An adaptive beamforming algorithm based on direction vector rotation and joint iterative optimization

An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVDR）, the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter. Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace. In addition, the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error. Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms.

RELIABILITY ASSESSMENT OF STRUCTURAL SYSTEMS BASED ON DIRECTIONAL VECTOR SIMULATION TECHNIQUE

The new improved directional vector simulation method foranalyzing the reliability of struc- tural systems failure probabilityis researched. This paper also points out the defects of generaldirectional vector simulation, and gives rise to a new higheraccuracy approximate integral formula of structural systems failureprobability. A new geometric meaning of characteristic function isobtained. A new simple method of generating uniformly distributedrandom vector sample sin n-dimensional unit hyper-spherical surfaceis put forward and strictly proved. This method is easy to put intopractice. Numerical examples are given to show the applicability andeffectiveness of the suggested approach to structural systemsreliability problems.

气溶胶光学特性偏振遥感反演算法

陆地上空大气顶的辐射主要由大气散射和地表反射组成,一般地表反射的贡献要大于大气散射的贡献,使得陆地上空气溶胶的特性提取非常困难,而偏振信息只强烈依赖于散射粒子的特性,发展基于偏振信息的气溶胶反演方法是非常有效的.通过对矢量辐射传输方程求解进行研究,基于倍加累加法矢量辐射传输模式分析影响气溶胶反演的主要因素,确定多维参数查找表建立方法,利用POLDER(Polarization and Directionality of the Earth's Reflec-tances)提供的反射率和偏振反射率数据,发展了一种利用反射率和偏振反射率查找表迭代查找反演气溶胶光学参数和地表反射率的算法,实现了对北京、香河、Dalanzadgad观测站上空气溶胶光学厚度、粒子半径、折射指数和地表反射率的反演.用AERONET(Aerosol Robotic Network)地基数据对反演结果进行了验证.

一类B-C-半预不变凸函数

引入了向量值映射的B-C-半预不变凸性概念,讨论了B-C-半预不变凸函数向量优化问题(VP)minx∈KF(x)的局部弱极小点与全局弱极小点的关系,并在弧方向可微的条件下建立了向量优化问题(VP)minx∈KF(x)与向量似变分不等式问题(VVLI)求x0∈K使得F′(x0;τ^(x,x0))-intC,x∈K的等价性。

Robust multi-objective optimization of rolling schedule for tandem cold rolling based on evolutionary direction differential evolution algorithm

According to the actual requirements,profile and rolling energy consumption are selected as objective functions of rolling schedule optimization for tandem cold rolling.Because of mechanical wear,roll diameter has some uncertainty during the rolling process,ignoring which will cause poor robustness of rolling schedule.In order to solve this problem,a robust multi-objective optimization model of rolling schedule for tandem cold rolling was established.A differential evolution algorithm based on the evolutionary direction was proposed.The algorithm calculated the horizontal angle of the vector,which was used to choose mutation vector.The chosen vector contained converging direction and it changed the random mutation operation in differential evolution algorithm.Efficiency of the proposed algorithm was verified by two benchmarks.Meanwhile,in order to ensure that delivery thicknesses have descending order like actual rolling schedule during evolution,a modified Latin Hypercube Sampling process was proposed.Finally,the proposed algorithm was applied to the model above.Results showed that profile was improved and rolling energy consumption was reduced compared with the actual rolling schedule.Meanwhile,robustness of solutions was ensured.

New adaptive vector control methods for induction motors with simpler structure and better performance

This paper deals with the vector control, including both the direct vector control(DVC) and the indirect vector control(Id VC),of induction motors. It is well known that the estimation of rotor flux plays a fundamental role in the DVC and the estimation of rotor resistance is vital in the slip compensation of the Id VC. In these estimations, the precision is significantly affected by the motor resistances. Therefore, online estimation of motor resistances is indispensable in practice.For a fast estimation of motor resistances, it is necessary to slow down the convergence rate of the current estimate. On the other hand, for a fast estimation of the rotor flux, it is necessary to speed up its convergence rate. It is very difficult to realize such a trade-off in convergence rates in a full order observer.In this paper, we propose to decouple the current observer from the flux observer so as to realize independent convergence rates. Then, the resistance estimation algorithm is applied to both DVC and Id VC. In particular, in the application to Id VC the flux observer needs not be used, which leads to a simpler structure. Meanwhile, independent convergence rates of current observer and flux observer yield an improved performance. A superior performance in the torque and flux responses in both cases is verified by numerous simulations.

Electromagnetic side-channel attack based on PSO directed acyclic graph SVM

Machine learning has a powerful potential for performing the template attack(TA) of cryptographic device. To improve the accuracy and time consuming of electromagnetic template attack(ETA), a multi-class directed acyclic graph support vector machine(DAGSVM) method is proposed to predict the Hamming weight of the key. The method needs to generate K(K ? 1)/2 binary support vector machine(SVM) classifiers and realizes the K-class prediction using a rooted binary directed acyclic graph(DAG) testing model. Further, particle swarm optimization(PSO) is used for optimal selection of DAGSVM model parameters to improve the performance of DAGSVM. By exploiting the electromagnetic emanations captured while a chip was implementing the RC4 algorithm in software, the computation complexity and performance of several multi-class machine learning methods, such as DAGSVM, one-versus-one(OVO)SVM, one-versus-all(OVA)SVM, Probabilistic neural networks(PNN), K-means clustering and fuzzy neural network(FNN) are investigated. In the same scenario, the highest classification accuracy of Hamming weight for the key reached 100%, 95.33%, 85%, 74%, 49.67% and 38% for DAGSVM, OVOSVM, OVASVM, PNN, K-means and FNN, respectively. The experiment results demonstrate the proposed model performs higher predictive accuracy and faster convergence speed.

THE SIMPLE BLOCK ITERATIVE ALGORITHM OF AN EXTENDED ENTROPY MODEL AND ITS CONVERGENCE

The paper discusses an extended entropy model for the prediction of trip amount and provides a method to solve it, called the simple block iterative algorithm, from the point of view of the system of nonlinear equations. Because the algorithm gives consideration to the characteristic of the model, it has better effect in our practice. The paper also studies the existence and uniqueness of the solution and convergence of the algorithm.