Partial discharge pattern classification using the fuzzy decision tree approach

Partial discharge (PD) measurement is a proven flaw detection technique for finding cavities that are defects in the insulating material. In this paper, a novel approach for the classification of cavity sizes, based on their maximum PD charge transfer-applied voltage (/spl Delta/Q-V) characteristics using a fuzzy decision tree system, is proposed. The (/spl Delta/Q-V) partial discharge patterns for different cavity sizes are represented by features extracted from their pulse shapes, and the classification rules are directly extracted from the data using the decision tree. The decision rules obtained from the decision tree are then converted to the fuzzy IF-then rules, and the back-propagation algorithm is utilized to tune the parameters of the membership functions employed in the fuzzy classifier. The neuro-fuzzy classification technique is shown to provide successful classification of void sizes in an easily interpretive fashion.     

Knowledge verification for fuzzy expert systems

Abstract

The introduction and use of fuzzy logic has strengthened knowledge representation and reasoning capability in expert systems; nevertheless, it also increases the complexity and difficulty of knowledge verification, which is known to be an important issue for building reliable and high performance expert systems. In the past decade, knowledge verification problems, e.g., redundancy, conflict, circularity and incompleteness of knowledge, have been widely discussed from the viewpoint of using binary logic; nevertheless, the issue of verifying fuzzy knowledge is seldom discussed. In this paper, we attempt to detect potential structural errors among fuzzy rules by proposing a fuzzy verification algorithm. Moreover, a system for verifying fuzzy knowledge base has been developed based on the novel approach.     

SOMRuler: a novel interpretable transmembrane helices predictor

Transmembrane helices (TMH) identification is one of the most important steps in membrane protein structure prediction. Existing TMH predictors tend to pursue accurate computational models without carefully considering the interpretability of these models and thus act as a black box. In this paper, a novel TMH predictor called SOMRuler with excellent interpretability while possessing high prediction accuracy is presented. The SOMRuler uses a self-organizing map (SOM) to learn helices distribution knowledge, which is encoded in the codebook vectors of the trained SOM, from the training samples. Human interpretable fuzzy rules are then extracted from the codebook vectors of the trained SOM. By extracting fuzzy rules from the learned knowledge rather than the original training samples, on the one hand, the computational burden of extracting fuzzy rules can be greatly reduced; on the other hand, the reliability of the extracted rules can also be enhanced since noise contained in the original samples can be smoothened by the learning procedure of SOM. The validity of the fuzzy rules extracted by SOMRuler is qualitatively and quantitatively analyzed. Experimental results on the benchmark dataset show that the SOMRuler outperforms most existing popular TMH predictors and is flexible to suite for a wide variety of problems in bioinformatics. The SOMRuler software is implemented by Java and Matlab and is available for academic use at: http://www.csbio.sjtu.edu.cn/bioinf/SOMRuler/.     

An approach of fuzzy logic evaluation and control in SPC

Quality control plays an important part in most industrial systems. Its role in providing relevant and timely data to management for decision‐making purposes is vital. A method that uses statistical techniques to monitor and control product quality is called statistical process control (SPC), where control charts are test tools frequently used for monitoring the manufacturing process. Engineers or managers can evaluate an abnormal process by using SPC zone rules in control charts. In the conventional use of the zone rules the user is only able to determine whether or not the process is out of control. What action should be taken to adjust the process is uncertain and is evaluated based on knowledge of the system and past experiences. This paper explores the integration of fuzzy logic and control charts to create and design a fuzzy–SPC evaluation and control (FSEC) method based on the application of fuzzy logic to the SPC zone rules. A simulation program implementing FSEC was written in Borland C++ 5.0 and simulation results were obtained and analysed. The abnormal processes simulated were automatically adjusted for each of the zone rules tested and showed an improved performance after the control action, thus confirming the merit of the technique as a special method with the specific numerical control action based on a quality evaluation criterion. Copyright © 2000 John Wiley & Sons, Ltd.     

