Invariant 2D object recognition using KRA and GRA

Computer vision has been extensively adopted in industry for the last two decades. It enhances productivity and quality management, and is flexibility, efficient, fast, inexpensive, reliable and robust. This study presents a new translation, rotation and scaling-free object recognition method for 2D objects. The proposed method comprises two parts: KRA feature extractor and GRA classifier. The KRA feature extractor employs K-curvature, re-sampling, and autocorrelation transformation to extract unique features of objects, and then gray relational analysis (GRA) classifies the extracted invariant features. The boundary of the digital object was first represented as the form of the K-curvature over a given region of support, and was then re-sampled and transformed with autocorrelation function. After that, the extracted features own the unique property that is invariant to translation, rotation and scaling. To verify and validate the proposed method, 50 synthetic and 50 real objects were digitized as standard patterns, and 10 extra images of each object (test images) which were taken at different positions, orientations and scales, were acquired and compared with the standard patterns. The experimental results reveal that the proposed method with either GRA or MD methods is effective and reliable for part recognition.     

基于阶比双谱分析的发动机故障特征提取

双谱估计是非线性、非高斯信号分析的有力工具，它能抑制加性有色噪声的影响，辨识非因果、非最小相位系统或重构非最小相位信号，抽取由于高斯性偏离引起的各种信息，检测和表征信号中的非线性以及辨识非线性系统。但是它是以分析稳态信号作为前提条件。严格上讲，用它去分析内燃机上所得到的非稳态信号是不合适的。阶比双谱估计是一种新方法，将等时间间隔采样(△t)所得的非稳态信号按等转角间隔(△θ)重采样，得到阶域中的稳定信号，再用双谱进行分析。通过仿真信号的分析对比显示了该方法的优越性。运用此方法对内燃机主轴承不同间隙下的噪声信号进行分析，结果表明，此方法能有效地提取故障的特征，实现状态监测。     

Enhancements of two-stage stochastic decomposition

This paper presents some enhancements associated with stochastic decomposition (SD). Specifically, we study two issues: (a) Are there any conditions under which the regularized version of SD generates a unique solution? (b) Is there a way to modify the SD algorithm so that a user can trade-off solution times with solution quality? The second issue addresses the scalability of SD for very large scale problems for which computational resources may be limited and the user may be willing to accept solutions that are “nearly optimal”. We show that by using bootstrapping (re-sampling) the regularized SD algorithm can be accelerated without significant loss of optimality. We report computational results that demonstrate the viability of this approach.     

一种基于手绘的自由形体建模方法研究

提出了一种基于手绘的自由形体建模方法。通过平滑去噪、重采样、曲线拟合等操作,将输入的手绘笔画转化为均匀间隔的点序列,根据提取到的几何信息生成三维网格模型,并通过对模型进行光照渲染和纹理贴图完成具有特定形状的自由形体的绘制。实验结果表明,该方法能够简单快速地实现符合用户输入意图的三维形体建模。     

Non-linear dynamic compensation and re-sampling for tactile scanning measurement of curved surface topography based on GPS standards

Tactile scanning method is widely used for curved surface topography measurement. Owing to the non-linear transformation process from the vertical translational displacement of the measured point (the coordinates of stylus tip) to the angular displacement of the stylus tip (measured by the cylindrical diffraction grating), the non-linear error exists within the horizontal and vertical coordinates of the obtained sampling data. And the non-linear error normally rises with the increasing of the measurement range.     

An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking

Particle filter algorithm is widely used for target tracking using video sequences, which is of great importance for intelligent surveillance applications. However, there is still much room for improvement, e.g. the so-called “sample impoverishment”. It is brought by re-sampling which aims to avoid particle degradation, and thus becomes the inherent shortcoming of the particle filter. In order to solve the problem of sample impoverishment, increase the number of meaningful particles and ensure the diversity of the particle set, an evolutionary particle filter with the immune genetic algorithm (IGA) for target tracking is proposed by adding IGA in front of the re-sampling process to increase particle diversity. Particles are regarded as the antibodies of the immune system, and the state of target being tracked is regarded as the external invading antigen. With the crossover and mutation process, the immune system produces a large number of new antibodies (particles), and thus the new particles can better approximate the true state by exploiting new areas. Regulatory mechanisms of antibodies, such as promotion and suppression, ensure the diversity of the particle set. In the proposed algorithm, the particle set optimized by IGA can better express the true state of the target, and the number of meaningful particles can be increased significantly. The effectiveness and robustness of the proposed particle filter are verified by target tracking experiments. Simulation results show that the proposed particle filter is better than the standard one in particle diversity and efficiency. The proposed algorithm can easily be extended to multiple objects tracking problems with occlusions.     

