多发性骨髓瘤患者超氧化物歧化酶和脂质过氧化物的改变及其意义

用邻苯三酚自氧化法和改良硫代巴比妥酸（ＴＢＡ）荧光微量法测定了多发性骨髓瘤（ＭＭ）患者初发期、缓解期和复发期红细胞超氧化物歧化酶（ＳＯＤ）活性和血浆脂质过氧化物（ＬＰＯ）含量。结果表明：ＭＭ初发和复发病例红细胞ＳＯＤ活性明显降低，血浆中ＬＰＯ含量明显增高，较正常对照均有明显差异。缓解期患者的ＳＯＤ活性和ＬＰＯ含量大致恢复正常水平。ＭＭ红细胞ＳＯＤ活力与血浆ＬＰＯ含量呈负相关（ｒ＝－０．４２５，Ｐ＜０．０５）），与血红蛋白浓度呈正相关（ｒ＝０．７２２，Ｐ＜０．００１），与骨髓中浆细胞百分数呈负相关（ｒ＝－０．５２６，Ｐ＜０．０５）。以上结果提示自由基损伤作用参与了ＭＭ的发生、发展和转归过程，且与病情的严重程度有关。     

Stability Analysis of Continuous-Time Fuzzy Large-Scale System

IntroductionSincefuzzylogicwasintroducedintotheanalysisanddesignofcontrolsystemsbyZadehin 1 96 5[1] ,ithasbecomeaverypopulartopicincontrolengineering[2 4] ,mainlybecauseofthefollowingtwopoints :1 )ithastheadvantagethatnoformalmathematicalmodelsisneededan…     

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在阐明ITS数据融合的意义及层次性的基础上，分析了数据层多源ITS数据融合及支持向量机的特点，根据支持向量机（SVM）的原理设计了利用支持向量机进行多源ITS数据融合的思路，并从支持向量机训练、训练结果评价以及支持向量机测试三个方面提出了该思路的实现步骤。在对日本阪神公路上堺入口的二源交通流数据进行支持向量机融合后，比较融合前后的数据，证明所提出的基于支持向量机技术的数据层多源ITS数据融合方法能够有效地进行数据质量控制，提高数据的精确度。     

A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.     

基于支持向量分类机和回归机的综合评价方法

采用支持向量多值分类机和回归机进行综合评价排序,以提高机器学习方法的综合评价排序能力,并以管理信息系统综合评价为例,与人工神经网络（ANN）方法进行了对比研究.试验结果表明,基于支持向量多值分类机综合评价得分之间的差异比ANN更明显,而且基于支持向量回归机综合评价得分的相对误差明显小于ANN.     

时空信息多模式融合模型应用研究

给出了时空多模式融合模型(MM模型)的实用建模方法.利用华北地震和北京SARS 2个时空数据集对MM模型进行了建模应用和性能评价,与单纯神经网络模型相比,MM模型的预测准确性与预测趋势变化的能力均有所提高.分析表明,利用MM模型对不同类型时空过程建模时,不同分量信息所起到的作用并不相同.     

MULTI SUPPORT VECTOR MACHINES DECISION MODEL AND ITS APPLICATION

IntroductionStatistical Learning Theory( SLT) is a small-sample statistical theory by Vapnik etal.SupportVector Machine( SVM) is a novel powerful ma-chine learning method developed from SLT.SVMis powerful for the problems will small sample,nonlinearity,high dimension and local minima.Currently,SVM has many applications in the pat-tern recognition,function estimation,signal pro-cession,control,and others field[1～ 3 ] .SVM en-hances generalization by principle of the structuralrisk min…     

多发性骨髓瘤患者氧自由基代谢状态的研究

测定了不同病期多发性骨髓瘤（MM）患者内源性抗氧化酶—红细胞超氧化物岐化酶（SOD）、谷胱甘肽过氧化物酶（GSH－Px）、过氧化氢酶（CAT）活性，血浆铜锌SOD（CuZn－SOD）、锰SOD（Mn－SOD）及总SOD（T-SOD）活性，血清铜、锌、锰、硒含量及血浆脂质过氧化物（LPO）含量。结果表明非缓解期MM患者红细胞3种抗氧化酶活性明显降低，血浆T－SOD、CuZn－SOD及Mn-SOD活性显著升高，血清锌、锰、硒含量降低，铜含量增高，血浆LPO含量明显增高。提示患者体内氧自由基含量增多，代谢增强，抗氧化能力明显减弱，且与疾病的发展及严重程度有关。     

FINE-GRAINED DISTRIBUTED MULTIMEDIA SYNCHRONIZA-TION MODEL——ENHANCED FUZZY-TIMING PETRI NET

IntroductionDistributed multimedia systems integrate a va-riety of media,including time dependent media,such as audio and video,and time independent me-dia,such as text,graphics,and images.Multime-dia synchronization refers to the temporal composi-tion of the data streams. Strams can be composedof temporal series of information units called Logi-cal Data Units (LDU) (e.g.,a video stream is atemporal series of video frames) .Multimedia synchronization can be modeledaccording to different gra…     

Wavelet De-noising of Speech Using Singular Spectrum Analysis for Decomposition Level Selection

The problem of speech enhancement using threshold de-noising in wavelet domain was considered.The appropriate decomposition level is another key factor pertinent to de-noising performance.This paper proposed a new wavelet-based de-noising scheme that can improve the enhancement performance significantly in the presence of additive white Gaussian noise.The proposed algorithm can adaptively select the optimal decomposition level of wavelet transformation according to the characteristics of noisy speech.The experimental results demonstrate that this proposed algorithm outperforms the classical wavelet-based de-noising method and effectively improves the practicability of this kind of techniques.     

