Applications of critical temperature in minimizing functions of continuous variables with simulated annealing algorithm

The simulated annealing (SA) algorithm has been recognized as a powerful technique for minimizing complicated functions. However, a critical disadvantage of the SA algorithm is its high computational cost. Therefore, it is the goal of this paper to investigate the use of the critical temperature in SA to reduce its computational cost. This paper presents a systematic study of the critical temperature and its applications in the minimization of functions of continuous variables with the SA algorithm. Based on this study, a new algorithm was developed to exploit the unique feature of the critical temperature in SA. The new algorithm combines SA and local search to determine global minimum effectively. Extensive tests on a variety of functions demonstrated that the new algorithm provides comparable performance to well-established SA techniques. Furthermore, the new algorithm also improves the determination of the starting temperature for the SA algorithm. The results obtained in this study are expected to be useful for improving the efficiency of SA algorithms, and for facilitating the development of temperature parallel SA algorithms.     

Comparison of approaches based on optimization and algebraic iteration for binary tomography

Binary tomography represents a special category of tomographic problems, in which only two values are possible for the sought image pixels. The binary nature of the problems can potentially lead to a significant reduction in the number of view angles required for a satisfactory reconstruction, thusly enabling many interesting applications. However, the limited view angles result in a severely underdetermined system of equations, which is challenging to solve. Various approaches have been proposed to address such a challenge, and two categories of approaches include those based on optimization and those based on algebraic iteration. However, the relative strengths, limitations, and applicable ranges of these approaches have not been clearly defined in the past. Therefore, it is the main objective of this work to conduct a systematic comparison of approaches from each category. This comparison suggested that the approaches based on algebraic iteration offered both superior reconstruction fidelity and computation efficiency at low (two or three) view angles, and these advantages diminished at high view angles. Meanwhile, this work also investigated the application of regularization techniques, the selection of optimal regularization parameter, and the use of a local search technique for binary problems. We expect the results and conclusions reported in this work to provide valuable guidance for the design and development of algorithms for binary tomography problems.     

B/S模式下条形码票证自定义打印的设计和实现

研究B/S模式下的票务管理信息系统中一维条形码票证的打印输出,提出一种基于ActiveX技术和条码打印机的条形码票证自定义打印方案.通过一个实例,详细阐述了该方案的设计和实现过程.该方案很好地解决了B/S模式下条形码票证的打印.     

新型国产近红外分析仪的菜籽菜粕快速检测技术研究

近红外光谱分析作为1种快速无损的绿色分析技术,可应用于检测油料原料和油脂产品品质,为粮油企业带来巨大经济效益.本文采用一款新型国产化的近红外漫反射光谱分析仪对菜籽和菜粕进行了检测分析,分别对菜籽的水分、蛋白、含油量、脂肪酸、硫苷含量和菜粕水分、蛋白和含油量指标建立模型并验证.结果显示近红外检测结果与国标参考方法检测结果相关性好、准确度高.表明新型国产化的近红外分析仪可满足油脂企业对菜籽和菜粕产品的质量控制要求.     

Deformation Transfer to Multi‐Component Objects

We present a simple and effective algorithm to transfer deformation between surface meshes with multiple components. The algorithm automatically computes spatial relationships between components of the target object, builds correspondences between source and target, and finally transfers deformation of the source onto the target while preserving cohesion between the target's components. We demonstrate the versatility of our approach on various complex models.     

空间数据库中一种自适应的缓存替换策略

随着近年来空间数据库研究和应用的不断深入,针对空间数据库中数据组织和查询的特征来设计缓存页面替换策略成为一个新的研究问题.Voronoi图是一种重要的空间数据库组织技术,在处理kNN查询时具有非常好的性能.针对Voronoi图组织的空间数据库,首先利用空间局部性提出了一种基于欧氏距离的替换策略,在发生页面失效时选择距离上一次访问页面欧氏距离最远的页面进行替换;进一步,针对不同kNN查询的搜索空间大小差异非常大的特点,在LIRS替换策略基础上提出一种自适应替换策略,通过对HIR页面占缓存比例自动调整来适应不同的查询.综合两者,形成基于欧氏距离的自适应缓存页面替换算法AELIRS.大量实验表明,在缓存大小与搜索空间大范围变动中,AELIRS始终优于其他替换策略.     

基于粗糙集与粗糙元神经网络的三峡库区水质评价模型

粗糙集和粗糙元神经网络相结合进行三峡库区水质评价.针对评价因子较多,通过粗糙集进行数据预处理,降低模型的复杂性.对于库区数据非精确值或范围值时,采用粗糙元神经网络建立评价模型,提高了系统的正确性和可理解性,并将其应用在三峡库区水环境安全系统中.     

Identifying mislabeled training data with the aid of unlabeled data

This paper presents a new approach for identifying and eliminating mislabeled training instances for supervised learning algorithms. The novelty of this approach lies in the using of unlabeled instances to aid the detection of mislabeled training instances. This is in contrast with existing methods which rely upon only the labeled training instances. Our approach is straightforward and can be applied to many existing noise detection methods with only marginal modifications on them as required. To assess the benefit of our approach, we choose two popular noise detection methods: majority filtering (MF) and consensus filtering (CF). MFAUD/CFAUD is the new proposed variant of MF/CF which relies on our approach and denotes majority/consensus filtering with the aid of unlabeled data. Empirical study validates the superiority of our approach and shows that MFAUD and CFAUD can significantly improve the performances of MF and CF under different noise ratios and labeled ratios. In addition, the improvement is more remarkable when the noise ratio is greater.     

Face recognition based on extreme learning machine

Extreme learning machine (ELM) is an efficient learning algorithm for generalized single hidden layer feedforward networks (SLFNs), which performs well in both regression and classification applications. It has recently been shown that from the optimization point of view ELM and support vector machine (SVM) are equivalent but ELM has less stringent optimization constraints. Due to the mild optimization constraints ELM can be easy of implementation and usually obtains better generalization performance. In this paper we study the performance of the one-against-all (OAA) and one-against-one (OAO) ELM for classification in multi-label face recognition applications. The performance is verified through four benchmarking face image data sets.