Fuzzy C-means Clustering Method Research Articles

Typhoon Maria Precipitation Retrieval and Evolution Based on the Infrared Brightness Temperature of the Feng-Yun 4A/Advanced Geosynchronous Radiation Imager

Recognizing the importance and challenges inherent in the remote sensing of precipitation in typhoon monitoring, a study of the Advanced Geosynchronous Radiation Imager (AGRI) data from Feng-Yun 4A on typhoon precipitation was conducted. First, Typhoon Maria was selected to statistically analyze the AGRI infrared brightness temperature in the “precipitation” and “nonprecipitation” channels of the field of view. When there was precipitation, the brightness temperature of the AGRI channel changed significantly. Second, the shrunken locally linear embedding algorithm (SLLE) was adopted to carry out the retrieval of precipitation based on the brightness temperatures of AGRI infrared channels 8–14. The contribution rate of the brightness temperature at different channels to the objective function of precipitation retrieval model was obtained by the Bayesian model averaging (BMA). Based on the preliminary experimental “quantification” evaluation index, we concluded that the method adopted in this paper can be used to retrieve precipitation in infrared data and to retrieve the spiral cloud rain bands of a typhoon. Finally, based on the AGRI channel brightness temperature of a 10.8-micron window channel, we applied the membership degree information of a typhoon’s dominant cloud system from the fuzzy c-means (FCM) clustering method to modify precipitation retrieval results. The results were used to obtain the main morphological structure of typhoon precipitation. By further analyzing the temporal variation of dominant cloud system development using the FCM method, we concluded that the brightness temperature gradient can assist in the analysis of the variation of a typhoon’s intensity. This method can be applied to the continuous retrieval of large-scale precipitation. Precipitation retrieval via the AGRI can yield indicators for typhoon precipitation warnings and forecasts, thus providing a reliable reference tool for disaster prevention and mitigation.

Robust System Separation Strategy Considering Online Wide-Area Coherency Identification and Uncertainties of Renewable Energy Sources

With the fast growth of renewable energy sources (RES), more and more uncertainties are involved and influencing the stable operation of power systems. Controlled islanding is the last measure to prevent power system blackouts, thus this paper aims to propose a novel model of system separation based on Online Coherency Identification and Adjustable Robust Optimization Programming (OCI-AROP) for minimizing load shedding considering the uncertainties of RES. First, Fuzzy C-Means (FCM) clustering method with F -statistics is utilized to identify the coherent generator groups with the frequency data measured by Phasor Measurement Units (PMUs). Then, the OCI-AROP model considering coherent group constraints, connectivity constraints and robustness constraints about RES are presented. Finally, the case studies on IEEE-39 bus system and WECC-179 bus system are employed to demonstrate the effectiveness of the proposed OCI-AROP model, and comparisons among the OCI-AROP model and the other models are also given to show its superiority.

Decision Support System For Determining The Single Tuition Group (UKT) In State University Of Medan Using Fuzzy C-Means

The Single Tuition System (UKT) is a portion of the Single Tuition Fee that is paid to each student based on his economic ability. UKT which applied at State Universities in Indonesian is a policy aimed at helping and reducing the cost of student education. Decree of Menristekdikti Number 91/M/KPT/2018 concerning Single Tuition Fee (BKT) and Single Tuition (UKT) at State Universities in the Ministry of Research, Technology, and Higher Education in 2018 divides UKT into seven groups. The purpose of this study is to develop a decision support system to determine the UKT group using the Fuzzy C-Means Clustering method. The decision support system is developed based on the web using the Fuzzy C-Means Clustering method. The data used as a sample is the data of undergraduate students of the Faculty of Mathematics and Natural Sciences, The State University of Medan in 2018/2019. The parameters used for grouping UKT are: the amount of parents’ income, the number of siblings based on the Family Card, the condition of the Father (Life or Death), the condition of the Mother (Life or Death), the Building Land Tax (PBB), the Motor Vehicle Tax (PKB) total last one year, and the last one month electricity costs. The grouping results showed that 76% were in groups 1 to 4, and 24% were in groups 5 to 7. However, the results of this clustering are not final due to inaccuracies in filling data for several parameters, so that data validation is required by checking from other sources of information.

