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Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance 认领 引用 被引量：4
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作者 Shaochun PANG Yijie +1 位作者 SHAO Sen JIANG Keyuan 《Journal of Shanghai Jiaotong university(Science)》 EI 2018年第5期636-642,共7页
This paper presents an advanced fuzzy C-means(FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of ... This paper presents an advanced fuzzy C-means(FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars' box-office data, and the classification accuracy of the first class stars achieves 92.625%. 展开更多
关键词 objective function clustering center fuzzy C-means （FCM） clustering algorithm degree of member-ship
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Research of Improved Fuzzy c-means Algorithm Based on a New Metric Norm 认领 引用 被引量：2
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作者 毛力 宋益春 +2 位作者 李引 杨弘 肖炜 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第1期51-55,共5页
For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FC... For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FCM and particle swarm optimization(PSO)clustering algorithm,and proposes a parallel optimization algorithm using an improved fuzzy c-means method combined with particle swarm optimization(AF-APSO).The experiment shows that the AF-APSO can avoid local optima,and get the best fitness and clustering performance significantly. 展开更多
关键词 fuzzy c-means(FCM) particle swarm optimization(PSO) clustering algorithm new metric norm
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Agent Based Segmentation of the MRI Brain Using a Robust C-Means Algorithm 认领 引用
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作者 Hanane Barrah Abdeljabbar Cherkaoui Driss Sarsri 《Journal of Computer and Communications》 2016年第10期13-21,共9页
In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many research... In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many researchers have adopted the fuzzy clustering approach to segment them. In this work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed. This system gets its robustness from a robust c-means algorithm (RFCM) and obtains its fastness from the beneficial properties of agents, such as autonomy, social ability and reactivity. To show the efficiency of the proposed method, we test it on a normal brain brought from the BrainWeb Simulated Brain Database. The experimental results are valuable in both robustness to noise and running times standpoints. 展开更多
关键词 Agents and MAS MR Images Fuzzy Clustering C-Means Algorithm Image Segmentation
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Partition region-based suppressed fuzzy C-means algorithm 认领 引用 被引量：3
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作者 Kun Zhang Weiren Kong +4 位作者 Peipei Liu Jiao Shi Yu Lei Jie Zou Min Liu 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2017年第5期996-1008,共13页
Aimed at the problem that the traditional suppressed fuzzy C-means clustering algorithms ignore the real needs of different objects, applying the same suppressed parameter for modifying membership degrees of all the o... Aimed at the problem that the traditional suppressed fuzzy C-means clustering algorithms ignore the real needs of different objects, applying the same suppressed parameter for modifying membership degrees of all the objects, a novel partition region-based suppressed fuzzy C-means clustering algorithm with better capacity of adaptability and robustness is proposed in this paper. The model based on the real needs of different objects is built, making it clear to decide whether to proceed with further determination; in addition, the external user-defined suppressed parameter is automatically selected according to the intrinsic structural characteristic of each dataset, making the proposed method become robust to the fluctuations in the incoming dataset and initial conditions. Experimental results show that the proposed method is more robust than its counterparts and overcomes the weakness of the original suppressed clustering algorithm in most cases. 展开更多
关键词 shadowed set suppressed fuzzy C-means clustering automatically parameter selection soft computing techniques
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Fingerprint image segmentation using modified fuzzy c-means algorithm 认领 引用 被引量：1
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作者 Jia-Yin Kang Cheng-Long Gong Wen-Juan Zhang 《Journal of Biomedical Science and Engineering》 2009年第8期656-660,共5页
Fingerprint segmentation is a crucial step in fingerprint recognition system, and determines the results of fingerprint analysis and recognition. This paper proposes an efficient approach for fingerprint segmentation ... Fingerprint segmentation is a crucial step in fingerprint recognition system, and determines the results of fingerprint analysis and recognition. This paper proposes an efficient approach for fingerprint segmentation based on modified fuzzy c-means (FCM). The proposed method is realized by modifying the objective function in the Szilagyi’s algorithm via introducing histogram-based weight. Experimental results show that the proposed approach has an efficient performance while segmenting both original fingerprint image and fingerprint images corrupted by different type of noises. 展开更多
