To address the issues of low accuracy,long time consumption,and high cost of the traditional temperature prediction methods for laser directed energy deposition(LDED),a machine learning model combined with numerical s...To address the issues of low accuracy,long time consumption,and high cost of the traditional temperature prediction methods for laser directed energy deposition(LDED),a machine learning model combined with numerical simulation was proposed to predict the temperature during LDED.A finite element(FE)thermal analysis model was established.The model's accuracy was verified through in-situ monitoring experiments,and a basic database for the predictive model was obtained based on FE simulations.Temperature prediction was performed using a generalized regression neural network(GRNN).To reduce dependence on human experience during GRNN parameter tuning and to enhance model prediction performance,an improved adaptive step-size fruit fly optimization algorithm(ASSFOA)was introduced.Finally,the prediction performance of ASSFOA-GRNN model was compared with that of back-propagation neural network model,GRNN model,and fruit fly optimization algorithm(FOA)-GRNN model.The evaluation metrics included the root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R^(2)),training time,and prediction time.Results show that the ASSFOA-GRNN model exhibits optimal performance regarding RMSE,MAE,and R^(2) indexes.Although its prediction efficiency is slightly lower than that of the FOA-GRNN model,its prediction accuracy is significantly better than that of the other models.This proposed method can be used for temperature prediction in LDED process and also provide a reference for similar methods.展开更多
Nowadays, distributed optimization algorithms are widely used in various complex networks. In order to expand the theory of distributed optimization algorithms in the direction of directed graph, the distributed conve...Nowadays, distributed optimization algorithms are widely used in various complex networks. In order to expand the theory of distributed optimization algorithms in the direction of directed graph, the distributed convex optimization problem with time-varying delays and switching topologies in the case of directed graph topology is studied. The event-triggered communication mechanism is adopted, that is, the communication between agents is determined by the trigger conditions, and the information exchange is carried out only when the conditions are met. Compared with continuous communication, this greatly saves network resources and reduces communication cost. Using Lyapunov-Krasovskii function method and inequality analysis, a new sufficient condition is proposed to ensure that the agent state finally reaches the optimal state. The upper bound of the maximum allowable delay is given. In addition, Zeno behavior will be proved not to exist during the operation of the algorithm. Finally, a simulation example is given to illustrate the correctness of the results in this paper.展开更多
In this paper we discuss the convergence of the directed graph-algorithm for solving a kind of optimization problems where the objective and subjective functions are all separable, and the parallel implementation proc...In this paper we discuss the convergence of the directed graph-algorithm for solving a kind of optimization problems where the objective and subjective functions are all separable, and the parallel implementation process for the directed graph -algorithm is introduced.展开更多
In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to gui...In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to guide the partitioning procedure. Finding best fitted mask application is converted to an functional optimizing problem, and we give out a GA solution to the problem. At last, we discuss the application of the proposed method in Fingerprint Classification.展开更多
Downhole microseismic data has the significant advantages of high signal-to-noise ratio and well-developed P and S waves and the core component of microseismic monitoring is microseismic event location associated with...Downhole microseismic data has the significant advantages of high signal-to-noise ratio and well-developed P and S waves and the core component of microseismic monitoring is microseismic event location associated with hydraulic fracturing in a relatively high confidence level and accuracy.In this study,we present a multidimensional DIRECT inversion method for microseismic locations and applicability tests over modeling data based on a downhole microseismic monitoring system.Synthetic tests inidcate that the objective function of locations can be defined as a multi-dimensional matrix space by employing the global optimization DIRECT algorithm,because it can be run without the initial value and objective function derivation,and the discretely scattered objective points lead to an expeditious contraction of objective functions in each dimension.This study shows that the DIRECT algorithm can be extensively applied in real downhole microseismic monitoring data from hydraulic fracturing completions.Therefore,the methodology,based on a multidimensional DIRECT algorithm,can provide significant high accuracy and convergent efficiency as well as robust computation for interpretable spatiotemporal microseismic evolution,which is more suitable for real-time processing of a large amount of downhole microseismic monitoring data.展开更多
This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different he...This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance.展开更多
Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.T...Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.展开更多
