An extended crowding genetic algorithm (ECGA) is introduced for solvingoptimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem hasbeen found to be non-convex, and the objective f...An extended crowding genetic algorithm (ECGA) is introduced for solvingoptimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem hasbeen found to be non-convex, and the objective function contained several local optima and globaloptimality could not be ensured by all the traditional MINLP optimization method. The concepts ofspecies conserving and composite encoding are introduced to crowding genetic algorithm (CGA) formaintain the diversity of population more effectively and coping with the continuous and/or discretevariables in MINLP problem. The solution of three-levels pump configuration got from DICOPT++software (OA algorithm) is also given. By comparing with the solutions obtained from DICOPT++, ECPmethod, and MIN-MIN method, the ECGA algorithm proved to be very effective in finding the globaloptimal solution of multi-levels pump configuration via using the problem-specific information.展开更多
An extended algorithm of flexibility analysis with a local adjusting method for flexibility region of chemical processes, which is based on the active constraint strategy, is proposed, which fully exploits the flexibi...An extended algorithm of flexibility analysis with a local adjusting method for flexibility region of chemical processes, which is based on the active constraint strategy, is proposed, which fully exploits the flexibility region of the process system operation. The hyperrectangular flexibility region determined by the extended algorithm is larger than that calculated by the previous algorithms. The limitation of the proposed algorithm due to imperfect convexity and its corresponding verification measure are also discussed. Both numerical and actual chemical process examples are presented to demonstrate the effectiveness of the new algorithm.展开更多
In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using...In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm (EPSO) was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm (GA) and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO.展开更多
This communique is opted to study the approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian matrices.We choose the geodesic distance betweenAHXXA an...This communique is opted to study the approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian matrices.We choose the geodesic distance betweenAHXXA and P as the cost function,and put forward the Extended Hamiltonian algorithm(EHA)and Natural gradient algorithm(NGA)for the solution.Finally,several numerical experiments give you an idea about the effectiveness of the proposed algorithms.We also show the comparison between these two algorithms EHA and NGA.Obtained results are provided and analyzed graphically.We also conclude that the extended Hamiltonian algorithm has better convergence speed than the natural gradient algorithm,whereas the trajectory of the solution matrix is optimal in case of Natural gradient algorithm(NGA)as compared to Extended Hamiltonian Algorithm(EHA).The aim of this paper is to show that the Extended Hamiltonian algorithm(EHA)has superior convergence properties as compared to Natural gradient algorithm(NGA).Upto the best of author’s knowledge,no approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian matrices is found so far in the literature.展开更多
A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filte...A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.展开更多
A modified sequential linear programming algorithm is presented, whose subproblem is always solvable, for the extended linear complementarity problem (XLCP), the global convergence of the algorithm under assumption of...A modified sequential linear programming algorithm is presented, whose subproblem is always solvable, for the extended linear complementarity problem (XLCP), the global convergence of the algorithm under assumption of X-row sufficiency or X-colunm monotonicity is proved. As a result, a sufficient condition for existence and boundedness of solution to the XLCP are obtained.展开更多
General active contour algorithm, which uses the intensity of the image, has been used to actively segment objects. Because the objects have a similar intensity but different colors, it is difficult to segment any obj...General active contour algorithm, which uses the intensity of the image, has been used to actively segment objects. Because the objects have a similar intensity but different colors, it is difficult to segment any object from the others, Moreover, this algodthm can only be used in the simple environment since it is very sensitive to noise. In tinter to solve these problems. This paper proposes an extended active contour algorithm based on a color variance. In complex images, the color variance energy as the image energy is introduced into the general active contour algorithm. Experimental results show that the proposed active contour algorithm is very effective in various environments.展开更多
At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns st...At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system symmetrically.Therefore,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative rates.In addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were shown.The results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten iterations.Whereas in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved parameters.Thus EDLA algorithm introduces novelty concerning its performance and particular activation function.This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner.This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses aftermodification.If the quantity of dataset records is enormous,then themethod’s computation power has to be updated.展开更多
