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AF-DHNN: fuzzy clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

SENSORS 2015年第7期15卷 17366-17396页

作者： Jin, Shan cui, Wen Jin, Zhigang Wang, Ying Tianjin Univ Sch Elect Informat Engn Tianjin 300072 Peoples R China Fire Brigade Hexi Dist Tianjin 300222 Peoples R China Tianjin Polytech Univ Sch Management Tianjin 300387 Peoples R China Nankai Hosp Tradit Chinese Med Tianjin 300102 Peoples R China Guangxi Expt Ctr Informat Sci Guilin 541004 Peoples R China

Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes' status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a fuzzy c-means algorithm (FcMA) and Sorting and classification algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors' detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability.

关键词： wireless sensor networks node fault diagnosis method fire detection adaptive Discrete Hopfield Neural Network fuzzy c-means algorithm fuzzy inference

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Performance analysis for clustering algorithms

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INTERNATIONAL JOURNAL OF cOMPUTING ScIENcE AND MATHEMATIcS 2016年第5期7卷 485-493页

作者： Xue, Yu Zhao, Binping Ma, Tinghuai Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing 210044 Jiangsu Peoples R China Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Jiangsu Peoples R China

There are lots of algorithms for optimal clustering. The main part of clustering algorithms includes the K-means, fuzzy c-means (FcM) and evolution algorithm. The main purpose of this paper was to research the performance and characteristics of these three types of algorithms. One criteria (clustering validity index), namely TRW, was used in the optimisation of classification and eight real-world datasets (glass, wine, ionosphere, biodegradation, connectionist bench, hill-valley, musk, madelon datasets), whose dimension became higher, were applied. We made a performance analysis and concluded that it was easy of the K-means and FcM to fall into a local minimum, and the hybrid algorithm was found more reliable and more efficient, especially on difficult tasks with high dimension.

关键词： optimal clustering K-means fuzzy c-means algorithm hybrid differential evolution algorithm performance analysis high dimension

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To Explore Intracerebral Hematoma with a Hybrid Approach and combination of Discriminative Factors

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METHODS OF INFORMATION IN MEDIcINE 2016年第5期55卷 450-454页

作者： chiu, Hui-chu chiu, Deng-Yiv Lee, Yao-Hsien Wang, chih-cheng Wang, chen-Shu Lee, chi-chung Ying, Ming-Hsiung Wu, Mei-Yu chang, Wen-chih Chung Hua Univ PhD Program Technol Management Hsinchu Taiwan Chung Hua Univ Dept Informat Management Hsinchu Taiwan Chung Hua Univ Dept Finance Hsinchu Taiwan Natl Taipei Univ Technol Dept Informat & Finance Management Taipei Taiwan

Objectives: To find discriminative combination of influential factors of Intracerebral hematoma (IcH) to cluster IcH patients with similar features to explore relationship among influential factors and 30-day mortality of IcH. Methods: The data of IcH patients are collected. We use a decision tree to find discriminative combination of the influential factors. We cluster IcH patients with similar features using fuzzy c-means algorithm (FcM) to construct a support vector machine (SVM) for each cluster to build a multi-SVM classifier. Finally, we designate each testing data into its appropriate cluster and apply the corresponding SVM classifier of the cluster to explore the relationship among impact factors and 30-day mortality. Results:The two influential factors chosen to split the decision tree are Glasgow coma scale (GcS) score and Hematoma size. FcM algorithm finds three centroids, one for high danger group, one for middle danger group, and the other for low danger group. The proposed approach outperforms benchmark experiments without FcM algorithm to cluster training data. conclusions: It is appropriate to construct a classifier for each cluster with similar features. The combination of factors with significant discrimination as input variables should outperform that with only single discriminative factor as input variable.