Direct algorithm of wavelet transform (WT) is the numerical algorithmobtained from the integral formula of WT by directly digitization. Some problems on realizing thealgorithm are studied. Some conclusions on the dire...Direct algorithm of wavelet transform (WT) is the numerical algorithmobtained from the integral formula of WT by directly digitization. Some problems on realizing thealgorithm are studied. Some conclusions on the direct algorithm of discrete wavelet transform (DWT),such as discrete convolution operation formula of wavelet coefficients and wavelet components,sampling principle and technology to wavelets, deciding method for scale range of wavelets, measuresto solve edge effect problem, etc, are obtained. The realization of direct algorithm of continuouswavelet transform (CWT) is also studied. The computing cost of direct algorithm and Mallat algorithmof DWT are still studied, and the computing formulae are obtained. These works are beneficial todeeply understand WT and Mallat algorithm. Examples in the end show that direct algorithm can alsobe applied widely.展开更多
The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parame...The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control(SOSMC) based on the super twisting algorithm(STA) combined with the fuzzy logic control(FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimization algorithm based on the genetic algorithm(GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.展开更多
According to the signal-to-noise ratio （SNR） loss of average algorithms in direct P-code acquisition method, this paper analyzes the SNR performance of the overlap average algorithm quantitatively, and derives the r...According to the signal-to-noise ratio （SNR） loss of average algorithms in direct P-code acquisition method, this paper analyzes the SNR performance of the overlap average algorithm quantitatively, and derives the relationship of SNR loss with overlap shift value and initial average phase difference in the overlap average algorithm. On this basis, the bidirectional overlap average algorithm based on optimal correlation SNR is proposed. The algorithm maintains SNR consistent in the entire initial average phase difference space, and has a better SNR performance than the overlap average algorithm. The effectiveness of the algorithm is verified by both theoretical analysis and simulation results. The SNR performance of the bidirectional overlap average algorithm is 5 dB better than that of the direct average algorithm, and 2 dB better than that of the overlap average algorithm, which provides the support for direct P-code acquisition in low SNR.展开更多
The car surface scratch detection adopts the traditional manual detection with poor efficiency and high missing rate.Because of the gray mark and the background of car surface scratches,the traditional edge detection ...The car surface scratch detection adopts the traditional manual detection with poor efficiency and high missing rate.Because of the gray mark and the background of car surface scratches,the traditional edge detection algorithm cannot meet the needs of car surface scratch detection.Therefore,the directional SUSAN algorithm based on CIELab color space is adopted in this paper.The direction template and the circle template to calculate the color difference of the color image are used in the algorithm which has been converted to the CIELab space.The edges and scratches are eliminated by matching and contrasting the detected image with the edge template.Experimental results show that the algorithm can effectively detect scratches on the surface of cars,improve the detection speed and reduce the undetected rate.展开更多
Among all kinds of wavefront control algorithms in adaptive optics systems, the direct gradient wavefront control algorithm is the most widespread and common method. This control algorithm obtains the actuator voltage...Among all kinds of wavefront control algorithms in adaptive optics systems, the direct gradient wavefront control algorithm is the most widespread and common method. This control algorithm obtains the actuator voltages directly from wavefront slopes through pre-measuring the relational matrix between deformable mirror actuators and Hartmann wavefront sensor with perfect real-time characteristic and stability. However, with increasing the number of sub-apertures in wavefront sensor and deformable mirror actuators of adaptive optics systems, the matrix operation in direct gradient algorithm takes too much time, which becomes a major factor influencing control effect of adaptive optics systems. In this paper we apply an iterative wavefront control algorithm to high-resolution adaptive optics systems, in which the voltages of each actuator are obtained through iteration arithmetic, which gains great advantage in calculation and storage. For AO system with thousands of actuators, the computational complexity estimate is about O(n2) ～ O(n3) in direct gradient wavefront control algorithm, while the computational complexity estimate in iterative wavefront control algorithm is about O(n) ～(O(n)3/2), in which n is the number of actuators of AO system. And the more the numbers of sub-apertures and deformable mirror actuators, the more significant advantage the iterative wavefront control algorithm exhibits.展开更多
