clustering validity function is an index used to judge the accuracy of clustering results. At present, most studies on clustering validity are based on single clustering validity function. Research shows that no clust...
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clustering validity function is an index used to judge the accuracy of clustering results. At present, most studies on clustering validity are based on single clustering validity function. Research shows that no clustering validity function can handle any data and always perform better than other indexes. Therefore, a hybrid weighted combination evaluation method based on fuzzyc-means (FcM) clustering validity functions was proposed. The weighting method combines expert weighting with information entropy weighting to improve the subjective factor influence of expert weighting and the shortcoming of information entropy weighting in the value judgment of each clustering validity function. Four clustering validity function combination methods of linear, exponential, logarithm and proportion was studied. Finally, the proposed fuzzyclustering validity evaluation method is verified by experiments on artificial data sets and UcI data sets. The experimental results show that the proposed fuzzyclustering validity evaluation method can overcome the shortcoming of single clustering validity function, and can get the optimal clustering number more accurately for different data sets.
As synthetic aperture radar (SAR) image change detection can continuously acquire target information under all weather conditions, it has been used in the past for various applications. However, it is very challenging...
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As synthetic aperture radar (SAR) image change detection can continuously acquire target information under all weather conditions, it has been used in the past for various applications. However, it is very challenging to use SAR images for small area change detection due to the presence of noise and the imbalance between classes. To deal with these problems this article proposes a SAR images change detection scheme which is based upon an Attention Mechanism and convolutional Wavelet Neural Network. First, employing Multiscale Superpixel Reconstructed Difference Image effectively enhances the edge information of the images. Second, a two-stage Weighted center constrained fuzzy c-means clustering algorithm is proposed. To constrain incorrect migration in the subsequent clustering phase, the algorithm uses a two-stage clusteringalgorithm. During the first stage weights in the clustering process to improve the accuracy of the resulting clusteringcenters are introduced while during the second stage, these refined cluster centers can be constrained. Third, a convolutional Squeeze-and-Excitation Attention Mechanism Wavelet Neural Network (cSWNN) is proposed which more accurately distinguishes uncertain pixels and thus reduces redundant features. The final Denoising Diffusion Probabilistic Model is introduced into the cSWNN to expand pseudolabeled samples. comparing this algorithm with seven sets of contrast experiments on six real SAR datasets shows that the accuracy of detecting changes in small regions is 2% -9% better than the classical algorithm.
Shield tunnelling presents numerous potential risks particularly in complex geological environments. In this study, we propose a novel fuzzy model for assessing the risk of tunnelling in soil-rock mixed strata. The pr...
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Shield tunnelling presents numerous potential risks particularly in complex geological environments. In this study, we propose a novel fuzzy model for assessing the risk of tunnelling in soil-rock mixed strata. The proposed model incorporates the fuzzy setpair analysis (FSPA) method into fuzzyc-means (FcM) clustering to overcome the limitations of conventional data normalisation. Data pertaining to tunnelling machine, deformation, and vibration are employed to construct an index system using mutual information algorithms for feature selection. The intercriteria importance though intercriteria correlation is employed to weight the indicators, and the FSPA method is adopted to calculate the connection number. Subsequently, the results are classified by the FcM with a modified objective function that considers the importance of risk indicators to derive the risk level of each ring in real time. The proposed model is applied to a case study of a shield tunnelling project in Guangzhou, china. The analysis results indicate a higher risk level from Ring 1572 onwards, which necessitates a judicious regulation of the thrust force and earth pressure. This novel method provides a practical and reliable tool for guiding risk decisions during the tunnel construction.
The cluster validity function is used to evaluate the quality of the cluster results, and giving the exact number of initial cluster categories will rationalize the cluster results. Most single cluster validity functi...
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The cluster validity function is used to evaluate the quality of the cluster results, and giving the exact number of initial cluster categories will rationalize the cluster results. Most single cluster validity functions and combined cluster validity functions generally have strong subjective problems, which also increases the burden on decision analysts and have great limitations in applications. To overcome the shortcomings of these clustering validity functions and improve the accuracy of the optimal cluster category classification for the datasets, based on the clustering performance evaluation components, a validity functional component construction method based on the exponential and log form was proposed. The weighting method adopts the combination of expert empowerment and standard separation method to combine the five weights so as to obtain 52 different fuzzyclustering validity functions. Then, based on the fuzzyc-mean (FcM) clusteringalgorithm, the performance analysis are carried out by using multiple data sets. Experimental simulation of these functions are proceeded on six commonly used UcI datasets. A clustering validity function with the simplest structure and the best classification effect was selected by comparison. Finally, this function is compared with 8 typical single clustering validity functions and four common clustering validity combination evaluation methods on 8 UcI data sets. Through experimental simulation, the proposed validity function is compared in processing data sets, but also has strong scientific theoretical basis. Thus, the feasibility and effectiveness of the proposed clustering validity function construction method are proved.
This paper presents a fuzzyc-meansclustering interval type-2 cerebellar model articulation neural network (FcM-IT2cMANN) method to help physicians improve diagnostic accuracy. The proposed method combines two classi...
