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
Augmented Reality(AR)tries to seamlessly integrate virtual content into the real world of the user.Ideally,the virtual content would behave exactly like real objects.This necessitates a correct and precise estimation ...Augmented Reality(AR)tries to seamlessly integrate virtual content into the real world of the user.Ideally,the virtual content would behave exactly like real objects.This necessitates a correct and precise estimation of the user’s viewpoint(or that of a camera)with regard to the virtual content’s coordinate sys-tem.Therefore,the real-time establishment of 3-dimension(3D)maps in real scenes is particularly important for augmented reality technology.So in this paper,we integrate Simultaneous Localization and Mapping(SLAM)technology into augmented reality.Our research is to implement an augmented reality system without markers using the ORB-SLAM2 framework algorithm.In this paper we propose an improved method for Oriented FAST and Rotated BRIEF(ORB)feature extraction and optimized key frame selection,as well as the use of the Progressive Sample Consensus(PROSAC)algorithm for planar estimation of augmented reality implementations,thus solving the problem of increased sys-tem runtime because of the loss of large amounts of texture information in images.In this paper,we get better results by comparing experiments and data analysis.However,there are some improved methods of PROSAC algorithm which are more suitable for the detection of plane feature points.展开更多
The purpose of the Combined Economic Emission Dispatch(CEED)of electric power is to offer the most exceptional schedule for production units,which must run with both low fuel costs and emission levels concurrently,the...The purpose of the Combined Economic Emission Dispatch(CEED)of electric power is to offer the most exceptional schedule for production units,which must run with both low fuel costs and emission levels concurrently,thereby meeting the lack of system equality and inequality constraints.Economic and emissions dispatching has become a primary and significant concern in power system networks.Consequences of using non-renewable fuels as input to exhaust power systems with toxic gas emissions and depleted resources for future generations.The optimal power allocation to generators serves as a solution to this problem.Emission dispatch reduces emissions while ignoring economic considerations.A collective strategy known as Combined Economic and Emission Dispatch is utilized to resolve the above-mentioned problems and investigate the trade-off relationship between fuel cost and emissions.Consequently,this work manages the Substantial Augmented Transformative Algorithm(SATA)to take care of the Combined Economic Emission Dispatch Problem(CEEDP)of warm units while fulfilling imperatives,for example,confines on generator limit,diminish the fuel cost,lessen the emission and decrease the force misfortune.SATA is a stochastic streamlining process that relies upon the development and knowledge of swarms.The goal is to minimize the total fuel cost of fossil-based thermal power generation units that generate and cause environmental pollution.The algorithm searches for solutions in the search space from the smallest to the largest in the case of forwarding search.The simulation of the proposed system is developed using MATLAB Simulink software.Simulation results show the effectiveness and practicability of this method in terms of economic and emission dispatching issues.The performance of the proposed system is compared with existing Artificial Bee Colony-Particle Swarm Optimization(ABC-PSO),Simulated Annealing(SA),and Differential Evolution(DE)methods.The fuel cost and gas emission of the proposed system are 128904$/hr and 138094.4652$/hr.展开更多
Reduced Q-matrix （Qr matrix） plays an important role in the rule space model （RSM） and the attribute hierarchy method （AHM）. Based on the attribute hierarchy, a valid/invalid item is defined. The judgment method...Reduced Q-matrix （Qr matrix） plays an important role in the rule space model （RSM） and the attribute hierarchy method （AHM）. Based on the attribute hierarchy, a valid/invalid item is defined. The judgment method of the valid/invalid item is developed on the relation between reachability matrix and valid items. And valid items are explained from the perspective of graph theory. An incremental augment algorithm for constructing Qr matrix is proposed based on the idea of incremental forward regression, and its validity is theoretically considered. Results of empirical tests are given in order to compare the performance of the incremental augment algo-rithm and the Tatsuoka algorithm upon the running time. Empirical evidence shows that the algorithm outper-forms the Tatsuoka algorithm, and the analysis of the two algorithms also show linear growth with respect to the number of valid items. Mathematical models with 10 attributes are built for the two algorithms by the linear regression analysis.展开更多