基于EM算法的人像合成图像的检测

数字篡改图像即使做的很逼真,没有留下肉眼可见的篡改证据,但图像原先的统计学规律已经改变.以人像合成图像为例,为了使篡改图像更逼真,篡改者需要改变图像大小、旋转图像或拉伸图像的某部分,这些操作需要把原图像重采样到新图像上去.虽然这种重采样通常难以察觉,但图像内部的相关性还是可以作为图像被篡改的证据.这种周期性可以通过EM算法(期望最大化算法)检测出来,从而通过检测合成图像的重采样可作为图像篡改的证据.     

一种基于改进粒子滤波的运动目标跟踪

基于传统粒子滤波的运动目标跟踪方法中存在重要密度函数选择困难、缺乏通用性、重采样设计难度大、粒子退化现象难以有效解决等问题。因此提出了一种改进的粒子滤波运动目标跟踪方法,该方法采用人工鱼群算法改进重要密度函数,通过粒子间的不断交互及协调行为,使其状态接近后验分布,从而提高重要密度函数的通用性。在此基础上,结合人工免疫算法的免疫算子改进重采样,平衡粒子群的收敛性和多样性,抑制早熟现象。实验结果表明,与传统粒子滤波算法相比,该方法通过参数调节,提高了运动目标跟踪的准确性和抗干扰能力,并能有效地抑制粒子退化现象。     

Statistical properties of quadratic-type performance indices

Performance indices used in the analysis of performance of univariate and multivariate processes are often computed as ratios of stochastic variables. Consequently, they have an inherent variability. In order to provide a basis for assessing performance using these measures, it is important to understand the sampling distribution of these indices. In this paper the statistical properties of quadratic-type performance indices used in the analysis of performance of univariate and multivariate processes are derived. When the system parameters are known, or are treated as know uncertainty in the performance indices can be quantified through calculation of exact confidence intervals using results from mathematical statistics. Given the extensive computational requirements for computing exact confidence intervals, high quality approximations can in turn be used. The influence of data length and autocorrelation structure of the process on the width on these the confidence intervals is seen explicitly in the derived expressions. It is shown that in the case of non-normal driving forces, the resulting confidence intervals are theoretically justified using the central limit theorem. The results of Desborough and Harris [Can. J. Chem. Eng. 70 (1992) 1186] are shown to be special case of the approach derived in this paper. When the parameters of the time series are estimated from data, the uncertainty in the performance indices must be investigated using a fundamentally different approach. Several methods are outlined. The methodologies developed in this paper are applicable to a wide variety of quadratic performance measures encountered in process monitoring and analysis, controller design and filtering.     

Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques: Case study of Hong Kong

The objective of this study was to apply preprocessing and ensemble artificial intelligence classifiers to forecast daily maximum ozone threshold exceedances in the Hong Kong area. Preprocessing methods, including over-sampling, under-sampling, and the synthetic minority over-sampling technique, were employed to address the imbalance data problem. Ensemble algorithms are proposed to improve the classifier's accuracy. Moreover, a distance-based regional data set was generated to capture ozone transportation characteristics. The results show that a combination of preprocessing methods and ensemble algorithms can effectively forecast ozone threshold exceedances. Furthermore, this study advises on the relative importance of the different variables for ozone pollution prediction and confirms that regional data facilitate better forecasting. The results of this research can be promoted by the Hong Kong authorities for improving the existing forecasting tools. Moreover, the results can facilitate researchers' selection of the appropriate techniques in their future research.