Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rel...Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.展开更多
Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous...Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions.展开更多
Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy cl...Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications.展开更多
Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology.The combination of domain knowledge and fuzzy clustering algorithms has some problems,such as ini...Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology.The combination of domain knowledge and fuzzy clustering algorithms has some problems,such as initialization sensitivity and information granule weight optimization.Therefore,we propose a weighted kernel fuzzy clustering algorithm based on a relative density view(RDVWKFC).Compared with the traditional density-based methods,RDVWKFC can capture the intrinsic structure of the data more accurately,thus improving the initial quality of the clustering.By introducing a Relative Density based Knowledge Extraction Method(RDKM)and adaptive weight optimization mechanism,we effectively solve the limitations of view initialization and information granule weight optimization.RDKM can accurately identify high-density regions and optimize the initialization process.The adaptive weight mechanism can reduce noise and outliers’interference in the initial cluster centre selection by dynamically allocating weights.Experimental results on 14 benchmark datasets show that the proposed algorithm is superior to the existing algorithms in terms of clustering accuracy,stability,and convergence speed.It shows adaptability and robustness,especially when dealing with different data distributions and noise interference.Moreover,RDVWKFC can also show significant advantages when dealing with data with complex structures and high-dimensional features.These advancements provide versatile tools for real-world applications such as bioinformatics,image segmentation,and anomaly detection.展开更多
Active semi-supervised fuzzy clustering integrates fuzzy clustering techniques with limited labeled data,guided by active learning,to enhance classification accuracy,particularly in complex and ambiguous datasets.Alth...Active semi-supervised fuzzy clustering integrates fuzzy clustering techniques with limited labeled data,guided by active learning,to enhance classification accuracy,particularly in complex and ambiguous datasets.Although several active semi-supervised fuzzy clustering methods have been developed previously,they typically face significant limitations,including high computational complexity,sensitivity to initial cluster centroids,and difficulties in accurately managing boundary clusters where data points often overlap among multiple clusters.This study introduces a novel Active Semi-Supervised Fuzzy Clustering algorithm specifically designed to identify,analyze,and correct misclassified boundary elements.By strategically utilizing labeled data through active learning,our method improves the robustness and precision of cluster boundary assignments.Extensive experimental evaluations conducted on three types of datasets—including benchmark UCI datasets,synthetic data with controlled boundary overlap,and satellite imagery—demonstrate that our proposed approach achieves superior performance in terms of clustering accuracy and robustness compared to existing active semi-supervised fuzzy clustering methods.The results confirm the effectiveness and practicality of our method in handling real-world scenarios where precise cluster boundaries are critical.展开更多
Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregatio...Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments.Additionally,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole issue.Moreover,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network performance.To address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile UWSNs.The proposed method has four main phases:clustering,CH selection,data aggregation,and re-clustering.During CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy efficiency.In the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving energy.To adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects CHs.Simulation results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.展开更多
The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show...The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data,as they rely on a single-dimensional membership value.To overcome these limitations,we propose an auto-weighted multi-view neutrosophic fuzzy clustering(AW-MVNFC)algorithm.Our method leverages the neutrosophic framework,an extension of fuzzy sets,to explicitly model imprecision and ambiguity through three membership degrees.The core novelty of AWMVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions of both individual data views and the importance of each feature within a view.Through a unified objective function,AW-MVNFC jointly optimizes the neutrosophic membership assignments,cluster centers,and the distributions of view and feature weights.Comprehensive experiments conducted on synthetic and real-world datasets demonstrate that our algorithm achieves more accurate and stable clustering than existing methods,demonstrating its effectiveness in handling the complexities of multi-view data.展开更多
