Assessment of obsessive-compulsive symptom dimensions: Development and evaluation of the Dimensional Obsessive-Compulsive Scale.

Although several measures of obsessive-compulsive (OC) symptoms exist, most are limited in that they are not consistent with the most recent empirical findings on the nature and dimensional structure of obsessions and compulsions. In the present research, the authors developed and evaluated a measure called the Dimensional Obsessive-Compulsive Scale (DOCS) to address limitations of existing OC symptom measures. The DOCS is a 20-item measure that assesses the four dimensions of OC symptoms most reliably replicated in previous structural research. Factorial validity of the DOCS was supported by exploratory and confirmatory factor analyses of 3 samples, including individuals with OC disorder, those with other anxiety disorders, and nonclinical individuals. Scores on the DOCS displayed good performance on indices of reliability and validity, as well as sensitivity to treatment and diagnostic sensitivity, and hold promise as a measure of OC symptoms in clinical and research settings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Quantitative evaluation of orientation-specific damage using elastic waves and probability-based diagnostic imaging

Different from the damage with relatively smooth boundaries or edges such as a through-thickness hole or delamination which scatters elastic waves omnidirectionally, orientation-specific damage of sizable length in a particular dimension (e.g., a crack or a notch) often exerts strong directivity to elastic wave propagation. As a consequence the damage-scattered waves may not be captured efficiently by sensors at certain locations, posing a challenging issue to elastic-wave-based damage identification. In this study, the influence of damage orientation on Lamb wave propagation was quantitatively scrutinised. Based on the established correlation between damage parameters (location, orientation, shape and size) and extracted signal features, a probability-based diagnostic imaging approach was developed, in conjunction with use of an active sensor network in conformity to a pulse-echo configuration. Relying on enhancive signal features including both the temporal information and signal intensity, this imaging approach is capable of indicating the orientation of individual damage edges clearly and further shape/size of the damage. The effectiveness of the approach was demonstrated by predicting orientation-specific damage cases including a triangular through-thickness hole (through finite element simulation), a through-thickness crack and an L-shape crack (through experiment) in aluminium plates. With the assistance of a two-level synthetic image fusion scheme, all damage cases were visually and quantitatively highlighted in the probability images.     

HT-7诊断中性束装置真空远程监控系统设计

本文介绍了真空实时远程监控系统的设计要求,讨论了该系统的硬件设计、软件设计及其工作原理,给出了部分实验结果。系统成功地应用于实验并获得理想的实验结果,为诊断中性束稳态运行提供可靠保障。     

使用表达式分析的通用故障诊断系统设计与实现

基于知识的故障诊断专家系统在应用时面临知识获取、知识表达和知识与诊断推理有机融合等几方面的困难。本文论述了一种使用表达式解析的故障诊断方法,并将其用于某型雷达系统的故障诊断保障系统中。该方法将知识表示为逻辑表达式,推理机使用表达式解析的方法推理知识。使用该方法,知识的获取和更新更加方便,并且推理机和表达式相互独立,具有相当的通用性。     

基于模糊神经网络的板级电路故障诊断研究

板级电路故障诊断过程复杂，故障征兆和故障原因之间存在着许多不确定因素，建立精确的故障诊断系统存在着许多困难，针对这种情况，提出基于模糊神经网络，利用模糊逻辑和神经网络相结合建立故障诊断系统的方法，并以某电路板为研究对象该方法作了验证，结果表明，该方法是可行的和有效的。     

带约束的非线性回归诊断混合模型分析

研究了带有约束的均值漂移和方差加权的混合非线性回归模型．得到了相应的一阶和二阶诊断统计量。同时证明了带约束的均值漂移非线性模型和带约束的数据删除非线性模型，它们相应的估计量相等。     

汽车电控系统的诊断技术

本文系统地论述了汽车电控系统的诊断方式和诊断方法,并提出了我国汽车电控系统诊断技术的发展方向。     

用激光技术研究燃油喷雾的粒径分布

介绍用激光全息摄影技术研究130型直喷式柴油机的喷油咀针阀开启压力和高压油泵柱塞直径对低惯量喷油器燃油喷雾的影响,并用激光快速滴谱分析装置处理了该燃油雾场的油滴尺寸分布。研究结果表明,提高启喷压力和增大柱塞直径均可以改善燃油的雾化均匀度。在高启喷压力下,直径在15～30μm的油滴占了绝大部分,滴径分布较窄,而低启喷压力时分布较宽。相比而言,增大供油速率(柱塞直径由φ11mm增大至φ12mm)对雾化均匀度的改善程度不如启喷压力影响得大。     

Comparison of different classification algorithms in clinical decision-making

Abstract: This paper gives an integrated view of implementing automated diagnostic systems for clinical decision-making. Because of the importance of making the right decision, better classification procedures are necessary for clinical decisions. The major objective of the paper is to be a guide for readers who want to develop an automated decision support system for clinical practice. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. Several different classification algorithms were tested and benchmarked for their performance. The performance of the classification algorithms is illustrated on two data sets: the Pima Indians diabetes and the Wisconsin breast cancer. The present research demonstrates that the support vector machines achieved diagnostic accuracies which were higher than those of other automated diagnostic systems.     

Influence diagnostics in log-Birnbaum-Saunders regression models with censored data

In this paper we discuss log-Birnbaum-Saunders regression models with censored observations. This kind of model has been largely applied to study material lifetime subject to failure or stress. The score functions and observed Fisher information matrix are given as well as the process for estimating the regression coefficients and shape parameter is discussed. The normal curvatures of local influence are derived under various perturbation schemes and two deviance-type residuals are proposed to assess departures from the log-Birnbaum-Saunders error assumption as well as to detect outlying observations. Finally, a data set from the medical area is analyzed under log-Birnbaum-Saunders regression models. A diagnostic analysis is performed in order to select an appropriate model.