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.     

Heavy rare earths,permanent magnets,and renewable energies: An imminent crisis

This article sounds the alarm that a significant build-out of efficient lighting and renewable energy technologies may be endangered by shortages of rare earths and rare earth permanent magnets. At the moment, China is the predominant supplier of both and its recent rare earth industrial policies combined with its own growing demand for rare earths have caused widespread concern. To diversify supplies, new mining—outside of China—is needed. But what many observers of the “rare earth problem” overlook is that China also dominates in (1) the processing of rare earths, particularly the less abundant heavy rare earths, and (2) the supply chains for permanent magnets. Heavy rare earths and permanent magnets are critical for many renewable energy technologies, and it will require decades to develop new non-Chinese deposits, processing capacity, and supply chains. This article clarifies several misconceptions, evaluates frequently proposed solutions, and urges policy makers outside of China to undertake measures to avert a crisis, such as greater support for research and development and for the cultivation of intellectual capital.     

浅谈DC/DC变换器控制新方法

文中针对DC/DC变换器的特点,在现有控制和建模方法的基础之上,对DC/DC变换器的现代控制方法进行了归纳和总结,简要讨论了其控制原理,并比较了各种现代控制方法的优缺点,最后对DC/DC变换器控制方法的发展作了一定的展望。     

Low Temperature Physics at the Laboratoire National des Champs Magnétiques Pulsés in Toulouse

An overview over past and present activities and future developments at the Toulouse pulsed magnetic field facility is given, both as far as technical developments of the infrastructure, as well as low temperature physics performed at the LNCMP are concerned.     

注射成形粘结NdFeB磁体:工艺·性能·应用

综合介绍了几种适于注射成形粘结NdFeB磁体的磁粉制备方法及特点；讨论了粘结剂、改性剂及磁场取向对注射成形NdFeB磁体最终磁性能的影响及其应用市场。在此基础上，指出了今后开展研究工作的方向。     

直流电动机参数测定

根据直流电机的可逆原理并利用常规的实验仪表，可以简便、准确地测出直流电动机的主要参数－电磁转矩、输出转矩和效率．     

LM2717型双降压直流/直流变换器的原理及其应用设计

LM2717是美国国家半导体公司推出的一款全新的高性能变换器,内含两个脉宽调制(PWM)直流/直流转换器,功能强大.介绍了该器件的主要参数、工作原理和引脚功能,讨论利用LM2717进行应用设计时选择外部器件的原则及设计中应注意的问题.     

Improvement of levitation characteristics in a magnetic bearing system using HTSC‐permanent magnet hybrid structure

Magnetic bearing using pinning force of a permanent magnet and a high‐temperature superconductor has been developed. Additional permanent magnet is introduced to increase the levitation force of the magnetic bearing. In this hybrid magnetic bearing system, levitation force is mainly given by the repulsive force of the permanent magnets, and stability for the lateral direction is given by pinning force of the superconductor. The experimental device is developed. A ring‐type superconductor and a bulk one are examined. Levitation characteristics of the hybrid magnetic bearing are measured. The bulk superconductor shows better characteristics of both levitation and lateral stability than the ring one. Levitation force of the hybrid system becomes about twice as large as that of the nonhybrid one. Although, the repulsive force of the permanent magnet decreases the lateral stability of the system, its influence becomes small by choosing an adequate position of the permanent magnets and the superconductor. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 150(1): 71–77, 2005; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10350