Fuzzy C-means Clustering Research Articles

Blind extraction of acoustic signals from rolling bearing composite faults based on ISCA-IMCKD

In production practice, the signal gathered by a sensor often includes strong ambient noise, and its composition is complex. Focusing on the problem that traditional methods are difficult to separate and extract fault frequencies from strong background noise, a novel compound fault blind extraction method based on improved sparse component analysis (ISCA) and improved maximum correlation kurtosis deconvolution (IMCKD)-named ISCA‐IMCKD- is suggested. Initially, the signal that the sensor has collected is shifted into time-frequency area signal by short-time Fourier transform (STFT). In addition, the single source domain characteristic data is screened by improved single source point detection to determine the number of sources. Second, the ISCA method is optimized by using cosine distance improved fuzzy C-means clustering, which is utilized to further process the characteristic data to calculate the mixing matrix. Moreover, the estimated source signal is initially extracted according to the membership degree of clustering results. Finally, the estimated source signal is shifted into the time area by inverse STFT transform, and the IMCKD is employed to enhance the characteristics of the projected source signals. Meanwhile, the initially estimated source signal is completely separated, and the defect frequencies of the composite faults are finally extracted by envelope analysis. Simulation experiments and measured data are employed to certify the viability of the proposed means. The defect detection of rolling bearings is finished while the time cost is significantly saved.

Deep learning-based framework for real-time transient stability prediction under stealthy data integrity attacks

Cyber-attacks can degrade the performance of online transient stability prediction (TSP) and assessment functions offered for power systems. This paper proposes a cyber-resilient real-time TSP framework created by considering both perspectives, of the attacker, and of the smart grid operator. From the attacker’s viewpoint, to minimize the attack cost, a model-free data-driven algorithm for finding the measurements to which the TSP function is vulnerable is considered. The attack vector that maximizes the TSP classifier’s inaccuracy reduces the performance of TSP significantly. From the perspective of the system operator and to defend the system against data integrity attacks, two unsupervised learning algorithms which are principal component analysis and fuzzy c-means clustering, are combined and employed to detect the falsified phasor measurement units (PMUs) in the system. In the proposed framework, a denoising autoencoder model is also developed to eliminate the effect of stealthy cyber-attacks. When a cyber-attack is detected, the denoising autoencoder-based model is triggered to recover the damage of the cyber-attack, while a well-tuned long short-term memory model is implemented for the TSP application. The proposed framework is tested on two IEEE test systems, including 39-bus and 127-bus systems, with various attack scenarios. Results show that the developed system is more resilient to the attacks than the existing well-established TSP functions.

An Adaptive Fuzzy C-Means Noise Image Segmentation Algorithm Combining Local and Regional Information

Noisy image segmentation using fuzzy c-mean clustering (FCM) algorithm is an important direction in the field of image segmentation, among which FCM using local information or non-local information constraints has made some progress. However, as the noise density increases, the local information cannot restore the real image. The non-local information-based segmentation method overly retains the noise and thus produces false edges resulting in the degradation of image segmentation accuracy. To address these problems, this paper use the regional and local information to develop an adaptive FCM image segmentation algorithm(RLFCM). First, the regional information and local information are obtained by the regional information filtering method and the local information filtering method. Then the weighted image is constructed in a certain way. Secondly, to make the objective function adaptively adjust the constraint ratio of the original image and the weighted image pixel by pixel, the degree of constraint is adaptively changed by using the difference between them as the coefficients of both through certain transformation. Finally, to speed up the convergence of the objective function, we add the number of clustering samples to the denominator of the objective function by certain transformations to reduce the number of iterations of the objective function. The proposed algorithm achieves more than 90% in image segmentation accuracy (SA), and mean Intersection-over-Union (mIoU) through synthetic and real-world image tests and acquires good performance in other indexes.

