基于引力搜索算法的分数阶变异时序回归GSA-TSGM(1,1)模型

为了利用分数阶累加算子在灰色短期预测中的高效性能,首次将分数阶累加算子引入变异时序回归模型以期取得更高的预测精度。主要方法如下：首先取湖北省链子崖某监测点1978—1987年的十年数据作为训练集并使用引力搜索算法确定最佳分数阶累加阶数,而1988—1993年的六年数据作为验证集验证提出的模型;其次对比了经典灰色模型GM(1,1)、分数阶累加灰色模型、变异时序回归模型TSGM(1,1)三种灰色模型。结果如下：首先修正了陈西江等人变异时序回归模型仿真时出现的错误,其次表明了相比于其他的模型,基于引力搜索算法的分数阶累加时序回归模型在进行灰色长期预测中具有较高的预测精度。因此,通过分数阶累加算子提高了灰色理论中长期预测模型的精度,为灰色长期预测提供了指导。     

大型活塞式压缩机异常振动的原因及对策

对我公司的1台6列M型活塞式压缩机组振动和噪声异常以及一级、二级连杆大头瓦快速失效的原因进行了分析。研究并实施了在轴系上加装配重体，以适度增大轴系的转动惯量来调整轴系扭转振动固有频率的改造措施，有效消减了轴系的扭转振动，大幅度减小了机组的振动和噪声，提高了连杆大头瓦的使用寿命。     

A general non-local denoising model using multi-kernel-induced measures

Noises are inevitably introduced in digital image acquisition processes, and thus image denoising is still a hot research problem. Different from local methods operating on local regions of images, the non-local methods utilize non-local information (even the whole image) to accomplish image denoising. Due to their superior performance, the non-local methods have recently drawn more and more attention in the image denoising community. However, these methods generally do not work well in handling complicated noises with different levels and types. Inspired by the fact in machine learning field that multi-kernel methods are more robust and effective in tackling complex problems than single-kernel ones, we establish a general non-local denoising model based on multi-kernel-induced measures (GNLMKIM for short), which provides us a platform to analyze some existing and design new filters. With the help of GNLMKIM, we reinterpret two well-known non-local filters in the united view and extend them to their novel multi-kernel counterparts. The comprehensive experiments indicate that these novel filters achieve encouraging denoising results in both visual effect and PSNR index.     

Preliminary evaluation of the anticorrosive properties of aircraft coatings by electrochemical methods

Strict regulations concerning the content of volatile organic compounds (VOCs) and heavy metals (Cr⁶⁺) in aircraft coating systems have increased the economic burden of the United States Air Force (USAF) in the area of coating maintenance. To this end, it is critical to have methods to characterize new coating systems in such a manner that the data can be used to predict accurately and reliably the expected lifetime of the coatings in service. Electrochemical noise method (ENM) and electrochemical impedance spectroscopy (EIS) are two techniques used to monitor extent and rate of corrosion. The USAF is currently employing these methods in order to supplement data acquired from traditional salt-spray methods. ENM and EIS are used to evaluate each component of the coating system and its contribution to corrosion prevention. Preliminary evaluations of an aircraft coating system on aluminum substrate (Al 2024-T3) produces resistance noise values of 10⁶ to 10⁷ Ω/cm². It is hoped that these results will form the basis of coatings that give increased USAF fleet service life and reduction in maintenance manpower and materials costs.     

New results on PWARX model identification based on clustering approach

This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.     

