Effects of Winding Attachment Positions on Output Characteristics of Flux‐Modulating Synchronous Machines

The flux‐modulating synchronous machine (FMSM) is a new type of multipole SM with nonoverlapping concentrated armature and field windings on the stator. This paper compares the output characteristics of two FMSMs through finite element analysis (FEA) and experiments. In both of the FMSMs, the attachment positions of the armature and field windings are swapped. To determine the reason for the discrepancies in their output characteristics, unsaturated inductances were calculated using a d‐‐q equivalent circuit. In addition, the calculated results of the inductances were confirmed through a visualization of the leakage fluxes using FEA. The results of the study show that the synchronous inductance can be reduced by attaching the armature winding to the air‐gap side of the stator teeth and that the reduction leads to an increase in output power.     

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%.     

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.     

Mixed coordination mechanisms for scheduling games on hierarchical machines

In this paper, we study scheduling games under mixed coordination mechanisms on hierarchical machines. The two scheduling policies involved are ‐ and ‐, where ‐ (resp., ‐) policy sequences jobs in nondecreasing order of their hierarchies, and jobs of the same hierarchy in nonincreasing (resp., nondecreasing) order of their processing times. We first show the existence of a Nash equilibrium. Then we present the price of anarchy and the price of stability for the games with social costs of minimizing the makespan and maximizing the minimum machine load. All the bounds given in this paper are tight.     

个性化公开密钥系统的设计

论文提出利用用户提供的个性化信息来随机产生关联大素数,保证了素数选取的随机化、个性化,提高了RSA算法的安全性。     

高性能大尺寸永磁体粘合强度的测定

介绍了一种用三点弯曲试验法测定高性能大尺寸粘接永磁体粘合强度的方法，及利用布氏硬度计的加载系统，实现100％检验粘接质量的方法。     

频谱分解技术在塔里木盆地北部TH地区碳酸盐岩缝洞型储层预测中的应用

本文从频谱分解技术的基本原理及计算方法入手,根据正演模型,通过对比缝洞型储层与地震反射特征的关系,总结出储层发育的反射特征,相应地精细调整频谱分解处理参数,进而进行预测。通过与钻井资料对比可知,频谱分解技术可以刻画不同频率强反射、弱反射的边界。     

Efficient Parallel Algorithms for Some Graph Theory Problems

In this paper,a sequential algorithm computing the aww vertex pair distance matrix D and the path matrix Pis given.On a PRAM EREW model with p,1≤p≤n^2,processors,a parallel version of the sequential algorithm is shown.This method can also be used to get a parallel algorithm to compute transitive closure array A^* of an undirected graph.The time complexity of the parallel algorithm is O(n^3/p).If D,P and A^* are known,it is shown that the problems to find all connected components,to compute the diameter of an undirected graph,to determine the center of a directed graph and to search for a directed cycle with the minimum(maximum)length in a directed graph can all be solved in O(n^2/p logp)time.     

引进意大利连续纺丝机车间空调节能设计

介绍了某化纤厂引进意大利十二台粘胶连续纺丝机车间与以往的纺丝车间相比,空调设计中的节能途径,体现了既满足工艺要求,又降低生产成本的设计目的。     

Mesh结构P/G布线网络层次化快速分析方法

本文提出了一个基于网络划分的P／G布线网络层次化快速分析方法。其中，对于子网运算，通过对Cholesky分解法三角化对称正定阵的图模型分析，并基于Mesh结构网络的自身特点，提出了一个基于图顶点排序的加速子网分析运算策略；并用基于MPI的并行结构实现了P／G布线网络分析的并行运算。