Top-Down Method Used for Pancreas Segmentation

Image segmentation is actively an imperative title role in image analysis. Image segmentation is advantageous in many applications like traffic detection, surface crack identification, medical image analysis, face recognition, crop disease detection. Two Approaches are used for automatic pancreas segmentation. Top-Down and Bottom-Up approach used for CT image segmentation. In Top Down approach, Grey Level Co-occurrence Matrix, Simple Linear Iterative Clustering, Scale-Invariant Feature transform, Novel Modified Kernel fuzzy c-means clustering (NMKFCM) and Kernel Density Estimator methods used and automatic bottomup technique is used for pancreas subgrouping in C.T. scans. Top-Dow approach accuracy rate is less than bottom-up approach. Top-down approach required less time period as compare to bottom-up approach. In top-down approach, input image manually selected and processed it. KDE, NMKFCM and SIFT are used to detect feature of image. NMKFCM works on neighborhood point value. In KDE, Edge detection based on the kernel estimation of the probability density function .In SIFT, comprehensive information of local feature of image is focused.

Sensitivity analysis of segmentation techniques and voxel resolution on rock physical properties by X-ray imaging

Pore and grain phases separation significantly affect the rock physical properties extracted by digital rock measurement techniques (DRMTs). Previous studies mainly focus on approaches to predict the physical properties of rocks, and less attention is paid on the effects of segmentation techniques and voxel resolution on rock physical properties. Physical properties of rocks are not still well understood using DRMTs with less experiment effort. In this work, 6 segmentation algorithms and 4 voxel resolutions combined with X-ray CT imaging are applied to the Stainton and Clashach sandstone, Indiana limestone and Estaillades carbonate samples to understand their effects on rock physical properties. Moreover, threshold-porosity relations are proposed to accurately segment the pores and grains in rocks. Results show that voxel resolution has little to no impact on the rock physical properties. However, the segmentation algorithms significantly affect these properties, in which the Ostu's method, Iterative self-organizing data (IsoData) method and Fuzzy c-mean clustering (FMC) method should be alternative methods to separate the pore and grain phases in sandstones. For carbonates, under-segmentation is still inevitable with these techniques. Rock physical properties calculated by the proposed threshold-porosity relations are closer to the experiment results than those calculated by the classical segmentation methods.

RETRACTED ARTICLE: Rough fuzzy region based bounded support fuzzy C-means clustering for brain MR image segmentation

Precise brain tissue segmentation and analysis in the presence of intensity non-uniformity (INU) and noise is the challenging task due to intensity overlaps between data pixels within the image. The clustering method is commonly used to variety of applications for grouping similar data items. Specifically, the fuzzy C-means (FCM) clustering method is extensively used in many real world and research applications. In this paper, the rough fuzzy region based bounded support fuzzy C-means (RFRBSFCM) clustering method is proposed for brain MR image INU estimation and correction, and segmentation. The rough fuzzy regions are estimated based on similarity distance vector and it is determined from both local and global spatial information. In addition, the proposed algorithm incorporates bounded support vector for estimating weighted image. The objective function of proposed algorithm is minimized for segmenting different tissues in brain MR image. The RFRBSFCM algorithm is tested with recent FCM clustering techniques using simulated T1 and T2-weighted brain MR images from public BrainWeb dataset. The quantitative results confirm that the proposed algorithm achieves superior performance than other recent state-of-the-art methods.

Improving Multi-Histogram-Based Reversible Watermarking Using Optimized Features and Adaptive Clustering Number

For the multi-histogram-based reversible watermarking (MHRW) scheme, the performance greatly depends on the multi-histogram construction, which remains a challenge in this field. To generate more desirable multi-histograms, this paper improves the MHRW using the Fuzzy C-Means (FCM) clustering technique by developing the following approaches: 1)optimize the original feature set, 2)adopt an alternative FCM (AFCM) clustering method, and 3)determine adaptively the optimal clustering number for low embedding rates. These approaches are then integrated to bring about the proposed scheme, i.e., the improved MHRW (IMHRW). Extensive simulations show that the proposed scheme improves the performance of multi-histogram-based reversible watermarking, and it is comparable to or even better than the state of the arts. This thus demonstrates the feasibility and effectiveness of the proposed scheme.