关键词 Fingerprint Segmentation Fuzzy C-means Histogram Robustness
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Hybrid Clustering Using Firefly Optimization and Fuzzy C-Means Algorithm 认领 引用
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作者 Krishnamoorthi Murugasamy Kalamani Murugasamy 《Circuits and Systems》 2016年第9期2339-2348,共10页
Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis... Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm. 展开更多
关键词 Clustering Optimization K-Means Fuzzy C-Means Firefly Algorithm F-Firefly
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Distributed C-Means Algorithm for Big Data Image Segmentation on a Massively Parallel and Distributed Virtual Machine Based on Cooperative Mobile Agents 认领 引用
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作者 Fatéma Zahra Benchara Mohamed Youssfi +2 位作者 Omar Bouattane Hassan Ouajji Mohammed Ouadi Bensalah 《Journal of Software Engineering and Applications》 2015年第3期103-113,共11页
The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is th... The aim of this paper is to present a distributed algorithm for big data classification, and its application for Magnetic Resonance Images (MRI) segmentation. We choose the well-known classification method which is the c-means method. The proposed method is introduced in order to perform a cognitive program which is assigned to be implemented on a parallel and distributed machine based on mobile agents. The main idea of the proposed algorithm is to execute the c-means classification procedure by the Mobile Classification Agents (Team Workers) on different nodes on their data at the same time and provide the results to their Mobile Host Agent (Team Leader) which computes the global results and orchestrates the classification until the convergence condition is achieved and the output segmented images will be provided from the Mobile Classification Agents. The data in our case are the big data MRI image of size (m × n) which is splitted into (m × n) elementary images one per mobile classification agent to perform the classification procedure. The experimental results show that the use of the distributed architecture improves significantly the big data segmentation efficiency. 展开更多
关键词 Multi-Agent System Distributed Algorithm Big Data Image Segmentation MRI Image C-Means Algorithm Mobile Agent
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Fuzzy C-Means Algorithm Based on Density Canopy and Manifold Learning 认领 引用
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作者 Jili Chen Hailan Wang Xiaolan Xie 《Computer Systems Science & Engineering》 2024年第3期645-663,共19页
Fuzzy C-Means(FCM)is an effective and widely used clustering algorithm,but there are still some problems.considering the number of clusters must be determined manually,the local optimal solutions is easily influenced ... Fuzzy C-Means(FCM)is an effective and widely used clustering algorithm,but there are still some problems.considering the number of clusters must be determined manually,the local optimal solutions is easily influenced by the random selection of initial cluster centers,and the performance of Euclid distance in complex high-dimensional data is poor.To solve the above problems,the improved FCM clustering algorithm based on density Canopy and Manifold learning(DM-FCM)is proposed.First,a density Canopy algorithm based on improved local density is proposed to automatically deter-mine the number of clusters and initial cluster centers,which improves the self-adaptability and stability of the algorithm.Then,considering that high-dimensional data often present a nonlinear structure,the manifold learning method is applied to construct a manifold spatial structure,which preserves the global geometric properties of complex high-dimensional data and improves the clustering effect of the algorithm on complex high-dimensional datasets.Fowlkes-Mallows Index(FMI),the weighted average of homogeneity and completeness(V-measure),Adjusted Mutual Information(AMI),and Adjusted Rand Index(ARI)are used as performance measures of clustering algorithms.The experimental results show that the manifold learning method is the superior distance measure,and the algorithm improves the clustering accuracy and performs superiorly in the clustering of low-dimensional and complex high-dimensional data. 展开更多
关键词 Fuzzy C-Means(FCM) cluster center density canopy ISOMAP clustering
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An Adaptive Fuzzy C-Means Algorithm for Improving MRI Segmentation 认领 引用
