一种改进的图像增强算法及其在铸坯图像增强中的应用

根据低照度图像暗、对比度低的特点,提出了一种基于3次样条插值函数的直方图规定化方法,并用其对连铸板坯表面图像进行对比度增强处理。该算法是在直方图规定化的理论基础上,采用3次样条插值函数来拟合原直方图,并以拟合后的函数为规定化函数对原图进行直方图规定化变换。通过试验并与采用高斯函数进行规定化处理后的效果图进行对比分析,验证了该算法的有效性和实用性。     

热轧带钢局部高点对冷轧带钢板形的影响

热轧带钢局部高点对冷轧带钢板厚和内应力分布具有重要影响。采用B3样条函数拟合带材厚度横向分布，定量研究热轧来料局部高点对冷轧带钢板厚和内应力分布的影响。研究结果表明：轧后带钢局部高点高度及附加张应力的大小与来料高点高度成正比，随总轧制压下率的增大而减小。     

用X射线衍射线形分析测定钨粉晶块分布的新方法

提出了一种用X射线衍射线形分析法求出纯物理展宽线形后再用瑞利分布函数进行强度数据最优化拟合,求解钨粉晶块大小分布函数的新方法。借助于电子计算机,使全部计算过程程序化。     

超细粉末粒度分布的X光小角散射分析(Ⅲ)——分割分布函数法求解的稳定性

分割分布函数法是以x光小角散射数据计算超细粉末粒度分布的方法之一。本文通过优化系数矩阵、另加阻尼因子以及最小二乘法处理,对分割分布函数法求解稳定性进行了研究。全部演算在计算机上完成。结果表明,当满足相应条件时,所求粒度分布的平均偏差不大于所测散射强度的误差。     

用样条函数拟合扇风机的特性曲线

扇风机特性曲线的数学处理,是通风网路计算的重要组成部份。本文提出的用样条函数拟合扇风机的特性曲线是一种新方法。     

采用发射光谱/脉冲分辨联用技术测定夹杂物粒度分布

通过解析发射光谱和脉冲分辨分析联用技术（OES/PDA）数据确定钢铁中夹杂物的类型及计算其粒度分布需要借助于高等数学方法,以便在大量的数据中滤出那些与夹杂物相关的OES/PDA数据,从而找到可靠的该数据集的离群数据。强度及大多的质量分布其分布函数都是非正态分布的,因此,通过对这些数据进行拟合即可得到非属于该正态分布的离群数据。离群数据确定后,通过这些信息可找到与一些特定夹杂物类型相关联的数据群。基于此目的,发现一种称为“自组织特征映射（SOM）”的数学方法非常适于进行此项研究。根据离群数据及夹杂物类型的相关信息,采用该方法可以计算出质量分布并能进一步进行不同夹杂物类型的粒度分布计算。     

计算动力触探修正系数的多项式拟合法

给出了用于计算动力触探修正系数的多项式拟合法.首先通过比对确定了拟合函数,指出用二次多项式拟合是考虑精确性和实用性的最佳契合点,通过数学分析介绍了双因素函数拟合的嵌套方法,并给出了重型动力触探修正系数在考虑杆长和实测锤击数条件下的二元二次多项式拟合函数及其行列式形式.通过拟合出的修正系数与规范所给的修正系数之间的误差分析判定拟合函数精度较好,适于工程应用.并且此公式适用于计算机计算.     

GPS高程拟合中多面函数及二次曲面函数的比较与分析

结合某区域的GPS和水准测量数据,用多面函数法、二次曲面函数法分别进行高程拟合,结果表明,在高程异常变化较大的区域用多面函数拟合GPS高程异常时,精度较高。     

用CAD技术进行露天矿台阶开采设计

本文论述了复杂露天矿台阶开采设计的基本原理，开发了一套台阶开采设计ＣＡＤ软件，用３次参数样条函数拟合矿体等边界曲线，用平面图和勘探线剖面图构成造矿体的立体图，并用人机对话的方法进行台阶开采设计，该软件在大新锰矿应用取得了明显的效果。     

六斜辊管材矫直过程精度解析

摘要：以管材六斜辊矫直过程为研究对象,推导了管材经一次反弯后,弹复曲率及残余曲率的演变过程。根据管材旋转一圈弯曲2次,计算了管材全流程矫直微元段的反弯次数;借助三次样条函数,以管材与矫直辊接触点为特征点,拟合出了管材在辊系中的弯曲形态。基于所获得的函数,可以获得任意位置处的弯曲曲率;对某一微梁段的反弯弹复过程进行循环迭代,计算出管材微梁段的最终残余曲率,进而得到最终的管材矫后的直线度精度。通过与现场矫直数据对比,证明该模型能够可靠地预测六斜辊矫直精度,为科学合理的设定矫直参数奠定了理论基础。     

