Kernel-based Fuzzy C-means Algorithm Research Articles

Dynamic Modeling Framework Based on Automatic Identification of Operating Conditions for Sintering Carbon Consumption Prediction

In iron and steel industry, sintering process requires huge carbon consumption. Achieving accurate and dynamic prediction of carbon consumption in this process is of evident significance to protect the environment and raise the economic efficiency of iron and steel industry. This paper develops an original dynamic modeling framework including automatic identification of operating conditions, modeling in different conditions, and dynamic prediction model of sintering carbon consumption. Firstly, an automatic kernel-based fuzzy C-means algorithm is presented for automatic identification of operating conditions. Dynamic relationships between the inputs and output of the model are analyzed by Copula Entropy, and then the relevant production data in each operating condition are determined by just-in-time learning method. Next, broad learning models are established under different operating conditions. Further, dynamic prediction model of sintering carbon consumption is designed, and the prediction error is adopted to quantify performance of the model and as one criteria to determine the update of production database. Finally, results of experiments using actual production data demonstrate the advantage and validity of the developed model compared with some advanced modeling methods. The developed model considers complex characteristics of the sintering process and presents a better dynamic performance and information mining performance.

Computer-aided diagnosis for early cancer detection using Adaptive Kernel Based Fuzzy Cuckoo Search Optimization Clustering from mammogram images

Early detection of breast cancer is major issue that must be addressed globally. Segmentation process play key role in detection. We propose a new hybrid approach called Adaptive Kernel Based Fuzzy Cuckoo Search Optimization Clustering to segment mammogram images. It is diversified into two stages. A kernel-based fuzzy c-means algorithm with cuckoo search optimization is used to obtain optimal cluster centers and obtained optimum centers act as centers to the KFCM clustering process to get different segmented regions in pre processing stage. In post processing stage, a level set method is employed to minimize boundary leakages. The simulation was performed on RIDER data set. The proposed approach is validated by calculating similarity indexing parameters. The simulation was performed on 12 distinct images from the RIDER mammography database. The suggested method's experimental results were compared to existing level set approaches such as IVC2010, IVC2013, and ESA2021.

Underdetermined Mixed Matrix Estimation Based on DPCKFCM Algorithm

Aiming at the problem of fuzzy C-means (FCM) in the estimation of underdetermined mixing matrix, that the estimation accuracy is not high and the robustness is poor, a density peak clustering (DPC) based on density peak clustering (DPC) is proposed. Improved Kernel-based Fuzzy C-means (KFCM). The kernel function is introduced into the FCM algorithm to construct the KFCM algorithm based on the Gaussian kernel function, which can effectively overcome the influence of noise points and isolated points on the clustering results and improve the estimation accuracy of the mixed matrix; the traditional DPC algorithm is improved and merged with the KFCM algorithm, and thresholds are set for the local density and high-density distance to achieve the initial clustering center of the KFCM algorithm and the automatic determination of the number of cluster centers improves the robustness of the algorithm. Experimental results show that the algorithm has greatly improved the estimation accuracy and robustness of the underdetermined mixed matrix.

Prediction Performance Improvement via Anomaly Detection and Correction of Actual Production Data in Iron Ore Sintering Process

The accuracy and integrity of the actual production data influence the reliability and stability of sintering process in steel industry. However, the actual production data may encounter various outliers due to noise, sensor failure, and operator negligence existing in this process. To tackle this issue, this article develops an original framework for the detection and correction of abnormal production data in the sintering process. First, an improved kernel-based Fuzzy C-Means algorithm is developed to effectively divide normal production data under multiple operating conditions. Then, different one-class support vector machine (SVM) classifiers are constructed for different operating conditions. According to which operating condition the actual production data belongs to, the one-class SVM under this operating condition is called to accurately detect abnormal production data. Finally, the most similar normal historical data in the operating condition is obtained to correct the abnormal data by using k nearest neighbor algorithm based on the Mahalanobis distance. Simulation results involving actual production data illustrate the effectiveness of the proposed method. By taking two existing models of the sintering process as examples, their prediction performance becomes improved after detecting and correcting the abnormal production data, so that the proposed framework has important engineering application impact.

