Simulation and analysis of resin flow in injection machine screw

A method with simulation and analysis of the resin flow in a screw is presented to ease the control of some problems that may affect the efficiency and the quality of the product among existing screws in an injection machine. The physical model of a screw is established to represent the stress, the strain, the relationship between velocity and stress, and the temperature of the cells. In this paper, a working case is considered where the velocity and the temperature distributions at any section of the flow are obtained. The analysis of the computational results shows an ability to master various parameters depending on the specifications.     

Optimal Rapid Restart of Heuristic Methods of NP Hard Problems

Many heuristic search methods exhibit a remarkable variability in the time required to solve some particular problem instances. Their cost distributions are often heavy-tailed. It has been demonstrated that, in most cases, rapid restart (RR) method can prominently suppress the heavy-tailed nature of the instances and improve computation efficiency. However, it is usually time-consuming to check whether an algorithm on a specific instance is heavy-tailed or not. Moreover, if the heavy-tailed distribution is confirmed and the RR method is relevant, an optimal RR threshold should be chosen to facilitate the RR mechanism. In this paper, an approximate approach is proposed to quickly check whether an algorithm on a specific instance is heavy-tailed or not. The method is realized by means of calculating the maximal Lyapunov exponent of its generic running trace. Then a statistical formula to estimate the optimal RR threshold is educed. The method is based on common nonparametric estimation, e.g. , Kernel estimation. Two heuristic methods are selected to verify our method. The experimental results are consistent with the theoretical consideration perfectly.     

A maximum a posteriori super resolution algorithm based on multidimensional Lorentzian distribution

This paper presents a threshold-free maximum a posteriori （MAP） super resolution （SR） algorithm to reconstruct high resolution （HR） images with sharp edges. The joint distribution of directional edge images is modeled as a multidimensional Lorentzian （MDL） function and regarded as a new image prior. This model makes full use of gradient information to restrict the solution space and yields an edge-preserving SR algorithm. The Lorentzian parameters in the cost function are replaced with a tunable variable, and graduated nonconvexity （GNC） optimization is used to guarantee that the proposed multidimensional Lor- entzian SR （MDLSR） algorithm converges to the global minimum. Simulation results show the effectiveness of the MDLSR algorithm as well as its superiority over conventional SR methods.     

Vibration shape testing of space sheet structure

The space sheet for a solar array structure is large in dimension, and flexible to induce complex distortion. It is difficult to measure the distortion. A large-scale flexible test prototype system is built for sheet structure vibration monitoring based on the vibration information of the solar array in space. The system provides an intelligent test method for flexible space structure with embedded sensors and non-visualization reinstruction. Multiple fiber Bragg grating （FBG） sensors are packaged and embedded into the structure with spatial division multiplexing （SDM） and wavelength division multiplexing （WDM）. The sensor interrogator distinguishes wavelength shifts that can be turned into curvature of the curve. The structure shape is reconstructed based on the curvature information and the arc-length, and then displayed. The experiment shows that the system has a high FBG sensitivity. The reconstruction algorithm is fast with high precision. The system has great potential for use in health monitoring of the space sheet structure.     

Parameter optimization model in electrical discharge machining process

Electrical discharge machining （EDM） process, at present is still an experience process, wherein selected parameters are often far from the optimum, and at the same time selecting optimization parameters is costly and time consuming. In this paper, artificial neural network （ANN） and genetic algorithm （GA） are used together to establish the parameter optimization model. An ANN model which adapts Levenberg-Marquardt algorithm has been set up to represent the relationship between material removal rate （MRR） and input parameters, and GA is used to optimize parameters, so that optimization results are obtained. The model is shown to be effective, and MRR is improved using optimized machining parameters.     

Image feature optimization based on nonlinear dimensionality reduction

Image feature optimization is an important means to deal with high-dimensional image data in image semantic un- derstanding and its applications. We formulate image feature optimization as the establishment of a mapping between high- and low-dimensional space via a five-tuple model. Nonlinear dimensionality reduction based on manifold learning provides a feasible way for solving such a problem. We propose a novel globular neighborhood based locally linear embedding （GNLLE） algorithm using neighborhood update and an incremental neighbor search scheme, which not only can handle sparse datasets but also has strong anti-noise capability and good topological stability. Given that the distance measure adopted in nonlinear dimensionality reduction is usually based on pairwise similarity calculation, we also present a globular neighborhood and path clustering based locally linear embedding （GNPCLLE） algorithm based on path-based clustering. Due to its full consideration of correlations between image data, GNPCLLE can eliminate the distortion of the overall topological structure within the dataset on the manifold. Experimental results on two image sets show the effectiveness and efficiency of the proposed algorithms.     