Tumor Detection in Brain Using MRI Images

This work provides a robust segmentation approach that combines Template-based K-means with the modified Fuzzy C-means (TKFCM) clustering algorithm. Operator and equipment error is reduced. The template is chosen in this method based on the convolution of grey level intensity in a tiny area of a brain image and a brain tumor image. The K-means method emphasizes initial segmentation through template selection. Distances from the cluster centroid to the cluster data points are used to update membership until it is optimal. This Euclidian distance is determined by the coarse image's intensity, entropy, contrast, dissimilarity, and homogeneity which were solely based on resemblance in conventional FCM. Then, using updated membership and automatic cluster selection, a sharp segmented image with red indicated tumor is created using the improved FCM technique. TKFCM detects minor differences in grey level intensity between normal and diseased tissue. The TKFCM method's performance is analyzed using a neural network to produce better regression and less error. The performance parameters yield meaningful results that are helpful at finding cancers in several different intensity-based brain scans brain. Keywords - Magnetic Resonance Imaging (MRI), Revised Fuzzy C-Means Algorithms and Template based K-Mean Clustering (TKFCM), gray level intensity, coarse image, features selection, Artificial Neural Network (ANN).

Short-term PV Power Prediction Based on SAGA-FCM and XGBoost

The stable and economic operation of the power grid cannot be achieved without accurate prediction of PV plant power. To address the problem of unstable clustering quality of the fuzzy c-mean clustering algorithm, this paper proposes to combine the genetic algorithm and simulated annealing algorithm to optimize the FCM clustering algorithm, and then combine it with XGBoost to construct a short-term PV power generation prediction model. Firstly, the robustness of the clustering algorithm is improved. Secondly, the differences in power generation characteristics under different weather types are considered, and the prediction models under three typical weather types are separately trained. Finally, to verify the improvement of the model on prediction accuracy, different clustering methods are used to compare the prediction results, and the results show that the algorithm proposed in this paper has higher accuracy compared with the other three methods.

A Fuzzy Inference System for enhanced groundwater quality assessment and index determination

Abstract Groundwater is a vital resource for human consumption, particularly in rural areas with limited access to treated water. The conventional Water Quality Index models used for this purpose have limitations related to data volatility and judgment uncertainties. To overcome these limitations, our study introduces a novel approach that employs a Fuzzy Inference System to determine the Water Quality Index. The dataset used in our research includes multiple parameters such as pH, EC, TDS, Ca, Mg, Na, K, HCO3, Cl, SO4, TH, DWQI, and other physio-chemical and chemical parameters. Our approach utilizes linguistic variables, fuzzy rules, and the hyperbolic tangent set function to handle imprecise and uncertain water quality data. By employing Fuzzy C-Means clustering, we group similar water samples based on quality parameters and map membership values to linguistic terms representing water quality categories. Suitable defuzzification methods are then applied to convert fuzzy outputs into precise results. This proposed approach provides a comprehensive framework for accurate water quality assessment, enabling informed decision-making and more reliable and precise evaluations of groundwater quality.

Enhancement of video condensation with video browsing and retrieval for surveillance videos using heuristic-derived deep learning algorithm

Video condensation or synopsis is an effective solution for problems regarding video storage and video browsing. The proposed model contributed to developing the video condensation framework for efficient video browsing and video retrieval. In the first stage, the videos are gathered from the surveillance videos. Here, the frames are generated, and then the video backgrounds are extracted. The objects from the frames are acquired through the support of Yolov3. Next, the optimal stitching is done based on the time and object activity of video frames using the Improved Blue Monkey Optimization (IBMO) algorithm. Moreover, video condensation is performed to get the compact video for making better browsing and retrieval of video. The video browsing and retrieval are performed under two phases such as training and testing phases and both phases are done by gathering the videos and followed by the feature extraction using VGG16, where the heuristic improvement is made by the same IBMO algorithm. Then, the extracted deep features from video segments are clustered based on Fuzzy C-means (FCM) clustering for combining the extracted features. These features are stored in the feature database in the training phase. Next, in the testing phase, video browsing and retrieval are performed by considering the queries gathered from the standard dataset. The features of query videos are extracted, which are compared based on Multi-Similarity Function (MSF) with the features in the database for retrieving the video segments. Experimental results show that the developed IBMO-VGG-MSF-based video condensation saves computational loads compared to the previous methods without compromising the condensation ratio and visual quality.

