Real-time 2D–3D filtering using order statistics based algorithms

The paper presents a review of the author’s own results obtained in the last several years. Some examples of real-time processing of 2D and 3D images are described. In particular, we discuss the noise model and objective criteria that can be applied to characterize the performance of the processing algorithms. Several proposed algorithms based on RM approach are compared with other known ones, demonstrating the advantages in noise suppressing and preservation of fine image details and edges. A number of 2D and 3D image denoising filters are implemented on DSP, realizing real-time mode in the image processing. The performances of the proposed processing algorithms and the known ones are discussed and evaluated here.

Implementing tactics and tacticals in a higher-order logic programming language

We argue that a logic programming language with a higher-order intuitionistic logic as its foundation can be used both to naturally specify and implement tactic-style theorem provers. The language extends traditional logic programming languages by replacing first-order terms with simply-typed -terms, replacing first-order unification with higher-order unification, and allowing implication and universal quantification in queries and the bodies of clauses. Inference rules for a variety of inference systems can be naturally specified in this language. The higher-order features of the language contribute to a concise specification of provisos concerning variable occurrences in formulas and the discharge of assumptions present in many inference systems. Tactics and tacticals, which provide a framework for high-level control over search for proofs, can be directly and naturally implemented in the extended language. This framework serves as a starting point for implementing theorem provers and proof systems that can integrate many diversified operations on formulas and proofs for a variety of logics. We present an extensive set of examples that have been implemented in the higher-order logic programming language Prolog.     

Strength Analysis of Yttria-Stabilized Tetragonal Zirconia Polycrystals

Tensile strength of Y₂O₃-stabilized ZrO₂ polycrystals (Y-TZP) was measured by a newly developed tensile testing method with a rectangular bar. The tensile strength of Y-TZP was lower than that of the three-point bend strength, and the shape of the tensile strength distribution was quite different from that of the three-point bend strength distribution. It was difficult to predict the distribution curve of the tensile strength using the data of the three-point bend strength by one-modal Weibull distribution. The distribution of the tensile strength was analyzed by two- or three-model Weibull distribution coupled with an analysis of fracture origins. The distribution curve of the three-point bend strength which was estimated by multimodal Weibull distribution agreed favorably with that of the measured three-point bend strength values. A two-modal Weibull distribution function was formulated approximately from the distributions of the tensile and three-point bend strengths, and the estimated two-modal Weibull distribution function for the four-point bend strength agreed well with the measured four-point bend strength.     

Systematic benchmarking of microarray data feature extraction and classification

A combination of microarrays with classification methods is a promising approach to supporting clinical management decisions in oncology. The aim of this paper is to systematically benchmark the role of classification models. Each classification model is a combination of one feature extraction method and one classification method. We consider four feature extraction methods and five classification methods, from which 20 classification models can be derived. The feature extraction methods are t-statistics, non-parametric Wilcoxon statistics, ad hoc signal-to-noise statistics, and principal component analysis (PCA), and the classification methods are Fisher linear discriminant analysis (FLDA), the support vector machine (SVM), the k nearest-neighbour classifier (kNN), diagonal linear discriminant analysis (DLDA), and diagonal quadratic discriminant analysis (DQDA). Twenty randomizations of each of three binary cancer classification problems derived from publicly available datasets are examined. PCA plus FLDA is found to be the optimal classification model.     