Design and Implementation of a Fuzzy Area-Based Image-Scaling Technique

In this paper, we propose the design and implementation of an interpolation scheme for performing image scaling by utilizing a dynamic mask combined with a sophisticated neighborhood averaging fuzzy algorithm. The functions that contribute to the final interpolated image are the areas of the input pixels, overlapped by a dynamic mask, and the difference in intensity between the input pixels. Fuzzy if–then rules for these two functions are presented to carry out the interpolation task. Simulation results have shown a fine high-frequency response and a low interpolation error, in comparison with other widely used algorithms. The interpolation can be applied to both gray-scale and color images for any scaling factor. The proposed hardware structure is implemented in a field-programmable gate array (FPGA) chip and is based on a sequence of pipeline stages and parallel processing to minimize computation times. The fuzzy image interpolation implementation combines a fuzzy inference system and an image-interpolation technique in one hardware system. Its main features are the ability to accurately approximate the Gaussian membership functions used by the fuzzy inference system with very few memory requirements and its high-frequency performance of 65 MHz, making it appropriate for real-time imaging applications. The system can magnify gray-scale images of up to 10-bit resolution. The maximum input image size is 1024 $times$ 1024 pixels for a maximum of 800% magnification.      

Efficient Landsat image fusion using fuzzy and stationary discrete wavelet transform

Image fusion is the concept to integrate multiple same scene images while drawing out maximum radiometric information from them by avoiding noise and fictional data. The main objective is to improve the radiometric quality of fused image compared to individual images of the same scene. Existing methods are found to be efficient, but if the similar radiometric information is fused into every image, it produces redundant high frequency of pixels. Therefore, to overcome this issue, in this paper a fuzzy and stationary discrete wavelet transform (FSDWT)-based image fusion technique is proposed. It decomposes Landsat image into stationary values, and then it preserves the radiometric data by using fuzzy if-then rules. In the last phase, FSDWT injects high-frequency blocks from input images and returns a single Landsat image with maximum radiometric data. Quantitative analysis has clearly demonstrated that FSDWT has better structural detail, spatial resolution and spectral information than existing methods.     

Recognition of defect spatial patterns in semiconductor fabrication

This research develops an analytical structure comprised of a filtering scheme and a fuzzy rule-based inference system to help identify defect spatial patterns. These defect spatial patterns include ring, scratch, zone and repeating types. A set of image processing masks is designed to locate defect positions and then the filtering scheme is applied to extract defect clusters on wafers. With clearly identified defect clusters, three features of minimum rectangle area are used to locate and describe the shape of defect clusters. When all possible defect clusters are well represented by minimum rectangle areas, a set of fuzzy rules are established to combine all the defect clusters and therefore defect spatial patterns are identified. The experiments show that the present approach can effectively identify different defect spatial patterns on wafers.     

Expert computer knowledge-based methodology using fuzzy sets for evaluating water conservation programs in Jordan

This paper aims to devise a new methodology for evaluating water conservation programs based on expert computer knowledge-based systems and fuzzy set analyses. Computer-programmed surveys were carried out on a random sample of water users in Amman City, Jordan, before and after they received water educational material. Relative weights were given for each response and a cumulative grade was estimated. The follow-up surveys reveal that a reasonable rise of water-related knowledge is achieved after respondents are exposed to water educational material. Fuzzy set analyses reveal that some fluctuations in the relative rise of public awareness programs may be attributed to insufficient knowledge. The fuzzy set logic technique enabled us to condense a great many data into a small set of variable rules. This in turn made our expert system effective in evaluating water conservation programs. Electronic Publication     