Multi-Scale Potential Field Data Integration Using Fuzzy C-Means Clustering for Automated Geological Mapping of North Singhbhum Mobile Belt, Eastern Indian Craton

Fuzzy C-Means (FCM) clustering is an unsupervised machine learning algorithm that helps to integrate multiple geophysical datasets and provides automated objective-oriented information. This study analyzed ground-based Bouguer gravity and aeromagnetic datasets using the FCM clustering algorithm to classify lithological units in the western part of the North Singhbhum Mobile Belt, a mineralized belt in the Eastern Indian Craton. The potential field signatures of clusters obtained using FCM correlate remarkably well with the existing surface geology on a broad scale. The cluster associated with the highest gravity signatures corresponds to the metabasic rocks, and the cluster with the highest magnetic response represents the mica schist rocks. The cluster characterized by the lowest gravity and magnetic responses reflects the granite gneiss rocks. However, few geological formations are represented by two or more clusters, probably due to the close association of similar rock types. The fuzzy membership scores for most of the data points in each cluster show above 0.8, indicating a consistent relationship between geophysical signatures and the existing lithological units. Further, the study reveals that integrating multi-scale geophysical data helps to disclose bedrock information and litho-units under the sediment cover.

Fuzzy clustering algorithm for university students' psychological fitness and performance detection

Students' psychological fitness is unavoidable, hindering personal development, social interactions, peer influence, and adolescence. Academic stress may be the most dominant factor affecting college students' mental well-being. Therefore, improving the monitoring of mental health issues among college students is a vital topic for study. However, identifying the student's stress level is challenging, leading to uncertainty. Hence, this paper suggests Heuristic Fuzzy C-means Clustering Algorithm (HFCA) for analyzing college students' stress levels, psychological well-being and academic performance detection. The data are collected from the Kaggle stress dataset for predicting student mental health. This study investigates the psychological factors affecting students' academic performance using the suggested HFCA. Students' performance may be predicted using the Fuzzy Cognitive Map (FCM) in this study. This study used fuzzy clustering algorithms to discover the most crucial aspects of student success, such as student involvement and satisfaction. A better understanding of the risk factors for and protective factors against poor mental health can serve as the basis for developing policies and targeted interventions to prevent mental health problems and guarantee that at-risk students can access the help they need. The experimental analysis shows the proposed method HFCA to achieve a high student performance ratio of 96.7%, cognitive development ratio of 97.2%, student engagement ratio of 97.5% and prediction ratio of 95.1% compared to other methods.

Collaborative Movie Recommendation System using Enhanced Fuzzy C-Means Clustering with Dove Swarm Optimization Algorithm

Recommender Systems (RSs) aid in filtering information seeking to envisage user and item ratings, primarily from huge data to recommend the likes. Movie RSs offer a scheme to help users categorize them based on comparable interests. This enables RSs to be a dominant part of websites and e-commerce applications. This paper proposes an optimized RS for movies, primarily aiming to suggest an RS by clustering data and Computational Intelligence (CI). Unsupervised clustering, a model-based Collaborative Filtering (CF) category, is preferred as it offers simple and practical recommendations. Nevertheless, it involves an increased error rate and consumes more iterations for converging. Enhanced Fuzzy C-Means (EFCM) clustering is proposed to handle these issues. Dove Swarm Optimisation Algorithm (DSOA)-based RS is proposed for optimising Data Points (DPs) in every cluster, providing effcient recommendations. The performance of the proposed EFCM-DSOA-based RS is analysed by performing an experimental study on benchmarked MovieLens Dataset. To ensure the effciency of the proposed EFCM-DSOA-based RS, the outcomes are compared with EFCM-Particle Swarm Optimization (EFCM-PSO) and EFCM-Cuckoo Search (EFCM-CS) based on standard optimization functions. The proposed EFCM-DSOA-based RS offers improved F-measure, Accuracy, and Fitness convergence.