Vectorized Kernel-Based Fuzzy C-Means: a Method to Apply KFCM on Crisp and Non-Crisp Numbers

Kernel methods are a class of algorithms for pattern analysis to robust them to noise, overlaps, outliers and also unequal sized clusters. In this paper, kernel-based fuzzy c-means (KFCM) method is extended to apply KFCM on any crisp and non-crisp input numbers only in a single structure. The proposed vectorized KFCM (VKFM) algorithm maps the input (crisp or non-crisp) features to crisp ones and applies the KFCM (with prototypes in feature space) on them. Finally the resulted crisp prototypes in the mapped space are influenced by an inverse mapping to obtain the prototypes’ (centers’) parameters in the input features space. The performance of the proposed method has been compared with the conventional FCM and KFCM and other new methods, to show its effectiveness in clustering of gene expression data and segmentation of land-cover using satellite images. Simulation results show good accuracy of proposed method in compare to other methods.

Taylor kernel fuzzy C-means clustering algorithm for trust and energy-aware cluster head selection in wireless sensor networks

Wireless sensor networks depend on the effective functioning of the nodes in the network, which is concerned regarding the energy that is essential for the extended network life-time. Clustering plays a major role in enabling energy efficiency, which extends the life-time of the network. Thus, the paper introduces a cluster head (CH) selection phenomenon based on the algorithm, Taylor kernel fuzzy C-means (Taylor KFCM), which is the modification of the kernel-based fuzzy c-means (KFCM) algorithm in the Taylor series. The developed algorithm chooses the cluster head using the selection phenomenon, acceptability factor, which is computed using the energy, distance, and trust. In other words, a node acts as a CH when the fitness constraints of minimal distance, maximal trust, and maximal energy are attained. The simulation environment is established using 50, 100, and 200 nodes with 5 and 10 CHs and the effectiveness of the proposed CH selection is revealed through the analysis depending on the metrics, throughput, energy, delay, and the number of alive nodes. The proposed Taylor kernel fuzzy C-means acquired a maximal throughput, energy, and alive nodes of 0.2857, 0.0947, and 31, and minimal delay and routing overhead of 0.1219, 0.0418 respectively.

Dynamic time alignment kernel-based fuzzy clustering of non-equal length vector time series

Time series clustering is an effective vehicle to explore and visualize the structure of a suite of time series. In this study, we generalize the kernel-based fuzzy c-means clustering algorithm by involving the dynamic time alignment kernel (DTAK) to cluster vector time series. In this method, the nonlinear time alignment embedded in DTAK makes the kernel-based fuzzy c-means available for sequences with variable lengths. However, it is noted that DTAK is not a strictly positive definite kernel, especially when the sample size is large. To overcome this, some strategies are presented to make the proposed algorithm available for large data sets. In addition, it is a challenge task to calculate the average sequence for a series of time series with different lengths. In kernel-based fuzzy c-means algorithm, it is not necessary to calculate the average sequence, which will increase the effectiveness of clustering techniques for time series. In the experiments, the kernel-based fuzzy c-means with DTAK is evaluated by both the data sets from the UCI KDD Archive and real-world data sets. Experimental results delivered by the proposed method demonstrate its effectiveness and robustness.

Low Speed Bearing Fault Diagnosis Based on EMD-CIIT Histogram Entropy and KFCM Clustering

In view of weak defect signals and large acoustic emission (AE) data in low speed bearing condition monitoring, we propose a bearing fault diagnosis technique based on a combination of empirical mode decomposition (EMD), clear iterative interval threshold (CIIT) and the kernel-based fuzzy c-means (KFCM) eigenvalue extraction. In this technique, we use EMD-CIIT and EMD to complete the noise removal and to extract the intrinsic mode functions (IMFs). Then we select the first three IMFs and calculate their histogram entropies as the main fault features. These features are used for bearing fault classification using KFCM technique. The result shows that the combined EMD-CIIT and KFCM algorithm can accurately identify various bearing faults based on AE signals acquired from a low speed bearing test rig.