一种基于软计算的转子故障诊断方法

提出了一种基于软计算的转子故障诊断方法。该方法充分利用软计算中的模糊集合理论,人工神经网 络,粗糙集理论和遗传算法等计算方法优势,弥补它们相互的不足,进行故障诊断。首先利用粗糙集理论对样本数据进 行初步规则获取,并计算规则的依赖度和条件覆盖度,然后根据这些规则进行网络设计,其中,网络隐层节点的数目等于 规则的数目,初始网络权重由规则的依赖度和条件覆盖度确定,最后用遗传算法对模糊神经网络参数进行优化。使用该 网络对转子类常见故障进行诊断。实验表明,和一般模糊神经网络相比,这种基于软计算的诊断方法具有训练时间短、 诊断准确率高的特点。     

A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming

We present an intelligent technique for image denoising problem of gray level images degraded with Gaussian white noise in spatial domain. The proposed technique consists of using fuzzy logic as a mapping function to decide whether a pixel needs to be krigged or not. Genetic programming is then used to evolve an optimal pixel intensity‐estimation function for restoring degraded images. The proposed system has shown considerable improvement when compared both qualitatively and quantitatively with the adaptive Wiener filter, methods based on fuzzy kriging, and a fuzzy‐based averaging technique. Experimental results conducted using an image database confirms that the proposed technique offers superior performance in terms of image quality measures. This also validates the use of hybrid techniques for image restoration. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 224–231, 2007     

Efficient impulse noise removal using hybrid neuro-fuzzy filter with optimized intelligent water drop technique

Impulse noise (IN) affects the digital image, during transmission, digital storage, and image acquisition. IN removal from an image is necessary as it retains the quality of the image. This work concentrates on the IN. A neuro-fuzzy (NF) system based on a fuzzy technique which is trained by a learning algorithm derived from neural network theory was implemented for the removal of noise. A NF network for noise filtering in grayscale images that combines two NF filters with a postprocessor to produce the output was presented. However, Sugeno-type is not intuitive technique and it also less accurate. To overcome these problems, a hybrid NF filter with optimized intelligent water drop (IWD) technique is introduced, where hybridized Sugeno–Mamdani-based fuzzy interference system is implemented in both the NF filters to obtain more efficient noise removal system. To improve the accuracy of the assignment of membership values to each input pixels, the optimized IWD technique is utilized, as the choice of membership function decides the efficiency of the noise removal in the images. Here, Fuzzy rules have been used to obtain the filtered output. The Hybrid method maintains the accuracy of the Sugeno model and also the interpretable capability of the Mamdani model. This method is robust against the IN and it is flexible, efficient, and accurate than existing filtering method in both noise attenuation and detail preservation and it has a great scope for better real-time applications.     

Generating interpretable fuzzy rules for adaptive job dispatching

Adaptive scheduling is an approach that selects and applies the most suitable strategy considering the current state of the system. The performance of an adaptive scheduling system relies on the effectiveness of the mapping knowledge between system states and the best rules in the states. This study proposes a new fuzzy adaptive scheduling method and an automated knowledge acquisition method to acquire and continuously update the required knowledge. In this method, the criteria for scheduling priority are selected to correspond to the performance measures of interest. The decisions are made by rules that reflect those criteria with appropriate weights that are determined according to the system states. A situated rule base for this mapping is built by an automated knowledge acquisition method based on system simulation. Distributed fuzzy sets are used for evaluating the criteria and recognizing the system states. The combined method is distinctive in its similarity to the way human schedulers accumulate and adjust their expertise: qualitatively establishing meaningful criteria and quantitatively optimizing the use of them. As a result, the developed rules may readily be interpreted, adopted and, when necessary, modified by human experts. An application of the proposed method to a job-dispatching problem in a hypothetical flexible manufacturing system (FMS) shows that the method can develop effective and robust rules.     

