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.     

国际微电子专用关键设备的现状和发展趋势（考察报告）

讲述了参观Ｓｅｍｉｃｏｎ／ｗｅｓｔ９４′展览会及考察美国和西德的５个公司、１所大学所了解到的投影光刻技术、电子束曝光技术、透镜面形测试技术的发展。     

食品高压装置

高压装置是研究高压食品科学、开发和生产高压食品的核心设备，能否提供性能优良、价格相对便宜的装置是高压食品产业化的关键。本文系统介绍食品高压处理装置的类型、结构、性能、特点及最新研究进展，并就如何降低高压装置造价，提高生产能力等关键问题进行探讨。     

Metrological Support and Standards in the Design,Production, and Use of High-Load Weighing Machines in Russia

An analysis is presented on development, production, and use of high-capacity weighing machines in Russia in the last ten years. Aspects arising in type approval testing are considered for high-capacity weighing machines working over wide temperature ranges.     

国外工具磨床发展现状

本文综述了近睥来国外工具磨床技术的发展现状，介绍了由CNC技术带来的几种新的磨削方法，分析了当代工具磨床典型产品的技术水平。     

Integrated distributed intelligent system for process-control system design

In process-control system design, the Computer Aided Design (CAD) techniques have evolved a new generation of design techniques. This also paves the way for implementing Computer Integrated Manufacturing Systems (CIMS). However, the key issue to accomplishing the objectives in CAD is the automation of conceptual design. This paper first introduces the fundamentals of conceptual design for process-control system design. Then, a strategy to solve the problems in conceptual design is proposed. The associated system configuration and software implementation platform for developing intelligent systems for process control system design are presented. Finally, an application case is studied.     

i－line光刻高均匀照明系统光能分布的计算模拟

叙述了ｉ－ｌｉｎｅ光刻高均匀照明系统的均匀照明原理，以及其具有特殊面形（非球面）、特殊结构（非共轴）、超光学表面（由多个非共轴光学表面组成）的光学系统的光能分布计算模拟的原理、方法。作为例子，用所编写的程序计算模拟了我们近来设计的一曝光系统几个重要光学表面上具有权重的点列图和光能分布曲线。