Intraoperative neurological monitoring

Intraoperative neurological monitoring (INM) is the evaluation of the nervous system within the operating room (OR) environment. In this paper, the INM system is tested in a clinical setting in comparison with conventional somatosensory evoked potential (SEP) monitoring. The study results demonstrate the capability of the INM system in extracting clearer and more stable SEP signals. The high SNR of SEP signals collected in various clinical environments, including the OR, makes the INM system a robust platform for continuous monitoring. While the current use of EP monitoring is limited to intermittent analysis by a highly trained clinical neurophysiologist, the authors believe in changing this paradigm by developing continuous monitoring systems, such as the INM system, capable of automated quantitative EP analysis. This noninvasive monitoring modality will allow for a wider range of use in clinical practice. Based on volunteer and clinical patient studies, the INM monitoring system demonstrates much greater reliability and accuracy via the artifact rejection and denoising strategies. It provides more strategic filtering options for different situations under which the clinical SEP response signal could be greatly contaminated and distorted. Furthermore, the INM system offers a promising approach to signal extraction in real-time monitoring during SEP research.     

China＇s Electric Power Reform：Different Economics and Challenges

China＇s electric power reform has followed the western reform format, however, it doest not seem to have endorsed the liberalization ideology underlying the reform model. This raises the concern of whether and how a competitive power market may be established and reform objectives reached. The new market competition model is easy to build in theory. The experience of many countries suggests that the market reform is extremely difficult because of the interruptions by different interest groups. To transform a centrally planned power sector into a competitive power market is a daunting task. Policy makers need to take into consideration the transitional nature of the economy and power industry as they implement the competitive power market model.     

Displacement Discontinuity Method for Fracture Mechanics Analysis of Reissner Plates: Static and Dynamic

This paper is concerned with the displacement discontinuity method applied to the shear deformable plates (Reissner’s and Mindlin’s theories) with cracks subjected to static and dynamic loads. Fundamental solutions of dislocation are derived using the Fourier transform method and the Laplace transformation technique. Boundary integral equations are presented in terms of rotations/displacement on the crack surfaces. The Chebyshev polynomials of the second kind are used to evaluate the integral equations with hypersingular kernels on the crack boundaries and determine the stress intensity factors at the crack tips. Comparisons are made with other numerical solutions to demonstrate the proposed method is accurate both for static and dynamic problems.     

广义频率响应函数的鲁棒建模新方法

针对基于非线性谱分析的故障诊断技术的需要,研究了非线性系统的广义频率响应函数的鲁棒在线建模方 法。首先将次元分析算法应用于Volterra级数模型的辨识,并通过对此算法的修正,得到了一种改进的Volterra系统的鲁 棒辨识算法。对其鲁棒性能进行的分析表明,改进算法的鲁棒抗噪性能被明显提高。在此基础上,提出了一种广义频率响 应函数的两步鲁棒建模新方法。仿真实验表明,该方法具有良好的鲁棒收敛性能,能够有效解决GFRF的在线建模问题。     

多属性神经网络地震反演在NB油田水平井钻探中的应用

NB油田位于渤海海域,为新近系河流相稠油油田,储层横向分布不稳定,砂体厚度薄、连通性较差,油水关系复杂,开发设计部署了大量水平井。选择神经网络反演方法,建立地震属性与储层参数的非线性关系,进而根据其关系反演得到储层参数数据体,利用该数据体沿层切片提取储层预测信息,研究储层发育规律,指导该油田水平井的部署和钻探,取得了较好的效果,降低了钻探风险。用神经网络地震反演方法进行储层预测研究,指导水平井钻探,在渤海海域尚属首次。     

大港油田关家堡地区不同含油层系气测解释方法探讨

大港油田关家堡地区油气藏特征复杂，不同含油层系油气显示特征不同，气测录井解释符合率相对较低。针对这一情况，通过分析关家堡地区明化镇组、馆陶组和沙河街组的地质和气测显示特征，结合试油结论，提出不同含油层系相应的气测录井解释方法——TR与Wh（湿度比）气测解释法和三角形图板法，归纳总结了全烃及组分在不同层系的变化规律。应用实例表明，效果较好，对于复杂油气藏多层系油气层的解释评价具有一定的借鉴作用。     

金属材料及防护涂层在两种淡水环境中的腐蚀试验研究

本文通过分析新疆某水库和三门峡水库中投放涂层和材料试样进行的试验中，金属涂层、封闭涂层、复合涂层系统在两种淡水环境下的腐蚀情况，比较了喷锌、喷铝、锌铝合金、稀土铝等金属涂层在淡水环境中对金属的保护效果；对部分碳钢材料、不锈钢材料、有色金属等在淡水环境中的自然腐蚀速度进行了研究。     

On denoising and compression of DNA microarray images

We address the problems of noise and huge data sizes in microarray images. First, we propose a mixture model for describing the statistical and structural properties of microarray images. Then, based on the microarray image model, we present methods for denoising and for compressing microarray images. The denoising method is based on a variant of the translation-invariant wavelet transform. The compression method introduces the notion of approximate contexts (rather than traditional exact contexts) in modeling the symbol probabilities in a microarray image. This inexact context modeling approach is important in dealing with the noisy nature of microarray images. Using the proposed denoising and compression methods, we describe a near-lossless compression scheme suitable for microarray images. Results on both denoising and compression are included, which show the performance of the proposed methods. Further experiments using the results of the proposed near-lossless compression scheme in gene clustering using cell-cycle microarray data for S. cerevisiae showed a general improvement in the clustering performance, when compared with using the original data. This provides an indirect validation of the effectiveness of the proposed denoising method.     

基于IPC的直流电机控制实验装置设计

介绍了工业控制计算机（IPC）在工业自动化领域中对直流机控制的应用，以工控机作为实验平台。设计了IPC测控系统之一的直流机测控子系统的实验装置，叙述了如何用IPC进行直流机转速的脉冲调宽式（PWM）控制，并通过软件实现在多任务实时操作系统（MROS）申直流机的速度测控和速度曲线绘制。     

Feature extraction using recursive cluster-based linear discriminant with application to face recognition.

A novel recursive procedure for extracting discriminant features, termed recursive cluster-based linear discriminant (RCLD), is proposed in this paper. Compared to the traditional Fisher linear discriminant (FLD) and its variations, RCLD has a number of advantages. First of all, it relaxes the constraint on the total number of features that can be extracted. Second, it fully exploits all information available for discrimination. In addition, RCLD is able to cope with multimodal distributions, which overcomes an inherent problem of conventional FLDs, which assumes uni-modal class distributions. Extensive experiments have been carried out on various types of face recognition problems for Yale, Olivetti Research Laboratory, and JAFFE databases to evaluate and compare the performance of the proposed algorithm with other feature extraction methods. The resulting improvement of performances by the new feature extraction scheme is significant.