In order to overcome the defects where the surface of the object lacks sufficient texture features and the algorithm cannot meet the real-time requirements of augmented reality,a markerless augmented reality tracking ...In order to overcome the defects where the surface of the object lacks sufficient texture features and the algorithm cannot meet the real-time requirements of augmented reality,a markerless augmented reality tracking registration method based on multimodal template matching and point clouds is proposed.The method first adapts the linear parallel multi-modal LineMod template matching method with scale invariance to identify the texture-less target and obtain the reference image as the key frame that is most similar to the current perspective.Then,we can obtain the initial pose of the camera and solve the problem of re-initialization because of tracking registration interruption.A point cloud-based method is used to calculate the precise pose of the camera in real time.In order to solve the problem that the traditional iterative closest point(ICP)algorithm cannot meet the real-time requirements of the system,Kdtree(k-dimensional tree)is used under the graphics processing unit(GPU)to replace the part of finding the nearest points in the original ICP algorithm to improve the speed of tracking registration.At the same time,the random sample consensus(RANSAC)algorithm is used to remove the error point pairs to improve the accuracy of the algorithm.The results show that the proposed tracking registration method has good real-time performance and robustness.展开更多
Nonlinear errors always exist in data obtained from tracker in augmented reality （AR）, which badly influence the effect of AR. This paper proposes to rectify the errors using BP neural network. As BP neural network ...Nonlinear errors always exist in data obtained from tracker in augmented reality （AR）, which badly influence the effect of AR. This paper proposes to rectify the errors using BP neural network. As BP neural network is prone to getting into local extrema and convergence is slow, genetic algorithm is employed to optimize the initial weights and threshold of neural network. This paper discusses how to set the crucial parameters in the algorithm. Experimental results show that the method ensures that the neural network achieves global convergence quickly and correctly. Tracking precision of AR system is improved after the tracker is rectified, and the third dimension of AR system is enhanced.展开更多
In this paper, a new augmented Lagrangian penalty function for constrained optimization problems is studied. The dual properties of the augmented Lagrangian objective penalty function for constrained optimization prob...In this paper, a new augmented Lagrangian penalty function for constrained optimization problems is studied. The dual properties of the augmented Lagrangian objective penalty function for constrained optimization problems are proved. Under some conditions, the saddle point of the augmented Lagrangian objective penalty function satisfies the first-order Karush-Kuhn-Tucker (KKT) condition. Especially, when the KKT condition holds for convex programming its saddle point exists. Based on the augmented Lagrangian objective penalty function, an algorithm is developed for finding a global solution to an inequality constrained optimization problem and its global convergence is also proved under some conditions.展开更多
This paper presents the design of stability augmentation system (SAS) for the airship, which is robust with respect to parametric plant uncertainties. A robust pole placement approach is adopted in the design, which u...This paper presents the design of stability augmentation system (SAS) for the airship, which is robust with respect to parametric plant uncertainties. A robust pole placement approach is adopted in the design, which uses genetic algorithm (GA) as the optimization tool to derive the most robust solution of the state-feedback gain matrix K. The method can guarantee the resulting closed-loop poles to remain in a specified allocation region despite plant parameter uncertainty. Thus, the longitudinal stability of the airship is augmented by robustly assigning the closed-loop poles in a prescribed region of the left half s-plane.展开更多
