FCM Clustering Algorithm Research Articles

Self-supervised change detection of heterogeneous images based on difference algorithms

ABSTRACT The presence of heterogeneous image disparities often leads to inferior quality in the generated difference images during change detection. This paper proposes a self-supervised change detection of heterogeneous images based on a difference algorithm. Firstly, a combination of phase consistency and a simplified pulse-coupled neural network (PC-SPCNN) is used to fuse the heterogeneous images, and the result is used to compute the difference image (DI). The new DI generation method can generate the standard and exponential difference images. Secondly, the hierarchical FCM clustering algorithm is improved to extract stable and correct self-supervised samples by difference images so that the clustering process is not overly dependent on thresholds. Then, the support vector machine classifier is trained based on the heterogeneous images, the fused images, and self-supervised sample sets, and the information from the fused images is utilized to increase the feature dimension for better detection of changes. Finally, the support vector machine classifier automatically detects whether the intermediate pixels are changed and produces the change detection results. The experimental results confirm the improvements made by the proposed method in difference image extraction, training sample selection, and clustering algorithm, and the stability of the method exceeds that of the state-of-the-art change detection methods.

Reform and Exploration of Multidimensional Aviation Service Teaching Mode Based on OBE

Abstract In order to ensure that relevant aviation talents have solid professional theoretical knowledge, it is necessary to raise the importance of teaching aviation theory courses in talent cultivation and lay the foundation for the improvement of the quality of aviation service talent cultivation. Based on OBE theory, this paper designs a multidimensional aviation service teaching model that encompasses four aspects: curriculum system, strategy, goal, and evaluation. Subsequently, the teaching model is applied to carry out service thinking teaching in G school, designing the service thinking development trajectory analysis framework to excavate the service thinking evolution process, characterizing the service thinking development law of student groups through the FCM clustering algorithm and visualization technology, and exploring the characteristics implied by the service thinking development law of different groups. Results: The service thinking ability of students of all labels under the OBE-based multidimensional aviation service teaching method was significantly improved, and all of them improved by at least 0.3 points, and the presentation and sharing part of teaching played a greater role in the development of students’ service thinking ability. In addition, the scores of students’ group resilience, self-control, and service dimensions under this model are more concentrated in the 20-30 points area, and most students in language and cooperation dimensions are still under 20 points, and the development of “all-round” dimensions is better than that of other labeled students.

The Logical Mechanism and Practical Progress of Digital Economy Contributing to Rural Revitalization in the Context of Big Data

Abstract This paper proposes spatial data mining and clustering analysis techniques based on big data and uses four distance similarity calculation methods, namely, Euclidean distance, Manhattan distance, Ming’s distance, and Ma’s distance, to constrain spatial clustering similarity metrics. Combine the K-means algorithm and FCM clustering algorithm to calculate the center value of fuzzy clustering, use the additive synthesis method to solve the combination weights, and construct the combination weighted distance, fuzzy C-mean algorithm model. Research on the mechanism and path of digital economy-enabled rural revitalization through the C-FCM model, and propose three correlation hypotheses for this paper. Empirical experiments are conducted to verify the authenticity of the hypotheses proposed in this paper and the use effect of the constructed model. The experimental results show that the Moran index range of digital economy and rural revitalization is [0.06, 0.12], [0.05, 0.16], respectively, and there is a significant aggregation phenomenon of the two under the geographic matrix. In the analysis of the spatial spillover effect, the values of spatial autoregressive coefficients of rural industrial revitalization are all >0, with significance p>0.05. The digital economy has a positive effect on rural revitalization in neighboring areas through the spatial spillover effect. In the clustering sample extraction test, the accuracy is most stable when the number of samples is 2200, and the accuracy rate is as high as 79.66%.

Knowledge Graph-based Interactive Sharing Platform for Teaching English Majors in Colleges and Universities

Abstract In this paper, we found that the traditional FCM clustering algorithm is over-sensitive to noise or outliers, and AP clustering is added on the basis of the FCM algorithm to improve and optimize the FCM algorithm. The improved and optimized algorithm is used to derive high-frequency words for teaching English majors, and the hot knowledge map of English teaching is studied according to the high-frequency words. Construct the knowledge map framework and utilize the knowledge map framework to build the U+ English teaching interactive sharing platform. Adopt a top-down approach to construct the knowledge graph, and after defining entity relationships and attributes, propose the learning path recommendation method of the U+ platform based on the knowledge graph. Comparative experiments are used to analyze the teaching effect of the U+ platform and students’ English learning engagement. After 8 weeks of interactive and shared learning on the U+ platform, the students in the experimental class improved their English single choice, perfect fill-in-the-blank, and reading comprehension scores by 2, 4, and 3 points, respectively, and the U+ platform had obvious help in improving the students’ English scores, and the platform was effective in teaching. The mean value of the positive dimension of learning engagement of the students in the experimental class is 4.1634, with an interval range of [4,4.3], and they are positively engaged in English learning, and the mean value of the negative dimension is 1.8341, with a range of [1.6,2.4], and there are fewer negative performances, and the U+ platform has a positive impact on the student’s English learning engagement.

