Bayesian network learning algorithm based on unconstrained optimization and ant colony optimization

Structure learning of Bayesian networks is a wellresearched but computationally hard task.For learning Bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization(U-ACO-B) to solve the drawbacks of the ant colony optimization(ACO-B).In this algorithm,firstly,an unconstrained optimization problem is solved to obtain an undirected skeleton,and then the ACO algorithm is used to orientate the edges,thus returning the final structure.In the experimental part of the paper,we compare the performance of the proposed algorithm with ACO-B algorithm.The experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm.     

On the Topology and Isotopic Meshing of Plane Algebraic Curves

This paper presents a symbolic algorithm to compute the topology of a plane curve. This is a full version of the authors' CASC15 paper. The algorithm mainly involves resultant computations and real root isolation for univariate polynomials. Compared to other symbolic methods based on elimination techniques, the novelty of the proposed method is that the authors use a technique of interval polynomials to solve the system f(α, y),?f/?y(α, y)and simultaneously obtain numerous simple roots of f(α, y) = 0 on the α fiber. This significantly improves the efficiency of the lifting step because the authors are no longer required to compute the simple roots of f(α, y) = 0. After the topology is computed, a revised Newton's method is presented to compute an isotopic meshing of the plane algebraic curve. Though the approximation method is numerical, the authors can ensure that the proposed method is a certified one, and the meshing is topologically correct. Several nontrivial examples confirm that the proposed algorithm performs well.     

Study of adaptive modulation and LDPC coding in multicarrier systems

An adaptive modulation （AM） algorithm is proposed and the application of the adapting algorithm together with low-density parity-check （LDPC） codes in multicarrier systems is investigated. The AM algorithm is based on minimizing the average bit error rate （BER） of systems, the combination of AM algorithm and LDPC codes with different code rates （half and three-fourths） are studied. The proposed AM algorithm with that of Fischer et al is compared. Simulation results show that the performance of the proposed AM algorithm is better than that of the Fischer＇s algorithm. The results also show that application of the proposed AM algorithm together with LDPC codes can greatly improve the performance of multicarrier systems. Results also show that the performance of the proposed algorithm is degraded with an increase in code rate when code length is the same.     

Degradation algorithm of compressive sensing

The compressive sensing(CS) theory allows people to obtain signal in the frequency much lower than the requested one of sampling theorem.Because the theory is based on the assumption of that the location of sparse values is unknown,it has many constraints in practical applications.In fact,in many cases such as image processing,the location of sparse values is knowable,and CS can degrade to a linear process.In order to take full advantage of the visual information of images,this paper proposes the concept of dimensionality reduction transform matrix and then selects sparse values by constructing an accuracy control matrix,so on this basis,a degradation algorithm is designed that the signal can be obtained by the measurements as many as sparse values and reconstructed through a linear process.In comparison with similar methods,the degradation algorithm is effective in reducing the number of sensors and improving operational efficiency.The algorithm is also used to achieve the CS process with the same amount of data as joint photographic exports group(JPEG) compression and acquires the same display effect.     

Optimal set integer programming algorithm for multiple maneuvering targets tracking in clutter

The aim of this paper is to solve the problems of multitarget tracking in clutter. Firstly, the data association of measurement-to-target is formulated as an integer programming problem. Through using the linear programming (LP) based branchand-bound method and adjusting the constraint conditions, an optimal set integer programming (OSIP) algorithm is then proposed for tracking multiple non-maneuvering targets in clutter. For the case of maneuvering targets, this paper introduces the OSIP algorithm into the filtering step of the interacting multiple model (IMM) algorithm resulting in the IMM based on OSIP algorithm. Extensive Monte Carlo simulations show that the presented algorithms can obtain superior estimations even in the case of high density noises.     

Distributed Solving Linear Algebraic Equations with Switched Fractional Order Dynamics

This paper proposes a novel distributed optimization algorithm with fractional order dynamics to solve linear algebraic equations. Firstly, the authors proposed “Consensus + Projection” flow with fractional order dynamics, which has more design freedom and the potential to obtain a better convergent performance than that of conventional first order algorithms. Moreover, the authors prove that the proposed algorithm is convergent under certain iteration order and step-size. Furthermore,the author...     