To realize automatic modeling and dynamic simulation of the educational assembling-type robot with open structure, a general dynamic model for the educational assembling-type robot and a fast simulation algorithm are ...To realize automatic modeling and dynamic simulation of the educational assembling-type robot with open structure, a general dynamic model for the educational assembling-type robot and a fast simulation algorithm are put forward. First, the educational robot system is abstracted to a multibody system and a general dynamic model of the educational robot is constructed by the Newton-Euler method. Then the dynamic model is simplified by a combination of components with fixed connections according to the structural characteristics of the educational robot. Secondly, in order to obtain a high efficiency simulation algorithm, based on the sparse matrix technique, the augmentation algorithm and the direct projective constraint stabilization algorithm are improved. Finally, a numerical example is given. The results show that the model and the fast algorithm are valid and effective. This study lays a dynamic foundation for realizing the simulation platform of the educational robot.展开更多
develop a mentation This paper considers the priority facility primal-dual 3-approximation algorithm for procedure, the authors further improve the location problem with penalties： The authors this problem. Combining...develop a mentation This paper considers the priority facility primal-dual 3-approximation algorithm for procedure, the authors further improve the location problem with penalties： The authors this problem. Combining with the greedy aug- previous ratio 3 to 1.8526.展开更多
The guidance and control for UAV aerial refueling docking based on dynamic inversion with L1 adaptive augmentation is studied.In order to improve the tracking performance of UAV aerial refueling docking,aguidance algo...The guidance and control for UAV aerial refueling docking based on dynamic inversion with L1 adaptive augmentation is studied.In order to improve the tracking performance of UAV aerial refueling docking,aguidance algorithm is developed to satisfy the tracking requirement of position and velocity,and it generates the UAV flight control loop commands.In flight control loop,based on the 6-DOF nonlinear model,the angular rate loop and the attitude loop are separated based on time-scale principle and the control law is designed using dynamic inversion.The throttle control is also derived from dynamic inversion method.Moreover,an L1 adaptive augmentation is developed to compensate for the undesirable effects of modeling uncertainty and disturbance.Nonlinear digital simulations are carried out.The results show that the guidance and control system has good tracking performance and robustness in achieving accurate aerial refueling docking.展开更多
In this study,a Dual Smoothing Ionospheric Gradient Monitor Algorithm(DSIGMA)was developed for Code-Carrier Divergence(CCD)faults of dual-frequency Ground-Based Augmentation Systems(GBAS)based on the Bei Dou Navigatio...In this study,a Dual Smoothing Ionospheric Gradient Monitor Algorithm(DSIGMA)was developed for Code-Carrier Divergence(CCD)faults of dual-frequency Ground-Based Augmentation Systems(GBAS)based on the Bei Dou Navigation Satellite System(BDS).Divergence-Free(DF)combinations of the signals were used to form test statistics for a dualfrequency DSIGMA.First,the single-frequency DSIGMA was reviewed,which supports the GBAS approach service type D(GAST-D)for protection against the effect of large ionospheric gradients.The single-frequency DSIGMA was used to create a novel input scheme for the dual-frequency DSIGMA by introducing DF combinations.The steady states of the test statistics were also analysed.The monitors were characterized using BDS measurement data,whereby standard deviations of 0.0432 and 0.0639 m for the proposed two test statistics were used to calculate the monitor threshold.An extensive simulation was designed to assess the monitor performance by comparing the Probability of Missed Detection(PMD)according to the differential error with the range domain PMD limits under different fault modes.The results showed that the proposed algorithm has a higher integrity performance than the single-frequency monitor.The minimum detectable divergence with the same missed probability is less than 50%that of GAST-D.展开更多
One of the surface mining methods is open-pit mining,by which a pit is dug to extract ore or waste downwards from the earth’s surface.In the mining industry,one of the most significant difficulties is long-term produ...One of the surface mining methods is open-pit mining,by which a pit is dug to extract ore or waste downwards from the earth’s surface.In the mining industry,one of the most significant difficulties is long-term production scheduling(LTPS)of the open-pit mines.Deterministic and uncertainty-based approaches are identified as the main strategies,which have been widely used to cope with this problem.Within the last few years,many researchers have highly considered a new computational type,which is less costly,i.e.,meta-heuristic methods,so as to solve the mine design and production