposed offers potential applications in wood classification,especially with homogeneous modified wood,and it also provides a basis for subsequent wood properties studies.展开更多
A new hierarchical identification algorithm is proposed for solving the identi-fication problem of the extended exponential model,which is used frequently in ecological,social and economical systems.By using the zero ...A new hierarchical identification algorithm is proposed for solving the identi-fication problem of the extended exponential model,which is used frequently in ecological,social and economical systems.By using the zero character of the optimal Lagrangianmultipliers of the equivalent identification problem,a two-level structure of the algorithmis derived first.Then,the convergence and the correspondence with the conventionalnonlinear approaches of the algorithm are proved.The results of simulation and applica-tion show that its convergent rate is greatly higher than that of the L-Mmethod.展开更多
Based on the idea of the set-membership identification, a modified recursive least squares algorithm with variable gain, variable forgetting factor and resetting is presented. The concept of the error tolerance level ...Based on the idea of the set-membership identification, a modified recursive least squares algorithm with variable gain, variable forgetting factor and resetting is presented. The concept of the error tolerance level is proposed. The selection criteria of the error tolerance level are also given according to the min-max principle. The algorithm is particularly suitable for tracing time-varying systems and is similar in computational complexity to the standard recursive least squares algorithm. The superior performance of the algorithm is verified ma simulation studies on a dynamic fermentation process.展开更多
Bilinear singular systems can be used in the investigation of different types of engineering systems.In the past decade,considerable attention has been paid to analyzing and synthesizing singular bilinear systems.Thei...Bilinear singular systems can be used in the investigation of different types of engineering systems.In the past decade,considerable attention has been paid to analyzing and synthesizing singular bilinear systems.Their importance lies in their real world application such as economic,ecological,and socioeconomic processes.They are also applied in several biological processes,such as population dynamics of biological species,water balance,temperature regulation in the human body,carbon dioxide control in lungs,blood pressure,immune system,cardiac regulation,etc.Bilinear singular systems naturally represent different physical processes such as the fundamental law of mass action,the DC motor,the induction motor drives,the mechanical brake systems,aerial combat between two aircraft,the missile intercept problem,modeling and control of small furnaces and hydraulic rotary multimotor systems.The current research work discusses the Legendre Neural Network’s implementation to evaluate time-varying singular bilinear systems for finding the exact solution.The results were obtained from two methods namely the RK-Butcher algorithm and the Runge Kutta Arithmetic Mean(RKAM)method.Compared with the results attained from Legendre Neural Network Method for time-varying singular bilinear systems,the output proved to be accurate.As such,this research article established that the proposed Legendre Neural Network could be easily implemented in MATLAB.One can obtain the solution for any length of time from this method in time-varying singular bilinear systems.展开更多
In this paper,by utilizing the angle of arrivals(AOAs) and imprecise positions of the sensors,a novel modified Levenberg-Marquardt algorithm to solve the source localization problem is proposed.Conventional source loc...In this paper,by utilizing the angle of arrivals(AOAs) and imprecise positions of the sensors,a novel modified Levenberg-Marquardt algorithm to solve the source localization problem is proposed.Conventional source localization algorithms,like Gauss-Newton algorithm and Conjugate gradient algorithm are subjected to the problems of local minima and good initial guess.This paper presents a new optimization technique to find the descent directions to avoid divergence,and a trust region method is introduced to accelerate the convergence rate.Compared with conventional methods,the new algorithm offers increased stability and is more robust,allowing for stronger non-linearity and wider convergence field to be identified.Simulation results demonstrate that the proposed algorithm improves the typical methods in both speed and robustness,and is able to avoid local minima.展开更多
We construct a fuzzy varying coefficient bilinear regression model to deal with the interval financial data and then adopt the least-squares method based on symmetric fuzzy number space. Firstly, we propose a varying ...We construct a fuzzy varying coefficient bilinear regression model to deal with the interval financial data and then adopt the least-squares method based on symmetric fuzzy number space. Firstly, we propose a varying coefficient model on the basis of the fuzzy bilinear regression model. Secondly, we develop the least-squares method according to the complete distance between fuzzy numbers to estimate the coefficients and test the adaptability of the proposed model by means of generalized likelihood ratio test with SSE composite index. Finally, mean square errors and mean absolutely errors are employed to evaluate and compare the fitting of fuzzy auto regression, fuzzy bilinear regression and fuzzy varying coefficient bilinear regression models, and also the forecasting of three models. Empirical analysis turns out that the proposed model has good fitting and forecasting accuracy with regard to other regression models for the capital market.展开更多
