A globally and quadratically convergent primal–dual augmented Lagrangian algorithm for equality constrained optimization

We present a primal–dual augmented Lagrangian method to solve an equality constrained minimization problem. This is a Newton-like method applied to a perturbation of the optimality system that follows from a reformulation of the initial problem by introducing an augmented Lagrangian function. An important aspect of this approach is that, by a choice of suitable updating rules of parameters, the algorithm reduces to a regularized Newton method applied to a sequence of optimality systems. The global convergence is proved under mild assumptions. An asymptotic analysis is also presented and quadratic convergence is proved under standard regularity assumptions. Some numerical results show that the method is very efficient and robust.     

Descent three-term conjugate gradient methods based on secant conditions for unconstrained optimization

The conjugate gradient method is an effective method for large-scale unconstrained optimization problems. Recent research has proposed conjugate gradient methods based on secant conditions to establish fast convergence of the methods. However, these methods do not always generate a descent search direction. In contrast, Y. Narushima, H. Yabe, and J.A. Ford [A three-term conjugate gradient method with sufficient descent property for unconstrained optimization, SIAM J. Optim. 21 (2011), pp. 212–230] proposed a three-term conjugate gradient method which always satisfies the sufficient descent condition. This paper makes use of both ideas to propose descent three-term conjugate gradient methods based on particular secant conditions, and then shows their global convergence properties. Finally, numerical results are given.     

Self-consistent gradient flow for shape optimization

We present a model for image segmentation and describe a gradient-descent method for level-set based shape optimization. It is commonly known that gradient-descent methods converge slowly due to zig–zag movement. This can also be observed for our problem, especially when sharp edges are present in the image. We interpret this in our specific context to gain a better understanding of the involved difficulties. One way to overcome slow convergence is the use of second-order methods. For our situation, they require derivatives of the potentially noisy image data and are thus undesirable. Hence, we propose a new method that can be interpreted as a self-consistent gradient flow and does not need any derivatives of the image data. It works very well in practice and leads to a far more efficient optimization algorithm. A related idea can also be used to describe the mean-curvature flow of a mean-convex surface. For this, we formulate a mean-curvature Eikonal equation, which allows a numerical propagation of the mean-curvature flow of a surface without explicit time stepping.     

New pixel driving circuit using self‐discharging compensation method for high‐resolution OLED micro displays on a silicon backplane

A new 4T2C pixel circuit formed on a silicon substrate is proposed to realize a high‐resolution 7.8‐μm pixel pitch AMOLED microdisplay. In order to achieve high luminance uniformity, the pixel circuit compensates its Vth variation of the MOSFET for the driving transistor internally by using self‐discharging method. Also presented are 0.5‐in Quad‐VGA and 1.25‐in wide Quad‐XGA microdisplays with the proposed pixel circuit.     

Dynamic neural network method-based improved PSO and BR algorithms for transient probabilistic analysis of flexible mechanism

To improve the computing efficiency and precision of transient probabilistic analysis of flexible mechanism, dynamic neural network method (DNNM)-based improved particle swarm optimization (PSO)/Bayesian regularization (BR) (called as PSO/BR-DNNM) is proposed based on the developed DNNM with the integration of extremum response surface method (ERSM) and artificial neural network (ANN). The mathematical model of DNNM is established based on ANN on the foundation of investigating ERSM. Aiming at the high nonlinearity and strong coupling characteristics of limit state function of flexible mechanism, accurate weights and thresholds of PSO/BR-DNNM function are discussed by searching initial weights and thresholds based on the improved PSO and training final weights and thresholds by the BR-based training performance function. The probabilistic analysis of two-link flexible robot manipulator (TFRM) was investigated with the proposed method. Reliability degree, distribution characteristics and major factors (section sizes of link-2) of TFRM are obtained, which provides a useful reference for a more effective TFRM design. Through the comparison of three methods (Monte Carlo method, DNNM, PSO/BR-DNNM), it is demonstrated that PSO/BR-DNNM reshapes the probability of flexible mechanism probabilistic analysis and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of flexible mechanism and thereby also enriches the theory and method of mechanical reliability design.     

On decomposition methods in interactive user-preference based optimization

Evolutionary multi-objective optimization (EMO) methodologies have been widely applied to find a well-distributed trade-off solutions approximating to the Pareto-optimal front in the past decades. However, integrating the user-preference into the optimization to find the region of interest (ROI) [1] or preferred Pareto-optimal solutions could be more efficient and effective for the decision maker (DM) straightforwardly. In this paper, we propose several methods by combining preference-based strategy (like the reference points) with the decomposition-based multi-objective evolutionary algorithm (MOEA/D) [2], and demonstrate how preferred sets or ROIs near the different reference points specified by the DM can be found simultaneously and interactively. The study is based on the experiments conducted on a set of test problems with objectives ranging from two to fifteen objectives. Experiments have proved that the proposed approaches are more efficient and effective especially on many-objective problems to provide a set of solutions to the DM's preference, so that a better and a more reliable decision can be made.     

基于模式的远监督关系抽取算法

远监督关系抽取算法能够自动将关系库中的关系与无标注的文本对齐,以进行文本中的关系抽取。目前提出的远监督关系抽取算法中,大多数是基于特征的。然而,此类算法在将实例转换为特征时,经常会出现关键信息不突出、数据集线性不可分等问题,影响关系抽取的效果。该文提出了一种基于模式的远监督关系抽取算法,其中引入了基于模式的向量,并使用了基于核的机器学习算法来克服上述问题。实验结果表明,该文提出的基于模式的远监督关系抽取算法,能够有效地提升远监督关系抽取的准确率。     

火焰温度场测试中的传感器动态特性补偿

在进行爆温测试时,爆炸伴随的高温火焰具有变化快、温度高、测试环境恶劣等特点.针对这种特定的测温场合,运用火焰温度源法研究了热电偶传感器在火焰温度场中的动态响应特性.为了减小热电偶的动态测量误差,通过粒子群优化算法(PSO)建立了测试系统的动态补偿滤波器模型.实验表明,这种研究方法有效地改善了热电偶的动态测量误差,对通过爆温测试进行弹丸热毁伤评价有一定的参考价值.     

中国微米纳米 花状Au-SnO2复合材料的制备及其对甲醛气敏性能的研究

利用水热法合成了花状SnO2微米材料,并以它作为基体,对其进行Au纳米颗粒修饰,得到了花状Au-SnO2复合材料.利用X射线衍射(XRD)、扫描电子显微镜(SEM)、X射线光电子能谱(XPS)分析等表征手段对所制备的Au-SnO2复合材料进行了表征分析,结果表明,这种花状结构的材料是由厚度为30 nm～60 nm的SnO2纳米片自组装而成,其尺寸为1 μm～2 μm.同时,我们用花状SnO2和Au修饰的花状SnO2复合材料对1×10-6～500×10-6的甲醛进行了气敏性能测试.结果表明,通过Au修饰,花状SnO2对甲醛的气敏性能有了极大的提高.当甲醛浓度为200×10-6时,其灵敏度(Ra/Rg)约为120,是花状SnO2敏感材料的8倍,表现出很高的响应.另外,由Au修饰的花状SnO2复合材料制成的敏感元件对甲醛表现出良好的选择性.这可能归因于Au纳米颗粒高效的催化活性促进了甲醛气体的扩散和O2的吸附,从而使其表现出更加优异的气敏性能.