SAR Change Detection Algorithm Combined with FFDNet Spatial Denoising

Objectives: When detecting changes in synthetic aperture radar (SAR) images, the quality of the difference map has an important impact on the detection results, and the speckle noise in the image interferes with the extraction of change information. In order to improve the detection accuracy of SAR image change detection and improve the quality of the difference map, this paper proposes a method that combines the popular deep neural network with the clustering algorithm.Methods: Firstly, the SAR image with speckle noise was constructed, and the FFDNet architecture was used to retrain the SAR image, and the network parameters with better effect on speckle noise suppression were obtained. Then the log ratio operator is generated by using the reconstructed image output from the network. Finally, K-means and FCM clustering algorithms are used to analyze the difference images, and the binary map of change detection results is generated. Results: The experimental results have high detection accuracy on Bern and Sulzberger's real data, which proves the effectiveness of the method.

Dual Construction of “Composition” and “Analysis” in Composition and Technical Theory in the Context of Big Data

Abstract Based on big data technology, this paper firstly collects and pre-processes educational resources and builds a dual teaching system based on two basic concepts of “creation” and “analysis”, combining technology and theory. Secondly, the FCM clustering algorithm is used to cluster the student learning data and get the optimal clustering center, and the Lagrange multiplier method is used to optimize the objective function, identify the student learning characteristics, and conduct a dynamic pre-testing assessment. Finally, the quantum optimization algorithm dynamically assigns the assessment data to eliminate redundant data and construct a feature set library for individual student learning. The practical analysis and application results show that the effect feedback in the two courses of composition and orchestration reached 88% and 93%. In the music analysis and music composition assessments, the percentage of the number of students with performance range values of 100-90 averaged 43% and 42.75%, respectively. In addition, 72.97% of the students found the system helpful in independent learning ability. It indicates that the teaching system constructed in this paper can better serve the teaching of composition technique and further improve the teaching model and process.

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

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

Remote sensing image segmentation using feature based fusion on FCM clustering algorithm

AbstractImage segmentation of heterogeneous comparable objects lying beneath the earth’s surface is a fundamental but challenging research area in remote sensing. Learning approaches are used in remote sensing image segmentation to improve segmentation accuracy at the expense of time and a large amount of data, but their performance need to be finely classified due to information diversity constraints. In this work, we proposed an novel feature based fuzzy C-means-extreme learning machine (FBFCM-ELM) algorithm for remote sensing image segmentation in which the classification based on entropy, intensity, and edge features is performed in such a way that it updates the intensity value to preserve the most local characteristics in the image while still being able to clearly distinguish the image’s boundaries by assigning the pixel values of each cluster to the peak value of the cluster’s sub-histogram. Using FBFCM, features are extracted and used as reliable samples for ELM training. Undetermined segmented pixels are obtained using the trained ELM classifier. Experiments performed over number of images that confirmed the proposed method yields a better segmented RGB image, as evidenced by observable details, edges, and improved appearance that resembles the ground truth image and outperforms state-of-the-art algorithms.

An effective method for optimal IOV network clustering using FCM clustering algorithm based on node energy density

An effective method for optimal IOV network clustering using FCM clustering algorithm based on node energy density

The multi-dimensional power big data mining based on improved grey clustering algorithm

In order to overcome the problems of the traditional power big data mining methods, such as the low integrity of data mining and the long time-consuming of data mining, this paper realizes multi-dimensional power big data mining by improving the grey clustering algorithm. Firstly, a relay multi hop network is established to collect power big data through the collector; Secondly, Lagrange interpolation method is used to fill the missing data of power data mining; Standardized processing of power consumption data; Finally, according to the grey theory and FCM clustering algorithm, the multi-dimensional power big data mining is realized. The experimental results show that the integrity of power big data mining in this method is up to 0.996, the mining time is not more than 3.05 s, and the mining integrity is up to 0.992, which indicates that this method can effectively improve the effect of power big data mining.