Imaging sonar devices generate sonar images by receiving echoes from objects,which are often accompanied by severe speckle noise,resulting in image distortion and information loss.Common optical denoising methods do n...Imaging sonar devices generate sonar images by receiving echoes from objects,which are often accompanied by severe speckle noise,resulting in image distortion and information loss.Common optical denoising methods do not work well in removing speckle noise from sonar images and may even reduce their visual quality.To address this issue,a sonar image denoising method based on fuzzy clustering and the undecimated dual-tree complex wavelet transform is proposed.This method provides a perfect translation invariance and an improved directional selectivity during image decomposition,leading to richer representation of noise and edges in high frequency coefficients.Fuzzy clustering can separate noise from useful information according to the amplitude characteristics of speckle noise,preserving the latter and achieving the goal of noise removal.Additionally,the low frequency coefficients are smoothed using bilateral filtering to improve the visual quality of the image.To verify the effectiveness of the algorithm,multiple groups of ablation experiments were conducted,and speckle sonar images with different variances were evaluated and compared with existing speckle removal methods in the transform domain.The experimental results show that the proposed method can effectively improve image quality,especially in cases of severe noise,where it still achieves a good denoising performance.展开更多
Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance.This method effectively integrates prior knowledge using pre-labeled data.While s...Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance.This method effectively integrates prior knowledge using pre-labeled data.While semi-supervised fuzzy clustering(SSFC)methods leverage limited labeled data to enhance accuracy,they remain highly susceptible to inappropriate or mislabeled prior knowledge,especially in noisy or overlapping datasets where cluster boundaries are ambiguous.To enhance the effectiveness of clustering algorithms,it is essential to leverage labeled data while ensuring the safety of the previous knowledge.Existing solutions,such as the Trusted Safe Semi-Supervised Fuzzy Clustering Method(TS3FCM),struggle with random centroid initialization,fixed neighbor radius formulas,and handling outliers or noise at cluster overlaps.A new framework called Active Safe Semi-Supervised Fuzzy Clustering with Pairwise Constraints Based on Cluster Boundary(AS3FCPC)is proposed in this paper to deal with these problems.It does this by combining pairwise constraints and active learning.AS3FCPC uses active learning to query only the most informative data instances close to the cluster boundaries.It also uses pairwise constraints to enforce the cluster structure,which makes the system more accurate and robust.Extensive test results on diverse datasets,including challenging noisy and overlapping scenarios,demonstrate that AS3FCPC consistently achieves superior performance compared to state-of-the-art methods like TS3FCM and other baselines,especially when the data is noisy and overlaps.This significant improvement underscores AS3FCPC’s potential for reliable and accurate semisupervised fuzzy clustering in complex,real-world applications,particularly by effectively managing mislabeled data and ambiguous cluster boundaries.展开更多
To guarantee safe and efficient tunneling of a tunnel boring machine(TBM),rapid and accurate judgment of the rock mass condition is essential.Based on fuzzy C-means clustering,this paper proposes a grouped machine lea...To guarantee safe and efficient tunneling of a tunnel boring machine(TBM),rapid and accurate judgment of the rock mass condition is essential.Based on fuzzy C-means clustering,this paper proposes a grouped machine learning method for predicting rock mass parameters.An elaborate data set on field rock mass is collected,which also matches field TBM tunneling.Meanwhile,target stratum samples are divided into several clusters by fuzzy C-means clustering,and multiple submodels are trained by samples in different clusters with the input of pretreated TBM tunneling data and the output of rock mass parameter data.Each testing sample or newly encountered tunneling condition can be predicted by multiple submodels with the weight of the membership degree of the sample to each cluster.The proposed method has been realized by 100 training samples and verified by 30 testing samples collected from the C1 part of the Pearl Delta water resources allocation project.The average percentage error of uniaxial compressive strength and joint frequency(Jf)of the 30 testing samples predicted by the pure back propagation(BP)neural network is 13.62%and 12.38%,while that predicted by the BP neural network combined with fuzzy C-means is 7.66%and6.40%,respectively.In addition,by combining fuzzy C-means clustering,the prediction accuracies of support vector regression and random forest are also improved to different degrees,which demonstrates that fuzzy C-means clustering is helpful for improving the prediction accuracy of machine learning and thus has good applicability.Accordingly,the proposed method is valuable for predicting rock mass parameters during TBM tunneling.展开更多