This paper develops a variational model for image noise removal using total curvature(TC), which is a high-order regularizer. The TC has the advantage of preserving image feature. Unfortunately, it also has the charac...This paper develops a variational model for image noise removal using total curvature(TC), which is a high-order regularizer. The TC has the advantage of preserving image feature. Unfortunately, it also has the characteristics of nonlinear, non-convex and non-smooth. Consequently, the numerical computation with the curvature regularization is difficult. In order to conquer the computation problem, the proposed model is transformed into an alternating optimization problem by importing auxiliary variables. Furthermore, based on alternating direction method of multipliers, we design a fast numerical approximation iterative scheme for proposed model. Finally, numerous experiments are implemented to indicate the advantages of the proposed model in image edge preserving, image contrast and corners preserving. Meanwhile, the high computational efficiency of the designed model is verified by comparing with traditional models, including the total variation(TV) and total Laplace(TL) model.展开更多
Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on f...Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.展开更多
To compensate for the imperfection of traditional bi-directional evolutionary structural optimization, material interpolation scheme and sensitivity filter functions are introduced. A suitable filter can overcome the ...To compensate for the imperfection of traditional bi-directional evolutionary structural optimization, material interpolation scheme and sensitivity filter functions are introduced. A suitable filter can overcome the checkerboard and mesh-dependency. And the historical information on accurate elemental sensitivity numbers are used to keep the objective function converging steadily. Apart from rational intervals of the relevant important parameters, the concept of distinguishing between active and non-active elements design is proposed, which can be widely used for improving the function and artistry of structures directly, especially for a one whose accurate size is not given. Furthermore, user-friendly software packages are developed to enhance its accessibility for practicing engineers and architects. And to reduce the time cost for large timeconsuming complex structure optimization, parallel computing is built-in in the MATLAB codes. The program is easy to use for engineers who may not be familiar with either FEA or structure optimization. And developers can make a deep research on the algorithm by changing the MATLAB codes. Several classical examples are given to show that the improved BESO method is superior for its handy and utility computer program software.展开更多
A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on gen- eralized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency ...A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on gen- eralized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency and computational speed are improved via the hybrid GA com- posed of standard GA and Nelder-Mead simplex algorithms. First, the objective function, with a form of generalized Rayleigh quotient, is derived via the standard D3LS algorithm. It is then taken as a fitness function and the unknown phases of all adaptive weights are taken as decision variables. Then, the nonlinear optimization is performed via the hybrid GA to obtain the optimized solution of phase-only adaptive weights. As a phase-only adaptive algorithm, the proposed algorithm is sim- pler than conventional algorithms when it comes to hardware implementation. Moreover, it proc- esses only a single snapshot data as opposed to forming sample covariance matrix and operating matrix inversion. Simulation results show that the proposed algorithm has a good signal recovery and interferences nulling performance, which are superior to that of the phase-only D3LS algorithm based on standard GA.展开更多
Directional modulation is one of the hot topics in data security researches.To fulfill the requirements of communication security in wireless environment with multiple paths,this study takes into account the factors o...Directional modulation is one of the hot topics in data security researches.To fulfill the requirements of communication security in wireless environment with multiple paths,this study takes into account the factors of reflections and antenna radiation pattern for directional modulation.Unlike other previous works,a novel multiple-reflection model,which is more realistic and complex than simplified two-ray reflection models,is proposed based on two reflectors.Another focus is a quantum genetic algorithm applied to optimize antenna excitation in a phased directional modulation antenna array.The quantum approach has strengths in convergence speed and the globe searching ability for the complicated model with the large-size antenna array and multiple paths.From this,a phased directional modulation transmission system can be optimized as regards communication safety and improve performance based on the constraint of the pattern of the antenna array.Our work can spur applications of the quantum evolutionary algorithm in directional modulation technology,which is also studied.展开更多
A theoretical relationship between the wavelet transform and the fast fourier transformation(FFT) methods in broadband wireless signal is proposed for solving the direction of arrivals(DOAs) estimation problem. This l...A theoretical relationship between the wavelet transform and the fast fourier transformation(FFT) methods in broadband wireless signal is proposed for solving the direction of arrivals(DOAs) estimation problem. This leads naturally to the derivation of minimum variance distortionless response(MVDR) algorithm, which combines the benefits of subspace methods with those of wavelet, and spatially smoothed versions are utilized which exhibits good performance against correlated signals. We test the method's performance by simulating and comparing the performance of proposed algorithm, FFT MVDR and MVDR with correlated signals, and an improved performance is obtained.展开更多