基于区分能力大小的决策表属性约简算法

The attribute reduction algorithms of decision table based on discernible matrix are required to construct discernible matrix, which reduces efficiency of algorithms. In this paper, the relationship between attribute discernible matrix and its discernibility is first established for general information systems. Based on the idea that the equivalent discernible matrix has a same attribute reduction, existing matrices are modified and a formula of attribute discernibility associated with algebraic reduction for decision table is proposed. A heuristic attribute reduction algorithm based on attribute discernibility is presented. Experimental results indicate that the algorithm can more easily explore an optimal or sub-optimal reduction, and is efficient.     

MOTOR METHOD OF ROBOT END EFFECTOR PATH GENERATION

According to the theory that rotation transformation from the basecoordinate to an arbitrary one can be described by a single equivalent rotation about agiven rotational axis,it is proposed that the orientation of robot can be described by usingthe vector of equivalent angular displacement.And the concept of pose motor and itsPlücker linear coordinate are established and used to describe the position and orientationof robot.Based on the fact that there is a one-to-one correspondence between the ori-ented straight lines in three-dimensional dual space,the interpolation plannings corre-sponding to the pose motors are developed in dual space.A new algorithm of robot CPmotion trajectory planning is presented.The correctness of the theory and derivation isconformed by an example.     

Non-Rigid Medical Image Registration with Joint Histogram Estimation Based on Mutual Information

A mutual information-based non-rigid medical image registration algorithm is presented. An approximate function of Hanning windowed sinc is used as kernel function of partial volume (PV) interpolation to estimate the joint histogram, which is the key to calculating the mutual information. And a new method is proposed to compute the gradient of mutual information with respect to the model parameters. The transformation of object is modeled by a free-form deformation (FFD) based on B-splines. The experiments on 3D synthetic and real image data show that the algorithm can converge at the global optimum and restrain the emergency of local extreme.     

An immune-tabu hybrid algorithm for thermal unit commitment of electric power systems

This paper presents a new method based on an immune-tabu hybrid algorithm to solve the thermal unit commitment （TUC） problem in power plant optimization. The mathematical model of the TUC problem is established by analyzing the generating units in modem power plants. A novel immune-tabu hybrid algorithm is proposed to solve this complex problem. In the algorithm, the objective function of the TUC problem is considered as an antigen and the solutions are considered as antibodies, which are determined by the affinity computation. The code length of an antibody is shortened by encoding the continuous operating time, and the optimum searching speed is improved. Each feasible individual in the immune algorithm （IA） is used as the initial solution of the tabu search （TS） algorithm after certain generations of IA iteration. As examples, the proposed method has been applied to several thermal unit systems for a period of 24 h. The computation results demonstrate the good global optimum searching performance of the proposed immune-tabu hybrid algorithm. The presented algorithm can also be used to solve other optimization problems in fields such as the chemical industry and the power industry.     

一种改进的BSP二值图像压缩算法研究

针对二值图像矩形划分编码在划分时存在的问题,本文提出一种改进的BSP二值图像的压缩算法.该算法以二叉树为数据结构,以达到减小中间结点的开销;同时该算法的时间复杂度和空间复杂度均低于采用四叉树结构来压缩二值图像.其压缩比可提高17%;同时适用于复杂图像.     

基于四叉树和特征融合的图像特征提取的研究

图像特征提取作为图像处理中的一项基本技术,仅依靠单一的图像特征很难准确、可靠地描述图像信息.利用四叉树分裂合并和局部特征SIFT算法的特点,本文提出了一种基于四叉树和特征融合的图像特征提取方法,首先利用四叉树对图像进行区域分割,然后对分割图像提取颜色直方图信息,接着使用局部特征SIFT算法进行特征精提取,达到特征信息的融合,从而更好识别图像特征信息.最后对此方法进行实例仿真,所提特征能够较完整地表示图像的特征信息,冗余度比较小,具有良好的尺度不变性.     