ACOMCD: A multiple cluster detection algorithm based on the spatial scan statistic and ant colony optimization

The spatial scan statistic (SaTScan) has become one of the most popular methods for detecting and evaluating spatial clusters. However, this method can only identify circular or elliptical clusters and is not a good fit for the detection of irregularly shaped clusters. Numerous methods have since been proposed to solve this problem. Nevertheless, if multiple clusters coexist, these methods may not identify the correct situation, because the interference between clusters can easily lead to a tree-like shaped cluster and cause confusion in the results. In this paper, we propose an Ant Colony Optimization based Multiple Cluster Detection (ACOMCD) algorithm, which combines classical SaTScan with the ant colony optimization (ACO) approach. In the initial stage, SaTScan is first used to mark the candidate cluster areas according to the significance of their maximum likelihood evaluations. Then ACO-based scan statistic is carried out separately on these candidate clusters to identify their natural shapes. The algorithm was designed for spatial regional count data only. Comparisons between ACOMCD, SaTScan, GaScan (genetic algorithm-based scan statistic), and FleXScan (flexibly shaped spatial scan statistic) on three kinds of simulated datasets show that ACOMCD performs the best in simultaneously determining the exact number of clusters and identifying multiple irregularly shaped clusters. A case study on esophageal cancer in eastern China further validates the correctness and effectiveness of ACOMCD.     

金沙江乌东德水库诱发地震统计预测研究

为了研究乌东德水库诱发地震的可能性,在选用国内外377座已建水库统计资料的基础上,对乌东德水库诱发地震进行预测分析。针对具体地质条件,共选出了10个影响因子,并将影响因子细化为3种不同状态,在此基础上建立了水库诱发地震预测统计模型。模型将诱震的10个概率因子表述为几个相应的地理位置可以完全叠加的矢量图层,然后进行GIS图层计算,实现了水库诱发地震的统计预测。研究结果表明,乌东德水库具备诱发地震的可能性。     

设计洪水估计值的抽样误差分析与应用

在设计洪水估计值的抽样误差分析中,采用B值诺模图方法估计均方误差简单易用。文章对在水电工程中经常采用的P-Ⅲ型经验适线法估计的设计洪水成果如何借助优化适线法参数来估算设计洪水值的抽样误差进行了分析说明。不同的优化适线法会得到不同的诺模系数B,估算设计洪水估计值的抽样误差时,选用某一准则的优化适线要使用相应的诺模系数B值。并分别对实测洪水的连序系列及含历史洪水的不连序系列资料进行了应用分析。     

基于Logistic模型的P2P资源流行程度分析

为了解决目标资源数量增多而带来的监管成本过高的问题,提出一种基于Logistic模型的资源流行程度分析方法.利用该方法估计目标资源的流行程度,剔除流行程度低的资源,以此缩小监控范围,减少节点的部署数量.在Kad中对不同资源进行追踪,统计资源的共享节点数量,估计Logistic模型的参数,并进行曲线拟合.通过决策树建模得出,资源的共享节点数量和资源大小是影响该方法准确性的关键因素.     

一种改进的基于秩检测的信道有效阶数估计算法

信道阶数估计是SOS类盲辨识算法的前端步骤。针对Liavas阶数估计算法对信噪比要求高的问题,通过对接收序列自相关矩阵进行"降噪"处理和改进秩检测目标函数,提出一种改进算法,有效降低了对信噪比的要求。分析了在不同信道条件下,各种阶数估计算法的性能,阐明随着信噪比降低,阶数估计误差不可避免,该结论是进一步研究对阶数估计误差鲁棒的盲辨识算法的重要依据。     

飞机载荷谱实测数据并行统计处理算法

针对飞机载荷谱数以万亿计的实测数据,为了提高统计处理运行效率,提出了飞机载荷谱实测数据处理的并行算法。本文对载荷谱实测数据处理模型进行了多级并行化分析,在此基础上论述了粗粒度、中粒度、细粒度级并行处理方式,建立了两种并行处理算法——基于机型数据流的粗粒度与中粒度并行数据处理算法和基于某起落数据流的中粒度与细粒度并行数据处理算法。在小规模对称多处理器(Symmetrical multi-processors,SMP)集群运算平台下进行比较测试表明,可大幅地提高载荷谱数据处理运行效率,在8核运算环境下,最高能获得5.82的加速比,为飞机载荷谱实测数据处理研究领域进行大规模科学计算和提高数据处理效率提供了新的技术途径。