Why Phenolic Acids Are Unlikely Primary Allelochemicals in Rice

Allelopathy in rice (Oryza sativa, L.) effective against weeds has been found in about 3.5% of tested rice germplasm in both laboratory and field experimentation. However, the allelochemicals responsible for growth inhibition of rice-associated weeds have not yet been identified. In the literature, phenolic acids are often mentioned as putative allelochemicals. If phenolic acids commonly reach growth inhibitory concentrations in rice ecosystems, it must be expected that the degree of tolerance to phenolic acids will vary among traditional rice cultivars or plant species adapted to rice environments having inherently different phenolic acid concentrations. Phenolic acids concentrations are normally greater in submerged than in aerobic soils. A dose–response study, however, showed that seedlings of rice cultivars adapted to submerged anaerobic soils did not have higher level of tolerance against p-hydroxybenzoic acid than did seedlings of varieties adapted to aerobic upland soils. Moreover, traditional rice cultivars had no greater tolerance than did improved cultivars that were recently bred for traits other than tolerance of phenolic acids. Similarly, there were no differences in tolerance of p-hydroxybenzoic acid between two Echinochloa weed species adapted to either anaerobic or aerobic growth conditions. Thus, neither the rice cultivars nor weed species had evolved different tolerance levels against the phenolic acid. However, all rice cultivars had significantly greater tolerance of p-hydroxybenzoic acid than did either weed species. In a second experiment, the rates at which rice plants released phenolic acids into solution cultures were measured for at least one month, the time period of greatest allelopathic activity following planting under field conditions. The maximum release rate of phenolic acids during the first month of growth was approximately 10 g/plant/day. At a conventional plant density, the release rate of phenolic acids would be approximately 1 mg/m²day. This order of release rate cannot provide concentrations remotely close to phytotoxic levels determined for these rice cultivars and weed species. The results presented in this paper do not preclude the possibility that phenolic acids might be one component in a mixture of chemicals that, when present simultaneously, are allelopathic.     

Noise characterization of surface processes of the Li/organic electrolyte interface

The processes occurring in aprotic electrolyte on a lithium electrode in the steady state conditions and under polarization are studied using the method of electrochemical noise characterization. The evidence of the electro-chemical noise measurements on polarized lithium electrodes indicates that the discharge of lithium ions under cathodic polarization, as well as lithium anodic dissolution, is localized under the passive film rather than on its surface. An increase in the polarizing current results in local breakdown of the film; in this case, the electrochemical process emerges on the electrode surface affecting the character of potential fluctuations. The intensity of electrochemical noise significantly increases in the course of cathodic polarization with high currents. The reason is that lithium metal crystals, which are formed under the passive film, perforate the film, and dendrites grow on its surface. The method shows the dependence of electrochemical noise intensity on the nature of the electrolyte and establishes the correlation between the stability of the lithium electrode in the course of cycling and the intensity of fluctuations. This offers an opportunity of using the method of electrochemical noise for screening organic electrolytes for lithium batteries.     

基于背景噪声的图像盲篡改检测

数字图像在成像过程中会产生特定的背景噪声,如果两幅不同噪声的图像拼接在一起,篡改区域和其他区的噪声会有差异。提出一种基于偏度统计特性的背景噪声估计算法,其通过对图像分块计算每块的噪声标准差,从而检测出噪声异常部分以达到篡改检测的目的。算法利用DCT变换去除原图细节部分,利用偏度统计特性估计噪声,利用条件最小值法求出噪声的标准差。算法改进了迭代求条件最小值法,利用微分方法求取最小值,避免了初始值设定问题,提高了算法的准确率。实验结果表明,提出的噪声估计算法正确率高,且对拼接篡改图像篡改检测有明显效果。     

分布式光纤测温系统在煤矿火灾检测中的应用

摘要：针对煤矿安全存在潜在的火灾问题,文中主要介绍分布式光纤测温原理及其在煤矿火灾监测中的应用。在系统结构上,本文提出了一种优化设计方案。此外,在信号处理方面,本文提出了一种命名为RLN-ELAM的新颖算法,用于检测并消除瑞利噪声对系统测量精度的影响,提高拉曼分布式光纤系统的测量精度。实验结果表明,利用此设计方案和算法后,可以使系统的温度分辨率、空间分辨率均有所提高,测量误差从7%降低到不足2%。在实验室进行的模拟实验中,对温度进行实际测量,结果表明,该煤矿火灾监测系统能够提供详细的温度分布情况并能够及时准确地报警,使工作人员随时掌握煤矿温度分布情况,及时准确地找到火灾发生区。     

一种基于PCA的MPSK信号码元速率估计方法

针对MPSK信号的码元速率估计问题, 研究了有限数据条件下循环谱的谱线特征受到背景色噪声干扰的现象, 提出了一种基于主分量分析(PCA)的循环谱特征码元速率估计方法。PCA变换抑制了信号循环谱中的背景色噪声, 提高了估计精度, 减小了估计方差。仿真表明, 该方法在有限数据条件下具有良好的估计性能, 适用于不同成形滤波系数的MPSK信号。