Directed networks such as gene regulation networks and neural networks are connected by arcs（directed links）. The nodes in a directed network are often strongly interwound by a huge number of directed cycles, which ...Directed networks such as gene regulation networks and neural networks are connected by arcs（directed links）. The nodes in a directed network are often strongly interwound by a huge number of directed cycles, which leads to complex information-processing dynamics in the network and makes it highly challenging to infer the intrinsic direction of information flow. In this theoretical paper, based on the principle of minimum-feedback, we explore the node hierarchy of directed networks and distinguish feedforward and feedback arcs. Nearly optimal node hierarchy solutions, which minimize the number of feedback arcs from lower-level nodes to higher-level nodes, are constructed by belief-propagation and simulated-annealing methods. For real-world networks, we quantify the extent of feedback scarcity by comparison with the ensemble of direction-randomized networks and identify the most important feedback arcs. Our methods are also useful for visualizing directed networks.展开更多
With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an i...With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an important foundation and inevitable development trend of future deepspace communication. In this paper, we design a deep space node model which is capable of combining the space division multiplexing with frequency division multiplexing. Furthermore, we propose the directional flooding routing algorithm(DFRA) for DSON based on our node model. This scheme selectively forwards the data packets in the routing, so that the energy consumption can be reduced effectively because only a portion of nodes will participate the flooding routing. Simulation results show that, compared with traditional flooding routing algorithm(TFRA), the DFRA can avoid the non-directional and blind transmission. Therefore, the energy consumption in message routing will be reduced and the lifespan of DSON can also be prolonged effectively. Although the complexity of routing implementation is slightly increased compared with TFRA, the energy of nodes can be saved and the transmission rate is obviously improved in DFRA. Thus the overall performance of DSON can be significantly improved.展开更多
基金National Key Research and Development Program of China(2022YFB4602200)。
文摘To address the issues of low accuracy,long time consumption,and high cost of the traditional temperature prediction methods for laser directed energy deposition(LDED),a machine learning model combined with numerical simulation was proposed to predict the temperature during LDED.A finite element(FE)thermal analysis model was established.The model's accuracy was verified through in-situ monitoring experiments,and a basic database for the predictive model was obtained based on FE simulations.Temperature prediction was performed using a generalized regression neural network(GRNN).To reduce dependence on human experience during GRNN parameter tuning and to enhance model prediction performance,an improved adaptive step-size fruit fly optimization algorithm(ASSFOA)was introduced.Finally,the prediction performance of ASSFOA-GRNN model was compared with that of back-propagation neural network model,GRNN model,and fruit fly optimization algorithm(FOA)-GRNN model.The evaluation metrics included the root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R^(2)),training time,and prediction time.Results show that the ASSFOA-GRNN model exhibits optimal performance regarding RMSE,MAE,and R^(2) indexes.Although its prediction efficiency is slightly lower than that of the FOA-GRNN model,its prediction accuracy is significantly better than that of the other models.This proposed method can be used for temperature prediction in LDED process and also provide a reference for similar methods.
文摘Nowadays, distributed optimization algorithms are widely used in various complex networks. In order to expand the theory of distributed optimization algorithms in the direction of directed graph, the distributed convex optimization problem with time-varying delays and switching topologies in the case of directed graph topology is studied. The event-triggered communication mechanism is adopted, that is, the communication between agents is determined by the trigger conditions, and the information exchange is carried out only when the conditions are met. Compared with continuous communication, this greatly saves network resources and reduces communication cost. Using Lyapunov-Krasovskii function method and inequality analysis, a new sufficient condition is proposed to ensure that the agent state finally reaches the optimal state. The upper bound of the maximum allowable delay is given. In addition, Zeno behavior will be proved not to exist during the operation of the algorithm. Finally, a simulation example is given to illustrate the correctness of the results in this paper.
文摘In this paper we discuss the convergence of the directed graph-algorithm for solving a kind of optimization problems where the objective and subjective functions are all separable, and the parallel implementation process for the directed graph -algorithm is introduced.
文摘In this work, we introduce a method of fingerprint directional image partitioning based on GA. According to the fingerprint topology, A set of dynamic partition masks and a cost estimating function are designed to guide the partitioning procedure. Finding best fitted mask application is converted to an functional optimizing problem, and we give out a GA solution to the problem. At last, we discuss the application of the proposed method in Fingerprint Classification.
