非接触测温技术对圆筒设备内部温度的定量检测

用红外技术获取设备外部信息并与计算传热学相结合，采用有限差分法，得到了推算设备内壁温度分布的空间步进迭代公式，数值求解了圆柱输热管道内壁上的温度分布。实现了对工业设备内部运行状态的定量诊断。     

激光远距作用的机制与效应

本文定量分析激光远距作用的机理并对它们在治癌中的作用作简要的论述。     

Predicting posttraumatic growth in breast cancer survivors.

Wide variability exists with respect to how breast cancer survivors respond to common psychological and psychosocial challenges of their disease, ranging from posttraumatic stress disorder to posttraumatic growth. This cross-sectional study examined contextual, disease-related, and intraindividual predictors of posttraumatic growth in 224 randomly selected breast cancer survivors. A series of hierarchical regression analyses found that age at diagnosis, marital status, employment, education, perceived intensity of disease, and active coping accounted for 34%, 35%, and 28% of the variance in growth in relationships with others, new possibilities, and appreciation for life. These findings suggest that a more comprehensive model of growth will be helpful in understanding the various factors that play a role in breast cancer survivors' perception of psychological and psychosocial growth. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于故障树与灰关联分析的鱼雷故障诊断

将灰色关联度与故障树联合运用于鱼雷故障诊断,为鱼雷的故障诊断提供了一种新途径。在鱼雷动力系统故障树诊断模式识别中,运用灰色系统理论中的灰关联分析,通过进行关联度计算及排序,对各种故障模式发生的可能性大小作出了判断,从而为处理故障的轻重缓急、控制故障的发生以及改进系统的可靠性提供了理论依据。实例诊断表明,这种方法是有效可行的,并且诊断结果可靠。     

基于主元分析的多变量统计过程的故障辨识技术

为了更好的进行故障检测与诊断，介绍了主元分析理论，给出了基于主元分析的过程故障辨识机理及策略。仿真实例表明，利用此方法建立的故障诊断模型，能够在不依赖过程机理的前提下高效抽取原始数据空间的主要变化信息，对过程的非正常变化做出反应，同时还能较正确地找出发生故障的原因以及相应环节。     

基于B点磁探针膛内电枢电弧长度与速度分布测量的计算机控制系统的研究

基于B点磁探针的测量原理，建立了电磁轨道炮膛内三维等离子体电枢电弧长度和电弧运动速度测量的数学模型，通过对该模型的分析给出了用于测量膛内电枢电弧长度和运动速度分布的相应推论。开发了用于测量膛内等离子体电枢电弧长度和速度的多通道计算机控制诊断系统，并进行了相应的试验研究。     

CMD－1压缩机组在线状态监测与故障诊断系统

浙江大学本文介绍了实现在线监测的多处理机主从系统以ＳＴＤ总线为基础的热旁待双机冗余高速数据采集部分的结构形式。对系统结构合理性、功能的实用性、运行的可靠性作了论述，并提供了该系统在生产现场实际运行中判断与分析机组故障实例的数据与图谱，该系统可以满足大中型企业旋转机械在线状态监测的要求，实践证明系统的设计和研制是成功的。     

Integrating integer programming and probabilistic deduction graphs for probabilistic reasoning

Optimal solutions of several variants of the probabilistic reasoning problem were found by a new technique that integrates integer programming and probabilistic deduction graphs (PDG). PDGs are extended from deduction graphs of the and-type via normal deduction graphs. The foregoing variants to be solved can involve multiple hypotheses and multiple evidences where the former is given and the latter is unknown and being found or vice versa. The relationship among these hypotheses and evidences with possible intermediaries is represented by a causal graph. The proposed method can handle a large causal graph of any type and find an optimal solution by invoking a linear integer programming package. In addition, formulating the reasoning problem to fit integer programming takes a polynomial time. H.-L. Li was visiting the Department of Computer Sciences, University of North Texas in 1988–1989. He is with the Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan, R.O.C.     

Application of the blind source separation method to feature extraction of machine sound signals

As the result of vibration emission in air, a machine sound signal carries important information about the working condition of machinery. But in practice, the sound signal is typically received with a very low signal-to-noise ratio. To obtain features of the original sound signal, uncorrelated sound signals must be removed and the wavelet coefficients related to fault condition must be retrieved. In this paper, the blind source separation technique is used to recover the wavelet coefficients of a monitored source from complex observed signals. Since in the proposed blind source separation (BSS) algorithms it is generally assumed that the number of sources is known, the Gerschgorin disk estimator method is introduced to determine the number of sound sources before applying the BSS method. This method can estimate the number of sound sources under non-Gaussian and non-white noise conditions. Then, the partial singular value analysis method is used to select these significant observations for BSS analysis. This method ensures that signals are separated with the smallest distortion. Afterwards, the time-frequency separation algorithm, converted to a suitable BSS algorithm for the separation of a non-stationary signal, is introduced. The transfer channel between observations and sources and the wavelet coefficients of the source signals can be blindly identified via this algorithm. The reconstructed wavelet coefficients can be used for diagnosis. Finally, the separation results obtained from the observed signals recorded in a semi-anechoic chamber demonstrate the effectiveness of the presented methods .