关键词： Intracerebral hematoma Glasgow coma scale score fuzzy c-means algorithm support vector machine decision tree

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Uncertainty quantification and estimation of closed curves based on noisy data

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cOMPUTATIONAL STATISTIcS 2021年第3期36卷 2161-2176页

作者： chen, Luming Ghosh, Sujit K. NC State Univ Dept Stat Raleigh NC 27695 USA

Estimating closed curves based on noisy data has been a popular and yet a challenging problem in many fields of applications. Yet, uncertainty quantification of such estimation methods has received much less attention in the literature. The primary challenge stems from the fact that the parametrization of a closed curve is not generally unique and hence popular curve fitting methods (e.g., weighted least squares based on known parametrization) does not work well due to initialization instabilities leading to larger uncertainties. First, an initial set of cluster points are obtained by means of a constrained fuzzy c-means algorithm and an initial curve is constructed by fitting a B-spline curve based on the cluster centers. Second, a novel tuning parameter selection procedure is proposed to obtain optimal number of knots for the B-spline curve. Experimental results with simulated noisy data show that the proposed method works well for a variety of unknown closed curves with sharp changes of slopes and complex curvatures, even when moderate to large noises are added with heteroskedastic errors. Finally, a new curvature preserving uncertainty quantification method is proposed based on an adaptation of bootstrap method that provides confidence band around the fitted curve, an aspect that is rarely provided by popular curve fitting methods.

关键词： B-spline curve closed curve fitting fuzzy c-means algorithm

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A robust fuzzy clustering algorithm using spatial information combined with local membership filtering for brain MR images

A robust fuzzy clustering algorithm using spatial informatio...

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IEEE International conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Li, Lanting cao, Peng Yang, Jinzhu Zhao, Dazhe Zaiane, Osmar R. Northeastern Univ Key Lab Intelligent Comp Med Image Coll Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Key Lab Intelligent Comp Med Image Coll Comp Sci & Engn Minist Educ Shenyang Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang Peoples R China Univ Alberta Alberta Machine Intelligence Inst Edmonton AB Canada

ISBN: (纸本)9781728162157

MRI brain segmentation plays an important part in computer-aided diagnosis, which visually reveals the changes in brain structure for doctors to quickly and accurately discover and treat diseases related to brain tissue morphology. The fuzzy c-means (FcM) algorithm performs well when the segmenting images with no noise and with intensity uniformity. However, the MRI brain images are always defective in noise and intensity non-uniformity and thus we propose a novel FcM algorithm named adaptive FcM with neighborhood membership (FcM_anm). We design a filtering process with neighborhood membership to reduce the negative influence of noise and a novel objective function which further considers the spatial membership information adaptively. Finally, to verify the performance of our method, several experiments comparing among the Experimental results demonstrate the proposed method consistently outperforms the state-of-the-art FcM-based algorithms in synthetic images, simulated and real brain MR images with effects of the noise and intensity non-uniformity.

关键词： MRI brain segmentation fuzzy c-means algorithm Spatial information Local membership filtering Adaptive penalty item

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Efficient feature extraction for fast segmentation of MR brain images

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15th Scandinavian conference on Image Analysis

作者： Szilagyi, Laszlo Szilagyi, Sndor M. Benylo, Zoltan Budapest Univ Technol & Econ Dept Control Engn & Informat Technol Budapest Hungary Univ Transylvania Sapientia Hungarian Sci Fac Techn & Human Sci Targu Mures Romania

ISBN: (纸本)9783540730392

Automated brain MR image segmentation is a challenging problem and received significant attention lately. Various techniques have been proposed, several improvements have been made to the standard fuzzy c-means (FcM) algorithm, in order to reduce its sensitivity to Gaussian, impulse, and intensity non-uniformity noises. In this paper we present a modified FcM algorithm, which aims at accurate segmentation in case of mixed noises, and performs at a high processing speed. As a first step, a scalar feature value is extracted from the neighborhood of each pixel, using a filtering technique that deals with both spatial and gray level distances. These features are clustered afterwards using the histogram-based approach of the enhanced FcM algorithm. The experiments using 2-D synthetic phantoms and real MR images show, that the proposed method provides better results compared to other reported FcM-based techniques. The produced segmentation and fuzzy membership values can serve as excellent support for level set based cortical surface reconstruction techniques.

关键词： image segmentation fuzzy c-means algorithm feature extraction noise elimination magnetic resonance imaging

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An efficient fault diagnosis strategy based on SVDD and fuzzy clustering for ground-based electronic equipment

An efficient fault diagnosis strategy based on SVDD and fuzz...