基金financially supported by the National Natural Science Foundation of China (Grant No. 41807296 and No. 41802006)Natural science found for universities of Anhui province (Grant No. KJ2017A036)
文摘Downhole microseismic data has the significant advantages of high signal-to-noise ratio and well-developed P and S waves and the core component of microseismic monitoring is microseismic event location associated with hydraulic fracturing in a relatively high confidence level and accuracy.In this study,we present a multidimensional DIRECT inversion method for microseismic locations and applicability tests over modeling data based on a downhole microseismic monitoring system.Synthetic tests inidcate that the objective function of locations can be defined as a multi-dimensional matrix space by employing the global optimization DIRECT algorithm,because it can be run without the initial value and objective function derivation,and the discretely scattered objective points lead to an expeditious contraction of objective functions in each dimension.This study shows that the DIRECT algorithm can be extensively applied in real downhole microseismic monitoring data from hydraulic fracturing completions.Therefore,the methodology,based on a multidimensional DIRECT algorithm,can provide significant high accuracy and convergent efficiency as well as robust computation for interpretable spatiotemporal microseismic evolution,which is more suitable for real-time processing of a large amount of downhole microseismic monitoring data.
基金Project supported by Faculty of Technology,Department of Electrical Engineering,University of Batna,Algeria
文摘This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance.
基金supported partly by the National Science and Technology Major Project of China(Grant No.2016ZX05025-001006)Major Science and Technology Project of CNPC(Grant No.ZD2019-183-007)
文摘Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.
基金This project is supported by National Natural Science Foundation of China (No.50135050)
文摘Direct algorithm of wavelet transform (WT) is the numerical algorithmobtained from the integral formula of WT by directly digitization. Some problems on realizing thealgorithm are studied. Some conclusions on the direct algorithm of discrete wavelet transform (DWT),such as discrete convolution operation formula of wavelet coefficients and wavelet components,sampling principle and technology to wavelets, deciding method for scale range of wavelets, measuresto solve edge effect problem, etc, are obtained. The realization of direct algorithm of continuouswavelet transform (CWT) is also studied. The computing cost of direct algorithm and Mallat algorithmof DWT are still studied, and the computing formulae are obtained. These works are beneficial todeeply understand WT and Mallat algorithm. Examples in the end show that direct algorithm can alsobe applied widely.
基金Project supported by the LEB Research LaboratoryDepartment of Electrical Engineering,University of Batna 2, Algeria。
文摘The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control(SOSMC) based on the super twisting algorithm(STA) combined with the fuzzy logic control(FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimization algorithm based on the genetic algorithm(GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.
基金supported by the National Natural Science Foundation of China(61102130)the Innovative Program of the Academy of Opto-Electtronics,Chinese Academy of Sciences(Y12414A01Y)
文摘According to the signal-to-noise ratio （SNR） loss of average algorithms in direct P-code acquisition method, this paper analyzes the SNR performance of the overlap average algorithm quantitatively, and derives the relationship of SNR loss with overlap shift value and initial average phase difference in the overlap average algorithm. On this basis, the bidirectional overlap average algorithm based on optimal correlation SNR is proposed. The algorithm maintains SNR consistent in the entire initial average phase difference space, and has a better SNR performance than the overlap average algorithm. The effectiveness of the algorithm is verified by both theoretical analysis and simulation results. The SNR performance of the bidirectional overlap average algorithm is 5 dB better than that of the direct average algorithm, and 2 dB better than that of the overlap average algorithm, which provides the support for direct P-code acquisition in low SNR.
基金supported in part by the Research Institute Joint Innovation Fund(No.BY2013003-06)
文摘The car surface scratch detection adopts the traditional manual detection with poor efficiency and high missing rate.Because of the gray mark and the background of car surface scratches,the traditional edge detection algorithm cannot meet the needs of car surface scratch detection.Therefore,the directional SUSAN algorithm based on CIELab color space is adopted in this paper.The direction template and the circle template to calculate the color difference of the color image are used in the algorithm which has been converted to the CIELab space.The edges and scratches are eliminated by matching and contrasting the detected image with the edge template.Experimental results show that the algorithm can effectively detect scratches on the surface of cars,improve the detection speed and reduce the undetected rate.
