Norman Joseph Slamecka (1928-2003).

Presents an obituary for Norman J. Slamecka (1928-2003). Slamecka and his wife Jan were killed suddenly in a pedestrian accident on August 2, 2003, in Lewes, Delaware. He was a Marine veteran of the Korean War, and after a few years of practice as a clinical psychologist for the Veterans Administration, he accepted a faculty position at the University of Vermont in Burlington. He then taught at the State University of New York at Buffalo for a short while before coming to the University of Toronto in 1970, where he remained until his retirement in 1994. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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 .     

Hypnosis and clinical pain.

Hypnosis has been demonstrated to reduce analogue pain, and studies on the mechanisms of laboratory pain reduction have provided useful applications to clinical populations. Studies showing central nervous system activity during hypnotic procedures offer preliminary information concerning possible physiological mechanisms of hypnotic analgesia. Randomized controlled studies with clinical populations indicate that hypnosis has a reliable and significant impact on acute procedural pain and chronic pain conditions, Methodological issues of this body of research are discussed, as are methods to better integrate hypnosis into comprehensive pain treatment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

冶钢小方坯连铸机结晶器液面控制系统故障分析及对策

本文针对某一钢铁厂的一套小方坯连铸机结晶器液面自动控制系统，对其故障原因进行了深入分析，指出导致系统控制性能达不到设计要求长期无法投入运行的主要原因，并提出了相应的解决方法。     

喷气客机涡轮风扇发动机的振动诊断与改进

通过动态分析和现场测试对一喷气客机的发动机振动作出诊断，发现涡轮叶轮的变柔导致其极转动惯量显著下降，因而改变发动机的动态特性，使其临界转速接近飞机的巡航转速，产生不应有的振动和噪声。文章提出了具体改进方案，已为研制单位采用。     

用瞬态频率波动法诊断齿轮故障

利用瞬态频率波动法实现频率调制信号的解调，提取齿轮的局部裂纹信息，并通过黄河ＴＮ２５２型汽车变速器疲劳试验的信号分析来验证。     

立足于实际的数字系统级诊断

本文主要提出了一个立足于当前实际的系统级诊断模型。在这模型下，系统级诊断变得很容易。模型基于下列假定：将系统划分成既具有自测试又具有测试其它单位能力的一种单位；其测试能力和测试结果用模糊数学来描述；自测试时在各种情况下均能给出正确的结果；测试其它单位时，除测试者本身具有严重故障外，也均能给出正确结果。     

Developing real-time knowledge-based systems with PERFECT

PERFECT (Programming EnviRonment For Expert systems Constrained in reasoning Time) is aimed at providing the necessary engineering support in real-time knowledge-based system development. PERFECT bridges the gap between the traditional analysis and design methodologies, and the implementation tools for these systems. It does so by providing the means to construct a knowledge model and to choose a suitable inference strategy. Subsequently the properties of the knowledge model and inference strategy may be analysed. For instance, it may be checked whether the knowledge model contains sufficient knowledge to diagnose a fault in an industrial process. Moreover, it may be checked whether the inference engine is able to provide an answer to a certain problem in time. If not, the analyser of PERFECT proposes an alternative structure of the knowledge model. When the constructed knowledge model and the chosen inference strategy show the required time efficiency, the compiler of PERFECT may translate them to an actual real-time knowledge based system in COGSYS. In addition, guidelines are provided with respect to the design of the human-machine interface. The resulting system is an instrument—a source of information that can be used by the human operator during problem-solving, rather than a prosthesis—a device that solves the entire problem by itself and presents the outcome to the human operator.     

神经网络在电路诊断中的应用

目的：阐述了目前电路基于数据库技术和人工智能专家系统及神经网络原理的故障诊断系统。方法：将所记录的模糊症状输入到系统中，通过模糊运算后，运用神经网络学习算法来寻找故障类型。结果：介绍了人工神经网络技术在电路诊断中的应用，并给出系统故障诊断软件的设计，结论：所用专家系统和神经网络相结合的方法改进电子电路故障诊断是可行的。