Cycle slip detection and repair is one of the key technologies for GNSS high-precision positioning.We introduce an enhanced methodology for detecting and repairing BDS four-frequency cycle slips,utilizing fuzzy cluste...Cycle slip detection and repair is one of the key technologies for GNSS high-precision positioning.We introduce an enhanced methodology for detecting and repairing BDS four-frequency cycle slips,utilizing fuzzy clustering analysis.Firstly,based on fuzzy clustering analysis,the optimal combinations for the BDS four-frequency,including extra-wide lane(EWL),wide lane(WL),and narrow lane(NL),were selected.Secondly,the feasibility of this method was verified using actual static and dynamic observation data,and different types of cycle slips were simulated for further validation.Meanwhile,the proposed method was compared with the classical Turbo-Edit method through experiments.Finally,cycle slips were repaired using the least squares method.According to the experimental results,the optimal geometry-free phase combinations(-2,2,1,-1),(1,-1,1,-1),(3,2,-2,-3),and the pseudo-range phase combination(-1,1,1,-1),selected based on fuzzy clustering analysis,were used for cycle slip detection.The proposed method accurately detected small,large,and specific cycle slips simulated in the actual data.Compared with the Turbo-Edit method,the proposed methodwas able to detect specific cycle slips that Turbo-Edit could not.It is worth noting that during the repair process,the coefficients of the combined observation values are integers,preserving the integer cycle characteristic of the observation values,which allows cycle slips to be fixed directly,eliminating the need for complex searching procedures.Consequently,by enhancing the precision and reliability of the detection of BDS four-frequency cycle slips,our proposed method provides the support for the high-precision localization of BDS multi-frequency observations.展开更多
Fuzzy C-means (FCM) is simple and widely used for complex data pattern recognition and image analyses. However, selecting an appropriate fuzzifier (m) is crucial in identifying an optimal number of patterns and achiev...Fuzzy C-means (FCM) is simple and widely used for complex data pattern recognition and image analyses. However, selecting an appropriate fuzzifier (m) is crucial in identifying an optimal number of patterns and achieving higher clustering accuracy, which few studies have investigated. Built upon two existing methods on selecting fuzzifier, we developed an integrated fuzzifier evaluation and selection algorithm and tested it using real datasets. Our findings indicate that the consistent optimal number of clusters can be learnt from testing different fuzzifiers for each dataset and the fuzzifier with the lowest value for this consistency should be selected for clustering. Our evaluation also shows that the fuzzifier impacts the clustering accuracy. For longitudinal data with missing values, m = 2 could be an empirical rule to start fuzzy clustering, and the best clustering accuracy was achieved for tested data, especially using our multiple-imputation based fuzzy clustering.展开更多
To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Con...To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Concept phrases, as well as the descriptions of final clusters, are presented using WordNet origin from key phrases. Initial centers and membership matrix are the most important factors affecting clustering performance. Orthogonal concept topic sub-spaces are built with the topic concept phrases representing topics of the texts and the initialization of centers and the membership matrix depend on the concept vectors in sub-spaces. The results show that, different from random initialization of traditional fuzzy c-means clustering, the initialization related to text content contributions can improve clustering precision.展开更多
A novel model of fuzzy clustering, i.e. an allied fuzzy c means （AFCM） model is proposed based on the combination of advantages of fuzzy c means （FCM） and possibilistic c means （PCM） clustering. PCM is sensitive...A novel model of fuzzy clustering, i.e. an allied fuzzy c means （AFCM） model is proposed based on the combination of advantages of fuzzy c means （FCM） and possibilistic c means （PCM） clustering. PCM is sensitive to initializations and often generates coincident clusters. AFCM overcomes this shortcoming and it is an ex tension of PCM. Membership and typicality values can be simultaneously produced in AFCM. Experimental re- suits show that noise data can be well processed, coincident clusters are avoided and clustering accuracy is better.展开更多
Aimed to the characters of pests forecast such as fuzziness, correlation, nonlinear and real-time as well as decline of generalization capacity of neural network in prediction with few observations, a method of pests ...Aimed to the characters of pests forecast such as fuzziness, correlation, nonlinear and real-time as well as decline of generalization capacity of neural network in prediction with few observations, a method of pests forecasting using the method of neural network based on fuzzy clustering was proposed in this experiment. The simulation results demonstrated that the method was simple and practical and could forecast pests fast and accurately, particularly, the method could obtain good results with few samples and samples correlation.展开更多
Due to the limitation and hesitation in one＇s knowledge, the membership degree of an element to a given set usually has a few different values, in which the conventional fuzzy sets are invalid. Hesitant fuzzy sets ar...Due to the limitation and hesitation in one＇s knowledge, the membership degree of an element to a given set usually has a few different values, in which the conventional fuzzy sets are invalid. Hesitant fuzzy sets are a powerful tool to treat this case. The present paper focuses on investigating the clustering technique for hesitant fuzzy sets based on the K-means clustering algorithm which takes the results of hierarchical clustering as the initial clusters. Finally, two examples demonstrate the validity of our algorithm.展开更多
Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-me...Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-means method the seeds are modified,and for each IFS a membership degree to each of the clusters is estimated.In the end of the algorithm,all the given IFSs are clustered according to the estimated membership degrees.Furthermore,the algorithm is extended for clustering interval-valued intuitionistic fuzzy sets(IVIFSs).Finally,the developed algorithms are illustrated through conducting experiments on both the real-world and simulated data sets.展开更多
High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization metho...High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization methods must be developed to relieve the computational burden.A new metamodel-based global optimization method using fuzzy clustering for design space reduction(MGO-FCR) is presented.The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel,whose accuracy is improved with increasing number of sample points gradually.Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively.Modeling efficiency and accuracy are directly related to the design space,so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms.The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated.The first pseudo reduction algorithm improves the speed of clustering,while the second pseudo reduction algorithm ensures the design space to be reduced.Through several numerical benchmark functions,comparative studies with adaptive response surface method,approximated unimodal region elimination method and mode-pursuing sampling are carried out.The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions.And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems.Based on this global design optimization method,a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms.This method possesses favorable performance on efficiency,robustness and capability of global convergence and gives a new optimization strategy for engineering design optimization problems involving expensive black box models.展开更多
Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and ...Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.展开更多
Reduced order models(ROMs) based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow confi...Reduced order models(ROMs) based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.展开更多
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.
基金supported by the Deanship of Research at the King Fahd University of Petroleum&Minerals,Dhahran,31261,Saudi Arabia,under Project No.EC241001.
文摘Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions.
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications.
文摘Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology.The combination of domain knowledge and fuzzy clustering algorithms has some problems,such as initialization sensitivity and information granule weight optimization.Therefore,we propose a weighted kernel fuzzy clustering algorithm based on a relative density view(RDVWKFC).Compared with the traditional density-based methods,RDVWKFC can capture the intrinsic structure of the data more accurately,thus improving the initial quality of the clustering.By introducing a Relative Density based Knowledge Extraction Method(RDKM)and adaptive weight optimization mechanism,we effectively solve the limitations of view initialization and information granule weight optimization.RDKM can accurately identify high-density regions and optimize the initialization process.The adaptive weight mechanism can reduce noise and outliers’interference in the initial cluster centre selection by dynamically allocating weights.Experimental results on 14 benchmark datasets show that the proposed algorithm is superior to the existing algorithms in terms of clustering accuracy,stability,and convergence speed.It shows adaptability and robustness,especially when dealing with different data distributions and noise interference.Moreover,RDVWKFC can also show significant advantages when dealing with data with complex structures and high-dimensional features.These advancements provide versatile tools for real-world applications such as bioinformatics,image segmentation,and anomaly detection.
文摘Active semi-supervised fuzzy clustering integrates fuzzy clustering techniques with limited labeled data,guided by active learning,to enhance classification accuracy,particularly in complex and ambiguous datasets.Although several active semi-supervised fuzzy clustering methods have been developed previously,they typically face significant limitations,including high computational complexity,sensitivity to initial cluster centroids,and difficulties in accurately managing boundary clusters where data points often overlap among multiple clusters.This study introduces a novel Active Semi-Supervised Fuzzy Clustering algorithm specifically designed to identify,analyze,and correct misclassified boundary elements.By strategically utilizing labeled data through active learning,our method improves the robustness and precision of cluster boundary assignments.Extensive experimental evaluations conducted on three types of datasets—including benchmark UCI datasets,synthetic data with controlled boundary overlap,and satellite imagery—demonstrate that our proposed approach achieves superior performance in terms of clustering accuracy and robustness compared to existing active semi-supervised fuzzy clustering methods.The results confirm the effectiveness and practicality of our method in handling real-world scenarios where precise cluster boundaries are critical.