A Fiedler’s Approach to LINEX Intuitionistic Fuzzy C-means Clustering Induced Spectral Initialization for Data Analysis

A Fiedler’s Approach to LINEX Intuitionistic Fuzzy C-means Clustering Induced Spectral Initialization for Data Analysis

Condition monitoring of wind turbine using novel deep learning method and dynamic kernel principal components Mahalanobis distance

Condition monitoring (CM) of wind turbine (WT) has been increasingly adopted for its fault diagnosis and maintenance decision-making. However, the data collected in CM is typically noisy, multidimensional, and highly nonlinear, which causes significant challenges in achieving the effective CM of WT. This paper proposes a novel CM method using a deep learning model with temporal pattern attention (TPA) and a dynamic kernel principal components Mahalanobis distance (DKPMD). The method can evaluate the WT performance accurately for detecting faults. First, outliers are recognized and removed using isolation forest improved by sparse autoencoder and fuzzy c-means clustering (FSIF) from raw wind turbine data of health state for enhancing the quality and reliability of data in modeling. Then, a gated recurrent unit (GRU) is developed for data reconstruction of the objective variables using LassoNet and TPA, which can capture the short- and long-term temporal relationships under different time steps based on selected variables. Meanwhile, kernel RMSE (KRMSE) is applied as a loss function, which avoids the negative effects of large reconstructed errors in parameter optimization. A condition index (CI) is constructed using DKPMD based on the reconstructed errors to consider the dynamic correlation between the variables. Finally, a delay perception-based IF(DPIF) is utilized to determine the threshold. Experiments with data from real WT demonstrate the effectiveness of the developed approach in detecting early abnormal conditions, which outperforms other state-of-the-art methods.

Identification of Nonlinear Behavior of Bridge Structures Using Time Series Analysis of Vibration Signals

ABSTRACT One of the most widely used linear techniques for solving damage identification problems and prediction of the system dynamic behavior is modal testing and analysis. However, most of real structures exhibit nonlinear behavior during their lifetime caused by a number of various reasons. The presence of nonlinearity in a structural system changes its dynamic characteristics; hence, the use of linear techniques is improper for prediction of the system behavior. In this paper, a vibration-based nonlinearity identification technique was proposed to identify early changes in a dynamic system prior to significant damages. This technique combines the time series Autoregressive Moving Average with Exogenous Inputs (ARMAX) modelling, probability theory, and Fuzzy C-Means (FCM) clustering algorithm to categorize the linear and nonlinear dynamic behaviors of a dynamic system. The technique aims to categorize the linear and nonlinear behaviors of a structure, when it is subjected to various levels of excitation source. To show the validity of the proposed method, a series of shake table tests was performed on a six-span steel truss bridge model. The bridge model was excited using different amplitudes of ground motion to control the nonlinearity degree of rubber-based supports. It can be concluded from the analysis results that the new vibration-based nonlinearity identification technique was able to identify nonlinear behavior of the bridge model once it was subjected to different levels of earthquake excitations. This algorithm was able to categorize the linear and nonlinear behaviors of the bridge model and define a specific threshold for different excitation sources.

Human Dental Age and Gender Assessment from Dental Radiographs Using Deep Convolutional Neural Network

Human gender and age identification play a prominent role in forensics, bio-archaeology, and anthropology. Dental images provide prominent indications used for the treatment or diagnosis of disease and forensic investigation. Numerous dental age identification techniques come with specific boundaries, namely minimum reliability and accuracy. Gender identification approaches are not widely researched, whereas the effectiveness and accuracy of classification are not practical and very minimal. Drawbacks in the existing system are considered in the formulation of the proposed approach. Deep learning approaches can effectively rectify issues of drawbacks in other classifiers. The accuracy and performance of a classifier are enhanced with the deep convolutional neural network. The fuzzy C-Means Clustering approach is used for segmentation, and Ant Lion Optimization is used for optimal feature score selection. The selected features are classified using a deep convolutional neural network (DCNN). The performance of the proposed technique is investigated with existing classifiers, and DCNN outperforms other classifiers. The proposed technique achieves 91.7% and 91% accuracy for the identification of gender and age, respectively.

