Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization

For many-objective optimization problems, how to get a set of solutions with good convergence and diversity is a difficult and challenging work. In this paper, a new decomposition based evolutionary algorithm with uniform designs is proposed to achieve the goal. The proposed algorithm adopts the uniform design method to set the weight vectors which are uniformly distributed over the design space, and the size of the weight vectors neither increases nonlinearly with the number of objectives nor considers a formulaic setting. A crossover operator based on the uniform design method is constructed to enhance the search capacity of the proposed algorithm. Moreover, in order to improve the convergence performance of the algorithm, a sub-population strategy is used to optimize each sub-problem. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII-CE, MOEA/D and HypE, on six benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence.     

Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

The ensemble learning paradigm has proved to be relevant to solving most challenging industrial problems. Despite its successful application especially in the Bioinformatics, the petroleum industry has not benefited enough from the promises of this machine learning technology. The petroleum industry, with its persistent quest for high-performance predictive models, is in great need of this new learning methodology. A marginal improvement in the prediction indices of petroleum reservoir properties could have huge positive impact on the success of exploration, drilling and the overall reservoir management portfolio. Support vector machines (SVM) is one of the promising machine learning tools that have performed excellently well in most prediction problems. However, its performance is a function of the prudent choice of its tuning parameters most especially the regularization parameter, C. Reports have shown that this parameter has significant impact on the performance of SVM. Understandably, no specific value has been recommended for it. This paper proposes a stacked generalization ensemble model of SVM that incorporates different expert opinions on the optimal values of this parameter in the prediction of porosity and permeability of petroleum reservoirs using datasets from diverse geological formations. The performance of the proposed SVM ensemble was compared to that of conventional SVM technique, another SVM implemented with the bagging method, and Random Forest technique. The results showed that the proposed ensemble model, in most cases, outperformed the others with the highest correlation coefficient, and the lowest mean and absolute errors. The study indicated that there is a great potential for ensemble learning in petroleum reservoir characterization to improve the accuracy of reservoir properties predictions for more successful explorations and increased production of petroleum resources. The results also confirmed that ensemble models perform better than the conventional SVM implementation.     

Stereo matching cost computation based on nonsubsampled contourlet transform

A new matching cost computation method based on nonsubsampled contourlet transform (NSCT) for stereo image matching is proposed in this paper. Firstly, stereo image is decomposed into high frequency sub-band images at different scales and along different directions by NSCT. Secondly, by utilizing coefficients in high frequency domain and grayscales in RGB color space, the computation model of weighted matching cost between two pixels is designed based on the gestalt laws. Lastly, two types of experiments are carried out with standard stereopairs in the Middlebury benchmark. One of the experiments is to confirm optimum values of NSCT scale and direction parameters, and the other is to compare proposed matching cost with nine known matching costs. Experimental results show that the optimum values of scale and direction parameters are respectively 2 and 3, and the matching accuracy of the proposed matching cost is twice higher than that of traditional NCC cost.     

砾岩储层精细描述中标准测井资料的应用

克拉玛依油田七区的标准测井资料是指使用标准电极系视电阻率测井、自然电位测井和井径测井,以相同的1:500深度比例尺及相同的横向比例进行测井作业所取得的资料.这种资料本来并不具备定量解释储层孔隙度和含油饱和度的能力,但这种资料占该区测井资料总量的比率高达34%.利用标准电极系视电阻率资料和岩心分析资料建立了视电阻率-岩性图版,利用自然电位减小系数α和岩心孔隙度分析资料φ建立了α-φ图版,根据综合测井资料求出标准测井视电阻率校正系数,进而确定了饱和度计算方程的参数.与相应的综合测井资料计算结果相对比,用该方法得到的孔隙度平均绝对误差及相对误差分别为1.85%和11.88%;含油饱和度平均绝对误差及相对误差分别为9.08%和24.75%.将该方法有选择地应用到该区砾岩储层精细描述研究中,弥补了缺乏综合测井资料无法进行测井储层评价及参数研究的缺陷.     

基于矢量平均的信道估计算法

提出一种适用于DS-CDMA无线通信系统的低计算量而性能良好的信道估计算法。在DS-CDMA系统中，当接收信号经过解扩或多用户检测等时域预处理后，可以认为干扰大大减弱、期望信号在处理后的信号中占主要地位。这样，用预处理后信号相关阵的最大特征值对应的特征矢量可以很好地近似期望信号的信道矢量。但是，直接特征分解需要很大的计算量，特征跟踪计算量较低，但瞬态性能较差。本文提出一种基于相关矩阵列矢量平均的信道估计算法，该算法不需要特征分解或跟踪。仿真结果表明：新算法在降低计算量的情况下可以获得同直接特征分解方法几乎相同的性能。     

环境敏感因素对电站正常蓄水位选择的影响

卜寺沟水电站预可阶段正常蓄水位是考虑规划成果、地形、地质务件、梯级衔接、水库淹没、筑坝技术、水能资源利用等因素综合初选的.从理论角度分析,水库淹没时水库内寺庙的影响,按全部淹没进行经济补偿处理代价较小;其社会环境敏感因素对正常蓄水位的选择不应起决定性作用.但是,考虑到少数民族群众、僧侣和地方政府的要求,适当降低正常蓄水位,以减少对扎西日岗寺庙的影响,将带来较积极的社会影响.     

Lattice structure on some fuzzy algebraic systems

In this paper, we study the lattice structure of some fuzzy algebraic systems such as (G-)fuzzy groups, some fuzzy ordered algebras and fuzzy hyperstructures. We prove that under suitable conditions, these structures form a distributive or modular lattice. This research partially is supported by the “ Fuzzy Systems and its Applications Center of Excelence, Shahid Bahonar University of Kerman, Iran”.     

实行全面质量管理 做好江口水电站工程设计

江口水电站是芙蓉江干流开发中的骨干工程。为认真履行江口工程的设计合同，确保优质、高效地完成江口工程的各项设计工作，长江委设计院实行全面质量管理，建立了一套规范化的统计管理程序，制订详细的设计计划，落实江口工程项目各级责任人的质量责任制，采取特殊技术措施，对工程设计过程中的各项质量活动进行全过程的管理，使江口工程在满足质量标准、合同进度的情况下，工程投资最省。     

线性时变系统的区间稳定性与鲁棒稳定性

本文应用向量比较定理研究线性时变系统的区间稳定性和具非线性时变摄动的线性时变系统的鲁棒稳定性，所得的新结果包含文献的一些主要结果作为特例，本文的研究方法说明向量比较方法是分析区间稳定性和鲁棒稳定性的一种自然而有力的工具。