基金funded by the Deanship of Scientific Research,the Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia under the project(KFU250420).
文摘Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments.Additionally,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole issue.Moreover,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network performance.To address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile UWSNs.The proposed method has four main phases:clustering,CH selection,data aggregation,and re-clustering.During CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy efficiency.In the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving energy.To adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects CHs.Simulation results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.
文摘The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data,as they rely on a single-dimensional membership value.To overcome these limitations,we propose an auto-weighted multi-view neutrosophic fuzzy clustering(AW-MVNFC)algorithm.Our method leverages the neutrosophic framework,an extension of fuzzy sets,to explicitly model imprecision and ambiguity through three membership degrees.The core novelty of AWMVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions of both individual data views and the importance of each feature within a view.Through a unified objective function,AW-MVNFC jointly optimizes the neutrosophic membership assignments,cluster centers,and the distributions of view and feature weights.Comprehensive experiments conducted on synthetic and real-world datasets demonstrate that our algorithm achieves more accurate and stable clustering than existing methods,demonstrating its effectiveness in handling the complexities of multi-view data.
基金the National Natural Science Foundation of China(No.62065001)the Yunnan Young and Middle-aged Academic and Technical Leaders Reserve Talent Project(No.202205AC160001)+1 种基金the Science and Technology Programs of Yunnan Provincial Science and Technology Department(No.202101BA070001-054)the Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities Association(No.2019FH001(-066))。
文摘Imaging sonar devices generate sonar images by receiving echoes from objects,which are often accompanied by severe speckle noise,resulting in image distortion and information loss.Common optical denoising methods do not work well in removing speckle noise from sonar images and may even reduce their visual quality.To address this issue,a sonar image denoising method based on fuzzy clustering and the undecimated dual-tree complex wavelet transform is proposed.This method provides a perfect translation invariance and an improved directional selectivity during image decomposition,leading to richer representation of noise and edges in high frequency coefficients.Fuzzy clustering can separate noise from useful information according to the amplitude characteristics of speckle noise,preserving the latter and achieving the goal of noise removal.Additionally,the low frequency coefficients are smoothed using bilateral filtering to improve the visual quality of the image.To verify the effectiveness of the algorithm,multiple groups of ablation experiments were conducted,and speckle sonar images with different variances were evaluated and compared with existing speckle removal methods in the transform domain.The experimental results show that the proposed method can effectively improve image quality,especially in cases of severe noise,where it still achieves a good denoising performance.
文摘Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance.This method effectively integrates prior knowledge using pre-labeled data.While semi-supervised fuzzy clustering(SSFC)methods leverage limited labeled data to enhance accuracy,they remain highly susceptible to inappropriate or mislabeled prior knowledge,especially in noisy or overlapping datasets where cluster boundaries are ambiguous.To enhance the effectiveness of clustering algorithms,it is essential to leverage labeled data while ensuring the safety of the previous knowledge.Existing solutions,such as the Trusted Safe Semi-Supervised Fuzzy Clustering Method(TS3FCM),struggle with random centroid initialization,fixed neighbor radius formulas,and handling outliers or noise at cluster overlaps.A new framework called Active Safe Semi-Supervised Fuzzy Clustering with Pairwise Constraints Based on Cluster Boundary(AS3FCPC)is proposed in this paper to deal with these problems.It does this by combining pairwise constraints and active learning.AS3FCPC uses active learning to query only the most informative data instances close to the cluster boundaries.It also uses pairwise constraints to enforce the cluster structure,which makes the system more accurate and robust.Extensive test results on diverse datasets,including challenging noisy and overlapping scenarios,demonstrate that AS3FCPC consistently achieves superior performance compared to state-of-the-art methods like TS3FCM and other baselines,especially when the data is noisy and overlaps.This significant improvement underscores AS3FCPC’s potential for reliable and accurate semisupervised fuzzy clustering in complex,real-world applications,particularly by effectively managing mislabeled data and ambiguous cluster boundaries.