Adaptive weighted fuzzy clustering based on intra-cluster data divergence

Fuzzy C-means clustering (FCM) approach is an effective method for clustering and has been successfully applied in numbers of real-world problems. In this paper, we propose an improving adaptive weighted FCM based on data divergence, with the merits of three aspects: 1) to avoid randomization of cluster centers, we propose a new cluster centers initialization method; 2) we present an adaptive parameter reflecting the changes of intra-cluster data divergence in the process of cluster formation from iteration to iteration for correcting the unreasonable factors resulting from the changes timely; 3) we propose a new data weighting method. By integrating the adaptive parameter and feature weighting method, we propose a novel adaptive objective function, by which the updating iterative formulas of the membership degrees, the feature weights and the cluster centers are obtained. Experimental results have shown that the novel clustering approach put forward can improve the clustering performance effectively.

Association Between Pseudoprogression of Vestibular Schwannoma After Radiosurgery and Radiological Features of Solid and Cystic Components.

The pathophysiology of vestibular schwannoma (VS) pseudoprogression after Gamma Knife radiosurgery (GKRS) remains unclear. Radiological features in pretreatment magnetic resonance images may help predict VS pseudoprogression. This study used VS radiological features quantified using an automated segmentation algorithm to predict pseudoprogression after GKRS treatment. This is a retrospective study comprising 330 patients with VS who received GKRS. After image preprocessing and T2W/contrast-enhanced T1-weighted image (CET1W) image generation, with fuzzy C-means clustering, VSs were segmented into solid and cystic components and classified as solid and cystic. Relevant radiological features were then extracted. The response to GKRS was classified into "nonpseudoprogression" and "pseudoprogression/fluctuation". The Z test for two proportions was used to compare solid and cystic VS for the likelihood of pseudoprogression/fluctuation. Logistic regression was used to assess the correlation between clinical variables and radiological features and response to GKRS. The likelihood of pseudoprogression/fluctuation after GKRS was significantly higher for solid VS compared with cystic VS (55% vs 31%, P < .001). For the entire VS cohort, multivariable logistic regression revealed that a lower mean tumor signal intensity (SI) in T2W/CET1W images was associated with pseudoprogression/fluctuation after GKRS ( P = .001). For the solid VS subgroup, a lower mean tumor SI in T2W/CET1W images ( P = .035) was associated with pseudoprogression/fluctuation after GKRS. For the cystic VS subgroup, a lower mean SI of the cystic component in T2W/CET1W images ( P = .040) was associated with pseudoprogression/fluctuation after GKRS. Pseudoprogression is more likely to occur in solid VS compared with cystic VS. Quantitative radiological features in pretreatment magnetic resonance images were associated with pseudoprogression after GKRS. In T2W/CET1W images, solid VS with a lower mean tumor SI and cystic VS with a lower mean SI of cystic component were more likely to have pseudoprogression after GKRS. These radiological features can help predict the likelihood of pseudoprogression after GKRS.

A Novel Method for Building Contour Extraction Based on CSAR Images

Circular synthetic aperture radar (CSAR) can obtain more complete scattering characteristics by observing the target with different azimuth angles. Therefore, extracting the complete structure of the target from CSAR images is of great significance for accurate interpretation. At present, the artificial target extraction based on CSAR images mostly uses anisotropic scattering features. For special targets such as buildings, as the walls and the ground form dihedral corner structures, there are also obvious strong scattering features such as double-scattering lines in SAR images. Therefore, combining the strong scattering features of buildings at specific aspects with anisotropic scattering characteristics at different aspects can obtain better extraction results, and how to extract these features accurately and efficiently is the key point. Based on this, this paper proposes a novel method for building contour extraction based on CSAR images. For strong scattering features, a fast fuzzy C-means (FCM) clustering algorithm was used to extract them. For anisotropic scattering features, aspect entropy was used to characterize the anisotropy degree, and K-means clustering was combined to extract. Finally, a more accurate result is obtained by merging the two feature extraction results. In order to verify the effectiveness and practicability of the proposed method, a lot of measured data acquired by the self-developed airborne L-band and Ku-band CSAR systems were processed. The experiments show that, compared with state-of-the-art algorithms, the proposed method can obtain more accurate results in less time.