Effect of correction function on image characteristics of Positologica: a positron CT device for the head

The effects of correction function on image characteristics were studied experimentally for a positron CT device Positologica. Correction functions were obtained by smoothing the Shepp and Logan function by convolution of Gaussian functions. Signal-to-noise ratio (SNR) and spatial resolution were measured for phantoms and related to the magnitude of smoothing. The relation between SNR and spatial resolution is also discussed. Some suggestions are made to indicate how to select correction functions for clinical images. A flow of data processing for Positologica is described together with an outline of its hardware.     

Internet paradox. A social technology that reduces social involvement and psychological well-being?

We present a method for estimating age- and time-specific HIV incidence using back-calculations of AIDS incidence data. Two-dimensional penalized likelihood is employed, using a flexible bivariate step function model of HIV incidence, together with a quadratic roughness penalty which leads to thin-plate spline smoothing. This allows incidence estimates to vary flexibly and smoothly in both age and time. We propose generalized cross-validation as a guide for choice of an appropriate level of smoothing and describe an EM algorithm for computing the estimates. We propose the method primarily for qualitative assessment of trends in age-specific incidence over time and apply it to a small Italian data set on men who have sex with men. The analysis suggests a trend over time of increasing relative incidence among younger individuals, consistent with incidence patterns observed in other countries.     

The place of life in our theories

The paper deals with the iterative three-dimensional (3D) smoothing of tomograms acquired by fast Magnetic Resonance (MR) imaging methods. The smoothing method explored, which is aimed basically at the improvement of 3D visualization quality, uses the physical concept of geometry-driven diffusion with a variable conductance function, based on a specific measure of the 3D neighborhood homogeneity. A novel stopping criterion is proposed for iterative 3D diffusion processing. A study of the transition from 2D to 3D algorithms is carried out. The main structure of the program implementation of the smoothing algorithms developed is described. Three smoothing/filtering methods, aimed at the improvement of 3D visualization of MR tomograms of the brain, are quantitatively and visually compared using real 3D MR images. The results of computer simulations with 3D smoothing, segmentation and visualization are presented and discussed.     

Pre-processing variance reducing techniques in multispectral positron emission tomography

Stochastic fluctuations and systematic errors severely restrict the potential of multispectral acquisition to improve scatter correction by energy-dependent processing in high-resolution positron emission tomography (PET). To overcome this limitation, three pre-processing approaches which reduce stochastic fluctuations and systematic errors without degrading spatial resolution were investigated: statistical variance was reduced by smoothing acquired data in energy space, systematic errors due to nonuniform detector efficiency were minimized by normalizing the data in the spatial domain and the overall variance was further reduced by selecting an optimal pre-processing sequence. Selection of the best protocol to reduce stochastic fluctuations entailed comparisons between four smoothing algorithms (prior constrained (PC) smoothing, weighted smoothing (WS), ideal low-pass filtering (ILF) and mean median (MM) smoothing) and permutations of three pre-processing procedures (smoothing, normalization and subtraction of random events). Results demonstrated that spectral smoothing by WS, ILF and MM efficiently reduces the statistical variance in both the energy and spatial domains without observable spatial resolution loss. The ILF algorithm was found to be the most convenient in terms of simplicity and efficiency. Regardless of the position of subtraction of randoms in the sequence, reduction of the systematic errors by normalization followed by spectral smoothing to suppress statistical noise produced the best results. However, subtraction of random events first in the sequence reduces computation load by half since the need to pre-process this distribution before subtraction is removed. In summary, normalizing data in the spatial domain and smoothing data in energy space are essential steps required to reduce systematic errors and statistical variance independently without degrading spatial resolution of multispectral PET data.     

Wrinkles induced by the use of smoothing procedures applied to serial growth data

This paper elucidates the effects of moving average filters when applied to serial growth measurements. This is a question of interest because smoothing procedures are inherently part of a number of analytical methods presently employed in auxological analyses. Particular attention is paid to sequential growth data analysed to identify what has been described as pulsatile, saltation and stasis patterns or mini-growth spurts. When applied to pulsatile, or saltatory, time series data the process of smoothing itself creates artifactual temporal patterns in the time series data similar to previously described mini growth spurts while removing the actual pulsatile characteristics of the data. These observations illustrate that smoothing approaches add noise to time series data while removing meaningful patterns in the original data sequence. Analyses employing such approaches produce results that include waveforms or other fluctuations compatible with an underlying pulsatile driving mechanism, but do not necessarily reflect the temporal characteristics of the original biological process.     