This work describes how a control algorithm can be implemented in a small (8-bit) microcontroller for the main purpose of merging embedded systems and control theory in electrical engineering undergraduate classes. Tw...This work describes how a control algorithm can be implemented in a small (8-bit) microcontroller for the main purpose of merging embedded systems and control theory in electrical engineering undergraduate classes. Two different methods for discretizing the control expression are compared: Euler transformation and bilinear transformation. The sampling rate’s impact on the algorithm is discussed and theoretical results are verified by an application to a temperature control system in a heating plant. Four control algorithms are compared: PID and PI algorithms discretized with Euler and bilinear transformation, respectively. It is shown that for the heating plant used in this work, a bilinear PI algorithm implemented in a small 8-bit microcontroller outperforms a commercial controller from Panasonic. It is also demonstrated that all the derived algorithms can be implemented using integer calculations only, obviating the need for expensive and time-consuming floating-point calculations. This work bridges the gap between control theory equations and the implementation of control systems in small embedded systems with no inherent floating-point processing power.展开更多
为了提高图像拼接速度并满足高分辨率图像的实时拼接需求,提出了一种基于ORB(Oriented Fast and Rotated Brief)算法和MSAC(M-estimator Sample Consensus)算法的快速图像拼接方法。ORB算法特征匹配速度快,能够满足实时性要求。首先采用...为了提高图像拼接速度并满足高分辨率图像的实时拼接需求,提出了一种基于ORB(Oriented Fast and Rotated Brief)算法和MSAC(M-estimator Sample Consensus)算法的快速图像拼接方法。ORB算法特征匹配速度快,能够满足实时性要求。首先采用ORB算法进行图像特征点提取;然后,采用MSAC算法对匹配点对进行优化,剔除图像拼接中的伪匹配点对,通过正确的匹配点对求解图像变换矩阵;最后,采用双线性插值融合算法消除可见接缝并去除拼接痕迹。实验结果表明,本文方法在保证图像拼接质量的同时具有更快的拼接速度。展开更多
This paper considers the real-time estimation problem of vehicle mass,which has a significant impact on driving comfort and safety.A bilinear parameter identification algorithm is proposed for a type of nonlinear iden...This paper considers the real-time estimation problem of vehicle mass,which has a significant impact on driving comfort and safety.A bilinear parameter identification algorithm is proposed for a type of nonlinear identification problems,which encompass vehicle mass estimation.The feature of this nonlinear model is that two parameters to be estimated are multiplied together,which brings great difficulties to identification compared to linear models.The main idea proposed in the algorithm design is to transform the original nonlinear model into two mutually dependent linear models,which are identified by the recursive algorithms.By constructing a combined Lyapunov function,it is theoretically proved that the algorithm converges under the input excitation condition,and the convergence rate O(1/t)is achieved based on some extra mild conditions.Finally,the algorithm is verified through practical experiments,with the estimated vehicle mass error of 1.06%on average,which shows the feasibility of the algorithm.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
基金supported by Scientific Research and Technology Development Project of CNPC(2021DJ4002,2022DJ3908).
文摘The numerical dispersion phenomenon in the finite-difference forward modeling simulations of the wave equation significantly affects the imaging accuracy in acoustic reflection logging.This issue is particularly pronounced in the reverse time migration(RTM)method used for shear-wave(S-wave)logging imaging.This not only affects imaging accuracy but also introduces ambiguities in the interpretation of logging results.To address this challenge,this study proposes the use of a least-squares difference coefficient optimization algorithm aiming to suppress the numerical dispersion phenomenon in the RTM of S-wave reflection imaging logging.By optimizing the difference coefficients,the high-precision finite-difference algorithm serves as an effective operator for both forward and backward RTM processes.This approach is instrumental in eliminating migration illusions,which are often caused by numerical dispersion.The effectiveness of this optimized algorithm is demonstrated through numerical results,which indicate that it can achieve more accurate forward imaging results across various conditions,including high-and low-velocity strata,and is effective in both large and small spatial grids.The results of processing real data demonstrate that numerical dispersion optimization effectively reduces migration artifacts and diminishes ambiguities in logging interpretations.This optimization offers crucial technical support to the RTM method,enhancing its capability for accurately modeling and imaging S-wave reflections.