Kernel Based Fuzzy C-Means Clustering for Chronic Sinusitis Classification

Sinusitis is an inflammation of the sinus wall, a small cavity interconnected through the airways in the skull bones. It is located on the back of the forehead, inside the cheek bone structure, on both sides of the nose, and behind the eyes. Chronic sinusitis is caused by infection, growth of nasal polyps, or irregularities of the nasal septum. This condition can affect teenagers, adults, and even children. To classify sinusitis we use Kernel Based Fuzzy C-Means (FCM) Clustering Algorithm, which is the development of Fuzzy C-Means (FCM) Algorithm. FCM is one of the widely used clustering technique. FCM algorithm comprises of sample points used to make whole and sub vector spaces according to the size of the distance. However, when non-linear data is separated, the convergence is inaccurate and slow. To overcome this problem, a Kernel-Based Fuzzy C-Means algorithm that makes use of kernel functions as a substitute for Euclidean distance utilized. It maps out samples to high-dimensional space to increase the differences between cluster centres, so they can overcome FCM deficiencies and improve linear machine capabilities. Data was obtained from the laboratory of Radiology at Cipto Mangunkusumo National General Hospital, Indonesia, with a 100% accuracy.

Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations.

Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data remains challenging owing to the complex structure of brain tumors, blurred boundaries, and external factors such as noise. To alleviate noise sensitivity and improve the stability of segmentation, an effective hybrid clustering algorithm combined with morphological operations is proposed for segmenting brain tumors in this paper. The main contributions of the paper are as follows: firstly, adaptive Wiener filtering is utilized for denoising, and morphological operations are used for removing nonbrain tissue, effectively reducing the method's sensitivity to noise. Secondly, K-means++ clustering is combined with the Gaussian kernel-based fuzzy C-means algorithm to segment images. This clustering not only improves the algorithm's stability, but also reduces the sensitivity of clustering parameters. Finally, the extracted tumor images are postprocessed using morphological operations and median filtering to obtain accurate representations of brain tumors. In addition, the proposed algorithm was compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity, and recall.

Dynamic image acquisition and particle recognition of laser-induced exit surface particle ejection in fused silica

Particle ejection is an important process during laser-induced exit surface damage in fused silica. Huge quantities of ejected particles, large ejection velocity, and long ejection duration make this phenomenon difficult to be directly observed. An in situ two-frame shadowgraphy system combined with a digital particle recognition algorithm was employed to capture the transient ejecting images and obtain the particle parameters. The experimental system is based on the principle of polarization splitting and can capture two images at each damage event. By combining multiple similar damage events at different time delays, the timeline of ejecting evolution can be obtained. Particle recognition is achieved by an adaptively regularized kernel-based fuzzy C-means algorithm based on a grey wolf optimizer. This algorithm overcomes the shortcoming of the adaptively regularized kernel-based fuzzy C-means algorithm easily falling into the local optimum and can resist strong image noises, including diffraction pattern, laser speckle, and motion artifact. This system is able to capture particles ejected after 600 ns with a time resolution of 6 ns and spatial resolution better than 5 μm under the particle recognition accuracy of 100%.

Semisupervised GDTW kernel-based fuzzy c-means algorithm for mapping vegetation dynamics in mining region using normalized difference vegetation index time series