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作者 Elnomery Allam Zanaty 《Open Journal of Medical Imaging》 2013年第4期125-135,共11页
In this paper, we propose new fuzzy c-means method for improving the magnetic resonance imaging (MRI) segmenta- tion. The proposed method called “possiblistic fuzzy c-means (PFCM)” which hybrids the fuzzy c-means (F... In this paper, we propose new fuzzy c-means method for improving the magnetic resonance imaging (MRI) segmenta- tion. The proposed method called “possiblistic fuzzy c-means (PFCM)” which hybrids the fuzzy c-means (FCM) and possiblistic c-means (PCM) functions. It is realized by modifying the objective function of the conventional PCM algorithm with Gaussian exponent weights to produce memberships and possibilities simultaneously, along with the usual point prototypes or cluster centers for each cluster. The membership values can be interpreted as degrees of possibility of the points belonging to the classes, i.e., the compatibilities of the points with the class prototypes. For that, the proposed algorithm is capable to avoid various problems of existing fuzzy clustering methods that solve the defect of noise sensitivity and overcomes the coincident clusters problem of PCM. The efficiency of the proposed algorithm is demonstrated by extensive segmentation experiments by applying them to the challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI) datasets and by comparison with other state of the art algorithms. The experimental results show that the proposed method produces accurate and stable results. 展开更多
关键词 Fuzzy Clustering Possiblistic C-Means Medical Image Segmentation
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Iris Recognition Based on Multilevel Thresholding Technique and Modified Fuzzy c-Means Algorithm 认领 引用
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作者 Slim Ben Chaabane Rafika Harrabi +1 位作者 Anas Bushnag Hassene Seddik 《Journal on Artificial Intelligence》 2022年第4期201-214,共14页
Biometrics represents the technology for measuring the characteristics of the human body.Biometric authentication currently allows for secure,easy,and fast access by recognizing a person based on facial,voice,and fing... Biometrics represents the technology for measuring the characteristics of the human body.Biometric authentication currently allows for secure,easy,and fast access by recognizing a person based on facial,voice,and fingerprint traits.Iris authentication is one of the essential biometric methods for identifying a person.This authentication type has become popular in research and practical applications.Unlike the face and hands,the iris is an internal organ,protected and therefore less likely to be damaged.However,the number of helpful information collected from the iris is much greater than the other biometric human organs.This work proposes a new iris identification model based on a multilevel thresholding technique and modified Fuzzy cmeans algorithm.The multilevel thresholding technique extracts the iris from its surroundings,such as specular reflections,eyelashes,pupils,and sclera.On the other hand,the modified Fuzzy c-means is used to combine and classify the most useful statistical features to maximize the accuracy of the collected information.Therefore,having the most optimal iris recognition.The proposed model results are validated using True Success Rate(TSR)and compared to other existing models.The results show how effective the combination of the two stages of the proposed model is:the Otsu method and modified Fuzzy c-means for the 400 tested images representing 40 people. 展开更多
关键词 Biometric authentication recognition iris recognition statistical features feature extraction fuzzy c-means TSR sensitivity classification
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Deep Fuzzy C-Means Clustering in a Federated Heterogeneous Scenario 认领 引用
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作者 Longmei Li Wei Lu Witold Pedrycz 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期633-646,共14页
In federated deep Fuzzy C-Means(FCM)clustering,conventional federated averaging(FedAvg)struggles with nonindependent and identically distributed(non-IID)data and dynamic device participation,leading to model drift and... In federated deep Fuzzy C-Means(FCM)clustering,conventional federated averaging(FedAvg)struggles with nonindependent and identically distributed(non-IID)data and dynamic device participation,leading to model drift and performance degradation during global aggregation.To address this challenge,we propose FedFCD,a federated deep FCM clustering method featuring a novel aggregation mechanism.FedFCD equips each client with a hybrid architecture comprising a contrastive autoencoder(CtAE)and an FCM network(FCMNet),which collaboratively learn stable low-dimensional embeddings and refine soft clustering assignments iteratively.At the server side,we design a two-phase aggregation strategy integrating Bayesian ensemble learning and knowledge