基金This project is supported by the National Natural Science Foundation of China
文摘This paper presents a new highly parallel algorithm for computing the minimum-norm least-squares solution of inconsistent linear equations Ax = b(A∈Rm×n,b∈R (A)). By this algorithm the solution x = A + b is obtained in T = n(log2m + log2(n - r + 1) + 5) + log2m + 1 steps with P=mn processors when m × 2(n - 1) and with P = 2n(n - 1) processors otherwise.
文摘Order-recursive least-squares(ORLS)algorithms are applied to the prob-lems of estimation and identification of FIR or ARMA system parameters where a fixedset of input signal samples is available and the desired order of the underlying model isunknown.On the basis of several universal formulae for updating nonsymmetric projec-tion operators,this paper presents three kinds of LS algorithms,called nonsymmetric,symmetric and square root normalized fast ORLS algorithms,respectively.As to the au-thors’ knowledge,the first and the third have not been so far provided,and the second isone of those which have the lowest computational requirement.Several simplified versionsof the algorithms are also considered.
基金This study was supported by the Fundamental Research Funds for the Central Universities(No.2572023DJ02).
文摘Effective development and utilization of wood resources is critical.Wood modification research has become an integral dimension of wood science research,however,the similarities between modified wood and original wood render it challenging for accurate identification and classification using conventional image classification techniques.So,the development of efficient and accurate wood classification techniques is inevitable.This paper presents a one-dimensional,convolutional neural network(i.e.,BACNN)that combines near-infrared spectroscopy and deep learning techniques to classify poplar,tung,and balsa woods,and PVA,nano-silica-sol and PVA-nano silica sol modified woods of poplar.The results show that BACNN achieves an accuracy of 99.3%on the test set,higher than the 52.9%of the BP neural network and 98.7%of Support Vector Machine compared with traditional machine learning methods and deep learning based methods;it is also higher than the 97.6%of LeNet,98.7%of AlexNet and 99.1%of VGGNet-11.Therefore,the classification method proposed offers potential applications in wood classification,especially with homogeneous modified wood,and it also provides a basis for subsequent wood properties studies.
文摘A new hierarchical identification algorithm is proposed for solving the identi-fication problem of the extended exponential model,which is used frequently in ecological,social and economical systems.By using the zero character of the optimal Lagrangianmultipliers of the equivalent identification problem,a two-level structure of the algorithmis derived first.Then,the convergence and the correspondence with the conventionalnonlinear approaches of the algorithm are proved.The results of simulation and applica-tion show that its convergent rate is greatly higher than that of the L-Mmethod.
文摘Based on the idea of the set-membership identification, a modified recursive least squares algorithm with variable gain, variable forgetting factor and resetting is presented. The concept of the error tolerance level is proposed. The selection criteria of the error tolerance level are also given according to the min-max principle. The algorithm is particularly suitable for tracing time-varying systems and is similar in computational complexity to the standard recursive least squares algorithm. The superior performance of the algorithm is verified ma simulation studies on a dynamic fermentation process.
文摘Bilinear singular systems can be used in the investigation of different types of engineering systems.In the past decade,considerable attention has been paid to analyzing and synthesizing singular bilinear systems.Their importance lies in their real world application such as economic,ecological,and socioeconomic processes.They are also applied in several biological processes,such as population dynamics of biological species,water balance,temperature regulation in the human body,carbon dioxide control in lungs,blood pressure,immune system,cardiac regulation,etc.Bilinear singular systems naturally represent different physical processes such as the fundamental law of mass action,the DC motor,the induction motor drives,the mechanical brake systems,aerial combat between two aircraft,the missile intercept problem,modeling and control of small furnaces and hydraulic rotary multimotor systems.The current research work discusses the Legendre Neural Network’s implementation to evaluate time-varying singular bilinear systems for finding the exact solution.The results were obtained from two methods namely the RK-Butcher algorithm and the Runge Kutta Arithmetic Mean(RKAM)method.Compared with the results attained from Legendre Neural Network Method for time-varying singular bilinear systems,the output proved to be accurate.As such,this research article established that the proposed Legendre Neural Network could be easily implemented in MATLAB.One can obtain the solution for any length of time from this method in time-varying singular bilinear systems.
