The appropriateness of the systematic framework to develop diagnosis procedures of nuclear power plants—an experimental verification

It has been well recognized that a diagnosis procedure that allows operators to successfully identify the nature of an on-going event is inevitable for an effective and appropriate recovery. Unfortunately, studies for a framework that can suggest a unified and consistent process in constructing a serviceable diagnosis procedure seem to be scant. Thus, Park et al. have suggested a systematic framework that can be used to construct a useful diagnosis procedure. In addition, the diagnosis procedure that is currently in use at the reference nuclear power plant (NPP) is reformed in order to demonstrate the appropriateness of the suggested framework. However, the necessity of a well-designed experiment is proposed to confirm the appropriateness of the suggested framework.In this regard, in this study, an experiment is conducted using a full-scope simulator of the reference NPP. From the experiment, two sets of operators' diagnosis performance data are collected, and then they are compared to investigate the change of an operator's diagnosis performance with respect to two types of diagnosis procedures.As a result, it is shown that an operator's diagnosis performance is improved when the revised diagnosis procedure is used. Therefore, it is reasonable to conclude that the suggested framework is useful in constructing an effective diagnosis procedure.     

Use of a neural network to link AFM etch patterns to plasma parameters

Plasmas play a critical role in depositing thin films or etching fine patterns while manufacturing integrated circuits. A new model for plasma diagnosis is presented. This was accomplished by linking atomic force microscopy (AFM) to plasma parameters using a neural network. Experimental AFM data were collected during the etching of silicon oxynitride films in C₂F₆ inductively coupled plasma. Surface roughness of etched patterns was characterized by means of discrete wavelet transformation. This led to the construction of three vertical (type I), diagonal (type II), and horizontal (type III) wavelet coefficient-based models. The performance of diagnosis models was evaluated in terms of the prediction and recognition accuracies. Both accuracies were optimized as a function of the number of hidden neurons. Comparisons revealed that the type I model yielded the largest recognition and the smallest prediction error. This was demonstrated even under stricter monitoring conditions. More improved diagnosis is expected by enhancing AFM resolution.     

模糊数学在电视机故障诊断中的应用

本文介绍了应用模糊数学方法进行计算机辅助诊断电视机的故障原因,这种方法有效性在于建立模型的因果关系正确性和自动搜索范围及速度。     

Current basis for the ultrastructural clinical diagnosis of tumors: A review

The ultrastructural diagnosis of tumors requires a careful analysis that should be done in an orderly fashion. It requires precise planning from the time of specimen collection to the selection of the area to be examined. Pictures must be taken systematically and every micrograph should allow to answer whether the number of cells photographed is adequate; whether mitoses are present, what is the pattern of the tumor; what is the appearance of the cell membrane; whether the cells are joined by junctional complexes; whether free surfaces possess microvilli or cilia; what organelles are present and how they are distributed; whether there are secretory granules, melanosomes, or other cytoplasmic elements. Nuclear and nucleolar size and shape have to be taken into consideration. The composition of the interstitial extracellular matrix is important in certain types of tumors. Although these questions are not the only ones to be addressed, their use in a logical fashion is helpful when it concerns the ultrastructural diagnosis.     

Evaluating an intelligent diagnosis system of historical text comprehension

This work aims to present and evaluate a Fuzzy-Case Based Reasoning Diagnosis system of Historical Text Comprehension. The synergism of fuzzy logic and case based reasoning techniques handles the uncertainty in the acquisition of human expert's knowledge regarding learner's observable behaviour and integrates the right balance between expert's knowledge described in the form of fuzzy sets and previous experiences documented in the form of cases. The formative evaluation focused on the comparison of the system's performance to the performance of human experts concerning the diagnosis accuracy. The system was also evaluated for its behaviour when using two different historical texts. Empirical evaluation conducted with human experts and real students indicated the need for revision of the diagnosis model. The evaluation results are encouraging for the system's educational impact on learners and for future work concerning an intelligent educational system for individualized learning.     

输血后丙型肝炎病毒NS4区抗体的动态变化及其诊断意义

利用重组NS4抗原及合成肽5－11抗原分别检测5例输血后丙型肝炎患者的系列血清136份，并与HCV总抗体及HCVRNA检测结果比较，发现抗NS4抗体的动态变化虽然有两种模式，但在多数情况下为一种模式，即抗体阳转后很少转阴，并且其动态变化基本与总抗体相似。同时发现2例病人抗NS4抗体阳转时间早于总抗体，因此，推测HCV检测试剂中增加NS4抗原可能对提高HCV诊断试剂的灵敏度有一定意义。     

一种汽车用液压电磁阀控制电路设计

液压电磁阀是现代汽车液压控制系统的重要部件,通过对电磁阀的精确驱动控制,全面的故障诊断,确保液压系统的安全可靠运行。本电路设计具有灵活性、安全性、可扩展性等特点,可以方便实现对不同类型、不同电气参数、不同数量的液压电磁阀的控制,对推动机电液一体化技术在汽车上的应用具有积极意义。     

基于SVM和RBFN的汽车主动降噪系统传感器故障诊断

汽车主动降噪系统的工作依赖于多个噪声传感器,一旦传感器发生故障,将严重影响降噪效果。为保证汽车主动降噪系统的性能,提出了由支持向量机(SVM)预测模型和径向基神经网络(RBFN)预测模型构成的传感器故障诊断系统,SVM模型判断是否发生传感器故障,RBFN模型则利用各传感器间的信息冗余关系定位故障传感器并对其信号进行重构。仿真结果表明,该诊断系统可有效实现汽车主动降噪系统中的传感器故障诊断及信号重构。与传统的汽车主动降噪系统相比,引入传感器故障诊断系统可保证更稳定的降噪性能。     

2D12型往复式压缩机气阀故障诊断的时频分析方法

分析了 2D12型往复式压缩机气阀的工作过程 ,针对气阀工作特点提取吸气阀与排气阀均关闭时刻的压缩阶段的局域声音信号 ,应用广义双线性时频法诊断气阀故障 ,给出了正常气阀和故障气阀的时频分析图     

关联维数在往复压缩机气阀故障诊断中的应用

针对传统方法难以有效进行往复压缩机气阀故障诊断的现状,提出了应用关联维数对往复压缩机气阀故障进行诊断的方法。通过对气阀常见故障类型的诊断,表明关联维数可以有效地分析出往复压缩机气阀的常见故障。