基金supported by the National Key Scientific and Research Equipment Development Project of China(Grant No.ZDYZ2013-2)the National Natural Science Foundation of China(Grant No.11173008)the Sichuan Provincial Outstanding Youth Academic Technology Leaders Program,China(Grant No.2012JQ0012)
文摘Among all kinds of wavefront control algorithms in adaptive optics systems, the direct gradient wavefront control algorithm is the most widespread and common method. This control algorithm obtains the actuator voltages directly from wavefront slopes through pre-measuring the relational matrix between deformable mirror actuators and Hartmann wavefront sensor with perfect real-time characteristic and stability. However, with increasing the number of sub-apertures in wavefront sensor and deformable mirror actuators of adaptive optics systems, the matrix operation in direct gradient algorithm takes too much time, which becomes a major factor influencing control effect of adaptive optics systems. In this paper we apply an iterative wavefront control algorithm to high-resolution adaptive optics systems, in which the voltages of each actuator are obtained through iteration arithmetic, which gains great advantage in calculation and storage. For AO system with thousands of actuators, the computational complexity estimate is about O(n2) ～ O(n3) in direct gradient wavefront control algorithm, while the computational complexity estimate in iterative wavefront control algorithm is about O(n) ～(O(n)3/2), in which n is the number of actuators of AO system. And the more the numbers of sub-apertures and deformable mirror actuators, the more significant advantage the iterative wavefront control algorithm exhibits.
基金supported by the National Natural Science Foundation of China(No.61602269)the China Postdoctoral Science Foundation(No.2015M571993)+1 种基金the Shandong Provincial Natural Science Foundation of China(No.ZR2017MD004)the Qingdao Postdoctoral Application Research Funded Project
文摘This paper develops a variational model for image noise removal using total curvature(TC), which is a high-order regularizer. The TC has the advantage of preserving image feature. Unfortunately, it also has the characteristics of nonlinear, non-convex and non-smooth. Consequently, the numerical computation with the curvature regularization is difficult. In order to conquer the computation problem, the proposed model is transformed into an alternating optimization problem by importing auxiliary variables. Furthermore, based on alternating direction method of multipliers, we design a fast numerical approximation iterative scheme for proposed model. Finally, numerous experiments are implemented to indicate the advantages of the proposed model in image edge preserving, image contrast and corners preserving. Meanwhile, the high computational efficiency of the designed model is verified by comparing with traditional models, including the total variation(TV) and total Laplace(TL) model.
文摘Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.
基金supported by the National Natural Science Foundation of China(No.51078311)
文摘To compensate for the imperfection of traditional bi-directional evolutionary structural optimization, material interpolation scheme and sensitivity filter functions are introduced. A suitable filter can overcome the checkerboard and mesh-dependency. And the historical information on accurate elemental sensitivity numbers are used to keep the objective function converging steadily. Apart from rational intervals of the relevant important parameters, the concept of distinguishing between active and non-active elements design is proposed, which can be widely used for improving the function and artistry of structures directly, especially for a one whose accurate size is not given. Furthermore, user-friendly software packages are developed to enhance its accessibility for practicing engineers and architects. And to reduce the time cost for large timeconsuming complex structure optimization, parallel computing is built-in in the MATLAB codes. The program is easy to use for engineers who may not be familiar with either FEA or structure optimization. And developers can make a deep research on the algorithm by changing the MATLAB codes. Several classical examples are given to show that the improved BESO method is superior for its handy and utility computer program software.
基金Supported by the Natural Science Foundation of Jiangsu Province (No.BK2004016).
文摘A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on gen- eralized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency and computational speed are improved via the hybrid GA com- posed of standard GA and Nelder-Mead simplex algorithms. First, the objective function, with a form of generalized Rayleigh quotient, is derived via the standard D3LS algorithm. It is then taken as a fitness function and the unknown phases of all adaptive weights are taken as decision variables. Then, the nonlinear optimization is performed via the hybrid GA to obtain the optimized solution of phase-only adaptive weights. As a phase-only adaptive algorithm, the proposed algorithm is sim- pler than conventional algorithms when it comes to hardware implementation. Moreover, it proc- esses only a single snapshot data as opposed to forming sample covariance matrix and operating matrix inversion. Simulation results show that the proposed algorithm has a good signal recovery and interferences nulling performance, which are superior to that of the phase-only D3LS algorithm based on standard GA.