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This paper presents a fuzzyc-meansclustering interval type-2 cerebellar model articulation neural network (FcM-IT2cMANN) method to help physicians improve diagnostic accuracy. The proposed method combines two classifiers, in which the IT2cMANN is the primary classifier and the fuzzyc-meansalgorithm is the pre-classifier. First, the data are divided into nc groups using the pre-classifier, and then, the main classifier is applied to determine whether the sample is in a healthy or diseased state. Implementing the gradient descent method, the adaptive laws for updating the FcM-IT2cMANN parameters are derived. Furthermore, the system convergence is proven by the Lyapunov stability theory. Finally, the classification of breast cancer and liver disease datasets from the University of california at Irvine is conducted to illustrate the effectiveness of the proposed classifier.
The business expansion installation can only simply record the most basic business information, which leads to the problems of complex power supply procedures and low efficiency. Therefore, a study on the optimal powe...
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The business expansion installation can only simply record the most basic business information, which leads to the problems of complex power supply procedures and low efficiency. Therefore, a study on the optimal power supply parameters of the business expansion installation based on grey correlation degree and fuzzy c-means clustering algorithm is proposed. Firstly, the grey correlation degree is used to process the optimal power supply parameter data of industrial expansion and installation, and the parameters of fuzzy c-means clustering algorithm are set. On this basis, an intelligent management system for the optimal power supply process of industrial expansion and installation is constructed, and the system development conditions are set up;According to the four business links of project reserve, business acceptance, collaborative operation and performance evaluation, the customer business expansion and installation function module is constructed, so as to realize the calculation of the optimal power supply line of the business expansion and installation and complete the research on the optimal power supply parameters. The experimental results show that the output stability, output throughput performance and parameter optimization ability of this method for the line impedance characteristiccontrol of the power supply of the industrial expansion device are good and are always on the rise. At 3 cm, the output throughput reaches 1.9%, and the parameter analysis ability can reach 350 pixels, which has certain application value.
In order to solve the defect of large error in current employment quality evaluation,an employment quality evaluation model based on grey correlation degree method and fuzzyc-means(FcM)is ***,it analyzes the related ...
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In order to solve the defect of large error in current employment quality evaluation,an employment quality evaluation model based on grey correlation degree method and fuzzyc-means(FcM)is ***,it analyzes the related research work of employment quality evaluation,establishes the employment quality evaluation index system,collects the index data,and normalizes the index data;Then,the weight value of employment quality evaluation index is determined by Grey relational analysis method,and some unimportant indexes are removed;Finally,the employment quality evaluation model is established by using fuzzycluster analysis algorithm,and compared with other employment quality evaluation *** test results show that the employment quality evaluation accuracy of the design model exceeds 93%,the employment quality evaluation error can meet the requirements of practical application,and the employment quality evaluation effect is much better than the comparison *** comparison test verifies the superiority of the model.
"Audiobook" is a multimedia-based reading technology that has emerged in recent years. Realizing the alignment of e-book text and book audio is the most important part of its processing. This article describ...
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"Audiobook" is a multimedia-based reading technology that has emerged in recent years. Realizing the alignment of e-book text and book audio is the most important part of its processing. This article describes an audio and text alignment algorithm using deep learning and neural network technology to improve the efficiency and quality of audiobook production. The algorithm first uses dual-threshold endpoint detection technology to segment long audio into short audio with sentence dimensions and recognizes it as short text. The threshold is calculated by AIc-FcM optimized based on simulated annealing geneticalgorithm. Then the algorithm uses Doc2vec optimized by the threshold prediction method based on the average length of the short text to calculate the text similarity. Finally, proofread and output the text sequence and audio segment aligned in the time dimension to meet the needs of audiobook production. Experiments show that compared to traditional audio and text alignment algorithms, the proposed algorithm is closer to the ideal segmentation result in long audio segmentation, and the alignment effect is basically the same as Doc2vec and the time complexity is reduced by about 35%.
This study conducted a questionnaire survey on the physical condition of high school students based on FIT. The correlation between sedentary behavior, exercise level and physical fitness was studied. In this paper, t...
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This study conducted a questionnaire survey on the physical condition of high school students based on FIT. The correlation between sedentary behavior, exercise level and physical fitness was studied. In this paper, the fuzzyc-average clustering method of the BP neural network is used to make statistics and classification of students' physical condition. This paper collates the biological quality data of college students. Then this paper makes a comprehensive classification, classification and quantitative assessment of the data. The study found that college students' sedentary habits varied from individual and family backgrounds. Family factors influence students' physical exercise. Physical exercise was positively correlated with sedentary behavior and body mass index in adolescents. The accuracy of clusteringcombined with the BP neural network can reach 94%.
In order to overcome the problems of high data noise, low prediction accuracy and long prediction time in the traditional short-term prediction method of lighting energy consumption of large buildings, a short-term pr...
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In order to overcome the problems of high data noise, low prediction accuracy and long prediction time in the traditional short-term prediction method of lighting energy consumption of large buildings, a short-term prediction method of lighting energy consumption of large buildings based on time series analysis is proposed in this paper. The improved threshold function is used to denoise the data, and the fuzzy c-means clustering algorithm is used to cluster the denoised data. The time series analysis method is used to construct the self-excitation threshold autoregressive model. When the model parameters are optimal, the clustered data are input into the model to output the short-term prediction results of lighting energy consumption of large buildings. The experimental results show that compared with the traditional method, the average data noise of this method is 12.3 dB, the prediction accuracy remains above 94% and the average prediction time is only 57 ms.
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