Prediction Model of Shield Performance During Tunneling via Incorporating Improved Particle Swarm Optimization Into ANFIS

This paper proposes a new computational model to predict the earth pressure balance (EPB) shield performance during tunnelling. The proposed model integrates an improved particle swarm optimization (PSO) with adaptive neurofuzzy inference system (ANFIS) based on the fuzzy C-mean (FCM) clustering method. In particular, the proposed model uses shield operational parameters as inputs and computes the advance rate as the output. Prior to modeling, critical operational parameters are identified through principle component analysis (PCA). The hybrid model is applied to the prediction of the shield performance in the tunnel section of Guangzhou Metro Line 9 in China. The prediction results indicate that the improved PSO-ANFIS model shows high accuracy in predicting the EPB shield performance in terms of the multiobjective fitness function [i.e. root mean square error $(RMSE) = 0.07$ , coefficient of determination ( $R^{2}) = 0.88$ , variance account $(VA) = 0.84$ for testing datasets, respectively]. The good agreement between the actual measurements and predicted values demonstrates that the proposed model is promising for predicting the EPB shield tunnel performance with good accuracy.

An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

Reliable and accurate real-time traffic flow state identification is crucial for an intelligent transportation system (ITS). This identification is a prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved traffic flow state identification model that is based on selective ensemble learning (SEL). First, we adopted the fuzzy C-means (FCM) clustering method to divide the traffic flow data into three main kinds of traffic flow states and obtained the parameters that correspond to each kind of traffic flow state. Second, we applied the random subspace (RS) algorithm as the ensemble method and support vector machine (SVM) model as base learners to construct the RS-SVM ensemble model for traffic flow identification. Significantly, the discrete binary particle swarm optimization (BPSO) algorithm with global optimization search ability was employed to select the classifiers obtained by the random subspace training in the ensemble system. We experimentally validated the effectiveness of the proposed BPSO–RS-SVM-SEL approach. The research results reveal that compared with other classical traffic flow state identification methods, the proposed model has a higher maximum accuracy of 98.68%. It can be seen that our model improves the classification accuracy of traffic flow state identification and the difference in the ensemble system to a certain extent.

A Novel Hybrid Active Contour Model for Intracranial Tuberculosis MRI Segmentation Applications

This paper proposes an improved region-based active contour model for segmenting magnetic resonance imaging (MRI) images of brain tuberculosis by combining a global energy fitting term and a local energy fitting term. First, a global energy fitting term is utilized to extract global image information, which guides the evolving curve globally and approximates the image intensity inside and outside the contour. Second, a local energy fitting term is proposed to describe the intensity inhomogeneity based on the local intensity variance and the adaptive image difference. Third, an improved Fuzzy C-Means (FCM) clustering method is applied to pre-segment the MRI images to automatically track the approximate location of brain tuberculosis and provide the initial contour for the hybrid model segmentation. By combining the global and local weighting functions, a hybrid region-based model is defined. Experiments demonstrate that the proposed model provides initialization of the contours automatically and offers superior segmentation performance for brain tuberculosis MRI medical images with intensity inhomogeneity.

ILivSpot: Secure Biometric System based on Iris Liveliness Detection

Liveliness detection aims to determine whether the iris presented to the sensor belongs to a live subject or it is a fake one. Liveliness detection is to classify input sample into one of the category between fake and real. This work proposes an improved biometric system which recognizes the liveliness of the iris samples in order to increase the security. In this work, the dataset of UBIRIS.v2 is used where input samples are segmented into pupil, sclera and iris and these individual segments are filtered to enhance the quality of the samples. Further, the segmentation using Fuzzy C-Mean and K-Mean clustering methods is done. Different features are extracted and fused thereafter. Fused features are then used as a training data. For testing purpose, a combined dataset of original and fake samples is used and accuracy of the system is calculated with a novel hybrid classifier AHyBrK which is a combination of ANN and KNN. Results achieve 97% accuracy in differentiating between fake and live which is 8.2% better than KNN and 5.1% better than ANN

A new method for determination of time-of-day breakpoints based on clustering and image segmentation

Signal control is an important part of the transportation system and plays an important role in improving the capacity of intersections. This paper proposes a new traffic time division method for multiperiod fixed-time control strategy. Firstly, we put forward a new concept — transportation overlap rate — to complete the clustering of daily traffic flow patterns. Then, all the daily traffic flow data belonging to the same category are composed into a matrix, which is converted into the corresponding image later with the aim of using the fast and robust fuzzy C-means clustering (FRFCM) method to segment it. Finally, the traffic time division and breakpoint location are obtained through further analysis and processing of the segmentation results. For each period, the optimal signal cycle and green split are separately calculated by Webster’s signal timing method to satisfy different traffic demands of each period and effectively improve the operation efficiency of the intersection. The simulation results at a certain intersection in the city of Mianyang demonstrate the effectiveness and practicability of the proposed method.