Angle estimation in bistatic MIMO radar using improved reduced dimension Capon algorithm

This paper discusses the problem of direction of departure (DOD) and direction of arrival(DOA) estimation for a bistatic multiple input multiple output(MIMO) radar,and proposes an improved reduced-dimension Capon algorithm therein.Compared with the reduced-dimension Capon algorithm which requires pair matching between the two-dimensional angle estimation,the proposed algorithm can obtain automatically paired DOD and DOA estimation without debasing the performance of angle estimation in bistatic MIMO radar.Furthermore,the proposed algorithm has a lower complexity than the reduced-dimension Capon algorithm, and it is suitable for non-uniform linear arrays.The complexity of the proposed algorithm is analyzed and the Cramer-Rao bound (CRB) is also derived.Simulation results verify the usefulness of the proposed algorithm.     

Hybrid algorithm for project scheduling with capacity constraint

Motivated by the projects constrained by space capacity and resource transporting time, a project scheduling probIem with capacity constraint was modeled. A hybrid algorithm is proposed, which uses the ideas of bi-level scheduling and project decomposition technology, and the genetic algorithm and tabu search is combined. Topological reordering technology is used to improve the efficiency of evaluation. Simulation results show the proposed algorithm can obtain satisfied scheduling results in acceptable time.     

AN ADAPTIVE TRUST REGION METHOD FOR EQUALITY CONSTRAINED OPTIMIZATION

In this paper, a trust region method for equality constrained optlmization based on nondiferentiable exact penalty is proposed. In this algorithin, the trail step is characterized by computation of its normal component being separated from computation of its tangential component, i.e., only the tangential component of the trail step is constrained by trust radius while the normal component and trail step itself have no constraints. The other main characteristic of the algorithm is the decision of trust region radius. Here, the decision of trust region radius uses the information of the gradient of objective function and reduced Hessian. However, Maratos effect will occur when we use the nondifferentiable exact penalty function as the merit function. In order to obtain the superlinear convergence of the algorithm, we use the twice order correction technique. Because of the speciality of the adaptive trust region method, we use twice order correction when p= 0 (the definition is as in Section 2) and this is different from the traditional trust region methods for equality constrained opthnization. So the computation of the algorithm in this paper is reduced. What is more, we can prove that the algorithm is globally and superlinearly convergent.     

Automatic differentiation for reduced sequential quadratic programming

In order to slove the large-scale nonlinear programming （NLP） problems efficiently, an efficient optimization algorithm based on reduced sequential quadratic programming （rSQP） and automatic differentiation （AD） is presented in this paper. With the characteristics of sparseness, relatively low degrees of freedom and equality constraints utilized, the nonlinear programming problem is solved by improved rSQP solver. In the solving process, AD technology is used to obtain accurate gradient information. The numerical results show that the combined algorithm, which is suitable for large-scale process optimization problems, can calculate more efficiently than rSQP itself.     

Shortest Path Routing Algorithm Based on Chaotic Neural Network

A shortest path routing algorithm based on transient chaotic neural network is proposed in this paper. Gam-pared with previous models adopting Hopfield neural network, this algorithm has a higher ability to overcome the local minimum, and achieves a better performance. By introducing a special post-processing technique for the output matrixes, our algorithm can obtain an optimal solution with a high probability even for the paths that need more hops in large-size networks.     

Genetic algorithm-based wide-band deterministic maximum likelihood direction finding algorithm

1 .INTRODUCTIONThe problemof esti mating direction of arriving waveusing array signal processing has wide applications inradar ,sonar ,seismology,astronomy and mobile andsatellite communication system, and has receivedmuch attentionin recent years .But the current liter-ature about direction finding algorithmpays more at-tention to narrow-band signal ,that is to say band-width of signal is far smaller than center frequency ofsignal and so envelop of signal keeps invariant in thesensors of …     

基于自适应阵列理论的波瓣置零综合

本文给出了一种基于Gram-Schmidt正交化方法的自适应波瓣置零综合的新算法,此算法采用的是单元零点向量法,它不仅保持了已有算法在波瓣置零综合中的全部优点,而且在对付宽频带干扰及设置较宽的零点凹口时,阵列副瓣电平上升不大。与通常的算法相比,在设置相同的零点凹口情况下,副瓣电平下降了3～5dB。作为应用举例,本文同时给出了新算法与现已报导的RAMONA波瓣置零综合算法的计算机仿真的数值比较结果。     

Multi-channel blind deconvolution algorithm for multiple-input multiple-output DS/CDMA system

Direct sequence spread spectrum transmission can be realized at low SNR,and has low probability of detection.It is aly problem how to obtain the original users'signal in a non-cooperative context.In practicality,the DS/CDMA sources received are linear convolute mixing.A more complex multichannel blind deconvolution MBD algorithm is required to achieve better source separation.An improved MBD algorithm for separating linear convolved mixtures of signals in CDMA system is proposed.This algorithm is based on minimizing the average squared cross-output-channel-correlation.The mixture coefficients are totally unknown.while some knowledge about temporal model exists.Results show that the proposed algorithm Can bring about the exactness and low computational complexity.     