Image Segmentation Method Based on Improved PSO Optimized FCM Algorithm and Its Application

<p>In image segmentation, FCM clustering algorithm can not find the optimal initial clustering center and fall into local extremum, which leads to the decrease of image segmentation accuracy. The PSO algorithm has strong optimization ability, so a new method based on improved PSO algorithm is proposed to optimize the FCM clustering center selection. Firstly, the optimization performance of the PSO algorithm is improved. The distance difference between each particle and the optimal particle is calculated, and the maximum distance difference is selected. The ratio of the distance difference to the maximum distance difference and the aggregation degree of particles are used to construct the natural exponential function. This natural exponential function is used to improve the calculation method of inertia weight value of PSO algorithm, so that the farther the particle is away from the optimal position, the larger the inertia weight value it will get, the stronger the global search ability of particle; on the contrary, the smaller the inertia weight value, the stronger the local search ability of particle, so as to improve the optimization ability of PSO algorithm. The improved PSO algorithm is called DDPSO (Distance Difference PSO). Then the optimized FCM algorithm is applied to the segmentation of standard image and eggshell damaged image to improve the accuracy of image segmentation. Finally, the experimental results show that the FCM algorithm optimized by DDPSO has higher segmentation accuracy than the traditional method.</p> <p> </p>

Forest fire identification method of UAV remote sensing image based on FCM clustering algorithm

Forest fire identification method of UAV remote sensing image based on FCM clustering algorithm

Cluster analysis-based big data mining method of e-commerce consumer behaviour

In order to overcome the problems of low precision and long time of data mining in traditional big data mining methods of consumer behaviour, a clustering analysis method for big data mining of e-commerce consumer behaviour is proposed. In this paper, the K-means algorithm is used to calculate the similarity of behaviour clustering nodes of fee payers, determine the clustering process of consumer behaviour data and determine the mining weight of behaviour data. According to the FCM clustering algorithm, the target function for data mining of e-commerce consumer behaviour is constructed. According to Lagrange multiplication, the membership degree of consumer behaviour data is obtained, and the big data mining of consumer behaviour in e-commerce is realised. The experimental results show that with the method proposed in this paper, when the number of consumers is 500, the time for big data mining of consumer behaviour is 15.6s and the accuracy of behaviour big data mining is 95.34%.

Segmentation algorithm of coal slurry foam with double-point directional extension based on the improved FCM clustering algorithm

In flotation production, the visual surface information of the flotation foam reflects the flotation effects, which are closely related to the flotation conditions and directly reflect the degree of mineralization of the foam layer. In this study, it was proposed a novel and efficient segmentation algorithm to extract the edge information of slime bubbles, as the boundaries are typically blurred and difficult to segment, due to the slime bubbles sticking to each other in the slime flotation foam image. First, the improved clustering algorithm and image morphology operation were used to extract the edges of the foam spots. Second, the image morphological operations were used as a starting point to look around the foam edge points. The pseudo-edge points were then removed using a region and spatial removal algorithm. Finally, the foam edges were extracted using the double-point directed expansion algorithm. A new criterion was proposed for segmentation effect determination based on the particularity of the segmented object. The feasibility and effectiveness of the foam segmentation method were investigated by comparative experiments. The experimental results showed that the proposed algorithm could obtain the foam surface properties more accurately and provide effective guidance for flotation production.

Research on Clothing Color Classification Method based on Improved FCM Clustering Algorithm

Research on Clothing Color Classification Method based on Improved FCM Clustering Algorithm

GPS Receivers Spoofing Detection Based on Subtractive, FCM and DBSCAN Clustering Algorithms

GPS receivers have a wide range of applications, but are not always secure. A spoofing attack is one source of conscious errors in which the counterfeit signal overcomes the authentic GPS signal and takes control of the receiver’s operation. Recently, GPS spoofing attack detection based on computational algorithms, such as machine learning, classification, wavelet transform and clustering, has been developing. This paper proposes multiple clustering algorithms for accurately clustering the authentic and spoofing signals, called subtractive, FCM and DBSCAN clustering. The spoofing attack is recognized using two distinct features: moving phase detector variance and norms of correlators. Spoofing and authentic signals have different patterns in the proposed features. According to the Dunn and Silhouette indexes, the validation of the results is investigated. The Dunn values for the proposed approaches are 0.8592, 0.5285 and 0.6039 for DBSCAN, FCM and subtractive clustering, respectively. Also, the DBSCAN algorithm is implemented at the RTL level because of its highest value for the Dunn index and algorithm verifiability. Using the Vivado tools, this algorithm is implemented and designed on a Xilinx Virtex 7 xc7vx690tffg1930-3 hardware device for two-dimensional data with 32-bit accuracy and 130 data points.