On the basis of Hartmann Shack sensor imaging analysis, a new method is presented with which the wavefront slope can be obtained when the object is incoherent and extended. This method, which is demonstrated by both ...On the basis of Hartmann Shack sensor imaging analysis, a new method is presented with which the wavefront slope can be obtained when the object is incoherent and extended. This method, which is demonstrated by both theoretical interpreting and computer simulation, explains how to measure the wavefront slope difference between two sub apertures through the determination of image displacements on detector plane. It includes a fast and accurate digital algorithm for detecting wavefront disturbance, which is much suitable for realization in such electrical hardwares as digital signal processors.展开更多
The behavior of the tip wake of a wind turbine is one of the hot issues in the wind power field.This problem can partially be tackled using Computational Fluid Dynamics(CFD).However,this approach lacks the ability to ...The behavior of the tip wake of a wind turbine is one of the hot issues in the wind power field.This problem can partially be tackled using Computational Fluid Dynamics(CFD).However,this approach lacks the ability to provide insights into the spatial structure of important high-order flows.Therefore,with the horizontal axis wind turbine as the main focus,in this work,firstly,we conduct CFD simulations of the wind turbine in order to obtain a data-driven basis relating to multiple working conditions for further analysis.Then,these data are studied using an extended Proper Orthogonal Decomposition(POD)algorithm.The quantitative results indicate that the tip vortex in the wake has a complicated spatio-temporal morphological configuration in the higher-order extended POD space.The radial velocity modes obtained are effective and credible,and such reconstructed flow of the tip vortex becomes clearer with the increase of the reconstruction orders.Interestingly,the changes of relatively high-order correlation coefficients are essentially affected by the periodic fusion of tip and central eddies in the wake.展开更多
In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. ...In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.展开更多
The current extended fuzzy description logics lack reasoning algorithms with TBoxes. The problem of the satisfiability of the extended fuzzy description logic EFALC cut concepts w. r. t. TBoxes is proposed, and a reas...The current extended fuzzy description logics lack reasoning algorithms with TBoxes. The problem of the satisfiability of the extended fuzzy description logic EFALC cut concepts w. r. t. TBoxes is proposed, and a reasoning algorithm is given. This algorithm is designed in the style of tableau algorithms, which is usually used in classical description logics. The transformation rules and the process of this algorithm is described and optimized with three main techniques： recursive procedure call, branch cutting and introducing sets of mesne results. The optimized algorithm is proved sound, complete and with an EXPTime complexity, and the satisfiability problem is EXPTime-complete.展开更多
The elliptic curve discrete logarithm problem(ECDLP)is a popular choice for cryptosystems due to its high level of security.However,with the advent of the extended Shor’s algorithm,there is concern that ECDLP may soo...The elliptic curve discrete logarithm problem(ECDLP)is a popular choice for cryptosystems due to its high level of security.However,with the advent of the extended Shor’s algorithm,there is concern that ECDLP may soon be vulnerable.While the algorithm does ofer hope in solving ECDLP,it is still uncertain whether it can pose a real threat in practice.From the perspective of the quantum circuits of the algorithm,this paper analyzes the feasibility of cracking ECDLP using an ion trap quantum computer with improved quantum circuits for the extended Shor’s algorithm.We give precise quantum circuits for extended Shor’s algorithm to calculate discrete logarithms on elliptic curves over prime felds,including modular subtraction,three diferent modular multiplication,and modular inverse.Additionally,we incorporate and improve upon windowed arithmetic in the circuits to reduce the CNOTcounts.Whereas previous studies mostly focused on minimizing the number of qubits or the depth of the circuit,we focus on minimizing the number of CNOT gates in the circuit,which greatly afects the running time of the algorithm on an ion trap quantum computer.Specifcally,we begin by presenting implementations of basic arithmetic operations with the lowest known CNOT-counts,along with improved constructions for modular inverse,point addition,and windowed arithmetic.Next,we precisely estimate that,to execute the extended Shor’s algorithm with the improved circuits to factor an n-bit integer,the CNOT-count required is1237n^(3)/log n+2n^(2)+n.Finally,we analyze the running time and feasibility of the extended Shor’s algorithm on an ion trap quantum computer.展开更多
The rolling mill vibration not only seriously causes the strip thickness heterogeneity, but also damages the rolling mill equipment and its electrical components. Existing vibration suppression methods are passive and...The rolling mill vibration not only seriously causes the strip thickness heterogeneity, but also damages the rolling mill equipment and its electrical components. Existing vibration suppression methods are passive and mainly tune mechanical, hydraulic, electrical and rolling process parameters. A new active vibration suppression method was thus proposed using the disturbance estimation and compensation algorithm. Firstly, the hydraulic-mechanical coupling model of the rolling mill vibration was established, and an active vibration suppressor was designed based on the extended state observer. Then, through the numerical simulation, it is found that the vibration energy is reduced by 35.3% using the vibration suppressor, and the vibration suppressor is valid when the vibration frequency is lower than 60 Hz Finally, the vibration suppressor was applied to the in-site manufacturing, and the expected vibration suppression was obtained. The method makes the produced steel strip have more uniform thickness and further significantly increases the finished product ratio.展开更多