Mapping vegetation dynamic types in mining areas is significant for revealing the mechanisms of environmental damage and for guiding ecological construction. Dynamic types of vegetation can be identified by applying interannual normalized difference vegetation index (NDVI) time series. However, phase differences and time shifts in interannual time series decrease mapping accuracy in mining regions. To overcome these problems and to increase the accuracy of mapping vegetation dynamics, an interannual Landsat time series for optimum vegetation growing status was constructed first by using the enhanced spatial and temporal adaptive reflectance fusion model algorithm. We then proposed a Markov random field optimized semisupervised Gaussian dynamic time warping kernel-based fuzzy c-means (FCM) cluster algorithm for interannual NDVI time series to map dynamic vegetation types in mining regions. The proposed algorithm has been tested in the Shengli mining region and Shendong mining region, which are typical representatives of China’s open-pit and underground mining regions, respectively. Experiments show that the proposed algorithm can solve the problems of phase differences and time shifts to achieve better performance when mapping vegetation dynamic types. The overall accuracies for the Shengli and Shendong mining regions were 93.32% and 89.60%, respectively, with improvements of 7.32% and 25.84% when compared with the original semisupervised FCM algorithm.

MKF-Firefly: Hybridization of Firefly and Multiple Kernel-Based Fuzzy C-Means Algorithm

MKF-Firefly: Hybridization of Firefly and Multiple Kernel-Based Fuzzy C-Means Algorithm

Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm

Fuzzy c-means clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself. Aimed at the problems existed in the FCM clustering algorithm, a kernel-based fuzzy c-means (KFCM) is clustering algorithm is proposed to optimize fuzzy c-means clustering, based on the Genetic Algorithm (GA) optimization which is combined of the improved genetic algorithm and the kernel technique (GAKFCM). In this algorithm, the improved adaptive genetic algorithm is used to optimize the initial clustering center firstly, and then the KFCM algorithm is availed to guide the categorization, so as to improve the clustering performance of the FCM algorithm. In the paper, Matlab is used to realize the simulation, and the performance of FCM algorithm, KFCM algorithm and GAKFCM algorithm is testified by test datasets. The results proved that the GAKFCM algorithm proposed overcomes FCM׳s defects efficiently and improves the clustering performance greatly.

Digital breast tomosynthesis guided near infrared spectroscopy: volumetric estimates of fibroglandular fraction and breast density from tomosynthesis reconstructions

A multimodality system combining a clinical prototype digital breast tomosynthesis with its imaging geometry modified to facilitate near-infrared spectroscopic imaging has been developed. The accuracy of parameters recovered from near-infrared spectroscopy (NIRS) is dependent on fibroglandular tissue content. Hence, in this study, volumetric estimates of fibroglandular tissue from tomosynthesis reconstructions were determined. A kernel-based fuzzy c-means algorithm was implemented to segment tomosynthesis reconstructed slices in order to estimate fibroglandular content and to provide anatomic priors for NIRS. This algorithm was used to determine volumetric breast density (VBD), defined as the ratio of fibroglandular tissue volume to the total breast volume, expressed as percentage, from 62 tomosynthesis reconstructions of 34 study participants. For a subset of study participants who subsequently underwent mammography, VBD from mammography matched for subject, breast laterality and mammographic view was quantified using commercial software and statistically analyzed to determine if it differed from tomosynthesis. Summary statistics of the VBD from all study participants were compared with prior independent studies. The fibroglandular volume from tomosynthesis and mammography were not statistically different (p = 0.211, paired t-test). After accounting for the compressed breast thickness, which were different between tomosynthesis and mammography, the VBD from tomosynthesis was correlated with (r = 0.809, p < 0.001), did not statistically differ from (p > 0.99, paired t-test), and was linearly related to, the VBD from mammography. Summary statistics of the VBD from tomosynthesis were not statistically different from prior studies using high-resolution dedicated breast computed tomography. The observation of correlation and linear association in VBD between mammography and tomosynthesis suggests that breast density associated risk measures determined for mammography are translatable to tomosynthesis. Accounting for compressed breast thickness is important when it differs between the two modalities. The fibroglandular volume from tomosynthesis reconstructions is similar to mammography indicating suitability for use during NIRS.