Fast interactive segmentation algorithm of image sequences based on relative fuzzy connectedness

1.INTRODUCTION Imagesegmentationistoseparatetheobjectofinter estfrombackground.Itistheimportantfoundation ofhigher levelimageanalysis,anditremainsanopen problem.Sofar,variousalgorithmshavebeenpro posed.However,mostofthealgorithmscanonly dealwithspecifickindsofimages.Theseavailableal gorithmscanbeclassifiedintothreegroupsaccording tothesegmentationstyle,automaticsegmentation,manualsegmentation,andinteractivesegmenta tion[1～5].Theautomaticsegmentationmethods,suchas algorithmsbasedonthegra…     

Parallelism of spatial data mining based on autocorrelation decision tree

1.INTRODUCTION Oneofmostimportantoperationsisspatialparallel processinginspatialdatabase.Itisnecessaryforusto retrievalallspatialpredictionsformtwoormorespa tialdatasets.Ingeneral,thequeryconsistsoftwo procedures:filtrationandsimplification.Sincethe complexityofspatialobjects,filtrationprocesscan generateaholdingitemsetofsatisfyingcertaincondi tionsintermsofparallelprocessingrules.Inorderto measuretheaccuracyofquerycondition,weneedre trievalpreciseshapeinformationofeachobjectfrom anotherm…     

An Algorithm for Estimating the Object Depth Accurately by Ground-Penetrating Radar

1 .ＩＮＴＲＯＤＵＣＴＩＯＮＧｒｏｕｎｄ ｐｅｎｅｔｒａｔｉｎｇｒａｄａｒ (ＧＰＲ)ｉｓａｎｉｎｃｒｅａｓｉｎｇｌｙｉｍｐｏｒｔａｎｔｔｏｏｌｆｏｒｓｈａｌｌｏｗ ｇｅｏｐｈｙｓｉｃａｌｉｎｖｅｓｔｉｇａｔｉｏｎｓ .Ｒｅｆｅｒｅｎｃｅｓ[1～ 4 ]ｓｕｍｍａｒｉｚｅｔｈｅｍｏｓｔｉｍｐｏｒｔａｎｔａｓｐｅｃｔｓａｎｄａｐｐｌｉｃａｔｉｏｎｓｏｆＧＰＲ .ＩｎＧＰＲｓｕｒｖｅｙｉｎｇ ,ｔｈｅｔｒａｎｓｍｉｔｔｉｎｇａｎｔｅｎｎａｏｆｔｈｅｒａｄａｒｅｍｉｔｓａｎｅｌｅｃｔｒｏｍａｇｎｅｔｉｃｐｕｌｓｅｔｈａ…     

Multisensor image fusion algorithm using nonseparable wavelet frame transform

1.INTRODUCTION Withtherapidimprovementofsensortechnology,numerousmultisensordata,whichoftencontaincom plementaryandredundantinformationaboutthere gionsurveyed,areobtainedinmanyfieldssuchasre motesensing,medicalimaging,machinevisionand militaryapplications.Sensorfusionisincreasinglybe comingapromisingresearcharea.Itcanbedivided intosignal,pixel,feature,andsymbollevels.The papermainlyaddressestheproblemofpixellevelim agefusion.Throughcombiningregisteredimages generatedbydifferentimagingsyst…     

并行广义预测自校正控制器(GPC)

本文旨在讨论ＧＰＣ算法［１，２］的并行化问题，通过对原串行算法的数据流及数据相关性的分析，得到了一种三角阵列的并行算法。该算法自然导致Ｓｙｓｔｏｌｉｃ结构，并具有良好的数值稳定性。对于一个 ｎ阶系统而言，本算法采用Ｏ（ｎ２）阶的处理器单元互连成三角阵列，可以把计算时间由原来的Ｏ（ｎ３）阶（内积运算）时间提高到 Ｏ（ｎ）阶，因而具有 Ｏ（ｎ２）的加速比，其处理器的利用效率得到了很大提高。     

1-GENERATOR QUASI-CYCLIC CODES

This paper discusses the enumeration of 1-generator quasi-cyclic codes and describes an algorithm which will obtain one, and only one, generator for each 1-generator quasi-cyclic code.