Double-suppressed possibilistic fuzzy Gustafson–Kessel clustering algorithm

Possibilistic fuzzy c-means clustering (PFCM) is an unsupervised hybrid clustering algorithm, which can partly inherit the stability of fuzzy c-means clustering (FCM) algorithm and the noise robustness of possibilistic c-means clustering (PCM) algorithm. However, there are still several limitations in the PFCM when clustering complex data with multiple characteristics: sensitivity to strong noise, coincident clustering problem in multi-class clustering, and disability to deal with strong correlations of feature components. Therefore, a double-suppressed possibilistic fuzzy Gustafson–Kessel​ clustering algorithm (DS-PFGK) is presented. Firstly, we design a new objective function with a modified Mahalanobis distance and an improved weight parameter setting method based on the stability of fuzzy memberships and the anti-noise ability of possibilistic memberships. Moreover, combined with the absolute attribute of possibilistic memberships and the covariance matrix in the modified Mahalanobis distance, an ellipsoidal cluster core which can depict the distribution of sample data is generated to divide each cluster into different regions for selecting appropriate learning objects and identifying noisy data adaptively. Then, a “double-suppressed competitive learning” strategy is designed for the selected learning objects to solve the coincident clustering problem in multi-class clustering by suppressing the nonwinner possibilistic memberships, and to reduce the iteration number by suppressing nonwinner fuzzy memberships and rewarding winner fuzzy memberships. Finally, a segmentation algorithm on noisy color images based on the DS-PFGK is proposed based on the generated cluster cores. Experiments on synthetic datasets, real datasets, and color images show the good performance of the proposed methods compared with several classical and state-of-the-art clustering algorithms.

Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection.

This paper reviews the potential use of fuzzy c-means clustering (FCM) and explores modifications to the distance function and centroid initialization methods to enhance image segmentation. The application of interest in the paper is the segmentation of breast tumours in mammograms. Breast cancer is the second leading cause of cancer deaths in Canadian women. Early detection reduces treatment costs and offers a favourable prognosis for patients. Classical methods, like mammograms, rely on radiologists to detect cancerous tumours, which introduces the potential for human error in cancer detection. Classical methods are labour-intensive, and, hence, expensive in terms of healthcare resources. Recent research supplements classical methods with automated mammogram analysis. The basic FCM method relies upon the Euclidean distance, which is not optimal for measuring non-spherical structures. To address these limitations, we review the implementation of a Mahalanobis-distance-based FCM (FCM-M). The three objectives of the paper are: (1) review FCM, FCM-M, and three centroid initialization algorithms in the literature, (2) illustrate the effectiveness of these algorithms in image segmentation, and (3) develop a Python package with the optimized algorithms to upload onto GitHub. Image analysis of the algorithms shows that using one of the three centroid initialization algorithms enhances the performance of FCM. FCM-M produced higher clustering accuracy and outlined the tumour structure better than basic FCM.

Full-parameter adaptive fuzzy clustering for noise image segmentation based on non-local and local spatial information

The fuzzy clustering C-means (FCM) algorithm is a compelling image segmentation method in image segmentation. However, the algorithm is less robust for images containing noise. This paper proposes a non-local full-parameter adaptive spatial information combined with a local fuzzy factor for noisy image segmentation. Firstly, we realize the adaptive calculation of the smoothing parameter, search term window, and neighborhood window in the non-local spatial information by defining the smoothness and designing the adaptive matching function. Secondly, the image’s non-local and local spatial information is considered comprehensively to reduce noise interference and segmentation ambiguity. Finally, the weighted average membership linking is used as the denominator of the objective function to reduce the number of iterations. The results in synthetic noise image and color image segmentation experiments show that the proposed algorithm has outstanding performance in various evaluation metrics and visual effects, outperforming most other variants of fuzzy clustering-based algorithms.