scheduling problem.Although the optimality of the final solution cannot be guaranteed,they are able to produce sufficiently good solutions with relatively less computational costs.In the present paper,two hybrid models between augmented Lagrangian relaxation(ALR)and a particle swarm optimization(PSO)and ALR and bat algorithm(BA)are suggested so that the LTPS problem is solved under the condition of grade uncertainty.It is suggested to carry out the ALR method on the LTPS problem to improve its performance and accelerate the convergence.Moreover,the Lagrangian coefficients are updated by using PSO and BA.The presented models have been compared with the outcomes of the ALR-genetic algorithm,the ALR-traditional sub-gradient method,and the conventional method without using the Lagrangian approach.The results indicated that the ALR is considered a more efficient approach which can solve a large-scale problem and make a valid solution.Hence,it is more effectual than the conventional method.Furthermore,the time and cost of computation are diminished by the proposed hybrid strategies.The CPU time using the ALR-BA method is about 7.4%higher than the ALR-PSO approach.展开更多
An active set truncated-Newton algorithm (ASTNA) is proposed to solve the large-scale bound constrained sub-problems. The global convergence of the algorithm is obtained and two groups of numerical experiments are mad...An active set truncated-Newton algorithm (ASTNA) is proposed to solve the large-scale bound constrained sub-problems. The global convergence of the algorithm is obtained and two groups of numerical experiments are made for the various large-scale problems of varying size. The comparison results between ASTNA and the subspace limited memory quasi-Newton algorithm and between the modified augmented Lagrange multiplier methods combined with ASTNA and the modified barrier function method show the stability and effectiveness of ASTNA for simultaneous optimization of distillation column.展开更多
The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of ...The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of civil aviation, satellite based augmentation system (SBAS) has been planned by various countries including USA, Europe, Japan and India. The Indian SBAS is named as GPS Aided Geo Augmented Navigation (GAGAN). The GAGAN network consists of several dual frequency GPS receivers located at various airports around the Indian subcontinent. The ionospheric delay, which is a function of the total electron content (TEC), is one of the main sources of error affecting GPS/SBAS accuracy. A dual frequency GPS receiver can be used to estimate the TEC. However, line-of-sight TEC derived from dual frequency GPS data is corrupted by the instrumental biases of the GPS receiver and satellites. The estimation of receiver instrumental bias is particularly important for obtaining accurate estimates of ionospheric delay. In this paper, two prominent techniques based on Kalman filter and Self-Calibration Of pseudo Range Error (SCORE) algorithm are used for estimation of instrumental biases. The estimated instrumental bias and TEC results for the GPS Aided Geo Augmented Navigation (GAGAN) station at Hyderabad (78.47°E, 17.45°N), India are presented.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
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
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supported by the Hainan Provincial Natural Science Foundation of China(project number:621QN269)the Sanya Science and Information Bureau Foundation(project number:2021GXYL251).
文摘Augmented Reality(AR)tries to seamlessly integrate virtual content into the real world of the user.Ideally,the virtual content would behave exactly like real objects.This necessitates a correct and precise estimation of the user’s viewpoint(or that of a camera)with regard to the virtual content’s coordinate sys-tem.Therefore,the real-time establishment of 3-dimension(3D)maps in real scenes is particularly important for augmented reality technology.So in this paper,we integrate Simultaneous Localization and Mapping(SLAM)technology into augmented reality.Our research is to implement an augmented reality system without markers using the ORB-SLAM2 framework algorithm.In this paper we propose an improved method for Oriented FAST and Rotated BRIEF(ORB)feature extraction and optimized key frame selection,as well as the use of the Progressive Sample Consensus(PROSAC)algorithm for planar estimation of augmented reality implementations,thus solving the problem of increased sys-tem runtime because of the loss of large amounts of texture information in images.In this paper,we get better results by comparing experiments and data analysis.However,there are some improved methods of PROSAC algorithm which are more suitable for the detection of plane feature points.