Fast automatic medical image segmentation based on spatial kernel fuzzy c-means on level set method

Fast two-cycle (FTC) model is an efficient and the fastest Level set image segmentation. But, its performance is highly dependent on appropriate manual initialization. This paper proposes a new algorithm by combining a spatially constrained kernel-based fuzzy c-means (SKFCM) algorithm and an FTC model to overcome the mentioned problem. The approach consists of two successive stages. First, the SKFCM makes a rough segmentation to select the initial contour automatically. Then, a fuzzy membership matrix of the region of interest, which is generated by the SKFCM, is used in the next stage to produce an initial contour. Eventually, the FTC scheme segments the image by a curve evolution based on the level set. Moreover, the fuzzy membership degree from the SKFCM is incorporated into the fidelity term of the Chan---Vese model to improve the robustness and accuracy, and it is utilized for the data-dependent speed term of the FTC. A performance evaluation of the proposed algorithm is carried out on the synthetic and real images. The experimental results show that the proposed algorithm has advantages in accuracy, computational time and robustness against noise in comparison with the KFCM, the SKFCM, the hybrid model of the KFCM and the FTC, and five different level set methods on medical image segmentation.

MKF-Cuckoo: Hybridization of Cuckoo Search and Multiple Kernel-based Fuzzy C-means Algorithm

Discovering of optimal cluster through the help of optimization procedure is a recent trend in clustering process. Accordingly, several algorithms have been developed in the literature to mine optimal clusters. Most of the optimization- based clustering algorithms presented in the literature are only focused on the same objective given in the well-known clustering process, k-means clustering. Instead of k-means objective, some more effective objective functions are designed by the researchers for clustering. So, hybridization of those effective objectives with optimization algorithms can lead the effective clustering results. With the aim of this, we have presented a hybrid algorithm, called MKF-Cuckoo which is the hybridization of cuckoo search algorithm with the multiple kernel-based fuzzy c means algorithm. Here, MKFCM objective is taken and the same objective is solved through the cuckoo search algorithm which is one of the recent optimization algorithm proved effective in many optimization problems. For proving the effectiveness of the algorithm, the performance of the algorithm is comparatively analyzed with some other algorithm using clustering accuracy, rand coefficient, jaccard coefficient and computational time with iris and wine datasets. From the results, we can prove that the hybrid algorithm obtained 96% accuracy in iris data and 67% accuracy in wine data.

Sustainability-oriented product modular design using kernel-based fuzzy c-means clustering and genetic algorithm

With increasing sustainable development consciousness, sustainable design plays an important role, not only in the design phase, but also in the manufacturing process. Existing design methods, including life cycle design and environmental design, have not fully considered sustainability requirements. A modular design methodology is proposed for achieving sustainable design as well as fulfilling functional requirements. Factors related to function and sustainability of a product, such as material, manufacturability, component life, and so on, are defined as modular drivers. A design structure matrix, with great advantages on analyzing the correlation between product system elements, is employed to establish a correlation matrix between components. A kernel-based fuzzy c-means algorithm is used to integrate components of a product into different modules based on their correlation distance. Meanwhile, a genetic algorithm is employed to determine the optimal clustering number on account of its efficiency in coming up with the global solutions. In addition, an assessment model for product sustainability is established considering economic, social, and environmental factors. Finally, a reduction gear is used as a case study example to show the effectiveness of the proposed sustainability-oriented modular design method.

Gaussian Kernelized Fuzzy c-means with Spatial Information Algorithm for Image Segmentation

FCM is used for image segmentation in some applications. It is based on a specific distance norm and does not use spatial information of the image, so it has some drawbacks. Various kinds of improvements have been developed to extend the adaptability, such as BFCM, SFCM and KFCM. These methods extend FCM from two aspects, one is replacing the Euclidean norm, and the other is considering the spatial information constraints for clustering. Kernel distance can improve the robustness for multi-distribution data sets. Spatial information can help eliminate the sensitivity to noises and outliers. In this paper, Gaussian kernel-based fuzzy c-means algorithm with spatial information (KSFCM) is proposed. KSFCM is more robust and adaptive. The experiment results showd that KSFCM has the better performance.

A Novel Kernel-based Fuzzy c-means Algorithm with Spatial Information for Image Segmentation

A Novel Kernel-based Fuzzy c-means Algorithm with Spatial Information for Image Segmentation