Efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization

The fuzzy c-partition entropy has been widely adopted as a global optimization technique for finding the optimized thresholds for multilevel image segmentation. However, it involves expensive computation as the number of thresholds increases and often yields noisy segmentation results since spatial coherence is not enforced. In this paper, an iterative calculation scheme is presented for reducing redundant computations in entropy evaluation. The efficiency of threshold selection is further improved through utilizing the artificial bee colony algorithm as the optimization technique. Finally, instead of performing thresholding for each pixel independently, the presented algorithm oversegments the input image into small regions and uses the probabilities of fuzzy events to define the costs of different label assignments for each region. The final segmentation results is computed using graph cut, which produces smooth segmentation results. The experimental results demonstrate the presented iterative calculation scheme can greatly reduce the running time and keep it stable as the number of required thresholds increases. Quantitative evaluations over 20 classic images also show that the presented algorithm outperforms existing multilevel segmentation approaches.     

A fuzzy neural network approach for online fault detection in waste water treatment process

In this paper, an effective strategy for fault detection of sludge volume index (SVI) sensor is proposed and tested on an experimental hardware setup in waste water treatment process (WWTP). The main objective of this fault detection strategy is to design a system which consists of the online sensors, the SVI predicting plant and fault diagnosis method. The SVI predicting plant is designed utilizing a fuzzy neural network (FNN), which is trained by a historical set of data collected during fault-free operation of WWTP. The fault diagnosis method, based on the difference between the measured concentration values and FNN predictions, allows a quick revealing of the faults. Then this proposed fault detection method is applied to a real WWTP and compared with other approaches. Experimental results show that the proposed fault detection strategy can obtain the fault signals of the SVI sensor online.     

On solutions to fuzzy stochastic differential equations with local martingales

This paper deals with a class of fuzzy stochastic differential equations (FSDEs) driven by a continuous local martingale under the Lipschitzian condition. Such equations can be useful in modeling hybrid systems, where the phenomena are simultaneously subjected to two kinds of uncertainties: randomness and fuzziness. The solutions of the FSDEs are the fuzzy stochastic processes, and their uniqueness is considered to be in a strong sense. Thus, the existence and uniqueness of solutions to the FSDEs under the Lipschitzian condition is first proven. Moreover, some asymptotic properties of the solutions to the FSDEs are investigated. Finally, an illustrating example on the interest term model is provided.     

基于证据理论的陶瓷智能感观评估

陶瓷感官评估结果的表达形式包括语言词汇、象征性符号和数值描述等，它们具有不确定性和不精确性。本文首先运用模糊逻辑技术对这些评估结果定量化，并基于证据理论提出了一种陶瓷智能感观数据融化方法，得到了基于感观数据的质量分级，并取得了较好的实验结果。     

基于EFPN的模糊离散事件系统可诊断性分析

针对模糊有限自动机及模糊Petri网在模糊离散事件系统(Fuzzy Discrete Event System,FDES)行为建模中的不足,提出一种扩展模糊Petri网(Extended Fuzzy Petri Net,EFPN),并基于EFPN构造了FDES行为模型及系统故障诊断器。EFPN以一种新的事件模型为基础对FDES的系统行为进行建模,并根据事件触发矩阵计算下一系统状态中各分量的可能性分布。与模糊有限自动机及模糊Petri网的对比分析表明,在描述FDES模糊事件时,对于同步距离为0的一组事件所造成的影响,EFPN的表达能力强于经典模糊Petri网;在对含有多个状态分量的FDES建模时,EFPN模型的规模远小于模糊有限自动机。在基于EFPN构造的系统行为模型中,运用可达状态标识图分析系统状态集,可有效地构造FDES的故障诊断器,并可据此进行系统可诊断性分析。     

FO-CA:一种基于距离差异度组合权重的多属性数据分类方法

为了解决多属性数据分类问题,提出了一种基于模糊优选模型与聚类分析的分类方法(FO-CA)。首先由模糊优选模型得到有序综合指标数据集,其中在权重阶段提出了距离差异度并以此为依据构建了一种组合主客观权重的赋权方法;然后采用聚类分析将有序综合指标数据集聚类为几个簇进而分类;最后选取UCI中的Iris、Wine和Ruspini 3个数据集进行仿真实验。实验结果表明,该分类方法相比模糊优选方法及K-Means算法能获得更好的分类结果,对决策者有一定的参考价值。     

均衡模糊C均值聚类算法

模糊C均值聚类算法没有考虑各类样本容量因素,当各类样本容量差异较大时,其聚类判决将向小样本类倾斜。提出一种新的聚类算法——均衡模糊C均值聚类,对模糊C均值聚类算法最小化目标函数进行修正,使得改进的目标函数包含了样本容量因素,利用粒子群算法并以样本模糊隶属度为编码对象求解参数优解。从理论上分析了该算法的性质,通过仿真实验验证了所提算法对平衡、不平衡数据集的有效性。     

一种基于计算机视觉的铁轨扣件缺失检测方法

针对传统扣件检测方法式效率低、可靠性差,不能满足现代铁路检修的需要,提出了一种基于计算机视觉的扣件缺失自动检测方法。在对灰度图像进行Canny边缘检测处理后采用十字交叉定位法对扣件位置进行定位,得到120×200像素的扣件区域,并提取扣件图像的20个边缘特征值;最后,利用模糊C均值聚类算法对这两类的特征量进行聚类分析,通过计算待诊断对象与标准模式的隶属度实现对扣件状态的分类。应用验证表明：采用的图像处理方法和识别分类算法能够有效检出轨道扣件缺失,检测速度快,鲁棒性好,检出率达96%。     

板翅式换热器模糊优化设计

应用结构模糊优化理论对板翅式换热器进行多目标优化设计；给出了构造隶属函数及确定目标函数权重的方法，通过线性加权法将多目标化为单目标求解。应用此软件设计了一台换热器，其实际使用结果表明完全达到设计要求。