文摘The purpose of the Combined Economic Emission Dispatch(CEED)of electric power is to offer the most exceptional schedule for production units,which must run with both low fuel costs and emission levels concurrently,thereby meeting the lack of system equality and inequality constraints.Economic and emissions dispatching has become a primary and significant concern in power system networks.Consequences of using non-renewable fuels as input to exhaust power systems with toxic gas emissions and depleted resources for future generations.The optimal power allocation to generators serves as a solution to this problem.Emission dispatch reduces emissions while ignoring economic considerations.A collective strategy known as Combined Economic and Emission Dispatch is utilized to resolve the above-mentioned problems and investigate the trade-off relationship between fuel cost and emissions.Consequently,this work manages the Substantial Augmented Transformative Algorithm(SATA)to take care of the Combined Economic Emission Dispatch Problem(CEEDP)of warm units while fulfilling imperatives,for example,confines on generator limit,diminish the fuel cost,lessen the emission and decrease the force misfortune.SATA is a stochastic streamlining process that relies upon the development and knowledge of swarms.The goal is to minimize the total fuel cost of fossil-based thermal power generation units that generate and cause environmental pollution.The algorithm searches for solutions in the search space from the smallest to the largest in the case of forwarding search.The simulation of the proposed system is developed using MATLAB Simulink software.Simulation results show the effectiveness and practicability of this method in terms of economic and emission dispatching issues.The performance of the proposed system is compared with existing Artificial Bee Colony-Particle Swarm Optimization(ABC-PSO),Simulated Annealing(SA),and Differential Evolution(DE)methods.The fuel cost and gas emission of the proposed system are 128904$/hr and 138094.4652$/hr.
基金Supported by the National Natural Science Foundation of China (30860084,60673014,60263005)the Backbone Young Teachers Foundation of Fujian Normal University(2008100244)the Department of Education Foundation of Fujian Province (ZA09047)~~
文摘Reduced Q-matrix （Qr matrix） plays an important role in the rule space model （RSM） and the attribute hierarchy method （AHM）. Based on the attribute hierarchy, a valid/invalid item is defined. The judgment method of the valid/invalid item is developed on the relation between reachability matrix and valid items. And valid items are explained from the perspective of graph theory. An incremental augment algorithm for constructing Qr matrix is proposed based on the idea of incremental forward regression, and its validity is theoretically considered. Results of empirical tests are given in order to compare the performance of the incremental augment algo-rithm and the Tatsuoka algorithm upon the running time. Empirical evidence shows that the algorithm outper-forms the Tatsuoka algorithm, and the analysis of the two algorithms also show linear growth with respect to the number of valid items. Mathematical models with 10 attributes are built for the two algorithms by the linear regression analysis.
基金This work was supported by National Natural Science Foundation of China(No.61125101).
文摘In order to overcome the defects where the surface of the object lacks sufficient texture features and the algorithm cannot meet the real-time requirements of augmented reality,a markerless augmented reality tracking registration method based on multimodal template matching and point clouds is proposed.The method first adapts the linear parallel multi-modal LineMod template matching method with scale invariance to identify the texture-less target and obtain the reference image as the key frame that is most similar to the current perspective.Then,we can obtain the initial pose of the camera and solve the problem of re-initialization because of tracking registration interruption.A point cloud-based method is used to calculate the precise pose of the camera in real time.In order to solve the problem that the traditional iterative closest point(ICP)algorithm cannot meet the real-time requirements of the system,Kdtree(k-dimensional tree)is used under the graphics processing unit(GPU)to replace the part of finding the nearest points in the original ICP algorithm to improve the speed of tracking registration.At the same time,the random sample consensus(RANSAC)algorithm is used to remove the error point pairs to improve the accuracy of the algorithm.The results show that the proposed tracking registration method has good real-time performance and robustness.
基金Project supported by Science Foundation of Shanghai Municipal Commission of Science and Technology (Grant No .025115008)
文摘Nonlinear errors always exist in data obtained from tracker in augmented reality （AR）, which badly influence the effect of AR. This paper proposes to rectify the errors using BP neural network. As BP neural network is prone to getting into local extrema and convergence is slow, genetic algorithm is employed to optimize the initial weights and threshold of neural network. This paper discusses how to set the crucial parameters in the algorithm. Experimental results show that the method ensures that the neural network achieves global convergence quickly and correctly. Tracking precision of AR system is improved after the tracker is rectified, and the third dimension of AR system is enhanced.