电力负荷的灰色预测

利用灰色GM(1,1)模型进行预测时,如果原始序列的变化不够光滑,预测误差很大.因此,对原始序列进行对数平滑处理,同时针对不同时段建模形成预测灰区间的特点,提出了用灰色系统理论进行电力负荷预测的具有对数平滑的灰色关联度加权预组合预测方法.该法使影响负荷的各个因素得到削弱或者抵消,提高了预测精度,并扩大了预测范围.     

Estimated numbers of deaths from coronary heart disease "caused" and "prevented" by alcohol: an example from Finland

OBJECTIVE: Epidemiological studies show that moderate alcohol consumption rather than abstention is associated with a lower risk of coronary heart disease (CHD) mortality. Our objective was to adjust established methods for calculating attributable fractions to a situation where the risk function is J-shaped and to estimate the number of CHD deaths "caused" and "prevented" by alcohol in Finland. METHOD: Point estimates of relative risk were obtained by a meta-analysis. They were pooled by fitting a nonparametric cubic smoothing spline to the data. Alcohol consumption distribution was estimated from survey data (N = 4,818; 2,488 women). The consequences of various assumptions about changes in alcohol consumption distribution on CHD mortality were estimated. The most detailed analyses are presented for men aged 30-69. The results for the men and women aged 30-79 are summarized. RESULTS: Among men aged 30-69, the beneficial effects of light to moderate alcohol consumption "prevent" some 400 CHD deaths each year which corresponds to 12-14% of the observed CHD deaths. Around 20 CHD deaths are "caused" by alcohol consumption exceeding the estimated optimum level. Among men aged 70-79 and women aged 30-79, the numbers of CHD deaths "prevented" by alcohol consumption were approximately 200 and 100, respectively, whereas there were only a few CHD deaths "caused" by alcohol. CONCLUSIONS: Our best estimates suggest that approximately one-tenth of the observed number of CHD deaths among middle-aged men in Finland is "prevented" by alcohol, while the relative effect is considerably smaller among older men and all women.     

The proliferation of mouse mammary epithelial cells in response to specific mitogens is modulated by the mammary fat pad in vitro

A volume-preserving three-dimensional smoothing approach is described that can be directly applied to 3D medical image data consisting of sets of 2D image slices, e.g., segmented intravascular ultrasound image sequences. Two local smoothing filters ℱ and 𝒢 were designed according to different smoothing goals and their performance was compared. Filtering with the ℱ filter of a relatively large frequency window keeps the important local characteristics of the object and results in little shrinkage while removing noise. Filtering with the Gaussian filter G that has an added volume compensation step results in no global shrinkage and may be used for multiscale filtering. The two filters can be easily extended to n-dimensional filtering.     

基于小波分解的尾矿坝浸润线预测方法研究

为了准确预测尾矿坝浸润线的位置变化，结合浸润线埋深非稳定、非线性的时间序列以及动态变化的特点，利用小波分解与重构，提出基于小波分解的时间序列指数平滑法和BP神经网络法，采用时间序列的指数平滑法和BP神经网络方法分别对多个细节信号序列和逼近信号序列进行拟合预测，并对其拟合结果进行叠加，实现对尾矿坝浸润线的预测。将预测结果与实际监测数据进行对比，结果表明小波分解预测方法的预测结果与传统单一的指数平滑法和神经网络法预测结果相比，在预测精确度和拟合度方面：小波分解>指数平滑>神经网络。     

基于EEMD-HW-PSO-ELM耦合模型的排土场边坡位移预测模型

为了准确预测小样本、非线性特点的排土场边坡位移,提出了一种基于经验模态分解法、三次指数平滑法和粒子群优化极限学习机的EEMD-HW-PSO-ELM边坡位移组合预测模型。以伊敏露天矿排土场GPS位移监测数据为例,验证该模型的有效性。研究结果表明：EEMD模型分解后的边坡位移时间序列包括4个IMF分量和1个余量,将分解后的数据重构为趋势项和波动项,物理意义明确。分别选择三次指数平滑法和粒子群优化极限学习机预测趋势项和波动项位移,将分项预测结果的等权叠加值作为最终预测结果,预测值的平均相对误差为0.38%,均方根误差为1.15。选择了BP模型和Elman模型进行对比预测,结果表明组合预测模型的预测效果较好,能够为边坡安全管理提供理论依据。