基于模糊逻辑和纹理分析的图像增强算法研究

提出了基于模糊逻辑和纹理分析的图像增强算法,通过图像模糊化、提取纹理信息和纹理信息模糊化、定义局部对比度、根据全局和局部信息来进行对比度的变换等措施,提高了增强算法的效果。测试结果表明该算法能很好地增强图像的边缘等细节信息,同时避免放大噪声和过增强的出现。     

基于DSP产生的SPWM波的谐波估计与分析

针对2000系列DSP产生的正弦脉宽调制（Sinusoidal Pulse Width Modulation,SPWM）波,使用傅里叶级数模型,提出了将改进的粒子群优化（Improved Particle Swarm Optimization,IPSO）用于各次谐波的幅值和相位的参数估计方法,着重分析了在不同的调制波频率和电压下,载波比与谐波总畸变率（Total Harmonic Distortion,THD）之间的关系,通过大量的实验,得到在一定范围的调制波频率和电压下,均能保持高次谐波较小,从而得到高质量的正弦输出。这一结论不仅对SPWM的产生起指导作用,而且对要求正弦交流电的用电设备有着降低高频损耗与节能的重要实际意义。     

IVIM及纹理分析在术前预测宫颈癌类型和淋巴结转移研究进展

本文综述了体素内不相干运动扩散加权成像（intravoxel incoherent motion diffusion-weighted imaging,IVIM-DWI）及纹理分析技术在术前鉴别宫颈癌组织学亚型及淋巴结转移的临床应用进展。MRI（magnetic resonance imaging）是宫颈癌临床术前最常用的影像学诊断和分期方法。宫颈癌的组织学类型及有无淋巴结转移与患者的生存及预后紧密相关。IVIM-DWI为MRI新型功能成像技术,其D值反映组织内单纯水分子的布朗运动信息、间接反映恶性肿瘤组织细胞密集度;其D^*值和f值能提供肿瘤组织内血流灌注的信息。影像组学的纹理分析技术（texture analysis,TA）通过提取肿瘤组织的纹理特征进行客观、定量分析,能检测人眼不能识别的肿瘤组织的微观改变,揭示更多肿瘤组织的灰度分布与定量数据特征,为临床术前预测宫颈癌不同组织学类型及转移淋巴结提供了可能。     

GLCM based extraction of flame image texture features and KPCA-GLVQ recognition method for rotary kiln combustion working conditions

According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process,a combustion working condition recognition method based on the generalized learning vector(GLVQ) neural network is proposed.Firstly,the numerical flame image is analyzed to extract texture features,such as energy,entropy and inertia,based on grey-level co-occurrence matrix(GLCM) to provide qualitative information on the changes in the visual appearance of the flame.Then the kernel principal component analysis(KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the GLVQ target dimension and network scale greatly.Finally,the GLVQ neural network is trained by using the normalized texture feature data.The test results show that the proposed KPCA-GLVQ classifer has an excellent performance on training speed and correct recognition rate,and it meets the requirement for real-time combustion working condition recognition for the rotary kiln process.     

Limit load and shakedown analysis of plastic structures under stochastic uncertainty

Problems from plastic analysis are based on the convex, linear or linearised yield/strength condition and the linear equilibrium equation for the stress (state) vector. In practice one has to take into account stochastic variations of several model parameters. Hence, in order to get robust maximum load factors, the structural analysis problem with random parameters must be replaced by an appropriate deterministic substitute problem. A direct approach is proposed based on the primary costs for missing carrying capacity and the recourse costs (e.g. costs for repair, compensation for weakness within the structure, damage, failure, etc.). Based on the mechanical survival conditions of plasticity theory, a quadratic error/loss criterion is developed. The minimum recourse costs can be determined then by solving an optimisation problem having a quadratic objective function and linear constraints. For each vector a(·) of model parameters and each design vector x, one obtains then an explicit representation of the “best” internal load distribution F^∗. Moreover, also the expected recourse costs can be determined explicitly. Consequently, an explicit stochastic nonlinear program results for finding a robust maximal load factor μ^∗. The analytical properties and possible solution procedures are discussed.     

Optimizing designs and operations of a single network or multiple interdependent infrastructures under stochastic arc disruption

In this paper, we consider an infrastructure as a network with supply, transshipment, and demand nodes. A subset of potential arcs can be constructed between node pairs for conveying service flows. The paper studies two optimization models under stochastic arc disruption. Model 1 focuses on a single network with small-scale failures, and repairs arcs for quick service restoration. Model 2 considers multiple interdependent infrastructures under large-scale disruptions, and mitigates cascading failures by selectively disconnecting failed components. We formulate both models as scenario-based stochastic mixed-integer programs, in which the first-stage problem builds arcs, and the second-stage problem optimizes recourse operations for restoring service or mitigating losses. The goal is to minimize the total cost of infrastructure design and recovery operations. We develop cutting-plane algorithms and several heuristic approaches for solving the two models. Model 1 is tested on an IEEE 118-bus system. Model 2 is tested on systems consisting of the 118-bus system, a 20-node network, and/or a 50-node network, with randomly generated interdependency sets in three different topological forms (i.e., chain, tree, and cycle). The computational results demonstrate that (i) decomposition and cutting-plane algorithms effectively solve Model 1, and (ii) heuristic approaches dramatically decrease the CPU time for Model 2, but yield worse bounds when cardinalities of interdependency sets increase. Future research includes developing special algorithms for optimizing Model 2 for complex multiple infrastructures with special topological forms of system interdependency.     

To the 70th anniversary of academician V. S. Mikhalevich academician V. S. Mikhalevich as a scientist and science organizer

The article is devoted to the 70th birthday of Academician V. S. Mikhalevich and presents a survey of his fruitful activity as scientist and science organizer. Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 77–100, January–February, 2000.     

Estimation of orientation of a textured planar surface using projective equations and separable analysis with M-channel wavelet decomposition

Estimation of the orientation of a textured planar surface is one of the basic tasks in the area of “shape from texture”. For the solution of this task, many successful approaches were proposed. In this paper, we have examined a few unaddressed questions: First, is there a mathematical formulation that relates the spectral characteristics of the texture pattern and the orientation of an inclined planar surface in a polar-coordinate system? Second, is there a good wavelet-based approach that produces an accurate estimate of the orientation angle of the textured planar surface by analyzing the spectral behavior of one single uncalibrated image?To answer these questions at first we present the formulation of a “texture projective equation”, which relates the depth and orientation of an inclined planar surface in a polar coordinate system with the spectral properties of its image texture. A suitable imaging geometry has been considered to enable separable analysis of the effect of inclination of the texture surface. Next, a method for shape from texture is presented based on discrete wavelet analysis to estimate the orientation of the planar surface. This approach although designed mainly for M-channel wavelets, is also applicable for dyadic wavelet analysis. Texture characteristics in the subbands of wavelet decomposition are analyzed using scalograms, and quantitatively evaluated based on texture projective equations. The proposed method of estimation of the orientation of a planar texture surface is evaluated using a set of simulated and real world textured images.