A Recognition Method of Truck Drivers’ Braking Patterns Based on FCM-LDA2vec

Taking truck drivers' braking patterns as the research objects, this study used a large amount of truck running data. A recognition method of truck drivers' braking patterns was proposed to determine the distribution of braking patterns during the operation of trucks. First, the segmented data of braking behaviors were collected in order to extract 25 characteristic parameters. Additionally, seven main correlation factors were obtained by dimensionality reduction. The FCM clustering algorithm and CH scores were used to identify nine categories of truck drivers' braking behaviors. Then the LDA2vec model was used to identify the distribution of different braking behavior words in braking patterns, and three categories of truck drivers' braking patterns were identified. The test results showed that the accuracy of the truck drivers' braking pattern recognition model based on LDA2vec was higher than 85%, and braking patterns of drivers in the daily operation process could be mined from vehicle operation data. Furthermore, through the monitoring and pre-warning of the braking patterns and targeted training of drivers, traffic accidents could be avoided. At the same time, this paper's results can be used to protect human life and health and reduce environmental pollution caused by traffic congestion or traffic accidents.

Construction of College Students’ Mental Health Assessment and Art Therapy System Aided by the Internet of Things and Big Data

In order to improve the effect of college students’ mental health assessment, this paper combines the Internet of Things and big data technology to build a college student’s mental health assessment system and analyzes and verifies the clustering effectiveness indicators by using FCM, GK, and GG clustering algorithms. Moreover, this paper explores the basic concepts and processes of FCM algorithm, GK algorithm, and GG algorithm and expounds the connection between the three algorithms. In addition, this paper uses four datasets to conduct clustering experiments and compares the CS indicator with several other indicators. The experimental results demonstrate the effectiveness of the CS indicator. The simulation study shows that the student mental health assessment system based on the Internet of Things and big data technology proposed in this paper can play a certain role in student mental health and art therapy, and it also verifies that art therapy plays a certain role in student psychotherapy.

Image segmentation based on super-pixel with neighborhood constrained and multi-level FCM clustering

In order to solve the problem of noise sensitivity and low precision in image segmentation, a new image segmentation method based on super-pixel with neighborhood constrained and multi-level FCM clustering is proposed. Firstly, an improved SLIC algorithm of FCM is designed based on the local pixel region of the image to get the refined super-pixel. Then, the improved FCM clustering algorithm is combined with the spatial relationship of neighborhood super-pixel to mark the super-pixel categories. In order to verify the effectiveness of the algorithm, the artificial synthetic image with noise and natural image without noise are used for comparison experiments. The experimental result show the algorithm proposed in this paper can not only suppress the noise, but also improve the processing accuracy.

Hybrid fuzzy multiple SVM classifier through feature fusion based on convolution neural networks and its practical applications

Lately, Convolutional Neural Networks (CNNs) have been introduced to extract features and further enhance the classification performance in different application areas. In this study, a hybrid fuzzy multiple (HFM)-SVM design with the convolution-base (which consists of a series of pooling and convolutional layers) and composite kernel function is proposed. The objective of the proposed classifier is to enhance the nonlinear fitting ability of the classifier and improve the classification performance in high-dimensional applications. The key points of the proposed HFM SVM are enumerated as follows: 1) The convolution-base of the proposed classifier extracts features. The extracted features exhibit flexibility and applicability in high-dimensional applications. 2) The proposed HFM SVM designed with the composite kernel could adjust the nonlinear fitting ability for improving classification performance. The procedure of the proposed HFM SVM is described as follows: Convolution-base is considered as a preprocessing unit for extracting features. The features are not always linearly separable especially when being extracted from high dimensional data. A composite kernel function is constructed by considering the complicated (nonlinear) classification boundary into several local linear boundaries. The structure of the extracted features is captured by FCM clustering and integrated into the composite kernel function for enhancing the nonlinear fitting ability of the proposed classifier. The nonlinearity of the composite function can fill the gap between linear and nonlinear kernel functions by adjusting the number of clusters obtained by the FCM clustering algorithm. The proposed HFM SVM classifier based on composite kernel could improve the classification performance on high dimensional datasets. The performance of the proposed HFM SVM is experimented with and demonstrated by using three high-dimensional applications to show the effectiveness as well as performance improvement.