Fuzzy C-Means Based Clustering and Rule Formation Approach for Classification of Bearing Faults Using Discrete Wavelet Transform

The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.

Detection of HTTP flooding attacks in cloud using fuzzy bat clustering

Cloud computing plays a major role in reducing the expenditure of infrastructural costs on the basis of pay per use model. Security is the major concern wherein detection of security attacks and crimes is very difficult. Due to the distributed nature of attacks and crimes in the cloud, there is a need for an efficient security mechanism. Traditional security mechanisms cannot be applied directly to identify the source of attack due to the dynamic changes in the cloud. Hypertext Transfer Protocol (HTTP) flooding attacks are identified by keeping track of all the activities of the virtual machine instances running in the cloud. It is hard to identify the source of an attack since an attacker deletes all the possible traces. So, in order to mitigate this issue, the proposed method reads the logs, extracts the relevant features and investigates HTTP flooding attacks by a grouping of similar input patterns using fuzzy bat clustering and determines the anomalous behavior using deviated anomalous score. The suspicious source is determined by finding the event correlation between the virtual machine instance issued by cloud service provider with the suspicious source list. The experimental results are compared with the existing approaches, viz., k-means clustering, fuzzy c-means clustering, bat clustering and Bartd method in which the proposed method determines the anomalies accurately with very few false alarm than existing approaches.

Optimal sensor placement for leak localization in water distribution networks based on a novel semi-supervised strategy

Optimal sensor placement (OSP) techniques are important to monitor water distribution networks (WDNs). However, these methods are only feasible under the premise of complete leak location information. In fact, partial leak locations may become lost or may not be recorded. Therefore, in this case, the previous OSP methods are not applicable. To overcome this problem, this paper proposes a new OSP strategy considering that some leak locations are unknown, meaning that they are under semi-supervised conditions. The proposed semi-supervised strategy and the original semi-JMI algorithm are applied to two benchmark networks. The results indicate that the monitoring nodes selected by the semi-JMI criterion will lead to the occurrence of monitoring blind zones, resulting in the degradation of the leak localization performance. The addition of the fuzzy c-means (FCM) clustering method will compensate for this problem and improve the accuracy and the stability of leak localization.

IPFCM Clustering Algorithm Under Euclidean and Hausdorff Distance Measure for Symbolic Interval Data

In this paper, a novel interval possibilistic fuzzy c-means (IPFCM) clustering method is proposed for clustering symbolic interval data. Clustering algorithms are important methods that required in pattern recognition, data mining and text mining, etc. Most of cluster algorithms focus on single-valued data. The interval fuzzy c-means (IFCM) clustering method is first proposed for symbolic interval data. However, the IFCM may not have good performance when facing noisy date or data with outliers. Hence, the advantages of the proposed IPFCM clustering algorithm in this study mainly overcome the noisy date or data with outliers in symbolic interval data. In the proposed approach, two different distances; namely, Euclidean and Hausdorff distance measures are all individually considered. That is, based on Lagrange multipliers nonlinear programming method, the proposed IPFCM clustering algorithm under Euclidean and Hausdorff distance measure was individually derived for symbolic interval data. From our experimental results, the proposed IPFCM clustering algorithm indeed has better performance than the IFCM and the interval fuzzy possibilistic c-means (IFPCM) clustering algorithm under different distance measures for the noisy date or data with outlier problem. Besides, the proposed IPFCM clustering algorithm is also implemented on windows smart phone platform and shown better performance as expected. Finally, a real data set is added for testing.