Nonlinear inverse modeling of sensor based on back-propagation fuzzy logical system

Objective To correct the nonlinear error of sensor output, a new approach to sensor inverse inodeling based on Back-Propagation Fuzzy Logical System （BP FS） is presented. Methods The BP FS is a computationaiiy efficient nonlinear universal approximator, which is capable of implementing complex nonlinear mapping from its input pattern space to the output with fast convergence speed. Results The neuro-fuzzy hybrid system, i.e. BP FS, is then applied to construct nonlinear inverse model of pressure sensor. The experimental results show that the proposed inverse modeling method automatically compensates the associated nonlinear error in pressure estimation, and thus the performance of pressure sensor is significantly improved. Conclusion The proposed method can be widely used in r nonlinearity correction of various kinds of sensors to compensate the effects of nonlinearity and temperature on sensor output.     

Inverse Learning Control of Nonlinear Systems Using Support Vector Machines

An inverse learning control scheme using the support vector machine (SVM) for regression was proposed. The inverse learning approach is originally researched in the neural networks. Compared with neural networks, SVMs overcome the problems of local minimum and curse of dimensionality. Additionally, the good generalization performance of SVMs increases the robustness of control system. The method of designing SVM inverse learning controller was presented. The proposed method is demonstrated on tracking problems and the performance is satisfactory.     

Support vector regression based color image restoration in YUV color space

A support vector regression(SVR) based color image restoration algorithm is proposed.The test color images are firstly mapped into the YUV color space,and then SVR is applied to build up a theoretical model between the degraded images and the original one.Performance comparisons of the proposed algorithm versus traditional filtering algorithms are given.Experimental results show that the proposed algorithm has better performance than traditional filtering algorithms and has less computation time than iterat...     

Application of support vector machine to ship steering

System identification is an effective way for modeling ship manoeuvring motion and ship manoeuvrability prediction. Support vector machine is proposed to identify the manoeuvring indices in four different response models of ship steering motion, including the first order linear, the first order nonlinear, the second order linear and the second order nonlinear models. Predictions of manoeuvres including trained samples by using the identified parameters are compared with the results of free-running model tests. It is discussed that the different four categories are consistent with each other both analytically and numerically. The generalization of the identified model is verified by predicting different untrained manoeuvres. The simulations and comparisons demonstrate the validity of the proposed method.     

尺度核支持向量机及在动态系统辨识中的应用

为了提高非线性动态系统辨识质量,提出了新的支持向量机尺度核函数构造方法.首先直接构造紧支撑尺度函数,然后根据小波多分辨分析理论,由紧支撑尺度函数生成具有多分辨率特性的尺度核函数.证明了这种核函数是满足Mercer定理的支持向量机核函数.动态系统辨识的仿真结果表明,尺度核函数支持向量机的建模和逼近能力优于基于三阶样条核函数或RBF核函数的支持向量机.     

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突发事件一旦发生,应急物资需求种类是纷繁复杂的,应急物流中的物资需求又是紧迫的、动态变化的. 为了提高应急物流的运作效率,救援管理人员需要对应急物资进行分级. 本文提出一种基于后验概率支持向量机的应急物资分类方法,并在此基础上建立应急物资分类模型. 把物资的重要性、时效性和缺口程度三个因素作为物资需求分级的评价标准,将模糊化后的评价值作为PPSVM的输入,选择后验概率最大所的类别作为测试样本的类别输出来完成整个分类过程,从而得到对应的分级结果. 实验结果表明,PPSVM用于应急物资分类是可行的,能够很好地解决应急物资的分类问题.     

基于小波变换与支持向量机的舰船水压信号检测方法

采用小波变换与支持向量机相结合对舰船水压场信号建立检测模型.该方法通过对海浪信号进行小波分解并对某一特定低频频段信号重构,利用支持向量机对重构信号建立预测模型,预测误差值作为特征值对舰船水压信号进行检测.通过检测验算表明此方法的有效性,特别是在低信噪比情况下,仍能较好地检测到目标信号.     

Forecasting of stock returns by using manifold wavelet support vector machine

An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine(SVM).Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities,the MWSVM can approximate arbitrary nonlinear functions and forecast stock returns accurately.T...     

基于LS—SVM逆系统方法的非线性动态矩阵控制

针对神经网络逆系统建模存在的诸多问题,提出了基于最小二乘法支持向量机的α阶逆系统方法的非线性动态矩阵控制新方法．将最小二乘支持向量机辨识非线性对象的α阶逆模型与原系统串联组成伪线性系统,根据线性动态矩阵预测控制方法对伪线性系统进行控制．仿真结果表明,系统存在扰动和模型参数发生变化时,依然具有很好的动、静态性能,且表现出很强的鲁棒性,证明了方法的有效性．该方法不依赖于系统的数学模型,简化了非线性对象动态矩阵控制器的设计,为非线性预测控制的研究提供了一种新途径．     

OKT_（10） POSITIVE CELLS IN PERIPHERAL BLOOD OF MULTIPLE MYELOMA AND MALIGNANT LYMPHOMA

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