文摘In this paper, a new augmented Lagrangian penalty function for constrained optimization problems is studied. The dual properties of the augmented Lagrangian objective penalty function for constrained optimization problems are proved. Under some conditions, the saddle point of the augmented Lagrangian objective penalty function satisfies the first-order Karush-Kuhn-Tucker (KKT) condition. Especially, when the KKT condition holds for convex programming its saddle point exists. Based on the augmented Lagrangian objective penalty function, an algorithm is developed for finding a global solution to an inequality constrained optimization problem and its global convergence is also proved under some conditions.
文摘This paper presents the design of stability augmentation system (SAS) for the airship, which is robust with respect to parametric plant uncertainties. A robust pole placement approach is adopted in the design, which uses genetic algorithm (GA) as the optimization tool to derive the most robust solution of the state-feedback gain matrix K. The method can guarantee the resulting closed-loop poles to remain in a specified allocation region despite plant parameter uncertainty. Thus, the longitudinal stability of the airship is augmented by robustly assigning the closed-loop poles in a prescribed region of the left half s-plane.
基金Hexa-Type Elites Peak Program of Jiangsu Province(No.2008144)Qing Lan Project of Jiangsu ProvinceFund for Excellent Young Teachers of Southeast University
文摘To realize automatic modeling and dynamic simulation of the educational assembling-type robot with open structure, a general dynamic model for the educational assembling-type robot and a fast simulation algorithm are put forward. First, the educational robot system is abstracted to a multibody system and a general dynamic model of the educational robot is constructed by the Newton-Euler method. Then the dynamic model is simplified by a combination of components with fixed connections according to the structural characteristics of the educational robot. Secondly, in order to obtain a high efficiency simulation algorithm, based on the sparse matrix technique, the augmentation algorithm and the direct projective constraint stabilization algorithm are improved. Finally, a numerical example is given. The results show that the model and the fast algorithm are valid and effective. This study lays a dynamic foundation for realizing the simulation platform of the educational robot.
基金supported by the National Natural Science Foundation of China under Grant No.11371001
文摘develop a mentation This paper considers the priority facility primal-dual 3-approximation algorithm for procedure, the authors further improve the location problem with penalties： The authors this problem. Combining with the greedy aug- previous ratio 3 to 1.8526.
基金supported by the National Natural Science Foundation of China(No.61273050)the Aeronautical Science Foundation of China(No.20121352026)
文摘The guidance and control for UAV aerial refueling docking based on dynamic inversion with L1 adaptive augmentation is studied.In order to improve the tracking performance of UAV aerial refueling docking,aguidance algorithm is developed to satisfy the tracking requirement of position and velocity,and it generates the UAV flight control loop commands.In flight control loop,based on the 6-DOF nonlinear model,the angular rate loop and the attitude loop are separated based on time-scale principle and the control law is designed using dynamic inversion.The throttle control is also derived from dynamic inversion method.Moreover,an L1 adaptive augmentation is developed to compensate for the undesirable effects of modeling uncertainty and disturbance.Nonlinear digital simulations are carried out.The results show that the guidance and control system has good tracking performance and robustness in achieving accurate aerial refueling docking.
