Assessing continuous bivariate effects among different groups through nonparametric regression models: An application to breast cancer detection

In many applications, the joint effect of two continuous covariates on the target binary response may vary across groups defined by levels of a given factor. A testing procedure that would enable this type of surface-by-factor interactions to be detected has been designed. To accomplish this goal, a logistic generalized additive model (GAM) with bivariate continuous interactions varying across groups defined by levels of a factor is considered. A local scoring algorithm based on local linear kernel smoothers was implemented to estimate the proposed logistic GAM. Bootstrap resampling techniques were used for the purpose of testing for factor-by-surface interactions. Given the high computational cost involved, binning techniques were used to speed up computation in the estimation and testing processes. The adequacy of the bootstrap-based test was assessed by means of a simulation study. If a factor-by-surface interaction is detected in the model, it is then established that the use of the odds-ratio curves is very useful in obtaining a direct interpretation of the fitted model. The benefits of using this methodology when analyzing real data are illustrated by applying the technique to the outputs produced by a computerized system dedicated to the early detection of breast cancer.     

A novel distributed approach to robust fault detection and identification

In this paper, we propose a novel distributed robust fault detection and identification (RFDI) scheme for a class of nonlinear systems. Firstly, a detection and identification estimator—robust fault tracking approximator (RFTA) is designed for online health monitoring. A novel feature of the RFTA is that it can simultaneously detect and accurately identify the shape and magnitude of the fault and disturbance. Moreover, it takes less online training time compared with the traditional neural network based fault diagnosis schemes. For some distributed systems, a network of distributed estimators is constructed where the RFTA is embedded into each estimator. Then we use consensus filter to filter the outputs of each estimator. One of the most important merits of the consensus filter is that its outputs can dramatically improve the accuracy of fault detection and identification. Next, the stability of the distributed RFDI scheme is rigorously investigated. Finally, two numerical examples are given to illustrate the feasibility and effectiveness of the proposed approach.     

应用金属迭片挠性联轴器改造D1000-32型风机

使用液力偶合器因故障频繁，对高炉稳产、高产造成严重威胁，而且维修费用较高，对偶合器改造后，应用金属迭片挠性联轴器其故障率、维修费用都很低。     

岔河集油田开发调整的主要做法与成效

岔河集油田有多个受构造、岩性双重因素控制的中低渗透复杂断块油藏,其主要储集层为河流相沉积。油田20余年的开发历程是典型的滚动勘探开发过程。通过精细科学的地质研究,不断的实践和认识,制定出与其油田地质特点相适应的开发方针,完善了井网部署与加密、产吸剖面调整、油水井配套综合治理等一系列开发对策,并取得了比较好的开发效果。岔河集油田的开发实践对于类似油田开发不乏成功的经验。     

如何提高康明斯发动机的使用寿命

康明斯发动机在白云铁矿使用已有十多年的历史，通过对发动机故障的认真分析，找出故障原因，进而在发动机的维修和使用中加强管理，从而提高发动机的使用寿命。     

Compte rendu d'L'insomnie: Traitement comportemental.

Reviews the book, L'insomnie: Traitement comportemental by Robert Ladouceur and Yves Gros-Louis (1984). The work offers a prospect broader than the medical vision which traditionally dominates the treatment of insomnia. More specifically, the work is articulated around two chapters. The first locates the relative source data of insomnia, and provides the criteria for a precise diagnosis, establishing a clear distinction between primary insomnia and secondary insomnia, and methods generally utilized to measure sleep. As for the second chapter, it is centered on the behavioral methods of treatment of insomnia. According to the authors, any treatment must be preceded by factual information on insomnia, to facilitate the application of the chosen method. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A neural network model for fault detection in conjunction with a programmable logic controller

This paper discusses the feasibility of using neural networks as a tool in the fault detection process. A neural network is integrated with a state language programmable logic controller, an important device in an automatic control system. Time series data related to time spent in a state is gathered and used as input into a neural network, for the purpose of identifying when a fault has occurred. A feedforward neural network is used to identify which (if any) of three types of faults may have occurred. Experimental results related to sensitivity and accuracy measures are presented. A brief review of related applications and research is also presented.     

一种评价机器磨损和润滑油质量的新方法

提出用表征大型机械系统健康状态的相对劣化度作为监控机械磨损状态和油品质量变化的综合判断指标，以便结合反映机器工作状态的其它因素，共同评定机器的健康状态。以柴油机为例，对监测得到的柴油机润滑油的铁谱参数、磨损金属元素含量和油品理化性能等数据进行综合分析和处理，所得相对劣化度的变化趋势，较准确地反映了柴油机的工作状况。该方法不仅简单实用，而且便于将油液监测技术与其他监测技术（如振动诊断技术）结合起来，用一统一指标描述机器的健康状况，达到监测和预报的目的，为开发基于多种监测技术的智能诊断系统打下基础。     

采金船采选工艺流程病症诊治专家系统

应用专家系统理论，建立了采金船采选工艺流程病症诊治专家系统，此系统对于采金船采选工艺流程中存在的技术病症，具有诊断、治理及致因分析等功能．     

HL-1M装置的真空运行与质谱诊断

通过对 HL- 1M装置真空运行模式、真空运行参数、氦辉光放电清洗和硅化壁处理手段等的规范化 ,显著地改善了装置的真空壁出气、本底杂质浓度、托卡马克放电杂质出气比和再循环 ,成功地实现了高参数放电、长脉冲放电和装置暴露大气后快速恢复放电 ,并成功地为演证低混杂电流驱动、离子回旋共振加热、电子回旋共振加热、中性束注入、弹丸注入和分子束注入实验和升级等离子体运行等提供了良好的真空壁条件。描述了 HL- 1M装置真空系统、壁出气和再循环控制、质谱诊断和程序脉冲送气等方面的主要实验成果 ,并为 HL- 2 A装置的真空系统研制和运行提供了有益的参考