For short-term wind power forecasting,an interval A2-C1 type-2(IT2)Takagi-Sugeno-Kang(TSK)fuzzy logic system(FLS)method(“A”means antecedent and“C”consequent)based on an extended Kalman filter(EKF)optimization algo...For short-term wind power forecasting,an interval A2-C1 type-2(IT2)Takagi-Sugeno-Kang(TSK)fuzzy logic system(FLS)method(“A”means antecedent and“C”consequent)based on an extended Kalman filter(EKF)optimization algorithm is proposed.Compared with the type-1(T1)FLS model,the IT2 TSK FLS method can simultaneously model both intra-and inter-individual uncertainty and further optimize the antecedent and consequent parameters using the EKF to improve forecasting performance further.The proposed IT2 A2-C1 FLS method is applied to Mackey-Glass chaotic time series and wind power forecasting instances in a certain region,under the same conditions.It is also compared with the T1 TSK FLS and IT2 TSK FLS methods with back propagation(BP)and particle swarm optimization(PSO)algorithms,as well as IT2 A2-C0 TSK FLS methods with EKF.The experimental results confirm that the proposed IT2 A2-C1 FLS method is superior to the other FLS methods regarding performance,which demonstrates its effectiveness and application potential.展开更多
Because of the ignored items after linearization,the extended Kalman filter(EKF)becomes a form of suboptimal gradient descent algorithm.The emanative tendency exists in GPS solution when the filter equations are ill-p...Because of the ignored items after linearization,the extended Kalman filter(EKF)becomes a form of suboptimal gradient descent algorithm.The emanative tendency exists in GPS solution when the filter equations are ill-posed.The deviation in the estimation cannot be avoided.Furthermore,the true solution may be lost in pseudorange positioning because the linearized pseudorange equations are partial solutions.To solve the above problems in GPS dynamic positioning by using EKF,a closed-form Kalman filter method called the two-stage algorithm is presented for the nonlinear algebraic solution of GPS dynamic positioning based on the global nonlinear least squares closed algorithm--Bancroft numerical algorithm of American.The method separates the spatial parts from temporal parts during processing the GPS filter problems,and solves the nonlinear GPS dynamic positioning,thus getting stable and reliable dynamic positioning solutions.展开更多
In order to overcome the limitations of a unitary reference station in mobile communication positioning network differential barometric altimetry(DBA)and broaden the action scope of the reference station and improve p...In order to overcome the limitations of a unitary reference station in mobile communication positioning network differential barometric altimetry(DBA)and broaden the action scope of the reference station and improve positioning accuracy of elevation,an integrated interpolation algorithm model based on generalized extended approximation(GEA)algorithm and Kriging interpolation in time-space domain of reference station is proposed.In the time domain,barometric measured data is considered the maximum value estimated by bilateral extension to avoid wrong direction of estimation,which is approaching true value.In the spatial domain,barometric relevance among multiple reference stations is utilized,the weighted coefficients of multiple reference stations is calculated by the integrated algorithm model based on the GEA algorithm and Kriging interpolation.The impact of each reference station to the measured station is quantified,so that a virtual reference station is constructed,which can overcome the limitation of barometric correction by a unitary reference station.In addition,the measurement error due to irregular change in atmospheric pressure will be eliminated.展开更多
In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accele...In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.展开更多
For the unforced dynamical non-linear state–space model,a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article.The proposed algorithm lends itself ...For the unforced dynamical non-linear state–space model,a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article.The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems.With the help of an ortho-normal triangularization method,which relies on numerically stable givens rotation,matrix inversion causes a computational burden,is reduced.Matrix computation possesses many excellent numerical properties such as singularity,symmetry,skew symmetry,and triangularity is achieved by using this algorithm.The proposed method is validated for the prediction of stationary and non-stationary Mackey–Glass Time Series,along with that a component in the x-direction of the Lorenz Times Series is also predicted to illustrate its usefulness.By the learning curves regarding mean square error(MSE)are witnessed for demonstration with prediction performance of the proposed algorithm from where it’s concluded that the proposed algorithm performs better than EKRLS.This new SREKRLS based design positively offers an innovative era towards non-linear systolic arrays,which is efficient in developing very-large-scale integration(VLSI)applications with non-linear input data.Multiple experiments are carried out to validate the reliability,effectiveness,and applicability of the proposed algorithm and with different noise levels compared to the Extended kernel recursive least-squares(EKRLS)algorithm.展开更多
基金This project is supported by Provincial Science Foundation of Hebei (No.01213553).