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IEEE International conference on Signal Processing, communications and computing (IcSPcc)

作者： Wang, cheng Yang, Huahui Meng, chen Army Engn Univ Measurement Engn Dept Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781728117089

In this paper, an efficient fault detection approach which employs the Support Vector Data Description (SVDD) and fuzzy c-means algorithm (FcM) is proposed for ground-based electronic equipment. Firstly, the FcM method is applied to fault pattern mining in which the prior knowledge of equipment faults is difficult to be known. Then SVDD model is trained with different faults data independently for multi-classification. This fault diagnosis strategy can be used in health condition monitoring for ground-based electronic equipment. The experimental results verify its effectiveness in fault diagnosis with high accuracy and real-time performance.

关键词： Fault diagnosis support vector domain description fuzzy c-means algorithm condition monitoring electronic equipment

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The color recognition of objects of survey and implementation on real-time video surveillance

The color recognition of objects of survey and implementatio...

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IEEE International conference on Systems, Man and cybernetics

作者： Kuo, Jong Yih Lai, Tai Yu Huang, Fu-chu Liu, Kevin Natl Taipei Univ Technol Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781424465880

For the video surveillance system nowadays, identifying the color of certain footage is paramount. Every time when it comes to a crime scene, the police will be able to extract useful information from the surveillance cameras on the scene. Among that important information, "color" plays a major role and is not affected by the size, location, time or changes in shape of an object. Facts affecting the accuracy and efficiency of real-time color identification include the material and the parameter of camera lens, the parameters of infrared for the hardware part, and efficiency of software algorithms, accuracy and practical degree for the software part, and so forth. Although many methods using static analysis of color have been proposed, they cannot effectively solve the color recognition in the real-time video system. In this paper, we provide an approach using dynamic algorithm for the real-time video system. The methodologies include the reduction of color dimension, color transformation, color classification and real-time color recognition. Lastly, we provide the methods for effectively improving both the efficiency and accuracy of a real-time surveillance system.

关键词： fuzzy c-means algorithm Hard c-means algorithm color Recognition

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construct Knowledge Structure of Linear Algebra

Construct Knowledge Structure of Linear Algebra

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International conference on Mechatronics and Intelligent Materials

作者： chen, chin-chun Lin, Yuan-Horng Yih, Jeng-Ming Huang, Sue-Fen Min Hwei Coll Hlth Care Management Tainan Taiwan Natl Taichung Univ Educat Dept Mat Educat Taichung Taiwan Natl Yunlin Univ Sci & Technol Touliu 64002 Yunlin Taiwan

ISBN: (纸本)9783037850695

Apply interpretive structural modeling to construct knowledge structure of linear algebra. New fuzzy clustering algorithms improved fuzzy c-means algorithm based on Mahalanobis distance has better performance than fuzzy c-means algorithm. Each cluster of data can easily describe features of knowledge structures individually. The results show that there are six clusters and each cluster has its own cognitive characteristics. The methodology can improve knowledge management in classroom more feasible.

关键词： interpretive structural modeling knowledge structure Mahalanobis distance fuzzy c-means algorithm

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White Matter Hyperintensity Segmentation using Multiple Stage FcM 10

White Matter Hyperintensity Segmentation using Multiple Stag...

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1st Amrita AcM-W celebration of Women in computing in India (A2cWic)

作者： Kurian, Dancy Jayasree, R. S. Wilscy, M. Gupta, A. K. Univ Kerala Dept Comp Sci Thiruvananthapuram Kerala India Sree Chitra Thirunal Inst Med Sci & Technol Dept Imaging Sci & Intervent Radiol Thiruvananthapuram Kerala India

ISBN: (纸本)9781450301947

The White Matter Hyperintensities (WMH) clearly visible on T2 weighted FLAIR brain MR images are associated with a number of neurodegenerative diseases including Alzheimer's Disease, vascular dementia, stroke, late-onset late-life depression, Multiple Sclerosis etc. Automated Segmentation of WMH from the brain MRI is used as a diagnostic tool in neuro medicine. In this work we have developed the algorithm to segment the WMH from T2 weighted FLAIR images using fuzzy c-means method in multiple stages. The steps involved are preprocessing of the brain image, skull stripping which uses the intensity based method and morphological operation, segmentation of the WMH. The developed technique is validated using the brain MR images of stroke patients. The volume of the segmented WMH found which can be used for research into risk factors and pathogenesis of neuropsychiatry disorders.

关键词： White Matter Hyperintensities fuzzy c-means algorithm Preprocessing Skull Stripping

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