基金Supported by the National High Technology Research and Development Programme of China(No.2011AA7014061)
文摘In this paper,by utilizing the angle of arrivals(AOAs) and imprecise positions of the sensors,a novel modified Levenberg-Marquardt algorithm to solve the source localization problem is proposed.Conventional source localization algorithms,like Gauss-Newton algorithm and Conjugate gradient algorithm are subjected to the problems of local minima and good initial guess.This paper presents a new optimization technique to find the descent directions to avoid divergence,and a trust region method is introduced to accelerate the convergence rate.Compared with conventional methods,the new algorithm offers increased stability and is more robust,allowing for stronger non-linearity and wider convergence field to be identified.Simulation results demonstrate that the proposed algorithm improves the typical methods in both speed and robustness,and is able to avoid local minima.
文摘We construct a fuzzy varying coefficient bilinear regression model to deal with the interval financial data and then adopt the least-squares method based on symmetric fuzzy number space. Firstly, we propose a varying coefficient model on the basis of the fuzzy bilinear regression model. Secondly, we develop the least-squares method according to the complete distance between fuzzy numbers to estimate the coefficients and test the adaptability of the proposed model by means of generalized likelihood ratio test with SSE composite index. Finally, mean square errors and mean absolutely errors are employed to evaluate and compare the fitting of fuzzy auto regression, fuzzy bilinear regression and fuzzy varying coefficient bilinear regression models, and also the forecasting of three models. Empirical analysis turns out that the proposed model has good fitting and forecasting accuracy with regard to other regression models for the capital market.
文摘This work describes how a control algorithm can be implemented in a small (8-bit) microcontroller for the main purpose of merging embedded systems and control theory in electrical engineering undergraduate classes. Two different methods for discretizing the control expression are compared: Euler transformation and bilinear transformation. The sampling rate’s impact on the algorithm is discussed and theoretical results are verified by an application to a temperature control system in a heating plant. Four control algorithms are compared: PID and PI algorithms discretized with Euler and bilinear transformation, respectively. It is shown that for the heating plant used in this work, a bilinear PI algorithm implemented in a small 8-bit microcontroller outperforms a commercial controller from Panasonic. It is also demonstrated that all the derived algorithms can be implemented using integer calculations only, obviating the need for expensive and time-consuming floating-point calculations. This work bridges the gap between control theory equations and the implementation of control systems in small embedded systems with no inherent floating-point processing power.
文摘为了提高图像拼接速度并满足高分辨率图像的实时拼接需求,提出了一种基于ORB(Oriented Fast and Rotated Brief)算法和MSAC(M-estimator Sample Consensus)算法的快速图像拼接方法。ORB算法特征匹配速度快,能够满足实时性要求。首先采用ORB算法进行图像特征点提取;然后,采用MSAC算法对匹配点对进行优化,剔除图像拼接中的伪匹配点对,通过正确的匹配点对求解图像变换矩阵;最后,采用双线性插值融合算法消除可见接缝并去除拼接痕迹。实验结果表明,本文方法在保证图像拼接质量的同时具有更快的拼接速度。
基金supported by the National Natural Science Foundation of China under Grant No.62025306CAS Project for Young Scientists in Basic Research under Grant No.YSBR-008。
文摘This paper considers the real-time estimation problem of vehicle mass,which has a significant impact on driving comfort and safety.A bilinear parameter identification algorithm is proposed for a type of nonlinear identification problems,which encompass vehicle mass estimation.The feature of this nonlinear model is that two parameters to be estimated are multiplied together,which brings great difficulties to identification compared to linear models.The main idea proposed in the algorithm design is to transform the original nonlinear model into two mutually dependent linear models,which are identified by the recursive algorithms.By constructing a combined Lyapunov function,it is theoretically proved that the algorithm converges under the input excitation condition,and the convergence rate O(1/t)is achieved based on some extra mild conditions.Finally,the algorithm is verified through practical experiments,with the estimated vehicle mass error of 1.06%on average,which shows the feasibility of the algorithm.
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.