文摘An extended crowding genetic algorithm (ECGA) is introduced for solvingoptimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem hasbeen found to be non-convex, and the objective function contained several local optima and globaloptimality could not be ensured by all the traditional MINLP optimization method. The concepts ofspecies conserving and composite encoding are introduced to crowding genetic algorithm (CGA) formaintain the diversity of population more effectively and coping with the continuous and/or discretevariables in MINLP problem. The solution of three-levels pump configuration got from DICOPT++software (OA algorithm) is also given. By comparing with the solutions obtained from DICOPT++, ECPmethod, and MIN-MIN method, the ECGA algorithm proved to be very effective in finding the globaloptimal solution of multi-levels pump configuration via using the problem-specific information.
文摘An extended algorithm of flexibility analysis with a local adjusting method for flexibility region of chemical processes, which is based on the active constraint strategy, is proposed, which fully exploits the flexibility region of the process system operation. The hyperrectangular flexibility region determined by the extended algorithm is larger than that calculated by the previous algorithms. The limitation of the proposed algorithm due to imperfect convexity and its corresponding verification measure are also discussed. Both numerical and actual chemical process examples are presented to demonstrate the effectiveness of the new algorithm.
基金Project(70671040) supported by the National Natural Science Foundation of China
文摘In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm (EPSO) was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm (GA) and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO.
文摘This communique is opted to study the approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian matrices.We choose the geodesic distance betweenAHXXA and P as the cost function,and put forward the Extended Hamiltonian algorithm(EHA)and Natural gradient algorithm(NGA)for the solution.Finally,several numerical experiments give you an idea about the effectiveness of the proposed algorithms.We also show the comparison between these two algorithms EHA and NGA.Obtained results are provided and analyzed graphically.We also conclude that the extended Hamiltonian algorithm has better convergence speed than the natural gradient algorithm,whereas the trajectory of the solution matrix is optimal in case of Natural gradient algorithm(NGA)as compared to Extended Hamiltonian Algorithm(EHA).The aim of this paper is to show that the Extended Hamiltonian algorithm(EHA)has superior convergence properties as compared to Natural gradient algorithm(NGA).Upto the best of author’s knowledge,no approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian matrices is found so far in the literature.
基金supported by National Natural Science Foundation of China (Nos.62265010,62061024)Gansu Province Science and Technology Plan (No.23YFGA0062)Gansu Province Innovation Fund (No.2022A-215)。
文摘A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.
文摘A modified sequential linear programming algorithm is presented, whose subproblem is always solvable, for the extended linear complementarity problem (XLCP), the global convergence of the algorithm under assumption of X-row sufficiency or X-colunm monotonicity is proved. As a result, a sufficient condition for existence and boundedness of solution to the XLCP are obtained.
基金supported by the Korea Research Foundation Grant funded by the Korean Government(MOEHRD),the MKE(The Ministry of knowledge Economy,Korea)the ITRC(Information Technology Research Center)support program(NIPA-2009-(C1090-0902-0007))
文摘General active contour algorithm, which uses the intensity of the image, has been used to actively segment objects. Because the objects have a similar intensity but different colors, it is difficult to segment any object from the others, Moreover, this algodthm can only be used in the simple environment since it is very sensitive to noise. In tinter to solve these problems. This paper proposes an extended active contour algorithm based on a color variance. In complex images, the color variance energy as the image energy is introduced into the general active contour algorithm. Experimental results show that the proposed active contour algorithm is very effective in various environments.
基金supported by Project No.R-2023-23 of the Deanship of Scientific Research at Majmaah University.
文摘At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system symmetrically.Therefore,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative rates.In addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were shown.The results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten iterations.Whereas in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved parameters.Thus EDLA algorithm introduces novelty concerning its performance and particular activation function.This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner.This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses aftermodification.If the quantity of dataset records is enormous,then themethod’s computation power has to be updated.
文摘On the basis of Hartmann Shack sensor imaging analysis, a new method is presented with which the wavefront slope can be obtained when the object is incoherent and extended. This method, which is demonstrated by both theoretical interpreting and computer simulation, explains how to measure the wavefront slope difference between two sub apertures through the determination of image displacements on detector plane. It includes a fast and accurate digital algorithm for detecting wavefront disturbance, which is much suitable for realization in such electrical hardwares as digital signal processors.