基金Natural Science Foundation of Shandong Province,Grant/Award Number:ZR202103010903Doctoral Fund of Shandong Jianzhu University,Grant/Award Number:X21101Z。
文摘To guarantee safe and efficient tunneling of a tunnel boring machine(TBM),rapid and accurate judgment of the rock mass condition is essential.Based on fuzzy C-means clustering,this paper proposes a grouped machine learning method for predicting rock mass parameters.An elaborate data set on field rock mass is collected,which also matches field TBM tunneling.Meanwhile,target stratum samples are divided into several clusters by fuzzy C-means clustering,and multiple submodels are trained by samples in different clusters with the input of pretreated TBM tunneling data and the output of rock mass parameter data.Each testing sample or newly encountered tunneling condition can be predicted by multiple submodels with the weight of the membership degree of the sample to each cluster.The proposed method has been realized by 100 training samples and verified by 30 testing samples collected from the C1 part of the Pearl Delta water resources allocation project.The average percentage error of uniaxial compressive strength and joint frequency(Jf)of the 30 testing samples predicted by the pure back propagation(BP)neural network is 13.62%and 12.38%,while that predicted by the BP neural network combined with fuzzy C-means is 7.66%and6.40%,respectively.In addition,by combining fuzzy C-means clustering,the prediction accuracies of support vector regression and random forest are also improved to different degrees,which demonstrates that fuzzy C-means clustering is helpful for improving the prediction accuracy of machine learning and thus has good applicability.Accordingly,the proposed method is valuable for predicting rock mass parameters during TBM tunneling.
基金supported by the National Natural Science Foundation of China(42174003)the Gansu Provincial Department of Education:Innovation Fund Project for College Teachers(2023A-035)+1 种基金Gansu Provincial Science and Technology Program(Joint Research Fund),24JRRA856the Lanzhou Talent Innovation Project,2023-RC-31.
文摘Cycle slip detection and repair is one of the key technologies for GNSS high-precision positioning.We introduce an enhanced methodology for detecting and repairing BDS four-frequency cycle slips,utilizing fuzzy clustering analysis.Firstly,based on fuzzy clustering analysis,the optimal combinations for the BDS four-frequency,including extra-wide lane(EWL),wide lane(WL),and narrow lane(NL),were selected.Secondly,the feasibility of this method was verified using actual static and dynamic observation data,and different types of cycle slips were simulated for further validation.Meanwhile,the proposed method was compared with the classical Turbo-Edit method through experiments.Finally,cycle slips were repaired using the least squares method.According to the experimental results,the optimal geometry-free phase combinations(-2,2,1,-1),(1,-1,1,-1),(3,2,-2,-3),and the pseudo-range phase combination(-1,1,1,-1),selected based on fuzzy clustering analysis,were used for cycle slip detection.The proposed method accurately detected small,large,and specific cycle slips simulated in the actual data.Compared with the Turbo-Edit method,the proposed methodwas able to detect specific cycle slips that Turbo-Edit could not.It is worth noting that during the repair process,the coefficients of the combined observation values are integers,preserving the integer cycle characteristic of the observation values,which allows cycle slips to be fixed directly,eliminating the need for complex searching procedures.Consequently,by enhancing the precision and reliability of the detection of BDS four-frequency cycle slips,our proposed method provides the support for the high-precision localization of BDS multi-frequency observations.
文摘Fuzzy C-means (FCM) is simple and widely used for complex data pattern recognition and image analyses. However, selecting an appropriate fuzzifier (m) is crucial in identifying an optimal number of patterns and achieving higher clustering accuracy, which few studies have investigated. Built upon two existing methods on selecting fuzzifier, we developed an integrated fuzzifier evaluation and selection algorithm and tested it using real datasets. Our findings indicate that the consistent optimal number of clusters can be learnt from testing different fuzzifiers for each dataset and the fuzzifier with the lowest value for this consistency should be selected for clustering. Our evaluation also shows that the fuzzifier impacts the clustering accuracy. For longitudinal data with missing values, m = 2 could be an empirical rule to start fuzzy clustering, and the best clustering accuracy was achieved for tested data, especially using our multiple-imputation based fuzzy clustering.