基金This work was supported by the NSFC(Grant Nos.61671087,61962009 and 61003287)the Fok Ying Tong Education Foundation(Grant No.131067)+3 种基金the Major Scientific and Technological Special Project of Guizhou Province(Grant No.20183001)the Foundation of State Key Laboratory of Public Big Data(Grant No.2018BDKFJJ018)the High-quality and Cutting-edge Disciplines Construction Project for Universities in Beijing(Internet Information,Communication University of China)the Fundamental Research Funds for the Central Universities(Nos.2019XD-A02,328201915,328201917 and 328201916).
文摘Directional modulation is one of the hot topics in data security researches.To fulfill the requirements of communication security in wireless environment with multiple paths,this study takes into account the factors of reflections and antenna radiation pattern for directional modulation.Unlike other previous works,a novel multiple-reflection model,which is more realistic and complex than simplified two-ray reflection models,is proposed based on two reflectors.Another focus is a quantum genetic algorithm applied to optimize antenna excitation in a phased directional modulation antenna array.The quantum approach has strengths in convergence speed and the globe searching ability for the complicated model with the large-size antenna array and multiple paths.From this,a phased directional modulation transmission system can be optimized as regards communication safety and improve performance based on the constraint of the pattern of the antenna array.Our work can spur applications of the quantum evolutionary algorithm in directional modulation technology,which is also studied.
基金supported by the Chinese Natural Science Foundation 61401075Central University Business Fee ZYGX2015J106
文摘A theoretical relationship between the wavelet transform and the fast fourier transformation(FFT) methods in broadband wireless signal is proposed for solving the direction of arrivals(DOAs) estimation problem. This leads naturally to the derivation of minimum variance distortionless response(MVDR) algorithm, which combines the benefits of subspace methods with those of wavelet, and spatially smoothed versions are utilized which exhibits good performance against correlated signals. We test the method's performance by simulating and comparing the performance of proposed algorithm, FFT MVDR and MVDR with correlated signals, and an improved performance is obtained.
基金Project by the National Basic Research Program of China(Grant No.2013CB932804)the National Natural Science Foundations of China(Grant Nos.11121403 and 11225526)support by Fondazione CRT under project SIBYL,initiative "La Ricerca dei Talenti"
文摘Directed networks such as gene regulation networks and neural networks are connected by arcs（directed links）. The nodes in a directed network are often strongly interwound by a huge number of directed cycles, which leads to complex information-processing dynamics in the network and makes it highly challenging to infer the intrinsic direction of information flow. In this theoretical paper, based on the principle of minimum-feedback, we explore the node hierarchy of directed networks and distinguish feedforward and feedback arcs. Nearly optimal node hierarchy solutions, which minimize the number of feedback arcs from lower-level nodes to higher-level nodes, are constructed by belief-propagation and simulated-annealing methods. For real-world networks, we quantify the extent of feedback scarcity by comparison with the ensemble of direction-randomized networks and identify the most important feedback arcs. Our methods are also useful for visualizing directed networks.
基金supported by National Natural Science Foundation of China (61471109, 61501104 and 91438110)Fundamental Research Funds for the Central Universities ( N140405005 , N150401002 and N150404002)Open Fund of IPOC (BUPT, IPOC2015B006)
文摘With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an important foundation and inevitable development trend of future deepspace communication. In this paper, we design a deep space node model which is capable of combining the space division multiplexing with frequency division multiplexing. Furthermore, we propose the directional flooding routing algorithm(DFRA) for DSON based on our node model. This scheme selectively forwards the data packets in the routing, so that the energy consumption can be reduced effectively because only a portion of nodes will participate the flooding routing. Simulation results show that, compared with traditional flooding routing algorithm(TFRA), the DFRA can avoid the non-directional and blind transmission. Therefore, the energy consumption in message routing will be reduced and the lifespan of DSON can also be prolonged effectively. Although the complexity of routing implementation is slightly increased compared with TFRA, the energy of nodes can be saved and the transmission rate is obviously improved in DFRA. Thus the overall performance of DSON can be significantly improved.