图象数据格式及其相互转换

栅格、矢量和四叉树是计算机图象显示和图象处理中三种最常见的数据格式。本文简要介绍了柳格到四叉树、矢量到栅格、栅格到矢量的转换算法。四叉树到矢量的转换最为复杂，虽有现成算法，但很难具体实现。本文给出了一种较为简单又易于实现的算法。     

二值图像的四边形区域压缩算法研究

从二值图像的区域特征出发，提出了基于四叉树的二值图像四边形区域压缩算法，该算法的思想是将二值图像用四叉树进行四分区域划分，在同一个区域中如果像素值相同则可用一个元素表示，否则继续划分，该算法的优点体现在如下两个方面：1．采用二进制移位的思想提高了MD码的生成效率。2．采用基于对数模型的方法进行区域像素压缩。该算法的内存开销小，运行速度高于其它相关方法。     

一种有效的小波域四叉树分形图象编码方案(英文)

基于Ｄａｖｉｓ提出的分形图象编码的小波理论,本文提出一种有效的小波域四叉树分形图象编码方案．在此方案中,利用小波系数的零树减少域块的个数,以降低表示分形编码局部信息所需要的比特代价;并对编码方案中采用的一组标量量化器、小波子树自量化器和判决树进行全面的熵约束最优化．实验结果表明：在低比特率下,编码方案的峰值信噪比性能比已报道的结果提高了约１ｄＢ．     

基于改进粒子群算法的图像匹配技术研究

在分析基本粒子群优化算法的基础上,对学习因子进行非线性异步策略调整,改变其固定常数模式,平衡算法在迭代过程中的局部和全局搜索能力;同时引入活力因子,对失活粒子执行变异操作,提高种群多样性。改进算法可以提升对多维空间的全局寻优能力,避免粒子产生早熟收敛现象。将改进粒子群算法引入图像匹配优化问题中,提出了一种基于改进粒子群算法的图像匹配算法,实验结果表明,该算法具有更快的匹配速度以及更高的匹配精度,具有强鲁棒性。     

基于广义混沌序列的多幅图像隐藏算法

给出一种基于广义混沌序列的多幅图像隐藏算法.利用图像的迭代混合可以将多幅图像隐藏于一幅图像之中,借助Logistic混沌动力学系统过程既非周期又不收敛,且对初始条件敏感性,产生实数值混沌序列,利用该混沌序列生成广义混沌序列,作为隐藏算法的混合因子,提高了隐藏算法的安全性.数值实验结果表明,算法简单易行,安全性好.     

多聚焦像素级数字图像信息融合

根据图像的清晰恢复结合数字图像处理的基本理论算法,采用多聚焦图像融合技术,提出基于像素级的多聚焦数字图像的融合算法.用统计学的方法计算出不同聚焦图像上的聚焦点,并将其取出合成为一张清晰图像.根据以上算法设计了图像融合软件,并进行了实验验证     

基于稀疏表示与加权核范数最小化的图像去噪算法

为解决一部分纹理数据在运用加权核范数最小化处理低秩矩阵逼近时出现丢失的问题,提出一种基于稀疏表示与加权核范数最小化的图像去噪算法。稀疏表示用于辅助重构清晰图像,加权核范数最小化用于图像块样本的低秩矩阵逼近。通过分析纹理数据找出熵较大的非平滑块,运用一种奇异值维纳滤波,从其差异矩阵中找出丢失的部分纹理信息,并将其与低秩去噪结果融合。实验结果表明,该算法能够保持图像的细小纹理,去噪效果更好,具有良好的鲁棒性与泛化性。     

一种基于混沌序列的多幅图像隐藏算法

给出了一种基于混沌序列的多幅图像隐藏算法.利用图像的迭代混合可以将多幅图像隐藏于一幅图像之中,借助Logistic混沌动力学系统过程既非周期又不收敛,且对初始条件敏感性,产生实数值混沌序列,作为隐藏算法的混合因子,提高了隐藏算法的安全性;为了进一步提高图像隐藏的安全性,对待隐藏图像进行基于象空间的置乱处理.实验结果证明,算法简单易行,安全性好.