distillation(KD).First,the Bayesian aggregation mechanism probabilistically fuses heterogeneous local models'inferences into a consensus assignment by treating each client's model as a candidate hypothesis,thereby constructing a posterior distribution over the global model space through iterative evidence accumulation.Subsequently,dual-source distillation harmonizes pseudo-labels derived from the Bayesian consensus with ground-truth labels from limited shared data,enabling the global model to align its predictions with both semantic anchors and aggregated soft assignments while preserving privacy through distillation loss.Comparative experiments on benchmark datasets demonstrate that Fed FCD outperforms baseline methods in clustering accuracy and exhibits enhanced stability under varying conditions,including data heterogeneity,device numbers,and device dropout. 展开更多
关键词 Bayesian inference data distillation deep embedding clustering federated learning fuzzy C-means(FCM) model aggregation
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Fuzzy C-Means Clustering-Driven Pooling for Robust and Generalizable Convolutional Neural Networks 认领 引用
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作者 Seunggyu Byeon Jung-hun Lee Jong-Deok Kim 《Computers, Materials & Continua》 SCIE EI 2026年第5期579-604,共26页
This paper introduces a fuzzy C-means-based pooling layer for convolutional neural networks that explicitly models local uncertainty and ambiguity.Conventional pooling operations,such as max and average,apply rigid ag... This paper introduces a fuzzy C-means-based pooling layer for convolutional neural networks that explicitly models local uncertainty and ambiguity.Conventional pooling operations,such as max and average,apply rigid aggregation and often discard fine-grained boundary information.In contrast,our method computes soft membershipswithin each receptive field and aggregates cluster-wise responses throughmembership-weighted pooling,thereby preserving informative structure while reducing dimensionality.Being differentiable,the proposed layer operates as standard two-dimensional pooling.We evaluate our approach across various CNN backbones and open datasets,including CIFAR-10/100,STL-10,LFW,and ImageNette,and further probe small training set restrictions on MNIST and Fashion-MNIST.In these settings,the proposed pooling consistently improves accuracy and weighted F1 over conventional baselines,with particularly strong gains when training data are scarce.Even with less than 1%of the training set,ourmethodmaintains reliable performance,indicating improved sample efficiency and robustness to noisy or ambiguous local patterns.Overall,integrating soft memberships into the pooling operator provides a practical and generalizable inductive bias that enhances robustness and generalization in modern CNN pipelines. 展开更多
关键词 Fuzzy logic fuzzy c-means clustering membership-based pooling convolutional neural networks downsampling feature extraction
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Grouped machine learning methods for predicting rock mass parameters in a tunnel boring machine-driven tunnel based on fuzzy C-means clustering 认领 引用
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作者 Ruirui Wang Yaodong Ni +1 位作者 Lingli Zhang Boyang Gao 《Deep Underground Science and Engineering》 EI CAS CSCD 2025年第1期55-71,共17页
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
关键词 fuzzy C-means clustering machine learning rock mass parameter tunnel boring machine
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Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm 认领 引用 被引量：4
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作者 YU XianChuan HE Hui +1 位作者 HU Dan ZHOU Wei 《Science China Earth Sciences》 SCIE EI CAS 2014年第6期1306-1313,共8页
There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling ... There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery. 展开更多
关键词 fuzzy c-means cluster interval-valued data remote sensing imagery land cover classification
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New two-dimensional fuzzy C-means clustering algorithm for image segmentation 认领 引用 被引量：11
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作者 周鲜成 申群太 刘利枚 《Journal of Central South University of Technology》 2008年第6期882-887,共6页
To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this... To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this method, the image segmentation was converted into an optimization problem. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels described by the improved two-dimensional histogram. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization, and the image segmentation could be accomplished. The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%. The proposed algorithm has strong anti-noise capability, high clustering accuracy and good segment effect, indicating that it is an effective algorithm for image segmentation. 展开更多