基金financial support from the National Natural Science Foundation of China(Nos.61871012,U1833125)a project from the Ministry of Industry and Information Technology(Airborne RAIM/ARAIM Technology)+2 种基金Open Fund Project of Intelligent Operation Key Laboratory of Civil Aviation Airport Group(No.KLAGIO20180405)the Young Top Talent Support Program of Beihang Universitythe Beijing Nova Program of Science and Technology(No.Z191100001119134)。
文摘In this study,a Dual Smoothing Ionospheric Gradient Monitor Algorithm(DSIGMA)was developed for Code-Carrier Divergence(CCD)faults of dual-frequency Ground-Based Augmentation Systems(GBAS)based on the Bei Dou Navigation Satellite System(BDS).Divergence-Free(DF)combinations of the signals were used to form test statistics for a dualfrequency DSIGMA.First,the single-frequency DSIGMA was reviewed,which supports the GBAS approach service type D(GAST-D)for protection against the effect of large ionospheric gradients.The single-frequency DSIGMA was used to create a novel input scheme for the dual-frequency DSIGMA by introducing DF combinations.The steady states of the test statistics were also analysed.The monitors were characterized using BDS measurement data,whereby standard deviations of 0.0432 and 0.0639 m for the proposed two test statistics were used to calculate the monitor threshold.An extensive simulation was designed to assess the monitor performance by comparing the Probability of Missed Detection(PMD)according to the differential error with the range domain PMD limits under different fault modes.The results showed that the proposed algorithm has a higher integrity performance than the single-frequency monitor.The minimum detectable divergence with the same missed probability is less than 50%that of GAST-D.
文摘One of the surface mining methods is open-pit mining,by which a pit is dug to extract ore or waste downwards from the earth’s surface.In the mining industry,one of the most significant difficulties is long-term production scheduling(LTPS)of the open-pit mines.Deterministic and uncertainty-based approaches are identified as the main strategies,which have been widely used to cope with this problem.Within the last few years,many researchers have highly considered a new computational type,which is less costly,i.e.,meta-heuristic methods,so as to solve the mine design and production scheduling problem.Although the optimality of the final solution cannot be guaranteed,they are able to produce sufficiently good solutions with relatively less computational costs.In the present paper,two hybrid models between augmented Lagrangian relaxation(ALR)and a particle swarm optimization(PSO)and ALR and bat algorithm(BA)are suggested so that the LTPS problem is solved under the condition of grade uncertainty.It is suggested to carry out the ALR method on the LTPS problem to improve its performance and accelerate the convergence.Moreover,the Lagrangian coefficients are updated by using PSO and BA.The presented models have been compared with the outcomes of the ALR-genetic algorithm,the ALR-traditional sub-gradient method,and the conventional method without using the Lagrangian approach.The results indicated that the ALR is considered a more efficient approach which can solve a large-scale problem and make a valid solution.Hence,it is more effectual than the conventional method.Furthermore,the time and cost of computation are diminished by the proposed hybrid strategies.The CPU time using the ALR-BA method is about 7.4%higher than the ALR-PSO approach.
基金Project (2002CB312200) supported by the National Key Basic Research and Development Program of China Project(03JJY3109) supported by the Natural Science Foundation of Hunan Province
文摘An active set truncated-Newton algorithm (ASTNA) is proposed to solve the large-scale bound constrained sub-problems. The global convergence of the algorithm is obtained and two groups of numerical experiments are made for the various large-scale problems of varying size. The comparison results between ASTNA and the subspace limited memory quasi-Newton algorithm and between the modified augmented Lagrange multiplier methods combined with ASTNA and the modified barrier function method show the stability and effectiveness of ASTNA for simultaneous optimization of distillation column.
文摘The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of civil aviation, satellite based augmentation system (SBAS) has been planned by various countries including USA, Europe, Japan and India. The Indian SBAS is named as GPS Aided Geo Augmented Navigation (GAGAN). The GAGAN network consists of several dual frequency GPS receivers located at various airports around the Indian subcontinent. The ionospheric delay, which is a function of the total electron content (TEC), is one of the main sources of error affecting GPS/SBAS accuracy. A dual frequency GPS receiver can be used to estimate the TEC. However, line-of-sight TEC derived from dual frequency GPS data is corrupted by the instrumental biases of the GPS receiver and satellites. The estimation of receiver instrumental bias is particularly important for obtaining accurate estimates of ionospheric delay. In this paper, two prominent techniques based on Kalman filter and Self-Calibration Of pseudo Range Error (SCORE) algorithm are used for estimation of instrumental biases. The estimated instrumental bias and TEC results for the GPS Aided Geo Augmented Navigation (GAGAN) station at Hyderabad (78.47°E, 17.45°N), India are presented.
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.
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.