基金supported by the PhD Start-up Fund from Chongqing University of Science and Technology(No.181903017)the Key R&D Project from Science and Technology of Chongqing(No.cstc2018jszx-cyztzx0003)the Key R&D Project from Science and Technology of Chongqing(No.cstc2018jszx-cyzd0092).
文摘The behavior of the tip wake of a wind turbine is one of the hot issues in the wind power field.This problem can partially be tackled using Computational Fluid Dynamics(CFD).However,this approach lacks the ability to provide insights into the spatial structure of important high-order flows.Therefore,with the horizontal axis wind turbine as the main focus,in this work,firstly,we conduct CFD simulations of the wind turbine in order to obtain a data-driven basis relating to multiple working conditions for further analysis.Then,these data are studied using an extended Proper Orthogonal Decomposition(POD)algorithm.The quantitative results indicate that the tip vortex in the wake has a complicated spatio-temporal morphological configuration in the higher-order extended POD space.The radial velocity modes obtained are effective and credible,and such reconstructed flow of the tip vortex becomes clearer with the increase of the reconstruction orders.Interestingly,the changes of relatively high-order correlation coefficients are essentially affected by the periodic fusion of tip and central eddies in the wake.
基金The National High Technology Research and Development Program of China (863 Program) (No.2008AA01Z227)the Cultivatable Fund of the Key Scientific and Technical Innovation Project of Ministry of Education of China (No.706028)
文摘In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.
基金The National Natural Science Foundation of China(No60403016),the Weaponry Equipment Foundation of PLA Equip-ment Ministry (No51406020105JB8103)
文摘The current extended fuzzy description logics lack reasoning algorithms with TBoxes. The problem of the satisfiability of the extended fuzzy description logic EFALC cut concepts w. r. t. TBoxes is proposed, and a reasoning algorithm is given. This algorithm is designed in the style of tableau algorithms, which is usually used in classical description logics. The transformation rules and the process of this algorithm is described and optimized with three main techniques： recursive procedure call, branch cutting and introducing sets of mesne results. The optimized algorithm is proved sound, complete and with an EXPTime complexity, and the satisfiability problem is EXPTime-complete.
基金supported by National Natural Science Foundation of China(Grant No.61672517)National Natural Science Foundation of China(Key Program,Grant No.61732021).
文摘The elliptic curve discrete logarithm problem(ECDLP)is a popular choice for cryptosystems due to its high level of security.However,with the advent of the extended Shor’s algorithm,there is concern that ECDLP may soon be vulnerable.While the algorithm does ofer hope in solving ECDLP,it is still uncertain whether it can pose a real threat in practice.From the perspective of the quantum circuits of the algorithm,this paper analyzes the feasibility of cracking ECDLP using an ion trap quantum computer with improved quantum circuits for the extended Shor’s algorithm.We give precise quantum circuits for extended Shor’s algorithm to calculate discrete logarithms on elliptic curves over prime felds,including modular subtraction,three diferent modular multiplication,and modular inverse.Additionally,we incorporate and improve upon windowed arithmetic in the circuits to reduce the CNOTcounts.Whereas previous studies mostly focused on minimizing the number of qubits or the depth of the circuit,we focus on minimizing the number of CNOT gates in the circuit,which greatly afects the running time of the algorithm on an ion trap quantum computer.Specifcally,we begin by presenting implementations of basic arithmetic operations with the lowest known CNOT-counts,along with improved constructions for modular inverse,point addition,and windowed arithmetic.Next,we precisely estimate that,to execute the extended Shor’s algorithm with the improved circuits to factor an n-bit integer,the CNOT-count required is1237n^(3)/log n+2n^(2)+n.Finally,we analyze the running time and feasibility of the extended Shor’s algorithm on an ion trap quantum computer.
文摘The rolling mill vibration not only seriously causes the strip thickness heterogeneity, but also damages the rolling mill equipment and its electrical components. Existing vibration suppression methods are passive and mainly tune mechanical, hydraulic, electrical and rolling process parameters. A new active vibration suppression method was thus proposed using the disturbance estimation and compensation algorithm. Firstly, the hydraulic-mechanical coupling model of the rolling mill vibration was established, and an active vibration suppressor was designed based on the extended state observer. Then, through the numerical simulation, it is found that the vibration energy is reduced by 35.3% using the vibration suppressor, and the vibration suppressor is valid when the vibration frequency is lower than 60 Hz Finally, the vibration suppressor was applied to the in-site manufacturing, and the expected vibration suppression was obtained. The method makes the produced steel strip have more uniform thickness and further significantly increases the finished product ratio.