关键词 image segmentation fuzzy C-means clustering particle swarm optimization two-dimensional histogram
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An improved Alpha-shape algorithm for extracting section contours of the super-high steel bridge tower using point clouds 认领 引用 被引量：2
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作者 ZHANG Yiming ZHAO Tianhao +2 位作者 LIAO Ruixuan LI Haoqing WANG Hao 《Journal of Southeast University(English Edition)》 EI CAS 2026年第1期26-35,共10页
The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,a... The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and temperature fluctuations can compromise the accuracy of contour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvatureadaptive voxel filtering to reduce acquisition noise.In addition,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geometries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency,highlighting its effectiveness in contour extraction tasks. 展开更多
关键词 super-high steel bridge tower point cloud contour extraction improved Alpha-shape algorithm
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Intuitionistic fuzzy C-means clustering algorithms 认领 引用 被引量：30
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作者 Zeshui Xu Junjie Wu 《Journal of Systems Engineering and Electronics》 SCIE EI 2010年第4期580-590,共11页
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
关键词 intuitionistic fuzzy set(IFS) intuitionistic fuzzy Cmeans algorithm clustering interval-valued intuitionistic fuzzy set(IVIFS).
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Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用 被引量：2
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作者 WU Jin GAO Yaqiong +2 位作者 SU Zhengdong CHONG Gege XIONG Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期828-842,I0002,共15页
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for... A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems. 展开更多
关键词 sea otter optimization algorithm(SOOA) swarm intelligence optimization wireless sensor network coverage optimization
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Study on the destabilizing damage precursors of cemented tailings backfill based on critical slowing down theory combined with multiple denoising algorithms under consideration of initial defect conditions 认领 引用 被引量：1
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作者 ZHAO Kang ZHONG Jun-cheng +3 位作者 YAN Ya-jing LIU Yang WEN Dao-tan XIAO Wei-ling 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第1期375-399,共25页
The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the... The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the stability of underground mining engineering,this paper simulates the generation of different degrees of initial defects inside the CTB by adding different contents of air-entraining agent(AEA),investigates the acoustic emission RA/AF eigenvalues of CTB with different contents of AEA under uniaxial compression,and adopts various denoising algorithms(e.g.,moving average smoothing,median filtering,and outlier detection)to improve the accuracy of the data.The variance and autocorrelation coefficients of RA/AF parameters were analyzed in conjunction with the critical slowing down(CSD)theory.The results show that the acoustic emission RA/AF values can be used to characterize the progressive damage evolution of CTB.The denoising algorithm processed the AE signals to reduce the effects of extraneous noise and anomalous spikes.Changes in the variance curves provide clear precursor information,while abrupt changes in the autocorrelation coefficient can be used as an auxiliary localization warning signal.The phenomenon of dramatic increase in the variance and autocorrelation coefficient curves during the compression-tightening stage,which is influenced by the initial defects,can lead to false warnings.As the initial defects of the CTB increase,its instability precursor time and instability time are prolonged,the peak stress decreases,and the time difference between the CTB and the instability damage is smaller.The results provide a new method for real-time monitoring and early warning of CTB instability damage. 展开更多
关键词 initial defects cemented tailings backfill critical slowing down acoustic emission RA/AF values denoising algorithms
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Optimization of the frequency offset increment of FDA-MIMO based on cuckoo search algorithm 认领 引用 被引量：2
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作者 WANG Bo ZHAO Yu +2 位作者 LI Yonglin YANG Rennong XUE Junjie 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期157-170,共14页
Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic e... Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe. 展开更多
关键词 frequency diverse array multiple-input multiple-output(FDA-MIMO) convex optimization cuckoo search algorithm beampattern
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