Collector System Topology for Large-Scale Offshore Wind Farms Considering Cross-Substation Incorporation

Double-sided ring topology has been widely applied in collector system planning for large-scale offshore wind farms (OWF), owing to its high reliability against cable faults. For collector systems with multiple substations, the conventional topology designs isolate the operation of substations, which requires high transformer capacity within each substation to satisfy the redundancy need. This paper first investigates the economic and reliability benefits of multi-substation incorporation (CSI), and then proposes a new double-sided ring topology with CSI for large-scale OWF collector systems. It encourages the transformer capacity sharing among multiple substations to improve the economic efficiency of collector system and in the meantime preserve the reliability advantage of ring topology. To achieve the optimal topology design, Fuzzy C-means (FCM) clustering method is adopted to locate substations and allocate turbines, and the Two-Phase Clark and Wright's saving algorithm is modified and applied to optimize the cable connection for the proposed topology so as to minimize the overall collector system cost. The proposed topology is tested and compared with traditional isolated topologies on two benchmark collector systems. The test results verify that the proposed the highest economic efficiency while maintaining the collector system reliability.

Typhoon Cloud System Identification and Forecasting Using the Feng-Yun 4A/Advanced Geosynchronous Radiation Imager Based on an Improved Fuzzy Clustering and Optical Flow Method

This study adopted an improved fuzzy clustering and optical flow method for the multiscale identification and forecasting of a cloud system based on the cloud images from a 10.8-micron infrared channel of the Advanced Geosynchronous Radiation Imager. First, we used the locally constrained fuzzy c-means (FCM) clustering method to identify typhoon-dominant cloud systems. Second, we coupled the background field-constrained optical flow method with the semi-Lagrangian scheme to forecast typhoon-dominant cloud systems. The experimental results for Typhoon Maria showed that the improved FCM method was able to effectively identify changes in the cloud system while retaining its edge information through the effective removal of the offset field. The identified dominant cloud system was consistent with the precipitation field of the Global Precipitation Measurement mission. We optimized the semi-Lagrangian nonlinear extrapolation of the optical flow field by introducing background field information, thus improving the forecast accuracy of the optical flow field. Based on the assessment indicators of structural similarity, normalized mutual information, peak signal-to-noise ratio, relative standard deviation, and root mean square error, the forecast results demonstrated that the forecast effect of the background field-constrained optical flow method was better than that of the standard optical flow method.

Intelligent image segmentation model for remote sensing applications

The conventional JSEG algorithm has a powerful detection capability on the homogeneity of regional texture features because it combines the spectral information with image texture features during the segmentation. However, the conventional JSEG method is not very accurate for the target edge locali zation in segmentation results. To solve this problem, this paper proposes an improved segmentation method of remotely sensed image based on JSEG algorithm and fuzzy c-means (FCM) with spatial constraints. Firstly, the FCM clustering method based on spatial neighborhood terms is used to replace the traditional HCM clustering method in the quantization step. Then the region growing method is applied to segment the class diagram after FCM clustering. Finally, the proposed method uses the improved regional merger approach to merger the over divided region after segmentation. According to the J index, the proposed algorithm is improved by 31% and 12% compared with the traditional JSEG segmentation method and improved by 17% and 8% compared with the FNEA segmentation algorithm for aerial image and the SPOT 5 image. The experimental results show that the proposed segmentation algorithm has good noise immunity because of the fuzzy clustering of spatial constraints and can extract the edge of the target more accurately.

Fuzzy clustering algorithm based on modified whale optimization algorithm for automobile insurance fraud detection

Fuzzy c-means (FCM) clustering method is used for performing the task of clustering. This method is the most widely used among various clustering techniques. However, it gets easily stuck in the local optima. whale optimization algorithm (WOA) is a stochastic global optimization algorithm, which is used to find out global optima of a provided dataset. The WOA is further modified to achieve better global optimum. In this paper, a fuzzy clustering method has been proposed by using the strengths of both modified whale optimization algorithm (MWOA) and FCM. The effectiveness of the proposed clustering technique is evaluated by considering some of the well-known existing metrics. The proposed hybrid clustering method based on MWOA is employed as an under sampling method to optimize the cluster centroids in the proposed automobile insurance fraud detection system (AIFDS). In the AIFDS, first the majority sample data set is trimmed by removing the outliers using proposed fuzzy clustering method, and then the modified dataset is undergone with some advanced classifiers such as CATBoost, XGBoost, Random Forest, LightGBM and Decision Tree. The classifiers are evaluated by measuring the performance parameters such as sensitivity, specificity and accuracy. The proposed AIFDS consisting of fuzzy clustering based on MWOA and CATBoost performs better than other compared methods.