基金Supported by the Key Project of Natural Science Foundation of Gansu Province(25JRRA150)the Gansu Provincial Natural Science Foundation(23JRRA876).
文摘For short-term wind power forecasting,an interval A2-C1 type-2(IT2)Takagi-Sugeno-Kang(TSK)fuzzy logic system(FLS)method(“A”means antecedent and“C”consequent)based on an extended Kalman filter(EKF)optimization algorithm is proposed.Compared with the type-1(T1)FLS model,the IT2 TSK FLS method can simultaneously model both intra-and inter-individual uncertainty and further optimize the antecedent and consequent parameters using the EKF to improve forecasting performance further.The proposed IT2 A2-C1 FLS method is applied to Mackey-Glass chaotic time series and wind power forecasting instances in a certain region,under the same conditions.It is also compared with the T1 TSK FLS and IT2 TSK FLS methods with back propagation(BP)and particle swarm optimization(PSO)algorithms,as well as IT2 A2-C0 TSK FLS methods with EKF.The experimental results confirm that the proposed IT2 A2-C1 FLS method is superior to the other FLS methods regarding performance,which demonstrates its effectiveness and application potential.
文摘Because of the ignored items after linearization,the extended Kalman filter(EKF)becomes a form of suboptimal gradient descent algorithm.The emanative tendency exists in GPS solution when the filter equations are ill-posed.The deviation in the estimation cannot be avoided.Furthermore,the true solution may be lost in pseudorange positioning because the linearized pseudorange equations are partial solutions.To solve the above problems in GPS dynamic positioning by using EKF,a closed-form Kalman filter method called the two-stage algorithm is presented for the nonlinear algebraic solution of GPS dynamic positioning based on the global nonlinear least squares closed algorithm--Bancroft numerical algorithm of American.The method separates the spatial parts from temporal parts during processing the GPS filter problems,and solves the nonlinear GPS dynamic positioning,thus getting stable and reliable dynamic positioning solutions.
基金Supported by the National Key Research Program of China"Collaborative Precision Positioning Project"(2016YFB 0501900)the National Natural Science Foundation of China(11603041)+1 种基金Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education(Guilin University of Electronic Technology)Guangxi Key Laboratory of Precision Navigation Technology and Application
文摘In order to overcome the limitations of a unitary reference station in mobile communication positioning network differential barometric altimetry(DBA)and broaden the action scope of the reference station and improve positioning accuracy of elevation,an integrated interpolation algorithm model based on generalized extended approximation(GEA)algorithm and Kriging interpolation in time-space domain of reference station is proposed.In the time domain,barometric measured data is considered the maximum value estimated by bilateral extension to avoid wrong direction of estimation,which is approaching true value.In the spatial domain,barometric relevance among multiple reference stations is utilized,the weighted coefficients of multiple reference stations is calculated by the integrated algorithm model based on the GEA algorithm and Kriging interpolation.The impact of each reference station to the measured station is quantified,so that a virtual reference station is constructed,which can overcome the limitation of barometric correction by a unitary reference station.In addition,the measurement error due to irregular change in atmospheric pressure will be eliminated.
基金Project(61301181) supported by the National Natural Science Foundation of China
文摘In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.
基金funded by Prince Sultan University,Riyadh,Saudi Arabia。
文摘For the unforced dynamical non-linear state–space model,a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article.The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems.With the help of an ortho-normal triangularization method,which relies on numerically stable givens rotation,matrix inversion causes a computational burden,is reduced.Matrix computation possesses many excellent numerical properties such as singularity,symmetry,skew symmetry,and triangularity is achieved by using this algorithm.The proposed method is validated for the prediction of stationary and non-stationary Mackey–Glass Time Series,along with that a component in the x-direction of the Lorenz Times Series is also predicted to illustrate its usefulness.By the learning curves regarding mean square error(MSE)are witnessed for demonstration with prediction performance of the proposed algorithm from where it’s concluded that the proposed algorithm performs better than EKRLS.This new SREKRLS based design positively offers an innovative era towards non-linear systolic arrays,which is efficient in developing very-large-scale integration(VLSI)applications with non-linear input data.Multiple experiments are carried out to validate the reliability,effectiveness,and applicability of the proposed algorithm and with different noise levels compared to the Extended kernel recursive least-squares(EKRLS)algorithm.