基金The National Natural Science Foundation of China(No60672056)Open Fund of MOE-MS Key Laboratory of Multime-dia Computing and Communication(No06120809)
文摘To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Concept phrases, as well as the descriptions of final clusters, are presented using WordNet origin from key phrases. Initial centers and membership matrix are the most important factors affecting clustering performance. Orthogonal concept topic sub-spaces are built with the topic concept phrases representing topics of the texts and the initialization of centers and the membership matrix depend on the concept vectors in sub-spaces. The results show that, different from random initialization of traditional fuzzy c-means clustering, the initialization related to text content contributions can improve clustering precision.
文摘A novel model of fuzzy clustering, i.e. an allied fuzzy c means （AFCM） model is proposed based on the combination of advantages of fuzzy c means （FCM） and possibilistic c means （PCM） clustering. PCM is sensitive to initializations and often generates coincident clusters. AFCM overcomes this shortcoming and it is an ex tension of PCM. Membership and typicality values can be simultaneously produced in AFCM. Experimental re- suits show that noise data can be well processed, coincident clusters are avoided and clustering accuracy is better.
基金Supported by Guangxi Science Research and Technology Explora-tion Plan Project(0815001-10)~~
文摘Aimed to the characters of pests forecast such as fuzziness, correlation, nonlinear and real-time as well as decline of generalization capacity of neural network in prediction with few observations, a method of pests forecasting using the method of neural network based on fuzzy clustering was proposed in this experiment. The simulation results demonstrated that the method was simple and practical and could forecast pests fast and accurately, particularly, the method could obtain good results with few samples and samples correlation.
基金Supported by the National Natural Science Foundation of China(61273209)
文摘Due to the limitation and hesitation in one＇s knowledge, the membership degree of an element to a given set usually has a few different values, in which the conventional fuzzy sets are invalid. Hesitant fuzzy sets are a powerful tool to treat this case. The present paper focuses on investigating the clustering technique for hesitant fuzzy sets based on the K-means clustering algorithm which takes the results of hierarchical clustering as the initial clusters. Finally, two examples demonstrate the validity of our algorithm.
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars(70625005)
文摘Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-means method the seeds are modified,and for each IFS a membership degree to each of the clusters is estimated.In the end of the algorithm,all the given IFSs are clustered according to the estimated membership degrees.Furthermore,the algorithm is extended for clustering interval-valued intuitionistic fuzzy sets(IVIFSs).Finally,the developed algorithms are illustrated through conducting experiments on both the real-world and simulated data sets.
基金supported by National Natural Science Foundation of China(Grant No.51105040)Aeronautic Science Foundation of China(Grant No.2011ZA72003)Excellent Young Scholars Research Fund of Beijing Institute of Technology(Grant No.2010Y0102)
文摘High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization methods must be developed to relieve the computational burden.A new metamodel-based global optimization method using fuzzy clustering for design space reduction(MGO-FCR) is presented.The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel,whose accuracy is improved with increasing number of sample points gradually.Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively.Modeling efficiency and accuracy are directly related to the design space,so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms.The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated.The first pseudo reduction algorithm improves the speed of clustering,while the second pseudo reduction algorithm ensures the design space to be reduced.Through several numerical benchmark functions,comparative studies with adaptive response surface method,approximated unimodal region elimination method and mode-pursuing sampling are carried out.The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions.And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems.Based on this global design optimization method,a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms.This method possesses favorable performance on efficiency,robustness and capability of global convergence and gives a new optimization strategy for engineering design optimization problems involving expensive black box models.
基金Supported by National Key R&D Projects(Grant No.2018YFB0905500)National Natural Science Foundation of China(Grant No.51875498)+1 种基金Hebei Provincial Natural Science Foundation of China(Grant Nos.E2018203439,E2018203339,F2016203496)Key Scientific Research Projects Plan of Henan Higher Education Institutions(Grant No.19B460001)
文摘Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.
基金Supported by National Natural Science Foundation of China(Grant No.11372036)
文摘Reduced order models(ROMs) based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.