用模糊差分滤波法检测闪光辐射照相物体边界

文中对闪光辐射照相的模糊成象过程进行了计算分析。利用计算分析获得的先验认识 ,提出了用模糊差分滤波法增强闪光辐射照相图象的高频分量 ,以突出边界轮廓 ,从而达到从模糊图象上精确定量检测物体边界的目的     

Fuzzy entropy‐based MR brain image segmentation using modified particle swarm optimization

This article presents an image segmentation technique based on fuzzy entropy, which is applied to magnetic resonance (MR) brain images in order to detect brain tumors. The proposed method performs image segmentation based on adaptive thresholding of the input MR images. The image is classified into two membership functions (MFs) of the fuzzy region: Z‐function and S‐function. The optimal parameters of these fuzzy MFs are obtained using modified particle swarm optimization (MPSO) algorithm. The objective function for obtaining the optimal fuzzy MF parameters is considered to be the maximum fuzzy entropy. Through a number of examples, The performance is compared with existing entropy based object segmentation approaches and the superiority of the proposed method is demonstrated. The experimental results are compared with the exhaustive search method and Otsu's segmentation technique. The result shows the proposed fuzzy entropy‐based segmentation method optimized using MPSO achieves maximum entropy with proper segmentation of infected areas and with minimum computational time. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 281–288, 2013     

消除图像脉冲噪声的模糊结合滤波器

提出模糊结合滤波用于消除脉冲噪声方法。它由噪声率检测、噪声污染程度W评价和模糊结合滤波器组成。根据选择中值滤波或最大最小排除均值滤波。由W确定模糊隶属函数及模糊判决规则。建立了模糊结合滤波的数学模型。模拟实验表明,椒盐噪声概率为80%时,滤波输出的峰值信噪比为25.8dB,均方误差为171,而且能很好地保护图像细节。     

A new approach to dynamic fuzzy modeling of genetic regulatory networks

In this paper, the dynamic fuzzy modeling approach is applied for modeling genetic regulatory networks from gene expression data. The parameters of the dynamic fuzzy model and the optimal number of fuzzy rules for the fuzzy gene network can be obtained via the proposed modeling approach from the measured gene expression data. One of the main features of the proposed approach is that the prior qualitative knowledge on the network structure can be easily incorporated in the proposed identification algorithm, so that the faster learning convergence of the algorithm can be achieved. Two sets of data, one the synthetic data, and the other the experimental SOS DNA repair network data with structural knowledge, have been used to validate the proposed modeling approach. It is shown that the proposed approach is effective in modeling genetic regulatory networks.     

模糊推理在边缘检测中的应用

针对传统边缘检测算法边缘轮廓模糊、易产生伪边缘等缺点,提出一种基于模糊逻辑推理策略的边缘检测新方法,在无需确定阈值的情况下对数字图像进行边缘提取。给出一种以变化的3×3模板为基准的模糊推理规则,通过对周围像素点的垂直和水平方向以及连续四点像素的黑白分布的判断,提取出图像中的黑点、白点和边缘部分。实验结果表明:与Robert算法相比,该方法对图像中噪声和双重边缘的抑制以及对曲线的圆度轮廓的提取都有很好的效果。     

基于模糊逻辑的产品意象造型设计研究

为设计出符合消费者感性需求的产品,采用基于模糊逻辑的产品意象造型设计方法。首先确定感性词汇与造型设计要素,利用模糊逻辑建立二者之间的关系。通过模糊化、模糊规则的构建、模糊推理以及反模糊化等过程进行模糊逻辑控制器的设计,用Matlab建立仿真模型,最后通过测试验证了模型的有效性。结合折叠自行车造型设计进行研究,结果表明该方法是正确可行的。     

基于证据理论的产品选型决策方法

 基于模糊数学原理和证据理论,提出了综合处理不确定性设计信息、合理选择产品机械结构类型的方法。为此,首先利用已有的产品实例为每个产品子类提取一组设计规则,并为每个子类构造一个可以描述其典型特征的模糊向量;然后在模糊机制下统一处理以普通实数、实数域上的区间值和模糊数表达的设计信息,采用模糊推理、证据理论等不精确推理方法处理不确定性信息,确定产品类型。上述方法可作为智能设计系统求解设计问题的推理策略,并实现知识的自动获取。通过一个选型设计的实例,验证了该方法的正确性。